From 25e1137312df9d7498e889d269791fe3c87d7f95 Mon Sep 17 00:00:00 2001 From: ptmexemexe Date: Sat, 10 May 2025 03:03:50 +0000 Subject: [PATCH] Create Prometheus-generated README file --- .kno/chunk_review.txt | 17251 ++++++++++++++++ .../data_level0.bin | Bin 0 -> 3212000 bytes .../header.bin | Bin 0 -> 100 bytes .../length.bin | Bin 0 -> 4000 bytes .../link_lists.bin | 0 .../chroma.sqlite3 | Bin 0 -> 6524928 bytes SECURITY_AUDIT_Prometheus-beta.md | 177 + 7 files changed, 17428 insertions(+) create mode 100644 .kno/chunk_review.txt create mode 100644 .kno/embedding_SBERTEmbedding_1746845907374_9651d09/41fb80a8-691f-45ed-9f91-53c322809202/data_level0.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746845907374_9651d09/41fb80a8-691f-45ed-9f91-53c322809202/header.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746845907374_9651d09/41fb80a8-691f-45ed-9f91-53c322809202/length.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746845907374_9651d09/41fb80a8-691f-45ed-9f91-53c322809202/link_lists.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746845907374_9651d09/chroma.sqlite3 create mode 100644 SECURITY_AUDIT_Prometheus-beta.md diff --git a/.kno/chunk_review.txt b/.kno/chunk_review.txt new file mode 100644 index 0000000..aaa6e6c --- /dev/null +++ b/.kno/chunk_review.txt @@ -0,0 +1,17251 @@ + +=== File: inference.py === + +-- Chunk 1 -- +// inference.py:57-94 + weak_persp_to_blender( + targets, + camera_scale, + camera_transl, + H, W, + sensor_width=36, + focal_length=5000): + ''' Converts weak-perspective camera to a perspective camera + ''' + if torch.is_tensor(camera_scale): + camera_scale = camera_scale.detach().cpu().numpy() + if torch.is_tensor(camera_transl): + camera_transl = camera_transl.detach().cpu().numpy() + + output = defaultdict(lambda: []) + for ii, target in enumerate(targets): + orig_bbox_size = target.get_field('orig_bbox_size') + bbox_center = target.get_field('orig_center') + z = 2 * focal_length / (camera_scale[ii] * orig_bbox_size) + + transl = [ + camera_transl[ii, 0].item(), camera_transl[ii, 1].item(), + z.item()] + shift_x = - (bbox_center[0] / W - 0.5) + shift_y = (bbox_center[1] - 0.5 * H) / W + focal_length_in_mm = focal_length / W * sensor_width + output['shift_x'].append(shift_x) + output['shift_y'].append(shift_y) + output['transl'].append(transl) + output['focal_length_in_mm'].append(focal_length_in_mm) + output['focal_length_in_px'].append(focal_length) + output['center'].append(bbox_center) + output['sensor_width'].append(sensor_width) + for key in output: + output[key] = np.stack(output[key], axis=0) + return output + + + +-- Chunk 2 -- +// inference.py:95-107 + undo_img_normalization(image, mean, std, add_alpha=True): + if torch.is_tensor(image): + image = image.detach().cpu().numpy().squeeze() + + out_img = (image * std[np.newaxis, :, np.newaxis, np.newaxis] + + mean[np.newaxis, :, np.newaxis, np.newaxis]) + if add_alpha: + out_img = np.pad( + out_img, [[0, 0], [0, 1], [0, 0], [0, 0]], + mode='constant', constant_values=1.0) + return out_img + + + +-- Chunk 3 -- +// inference.py:109-258 + main( + exp_cfg, + show=False, + demo_output_folder='demo_output', + pause=-1, + focal_length=5000, sensor_width=36, + save_vis=True, + save_params=False, + save_mesh=False, + degrees=[], +): + + device = torch.device('cuda') + if not torch.cuda.is_available(): + logger.error('CUDA is not available!') + sys.exit(3) + + logger.remove() + logger.add(lambda x: tqdm.write(x, end=''), + level=exp_cfg.logger_level.upper(), + colorize=True) + + demo_output_folder = osp.expanduser(osp.expandvars(demo_output_folder)) + logger.info(f'Saving results to: {demo_output_folder}') + os.makedirs(demo_output_folder, exist_ok=True) + + model = SMPLXNet(exp_cfg) + try: + model = model.to(device=device) + except RuntimeError: + # Re-submit in case of a device error + sys.exit(3) + + checkpoint_folder = osp.join( + exp_cfg.output_folder, exp_cfg.checkpoint_folder) + checkpointer = Checkpointer(model, save_dir=checkpoint_folder, + pretrained=exp_cfg.pretrained) + + arguments = {'iteration': 0, 'epoch_number': 0} + extra_checkpoint_data = checkpointer.load_checkpoint() + for key in arguments: + if key in extra_checkpoint_data: + arguments[key] = extra_checkpoint_data[key] + + model = model.eval() + + means = np.array(exp_cfg.datasets.body.transforms.mean) + std = np.array(exp_cfg.datasets.body.transforms.std) + + render = save_vis or show + body_crop_size = exp_cfg.get('datasets', {}).get('body', {}).get( + 'transforms').get('crop_size', 256) + if render: + hd_renderer = HDRenderer(img_size=body_crop_size) + + dataloaders = make_all_data_loaders(exp_cfg, split='test') + + body_dloader = dataloaders['body'][0] + + total_time = 0 + cnt = 0 + for bidx, batch in enumerate(tqdm(body_dloader, dynamic_ncols=True)): + + full_imgs_list, body_imgs, body_targets = batch + if full_imgs_list is None: + continue + + full_imgs = to_image_list(full_imgs_list) + body_imgs = body_imgs.to(device=device) + body_targets = [target.to(device) for target in body_targets] + full_imgs = full_imgs.to(device=device) + + torch.cuda.synchronize() + start = time.perf_counter() + model_output = model(body_imgs, body_targets, full_imgs=full_imgs, + device=device) + torch.cuda.synchronize() + elapsed = time.perf_counter() - start + cnt += 1 + total_time += elapsed + + hd_imgs = full_imgs.images.detach().cpu().numpy().squeeze() + body_imgs = body_imgs.detach().cpu().numpy() + body_output = model_output.get('body') + + _, _, H, W = full_imgs.shape + # logger.info(f'{H}, {W}') + # H, W, _ = hd_imgs.shape + if render: + hd_imgs = np.transpose(undo_img_normalization(hd_imgs, means, std), + [0, 2, 3, 1]) + hd_imgs = np.clip(hd_imgs, 0, 1.0) + right_hand_crops = body_output.get('right_hand_crops') + left_hand_crops = torch.flip( + body_output.get('left_hand_crops'), dims=[-1]) + head_crops = body_output.get('head_crops') + bg_imgs = undo_img_normalization(body_imgs, means, std) + + right_hand_crops = undo_img_normalization( + right_hand_crops, means, std) + left_hand_crops = undo_img_normalization( + left_hand_crops, means, std) + head_crops = undo_img_normalization(head_crops, means, std) + + body_output = model_output.get('body', {}) + num_stages = body_output.get('num_stages', 3) + stage_n_out = body_output.get(f'stage_{num_stages - 1:02d}', {}) + model_vertices = stage_n_out.get('vertices', None) + + if stage_n_out is not None: + model_vertices = stage_n_out.get('vertices', None) + + faces = stage_n_out['faces'] + if model_vertices is not None: + model_vertices = model_vertices.detach().cpu().numpy() + camera_parameters = body_output.get('camera_parameters', {}) + camera_scale = camera_parameters['scale'].detach() + camera_transl = camera_parameters['translation'].detach() + + out_img = OrderedDict() + + final_model_vertices = None + stage_n_out = model_output.get('body', {}).get('final', {}) + if stage_n_out is not None: + final_model_vertices = stage_n_out.get('vertices', None) + + if final_model_vertices is not None: + final_model_vertices = final_model_vertices.detach().cpu().numpy() + camera_parameters = model_output.get('body', {}).get( + 'camera_parameters', {}) + camera_scale = camera_parameters['scale'].detach() + camera_transl = camera_parameters['translation'].detach() + + hd_params = weak_persp_to_blender( + body_targets, + camera_scale=camera_scale, + camera_transl=camera_transl, + H=H, W=W, + sensor_width=sensor_width, + focal_length=focal_length, + ) + + if save_vis: + bg_hd_imgs = np.transpose(hd_imgs, [0, 3, 1, 2]) + out_img['hd_imgs'] = bg_hd_imgs + if render: + # Render the initial predictions on the original image resolution + hd_orig_overlays = hd_renderer( + model_vertices, faces, + focal_length=hd_params['focal_length_in_px'], + +-- Chunk 4 -- +// inference.py:259-373 + camera_translation=hd_params['transl'], + camera_center=hd_params['center'], + bg_imgs=bg_hd_imgs, + return_with_alpha=True, + ) + out_img['hd_orig_overlay'] = hd_orig_overlays + + # Render the overlays of the final prediction + if render: + hd_overlays = hd_renderer( + final_model_vertices, + faces, + focal_length=hd_params['focal_length_in_px'], + camera_translation=hd_params['transl'], + camera_center=hd_params['center'], + bg_imgs=bg_hd_imgs, + return_with_alpha=True, + body_color=[0.4, 0.4, 0.7] + ) + out_img['hd_overlay'] = hd_overlays + + for deg in degrees: + hd_overlays = hd_renderer( + final_model_vertices, faces, + focal_length=hd_params['focal_length_in_px'], + camera_translation=hd_params['transl'], + camera_center=hd_params['center'], + bg_imgs=bg_hd_imgs, + return_with_alpha=True, + render_bg=False, + body_color=[0.4, 0.4, 0.7], + deg=deg, + ) + out_img[f'hd_rendering_{deg:03.0f}'] = hd_overlays + + if save_vis: + for key in out_img.keys(): + out_img[key] = np.clip( + np.transpose( + out_img[key], [0, 2, 3, 1]) * 255, 0, 255).astype( + np.uint8) + + for idx in tqdm(range(len(body_targets)), 'Saving ...'): + fname = body_targets[idx].get_field('fname') + curr_out_path = osp.join(demo_output_folder, fname) + os.makedirs(curr_out_path, exist_ok=True) + + if save_vis: + for name, curr_img in out_img.items(): + pil_img.fromarray(curr_img[idx]).save( + osp.join(curr_out_path, f'{name}.png')) + + if save_mesh: + # Store the mesh predicted by the body-crop network + naive_mesh = o3d.geometry.TriangleMesh() + naive_mesh.vertices = Vec3d( + model_vertices[idx] + hd_params['transl'][idx]) + naive_mesh.triangles = Vec3i(faces) + mesh_fname = osp.join(curr_out_path, f'body_{fname}.ply') + o3d.io.write_triangle_mesh(mesh_fname, naive_mesh) + + # Store the final mesh + expose_mesh = o3d.geometry.TriangleMesh() + expose_mesh.vertices = Vec3d( + final_model_vertices[idx] + hd_params['transl'][idx]) + expose_mesh.triangles = Vec3i(faces) + mesh_fname = osp.join(curr_out_path, f'{fname}.ply') + o3d.io.write_triangle_mesh(mesh_fname, expose_mesh) + + if save_params: + params_fname = osp.join(curr_out_path, f'{fname}_params.npz') + out_params = dict(fname=fname) + for key, val in stage_n_out.items(): + if torch.is_tensor(val): + val = val.detach().cpu().numpy()[idx] + out_params[key] = val + for key, val in hd_params.items(): + if torch.is_tensor(val): + val = val.detach().cpu().numpy() + if np.isscalar(val[idx]): + out_params[key] = val[idx].item() + else: + out_params[key] = val[idx] + np.savez_compressed(params_fname, **out_params) + + if show: + nrows = 1 + ncols = 4 + len(degrees) + fig, axes = plt.subplots( + ncols=ncols, nrows=nrows, num=0, + gridspec_kw={'wspace': 0, 'hspace': 0}) + axes = axes.reshape(nrows, ncols) + for ax in axes.flatten(): + ax.clear() + ax.set_axis_off() + + axes[0, 0].imshow(hd_imgs[idx]) + axes[0, 1].imshow(out_img['rgb'][idx]) + axes[0, 2].imshow(out_img['hd_orig_overlay'][idx]) + axes[0, 3].imshow(out_img['hd_overlay'][idx]) + start = 4 + for deg in degrees: + axes[0, start].imshow( + out_img[f'hd_rendering_{deg:03.0f}'][idx]) + start += 1 + + plt.draw() + if pause > 0: + plt.pause(pause) + else: + plt.show() + + logger.info(f'Average inference time: {total_time / cnt}') + + + +=== File: demo.py === + +-- Chunk 1 -- +// demo.py:64-74 + collate_fn(batch): + output_dict = dict() + + for d in batch: + for key, val in d.items(): + if key not in output_dict: + output_dict[key] = [] + output_dict[key].append(val) + return output_dict + + + +-- Chunk 2 -- +// demo.py:75-159 + preprocess_images( + image_folder: str, + exp_cfg, + num_workers: int = 8, batch_size: int = 1, + min_score: float = 0.5, + scale_factor: float = 1.2, + device: Optional[torch.device] = None +) -> dutils.DataLoader: + + if device is None: + device = torch.device('cuda') + if not torch.cuda.is_available(): + logger.error('CUDA is not available!') + sys.exit(3) + + rcnn_model = keypointrcnn_resnet50_fpn(pretrained=True) + rcnn_model.eval() + rcnn_model = rcnn_model.to(device=device) + + transform = Compose( + [ToTensor(), ] + ) + + # Load the images + dataset = ImageFolder(image_folder, transforms=transform) + rcnn_dloader = dutils.DataLoader( + dataset, batch_size=batch_size, num_workers=num_workers, + collate_fn=collate_fn + ) + + out_dir = osp.expandvars('$HOME/Dropbox/boxes') + os.makedirs(out_dir, exist_ok=True) + + img_paths = [] + bboxes = [] + for bidx, batch in enumerate( + tqdm(rcnn_dloader, desc='Processing with R-CNN')): + batch['images'] = [x.to(device=device) for x in batch['images']] + + output = rcnn_model(batch['images']) + for ii, x in enumerate(output): + img = np.transpose( + batch['images'][ii].detach().cpu().numpy(), [1, 2, 0]) + img = (img * 255).astype(np.uint8) + + img_path = batch['paths'][ii] + _, fname = osp.split(img_path) + fname, _ = osp.splitext(fname) + + # out_path = osp.join(out_dir, f'{fname}_{ii:03d}.jpg') + for n, bbox in enumerate(output[ii]['boxes']): + bbox = bbox.detach().cpu().numpy() + if output[ii]['scores'][n].item() < min_score: + continue + img_paths.append(img_path) + bboxes.append(bbox) + + # cv2.rectangle(img, tuple(bbox[:2]), tuple(bbox[2:]), + # (255, 0, 0)) + # cv2.imwrite(out_path, img[:, :, ::-1]) + + dataset_cfg = exp_cfg.get('datasets', {}) + body_dsets_cfg = dataset_cfg.get('body', {}) + + body_transfs_cfg = body_dsets_cfg.get('transforms', {}) + transforms = build_transforms(body_transfs_cfg, is_train=False) + batch_size = body_dsets_cfg.get('batch_size', 64) + + expose_dset = ImageFolderWithBoxes( + img_paths, bboxes, scale_factor=scale_factor, transforms=transforms) + + expose_collate = functools.partial( + collate_batch, use_shared_memory=num_workers > 0, + return_full_imgs=True) + expose_dloader = dutils.DataLoader( + expose_dset, + batch_size=batch_size, + num_workers=num_workers, + collate_fn=expose_collate, + drop_last=False, + pin_memory=True, + ) + return expose_dloader + + + +-- Chunk 3 -- +// demo.py:160-197 + weak_persp_to_blender( + targets, + camera_scale, + camera_transl, + H, W, + sensor_width=36, + focal_length=5000): + ''' Converts weak-perspective camera to a perspective camera + ''' + if torch.is_tensor(camera_scale): + camera_scale = camera_scale.detach().cpu().numpy() + if torch.is_tensor(camera_transl): + camera_transl = camera_transl.detach().cpu().numpy() + + output = defaultdict(lambda: []) + for ii, target in enumerate(targets): + orig_bbox_size = target.get_field('orig_bbox_size') + bbox_center = target.get_field('orig_center') + z = 2 * focal_length / (camera_scale[ii] * orig_bbox_size) + + transl = [ + camera_transl[ii, 0].item(), camera_transl[ii, 1].item(), + z.item()] + shift_x = - (bbox_center[0] / W - 0.5) + shift_y = (bbox_center[1] - 0.5 * H) / W + focal_length_in_mm = focal_length / W * sensor_width + output['shift_x'].append(shift_x) + output['shift_y'].append(shift_y) + output['transl'].append(transl) + output['focal_length_in_mm'].append(focal_length_in_mm) + output['focal_length_in_px'].append(focal_length) + output['center'].append(bbox_center) + output['sensor_width'].append(sensor_width) + for key in output: + output[key] = np.stack(output[key], axis=0) + return output + + + +-- Chunk 4 -- +// demo.py:198-210 + undo_img_normalization(image, mean, std, add_alpha=True): + if torch.is_tensor(image): + image = image.detach().cpu().numpy().squeeze() + + out_img = (image * std[np.newaxis, :, np.newaxis, np.newaxis] + + mean[np.newaxis, :, np.newaxis, np.newaxis]) + if add_alpha: + out_img = np.pad( + out_img, [[0, 0], [0, 1], [0, 0], [0, 0]], + mode='constant', constant_values=1.0) + return out_img + + + +-- Chunk 5 -- +// demo.py:212-361 + main( + image_folder: str, + exp_cfg, + show: bool = False, + demo_output_folder: str = 'demo_output', + pause: float = -1, + focal_length: float = 5000, + rcnn_batch: int = 1, + sensor_width: float = 36, + save_vis: bool = True, + save_params: bool = False, + save_mesh: bool = False, + degrees: Optional[List[float]] = [], +) -> None: + + device = torch.device('cuda') + if not torch.cuda.is_available(): + logger.error('CUDA is not available!') + sys.exit(3) + + logger.remove() + logger.add(lambda x: tqdm.write(x, end=''), + level=exp_cfg.logger_level.upper(), + colorize=True) + + expose_dloader = preprocess_images( + image_folder, exp_cfg, batch_size=rcnn_batch, device=device) + + demo_output_folder = osp.expanduser(osp.expandvars(demo_output_folder)) + logger.info(f'Saving results to: {demo_output_folder}') + os.makedirs(demo_output_folder, exist_ok=True) + + model = SMPLXNet(exp_cfg) + try: + model = model.to(device=device) + except RuntimeError: + # Re-submit in case of a device error + sys.exit(3) + + output_folder = exp_cfg.output_folder + checkpoint_folder = osp.join(output_folder, exp_cfg.checkpoint_folder) + checkpointer = Checkpointer( + model, save_dir=checkpoint_folder, pretrained=exp_cfg.pretrained) + + arguments = {'iteration': 0, 'epoch_number': 0} + extra_checkpoint_data = checkpointer.load_checkpoint() + for key in arguments: + if key in extra_checkpoint_data: + arguments[key] = extra_checkpoint_data[key] + + model = model.eval() + + means = np.array(exp_cfg.datasets.body.transforms.mean) + std = np.array(exp_cfg.datasets.body.transforms.std) + + render = save_vis or show + body_crop_size = exp_cfg.get('datasets', {}).get('body', {}).get( + 'transforms').get('crop_size', 256) + if render: + hd_renderer = HDRenderer(img_size=body_crop_size) + + total_time = 0 + cnt = 0 + for bidx, batch in enumerate(tqdm(expose_dloader, dynamic_ncols=True)): + + full_imgs_list, body_imgs, body_targets = batch + if full_imgs_list is None: + continue + + full_imgs = to_image_list(full_imgs_list) + body_imgs = body_imgs.to(device=device) + body_targets = [target.to(device) for target in body_targets] + full_imgs = full_imgs.to(device=device) + + torch.cuda.synchronize() + start = time.perf_counter() + model_output = model(body_imgs, body_targets, full_imgs=full_imgs, + device=device) + torch.cuda.synchronize() + elapsed = time.perf_counter() - start + cnt += 1 + total_time += elapsed + + hd_imgs = full_imgs.images.detach().cpu().numpy().squeeze() + body_imgs = body_imgs.detach().cpu().numpy() + body_output = model_output.get('body') + + _, _, H, W = full_imgs.shape + # logger.info(f'{H}, {W}') + # H, W, _ = hd_imgs.shape + if render: + hd_imgs = np.transpose(undo_img_normalization(hd_imgs, means, std), + [0, 2, 3, 1]) + hd_imgs = np.clip(hd_imgs, 0, 1.0) + right_hand_crops = body_output.get('right_hand_crops') + left_hand_crops = torch.flip( + body_output.get('left_hand_crops'), dims=[-1]) + head_crops = body_output.get('head_crops') + bg_imgs = undo_img_normalization(body_imgs, means, std) + + right_hand_crops = undo_img_normalization( + right_hand_crops, means, std) + left_hand_crops = undo_img_normalization( + left_hand_crops, means, std) + head_crops = undo_img_normalization(head_crops, means, std) + + body_output = model_output.get('body', {}) + num_stages = body_output.get('num_stages', 3) + stage_n_out = body_output.get(f'stage_{num_stages - 1:02d}', {}) + model_vertices = stage_n_out.get('vertices', None) + + if stage_n_out is not None: + model_vertices = stage_n_out.get('vertices', None) + + faces = stage_n_out['faces'] + if model_vertices is not None: + model_vertices = model_vertices.detach().cpu().numpy() + camera_parameters = body_output.get('camera_parameters', {}) + camera_scale = camera_parameters['scale'].detach() + camera_transl = camera_parameters['translation'].detach() + + out_img = OrderedDict() + + final_model_vertices = None + stage_n_out = model_output.get('body', {}).get('final', {}) + if stage_n_out is not None: + final_model_vertices = stage_n_out.get('vertices', None) + + if final_model_vertices is not None: + final_model_vertices = final_model_vertices.detach().cpu().numpy() + camera_parameters = model_output.get('body', {}).get( + 'camera_parameters', {}) + camera_scale = camera_parameters['scale'].detach() + camera_transl = camera_parameters['translation'].detach() + + hd_params = weak_persp_to_blender( + body_targets, + camera_scale=camera_scale, + camera_transl=camera_transl, + H=H, W=W, + sensor_width=sensor_width, + focal_length=focal_length, + ) + + if save_vis: + bg_hd_imgs = np.transpose(hd_imgs, [0, 3, 1, 2]) + out_img['hd_imgs'] = bg_hd_imgs + if render: + # Render the initial predictions on the original image resolution + hd_orig_overlays = hd_renderer( + +-- Chunk 6 -- +// demo.py:362-478 + model_vertices, faces, + focal_length=hd_params['focal_length_in_px'], + camera_translation=hd_params['transl'], + camera_center=hd_params['center'], + bg_imgs=bg_hd_imgs, + return_with_alpha=True, + ) + out_img['hd_orig_overlay'] = hd_orig_overlays + + # Render the overlays of the final prediction + if render: + hd_overlays = hd_renderer( + final_model_vertices, + faces, + focal_length=hd_params['focal_length_in_px'], + camera_translation=hd_params['transl'], + camera_center=hd_params['center'], + bg_imgs=bg_hd_imgs, + return_with_alpha=True, + body_color=[0.4, 0.4, 0.7] + ) + out_img['hd_overlay'] = hd_overlays + + for deg in degrees: + hd_overlays = hd_renderer( + final_model_vertices, faces, + focal_length=hd_params['focal_length_in_px'], + camera_translation=hd_params['transl'], + camera_center=hd_params['center'], + bg_imgs=bg_hd_imgs, + return_with_alpha=True, + render_bg=False, + body_color=[0.4, 0.4, 0.7], + deg=deg, + ) + out_img[f'hd_rendering_{deg:03.0f}'] = hd_overlays + + if save_vis: + for key in out_img.keys(): + out_img[key] = np.clip( + np.transpose( + out_img[key], [0, 2, 3, 1]) * 255, 0, 255).astype( + np.uint8) + + for idx in tqdm(range(len(body_targets)), 'Saving ...'): + fname = body_targets[idx].get_field('fname') + curr_out_path = osp.join(demo_output_folder, fname) + os.makedirs(curr_out_path, exist_ok=True) + + if save_vis: + for name, curr_img in out_img.items(): + pil_img.fromarray(curr_img[idx]).save( + osp.join(curr_out_path, f'{name}.png')) + + if save_mesh: + # Store the mesh predicted by the body-crop network + naive_mesh = o3d.geometry.TriangleMesh() + naive_mesh.vertices = Vec3d( + model_vertices[idx] + hd_params['transl'][idx]) + naive_mesh.triangles = Vec3i(faces) + mesh_fname = osp.join(curr_out_path, f'body_{fname}.ply') + o3d.io.write_triangle_mesh(mesh_fname, naive_mesh) + + # Store the final mesh + expose_mesh = o3d.geometry.TriangleMesh() + expose_mesh.vertices = Vec3d( + final_model_vertices[idx] + hd_params['transl'][idx]) + expose_mesh.triangles = Vec3i(faces) + mesh_fname = osp.join(curr_out_path, f'{fname}.ply') + o3d.io.write_triangle_mesh(mesh_fname, expose_mesh) + + if save_params: + params_fname = osp.join(curr_out_path, f'{fname}_params.npz') + out_params = dict(fname=fname) + for key, val in stage_n_out.items(): + if torch.is_tensor(val): + val = val.detach().cpu().numpy()[idx] + out_params[key] = val + for key, val in hd_params.items(): + if torch.is_tensor(val): + val = val.detach().cpu().numpy() + if np.isscalar(val[idx]): + out_params[key] = val[idx].item() + else: + out_params[key] = val[idx] + np.savez_compressed(params_fname, **out_params) + + if show: + nrows = 1 + ncols = 4 + len(degrees) + fig, axes = plt.subplots( + ncols=ncols, nrows=nrows, num=0, + gridspec_kw={'wspace': 0, 'hspace': 0}) + axes = axes.reshape(nrows, ncols) + for ax in axes.flatten(): + ax.clear() + ax.set_axis_off() + + axes[0, 0].imshow(hd_imgs[idx]) + axes[0, 1].imshow(out_img['rgb'][idx]) + axes[0, 2].imshow(out_img['hd_orig_overlay'][idx]) + axes[0, 3].imshow(out_img['hd_overlay'][idx]) + start = 4 + for deg in degrees: + axes[0, start].imshow( + out_img[f'hd_rendering_{deg:03.0f}'][idx]) + start += 1 + + plt.draw() + if pause > 0: + plt.pause(pause) + else: + plt.show() + + logger.info(f'Average inference time: {total_time / cnt}') + + + +=== File: requirements.txt === + +-- Chunk 1 -- +// /app/repos/repo_8/requirements.txt:1-14 +fvcore>=0.1.1.post20200716 +loguru>=0.5.1 +matplotlib>=3.3.1 +numpy>=1.19.1 +open3d>=0.10.0.0 +opencv-python>=3.4.3 +Pillow>=7.2.0 +pyrender>=0.1.43 +smplx>=0.1.21 +threadpoolctl>=2.1.0 +torch>=1.6.0 +torchvision>=0.7.0+cu101 +tqdm>=4.48.2 +trimesh>=3.8.1 + +=== File: README.md === + +-- Chunk 1 -- +// /app/repos/repo_8/README.md:1-150 +## ExPose: Monocular Expressive Body Regression through Body-Driven Attention + +[![report](https://img.shields.io/badge/arxiv-report-red)](https://arxiv.org/abs/2008.09062) + +[[Project Page](https://expose.is.tue.mpg.de/)] +[[Paper](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/620/0983.pdf)] +[[Supp. Mat.](https://ps.is.tuebingen.mpg.de/uploads_file/attachment/attachment/621/0983-supp_no_header_compressed.pdf)] + +![SMPL-X Examples](./images/expose.png) + +| Short Video | Long Video | +| --- | --- | +| [![ShortVideo](https://img.youtube.com/vi/a-sVItuoPek/0.jpg)](https://www.youtube.com/watch?v=a-sVItuoPek) | [![LongVideo](https://img.youtube.com/vi/lNTmHLYTiB8/0.jpg)](https://www.youtube.com/watch?v=lNTmHLYTiB8) | + +## Table of Contents + * [License](#license) + * [Description](#description) + * [Dependencies](#dependencies) + * [Preparing the data](#preparing-the-data) + * [Demo](#demo) + * [Inference](#inference) + * [Citation](#citation) + * [Acknowledgments](#acknowledgments) + * [Contact](#contact) + + +## License + +Software Copyright License for non-commercial scientific research purposes. +Please read carefully the following [terms and conditions](LICENSE) and any accompanying +documentation before you download and/or use the ExPose data, model and +software, (the "Data & Software"), including 3D meshes, images, videos, +textures, software, scripts, and animations. By downloading and/or using the +Data & Software (including downloading, cloning, installing, and any other use +of the corresponding github repository), you acknowledge that you have read +these [terms and conditions](LICENSE), understand them, and agree to be bound by them. If +you do not agree with these [terms and conditions](LICENSE), you must not download and/or +use the Data & Software. Any infringement of the terms of this agreement will +automatically terminate your rights under this [License](LICENSE). + +## Description + +**EX**pressive **PO**se and **S**hape r**E**gression (ExPose) is a method +that estimates 3D body pose and shape, hand articulation and facial expression +of a person from a single RGB image. For more details, please see our ECCV paper +[Monocular Expressive Body Regression through Body-Driven Attention](https://expose.is.tue.mpg.de/). +This repository contains: +* A PyTorch demo to run ExPose on images. +* An inference script for the supported datasets. + +## Installation + +To install the necessary dependencies run the following command: +```shell + pip install -r requirements.txt +``` +The code has been tested with two configurations: a) with Python 3.7, CUDA 10.1, CuDNN 7.5 and PyTorch 1.5 on Ubuntu 18.04, and b) with Python 3.6, CUDA 10.2 and PyTorch 1.6 on Ubuntu 18.04. + + +### Preparing the data + +First, you should head to the [project website](https://expose.is.tue.mpg.de/) and create an account. +If you want to stay informed, please opt-in for email communication +and we will reach out with any updates on the project. +Once you have your account, login and head to the download section +to get the pre-trained **ExPose** model. +Create a folder named *data* and extract the downloaded zip there. +You should now have a folder with the following structure: +```bash +data +├── checkpoints +├── all_means.pkl +├── conf.yaml +├── shape_mean.npy +├── SMPLX_to_J14.pkl +``` +For more information on the data, please read the [data documentation](doc/data.md). +If you don't already have an account on the [SMPL-X website](https://smpl-x.is.tue.mpg.de/), +please register to be able to download the model. Afterward, extract the SMPL-X model +zip inside the data folder you created above. +```bash +data +├── models +│   ├── smplx +``` +You are now ready to run the demo and inference scripts. + +### Demo + +We provide a script to run **ExPose** directly on images. +To get you started, we provide a sample folder, taken from [pexels](https://pexels.com), +which can be processed with the the following command: +```shell + python demo.py --image-folder samples \ + --exp-cfg data/conf.yaml \ + --show=False \ + --output-folder OUTPUT_FOLDER \ + --save-params [True/False] \ + --save-vis [True/False] \ + --save-mesh [True/False] +``` +The script will use a *Keypoint R-CNN* from *torchvision* to detect people in +the images and then produce a SMPL-X prediction for each using **ExPose**. +You should see the following output for the sample image: + +| ![Sample](samples/man-in-red-crew-neck-sweatshirt-photography-941693.png) | ![HD Overlay](images/hd_overlay.png) | +| --- | --- | + +### Inference + +The [inference](inference.py) script can be used to run inference on one of the supported +datasets. For example, if you have a folder with images and OpenPose keypoints +with the following structure: +```bash +folder +├── images +│   ├── img0001.jpg +│   └── img0002.jpg +│   └── img0002.jpg +├── keypoints +│   ├── img0001_keypoints.json +│   └── img0002_keypoints.json +│   └── img0002_keypoints.json +``` +Then you can use the following command to run ExPose for each person: +```shell +python inference.py --exp-cfg data/conf.yaml \ + --datasets openpose \ + --exp-opts datasets.body.batch_size B datasets.body.openpose.data_folder folder \ + --show=[True/False] \ + --output-folder OUTPUT_FOLDER \ + --save-params [True/False] \ + --save-vis [True/False] \ + --save-mesh [True/False] +``` +You can select if you want to save the estimated parameters, meshes, and renderings by +setting the corresponding flags. + +## Citation + +If you find this Model & Software useful in your research we would kindly ask you to cite: + +```bibtex +@inproceedings{ExPose:2020, + title= {Monocular Expressive Body Regression through Body-Driven Attention}, + author= {Choutas, Vasileios and Pavlakos, Georgios and Bolkart, Timo and Tzionas, Dimitrios and Black, Michael J.}, + booktitle = {European Conference on Computer Vision (ECCV)}, + year = {2020}, + url = {https://expose.is.tue.mpg.de} +} + +-- Chunk 2 -- +// /app/repos/repo_8/README.md:151-173 +``` +```bibtex +@inproceedings{SMPL-X:2019, + title = {Expressive Body Capture: 3D Hands, Face, and Body from a Single Image}, + author = {Pavlakos, Georgios and Choutas, Vasileios and Ghorbani, Nima and Bolkart, Timo and Osman, Ahmed A. A. and Tzionas, Dimitrios and Black, Michael J.}, + booktitle = {Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR)}, + year = {2019} +} +``` + +## Acknowledgments + +We thank Haiwen Feng for the FLAME fits, +Nikos Kolotouros, Muhammed Kocabas and Nikos Athanasiou for helpful discussions, +Sai Kumar Dwivedi and Lea Muller for proofreading, +Mason Landry and Valerie Callaghan for video voiceovers. + +## Contact +The code of this repository was implemented by [Vassilis Choutas](mailto:vassilis.choutas@tuebingen.mpg.de). + +For questions, please contact [expose@tue.mpg.de](mailto:expose@tue.mpg.de). + +For commercial licensing (and all related questions for business applications), please contact [ps-licensing@tue.mpg.de](mailto:ps-licensing@tue.mpg.de). + +=== File: expose/evaluation.py === + +-- Chunk 1 -- +// evaluation.py:52-57 + make_filter(name): + def filter(record): + return record['extra'].get('key_name') == name + return filter + + + +-- Chunk 2 -- +// evaluation.py:58-207 +ss Evaluator(object): + def __init__(self, exp_cfg, rank=0, distributed=False): + super(Evaluator, self).__init__() + self.rank = rank + self.distributed = distributed + + self.alpha_blend = exp_cfg.get('alpha_blend', 0.7) + j14_regressor_path = exp_cfg.j14_regressor_path + with open(j14_regressor_path, 'rb') as f: + self.J14_regressor = pickle.load(f, encoding='latin1') + part_map_path = osp.expandvars(exp_cfg.part_map) + with open(part_map_path, 'rb') as f: + data = pickle.load(f) + self.num2part = data['num2part'] + self.segm = data['segm'] + + smplx_valid_verts_fn = osp.expandvars( + exp_cfg.get('smplx_valid_verts_fn', '')) + self.use_body_verts = osp.exists(smplx_valid_verts_fn) + if self.use_body_verts: + self.use_hands_for_shape = exp_cfg.get( + 'use_hands_for_shape', False) + verts_data = np.load(smplx_valid_verts_fn) + if self.use_hands_for_shape: + # First column should be SMPL vertices + self.smplx_valid_verts = verts_data['mapping'][:, 1] + else: + self.smplx_valid_verts = verts_data['no_hands_mapping'][:, 1] + self.smplx_valid_verts = np.asarray( + self.smplx_valid_verts, dtype=np.int64) + + body_vertex_ids_path = osp.expandvars( + exp_cfg.get('body_vertex_ids_path', '')) + body_vertex_ids = None + if osp.exists(body_vertex_ids_path): + body_vertex_ids = np.load(body_vertex_ids_path).astype(np.int32) + self.body_vertex_ids = body_vertex_ids + + face_vertex_ids_path = osp.expandvars( + exp_cfg.get('face_vertex_ids_path', '')) + face_vertex_ids = None + if osp.exists(face_vertex_ids_path): + face_vertex_ids = np.load(face_vertex_ids_path).astype(np.int32) + self.face_vertex_ids = face_vertex_ids + + hand_vertex_ids_path = osp.expandvars( + exp_cfg.get('hand_vertex_ids_path', '')) + left_hand_vertex_ids, right_hand_vertex_ids = None, None + if osp.exists(hand_vertex_ids_path): + with open(hand_vertex_ids_path, 'rb') as f: + vertex_idxs_data = pickle.load(f, encoding='latin1') + left_hand_vertex_ids = vertex_idxs_data['left_hand'] + right_hand_vertex_ids = vertex_idxs_data['right_hand'] + + self.left_hand_vertex_ids = left_hand_vertex_ids + self.right_hand_vertex_ids = right_hand_vertex_ids + + self.imgs_per_row = exp_cfg.get('imgs_per_row', 2) + + self.save_part_v2v = exp_cfg.save_part_v2v + + self.exp_cfg = exp_cfg.clone() + self.output_folder = osp.expandvars(exp_cfg.output_folder) + + self.summary_folder = osp.join(self.output_folder, + exp_cfg.summary_folder) + os.makedirs(self.summary_folder, exist_ok=True) + self.summary_steps = exp_cfg.summary_steps + + self.results_folder = osp.join(self.output_folder, + exp_cfg.results_folder) + os.makedirs(self.results_folder, exist_ok=True) + self.loggers = defaultdict(lambda: None) + + self.body_degrees = exp_cfg.get('degrees', {}).get( + 'body', [90, 180, 270]) + self.hand_degrees = exp_cfg.get('degrees', {}).get( + 'hand', [90, 180, 270]) + self.head_degrees = exp_cfg.get('degrees', {}).get( + 'head', [90, 180, 270]) + + self.body_alignments = {'procrustes': ProcrustesAlignmentMPJPE(), + 'pelvis': PelvisAlignmentMPJPE() + } + hand_fscores_thresh = exp_cfg.get('fscores_thresh', {}).get( + 'hand', [5.0 / 1000, 15.0 / 1000]) + self.hand_fscores_thresh = hand_fscores_thresh + + self.hand_alignments = { + 'procrustes': ProcrustesAlignmentMPJPE( + fscore_thresholds=hand_fscores_thresh), + } + head_fscores_thresh = exp_cfg.get('fscores_thresh', {}).get( + 'head', [5.0 / 1000, 15.0 / 1000]) + self.head_fscores_thresh = head_fscores_thresh + self.head_alignments = { + 'procrustes': ProcrustesAlignmentMPJPE( + fscore_thresholds=head_fscores_thresh)} + + self.plot_conf_thresh = exp_cfg.plot_conf_thresh + + idxs_dict = get_part_idxs() + self.body_idxs = idxs_dict['body'] + self.hand_idxs = idxs_dict['hand'] + self.left_hand_idxs = idxs_dict['left_hand'] + self.right_hand_idxs = idxs_dict['right_hand'] + self.flame_idxs = idxs_dict['flame'] + + self.means = np.array(self.exp_cfg.datasets.body.transforms.mean) + self.std = np.array(self.exp_cfg.datasets.body.transforms.std) + + body_crop_size = exp_cfg.get('datasets', {}).get('body', {}).get( + 'crop_size', 256) + self.body_renderer = OverlayRenderer(img_size=body_crop_size) + + hand_crop_size = exp_cfg.get('datasets', {}).get('hand', {}).get( + 'crop_size', 256) + self.hand_renderer = OverlayRenderer(img_size=hand_crop_size) + + head_crop_size = exp_cfg.get('datasets', {}).get('head', {}).get( + 'crop_size', 256) + self.head_renderer = OverlayRenderer(img_size=head_crop_size) + + self.render_gt_meshes = exp_cfg.get('render_gt_meshes', True) + if self.render_gt_meshes: + self.gt_body_renderer = GTRenderer(img_size=body_crop_size) + self.gt_hand_renderer = GTRenderer(img_size=hand_crop_size) + self.gt_head_renderer = GTRenderer(img_size=head_crop_size) + + @torch.no_grad() + def __enter__(self): + self.filewriter = SummaryWriter(self.summary_folder, max_queue=1) + return self + + def __exit__(self, exception_type, exception_value, traceback): + self.filewriter.close() + + def create_summaries(self, step, dset_name, images, targets, + model_output, camera_parameters, + renderer=None, gt_renderer=None, + degrees=None, prefix=''): + if not hasattr(self, 'filewriter'): + return + if degrees is None: + degrees = [] + + crop_size = images.shape[-1] + + imgs = (images * self.std[np.newaxis, :, np.newaxis, np.newaxis] + + self.means[np.newaxis, :, np.newaxis, np.newaxis]) + +-- Chunk 3 -- +// evaluation.py:208-357 + summary_imgs = OrderedDict() + summary_imgs['rgb'] = imgs + + gt_keyp_imgs = [] + for img_idx in range(imgs.shape[0]): + input_img = np.ascontiguousarray( + np.transpose(imgs[img_idx], [1, 2, 0])) + gt_keyp2d = targets[img_idx].smplx_keypoints.detach( + ).cpu().numpy() + gt_conf = targets[img_idx].conf.detach().cpu().numpy() + + gt_keyp2d[:, 0] = ( + gt_keyp2d[:, 0] * 0.5 + 0.5) * crop_size + gt_keyp2d[:, 1] = ( + gt_keyp2d[:, 1] * 0.5 + 0.5) * crop_size + + gt_keyp_img = create_skel_img( + input_img, gt_keyp2d, + targets[img_idx].CONNECTIONS, + gt_conf > 0, + names=KEYPOINT_NAMES) + + gt_keyp_img = np.transpose(gt_keyp_img, [2, 0, 1]) + gt_keyp_imgs.append(gt_keyp_img) + gt_keyp_imgs = np.stack(gt_keyp_imgs) + + # Add the ground-truth keypoints + summary_imgs['gt_keypoints'] = gt_keyp_imgs + + proj_joints = model_output.get('proj_joints', None) + if proj_joints is not None: + proj_points = model_output[ + 'proj_joints'].detach().cpu().numpy() + proj_points = (proj_points * 0.5 + 0.5) * crop_size + + reproj_joints_imgs = [] + for img_idx in range(imgs.shape[0]): + gt_conf = targets[img_idx].conf.detach().cpu().numpy() + + input_img = np.ascontiguousarray( + np.transpose(imgs[img_idx], [1, 2, 0])) + + reproj_joints_img = create_skel_img( + input_img, + proj_points[img_idx], + targets[img_idx].CONNECTIONS, + valid=gt_conf > 0, names=KEYPOINT_NAMES) + + reproj_joints_img = np.transpose( + reproj_joints_img, [2, 0, 1]) + reproj_joints_imgs.append(reproj_joints_img) + + # Add the the projected keypoints + reproj_joints_imgs = np.stack(reproj_joints_imgs) + summary_imgs['proj_joints'] = reproj_joints_imgs + + render_gt_meshes = (self.render_gt_meshes and + any([t.has_field('vertices') for t in targets])) + if render_gt_meshes: + gt_mesh_imgs = [] + faces = model_output['faces'] + for bidx, t in enumerate(targets): + if not (t.has_field('vertices') and t.has_field('intrinsics')): + gt_mesh_imgs.append(np.zeros_like(imgs[bidx])) + continue + + curr_gt_vertices = t.get_field( + 'vertices').vertices.detach().cpu().numpy().squeeze() + intrinsics = t.get_field('intrinsics') + + mesh_img = gt_renderer( + curr_gt_vertices[np.newaxis], faces=faces, + intrinsics=intrinsics[np.newaxis], + bg_imgs=imgs[[bidx]]) + gt_mesh_imgs.append(mesh_img.squeeze()) + + gt_mesh_imgs = np.stack(gt_mesh_imgs) + B, C, H, W = gt_mesh_imgs.shape + row_pad = (crop_size - H) // 2 + gt_mesh_imgs = np.pad( + gt_mesh_imgs, + [[0, 0], [0, 0], [row_pad, row_pad], [row_pad, row_pad]]) + summary_imgs['gt_meshes'] = gt_mesh_imgs + + vertices = model_output.get('vertices', None) + if vertices is not None: + body_imgs = [] + + camera_scale = camera_parameters.scale.detach() + camera_transl = camera_parameters.translation.detach() + + vertices = vertices.detach().cpu().numpy() + faces = model_output['faces'] + body_imgs = renderer( + vertices, faces, + camera_scale, camera_transl, + bg_imgs=imgs, + return_with_alpha=False, + ) + # Add the rendered meshes + summary_imgs['overlay'] = body_imgs.copy() + + for deg in degrees: + body_imgs = renderer( + vertices, faces, + camera_scale, camera_transl, + deg=deg, + return_with_alpha=False, + ) + summary_imgs[f'{deg:03d}'] = body_imgs.copy() + + summary_imgs = np.concatenate( + list(summary_imgs.values()), axis=3) + img_grid = make_grid( + torch.from_numpy(summary_imgs), nrow=self.imgs_per_row) + img_tab_name = (f'{dset_name}/{prefix}/Images' if len(prefix) > 0 else + f'{dset_name}/Images') + self.filewriter.add_image(img_tab_name, img_grid, step) + return + + def build_metric_logger(self, name): + output_fn = osp.join( + self.results_folder, name + '.log') + if self.loggers[name] is None: + logger.add(output_fn, filter=make_filter(name)) + self.loggers[name] = logger.bind(key_name=name) + + def compute_mpjpe(self, model_joints, targets, + alignments, + gt_joint_idxs=None, + joint_idxs=None): + gt_keyps = [target.get_field( + 'keypoints3d'). smplx_keypoints.detach().cpu().numpy() + for target in targets + if target.has_field('keypoints3d')] + gt_conf = [target.get_field('keypoints3d').conf.detach().cpu().numpy() + for target in targets + if target.has_field('keypoints3d')] + idxs = [idx + for idx, target in enumerate(targets) + if target.has_field('keypoints3d')] + if len(gt_keyps) < 1: + out_array = { + key: np.zeros(model_joints.shape[:2], dtype=model_joints.dtype) + for key in alignments + } + return {'error': defaultdict(lambda: 0.0), + 'valid': 0, 'array': out_array} + if model_joints is None: + return {'error': defaultdict(lambda: 0.0), + +-- Chunk 4 -- +// evaluation.py:358-507 + 'valid': 0, 'array': out_array} + + if torch.is_tensor(model_joints): + model_joints = model_joints.detach().cpu().numpy() + if joint_idxs is None: + joint_idxs = np.arange(0, model_joints.shape[1]) + + gt_keyps = np.asarray(gt_keyps) + gt_conf = np.asarray(gt_conf) + if gt_joint_idxs is not None: + gt_keyps = gt_keyps[:, gt_joint_idxs] + gt_conf = gt_conf[:, gt_joint_idxs] + if joint_idxs is not None: + model_joints = model_joints[:, joint_idxs] + num_valid_joints = (gt_conf > 0).sum() + idxs = np.asarray(idxs) + + mpjpe_err = {} + for alignment_name, alignment in alignments.items(): + mpjpe_err[alignment_name] = [] + for bidx in range(gt_keyps.shape[0]): + align_out = alignment( + model_joints[bidx, :], + gt_keyps[bidx, :]) + mpjpe_err[alignment_name].append( + align_out['point']) + mpjpe_err[alignment_name] = np.stack(mpjpe_err[alignment_name]) + + return { + 'valid': num_valid_joints, + 'array': mpjpe_err + } + + def compute_v2v(self, model_vertices, targets, alignments, vids=None): + if model_vertices is None: + return {'valid': 0, + 'fscore': {}, + 'point': {}} + + gt_vertices = [target.get_field('vertices'). + vertices.detach().cpu().numpy() + for target in targets + if target.has_field('vertices')] + if len(gt_vertices) < 1: + out_array = { + key: np.zeros( + model_vertices.shape[:2], dtype=np.float32) + for key in alignments + } + return {'fscore': {}, + 'valid': 0, 'point': out_array} + gt_vertices = np.array(gt_vertices) + if torch.is_tensor(model_vertices): + model_vertices = model_vertices.detach().cpu().numpy() + + if vids is not None: + gt_vertices = gt_vertices[:, vids] + model_vertices = model_vertices[:, vids] + + v2v_err = {} + fscores = {} + for alignment_name, alignment in alignments.items(): + v2v_err[alignment_name] = [] + fscores[alignment_name] = defaultdict(lambda: []) + + for bidx in range(gt_vertices.shape[0]): + align_out = alignment( + model_vertices[bidx], gt_vertices[bidx]) + v2v_err[alignment_name].append(align_out['point']) + for thresh, val in align_out['fscore'].items(): + fscores[alignment_name][thresh].append( + val['fscore'].copy()) + + v2v_err[alignment_name] = np.stack(v2v_err[alignment_name]) + for thresh in fscores[alignment_name]: + fscores[alignment_name][thresh] = np.stack( + fscores[alignment_name][thresh]) + # logger.info(f'{alignment_name}: {v2v_err[alignment_name].shape}') + + return {'point': v2v_err, 'fscore': fscores} + + def run_head_eval(self, dataloaders, model, step, alignments=None, + device=None): + head_model = model.get_head_model() + if alignments is None: + alignments = {'procrustes': ProcrustesAlignmentMPJPE(), + 'root': RootAlignmentMPJPE()} + if device is None: + device = torch.device('cpu') + + for dataloader in dataloaders: + dset = dataloader.dataset + dset_name = dset.name() + dset_metrics = dset.metrics + + compute_v2v = 'v2v' in dset_metrics + if compute_v2v: + v2v_err = {key: [] for key in alignments} + self.build_metric_logger(f'{dset_name}_v2v') + + fscores = {} + for alignment_name in alignments: + fscores[alignment_name] = {} + for thresh in self.head_fscores_thresh: + fscores[alignment_name][thresh] = [] + self.build_metric_logger( + f'{dset_name}_fscore_{thresh}') + + desc = f'Evaluating dataset: {dset_name}' + for idx, batch in enumerate( + tqdm.tqdm(dataloader, desc=desc, dynamic_ncols=True)): + _, head_imgs, head_targets = batch + + head_imgs = head_imgs.to(device=device) + head_targets = [t.to(device=device) for t in head_targets] + + model_output = head_model(head_imgs=head_imgs, + num_head_imgs=len(head_imgs)) + + head_vertices = model_output.get('vertices') + + out_params = {} + for key, val in model_output.items(): + if not torch.is_tensor(val): + continue + out_params[key] = val.detach().cpu().numpy() + + if compute_v2v: + v2v_output = self.compute_v2v( + head_vertices, head_targets, alignments) + for alignment_name, val in v2v_output['point'].items(): + v2v_err[alignment_name].append(val.copy()) + + for alignment_name, val in v2v_output['fscore'].items(): + for thresh, fscore_val in val.items(): + fscores[alignment_name][thresh].append( + fscore_val) + if idx == 0: + camera_parameters = model_output.get('camera_parameters') + self.create_summaries( + step, dset_name, + head_imgs.detach().cpu().numpy(), + head_targets, + model_output, + camera_parameters=camera_parameters, + degrees=self.head_degrees, + renderer=self.head_renderer, + gt_renderer=self.gt_head_renderer, + prefix='Head', + ) + +-- Chunk 5 -- +// evaluation.py:508-657 + + if compute_v2v: + for key, val in v2v_err.items(): + val = np.concatenate(val, axis=0) + # Divide by the number of items in the dataset and the + # number of vertices + metric_value = val.mean() * 1000 + alignment_name = key.title() + + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/Head_V2V' + # summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + + for alignment_name, val in fscores.items(): + for thresh, fscore_arr in val.items(): + fscore_arr = np.concatenate(fscore_arr) + if len(fscore_arr) < 1: + continue + metric_value = np.asarray(fscore_arr).mean() + logger.info( + '[{:06d}] {}: F-Score@{:.1f}/{}: {:.3f} ', + step, dset_name, thresh * 1000, + alignment_name, metric_value) + + summary_name = (f'{dset_name}/F@{thresh * 1000:.1f}/' + f'{alignment_name}') + self.filewriter.add_scalar( + summary_name, metric_value, step) + return + + def run_hand_eval(self, dataloaders, model, step, alignments=None, + device=None): + hand_model = model.get_hand_model() + if alignments is None: + alignments = {'procrustes': ProcrustesAlignmentMPJPE(), + 'root': RootAlignmentMPJPE()} + if device is None: + device = torch.device('cpu') + + for dataloader in dataloaders: + dset = dataloader.dataset + dset_name = dset.name() + dset_metrics = dset.metrics + + compute_mpjpe = 'mpjpe' in dset_metrics + if compute_mpjpe: + hand_valid = 0 + mpjpe_err = { + alignment_name: [] for alignment_name in alignments} + self.build_metric_logger(f'{dset_name}_mpjpe') + self.build_metric_logger(f'{dset_name}_hand_mpjpe') + + compute_v2v = 'v2v' in dset_metrics + if compute_v2v: + v2v_err = {key: [] for key in alignments} + self.build_metric_logger(f'{dset_name}_v2v') + + fscores = {} + for alignment_name in alignments: + fscores[alignment_name] = {} + for thresh in self.hand_fscores_thresh: + fscores[alignment_name][thresh] = [] + self.build_metric_logger( + f'{dset_name}_fscore_{thresh}') + + desc = f'Evaluating dataset: {dset_name}' + for idx, batch in enumerate( + tqdm.tqdm(dataloader, desc=desc, dynamic_ncols=True)): + _, hand_imgs, hand_targets = batch + + hand_imgs = hand_imgs.to(device=device) + hand_targets = [t.to(device=device) for t in hand_targets] + + model_output = hand_model(hand_imgs=hand_imgs, + num_hand_imgs=len(hand_imgs)) + + hand_vertices = model_output.get('vertices') + hand_joints = model_output.get('joints') + + out_params = {} + for key, val in model_output.items(): + if not torch.is_tensor(val): + continue + out_params[key] = val.detach().cpu().numpy() + + if compute_mpjpe: + hand_mpjpe_out = self.compute_mpjpe( + hand_joints, hand_targets, + gt_joint_idxs=self.right_hand_idxs, + alignments=alignments) + hand_valid += hand_mpjpe_out['valid'].sum() + + for alignment_name, val in hand_mpjpe_out['array'].items(): + if len(val) < 1: + continue + mpjpe_err[alignment_name].append(val) + + if compute_v2v: + v2v_output = self.compute_v2v( + hand_vertices, hand_targets, alignments) + for alignment_name, val in v2v_output['point'].items(): + v2v_err[alignment_name].append(val) + + for alignment_name, val in v2v_output['fscore'].items(): + for thresh, fscore_val in val.items(): + fscores[alignment_name][thresh].append(fscore_val) + if idx == 0: + camera_parameters = model_output.get('camera_parameters') + self.create_summaries( + step, dset_name, + hand_imgs.detach().cpu().numpy(), + hand_targets, + model_output, + camera_parameters=camera_parameters, + degrees=self.hand_degrees, + renderer=self.hand_renderer, + gt_renderer=self.gt_hand_renderer, + prefix='Hand', + ) + + # Compute hand Mean per Joint Point Error (MPJPE) + if compute_mpjpe: + for key, val in mpjpe_err.items(): + val = np.concatenate(val) + metric_value = val.sum() / hand_valid * 1000 + alignment_name = key.title() + + # Store the Procrustes aligned MPJPE + self.loggers[f'{dset_name}_mpjpe'].info( + '[{:06d}] {}: {} 3D Hand Keypoint error: {:.4f} mm', + step, + dset_name, + alignment_name, + metric_value) + + metric_name = f'{dset_name}/{alignment_name}/Hand' + self.filewriter.add_scalar(metric_name, metric_value, step) + + if compute_v2v: + for key, val in v2v_err.items(): + val = np.concatenate(val, axis=0) + # Divide by the number of items in the dataset and the + # number of vertices + metric_value = val.mean() * 1000 + alignment_name = key.title() + + +-- Chunk 6 -- +// evaluation.py:658-807 + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/Hand_V2V' + # summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + + for alignment_name, val in fscores.items(): + for thresh, fscore_arr in val.items(): + metric_value = np.concatenate( + fscore_arr, axis=0).mean() + summary_name = (f'{dset_name}/F@{thresh * 1000:.1f}/' + f'{alignment_name}') + self.filewriter.add_scalar( + summary_name, metric_value, step) + return + + def run_body_eval(self, dataloaders, model, step, alignments=None, + device=None): + if alignments is None: + alignments = {'procrustes': ProcrustesAlignmentMPJPE(), + # 'root': RootAlignmentMPJPE(), + } + if device is None: + device = torch.device('cpu') + + for dataloader in dataloaders: + dset = dataloader.dataset + + dset_name = dset.name() + dset_metrics = dset.metrics + + compute_body_mpjpe = 'body_mpjpe' in dset_metrics + if compute_body_mpjpe: + body_valid = 0 + body_mpjpe_err = { + alignment_name: [] for alignment_name in alignments} + self.build_metric_logger(f'{dset_name}_body_mpjpe') + + compute_hand_mpjpe = 'hand_mpjpe' in dset_metrics + if compute_hand_mpjpe: + left_hand_valid = 0 + left_hand_mpjpe_err = { + alignment_name: [] for alignment_name in alignments} + + right_hand_valid = 0 + right_hand_mpjpe_err = { + alignment_name: [] for alignment_name in alignments} + self.build_metric_logger(f'{dset_name}_left_hand_mpjpe') + self.build_metric_logger(f'{dset_name}_right_hand_mpjpe') + + compute_head_mpjpe = 'head_mpjpe' in dset_metrics + if compute_head_mpjpe: + head_valid = 0 + head_mpjpe_err = { + alignment_name: [] for alignment_name in alignments} + self.build_metric_logger(f'{dset_name}_head_mpjpe') + + compute_mpjpe14 = 'mpjpe14' in dset_metrics + if compute_mpjpe14: + mpjpe14_err = { + alignment_name: [] for alignment_name in alignments} + self.build_metric_logger(f'{dset_name}_mpjpe14') + + compute_v2v = 'v2v' in dset_metrics + if compute_v2v: + # num_verts = len(self.segm) + v2v_err = {key: [] for key in alignments} + self.build_metric_logger(f'{dset_name}_v2v') + + body_v2v_err = {key: [] for key in alignments} + left_hand_v2v_err = {key: [] for key in alignments} + right_hand_v2v_err = {key: [] for key in alignments} + face_v2v_err = {key: [] for key in alignments} + + if not any([compute_mpjpe14, compute_body_mpjpe, compute_v2v]): + continue + + desc = f'Evaluating dataset: {dset_name}' + + for idx, batch in enumerate( + tqdm.tqdm(dataloader, desc=desc, dynamic_ncols=True)): + + full_imgs_list, body_imgs, body_targets = batch + full_imgs = to_image_list(full_imgs_list) + + hand_imgs, hand_targets = None, None + head_imgs, head_targets = None, None + + if full_imgs is not None: + full_imgs = full_imgs.to(device=device) + body_imgs = body_imgs.to(device=device) + body_targets = [target.to(device) for target in body_targets] + + model_output = model( + body_imgs, body_targets, + hand_imgs=hand_imgs, hand_targets=hand_targets, + head_imgs=head_imgs, head_targets=head_targets, + full_imgs=full_imgs, + device=device) + + body_vertices = None + body_output = model_output.get('body') + body_stage_n_out = body_output.get('final', {}) + if body_stage_n_out is not None: + body_vertices = body_stage_n_out.get('vertices', None) + body_joints = body_stage_n_out.get('joints', None) + if body_vertices is None: + num_stages = body_output.get('num_stages', 1) + body_stage_n_out = body_output.get( + f'stage_{num_stages - 1:02d}', {}) + if body_stage_n_out is not None: + body_vertices = body_stage_n_out.get('vertices', None) + body_joints = body_stage_n_out.get('joints', None) + + out_params = {} + for key, val in body_stage_n_out.items(): + if not torch.is_tensor(val): + continue + out_params[key] = val.detach().cpu().numpy() + + if compute_body_mpjpe: + body_mpjpe_out = self.compute_mpjpe( + body_joints, body_targets, + gt_joint_idxs=self.body_idxs, + joint_idxs=self.body_idxs, + alignments=alignments) + body_valid += body_mpjpe_out['valid'] + + computed_errors = body_mpjpe_out['array'] + for alignment_name, val in computed_errors.items(): + logger.info( + f'{alignment_name}: ' + f'{val.shape}') + if alignment_name == 'pelvis': + continue + body_mpjpe_err[alignment_name].append( + val) + + if compute_head_mpjpe: + head_mpjpe_out = self.compute_mpjpe( + body_joints, head_targets, + gt_joint_idxs=self.head_idxs, + joint_idxs=self.head_idxs, + alignments=alignments) + head_valid += head_mpjpe_out['valid'] + + computed_errors = head_mpjpe_out['array'] + for alignment_name, val in computed_errors.items(): + +-- Chunk 7 -- +// evaluation.py:808-957 + if alignment_name == 'pelvis': + continue + head_mpjpe_err[alignment_name].append(val) + + if compute_hand_mpjpe: + left_hand_mpjpe_out = self.compute_mpjpe( + body_joints, body_targets, + gt_joint_idxs=self.left_hand_idxs, + joint_idxs=self.left_hand_idxs, + alignments=alignments) + left_hand_valid += left_hand_mpjpe_out['valid'] + + computed_errors = left_hand_mpjpe_out['array'] + for alignment_name, val in computed_errors.items(): + if alignment_name == 'pelvis': + continue + left_hand_mpjpe_err[alignment_name].append(val) + + right_hand_mpjpe_out = self.compute_mpjpe( + body_joints, body_targets, + gt_joint_idxs=self.right_hand_idxs, + joint_idxs=self.right_hand_idxs, + alignments=alignments) + right_hand_valid += right_hand_mpjpe_out['valid'] + + computed_errors = right_hand_mpjpe_out['array'] + for alignment_name, val in computed_errors.items(): + if alignment_name == 'pelvis': + continue + right_hand_mpjpe_err[alignment_name].append(val) + + if compute_v2v: + v2v_output = self.compute_v2v( + body_vertices, body_targets, alignments) + for alignment_name, val in v2v_output['point'].items(): + if alignment_name == 'pelvis': + continue + v2v_err[alignment_name].append(val) + + if self.body_vertex_ids is not None: + body_v2v_output = self.compute_v2v( + body_vertices, body_targets, + alignments, vids=self.body_vertex_ids + ) + for alignment_name, val in body_v2v_output['point'].items(): + if alignment_name == 'pelvis': + continue + body_v2v_err[alignment_name].append(val) + if self.left_hand_vertex_ids is not None: + left_hand_v2v_output = self.compute_v2v( + body_vertices, body_targets, + alignments, vids=self.left_hand_vertex_ids + ) + iterator = left_hand_v2v_output['point'].items() + for alignment_name, val in iterator: + if alignment_name == 'pelvis': + continue + left_hand_v2v_err[alignment_name].append(val) + if self.right_hand_vertex_ids is not None: + right_hand_v2v_output = self.compute_v2v( + body_vertices, body_targets, + alignments, vids=self.right_hand_vertex_ids + ) + iterator = right_hand_v2v_output['point'].items() + for alignment_name, val in iterator: + if alignment_name == 'pelvis': + continue + right_hand_v2v_err[alignment_name].append(val) + if self.face_vertex_ids is not None: + face_v2v_output = self.compute_v2v( + body_vertices, body_targets, + alignments, vids=self.face_vertex_ids + ) + for alignment_name, val in face_v2v_output['point'].items(): + if alignment_name == 'pelvis': + continue + face_v2v_err[alignment_name].append(val) + + if compute_mpjpe14 and body_vertices is not None: + gt_joints14 = [target.get_field('joints14'). + joints.detach().cpu().numpy() + for target in body_targets + if target.has_field('joints14')] + if len(gt_joints14) > 0: + gt_joints14 = np.asarray(gt_joints14) + if torch.is_tensor(body_vertices): + body_vertices = body_vertices.detach( + ).cpu().numpy() + + pred_joints = np.einsum( + 'jv,bvm->bjm', self.J14_regressor, body_vertices) + for alignment_name, alignment in alignments.items(): + for bidx in range(gt_joints14.shape[0]): + mpjpe14_err[alignment_name].append( + alignment( + pred_joints[bidx], + gt_joints14[bidx])['point']) + + if idx == 0: + camera_parameters = body_output.get('camera_parameters') + self.create_summaries( + step, dset_name, + body_imgs.detach().cpu().numpy(), + body_targets, + body_stage_n_out, + camera_parameters=camera_parameters, + renderer=self.body_renderer, + gt_renderer=self.gt_body_renderer, + degrees=self.body_degrees, + ) + + # Compute Body Mean per Joint point error + if compute_body_mpjpe: + for key, val in body_mpjpe_err.items(): + val = np.concatenate(val) + logger.info(f'{key}: {val.shape}') + # Compute the mean over the dataset and convert to + # millimeters + logger.info(f'body valid: {body_valid}') + metric_value = val.sum() / body_valid * 1000 + alignment_name = key.title() + + # Store the Procrustes aligned MPJPE + self.loggers[f'{dset_name}_body_mpjpe'].info( + '[{:06d}] {}: {} 3D Keypoint error: {:.4f} mm', + step, dset_name, + alignment_name, + metric_value) + + metric_name = f'{dset_name}/{alignment_name}/MPJPE' + self.filewriter.add_scalar( + metric_name, metric_value, step) + + # Compute Hand Mean per Joint point error + if compute_hand_mpjpe: + for key, val in left_hand_mpjpe_err.items(): + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + # Compute the mean over the dataset and convert to + # millimeters + metric_value = val.sum() / left_hand_valid * 1000 + alignment_name = key.title() + # Store the Procrustes aligned MPJPE + # self.loggers[f'{dset_name}_hand_mpjpe'].info( + logger.info( + '[{:06d}] {}: {} 3D Left Hand Keypoint error: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + metric_name = f'{dset_name}/{alignment_name}/LeftHand' + self.filewriter.add_scalar( + +-- Chunk 8 -- +// evaluation.py:958-1107 + metric_name, metric_value, step) + for key, val in right_hand_mpjpe_err.items(): + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + # Compute the mean over the dataset and convert to + # millimeters + metric_value = val.sum() / right_hand_valid * 1000 + alignment_name = key.title() + # Store the Procrustes aligned MPJPE + # self.loggers[f'{dset_name}_hand_mpjpe'].info( + logger.info( + '[{:06d}] {}: {} 3D Right Hand Keypoint error: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + metric_name = f'{dset_name}/{alignment_name}/RightHand' + self.filewriter.add_scalar( + metric_name, metric_value, step) + + # Compute Head Mean per Joint point error + if compute_head_mpjpe: + for key, val in head_mpjpe_err.items(): + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + metric_value = val.sum() / head_valid * 1000 + alignment_name = key.title() + + # Store the Procrustes aligned MPJPE + self.loggers[f'{dset_name}_head_mpjpe'].info( + '[{:06d}] {}: {} 3D Head Keypoint error: {:.4f} mm', + step, + dset_name, + alignment_name, + metric_value) + + metric_name = f'{dset_name}/{alignment_name}/Head' + self.filewriter.add_scalar(metric_name, metric_value, step) + + # Compute Mean per Joint point error + if compute_mpjpe14: + for key, val in mpjpe14_err.items(): + if len(val) < 1: + continue + val = np.asarray(val) + metric_value = np.mean(val) * 1000 + alignment_name = key.title() + + # Store the Procrustes aligned MPJPE + self.loggers[f'{dset_name}_mpjpe14'].info( + '[{:06d}] {}: {} MPJPE: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/MPJPE' + self.filewriter.add_scalar(metric_name, metric_value, step) + + if compute_v2v: + summary_dict = {} + for key, val in v2v_err.items(): + # Divide by the number of items in the dataset and the + # number of vertices + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + metric_value = np.mean(val) * 1000 + alignment_name = key.title() + + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/V2V' + summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + for key, val in body_v2v_err.items(): + # Divide by the number of items in the dataset and the + # number of vertices + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + metric_value = np.mean(val) * 1000 + alignment_name = key.title() + + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Body Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/BodyV2V' + summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + for key, val in left_hand_v2v_err.items(): + # Divide by the number of items in the dataset and the + # number of vertices + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + metric_value = np.mean(val) * 1000 + alignment_name = key.title() + + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Left Hand Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/LeftHandV2V' + summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + + for key, val in right_hand_v2v_err.items(): + # Divide by the number of items in the dataset and the + # number of vertices + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + metric_value = np.mean(val) * 1000 + alignment_name = key.title() + + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Right Hand Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/RightHandV2V' + summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + + for key, val in face_v2v_err.items(): + # Divide by the number of items in the dataset and the + # number of vertices + if len(val) < 1: + continue + val = np.concatenate(val, axis=0) + metric_value = np.mean(val) * 1000 + alignment_name = key.title() + + self.loggers[f'{dset_name}_v2v'].info( + '[{:06d}] {}: Face Vertex-To-Vertex/{}: {:.4f} mm', + step, dset_name, alignment_name, metric_value) + + metric_name = f'{dset_name}/{alignment_name}/FaceV2V' + summary_dict[metric_name] = val + self.filewriter.add_scalar(metric_name, metric_value, step) + + return + + @torch.no_grad() + def run(self, model, dataloaders, exp_cfg, device, step=0): + if self.rank > 0: + return + model.eval() + assert not (model.training), 'Model is in training mode!' + + body_dloader = dataloaders.get('body', None) + +-- Chunk 9 -- +// evaluation.py:1108-1130 + hand_dloader = dataloaders.get('hand', None) + head_dloader = dataloaders.get('head', None) + + if self.distributed: + eval_model = deepcopy(model.module) + else: + eval_model = deepcopy(model) + + eval_model.eval() + assert not (eval_model.training), 'Model is in training mode!' + if body_dloader is not None: + self.run_body_eval(body_dloader, eval_model, + alignments=self.body_alignments, + step=step, device=device) + if hand_dloader is not None: + self.run_hand_eval(hand_dloader, eval_model, + alignments=self.hand_alignments, + step=step, + device=device) + if head_dloader is not None: + self.run_head_eval(head_dloader, eval_model, + alignments=self.head_alignments, + step=step, device=device) + +=== File: expose/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/__init__.py:1-15 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +=== File: doc/data.md === + +-- Chunk 1 -- +// /app/repos/repo_8/doc/data.md:1-32 +## ExPose Model - Documentation +For suggestions on improving documentation, please contact [expose@tue.mpg.de](mailto:expose@tue.mpg.de). + +Once you download and extract the zip with the pre-trained model you should have the following files: +* all_means.pkl : The mean pose parameters, which are used as the initial point for the iterative regression, in different pose representations ( axis-angle, PCA for the hands only, etc). +* shape_mean.npy: The mean shape parameters used to initialize the iterative regressor. +* SMPLX_to_J14.pkl: A linear regressor that computes the 14 LSP-like joints used to compute the mean per-joint point error (MPJPE). +* conf.yaml: Contains all the arguments needed to run ExPose. +* checkpoints: The pre-trained checkpoint. +* ExPose Dataset - Documentation + +### Curated fits +Downloading and extracting the curated fits zip should give you the following +two files: +* train.npz + * img_fns: The name of the image to read. + * betas: A Nx10 numpy array with the shape coefficients of each instance. + * expression: A Nx10 numpy array with the expression coefficients of each instance. + * keypoints2D: The OpenPose keypoints used to generate the fits. + * pose: A numpy array that contains the estimated SMPL-X pose vector in axis-angle format. +* val.npz + * img_fns: The name of the image to read. + * betas: A Nx10 numpy array with the shape coefficients of each instance. + * expression: A Nx10 numpy array with the expression coefficients of each instance. + * keypoints2D: The OpenPose keypoints used to generate the fits. + * pose: A numpy array that contains the estimated SMPL-X pose vector in axis-angle format. + * vertices: A numpy array that contains the estimated SMPL-X vertices. + * joints: The 14 LSP-like joints used to compute the mean per-joint point error metric. + +### SPIN in SMPL-X + +The data format is exactly the same as the one in SPIN, see the [original page](https://github.com/nkolot/SPIN#final-fits) for more details. + +=== File: expose/optimizers/build.py === + +-- Chunk 1 -- +// build.py:26-51 + build_optimizer( + model: nn.Module, + optim_cfg: Dict, + exclude: str = '', +) -> optim.Optimizer: + params = [] + + for key, value in model.named_parameters(): + if not value.requires_grad: + continue + lr = optim_cfg.lr + weight_decay = optim_cfg.weight_decay + if "bias" in key: + lr = optim_cfg.lr * optim_cfg.bias_lr_factor + weight_decay = optim_cfg.weight_decay_bias + + if len(exclude) > 0 and exclude in key: + continue + params += [{"params": [value], "lr": lr, "weight_decay": weight_decay}] + + lr = optim_cfg.lr + + optimizer = get_optimizer(params, optim_cfg) + return optimizer + + + +-- Chunk 2 -- +// build.py:52-69 + get_optimizer(params, optim_cfg): + lr = optim_cfg.lr + optimizer_type = optim_cfg.type + logger.debug('Building optimizer: {}', optimizer_type.upper()) + if optimizer_type == 'sgd': + optimizer = optim.SGD(params, lr, + **optim_cfg.sgd) + elif optimizer_type == 'adam': + optimizer = optim.Adam(params, lr, **optim_cfg.adam) + elif optimizer_type == 'rmsprop': + optimizer = optim.RMSprop(params, lr, **optim_cfg.rmsprop) + elif optimizer_type == 'lbfgs': + optimizer = optim.LBFGS(params, **optim_cfg.get('lbfgs', {})) + else: + raise ValueError(f'Unknown optimizer type: {optimizer_type}') + return optimizer + + + +-- Chunk 3 -- +// build.py:70-91 + build_scheduler( + optimizer: optim.Optimizer, + sched_cfg: Dict +) -> optim.lr_scheduler._LRScheduler: + scheduler_type = sched_cfg.type + if scheduler_type == 'none': + return None + elif scheduler_type == 'step-lr': + step_size = sched_cfg.step_size + gamma = sched_cfg.gamma + logger.info('Building scheduler: StepLR(step_size={}, gamma={})', + step_size, gamma) + return scheduler.StepLR(optimizer, step_size, gamma) + elif scheduler_type == 'multi-step-lr': + gamma = sched_cfg.gamma + milestones = sched_cfg.milestones + logger.info('Building scheduler: MultiStepLR(milestone={}, gamma={})', + milestones, gamma) + return scheduler.MultiStepLR( + optimizer, milestones=milestones, gamma=gamma) + else: + raise ValueError(f'Unknown scheduler type: {scheduler_type}') + +=== File: expose/optimizers/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/optimizers/__init__.py:1-19 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from .build import build_optimizer +from .build import build_scheduler +from .build import get_optimizer + +=== File: expose/utils/plot_utils.py === + +-- Chunk 1 -- +// plot_utils.py:92-95 + blend_images(img1, img2, alpha=0.7): + return img1 * alpha + (1 - alpha) * img2 + + + +-- Chunk 2 -- +// plot_utils.py:96-121 + target_to_part_mask_img(target, num_parts=14, cmap_name='tab20'): + cmap = mpl_cm.get_cmap(name='tab20') + norm = mpl_colors.Normalize(0, num_parts + 1) + + full_mask = np.full(tuple(target.size), num_parts + 1, + dtype=np.float32) + + for part_idx in range(num_parts): + if not target.has_field(f'part_mask{part_idx}'): + continue + + masks = target.get_field(f'part_mask{part_idx}') + masks = masks.get_mask_tensor() + masks = masks.detach().cpu().numpy().astype(np.float32) + + full_mask[masks > 0] = part_idx + # color = np.asarray(cmap(norm(part_idx)))[:3].reshape(1, 1, 3) + # if colored_mask is None: + # colored_mask = np.zeros(masks.shape + (3,), dtype=masks.dtype) + # colored_mask += masks[:, :, np.newaxis] * color + colored_mask = cmap(norm(full_mask))[:, :, :3] + colored_mask = np.clip(colored_mask, 0.0, 1.0) + + return colored_mask + + + +-- Chunk 3 -- +// plot_utils.py:122-189 + create_skel_img(img, keypoints, connections, valid=None, + names=None, + color_left=[0.9, 0.0, 0.0], + color_right=[0.0, 0.0, 0.9], + color_else=[1.0, 1.0, 1.0], + marker_size=2, linewidth=2, draw_skel=True, + draw_text=True, + ): + kp_mask = np.copy(img) + if valid is None: + valid = np.ones([keypoints.shape[0]]) + + for idx, pair in enumerate(connections): + if pair[0] > len(valid) or pair[1] > len(valid): + continue + if not valid[pair[0]] or not valid[pair[1]]: + continue + + curr_line_width = linewidth + if pair[1] >= 22: + curr_marker_size = int(0.1 * marker_size) + # curr_line_width = 1 + else: + curr_marker_size = marker_size + + if names is not None: + curr_name = names[pair[1]] + + if any([finger_name in curr_name for finger_name in FINGER_NAMES]): + if 'left' in curr_name: + color = HAND_COLORS[LEFT_FINGER.index(curr_name)] + else: + color = HAND_COLORS[RIGHT_FINGER.index(curr_name)] + elif 'left' in curr_name: + color = color_left + elif 'right' in curr_name: + color = color_right + else: + color = color_else + else: + color = color_else + + if pair[1] >= keypoints.shape[0] or pair[0] >= keypoints.shape[0]: + continue + center = tuple(keypoints[pair[1], :].astype(np.int32).tolist()) + + cv2.circle(kp_mask, center, curr_marker_size, color) + + if draw_skel: + if not valid[pair[0]] and not valid[pair[1]]: + continue + start_pt = tuple(keypoints[pair[0], :2].astype(np.int32).tolist()) + end_pt = tuple(keypoints[pair[1], :2].astype(np.int32).tolist()) + cv2.line(kp_mask, start_pt, end_pt, + color, thickness=curr_line_width, + lineType=cv2.LINE_AA) + + if pair[1] <= 22 and draw_text: + cv2.putText(kp_mask, f'{pair[1]}', + center, cv2.FONT_HERSHEY_PLAIN, fontScale=1.0, + color=[0.0, 0.0, 0.0], thickness=4) + cv2.putText(kp_mask, f'{pair[1]}', + center, cv2.FONT_HERSHEY_PLAIN, fontScale=1.0, + color=color, thickness=2) + + return kp_mask + + + +-- Chunk 4 -- +// plot_utils.py:190-199 + create_bbox_img(img, bounding_box, color=(0.0, 0.0, 0.0), + linewidth=2): + bbox_img = img.copy() + xmin, ymin, xmax, ymax = bounding_box.reshape(4) + + cv2.rectangle(bbox_img, (xmin, ymin), (xmax, ymax), + color, thickness=linewidth) + return bbox_img + + + +-- Chunk 5 -- +// plot_utils.py:200-216 + create_dp_img(img, dp_points, cmap='viridis', marker_size=4): + ''' Creates a Dense Pose visualization + ''' + dp_img = np.copy(img) + + cm = mpl_cm.get_cmap(name=cmap) + + num_points = dp_points.shape[0] + colors = cm(np.linspace(0, 1, num_points))[:, :3] + for idx in range(num_points): + center = tuple(dp_points[idx, :].astype(np.int32).tolist()) + cv2.circle(dp_img, center, marker_size, + colors[idx], -1) + + return dp_img + + + +-- Chunk 6 -- +// plot_utils.py:217-257 +ss OpenCVCamera(pyrender.Camera): + PIXEL_CENTER_OFFSET = 0.5 + + def __init__(self, + focal_length=1000, + znear=pyrender.camera.DEFAULT_Z_NEAR, + zfar=None, + name=None): + super(OpenCVCamera, self).__init__( + znear=znear, + zfar=zfar, + name=name, + ) + self.focal_length = focal_length + + def get_projection_matrix(self, width=None, height=None): + cx = 0.5 * width + cy = 0.5 * height + + right = (width - (cx + self.PIXEL_CENTER_OFFSET)) * ( + self.znear / self.focal_length) + left = -(cx + self.PIXEL_CENTER_OFFSET) * (self.znear / + self.focal_length) + top = -(height - (cy + self.PIXEL_CENTER_OFFSET)) * ( + self.znear / self.focal_length) + bottom = (cy + self.PIXEL_CENTER_OFFSET) * ( + self.znear / self.focal_length) + + P = np.zeros([4, 4]) + + P[0][0] = 2 * self.znear / (right - left) + P[1, 1] = -2 * self.znear / (top - bottom) + P[0, 2] = (right + left) / (right - left) + P[1, 2] = (top + bottom) / (top - bottom) + P[2, 2] = - (self.zfar + self.znear) / (self.zfar - self.znear) + P[3, 2] = -1.0 + P[2][3] = (2 * self.zfar * self.znear) / (self.znear - self.zfar) + + return P + + + +-- Chunk 7 -- +// plot_utils.py:258-356 +ss Renderer(object): + def __init__(self, near=0.1, far=200, width=224, height=224, + bg_color=(0.0, 0.0, 0.0, 0.0), ambient_light=None, + use_raymond_lighting=True, + light_color=None, light_intensity=3.0): + if light_color is None: + light_color = np.ones(3) + + self.near = near + self.far = far + + self.renderer = pyrender.OffscreenRenderer(viewport_width=width, + viewport_height=height, + point_size=1.0) + + if ambient_light is None: + ambient_light = (0.1, 0.1, 0.1) + + self.scene = pyrender.Scene(bg_color=bg_color, + ambient_light=ambient_light) + + pc = pyrender.PerspectiveCamera(yfov=np.pi / 3.0, + aspectRatio=float(width) / height) + camera_pose = np.eye(4) + camera_pose[:3, 3] = np.array([0, 0, 2]) + self.scene.add(pc, pose=camera_pose) + + if use_raymond_lighting: + light_nodes = self._create_raymond_lights() + for node in light_nodes: + self.scene.add_node(node) + + def _create_raymond_lights(self): + thetas = np.pi * np.array([1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0]) + phis = np.pi * np.array([0.0, 2.0 / 3.0, 4.0 / 3.0]) + + nodes = [] + + for phi, theta in zip(phis, thetas): + xp = np.sin(theta) * np.cos(phi) + yp = np.sin(theta) * np.sin(phi) + zp = np.cos(theta) + + z = np.array([xp, yp, zp]) + z = z / np.linalg.norm(z) + x = np.array([-z[1], z[0], 0.0]) + if np.linalg.norm(x) == 0: + x = np.array([1.0, 0.0, 0.0]) + x = x / np.linalg.norm(x) + y = np.cross(z, x) + + matrix = np.eye(4) + matrix[:3, :3] = np.c_[x, y, z] + nodes.append( + pyrender.Node( + light=pyrender.DirectionalLight(color=np.ones(3), + intensity=1.0), + matrix=matrix + )) + + return nodes + + def __call__(self, vertices, faces, img=None, + img_size=224, + body_color=(1.0, 1.0, 1.0, 1.0), + **kwargs): + + centered_verts = vertices - np.mean(vertices, axis=0, keepdims=True) + meshes = self.create_mesh(centered_verts, faces, + vertex_color=body_color) + + for node in self.scene.get_nodes(): + if node.name == 'mesh': + self.scene.remove_node(node) + for mesh in meshes: + self.scene.add(mesh, name='mesh') + + color, _ = self.renderer.render(self.scene) + + return color.astype(np.uint8) + + def create_mesh(self, vertices, faces, + vertex_color=(0.9, 0.9, 0.7, 1.0)): + + tri_mesh = trimesh.Trimesh(vertices=vertices, faces=faces) + rot = trimesh.transformations.rotation_matrix(np.radians(180), + [1, 0, 0]) + tri_mesh.apply_transform(rot) + + meshes = [] + + material = pyrender.MetallicRoughnessMaterial( + metallicFactor=0.0, + baseColorFactor=vertex_color) + mesh = pyrender.Mesh.from_trimesh(tri_mesh, material=material) + meshes.append(mesh) + return meshes + + + +-- Chunk 8 -- +// plot_utils.py:357-384 +ss WeakPerspectiveCamera(pyrender.Camera): + PIXEL_CENTER_OFFSET = 0.5 + + def __init__(self, + scale, + translation, + znear=pyrender.camera.DEFAULT_Z_NEAR, + zfar=pyrender.camera.DEFAULT_Z_FAR, + name=None): + super(WeakPerspectiveCamera, self).__init__( + znear=znear, + zfar=zfar, + name=name, + ) + self.scale = scale + self.translation = translation + + def get_projection_matrix(self, width=None, height=None): + P = np.eye(4) + P[0, 0] = self.scale + P[1, 1] = self.scale + P[0, 3] = self.translation[0] * self.scale + P[1, 3] = -self.translation[1] * self.scale + P[2, 2] = -1 + + return P + + + +-- Chunk 9 -- +// plot_utils.py:385-412 +ss WeakPerspectiveCameraNonSquare(pyrender.Camera): + PIXEL_CENTER_OFFSET = 0.5 + + def __init__(self, + scale, + translation, + znear=pyrender.camera.DEFAULT_Z_NEAR, + zfar=pyrender.camera.DEFAULT_Z_FAR, + name=None): + super(WeakPerspectiveCameraNonSquare, self).__init__( + znear=znear, + zfar=zfar, + name=name, + ) + self.scale = scale + self.translation = translation + + def get_projection_matrix(self, width=None, height=None): + P = np.eye(4) + P[0, 0] = self.scale[0] + P[1, 1] = self.scale[1] + P[0, 3] = self.translation[0] * self.scale[0] + P[1, 3] = -self.translation[1] * self.scale[1] + P[2, 2] = -1 + + return P + + + +-- Chunk 10 -- +// plot_utils.py:413-506 +ss AbstractRenderer(object): + def __init__(self, faces=None, img_size=224, use_raymond_lighting=True): + super(AbstractRenderer, self).__init__() + + self.img_size = img_size + self.renderer = pyrender.OffscreenRenderer( + viewport_width=img_size, + viewport_height=img_size, + point_size=1.0) + self.mat_constructor = pyrender.MetallicRoughnessMaterial + self.mesh_constructor = trimesh.Trimesh + self.trimesh_to_pymesh = pyrender.Mesh.from_trimesh + self.transf = trimesh.transformations.rotation_matrix + + self.scene = pyrender.Scene(bg_color=[0.0, 0.0, 0.0, 0.0], + ambient_light=(0.0, 0.0, 0.0)) + if use_raymond_lighting: + light_nodes = self._create_raymond_lights() + for node in light_nodes: + self.scene.add_node(node) + + def _create_raymond_lights(self): + thetas = np.pi * np.array([1.0 / 6.0, 1.0 / 6.0, 1.0 / 6.0]) + phis = np.pi * np.array([0.0, 2.0 / 3.0, 4.0 / 3.0]) + + nodes = [] + + for phi, theta in zip(phis, thetas): + xp = np.sin(theta) * np.cos(phi) + yp = np.sin(theta) * np.sin(phi) + zp = np.cos(theta) + + z = np.array([xp, yp, zp]) + z = z / np.linalg.norm(z) + x = np.array([-z[1], z[0], 0.0]) + if np.linalg.norm(x) == 0: + x = np.array([1.0, 0.0, 0.0]) + x = x / np.linalg.norm(x) + y = np.cross(z, x) + + matrix = np.eye(4) + matrix[:3, :3] = np.c_[x, y, z] + nodes.append( + pyrender.Node( + light=pyrender.DirectionalLight(color=np.ones(3), + intensity=1.0), + matrix=matrix + )) + + return nodes + + def is_active(self): + return self.viewer.is_active + + def close_viewer(self): + if self.viewer.is_active: + self.viewer.close_external() + + def create_mesh(self, vertices, faces, color=(0.3, 0.3, 0.3, 1.0), + wireframe=False, deg=0): + + material = self.mat_constructor( + metallicFactor=0.0, + alphaMode='BLEND', + baseColorFactor=color) + + mesh = self.mesh_constructor(vertices, faces, process=False) + + curr_vertices = vertices.copy() + mesh = self.mesh_constructor( + curr_vertices, faces, process=False) + if deg != 0: + rot = self.transf( + np.radians(deg), [0, 1, 0], + point=np.mean(curr_vertices, axis=0)) + mesh.apply_transform(rot) + + rot = self.transf(np.radians(180), [1, 0, 0]) + mesh.apply_transform(rot) + + return self.trimesh_to_pymesh(mesh, material=material) + + def update_mesh(self, vertices, faces, body_color=(1.0, 1.0, 1.0, 1.0), + deg=0): + for node in self.scene.get_nodes(): + if node.name == 'body_mesh': + self.scene.remove_node(node) + break + + body_mesh = self.create_mesh( + vertices, faces, color=body_color, deg=deg) + self.scene.add(body_mesh, name='body_mesh') + + + +-- Chunk 11 -- +// plot_utils.py:507-573 +ss SMPLifyXRenderer(AbstractRenderer): + def __init__(self, faces=None, img_size=224): + super(SMPLifyXRenderer, self).__init__(faces=faces, img_size=img_size) + + def update_camera(self, translation, rotation=None, focal_length=5000, + camera_center=None): + for node in self.scene.get_nodes(): + if node.name == 'camera': + self.scene.remove_node(node) + if rotation is None: + rotation = np.eye(3, dtype=translation.dtype) + if camera_center is None: + camera_center = np.array( + [self.img_size, self.img_size], dtype=translation.dtype) * 0.5 + + camera_transl = translation.copy() + camera_transl[0] *= -1.0 + pc = pyrender.camera.IntrinsicsCamera( + fx=focal_length, fy=focal_length, + cx=camera_center[0], cy=camera_center[1]) + camera_pose = np.eye(4) + camera_pose[:3, :3] = rotation + camera_pose[:3, 3] = camera_transl + self.scene.add(pc, pose=camera_pose, name='camera') + + @torch.no_grad() + def __call__(self, vertices, faces, + camera_translation, bg_imgs=None, + body_color=(1.0, 1.0, 1.0), + upd_color=None, + **kwargs): + if upd_color is None: + upd_color = {} + + if torch.is_tensor(vertices): + vertices = vertices.detach().cpu().numpy() + if torch.is_tensor(camera_translation): + camera_translation = camera_translation.cpu().numpy() + batch_size = vertices.shape[0] + + output_imgs = [] + for bidx in range(batch_size): + self.update_camera(camera_translation[bidx]) + + curr_col = upd_color.get(bidx, None) + if curr_col is None: + curr_col = body_color + self.update_mesh(vertices[bidx], faces, body_color=curr_col) + + flags = (pyrender.RenderFlags.RGBA | + pyrender.RenderFlags.SKIP_CULL_FACES) + color, depth = self.renderer.render(self.scene, flags=flags) + + color = np.transpose(color, [2, 0, 1]).astype(np.float32) / 255.0 + color = np.clip(color, 0, 1) + + if bg_imgs is None: + output_imgs.append(color[:-1]) + else: + valid_mask = (color[3] > 0)[np.newaxis] + + output_img = (color[:-1] * valid_mask + + (1 - valid_mask) * bg_imgs[bidx]) + output_imgs.append(np.clip(output_img, 0, 1)) + return np.stack(output_imgs, axis=0) + + + +-- Chunk 12 -- +// plot_utils.py:574-654 +ss OverlayRenderer(AbstractRenderer): + def __init__(self, faces=None, img_size=224, tex_size=1): + super(OverlayRenderer, self).__init__(faces=faces, img_size=img_size) + + def update_camera(self, scale, translation): + for node in self.scene.get_nodes(): + if node.name == 'camera': + self.scene.remove_node(node) + + pc = WeakPerspectiveCamera(scale, translation, + znear=1e-5, + zfar=1000) + camera_pose = np.eye(4) + self.scene.add(pc, pose=camera_pose, name='camera') + + @torch.no_grad() + def __call__(self, vertices, faces, + camera_scale, camera_translation, bg_imgs=None, + deg=0, + return_with_alpha=False, + body_color=None, + **kwargs): + + if torch.is_tensor(vertices): + vertices = vertices.detach().cpu().numpy() + if torch.is_tensor(camera_scale): + camera_scale = camera_scale.detach().cpu().numpy() + if torch.is_tensor(camera_translation): + camera_translation = camera_translation.detach().cpu().numpy() + batch_size = vertices.shape[0] + + output_imgs = [] + for bidx in range(batch_size): + if body_color is None: + body_color = COLORS['N'] + + if bg_imgs is not None: + _, H, W = bg_imgs[bidx].shape + # Update the renderer's viewport + self.renderer.viewport_height = H + self.renderer.viewport_width = W + + self.update_camera(camera_scale[bidx], camera_translation[bidx]) + self.update_mesh(vertices[bidx], faces, body_color=body_color, + deg=deg) + + flags = (pyrender.RenderFlags.RGBA | + pyrender.RenderFlags.SKIP_CULL_FACES) + color, depth = self.renderer.render(self.scene, flags=flags) + color = np.transpose(color, [2, 0, 1]).astype(np.float32) / 255.0 + color = np.clip(color, 0, 1) + + if bg_imgs is None: + if return_with_alpha: + output_imgs.append(color) + else: + output_imgs.append(color[:-1]) + else: + if return_with_alpha: + valid_mask = (color[3] > 0)[np.newaxis] + + if bg_imgs[bidx].shape[0] < 4: + curr_bg_img = np.concatenate( + [bg_imgs[bidx], + np.ones_like(bg_imgs[bidx, [0], :, :]) + ], axis=0) + else: + curr_bg_img = bg_imgs[bidx] + + output_img = (color * valid_mask + + (1 - valid_mask) * curr_bg_img) + output_imgs.append(np.clip(output_img, 0, 1)) + else: + valid_mask = (color[3] > 0)[np.newaxis] + + output_img = (color[:-1] * valid_mask + + (1 - valid_mask) * bg_imgs[bidx]) + output_imgs.append(np.clip(output_img, 0, 1)) + return np.stack(output_imgs, axis=0) + + + +-- Chunk 13 -- +// plot_utils.py:655-732 +ss GTRenderer(AbstractRenderer): + def __init__(self, faces=None, img_size=224): + super(GTRenderer, self).__init__(faces=faces, img_size=img_size) + + def update_camera(self, intrinsics): + for node in self.scene.get_nodes(): + if node.name == 'camera': + self.scene.remove_node(node) + pc = pyrender.IntrinsicsCamera( + fx=intrinsics[0, 0], + fy=intrinsics[1, 1], + cx=intrinsics[0, 2], + cy=intrinsics[1, 2], + zfar=1000) + camera_pose = np.eye(4) + self.scene.add(pc, pose=camera_pose, name='camera') + + @torch.no_grad() + def __call__(self, vertices, faces, + intrinsics, bg_imgs=None, deg=0, + return_with_alpha=False, + **kwargs): + ''' Returns a B3xHxW batch of mesh overlays + ''' + + if torch.is_tensor(vertices): + vertices = vertices.detach().cpu().numpy() + if torch.is_tensor(intrinsics): + intrinsics = intrinsics.detach().cpu().numpy() + batch_size = vertices.shape[0] + + body_color = COLORS['GT'] + output_imgs = [] + for bidx in range(batch_size): + if bg_imgs is not None: + _, H, W = bg_imgs[bidx].shape + # Update the renderer's viewport + self.renderer.viewport_height = H + self.renderer.viewport_width = W + self.update_camera(intrinsics[bidx]) + self.update_mesh(vertices[bidx], faces, body_color=body_color, + deg=deg) + + flags = (pyrender.RenderFlags.RGBA | + pyrender.RenderFlags.SKIP_CULL_FACES) + color, depth = self.renderer.render(self.scene, flags=flags) + color = np.transpose(color, [2, 0, 1]).astype(np.float32) / 255.0 + color = np.clip(color, 0, 1) + + if bg_imgs is None: + if return_with_alpha: + output_imgs.append(color) + else: + output_imgs.append(color[:-1]) + else: + if return_with_alpha: + valid_mask = (color[3] > 0)[np.newaxis] + + if bg_imgs[bidx].shape[0] < 4: + curr_bg_img = np.concatenate( + [bg_imgs[bidx], + np.ones_like(bg_imgs[bidx, [0], :, :]) + ], axis=0) + else: + curr_bg_img = bg_imgs[bidx] + + output_img = (color * valid_mask + + (1 - valid_mask) * curr_bg_img) + output_imgs.append(np.clip(output_img, 0, 1)) + else: + valid_mask = (color[3] > 0)[np.newaxis] + + output_img = (color[:-1] * valid_mask + + (1 - valid_mask) * bg_imgs[bidx]) + output_imgs.append(np.clip(output_img, 0, 1)) + return np.stack(output_imgs, axis=0) + + + +-- Chunk 14 -- +// plot_utils.py:733-855 +ss HDRenderer(OverlayRenderer): + def __init__(self, **kwargs): + super(HDRenderer, self).__init__(**kwargs) + + def update_camera(self, focal_length, translation, center): + for node in self.scene.get_nodes(): + if node.name == 'camera': + self.scene.remove_node(node) + + pc = pyrender.IntrinsicsCamera( + fx=focal_length, + fy=focal_length, + cx=center[0], + cy=center[1], + ) + camera_pose = np.eye(4) + camera_pose[:3, 3] = translation.copy() + camera_pose[0, 3] *= (-1) + self.scene.add(pc, pose=camera_pose, name='camera') + + @torch.no_grad() + def __call__(self, + vertices: Tensor, + faces: Union[Tensor, Array], + focal_length: Union[Tensor, Array], + camera_translation: Union[Tensor, Array], + camera_center: Union[Tensor, Array], + bg_imgs: Array, + render_bg: bool = True, + deg: float = 0, + return_with_alpha: bool = False, + body_color: List[float] = None, + **kwargs): + ''' + Parameters + ---------- + vertices: BxVx3, torch.Tensor + The torch Tensor that contains the current vertices to be drawn + faces: Fx3, np.array + The faces of the meshes to be drawn. Right now only support a + batch of meshes with the same topology + focal_length: B, torch.Tensor + The focal length used by the perspective camera + camera_translation: Bx3, torch.Tensor + The translation of the camera estimated by the network + camera_center: Bx2, torch.Tensor + The center of the camera in pixels + bg_imgs: np.ndarray + Optional background images used for overlays + render_bg: bool, optional + Render on top of the background image + deg: float, optional + Degrees to rotate the mesh around itself. Used to render the + same mesh from multiple viewpoints. Defaults to 0 degrees + return_with_alpha: bool, optional + Whether to return the rendered image with an alpha channel. + Default value is False. + body_color: list, optional + The color used to render the image. + ''' + if torch.is_tensor(vertices): + vertices = vertices.detach().cpu().numpy() + if torch.is_tensor(faces): + faces = faces.detach().cpu().numpy() + if torch.is_tensor(focal_length): + focal_length = focal_length.detach().cpu().numpy() + if torch.is_tensor(camera_translation): + camera_translation = camera_translation.detach().cpu().numpy() + if torch.is_tensor(camera_center): + camera_center = camera_center.detach().cpu().numpy() + batch_size = vertices.shape[0] + + output_imgs = [] + for bidx in range(batch_size): + if body_color is None: + body_color = COLORS['N'] + + _, H, W = bg_imgs[bidx].shape + # Update the renderer's viewport + self.renderer.viewport_height = H + self.renderer.viewport_width = W + + self.update_camera( + focal_length=focal_length[bidx], + translation=camera_translation[bidx], + center=camera_center[bidx], + ) + self.update_mesh( + vertices[bidx], faces, body_color=body_color, deg=deg) + + flags = (pyrender.RenderFlags.RGBA | + pyrender.RenderFlags.SKIP_CULL_FACES) + color, depth = self.renderer.render(self.scene, flags=flags) + color = np.transpose(color, [2, 0, 1]).astype(np.float32) / 255.0 + color = np.clip(color, 0, 1) + + if render_bg: + if return_with_alpha: + valid_mask = (color[3] > 0)[np.newaxis] + + if bg_imgs[bidx].shape[0] < 4: + curr_bg_img = np.concatenate( + [bg_imgs[bidx], + np.ones_like(bg_imgs[bidx, [0], :, :]) + ], axis=0) + else: + curr_bg_img = bg_imgs[bidx] + + output_img = (color * valid_mask + + (1 - valid_mask) * curr_bg_img) + output_imgs.append(np.clip(output_img, 0, 1)) + else: + valid_mask = (color[3] > 0)[np.newaxis] + + output_img = (color[:-1] * valid_mask + + (1 - valid_mask) * bg_imgs[bidx]) + output_imgs.append(np.clip(output_img, 0, 1)) + else: + if return_with_alpha: + output_imgs.append(color) + else: + output_imgs.append(color[:-1]) + return np.stack(output_imgs, axis=0) + +=== File: expose/utils/img_utils.py === + +-- Chunk 1 -- +// img_utils.py:28-33 + read_img(img_fn: str, dtype=np.float32) -> Array: + img = cv2.cvtColor(cv2.imread(img_fn), cv2.COLOR_BGR2RGB) + if dtype == np.float32: + if img.dtype == np.uint8: + img = img.astype(dtype) / 255.0 + return img + +=== File: expose/utils/data_structs.py === + +-- Chunk 1 -- +// data_structs.py:18-25 +ss Struct(object): + def __init__(self, **kwargs): + self.keys = list(kwargs.keys()) + for key, val in kwargs.items(): + setattr(self, key, val) + + def keys(self): + return self.keys + +=== File: expose/utils/rotation_utils.py === + +-- Chunk 1 -- +// rotation_utils.py:20-54 + batch_rodrigues(rot_vecs, epsilon=1e-8): + ''' Calculates the rotation matrices for a batch of rotation vectors + Parameters + ---------- + rot_vecs: torch.tensor Nx3 + array of N axis-angle vectors + Returns + ------- + R: torch.tensor Nx3x3 + The rotation matrices for the given axis-angle parameters + ''' + + batch_size = rot_vecs.shape[0] + device = rot_vecs.device + dtype = rot_vecs.dtype + + angle = torch.norm(rot_vecs + epsilon, dim=1, keepdim=True, p=2) + rot_dir = rot_vecs / angle + + cos = torch.unsqueeze(torch.cos(angle), dim=1) + sin = torch.unsqueeze(torch.sin(angle), dim=1) + + # Bx1 arrays + rx, ry, rz = torch.split(rot_dir, 1, dim=1) + K = torch.zeros((batch_size, 3, 3), dtype=dtype, device=device) + + zeros = torch.zeros((batch_size, 1), dtype=dtype, device=device) + K = torch.cat([zeros, -rz, ry, rz, zeros, -rx, -ry, rx, zeros], dim=1) \ + .view((batch_size, 3, 3)) + + ident = torch.eye(3, dtype=dtype, device=device).unsqueeze(dim=0) + rot_mat = ident + sin * K + (1 - cos) * torch.bmm(K, K) + return rot_mat + + + +-- Chunk 2 -- +// rotation_utils.py:55-98 + batch_rot2aa(Rs, epsilon=1e-7): + """ + Rs is B x 3 x 3 + void cMathUtil::RotMatToAxisAngle(const tMatrix& mat, tVector& out_axis, + double& out_theta) + { + double c = 0.5 * (mat(0, 0) + mat(1, 1) + mat(2, 2) - 1); + c = cMathUtil::Clamp(c, -1.0, 1.0); + + out_theta = std::acos(c); + + if (std::abs(out_theta) < 0.00001) + { + out_axis = tVector(0, 0, 1, 0); + } + else + { + double m21 = mat(2, 1) - mat(1, 2); + double m02 = mat(0, 2) - mat(2, 0); + double m10 = mat(1, 0) - mat(0, 1); + double denom = std::sqrt(m21 * m21 + m02 * m02 + m10 * m10); + out_axis[0] = m21 / denom; + out_axis[1] = m02 / denom; + out_axis[2] = m10 / denom; + out_axis[3] = 0; + } + } + """ + + cos = 0.5 * (torch.einsum('bii->b', [Rs]) - 1) + cos = torch.clamp(cos, -1 + epsilon, 1 - epsilon) + + theta = torch.acos(cos) + + m21 = Rs[:, 2, 1] - Rs[:, 1, 2] + m02 = Rs[:, 0, 2] - Rs[:, 2, 0] + m10 = Rs[:, 1, 0] - Rs[:, 0, 1] + denom = torch.sqrt(m21 * m21 + m02 * m02 + m10 * m10 + epsilon) + + axis0 = torch.where(torch.abs(theta) < 0.00001, m21, m21 / denom) + axis1 = torch.where(torch.abs(theta) < 0.00001, m02, m02 / denom) + axis2 = torch.where(torch.abs(theta) < 0.00001, m10, m10 / denom) + + return theta.unsqueeze(1) * torch.stack([axis0, axis1, axis2], 1) + +=== File: expose/utils/checkpointer.py === + +-- Chunk 1 -- +// checkpointer.py:27-150 +ss Checkpointer(object): + def __init__(self, model, optimizer=None, scheduler=None, + adv_optimizer=None, + pretrained='', + distributed=False, + rank=0, + save_dir='/tmp/exp'): + self.rank = rank + self.distributed = distributed + + self.model = model + self.optimizer = optimizer + self.scheduler = scheduler + self.adv_optimizer = adv_optimizer + + self.save_dir = save_dir + if self.rank == 0: + logger.info(f'Creating directory {self.save_dir}') + os.makedirs(self.save_dir, exist_ok=True) + self.pretrained = pretrained + + def save_checkpoint(self, name, **kwargs): + if self.rank > 0: + return + ckpt_data = {} + ckpt_data['model'] = self.model.state_dict() + + if self.optimizer is not None: + logger.info('Adding optimizer state ...') + ckpt_data['optimizer'] = self.optimizer.state_dict() + if self.scheduler is not None: + logger.info('Adding scheduler state ...') + ckpt_data['scheduler'] = self.scheduler.state_dict() + if self.adv_optimizer is not None: + logger.info('Adding discriminator optimizer state ...') + ckpt_data['adv_optimizer'] = self.adv_optimizer.state_dict() + + ckpt_data.update(kwargs) + + curr_ckpt_fn = osp.join(self.save_dir, name) + logger.info('Saving checkpoint to {}'.format(curr_ckpt_fn)) + torch.save(ckpt_data, curr_ckpt_fn) + with open(osp.join(self.save_dir, 'latest_checkpoint'), 'w') as f: + f.write(curr_ckpt_fn) + ckpt_data.clear() + + def load_checkpoint(self): + save_fn = osp.join(self.save_dir, 'latest_checkpoint') + + load_pretrained = False + if not osp.exists(save_fn): + # If no previous checkpoint exists, load from the pretrained model + if len(self.pretrained) > 1: + self.pretrained = osp.expandvars(self.pretrained) + load_pretrained = True + save_fn = osp.join( + self.pretrained, 'checkpoints', 'latest_checkpoint') + # If neither the pretrained model exists nor there is a previous + # checkpoint then initialize from scratch + if not osp.exists(save_fn): + logger.warning(f'No checkpoint found in {self.save_dir}!') + return {} + + logger.info('Load pretrained: {}', load_pretrained) + with open(save_fn, 'r') as f: + latest_ckpt_fn = f.read().strip() + logger.warning(f'Loading checkpoint from {latest_ckpt_fn}!') + + if self.distributed: + map_location = torch.device(f'cuda:{self.rank}') + else: + map_location = torch.device('cpu') + ckpt_data = torch.load(latest_ckpt_fn, map_location=map_location) + + if load_pretrained: + if 'face_idxs' in ckpt_data['model']: + del ckpt_data['model']['face_idxs'] + if 'smplx.smplx_loss.body_idxs' in ckpt_data['model']: + del ckpt_data['model']['smplx.smplx_loss.body_idxs'] + if 'smplx.smplx_loss.hand_idxs' in ckpt_data['model']: + del ckpt_data['model']['smplx.smplx_loss.hand_idxs'] + if 'smplx.smplx_loss.face_idxs' in ckpt_data['model']: + del ckpt_data['model']['smplx.smplx_loss.face_idxs'] + if 'smplx.smplx_loss.left_hand_idxs' in ckpt_data['model']: + del ckpt_data['model']['smplx.smplx_loss.left_hand_idxs'] + if 'smplx.smplx_loss.right_hand_idxs' in ckpt_data['model']: + del ckpt_data['model']['smplx.smplx_loss.right_hand_idxs'] + if 'smplx.head_idxs' in ckpt_data['model']: + del ckpt_data['model']['smplx.head_idxs'] + + missing, unexpected = self.model.load_state_dict( + # ckpt_data['model'], strict=not load_pretrained) + ckpt_data['model'], strict=False) + if len(missing) > 0: + logger.warning( + f'The following keys were not found: {missing}') + if len(unexpected): + logger.warning( + f'The following keys were not expected: {unexpected}') + + if self.optimizer is not None and 'optimizer' in ckpt_data: + if not load_pretrained: + logger.warning('Loading optimizer data from: {}'.format( + self.save_dir)) + self.optimizer.load_state_dict(ckpt_data['optimizer']) + + if self.scheduler is not None and 'scheduler' in ckpt_data: + if not load_pretrained: + logger.warning('Loading scheduler data from: {}'.format( + self.save_dir)) + self.scheduler.load_state_dict(ckpt_data['scheduler']) + if self.adv_optimizer is not None and 'adv_optimizer' in ckpt_data: + if not load_pretrained: + logger.warning( + 'Loading discriminator optim data from: {}'.format( + self.save_dir)) + self.adv_optimizer.load_state_dict( + ckpt_data['adv_optimizer']) + + if load_pretrained: + ckpt_data['iteration'] = 0 + ckpt_data['epoch_number'] = 0 + + return ckpt_data + +=== File: expose/utils/timer.py === + +-- Chunk 1 -- +// timer.py:24-42 +ss Timer(object): + def __init__(self, name='', sync=False): + super(Timer, self).__init__() + self.elapsed = [] + self.name = name + self.sync = sync + + def __enter__(self): + if self.sync: + torch.cuda.synchronize() + self.start = time.perf_counter() + + def __exit__(self, type, value, traceback): + if self.sync: + torch.cuda.synchronize() + elapsed = time.perf_counter() - self.start + self.elapsed.append(elapsed) + logger.info( + f'[{self.name}]: {elapsed:.3f}, {np.mean(self.elapsed):.3f}') + +=== File: expose/utils/cfg_utils.py === + +-- Chunk 1 -- +// cfg_utils.py:20-27 + cfg_to_dict(cfg_node): + if type(cfg_node) in BUILTINS: + return cfg_node + else: + curr_dict = dict(cfg_node) + for key, val in curr_dict.items(): + curr_dict[key] = cfg_to_dict(val) + return curr_dict + +=== File: expose/utils/torch_utils.py === + +-- Chunk 1 -- +// torch_utils.py:23-26 + no_reduction(arg): + return arg + + + +-- Chunk 2 -- +// torch_utils.py:27-37 + to_tensor( + tensor: Union[Tensor, Array], + device=None, + dtype=torch.float32 +) -> Tensor: + if torch.is_tensor(tensor): + return tensor + else: + return torch.tensor(tensor, dtype=dtype, device=device) + + + +-- Chunk 3 -- +// torch_utils.py:38-49 + get_reduction_method(reduction='mean'): + if reduction == 'mean': + reduction = torch.mean + elif reduction == 'sum': + reduction = torch.sum + elif reduction == 'none': + reduction = no_reduction + else: + raise ValueError('Unknown reduction type: {}'.format(reduction)) + return reduction + + + +-- Chunk 4 -- +// torch_utils.py:50-56 + tensor_to_numpy(tensor: Tensor, default=None) -> Array: + if tensor is None: + return default + else: + return tensor.detach().cpu().numpy() + + + +-- Chunk 5 -- +// torch_utils.py:57-63 + rot_mat_to_euler(rot_mats: Tensor) -> Tensor: + # Calculates rotation matrix to euler angles + # Careful for extreme cases of eular angles like [0.0, pi, 0.0] + + sy = torch.sqrt(rot_mats[:, 0, 0] * rot_mats[:, 0, 0] + + rot_mats[:, 1, 0] * rot_mats[:, 1, 0]) + return torch.atan2(-rot_mats[:, 2, 0], sy) + +=== File: expose/utils/typing_utils.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/utils/typing_utils.py:1-27 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from typing import NewType, List, Union +import numpy as np +import torch + +__all__ = [ + 'Tensor', + 'Array', +] + +Tensor = NewType('Tensor', torch.Tensor) +Array = NewType('Array', np.ndarray) + +=== File: expose/utils/metrics.py === + +-- Chunk 1 -- +// metrics.py:26-36 +ss NoAligment(object): + def __init__(self): + super(NoAligment, self).__init__() + + def __repr__(self): + return 'NoAlignment' + + def __call__(self, S1, S2): + return S1 + + + +-- Chunk 2 -- +// metrics.py:37-94 +ss ProcrustesAlignment(object): + def __init__(self): + super(ProcrustesAlignment, self).__init__() + + def __repr__(self): + return 'ProcrustesAlignment' + + def __call__(self, S1, S2): + ''' + Computes a similarity transform (sR, t) that takes + a set of 3D points S1 (3 x N) closest to a set of 3D points S2, + where R is an 3x3 rotation matrix, t 3x1 translation, s scale. + i.e. solves the orthogonal Procrustes problem. + ''' + transposed = False + if S1.shape[0] != 3 and S1.shape[0] != 2: + S1 = S1.T + S2 = S2.T + transposed = True + assert(S2.shape[1] == S1.shape[1]) + + # 1. Remove mean. + mu1 = S1.mean(axis=1, keepdims=True) + mu2 = S2.mean(axis=1, keepdims=True) + X1 = S1 - mu1 + X2 = S2 - mu2 + + # 2. Compute variance of X1 used for scale. + var1 = np.sum(X1**2) + + # 3. The outer product of X1 and X2. + K = X1.dot(X2.T) + + # 4. Solution that Maximizes trace(R'K) is R=U*V', where U, V are + # singular vectors of K. + U, s, Vh = np.linalg.svd(K) + V = Vh.T + # Construct Z that fixes the orientation of R to get det(R)=1. + Z = np.eye(U.shape[0]) + Z[-1, -1] *= np.sign(np.linalg.det(U.dot(V.T))) + # Construct R. + R = V.dot(Z.dot(U.T)) + + # 5. Recover scale. + scale = np.trace(R.dot(K)) / var1 + + # 6. Recover translation. + t = mu2 - scale * (R.dot(mu1)) + + # 7. Error: + S1_hat = scale * R.dot(S1) + t + + if transposed: + S1_hat = S1_hat.T + + return S1_hat + + + +-- Chunk 3 -- +// metrics.py:95-124 +ss ProcrustesAlignmentMPJPE(ProcrustesAlignment): + def __init__(self, fscore_thresholds=None): + super(ProcrustesAlignmentMPJPE, self).__init__() + self.fscore_thresholds = fscore_thresholds + + def __repr__(self): + msg = [super(ProcrustesAlignment).__repr__()] + if self.fscore_thresholds is not None: + msg.append( + 'F-Score thresholds: ' + + f'(mm), '.join(map(lambda x: f'{x * 1000}', + self.fscore_thresholds)) + ) + return '\n'.join(msg) + + def __call__(self, est_points, gt_points): + aligned_est_points = super(ProcrustesAlignmentMPJPE, self).__call__( + est_points, gt_points) + + fscore = {} + if self.fscore_thresholds is not None: + for thresh in self.fscore_thresholds: + fscore[thresh] = point_fscore( + aligned_est_points, gt_points, thresh) + return { + 'point': mpjpe(aligned_est_points, gt_points), + 'fscore': fscore + } + + + +-- Chunk 4 -- +// metrics.py:125-170 +ss ScaleAlignment(object): + def __init__(self): + super(ScaleAlignment, self).__init__() + + def __repr__(self): + return 'ScaleAlignment' + + def __call__(self, S1, S2): + ''' + Computes a similarity transform (sR, t) that takes + a set of 3D points S1 (3 x N) closest to a set of 3D points S2, + where R is an 3x3 rotation matrix, t 3x1 translation, s scale. + i.e. solves the orthogonal Procrutes problem. + ''' + transposed = False + if S1.shape[0] != 3 and S1.shape[0] != 2: + S1 = S1.T + S2 = S2.T + transposed = True + assert(S2.shape[1] == S1.shape[1]) + + # 1. Remove mean. + mu1 = S1.mean(axis=1, keepdims=True) + mu2 = S2.mean(axis=1, keepdims=True) + X1 = S1 - mu1 + X2 = S2 - mu2 + + # 2. Compute variance of X1 used for scale. + var1 = np.sum(X1**2) + var2 = np.sum(X2**2) + + # 5. Recover scale. + scale = np.sqrt(var2 / var1) + + # 6. Recover translation. + t = mu2 - scale * (mu1) + + # 7. Error: + S1_hat = scale * S1 + t + + if transposed: + S1_hat = S1_hat.T + + return S1_hat + + + +-- Chunk 5 -- +// metrics.py:171-198 +ss RootAlignmentMPJPE(object): + def __init__(self, root=0, fscore_thresholds=None): + super(RootAlignmentMPJPE, self).__init__() + self.root = root + self.fscore_thresholds = fscore_thresholds + + def align_by_root(self, joints): + root_joint = joints[self.root, :] + return {'joints': joints - root_joint, 'root': root_joint} + + def __call__(self, gt, est): + gt_out = self.align_by_root(gt) + est_out = self.align_by_root(est) + + aligned_gt_joints = gt_out['joints'] + aligned_est_joints = est_out['joints'] + fscore = {} + if self.fscore_thresholds is not None: + for thresh in self.fscore_thresholds: + fscore[thresh] = point_fscore( + aligned_est_joints, aligned_gt_joints, thresh) + + return { + 'point': mpjpe(aligned_est_joints, aligned_gt_joints), + 'fscore': fscore + } + + + +-- Chunk 6 -- +// metrics.py:199-219 +ss PelvisAlignment(object): + def __init__(self, hips_idxs=None): + super(PelvisAlignment, self).__init__() + if hips_idxs is None: + hips_idxs = [2, 3] + self.hips_idxs = hips_idxs + + def align_by_pelvis(self, joints): + pelvis = joints[self.hips_idxs, :].mean(axis=0, keepdims=True) + return {'joints': joints - pelvis, 'pelvis': pelvis} + + def __call__(self, gt, est): + gt_out = self.align_by_pelvis(gt) + est_out = self.align_by_pelvis(est) + + aligned_gt_joints = gt_out['joints'] + aligned_est_joints = est_out['joints'] + + return aligned_gt_joints, aligned_est_joints + + + +-- Chunk 7 -- +// metrics.py:220-249 +ss PelvisAlignmentMPJPE(PelvisAlignment): + def __init__(self, fscore_thresholds=None): + super(PelvisAlignmentMPJPE, self).__init__() + self.fscore_thresholds = fscore_thresholds + + def __repr__(self): + msg = [super(PelvisAlignmentMPJPE).__repr__()] + if self.fscore_thresholds is not None: + msg.append( + 'F-Score thresholds: ' + + f'(mm), '.join(map(lambda x: f'{x * 1000}', + self.fscore_thresholds)) + ) + return '\n'.join(msg) + + def __call__(self, est_points, gt_points): + aligned_gt_points, aligned_est_points = super( + PelvisAlignmentMPJPE, self).__call__(gt_points, est_points) + + fscore = {} + if self.fscore_thresholds is not None: + for thresh in self.fscore_thresholds: + fscore[thresh] = point_fscore( + aligned_est_points, gt_points, thresh) + return { + 'point': mpjpe(aligned_est_points, aligned_gt_points), + 'fscore': fscore + } + + + +-- Chunk 8 -- +// metrics.py:250-267 + mpjpe(input_joints, target_joints): + ''' Calculate mean per-joint point error + + Parameters + ---------- + input_joints: numpy.array, Jx3 + The joints predicted by the model + target_joints: numpy.array, Jx3 + The ground truth joints + Returns + ------- + numpy.array, BxJ + The per joint point error for each element in the batch + ''' + + return np.sqrt(np.power(input_joints - target_joints, 2).sum(axis=-1)) + + + +-- Chunk 9 -- +// metrics.py:268-271 + vertex_to_vertex_error(input_vertices, target_vertices): + return np.sqrt(np.power(input_vertices - target_vertices, 2).sum(axis=-1)) + + + +-- Chunk 10 -- +// metrics.py:272-298 + point_fscore( + pred: torch.Tensor, + gt: torch.Tensor, + thresh: float) -> Dict[str, float]: + if torch.is_tensor(pred): + pred = pred.detach().cpu().numpy() + if torch.is_tensor(gt): + gt = gt.detach().cpu().numpy() + + pred_pcl = np2o3d_pcl(pred) + gt_pcl = np2o3d_pcl(gt) + + gt_to_pred = np.asarray(gt_pcl.compute_point_cloud_distance(pred_pcl)) + pred_to_gt = np.asarray(pred_pcl.compute_point_cloud_distance(gt_pcl)) + + recall = (pred_to_gt < thresh).sum() / len(pred_to_gt) + precision = (gt_to_pred < thresh).sum() / len(gt_to_pred) + if recall + precision > 0.0: + fscore = 2 * recall * precision / (recall + precision) + else: + fscore = 0.0 + + return { + 'fscore': fscore, + 'precision': precision, + 'recall': recall, + } + +=== File: expose/utils/np_utils.py === + +-- Chunk 1 -- +// np_utils.py:21-24 + rel_change(prev_val, curr_val): + return (prev_val - curr_val) / max([np.abs(prev_val), np.abs(curr_val), 1]) + + + +-- Chunk 2 -- +// np_utils.py:25-28 + max_grad_change(grad_arr): + return grad_arr.abs().max() + + + +-- Chunk 3 -- +// np_utils.py:29-34 + to_np(array, dtype=np.float32): + if 'scipy.sparse' in str(type(array)): + array = array.todense() + return np.array(array, dtype=dtype) + + + +-- Chunk 4 -- +// np_utils.py:35-39 + np2o3d_pcl(x: np.ndarray) -> o3d.geometry.PointCloud: + pcl = o3d.geometry.PointCloud() + pcl.points = o3d.utility.Vector3dVector(x) + + return pcl + +=== File: expose/utils/transf_utils.py === + +-- Chunk 1 -- +// transf_utils.py:28-61 + get_transform( + center: Array, scale: float, + res: Tuple[int], + rot: float = 0 +) -> Array: + """ + General image processing functions + """ + # Generate transformation matrix + h = 200 * scale + t = np.zeros((3, 3), dtype=np.float32) + t[0, 0] = float(res[1]) / h + t[1, 1] = float(res[0]) / h + t[0, 2] = res[1] * (-float(center[0]) / h + .5) + t[1, 2] = res[0] * (-float(center[1]) / h + .5) + t[2, 2] = 1 + if not rot == 0: + rot = -rot # To match direction of rotation from cropping + rot_mat = np.zeros((3, 3), dtype=np.float32) + rot_rad = rot * np.pi / 180 + sn, cs = np.sin(rot_rad), np.cos(rot_rad) + rot_mat[0, :2] = [cs, -sn] + rot_mat[1, :2] = [sn, cs] + rot_mat[2, 2] = 1 + # Need to rotate around center + t_mat = np.eye(3) + t_mat[0, 2] = -res[1] / 2 + t_mat[1, 2] = -res[0] / 2 + t_inv = t_mat.copy() + t_inv[:2, 2] *= -1 + t = np.dot(t_inv, np.dot(rot_mat, np.dot(t_mat, t))) + return t.astype(np.float32) + + + +-- Chunk 2 -- +// transf_utils.py:64-73 + transform(pt, center, scale, res, invert=0, rot=0): + # Transform pixel location to different reference + t = get_transform(center, scale, res, rot=rot) + if invert: + t = np.linalg.inv(t) + new_pt = np.array([pt[0] - 1, pt[1] - 1, 1.], dtype=np.float32).T + new_pt = np.dot(t, new_pt) + return new_pt[:2].astype(int) + 1 + + + +-- Chunk 3 -- +// transf_utils.py:74-119 + crop(img, center, scale, res, rot=0, dtype=np.float32): + # Upper left point + ul = np.array(transform([1, 1], center, scale, res, invert=1)) - 1 + # Bottom right point + br = np.array(transform([res[0] + 1, res[1] + 1], + center, scale, res, invert=1)) - 1 + # size of cropped image + # crop_shape = [br[1] - ul[1], br[0] - ul[0]] + # Padding so that when rotated proper amount of context is included + pad = int(np.linalg.norm(br - ul) / 2 - float(br[1] - ul[1]) / 2) + + if not rot == 0: + ul -= pad + br += pad + + new_shape = [br[1] - ul[1], br[0] - ul[0]] + if len(img.shape) > 2: + new_shape += [img.shape[2]] + new_shape = list(map(int, new_shape)) + new_img = np.zeros(new_shape, dtype=img.dtype) + + # Range to fill new array + new_x = max(0, -ul[0]), min(br[0], len(img[0])) - ul[0] + new_y = max(0, -ul[1]), min(br[1], len(img)) - ul[1] + + # Range to sample from original image + old_x = max(0, ul[0]), min(len(img[0]), br[0]) + old_y = max(0, ul[1]), min(len(img), br[1]) + # Range to sample from original image + new_img[new_y[0]:new_y[1], new_x[0]:new_x[1] + ] = img[old_y[0]:old_y[1], old_x[0]:old_x[1]] + + # pixel_scale = 1.0 if new_img.max() > 1.0 else 255 + # resample = pil_img.BILINEAR + if not rot == 0: + new_H, new_W, _ = new_img.shape + + rotn_center = (new_W / 2.0, new_H / 2.0) + M = cv2.getRotationMatrix2D(rotn_center, rot, 1.0).astype(np.float32) + + new_img = cv2.warpAffine(new_img, M, tuple(new_shape[:2]), + cv2.INTER_LINEAR_EXACT) + new_img = new_img[pad:new_H - pad, pad:new_W - pad] + + output = cv2.resize(new_img, tuple(res), interpolation=cv2.INTER_LINEAR) + return output.astype(np.float32) + +=== File: expose/utils/__init__.py === + +-- Chunk 1 -- +// __init__.py:18-19 + nand(x: bool, y: bool) -> bool: + return not (x and y) + +=== File: expose/config/cmd_parser.py === + +-- Chunk 1 -- +// cmd_parser.py:15-20 +def set_face_contour(node, use_face_contour=False): + for key in node: + if 'use_face_contour' in key: + node[key] = use_face_contour + if isinstance(node[key], CN): + set_face_contour(node[key], use_face_contour=use_face_contour) + +-- Chunk 2 -- +// cmd_parser.py:23-58 +def parse_args(argv=None): + arg_formatter = argparse.ArgumentDefaultsHelpFormatter + + description = 'PyTorch SMPL-X Regressor with Attention' + parser = argparse.ArgumentParser(formatter_class=arg_formatter, + description=description) + + parser.add_argument('--exp-cfg', type=str, dest='exp_cfg', + help='The configuration of the experiment') + parser.add_argument('--exp-opts', default=[], dest='exp_opts', + nargs='*', + help='The configuration of the Detector') + parser.add_argument('--local_rank', default=0, type=int, + help='ranking within the nodes') + parser.add_argument('--num-gpus', dest='num_gpus', + default=1, type=int, + help='Number of gpus') + parser.add_argument('--backend', dest='backend', + default='nccl', type=str, + choices=['nccl', 'gloo'], + help='Backend used for multi-gpu training') + + cmd_args = parser.parse_args() + + cfg.merge_from_file(cmd_args.exp_cfg) + cfg.merge_from_list(cmd_args.exp_opts) + + use_face_contour = cfg.datasets.use_face_contour + set_face_contour(cfg, use_face_contour=use_face_contour) + + cfg.network.use_sync_bn = (cfg.network.use_sync_bn and + cmd_args.num_gpus > 1) + cfg.local_rank = cmd_args.local_rank + cfg.num_gpus = cmd_args.num_gpus + + return cfg + +=== File: expose/config/datasets_defaults.py === + +-- Chunk 1 -- +// datasets_defaults.py:8-37 +def build_transform_cfg(node, key='transforms', flip_prob=0.0, + downsample_factor_min=1.0, + downsample_factor_max=1.0, + center_jitter_factor=0.0, + downsample_dist='categorical', + ): + if key not in node: + node[key] = CN() + node[key].flip_prob = flip_prob + node[key].downsample_dist = downsample_dist + node[key].downsample_factor_min = downsample_factor_min + node[key].downsample_factor_max = downsample_factor_max + node[key].downsample_cat_factors = (1.0,) + node[key].center_jitter_factor = center_jitter_factor + node[key].center_jitter_dist = 'normal' + node[key].crop_size = 256 + node[key].scale_factor_min = 1.0 + node[key].scale_factor_max = 1.0 + node[key].scale_factor = 0.0 + node[key].scale_dist = 'uniform' + node[key].noise_scale = 0.0 + node[key].rotation_factor = 0.0 + node[key].mean = [0.485, 0.456, 0.406] + node[key].std = [0.229, 0.224, 0.225] + node[key].brightness = 0.0 + node[key].saturation = 0.0 + node[key].hue = 0.0 + node[key].contrast = 0.0 + + return node[key] + +-- Chunk 2 -- +// datasets_defaults.py:40-46 +def build_num_workers_cfg(node, key='num_workers'): + if key not in node: + node[key] = CN() + node[key].train = 8 + node[key].val = 2 + node[key].test = 2 + return node[key] + +=== File: expose/config/loss_defaults.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/config/loss_defaults.py:1-150 +from copy import deepcopy +# from yacs.config import CfgNode as CN +from fvcore.common.config import CfgNode as CN + +_C = CN() + + +_C.stages_to_penalize = [-1] +_C.stages_to_regularize = [-1] + +_C.body_joints_2d = CN() +_C.body_joints_2d.type = 'keypoints' +_C.body_joints_2d.robustifier = 'none' +_C.body_joints_2d.norm_type = 'l1' +_C.body_joints_2d.rho = 100.0 +_C.body_joints_2d.beta = 5.0 / 100 * 2 +_C.body_joints_2d.size_average = True +_C.body_joints_2d.weight = 1.0 +_C.body_joints_2d.enable = 0 + +_C.hand_joints_2d = CN() +_C.hand_joints_2d.type = 'keypoints' +_C.hand_joints_2d.norm_type = 'l1' +_C.hand_joints_2d.robustifier = 'none' +_C.hand_joints_2d.rho = 100.0 +_C.hand_joints_2d.beta = 5.0 / 100 * 2 +_C.hand_joints_2d.size_average = True +_C.hand_joints_2d.weight = 1.0 +_C.hand_joints_2d.enable = 0 + +_C.face_joints_2d = CN() +_C.face_joints_2d.type = 'keypoints' +_C.face_joints_2d.norm_type = 'l1' +_C.face_joints_2d.robustifier = 'none' +_C.face_joints_2d.rho = 100.0 +_C.face_joints_2d.beta = 5.0 / 100 * 2 +_C.face_joints_2d.size_average = True +_C.face_joints_2d.weight = 1.0 +_C.face_joints_2d.enable = 0 + + +_C.head_crop_keypoints = CN() +_C.head_crop_keypoints.type = 'keypoints' +_C.head_crop_keypoints.norm_type = 'l1' +_C.head_crop_keypoints.robustifier = 'none' +_C.head_crop_keypoints.rho = 100.0 +_C.head_crop_keypoints.beta = 5.0 / 100 * 2 +_C.head_crop_keypoints.size_average = True +_C.head_crop_keypoints.weight = 0.0 +_C.head_crop_keypoints.enable = 0 + +_C.left_hand_crop_keypoints = CN() +_C.left_hand_crop_keypoints.type = 'keypoints' +_C.left_hand_crop_keypoints.norm_type = 'l1' +_C.left_hand_crop_keypoints.robustifier = 'none' +_C.left_hand_crop_keypoints.rho = 100.0 +_C.left_hand_crop_keypoints.beta = 5.0 / 100 * 2 +_C.left_hand_crop_keypoints.size_average = True +_C.left_hand_crop_keypoints.weight = 0.0 +_C.left_hand_crop_keypoints.enable = 0 + +_C.right_hand_crop_keypoints = CN() +_C.right_hand_crop_keypoints.type = 'keypoints' +_C.right_hand_crop_keypoints.norm_type = 'l1' +_C.right_hand_crop_keypoints.robustifier = 'none' +_C.right_hand_crop_keypoints.rho = 100.0 +_C.right_hand_crop_keypoints.beta = 5.0 / 100 * 2 +_C.right_hand_crop_keypoints.size_average = True +_C.right_hand_crop_keypoints.weight = 0.0 +_C.right_hand_crop_keypoints.enable = 0 + +_C.body_edge_2d = CN() +_C.body_edge_2d.norm_type = 'l2' +_C.body_edge_2d.rho = 100.0 +_C.body_edge_2d.beta = 5.0 / 100 * 2 +_C.body_edge_2d.size_average = True +_C.body_edge_2d.weight = 0.0 +_C.body_edge_2d.enable = 0 +_C.body_edge_2d.robustifier = 'none' +_C.body_edge_2d.scale = 1.0 +_C.body_edge_2d.threshold = 1.0 + + +_C.hand_edge_2d = CN() +_C.hand_edge_2d.norm_type = 'l2' +_C.hand_edge_2d.rho = 100.0 +_C.hand_edge_2d.beta = 5.0 / 100 * 2 +_C.hand_edge_2d.size_average = True +_C.hand_edge_2d.weight = 0.0 +_C.hand_edge_2d.enable = 0 +_C.hand_edge_2d.robustifier = 'none' +_C.hand_edge_2d.scale = 1.0 +_C.hand_edge_2d.threshold = 1.0 + + +_C.face_edge_2d = CN() +_C.face_edge_2d.norm_type = 'l2' +_C.face_edge_2d.rho = 100.0 +_C.face_edge_2d.beta = 5.0 / 100 * 2 +_C.face_edge_2d.size_average = True +_C.face_edge_2d.weight = 0.0 +_C.face_edge_2d.enable = 0 +_C.face_edge_2d.robustifier = 'none' +_C.face_edge_2d.scale = 1.0 +_C.face_edge_2d.threshold = 1.0 + +_C.body_joints_3d = CN() +_C.body_joints_3d.type = 'keypoints' +_C.body_joints_3d.norm_type = 'l1' +_C.body_joints_3d.rho = 100.0 +_C.body_joints_3d.beta = 5.0 / 100 * 2 +_C.body_joints_3d.size_average = True +_C.body_joints_3d.weight = 0.0 +_C.body_joints_3d.enable = 0 + + +_C.hand_joints_3d = CN() +_C.hand_joints_3d.type = 'keypoints' +_C.hand_joints_3d.norm_type = 'l1' +_C.hand_joints_3d.rho = 100.0 +_C.hand_joints_3d.beta = 5.0 / 100 * 2 +_C.hand_joints_3d.size_average = True +_C.hand_joints_3d.weight = 0.0 +_C.hand_joints_3d.enable = 500 * 1000 + + +_C.face_joints_3d = CN() +_C.face_joints_3d.type = 'keypoints' +_C.face_joints_3d.norm_type = 'l1' +_C.face_joints_3d.rho = 100.0 +_C.face_joints_3d.beta = 5.0 / 100 * 2 +_C.face_joints_3d.size_average = True +_C.face_joints_3d.weight = 0.0 +_C.face_joints_3d.enable = 500 * 1000 + + +_C.shape = CN() +_C.shape.type = 'l2' +_C.shape.weight = 1.0 +_C.shape.enable = 0 +_C.shape.prior = CN() +_C.shape.prior.type = 'l2' +_C.shape.prior.weight = 0.0 +_C.shape.prior.margin = 1.0 +_C.shape.prior.norm = 'l2' +_C.shape.prior.use_vector = True +_C.shape.prior.barrier = 'log' +_C.shape.prior.epsilon = 1e-7 + +_C.expression = CN() + +-- Chunk 2 -- +// /app/repos/repo_8/expose/config/loss_defaults.py:151-300 +_C.expression.type = 'l2' +_C.expression.weight = 1.0 +_C.expression.enable = 0 +_C.expression.use_conf_weight = False +_C.expression.prior = CN() +_C.expression.prior.type = 'l2' +_C.expression.prior.weight = 0.0 +_C.expression.prior.margin = 1.0 +_C.expression.prior.use_vector = True +_C.expression.prior.norm = 'l2' +_C.expression.prior.barrier = 'log' +_C.expression.prior.epsilon = 1e-7 + +_C.global_orient = CN() +_C.global_orient.type = 'rotation' +_C.global_orient.enable = 0 +_C.global_orient.weight = 1.0 +_C.global_orient.prior = CN() + +_C.body_pose = CN() +_C.body_pose.type = 'rotation' +_C.body_pose.enable = 0 +_C.body_pose.weight = 1.0 +_C.body_pose.prior = CN() +_C.body_pose.prior.type = 'l2' +_C.body_pose.prior.use_max = False +_C.body_pose.prior.weight = 0.0 +_C.body_pose.prior.path = 'data/priors/gmm_08.pkl' +_C.body_pose.prior.num_gaussians = 8 + +_C.left_hand_pose = CN() +_C.left_hand_pose.use_conf_weight = False +_C.left_hand_pose.type = 'rotation' +_C.left_hand_pose.enable = 0 +_C.left_hand_pose.weight = 1.0 +_C.left_hand_pose.prior = CN() +_C.left_hand_pose.prior.type = 'l2' +_C.left_hand_pose.prior.weight = 0.0 +_C.left_hand_pose.prior.num_gaussians = 6 +_C.left_hand_pose.prior.path = 'data/priors/gmm_left_06.pkl' + +_C.right_hand_pose = CN() +_C.right_hand_pose.use_conf_weight = False +_C.right_hand_pose.type = 'rotation' +_C.right_hand_pose.enable = 0 +_C.right_hand_pose.weight = 1.0 +_C.right_hand_pose.prior = CN() +_C.right_hand_pose.prior.type = 'l2' +_C.right_hand_pose.prior.weight = 0.0 +_C.right_hand_pose.prior.num_gaussians = 6 +_C.right_hand_pose.prior.path = 'data/priors/gmm_right_06.pkl' + +_C.jaw_pose = CN() +_C.jaw_pose.type = 'rotation' +_C.jaw_pose.use_conf_weight = False +_C.jaw_pose.enable = 0 +_C.jaw_pose.weight = 1.0 +_C.jaw_pose.prior = CN() +_C.jaw_pose.prior.type = 'l2' +_C.jaw_pose.prior.weight = 0.0 +_C.jaw_pose.prior.reduction = 'mean' + +_C.edge = CN() +_C.edge.weight = 0.0 +_C.edge.type = 'vertex-edge' +_C.edge.norm_type = 'l2' +_C.edge.gt_edge_path = '' +_C.edge.est_edge_path = '' +_C.edge.rho = 100.0 +_C.edge.size_average = True +_C.edge.enable = 0 + +_C.hand = CN() + +_C.hand.joints_2d = CN() +_C.hand.joints_2d.weight = 1.0 +_C.hand.joints_2d.type = 'keypoints' +_C.hand.joints_2d.norm_type = 'l1' +_C.hand.joints_2d.robustifier = 'none' +_C.hand.joints_2d.rho = 100.0 +_C.hand.joints_2d.beta = 5.0 / 100 * 2 +_C.hand.joints_2d.size_average = True +_C.hand.joints_2d.enable = 0 + +_C.hand.vertices = CN() +_C.hand.vertices.weight = 0.0 +_C.hand.vertices.type = 'weighted-l1' +_C.hand.vertices.rho = 100.0 +_C.hand.vertices.beta = 5.0 / 100 * 2 +_C.hand.vertices.size_average = True +_C.hand.vertices.enable = 0 + +_C.hand.edge = CN() +_C.hand.edge.weight = 0.0 +_C.hand.edge.type = 'vertex-edge' +_C.hand.edge.norm_type = 'l2' +_C.hand.edge.gt_edge_path = '' +_C.hand.edge.est_edge_path = '' +_C.hand.edge.rho = 100.0 +_C.hand.edge.size_average = True +_C.hand.edge.enable = 0 + +_C.hand.hand_edge_2d = CN() +_C.hand.hand_edge_2d.weight = 0.0 +_C.hand.hand_edge_2d.norm_type = 'l2' +_C.hand.hand_edge_2d.rho = 100.0 +_C.hand.hand_edge_2d.beta = 5.0 / 100 * 2 +_C.hand.hand_edge_2d.size_average = True +_C.hand.hand_edge_2d.enable = 0 +_C.hand.hand_edge_2d.robustifier = 'none' +_C.hand.hand_edge_2d.scale = 1.0 +_C.hand.hand_edge_2d.threshold = 1.0 + + +_C.hand.joints_3d = CN() +_C.hand.joints_3d.weight = 0.0 +_C.hand.joints_3d.type = 'keypoints' +_C.hand.joints_3d.norm_type = 'l1' +_C.hand.joints_3d.rho = 100.0 +_C.hand.joints_3d.beta = 5.0 / 100 * 2 +_C.hand.joints_3d.size_average = True +_C.hand.joints_3d.enable = 500 * 1000 + + +_C.hand.shape = CN() +_C.hand.shape.type = 'l2' +_C.hand.shape.weight = 0.0 +_C.hand.shape.enable = 0 +_C.hand.shape.prior = CN() +_C.hand.shape.prior.weight = 0.0 +_C.hand.shape.prior.type = 'l2' +_C.hand.shape.prior.margin = 1.0 +_C.hand.shape.prior.norm = 'l2' +_C.hand.shape.prior.use_vector = True +_C.hand.shape.prior.barrier = 'log' +_C.hand.shape.prior.epsilon = 1e-7 + +_C.hand.global_orient = CN() +_C.hand.global_orient.type = 'rotation' +_C.hand.global_orient.enable = 0 +_C.hand.global_orient.weight = 1.0 +_C.hand.global_orient.prior = CN() + +_C.hand.hand_pose = CN() +_C.hand.hand_pose.use_conf_weight = False +_C.hand.hand_pose.type = 'rotation' +_C.hand.hand_pose.enable = 0 +_C.hand.hand_pose.weight = 1.0 +_C.hand.hand_pose.prior = CN() +_C.hand.hand_pose.prior.type = 'l2' + +-- Chunk 3 -- +// /app/repos/repo_8/expose/config/loss_defaults.py:301-397 +_C.hand.hand_pose.prior.weight = 0.0 +_C.hand.hand_pose.prior.num_gaussians = 6 +_C.hand.hand_pose.prior.margin = 1.0 +_C.hand.hand_pose.prior.path = 'data/priors/gmm_left_06.pkl' + +# Losses +_C.head = CN() + +_C.head.joints_2d = CN() +_C.head.joints_2d.type = 'keypoints' +_C.head.joints_2d.norm_type = 'l1' +_C.head.joints_2d.robustifier = 'none' +_C.head.joints_2d.rho = 100.0 +_C.head.joints_2d.beta = 5.0 / 100 * 2 +_C.head.joints_2d.size_average = True +_C.head.joints_2d.weight = 0.0 +_C.head.joints_2d.enable = 0.0 + +_C.head.edge_2d = CN() +_C.head.edge_2d.weight = 0.0 +_C.head.edge_2d.norm_type = 'l2' +_C.head.edge_2d.rho = 100.0 +_C.head.edge_2d.beta = 5.0 / 100 * 2 +_C.head.edge_2d.size_average = True +_C.head.edge_2d.enable = 0 +_C.head.edge_2d.robustifier = 'none' +_C.head.edge_2d.scale = 0.0 +_C.head.edge_2d.threshold = 1.0 + +_C.head.vertices = CN() +_C.head.vertices.weight = 0.0 +_C.head.vertices.type = 'weighted-l1' +_C.head.vertices.rho = 100.0 +_C.head.vertices.beta = 5.0 / 100 * 2 +_C.head.vertices.size_average = True +_C.head.vertices.enable = 0 + +_C.head.edge = CN() +_C.head.edge.weight = 0.0 +_C.head.edge.type = 'vertex-edge' +_C.head.edge.norm_type = 'l2' +_C.head.edge.gt_edge_path = '' +_C.head.edge.est_edge_path = '' +_C.head.edge.rho = 100.0 +_C.head.edge.size_average = True +_C.head.edge.enable = 0 + +_C.head.joints_3d = CN() +_C.head.joints_3d.weight = 0.0 +_C.head.joints_3d.type = 'keypoints' +_C.head.joints_3d.norm_type = 'l1' +_C.head.joints_3d.rho = 100.0 +_C.head.joints_3d.beta = 5.0 / 100 * 2 +_C.head.joints_3d.size_average = True +_C.head.joints_3d.enable = 0.0 + +_C.head.shape = CN() +_C.head.shape.type = 'l2' +_C.head.shape.weight = 1.0 +_C.head.shape.enable = 0 +_C.head.shape.prior = CN() +_C.head.shape.prior.type = 'l2' +_C.head.shape.prior.weight = 0.0 +_C.head.shape.prior.margin = 1.0 +_C.head.shape.prior.norm = 'l2' +_C.head.shape.prior.use_vector = True +_C.head.shape.prior.barrier = 'log' +_C.head.shape.prior.epsilon = 1e-7 + +_C.head.expression = CN() +_C.head.expression.type = 'l2' +_C.head.expression.weight = 1.0 +_C.head.expression.enable = 0 +_C.head.expression.use_conf_weight = False +_C.head.expression.prior = CN() +_C.head.expression.prior.type = 'l2' +_C.head.expression.prior.weight = 0.0 +_C.head.expression.prior.margin = 1.0 +_C.head.expression.prior.use_vector = True +_C.head.expression.prior.norm = 'l2' +_C.head.expression.prior.barrier = 'log' +_C.head.expression.prior.epsilon = 1e-7 + +_C.head.global_orient = CN() +_C.head.global_orient.type = 'rotation' +_C.head.global_orient.enable = 0 +_C.head.global_orient.weight = 1.0 +_C.head.global_orient.prior = CN() + +_C.head.jaw_pose = CN() +_C.head.jaw_pose.type = 'rotation' +_C.head.jaw_pose.use_conf_weight = False +_C.head.jaw_pose.enable = 0 +_C.head.jaw_pose.weight = 1.0 +_C.head.jaw_pose.prior = CN() +_C.head.jaw_pose.prior.type = 'l2' +_C.head.jaw_pose.prior.weight = 0.0 + +=== File: expose/config/defaults.py === + +-- Chunk 1 -- +// defaults.py:12-28 +def create_camera_config(node): + node.camera = CN() + node.camera.type = 'weak-persp' + node.camera.pos_func = 'softplus' + + node.camera.weak_persp = CN() + node.camera.weak_persp.regress_translation = True + node.camera.weak_persp.regress_scale = True + node.camera.weak_persp.regress_scale = True + node.camera.weak_persp.mean_scale = 0.9 + + node.camera.perspective = CN() + node.camera.perspective.regress_translation = False + node.camera.perspective.regress_rotation = False + node.camera.perspective.regress_focal_length = False + node.camera.perspective.focal_length = 5000 + return node.camera + +-- Chunk 2 -- +// defaults.py:31-45 +def create_mlp_config(node, key='mlp'): + if key not in node: + node[key] = CN() + + node[key].layers = (1024, 1024) + node[key].activ_type = 'relu' + node[key].lrelu_slope = 0.2 + node[key].norm_type = 'none' + node[key].num_groups = 32 + node[key].dropout = 0.0 + node[key].init_type = 'xavier' + node[key].gain = 0.01 + node[key].bias_init = 0.0 + + return node[key] + +-- Chunk 3 -- +// defaults.py:48-55 +def create_conv_layers(node, key='layer'): + if key not in node: + node[key] = CN() + + node[key].num_layers = 5 + node[key].num_filters = 2048 + node[key].stride = 1 + return node[key] + +-- Chunk 4 -- +// defaults.py:58-70 +def create_subsample_layer(node, num_layers=3, key='layer', + kernel_size=3, stride=2): + if key not in node: + node[key] = CN() + + node[key].num_filters = (512,) * num_layers + node[key].norm_type = 'bn' + node[key].activ_type = 'relu' + node[key].dim = 2 + node[key].kernel_sizes = [kernel_size] * len(node[key].num_filters) + node[key].strides = [stride] * len(node[key].num_filters) + node[key].padding = 1 + return node[key] + +-- Chunk 5 -- +// defaults.py:73-145 +def create_backbone_cfg(node, backbone_type='resnet50'): + if 'backbone' not in node: + node.backbone = CN() + node.backbone.type = backbone_type + node.backbone.pretrained = True + + node.backbone.resnet = CN() + node.backbone.resnet.replace_stride_with_dilation = (False, False, False) + + node.backbone.fpn = CN() + node.backbone.fpn.pooling_type = 'concat' + node.backbone.fpn.concat = CN() + node.backbone.fpn.concat.use_max = True + node.backbone.fpn.concat.use_avg = True + + node.backbone.hrnet = CN() + node.backbone.hrnet.pretrained_layers = ['*'] + node.backbone.hrnet.pretrained_path = ( + 'data/' + 'network_weights/hrnet/' + 'imagenet/hrnet_w48-8ef0771d.pth' + ) + + node.backbone.hrnet.stage1 = CN() + node.backbone.hrnet.stage1.num_modules = 1 + node.backbone.hrnet.stage1.num_branches = 1 + node.backbone.hrnet.stage1.num_blocks = [4] + node.backbone.hrnet.stage1.num_channels = [64] + node.backbone.hrnet.stage1.block = 'BOTTLENECK' + node.backbone.hrnet.stage1.fuse_method = 'SUM' + + node.backbone.hrnet.stage2 = CN() + node.backbone.hrnet.stage2.num_modules = 1 + node.backbone.hrnet.stage2.num_branches = 2 + node.backbone.hrnet.stage2.num_blocks = [4, 4] + node.backbone.hrnet.stage2.num_channels = [48, 96] + node.backbone.hrnet.stage2.block = 'BASIC' + node.backbone.hrnet.stage2.fuse_method = 'SUM' + + node.backbone.hrnet.stage3 = CN() + node.backbone.hrnet.stage3.num_modules = 4 + node.backbone.hrnet.stage3.num_branches = 3 + node.backbone.hrnet.stage3.num_blocks = [4, 4, 4] + node.backbone.hrnet.stage3.num_channels = [48, 96, 192] + node.backbone.hrnet.stage3.block = 'BASIC' + node.backbone.hrnet.stage3.fuse_method = 'SUM' + + node.backbone.hrnet.stage4 = CN() + node.backbone.hrnet.stage4.num_modules = 3 + node.backbone.hrnet.stage4.num_branches = 4 + node.backbone.hrnet.stage4.num_blocks = [4, 4, 4, 4] + node.backbone.hrnet.stage4.num_channels = [48, 96, 192, 384] + node.backbone.hrnet.stage4.block = 'BASIC' + node.backbone.hrnet.stage4.fuse_method = 'SUM' + + node.backbone.hrnet.stage2.subsample = create_subsample_layer( + node.backbone.hrnet.stage2, key='subsample', num_layers=2) + node.backbone.hrnet.stage2.subsample.num_filters = [96, 192] + node.backbone.hrnet.stage2.subsample.num_filters = [384] + node.backbone.hrnet.stage2.subsample.kernel_sizes = [3] + node.backbone.hrnet.stage2.subsample.strides = [2] + + node.backbone.hrnet.stage3.subsample = create_subsample_layer( + node.backbone.hrnet.stage3, key='subsample', num_layers=1) + node.backbone.hrnet.stage3.subsample.num_filters = [192, 384] + node.backbone.hrnet.stage3.subsample.kernel_sizes = [3, 3] + node.backbone.hrnet.stage3.subsample.strides = [2, 2] + + node.backbone.hrnet.final_conv = create_conv_layers( + node.backbone.hrnet, key='final_conv') + node.backbone.hrnet.final_conv.num_filters = 2048 + + return node.backbone + +-- Chunk 6 -- +// defaults.py:345-349 +def get_cfg_defaults(): + """Get a yacs CfgNode object with default values for my_project.""" + # Return a clone so that the defaults will not be altered + # This is for the "local variable" use pattern + return _C.clone() + +=== File: expose/config/body_model.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/config/body_model.py:1-96 +from fvcore.common.config import CfgNode as CN +# from yacs.config import CfgNode as CN + +_C = CN() + +_C.body_model = CN() + +_C.body_model.j14_regressor_path = '' +_C.body_model.mean_pose_path = '' +_C.body_model.shape_mean_path = 'data/shape_mean.npy' +_C.body_model.type = 'smplx' +_C.body_model.model_folder = 'models' +_C.body_model.use_compressed = True +_C.body_model.gender = 'neutral' +_C.body_model.num_betas = 10 +_C.body_model.num_expression_coeffs = 10 +_C.body_model.use_feet_keypoints = True +_C.body_model.use_face_keypoints = True +_C.body_model.use_face_contour = False + +_C.body_model.global_orient = CN() +# The configuration for the parameterization of the body pose +_C.body_model.global_orient.param_type = 'cont_rot_repr' + +_C.body_model.body_pose = CN() +# The configuration for the parameterization of the body pose +_C.body_model.body_pose.param_type = 'cont_rot_repr' +_C.body_model.body_pose.finetune = False + +_C.body_model.left_hand_pose = CN() +# The configuration for the parameterization of the left hand pose +_C.body_model.left_hand_pose.param_type = 'pca' +_C.body_model.left_hand_pose.num_pca_comps = 12 +_C.body_model.left_hand_pose.flat_hand_mean = False +# The type of prior on the left hand pose + +_C.body_model.right_hand_pose = CN() +# The configuration for the parameterization of the left hand pose +_C.body_model.right_hand_pose.param_type = 'pca' +_C.body_model.right_hand_pose.num_pca_comps = 12 +_C.body_model.right_hand_pose.flat_hand_mean = False + +_C.body_model.jaw_pose = CN() +_C.body_model.jaw_pose.param_type = 'cont_rot_repr' +_C.body_model.jaw_pose.data_fn = 'clusters.pkl' + +####### HAND MODEL ######## + +_C.hand_model = CN() +_C.hand_model.j14_regressor_path = '' +_C.hand_model.mean_pose_path = '' +_C.hand_model.shape_mean_path = 'data/shape_mean.npy' +_C.hand_model.type = 'mano-from-smplx' +_C.hand_model.model_folder = 'models' +_C.hand_model.use_compressed = True +_C.hand_model.gender = 'neutral' +_C.hand_model.num_betas = 10 +_C.hand_model.num_expression_coeffs = 10 +_C.hand_model.use_feet_keypoints = True +_C.hand_model.use_face_keypoints = True + +_C.hand_model.return_hand_vertices_only = True +_C.hand_model.vertex_idxs_path = '' + +_C.hand_model.global_orient = CN() +# The configuration for the parameterization of the body pose +_C.hand_model.global_orient.param_type = 'cont_rot_repr' + +_C.hand_model.hand_pose = CN() +_C.hand_model.hand_pose.param_type = 'pca' +_C.hand_model.hand_pose.num_pca_comps = 12 +_C.hand_model.hand_pose.flat_hand_mean = False + +#### HEAD MODEL ########### +_C.head_model = CN() +_C.head_model.j14_regressor_path = '' +_C.head_model.mean_pose_path = '' +_C.head_model.shape_mean_path = 'data/shape_mean.npy' +_C.head_model.type = 'flame-from-smplx' +_C.head_model.model_folder = 'models' +_C.head_model.use_compressed = True +_C.head_model.gender = 'neutral' +_C.head_model.num_betas = 10 +_C.head_model.num_expression_coeffs = 10 +_C.head_model.use_feet_keypoints = True +_C.head_model.use_face_keypoints = True +_C.head_model.use_face_contour = True +_C.head_model.return_head_vertices_only = True +_C.head_model.vertex_idxs_path = '' + +_C.head_model.global_orient = CN() +# The configuration for the parameterization of the body pose +_C.head_model.global_orient.param_type = 'cont_rot_repr' +# +_C.head_model.jaw_pose = CN() +_C.head_model.jaw_pose.param_type = 'cont_rot_repr' + +=== File: expose/config/optim_defaults.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/config/optim_defaults.py:1-38 +from copy import deepcopy +from fvcore.common.config import CfgNode as CN + +_C = CN() + +_C = CN() +_C.type = 'sgd' +_C.num_epochs = 300 +_C.lr = 1e-4 +_C.offsets_decay = 1e-4 + +_C.steps = (30000,) + +_C.sgd = CN() +_C.sgd.momentum = 0.9 +_C.sgd.nesterov = True + +_C.scheduler = CN() +_C.scheduler.type = 'none' +_C.scheduler.gamma = 0.1 +_C.scheduler.milestones = [] +_C.scheduler.step_size = 1000 +_C.scheduler.warmup_factor = 1.0e-1 / 3 +_C.scheduler.warmup_iters = 500 +_C.scheduler.warmup_method = "linear" + +# Adam parameters +_C.adam = CN() +_C.adam.betas = [0.9, 0.999] +_C.adam.eps = 1e-08 +_C.adam.amsgrad = False + +_C.rmsprop = CN() +_C.rmsprop.alpha = 0.99 + +_C.weight_decay = 0.0 +_C.weight_decay_bias = 0.0 +_C.bias_lr_factor = 1.0 + +=== File: expose/config/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/config/__init__.py:1-2 +from .defaults import _C as cfg +from .cmd_parser import parse_args + +=== File: expose/data/build.py === + +-- Chunk 1 -- +// build.py:45-53 + make_data_sampler(dataset, is_train=True, + shuffle=True, is_distributed=False): + if is_train: + sampler = dutils.RandomSampler(dataset) + else: + sampler = dutils.SequentialSampler(dataset) + return sampler + + + +-- Chunk 2 -- +// build.py:54-87 + make_head_dataset(name, dataset_cfg, transforms, + num_betas=10, num_expression_coeffs=10, + **kwargs): + if name == 'ehf': + obj = datasets.EHF + elif name == 'curated_fits': + obj = datasets.CuratedFittings + elif name == 'spinx': + obj = datasets.SPINX + elif name == 'ffhq': + obj = datasets.FFHQ + elif name == 'openpose': + obj = datasets.OpenPose + elif name == 'stirling3d': + obj = datasets.Stirling3D + else: + raise ValueError('Unknown dataset: {}'.format(name)) + + args = dict(**dataset_cfg[name]) + args.update(kwargs) + + vertex_flip_correspondences = osp.expandvars(dataset_cfg.get( + 'vertex_flip_correspondences', '')) + dset_obj = obj(transforms=transforms, + head_only=True, + num_betas=num_betas, + num_expression_coeffs=num_expression_coeffs, + vertex_flip_correspondences=vertex_flip_correspondences, + **args) + + logger.info(f'Created head dataset: {dset_obj.name()}') + return dset_obj + + + +-- Chunk 3 -- +// build.py:88-118 + make_hand_dataset(name, dataset_cfg, transforms, + num_betas=10, num_expression_coeffs=10, + **kwargs): + if name == 'ehf': + obj = datasets.EHF + elif name == 'curated_fits': + obj = datasets.CuratedFittings + elif name == 'spinx': + obj = datasets.SPINX + elif name == 'openpose': + obj = datasets.OpenPose + elif name == 'freihand': + obj = datasets.FreiHand + else: + raise ValueError(f'Unknown dataset: {name}') + + logger.info(f'Building dataset: {name}') + args = dict(**dataset_cfg[name]) + args.update(kwargs) + vertex_flip_correspondences = osp.expandvars(dataset_cfg.get( + 'vertex_flip_correspondences', '')) + + dset_obj = obj(transforms=transforms, num_betas=num_betas, hand_only=True, + num_expression_coeffs=num_expression_coeffs, + vertex_flip_correspondences=vertex_flip_correspondences, + **args) + + logger.info(f'Created dataset: {dset_obj.name()}') + return dset_obj + + + +-- Chunk 4 -- +// build.py:119-155 + make_body_dataset(name, dataset_cfg, transforms, + num_betas=10, + num_expression_coeffs=10, + **kwargs): + if name == 'ehf': + obj = datasets.EHF + elif name == 'curated_fits': + obj = datasets.CuratedFittings + elif name == 'threedpw': + obj = datasets.ThreeDPW + elif name == 'spin': + obj = datasets.SPIN + elif name == 'spinx': + obj = datasets.SPINX + elif name == 'lsp_test': + obj = datasets.LSPTest + elif name == 'openpose': + obj = datasets.OpenPose + elif name == 'tracks': + obj = datasets.OpenPoseTracks + else: + raise ValueError(f'Unknown dataset: {name}') + + args = dict(**dataset_cfg[name]) + args.update(kwargs) + + vertex_flip_correspondences = osp.expandvars(dataset_cfg.get( + 'vertex_flip_correspondences', '')) + dset_obj = obj(transforms=transforms, num_betas=num_betas, + vertex_flip_correspondences=vertex_flip_correspondences, + num_expression_coeffs=num_expression_coeffs, + **args) + + logger.info('Created dataset: {}', dset_obj.name()) + return dset_obj + + + +-- Chunk 5 -- +// build.py:156-182 +ss MemoryPinning(object): + def __init__( + self, + full_img_list: Union[ImageList, List[Tensor]], + images: Tensor, + targets: List[GenericTarget] + ): + super(MemoryPinning, self).__init__() + self.img_list = full_img_list + self.images = images + self.targets = targets + + def pin_memory( + self + ) -> Tuple[Union[ImageList, List[Tensor]], Tensor, List[GenericTarget]]: + if self.img_list is not None: + if isinstance(self.img_list, ImageList): + self.img_list.pin_memory() + elif isinstance(self.img_list, (list, tuple)): + self.img_list = [x.pin_memory() for x in self.img_list] + return ( + self.img_list, + self.images.pin_memory(), + self.targets, + ) + + + +-- Chunk 6 -- +// build.py:183-235 + collate_batch(batch, use_shared_memory=False, return_full_imgs=False, + pin_memory=True): + if return_full_imgs: + images, cropped_images, targets, _ = zip(*batch) + else: + _, cropped_images, targets, _ = zip(*batch) + + out_targets = [] + for t in targets: + if t is None: + continue + if type(t) == list: + out_targets += t + else: + out_targets.append(t) + out_cropped_images = [] + for img in cropped_images: + if img is None: + continue + if len(img.shape) < 4: + img.unsqueeze_(dim=0) + out_cropped_images.append(img.clone()) + + if len(out_cropped_images) < 1: + return None, None, None + + full_img_list = None + if return_full_imgs: + # full_img_list = to_image_list(images) + full_img_list = images + out = None + if use_shared_memory: + numel = sum([x.numel() for x in out_cropped_images if x is not None]) + storage = out_cropped_images[0].storage()._new_shared(numel) + out = out_cropped_images[0].new(storage) + + # if not return_full_imgs: + # del images + # images = None + + batch.clear() + # del targets, batch + if pin_memory: + return MemoryPinning( + full_img_list, + torch.cat(out_cropped_images, 0, out=out), + out_targets + ) + else: + return full_img_list, torch.cat( + out_cropped_images, 0, out=out), out_targets + + + +-- Chunk 7 -- +// build.py:236-243 + make_equal_sampler(datasets, batch_size=32, shuffle=True, ratio_2d=0.5): + batch_sampler = EqualSampler( + datasets, batch_size=batch_size, shuffle=shuffle, ratio_2d=ratio_2d) + out_dsets_lst = [dutils.ConcatDataset(datasets) if len(datasets) > 1 else + datasets[0]] + return batch_sampler, out_dsets_lst + + + +-- Chunk 8 -- +// build.py:244-275 + make_data_loader(dataset, batch_size=32, num_workers=0, + is_train=True, sampler=None, collate_fn=None, + shuffle=True, is_distributed=False, + batch_sampler=None): + if batch_sampler is None: + sampler = make_data_sampler( + dataset, is_train=is_train, + shuffle=shuffle, is_distributed=is_distributed) + + if batch_sampler is None: + assert sampler is not None, ( + 'Batch sampler and sampler can\'t be "None" at the same time') + data_loader = torch.utils.data.DataLoader( + dataset, + batch_size=batch_size, + num_workers=num_workers, + sampler=sampler, + collate_fn=collate_fn, + drop_last=True and is_train, + pin_memory=True, + ) + else: + data_loader = torch.utils.data.DataLoader( + dataset, + num_workers=num_workers, + collate_fn=collate_fn, + batch_sampler=batch_sampler, + pin_memory=True, + ) + return data_loader + + + +-- Chunk 9 -- +// build.py:276-425 + make_all_data_loaders(exp_cfg, split='train', start_iter=0, **kwargs): + is_train = 'train' in split + num_betas = exp_cfg.body_model.num_betas + num_expression_coeffs = exp_cfg.body_model.num_expression_coeffs + + dataset_cfg = exp_cfg.get('datasets', {}) + + body_dsets_cfg = dataset_cfg.get('body', {}) + body_dset_names = body_dsets_cfg.get('splits', {})[split] + + body_transfs_cfg = body_dsets_cfg.get('transforms', {}) + body_transforms = build_transforms(body_transfs_cfg, is_train=is_train) + + hand_dsets_cfg = dataset_cfg.get('hand', {}) + hand_dset_names = hand_dsets_cfg.get('splits', {})[split] + hand_transfs_cfg = hand_dsets_cfg.get('transforms', {}) + hand_transforms = build_transforms(hand_transfs_cfg, is_train=is_train) + + head_dsets_cfg = dataset_cfg.get('head', {}) + head_dset_names = head_dsets_cfg.get('splits', {})[split] + head_transfs_cfg = head_dsets_cfg.get('transforms', {}) + head_transforms = build_transforms(head_transfs_cfg, is_train=is_train) + + body_datasets = [] + for dataset_name in body_dset_names: + dset = make_body_dataset(dataset_name, body_dsets_cfg, + transforms=body_transforms, + num_betas=num_betas, + num_expression_coeffs=num_expression_coeffs, + is_train=is_train, split=split, **kwargs) + body_datasets.append(dset) + + hand_datasets = [] + for dataset_name in hand_dset_names: + dset = make_hand_dataset(dataset_name, hand_dsets_cfg, + transforms=hand_transforms, + num_betas=num_betas, + num_expression_coeffs=num_expression_coeffs, + is_train=is_train, split=split, **kwargs) + hand_datasets.append(dset) + + head_datasets = [] + for dataset_name in head_dset_names: + dset = make_head_dataset(dataset_name, head_dsets_cfg, + transforms=head_transforms, + num_betas=num_betas, + num_expression_coeffs=num_expression_coeffs, + is_train=is_train, split=split, **kwargs) + head_datasets.append(dset) + + use_equal_sampling = exp_cfg.datasets.use_equal_sampling + + # Hard-coded for now + shuffle = is_train + is_distributed = False + + body_batch_size = body_dsets_cfg.get('batch_size', 64) + body_ratio_2d = body_dsets_cfg.get('ratio_2d', 0.5) + + hand_batch_size = hand_dsets_cfg.get('batch_size', 64) + hand_ratio_2d = hand_dsets_cfg.get('ratio_2d', 0.5) + + head_batch_size = head_dsets_cfg.get('batch_size', 64) + head_ratio_2d = head_dsets_cfg.get('ratio_2d', 0.5) + + body_num_workers = body_dsets_cfg.get( + 'num_workers', DEFAULT_NUM_WORKERS).get(split, 0) + logger.info(f'{split.upper()} Body num workers: {body_num_workers}') + + network_cfg = exp_cfg.network + return_full_imgs = (network_cfg.get('apply_hand_network_on_body', True) or + network_cfg.get('apply_head_network_on_body', True)) + logger.info(f'Return full resolution images: {return_full_imgs}') + body_collate_fn = functools.partial( + collate_batch, use_shared_memory=body_num_workers > 0, + return_full_imgs=return_full_imgs) + + hand_num_workers = hand_dsets_cfg.get( + 'num_workers', DEFAULT_NUM_WORKERS).get(split, 0) + hand_collate_fn = functools.partial( + collate_batch, use_shared_memory=hand_num_workers > 0) + # collate_batch, use_shared_memory=False) + + head_num_workers = head_dsets_cfg.get( + 'num_workers', DEFAULT_NUM_WORKERS).get(split, 0) + head_collate_fn = functools.partial( + collate_batch, use_shared_memory=head_num_workers > 0) + # collate_batch, use_shared_memory=False) + + body_batch_sampler, hand_batch_sampler, head_batch_sampler = [None] * 3 + # Equal sampling should only be used during training and only if there + # are multiple datasets + if is_train and use_equal_sampling: + body_batch_sampler, body_datasets = make_equal_sampler( + body_datasets, batch_size=body_batch_size, + shuffle=shuffle, ratio_2d=body_ratio_2d) + if len(hand_datasets) > 0: + hand_batch_sampler, hand_datasets = make_equal_sampler( + hand_datasets, batch_size=hand_batch_size, + shuffle=shuffle, ratio_2d=hand_ratio_2d) + if len(head_datasets) > 0: + head_batch_sampler, head_datasets = make_equal_sampler( + head_datasets, batch_size=head_batch_size, + shuffle=shuffle, ratio_2d=head_ratio_2d) + + body_data_loaders = [] + for body_dataset in body_datasets: + body_data_loaders.append( + make_data_loader(body_dataset, batch_size=body_batch_size, + num_workers=body_num_workers, + is_train=is_train, + batch_sampler=body_batch_sampler, + collate_fn=body_collate_fn, + shuffle=shuffle, is_distributed=is_distributed)) + hand_data_loaders = [] + for hand_dataset in hand_datasets: + hand_data_loaders.append( + make_data_loader(hand_dataset, batch_size=hand_batch_size, + num_workers=hand_num_workers, + is_train=is_train, + batch_sampler=hand_batch_sampler, + collate_fn=hand_collate_fn, + shuffle=shuffle, is_distributed=is_distributed)) + head_data_loaders = [] + for head_dataset in head_datasets: + head_data_loaders.append( + make_data_loader(head_dataset, batch_size=head_batch_size, + num_workers=head_num_workers, + is_train=is_train, + batch_sampler=head_batch_sampler, + collate_fn=head_collate_fn, + shuffle=shuffle, is_distributed=is_distributed)) + + use_adv_training = exp_cfg.use_adv_training + if is_train: + assert len(body_data_loaders) == 1, ( + 'There should be a single body loader,' + f' not {len(body_data_loaders)}') + # assert len(hand_data_loaders) == 1, ( + # 'There should be a single hand loader,' + # f' not {len(hand_data_loaders)}') + # assert len(head_data_loaders) == 1, ( + # 'There should be a single head loader,' + # f' not {len(head_data_loaders)}') + dloaders = { + 'body': body_data_loaders[0], + } + if len(hand_data_loaders) > 0: + dloaders['hand'] = hand_data_loaders[0] + if len(head_data_loaders) > 0: + +-- Chunk 10 -- +// build.py:426-435 + dloaders['head'] = head_data_loaders[0] + if use_adv_training: + raise NotImplementedError + return dloaders + + return { + 'body': body_data_loaders, + 'hand': hand_data_loaders, + 'head': head_data_loaders, + } + +=== File: expose/data/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/data/__init__.py:1-17 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from .build import make_all_data_loaders + +=== File: expose/losses/utils.py === + +-- Chunk 1 -- +// utils.py:21-29 + get_reduction_method(reduction='mean'): + if reduction == 'mean': + return torch.mean + elif reduction == 'sum': + return torch.sum + elif reduction == 'none': + return lambda x: x + else: + raise ValueError('Unknown reduction method: {}'.format(reduction)) + +=== File: expose/losses/robustifiers.py === + +-- Chunk 1 -- +// robustifiers.py:29-37 + build_robustifier(robustifier_type: str = None, **kwargs) -> nn.Module: + if robustifier_type is None or robustifier_type == 'none': + return None + elif robustifier_type == 'gmof': + return GMOF(**kwargs) + else: + raise ValueError(f'Unknown robustifier: {robustifier_type}') + + + +-- Chunk 2 -- +// robustifiers.py:38-48 +ss GMOF(nn.Module): + def __init__(self, rho: float = 100, **kwargs) -> None: + super(GMOF, self).__init__() + self.rho = rho + + def extra_repr(self): + return f'Rho = {self.rho}' + + def forward(self, residual): + squared_residual = residual.pow(2) + return torch.div(squared_residual, squared_residual + self.rho ** 2) + +=== File: expose/losses/losses.py === + +-- Chunk 1 -- +// losses.py:49-54 + GMof(residual, rho=1): + squared_res = residual ** 2 + dist = torch.div(squared_res, squared_res + rho ** 2) + return rho ** 2 * dist + + + +-- Chunk 2 -- +// losses.py:55-86 + build_loss(type='l2', rho=100, reduction='mean', size_average=True, + ignore_index=-100, + **kwargs) -> nn.Module: + logger.debug(f'Building loss: {type}') + if type == 'gmof': + return GMofLoss(rho=rho, reduction=reduction, **kwargs) + elif type == 'keypoints': + return KeypointLoss(reduction=reduction, **kwargs) + elif type == 'l2': + return WeightedMSELoss(reduction=reduction, **kwargs) + elif type == 'weighted-l1': + return WeightedL1Loss( + reduction=reduction, size_average=size_average, **kwargs) + elif type == 'keypoint-edge': + return KeypointEdgeLoss(reduction=reduction, **kwargs) + elif type == 'vertex-edge': + return VertexEdgeLoss(reduction=reduction, **kwargs) + elif type == 'bce': + return nn.BCELoss() + elif type == 'bce-logits': + return nn.BCEWithLogitsLoss() + elif type == 'cross-entropy': + return nn.CrossEntropyLoss( + reduction=reduction, ignore_index=ignore_index) + elif type == 'l1': + return nn.L1Loss() + elif type == 'rotation': + return RotationDistance(reduction=reduction, **kwargs) + else: + raise ValueError(f'Unknown loss type: {type}') + + + +-- Chunk 3 -- +// losses.py:87-106 +ss SmoothL1LossModule(nn.Module): + def __init__(self, size_average=True, beta=1. / 9): + super(SmoothL1LossModule, self).__init__() + self.size_average = size_average + self.beta = beta + + def extra_repr(self): + return 'beta={}, size_average={}'.format(self.beta, + self.size_average) + + def forward(self, input, target): + n = torch.abs(input - target) + cond = n < self.beta + loss = torch.where(cond, 0.5 * n ** 2 / self.beta, + n - 0.5 * self.beta) + if self.size_average: + return loss.mean() + return loss.sum() + + + +-- Chunk 4 -- +// losses.py:107-146 +ss KeypointLoss(nn.Module): + def __init__(self, norm_type='l1', binarize=True, + robustifier=None, epsilon=1e-6, + **kwargs): + super(KeypointLoss, self).__init__() + self.norm_type = norm_type + assert self.norm_type in ['l1', 'l2'], 'Keypoint loss must be L1, L2' + self.binarize = binarize + self.robustifier = build_robustifier( + robustifier_type=robustifier, **kwargs) + self.epsilon = epsilon + + def extra_repr(self): + return 'Norm type: {}'.format(self.norm_type.title()) + + def forward(self, input, target, weights=None, epsilon=1e-9): + assert weights is not None + keyp_dim = input.shape[-1] + + if self.binarize: + weights = weights.gt(0).to(dtype=input.dtype) + + raw_diff = input - target + # Should be B + # Should contain the number of visible keypoints per batch item + # visibility = (weights.sum(dim=-1) * keyp_dim).view(-1, 1, 1) + + if self.robustifier is not None: + diff = self.robustifier(raw_diff) + else: + if self.norm_type == 'l1': + diff = raw_diff.abs() + elif self.norm_type == 'l2': + diff = raw_diff.pow(2) + weighted_diff = diff * weights.unsqueeze(dim=-1) + + return torch.sum(weighted_diff) / weighted_diff.shape[0] + # return torch.sum(weighted_diff) / (torch.sum(visibility) + epsilon) + + + +-- Chunk 5 -- +// losses.py:147-162 +ss WeightedL1Loss(nn.Module): + def __init__(self, reduction='mean', **kwargs): + super(WeightedL1Loss, self).__init__() + self.reduce_str = reduction + self.reduce = get_reduction_method(reduction) + + def forward(self, input, target, weights=None): + diff = input - target + if weights is None: + return diff.abs().sum() / diff.shape[0] + else: + diff = input - target + weighted_diff = weights.unsqueeze(dim=-1) * diff.abs() + return weighted_diff.sum() / diff.shape[0] + + + +-- Chunk 6 -- +// losses.py:163-177 +ss WeightedMSELoss(nn.Module): + def __init__(self, reduction='mean', **kwargs): + super(WeightedMSELoss, self).__init__() + self.reduce_str = reduction + self.reduce = get_reduction_method(reduction) + + def forward(self, input, target, weights=None): + diff = input - target + if weights is None: + return diff.pow(2).sum() / diff.shape[0] + else: + return ( + weights.unsqueeze(dim=-1) * diff.pow(2)).sum() / diff.shape[0] + + + +-- Chunk 7 -- +// losses.py:178-200 +ss GMofLoss(nn.Module): + + def __init__(self, rho=100, reduction='mean', **kwargs): + super(GMofLoss, self).__init__() + self.rho = rho + self.reduction = get_reduction_method(reduction) + self.reduction_str = reduction + + def extra_repr(self): + return 'rho={}, reduction={}'.format(self.rho, + self.reduction_str) + + def forward(self, module_input, target, weights=None): + batch_size = module_input.shape[0] + squared_residual = (module_input - target).pow(2) + dist = torch.div(squared_residual, squared_residual + self.rho ** 2) + output = self.rho ** 2 * dist + if weights is not None: + output *= weights.view(batch_size, -1, 1).pow(2) + + return self.reduction(output) + + + +-- Chunk 8 -- +// losses.py:201-238 +ss RotationDistance(nn.Module): + def __init__(self, reduction='mean', epsilon=1e-7, + robustifier='none', + **kwargs): + super(RotationDistance, self).__init__() + self.reduction = get_reduction_method(reduction) + self.reduction_str = reduction + self.epsilon = epsilon + self.robustifier = build_robustifier( + robustifier_type=robustifier, epsilon=epsilon, **kwargs) + + def extra_repr(self) -> str: + msg = [] + msg.append(f'Reduction: {self.reduction_str}') + msg.append(f'Epsilon: {self.epsilon}') + return '\n'.join(msg) + + def forward(self, module_input, target, weights=None): + tr = torch.einsum( + 'bij,bij->b', + [module_input.view(-1, 3, 3), + target.view(-1, 3, 3)]) + + theta = (tr - 1) * 0.5 + loss = torch.acos( + torch.clamp(theta, -1 + self.epsilon, 1 - self.epsilon)) + if self.robustifier is not None: + loss = self.robustifier(loss) + if weights is not None: + loss = loss.view( + module_input.shape[0], -1) * weights.view( + module_input.shape[0], -1) + return loss.sum() / ( + weights.gt(0).to(loss.dtype).sum() + self.epsilon) + else: + return loss.sum() / module_input.shape[0] + + + +-- Chunk 9 -- +// losses.py:239-310 +ss VertexEdgeLoss(nn.Module): + def __init__(self, norm_type='l2', + gt_edge_path='', + est_edge_path='', + robustifier=None, + edge_thresh=0.0, epsilon=1e-8, **kwargs): + super(VertexEdgeLoss, self).__init__() + + assert norm_type in ['l1', 'l2'], 'Norm type must be [l1, l2]' + self.norm_type = norm_type + self.epsilon = epsilon + self.robustifier = build_robustifier( + robustifier_type=robustifier, **kwargs) + + gt_edge_path = osp.expandvars(gt_edge_path) + est_edge_path = osp.expandvars(est_edge_path) + self.has_connections = osp.exists(gt_edge_path) and osp.exists( + est_edge_path) + if self.has_connections: + gt_edges = np.load(gt_edge_path) + est_edges = np.load(est_edge_path) + + self.register_buffer( + 'gt_connections', torch.tensor(gt_edges, dtype=torch.long)) + self.register_buffer( + 'est_connections', torch.tensor(est_edges, dtype=torch.long)) + + def extra_repr(self): + msg = [ + f'Norm type: {self.norm_type}', + ] + if self.has_connections: + msg.append( + f'GT Connections shape: {self.gt_connections.shape}' + ) + msg.append( + f'Est Connections shape: {self.est_connections.shape}' + ) + return '\n'.join(msg) + + def compute_edges(self, points, connections): + start = torch.index_select( + points, 1, connections[:, 0]) + end = torch.index_select(points, 1, connections[:, 1]) + return start - end + + def forward(self, gt_vertices, est_vertices, weights=None): + if not self.has_connections: + return 0.0 + + # Compute the edges for the ground truth keypoints and the model keypoints + # Remove the confidence from the ground truth keypoints + gt_edges = self.compute_edges( + gt_vertices, connections=self.gt_connections) + est_edges = self.compute_edges( + est_vertices, connections=self.est_connections) + + raw_edge_diff = (gt_edges - est_edges) + + batch_size = gt_vertices.shape[0] + if self.robustifier is not None: + raise NotImplementedError + else: + if self.norm_type == 'l2': + return (raw_edge_diff.pow(2).sum(dim=-1)).sum() / batch_size + elif self.norm_type == 'l1': + return (raw_edge_diff.pow(2).sum(dim=-1)).sum() / batch_size + else: + raise NotImplementedError( + f'Loss type not implemented: {self.loss_type}') + + + +-- Chunk 10 -- +// losses.py:311-379 +ss KeypointEdgeLoss(nn.Module): + def __init__(self, norm_type='l2', connections=None, + robustifier=None, + edge_thresh=0.0, epsilon=1e-8, **kwargs): + super(KeypointEdgeLoss, self).__init__() + if connections is not None: + connections = torch.tensor(connections).reshape(-1, 2) + self.register_buffer('connections', connections) + else: + self.connections = None + self.edge_thresh = edge_thresh + + assert norm_type in ['l1', 'l2'], 'Norm type must be [l1, l2]' + self.norm_type = norm_type + self.epsilon = epsilon + self.robustifier = build_robustifier( + robustifier_type=robustifier, **kwargs) + + def extra_repr(self): + msg = [ + f'Edge threshold: {self.edge_thresh}', + f'Norm type: {self.norm_type}', + f'Connections shape: {self.connections.shape}' + ] + return '\n'.join(msg) + + def compute_edges(self, keypoints): + start = torch.index_select( + keypoints, 1, self.connections[:, 0]) + end = torch.index_select(keypoints, 1, self.connections[:, 1]) + return start - end + + def forward(self, gt_keypoints, model_keypoints, weights=None): + if self.connections is None or len(self.connections) < 1: + return 0.0 + + # Compute the edges for the ground truth keypoints and the model keypoints + # Remove the confidence from the ground truth keypoints + gt_edges = self.compute_edges(gt_keypoints) + model_edges = self.compute_edges(model_keypoints) + + # Compute the confidence of the edge as the harmonic mean of the + # confidences + # Weights: BxC + if weights is not None: + weight_start_pt = torch.index_select( + weights, 1, self.connections[:, 0]) + weight_end_pt = torch.index_select( + weights, 1, self.connections[:, 1]) + edge_weight = 2.0 * weight_start_pt * weight_end_pt / ( + weight_start_pt + weight_end_pt + self.epsilon) + edge_weight[torch.isnan(edge_weight)] = 0 + else: + edge_weight = torch.ones_like(gt_edges[:, :, 0]) + + # num_visible = edge_weight.gt( + # self.edge_thresh).to(dtype=gt_edges.dtype).sum() + + raw_edge_diff = (gt_edges - model_edges) + + if self.robustifier is not None: + raise NotImplementedError + else: + if self.norm_type == 'l2': + return (raw_edge_diff.pow(2).sum(dim=-1) * + edge_weight).sum() / gt_keypoints.shape[0] + else: + raise NotImplementedError( + f'Loss type not implemented: {self.loss_type}') + +=== File: expose/losses/priors.py === + +-- Chunk 1 -- +// priors.py:44-66 + build_prior(prior_type, rho=100, reduction='mean', size_average=True, + **kwargs): + logger.debug('Building prior: {}', prior_type) + if prior_type == 'l2': + return L2Prior(reduction=reduction, **kwargs) + elif prior_type == 'l1': + return L1Prior(reduction=reduction, **kwargs) + elif prior_type == 'identity': + return IdentityPrior(reduction=reduction, **kwargs) + elif prior_type == 'mean': + return MeanPrior(reduction=reduction, **kwargs) + elif prior_type == 'penalty': + return PenaltyPrior(reduction=reduction, **kwargs) + elif prior_type == 'barrier': + return BarrierPrior(reduction=reduction, **kwargs) + elif prior_type == 'threshold': + return ThresholdPrior(reduction=reduction, **kwargs) + elif prior_type == 'gmm': + return GMMPrior(reduction=reduction, **kwargs) + else: + raise ValueError('Unknown prior type: {}'.format(prior_type)) + + + +-- Chunk 2 -- +// priors.py:67-83 +ss MeanPrior(nn.Module): + def __init__(self, mean=None, reduction='mean', **kwargs): + super(MeanPrior, self).__init__() + assert mean is not None, 'Request MeanPrior, but mean was not given!' + if type(mean) is not torch.Tensor: + mean = torch.tensor(mean) + self.register_buffer('mean', mean.view(1, *list(mean.shape))) + self.reduction_str = reduction + self.reduction = get_reduction_method(reduction) + + def extra_repr(self): + return f'Mean: {self.mean.shape}' + + def forward(self, module_input, *args, **kwargs): + return (module_input - self.mean).pow(2).sum() / module_input.shape[0] + + + +-- Chunk 3 -- +// priors.py:84-101 +ss IdentityPrior(nn.Module): + def __init__(self, reduction='mean', **kwargs): + ''' Penalizes inputs to be close to identity matrix + ''' + super(IdentityPrior, self).__init__() + self.reduction_str = reduction + self.reduction = get_reduction_method(reduction) + + self.register_buffer( + 'identity', torch.eye(3, dtype=torch.float32).unsqueeze(dim=0)) + + def forward(self, module_input, *args, **kwargs): + x = module_input.view(-1, 3, 3) + batch_size = module_input.shape[0] + + return (x - self.identity).pow(2).sum() / batch_size + + + +-- Chunk 4 -- +// priors.py:102-136 +ss ThresholdPrior(nn.Module): + def __init__(self, reduction='mean', margin=1, norm='l2', epsilon=1e-7, + **kwargs): + super(ThresholdPrior, self).__init__() + self.reduction_str = reduction + self.reduction = get_reduction_method(reduction) + self.margin = margin + assert norm in ['l1', 'l2'], 'Norm variable must me l1 or l2' + self.norm = norm + self.epsilon = epsilon + + def extra_repr(self): + msg = 'Reduction: {}\n'.format(self.reduction_str) + msg += 'Margin: {}\n'.format(self.margin) + msg += 'Norm: {}'.format(self.norm) + return msg + + def forward(self, module_input, *args, **kwargs): + batch_size = module_input.shape[0] + + abs_values = module_input.abs() + mask = abs_values.gt(self.margin) + + invalid_values = torch.masked_select(module_input, mask) + + if self.norm == 'l1': + return invalid_values.abs().sum() / ( + mask.to(dtype=module_input.dtype).sum() + self.epsilon + ) + elif self.norm == 'l2': + return invalid_values.pow(2).sum() / ( + mask.to(dtype=module_input.dtype).sum() + self.epsilon + ) + + + +-- Chunk 5 -- +// priors.py:137-195 +ss PenaltyPrior(nn.Module): + def __init__(self, reduction='mean', margin=1, norm='l2', epsilon=1e-7, + use_vector=True, + **kwargs): + ''' Soft constraint to prevent parameters for leaving feasible set + + Implements a penalty constraint that encourages the parameters to + stay in the feasible set of solutions. Assumes that the initial + estimate is already in this set + ''' + super(PenaltyPrior, self).__init__() + self.reduction_str = reduction + self.reduction = get_reduction_method(reduction) + self.margin = margin + assert norm in ['l1', 'l2'], 'Norm variable must me l1 or l2' + self.norm = norm + self.epsilon = epsilon + self.use_vector = use_vector + + def extra_repr(self): + msg = 'Reduction: {}\n'.format(self.reduction_str) + msg += 'Margin: {}\n'.format(self.margin) + msg += 'Norm: {}'.format(self.norm) + return msg + + def forward(self, module_input, *args, **kwargs): + batch_size = module_input.shape[0] + if self.use_vector: + + if self.norm == 'l1': + param_norm = module_input.abs().view( + batch_size, -1).sum(dim=-1) + margin = self.margin + elif self.norm == 'l2': + param_norm = module_input.pow(2).view( + batch_size, -1).sum(dim=-1) + margin = self.margin ** 2 + + thresholded_vals = F.relu(param_norm - margin) + non_zeros = ( + thresholded_vals.gt(0).to(torch.float32).sum() + self.epsilon) + return (thresholded_vals.sum() / non_zeros) + else: + upper_margin = F.relu(module_input - self.margin) + lower_margin = F.relu(-(module_input + self.margin)) + with torch.no_grad(): + upper_non_zeros = ( + upper_margin.gt(0).to(torch.float32).sum() + self.epsilon) + lower_non_zeros = ( + lower_margin.gt(0).to(torch.float32).sum() + self.epsilon) + + if self.norm == 'l1': + return (upper_margin.abs().sum() / upper_non_zeros + + lower_margin.abs().sum() / lower_non_zeros) + elif self.norm == 'l2': + return (upper_margin.pow(2).sum() / upper_non_zeros + + lower_margin.pow(2).sum() / lower_non_zeros) + + + +-- Chunk 6 -- +// priors.py:196-236 +ss BarrierPrior(nn.Module): + def __init__(self, reduction='mean', margin=1, barrier='log', + epsilon=1e-7, symmetric=True, **kwargs): + ''' Soft constraint that pushes parameters away from the border + + Implements a barrier constraint that encourages the parameters to + stay away from the border of the feasible set. Assumes that the initial + estimate is already in this set + ''' + super(BarrierPrior, self).__init__() + self.reduction_str = reduction + self.reduction = get_reduction_method(reduction) + assert barrier in ['log', 'inv'], 'Norm variable must me inv or log' + self.barrier = barrier + self.epsilon = epsilon + self.symmetric = symmetric + self.register_buffer('margin', torch.tensor(margin)) + + def extra_repr(self): + msg = 'Reduction: {}\n'.format(self.reduction_str) + msg += 'Margin: {}\n'.format(self.margin) + msg += 'Barrier: {}'.format(self.barrier) + msg += 'Symmetric: {}'.format(self.symmetric) + return msg + + def forward(self, module_input, *args, **kwargs): + if self.barrier == 'log': + loss = -torch.log(self.margin) - torch.log( + -(module_input - self.margin) + self.epsilon).mean() + if self.symmetric: + loss += -torch.log(self.margin) - torch.log( + (module_input + self.margin) + self.epsilon).mean() + elif self.barrier == 'inv': + loss = - 1 / (module_input - self.margin + self.epsilon).mean() + if self.symmetric: + loss += 1 / (module_input + self.margin) + # Compensate for the minimum to make it zero + loss -= 1 + return loss + + + +-- Chunk 7 -- +// priors.py:237-245 +ss L1Prior(nn.Module): + def __init__(self, dtype=torch.float32, reduction='mean', **kwargs): + super(L1Prior, self).__init__() + self.reduction = get_reduction_method(reduction) + + def forward(self, module_input, *args): + return self.reduction(module_input.abs().sum(dim=-1)) + + + +-- Chunk 8 -- +// priors.py:246-254 +ss L2Prior(nn.Module): + def __init__(self, dtype=torch.float32, reduction='mean', **kwargs): + super(L2Prior, self).__init__() + self.reduction = get_reduction_method(reduction) + + def forward(self, module_input, *args): + return self.reduction(module_input.pow(2)) + + + +-- Chunk 9 -- +// priors.py:255-375 +ss GMMPrior(nn.Module): + + def __init__(self, path, + num_gaussians=6, dtype=torch.float32, epsilon=1e-16, + reduction='mean', + use_max=False, + **kwargs): + super(GMMPrior, self).__init__() + + logger.debug('Loading GMMPrior from {}', path) + if dtype == torch.float32: + np_dtype = np.float32 + elif dtype == torch.float64: + np_dtype = np.float64 + else: + raise ValueError( + 'Unknown float type {}.format(exiting)!'.format(dtype)) + + self.num_gaussians = num_gaussians + self.epsilon = epsilon + self.reduction = get_reduction_method(reduction) + self.use_max = use_max + self.dtype = dtype + + path = osp.expanduser(osp.expandvars(path)) + with open(path, 'rb') as f: + gmm = pickle.load(f, encoding='latin1') + + if type(gmm) == dict: + means = gmm['means'] + covs = gmm['covars'] + weights = gmm['weights'] + elif 'sklearn.mixture.gmm.GMM' in str(type(gmm)): + means = gmm.means_ + covs = gmm.covars_ + weights = gmm.weights_ + else: + msg = 'Unknown type for the prior: {}, exiting!'.format(type(gmm)) + raise ValueError(msg) + + self.register_buffer('means', torch.tensor(means, dtype=dtype)) + self.register_buffer('covs', torch.tensor(covs, dtype=dtype)) + + precisions = [np.linalg.inv(cov) for cov in covs] + precisions = np.stack(precisions) + + self.register_buffer('precisions', + torch.tensor(precisions, dtype=dtype)) + + nll_weights = np.asarray(gmm['weights']) + nll_weights = torch.tensor(nll_weights, dtype=dtype).unsqueeze(dim=0) + + nll_weights = torch.log(nll_weights) + self.register_buffer('nll_weights', nll_weights) + + weights = torch.tensor(gmm['weights'], dtype=dtype).unsqueeze(dim=0) + self.register_buffer('weights', weights) + + self.register_buffer('pi_term', + torch.log(torch.tensor(2 * np.pi, dtype=dtype))) + + cov_dets = [np.log(np.linalg.det(covs[idx])) + for idx in range(covs.shape[0])] + + self.register_buffer('cov_dets', + torch.tensor(cov_dets, dtype=dtype)) + + # The dimensionality of the random variable + self.random_var_dim = self.means.shape[1] + + def extra_repr(self): + msg = [] + msg.append(f'Mean: {self.means.shape}') + msg.append(f'Covariance: {self.covs.shape}') + return '\n'.join(msg) + + def get_mean(self): + ''' Returns the mean of the mixture ''' + mean_pose = torch.matmul(self.weights, self.means) + return mean_pose + + def max_log_likelihood(self, pose, *args): + diff_from_mean = pose.unsqueeze(dim=1) - self.means + + prec_diff_prod = torch.einsum('mij,bmj->bmi', + [self.precisions, diff_from_mean]) + diff_prec_quadratic = (prec_diff_prod * diff_from_mean).sum(dim=-1) + + curr_loglikelihood = -0.5 * (diff_prec_quadratic + + self.cov_dets + + self.random_var_dim * self.pi_term) + curr_loglikelihood += (-self.nll_weights) + # curr_loglikelihood = 0.5 * diff_prec_quadratic - \ + # torch.log(self.nll_weights) + + min_likelihood, _ = torch.min(curr_loglikelihood, dim=1) + return self.reduction(min_likelihood) + + def logsumexp_likelihood(self, pose, *args, **kwargs): + diff_from_mean = pose.unsqueeze(dim=1) - self.means + + prec_diff_prod = torch.einsum('mij,bmj->bmi', + [self.precisions, diff_from_mean]) + diff_prec_quadratic = (prec_diff_prod * diff_from_mean).sum(dim=-1) + + exponent = (self.nll_weights - + 0.5 * self.random_var_dim * self.pi_term - + 0.5 * self.cov_dets - + 0.5 * diff_prec_quadratic) + logsumexp = -torch.logsumexp(exponent, dim=-1) + + return self.reduction(logsumexp) + + def forward(self, pose, *args): + if len(pose.shape) == 4: + raise NotImplementedError + + if self.use_max: + return self.max_log_likelihood(pose, *args) + else: + return self.logsumexp_likelihood(pose, *args) + +=== File: expose/losses/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/losses/__init__.py:1-18 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from .priors import * +from .losses import * + +=== File: expose/models/smplx_net.py === + +-- Chunk 1 -- +// smplx_net.py:30-77 +ss SMPLXNet(nn.Module): + + def __init__(self, exp_cfg): + super(SMPLXNet, self).__init__() + + self.exp_cfg = exp_cfg.clone() + network_cfg = exp_cfg.get('network', {}) + self.net_type = network_cfg.get('type', 'attention') + if self.net_type == 'attention': + self.smplx = build_attention_head(exp_cfg) + else: + raise ValueError(f'Unknown network type: {self.net_type}') + + def toggle_hands_and_face(self, iteration): + pass + + def toggle_losses(self, iteration): + self.smplx.toggle_losses(iteration) + + def get_hand_model(self) -> nn.Module: + return self.smplx.get_hand_model() + + def get_head_model(self) -> nn.Module: + return self.smplx.get_head_model() + + def toggle_param_prediction(self, iteration) -> None: + self.smplx.toggle_param_prediction(iteration) + + def forward(self, images, targets, + hand_imgs=None, hand_targets=None, + head_imgs=None, head_targets=None, + full_imgs=None, + device=None): + + if not self.training: + pass + if device is None: + device = torch.device('cpu') + + losses = {} + + output = self.smplx(images, targets=targets, + hand_imgs=hand_imgs, hand_targets=hand_targets, + head_imgs=head_imgs, head_targets=head_targets, + full_imgs=full_imgs) + + output['losses'] = losses + return output + +=== File: expose/models/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/__init__.py:1-15 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +=== File: expose/data/datasets/threedpw.py === + +-- Chunk 1 -- +// threedpw.py:38-187 +ss ThreeDPW(dutils.Dataset): + def __init__(self, data_path='data/3dpw', + img_folder='', + seq_folder='sequenceFiles', + param_folder='smplx_npz_data', + split='val', + use_face=True, use_hands=True, use_face_contour=False, + model_type='smplx', + dtype=torch.float32, + vertex_folder='smplx_vertices', + return_vertices=True, + joints_to_ign=None, + use_joint_conf=True, + metrics=None, + transforms=None, + body_thresh=0.3, + binarization=True, + min_visible=6, + **kwargs): + super(ThreeDPW, self).__init__() + + if metrics is None: + metrics = [] + self.metrics = metrics + self.binarization = binarization + self.return_vertices = return_vertices + + self.split = split + self.is_train = 'train' in split + + self.data_path = osp.expandvars(osp.expanduser(data_path)) + seq_path = osp.join(self.data_path, seq_folder) + if self.split == 'train': + seq_split_path = osp.join(seq_path, 'train') + npz_fn = osp.join(self.data_path, param_folder, '3dpw_train.npz') + elif self.split == 'val': + seq_split_path = osp.join(seq_path, 'validation') + npz_fn = osp.join( + self.data_path, param_folder, '3dpw_validation.npz') + elif self.split == 'test': + seq_split_path = osp.join(seq_path, 'test') + npz_fn = osp.join(self.data_path, param_folder, '3dpw_test.npz') + + self.vertex_folder = osp.join( + self.data_path, vertex_folder, self.split) + + self.img_folder = osp.join(self.data_path, img_folder) + folder_map_fname = osp.expandvars( + osp.join(self.img_folder, split, FOLDER_MAP_FNAME)) + self.use_folder_split = osp.exists(folder_map_fname) + if self.use_folder_split: + with open(folder_map_fname, 'rb') as f: + data_dict = pickle.load(f) + self.items_per_folder = max(data_dict.values()) + self.img_folder = osp.join(self.img_folder, split) + + data_dict = np.load(npz_fn) + # data_dict = {key: data[key] for key in data.keys()} + + if 'cam_intrinsics' in data_dict: + self.cam_intrinsics = data_dict['cam_intrinsics'] + + self.img_paths = np.asarray(data_dict['img_paths']) + + # idxs = [ii for ii, path in enumerate(self.img_paths) + # if 'downtown_walking_00' in path] + idxs = np.arange(len(self.img_paths)) + # idxs = np.array(idxs) + self.idxs = idxs + self.img_paths = self.img_paths[idxs] + + if 'keypoints2d' in data_dict: + self.keypoints2d = np.asarray( + data_dict['keypoints2d']).astype(np.float32)[idxs] + elif 'keypoints2D' in data_dict: + self.keypoints2d = np.asarray( + data_dict['keypoints2D']).astype(np.float32)[idxs] + else: + raise KeyError(f'Keypoints2D not in 3DPW {split} dictionary') + self.joints3d = np.asarray( + data_dict['joints3d']).astype(np.float32)[idxs] + # self.v_shaped = np.asarray(data_dict['v_shaped']).astype(np.float32) + self.num_items = len(self.img_paths) + # self.pids = np.asarray(data_dict['person_ids'], dtype=np.int32) + self.pids = np.asarray(data_dict['pid'], dtype=np.int32) + self.center = np.asarray( + data_dict['center'], dtype=np.float32)[idxs] + self.scale = np.asarray( + data_dict['scale'], dtype=np.float32)[idxs] + self.bbox_size = np.asarray( + data_dict['bbox_size'], dtype=np.float32)[idxs] + + self.transforms = transforms + self.dtype = dtype + + self.use_face = use_face + self.use_hands = use_hands + self.use_face_contour = use_face_contour + self.model_type = model_type + self.use_joint_conf = use_joint_conf + self.body_thresh = body_thresh + + source_idxs, target_idxs = dset_to_body_model( + dset='3dpw', model_type='smplx', + use_face_contour=self.use_face_contour) + self.source_idxs = np.asarray(source_idxs) + self.target_idxs = np.asarray(target_idxs) + + def get_elements_per_index(self): + return 1 + + def __repr__(self): + return '3DPW( \n\t Split: {}\n)'.format(self.split) + + def name(self): + return '3DPW/{}'.format(self.split) + + def get_num_joints(self): + return 14 + + def __len__(self): + return self.num_items + + def only_2d(self): + return False + + def __getitem__(self, index): + # start = time.perf_counter() + img_fn = self.img_paths[index] + + if self.use_folder_split: + folder_idx = (index + self.idxs[0]) // self.items_per_folder + img_fn = osp.join(self.img_folder, + 'folder_{:010d}'.format(folder_idx), + f'{index + self.idxs[0]:010d}.jpg') + img = read_img(img_fn) + # print('read img:', time.perf_counter() - start) + + keypoints2d = self.keypoints2d[index, :] + # print('read data:', time.perf_counter() - start) + # start = time.perf_counter() + # logger.info('V + J: {}'.format(time.perf_counter() - start)) + + # # Pad to compensate for extra keypoints + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + output_keypoints2d[self.target_idxs] = keypoints2d[self.source_idxs] + + # Remove joints with negative confidence + +-- Chunk 2 -- +// threedpw.py:188-250 + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + output_keypoints2d[ + output_keypoints2d[:, -1] < self.body_thresh, -1] = 0 + + # If we don't want to use the confidence scores as weights for the loss + if self.binarization: + # then set those above the conf thresh to 1 + output_keypoints2d[:, -1] = ( + output_keypoints2d[:, -1] >= self.body_thresh).astype( + output_keypoints2d.dtype) + + center = self.center[index] + scale = self.scale[index] + bbox_size = self.bbox_size[index] + + # keypoints = output_keypoints2d[:, :-1] + # conf = output_keypoints2d[:, -1] + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + target.add_field('center', center) + target.add_field('orig_center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + target.add_field('orig_bbox_size', bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + + target.add_field('filename', self.img_paths[index]) + + head, fname = osp.split(self.img_paths[index]) + _, seq_name = osp.split(head) + target.add_field('fname', f'{seq_name}/{fname}_{self.pids[index]}') + + if self.return_vertices: + vertex_fname = osp.join( + self.vertex_folder, + f'{index + self.idxs[0]:06d}.npy') + vertices = np.load(vertex_fname) + + vertex_field = Vertices(vertices.reshape(-1, 3)) + target.add_field('vertices', vertex_field) + + intrinsics = self.cam_intrinsics[index] + target.add_field('intrinsics', intrinsics) + + if not self.is_train: + joints3d = self.joints3d[index] + joints = Joints(joints3d[:14]) + target.add_field('joints14', joints) + + if hasattr(self, 'v_shaped'): + v_shaped = self.v_shaped[index] + target.add_field('v_shaped', Vertices(v_shaped)) + # print('SMPL-HF Field {}'.format(time.perf_counter() - start)) + + # start = time.perf_counter() + if self.transforms is not None: + img, cropped_image, target = self.transforms( + img, target, dset_scale_factor=1.2, force_flip=False) + # logger.info('Transforms: {}'.format(time.perf_counter() - start)) + + return img, cropped_image, target, index + +=== File: expose/data/datasets/image_folder.py === + +-- Chunk 1 -- +// image_folder.py:37-68 +ss ImageFolder(dutils.Dataset): + def __init__(self, + data_folder='data/images', + transforms=None, + **kwargs): + super(ImageFolder, self).__init__() + + paths = [] + self.transforms = transforms + data_folder = osp.expandvars(data_folder) + for fname in os.listdir(data_folder): + if not any(fname.endswith(ext) for ext in EXTS): + continue + paths.append(osp.join(data_folder, fname)) + + self.paths = np.stack(paths) + + def __len__(self): + return len(self.paths) + + def __getitem__(self, index): + img = read_img(self.paths[index]) + + if self.transforms is not None: + img = self.transforms(img) + + return { + 'images': img, + 'paths': self.paths[index] + } + + + +-- Chunk 2 -- +// image_folder.py:69-108 +ss ImageFolderWithBoxes(dutils.Dataset): + def __init__(self, + img_paths, + bboxes, + transforms=None, + scale_factor=1.2, + **kwargs): + super(ImageFolderWithBoxes, self).__init__() + + self.transforms = transforms + + self.paths = np.stack(img_paths) + self.bboxes = np.stack(bboxes) + self.scale_factor = scale_factor + + def __len__(self): + return len(self.paths) + + def __getitem__(self, index): + img = read_img(self.paths[index]) + + bbox = self.bboxes[index] + + target = BoundingBox(bbox, size=img.shape) + + center, scale, bbox_size = bbox_to_center_scale( + bbox, dset_scale_factor=self.scale_factor) + target.add_field('bbox_size', bbox_size) + target.add_field('orig_bbox_size', bbox_size) + target.add_field('orig_center', center) + target.add_field('center', center) + target.add_field('scale', scale) + + _, fname = osp.split(self.paths[index]) + target.add_field('fname', f'{fname}_{index:03d}') + + if self.transforms is not None: + full_img, cropped_image, target = self.transforms(img, target) + + return full_img, cropped_image, target, index + +=== File: expose/data/datasets/stirling.py === + +-- Chunk 1 -- +// stirling.py:38-105 +ss Stirling3D(dutils.Dataset): + def __init__(self, data_path='data/stirling/HQ', + head_only=True, + split='train', + dtype=torch.float32, + metrics=None, + transforms=None, + **kwargs): + super(Stirling3D, self).__init__() + assert head_only, 'Stirling3D can only be used as a head only dataset' + + self.split = split + assert 'test' in split, ( + f'Stirling3D can only be used for testing, but got split: {split}' + ) + if metrics is None: + metrics = [] + self.metrics = metrics + + self.data_path = osp.expandvars(osp.expanduser(data_path)) + self.transforms = transforms + self.dtype = dtype + + self.img_paths = np.array( + [osp.join(self.data_path, fname) + for fname in sorted(os.listdir(self.data_path))] + ) + self.num_items = len(self.img_paths) + + def get_elements_per_index(self): + return 1 + + def __repr__(self): + return 'Stirling3D( \n\t Split: {self.split}\n)' + + def name(self): + return f'Stirling3D/{self.split}' + + def get_num_joints(self): + return 0 + + def only_2d(self): + return False + + def __len__(self): + return self.num_items + + def __getitem__(self, index): + img = read_img(self.img_paths[index]) + + H, W, _ = img.shape + bbox = np.array([0, 0, W - 1, H - 1], dtype=np.float32) + target = BoundingBox(bbox, size=img.shape) + + center = np.array([W, H], dtype=np.float32) * 0.5 + target.add_field('center', center) + + center, scale, bbox_size = bbox_to_center_scale(bbox) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + target.add_field('image_size', img.shape) + + if self.transforms is not None: + img, cropped_image, target = self.transforms(img, target) + + target.add_field('name', self.name()) + target.add_field('fname', osp.split(self.img_paths[index])[1]) + return img, cropped_image, target, index + +=== File: expose/data/datasets/ehf.py === + +-- Chunk 1 -- +// ehf.py:43-192 +ss EHF(dutils.Dataset): + + def __init__(self, data_folder, img_folder='images', + # keyp_folder='keypoints', + alignments_folder='alignments', + num_betas=10, num_expr_coeffs=10, + use_face_contour=False, + dtype=torch.float32, + transforms=None, + split='train', + keyp_format='coco25', + metrics=None, + use_joint_conf=True, + head_only=False, + hand_only=False, + is_right=True, + binarization=True, + body_thresh=0.1, + hand_thresh=0.2, + face_thresh=0.4, + **kwargs): + super(EHF, self).__init__() + if metrics is None: + metrics = ['v2v'] + self.metrics = metrics + + self.dtype = dtype + self.data_folder = osp.expandvars(data_folder) + self.img_folder = img_folder + # self.keyp_folder = keyp_folder + self.alignments_folder = alignments_folder + self.use_joint_conf = use_joint_conf + + # keypoint_fname = osp.join(self.data_folder, 'gt_keyps.npy') + keypoint_fname = osp.join(self.data_folder, 'gt_keyps.npz') + keypoint_data = np.load(keypoint_fname) + self.keypoints = keypoint_data['gt_keypoints_2d'] + self.keypoints3d = keypoint_data['gt_keypoints_3d'] + self.joints14 = keypoint_data['gt_joints14'] + if not use_face_contour: + self.keypoints = self.keypoints[:, :-17] + + self.is_train = 'train' in split + self.split = split + self.keyp_format = keyp_format + self.is_right = is_right + self.head_only = head_only + self.hand_only = hand_only + self.body_thresh = body_thresh + self.hand_thresh = hand_thresh + self.face_thresh = face_thresh + self.binarization = binarization + + annot_fn = osp.join(self.data_folder, 'annotations.yaml') + with open(annot_fn, 'r') as annot_file: + annotations = yaml.load(annot_file) + self.annotations = annotations + self.annotations = (self.annotations['train'] + + self.annotations['test']) + + self.transforms = transforms + + self.num_betas = num_betas + self.num_expr_coeffs = num_expr_coeffs + self.use_face_contour = use_face_contour + + self.img_fns = sorted( + os.listdir(osp.join(self.data_folder, self.img_folder))) + # source_idxs, target_idxs = dset_to_body_model( + # dset='openpose25+hands+face', + # model_type='smplx', use_hands=True, use_face=True, + # use_face_contour=self.use_face_contour, + # keyp_format=self.keyp_format) + + # self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + # self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + face_idxs = idxs_dict['face'] + if not use_face_contour: + face_idxs = face_idxs[:-17] + + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.face_idxs = np.asarray(face_idxs) + self.left_hand_idxs = np.asarray(left_hand_idxs) + self.right_hand_idxs = np.asarray(right_hand_idxs) + + self.body_dset_factor = 1.2 + self.head_dset_factor = 2.0 + self.hand_dset_factor = 2.0 + + def __repr__(self): + return 'EHF' + + def name(self): + return 'EHF/Test' + + def get_num_joints(self): + return 14 + + def __len__(self): + return len(self.img_fns) + + def get_elements_per_index(self): + return 1 + + def __getitem__(self, index): + fn = self.annotations[index] + img_path = osp.join(self.data_folder, self.img_folder, + fn + '.png') + img = read_img(img_path) + + _, fn = os.path.split(fn) + + # TODO: Add 3D Keypoints + # keypoints2d = data_tuple['keypoints'].squeeze() + + # Copy keypoints from the GT data + output_keypoints2d = np.zeros( + [127 + 17 * self.use_face_contour, 3], dtype=np.float32) + output_keypoints2d[:, :-1] = self.keypoints[index].copy() + output_keypoints2d[:, -1] = 1.0 + + output_keypoints3d = np.zeros( + [127 + 17 * self.use_face_contour, 4], dtype=np.float32) + output_keypoints3d[:, :-1] = self.keypoints3d[index].copy() + output_keypoints3d[:, -1] = 1.0 + + is_right = self.is_right + # Remove joints with negative confidence + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + body_conf = output_keypoints2d[self.body_idxs, -1] + face_conf = output_keypoints2d[self.face_idxs, -1] + if self.head_only or self.hand_only: + body_conf[:] = 0.0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf = output_keypoints2d[self.left_hand_idxs, -1] + right_hand_conf = output_keypoints2d[self.right_hand_idxs, -1] + if self.head_only: + left_hand_conf[:] = 0.0 + right_hand_conf[:] = 0.0 + + face_conf = output_keypoints2d[self.face_idxs, -1] + +-- Chunk 2 -- +// ehf.py:193-293 + if self.hand_only: + face_conf[:] = 0.0 + if is_right: + left_hand_conf[:] = 0 + else: + right_hand_conf[:] = 0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf[left_hand_conf < self.hand_thresh] = 0.0 + right_hand_conf[right_hand_conf < self.hand_thresh] = 0.0 + face_conf[face_conf < self.face_thresh] = 0.0 + if self.binarization: + body_conf = ( + body_conf >= self.body_thresh).astype( + output_keypoints2d.dtype) + left_hand_conf = ( + left_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + right_hand_conf = ( + right_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + face_conf = ( + face_conf >= self.face_thresh).astype( + output_keypoints2d.dtype) + + output_keypoints2d[self.body_idxs, -1] = body_conf + output_keypoints2d[self.left_hand_idxs, -1] = left_hand_conf + output_keypoints2d[self.right_hand_idxs, -1] = right_hand_conf + output_keypoints2d[self.face_idxs, -1] = face_conf + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + keypoints = output_keypoints2d[:, :-1] + conf = output_keypoints2d[:, -1] + if self.head_only: + dset_scale_factor = self.head_dset_factor + elif self.hand_only: + dset_scale_factor = self.hand_dset_factor + else: + dset_scale_factor = self.body_dset_factor + + center, scale, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.size), + dset_scale_factor=dset_scale_factor, + ) + if center is None: + return None, None, None, None + + if self.hand_only: + target.add_field('is_right', is_right) + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + + target.add_field( + 'keypoints3d', + Keypoints3D(output_keypoints3d, img.shape, flip_axis=0) + ) + + orig_center, _, orig_bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.size), + dset_scale_factor=1.0, + ) + target.add_field('orig_center', orig_center) + target.add_field('orig_bbox_size', bbox_size) + + alignment_path = osp.join(self.data_folder, self.alignments_folder, + fn.replace('.07_C', '') + '.pkl') + with open(alignment_path, 'rb') as alignment_file: + alignment_data = pickle.load(alignment_file, encoding='latin1') + transl = np.array([-0.03609917, 0.43416458, 2.37101226]) + camera_pose = np.array([-2.9874789618512025, 0.011724572107320893, + -0.05704686818955933]) + camera_pose = cv2.Rodrigues(camera_pose)[0] + + vertices = alignment_data['v'] + cam_vertices = vertices.dot(camera_pose.T) + transl.reshape(1, 3) + + vertices_field = Vertices(cam_vertices) + target.add_field('vertices', vertices_field) + + H, W, _ = img.shape + intrinsics = np.array([[1498.22426237, 0, 790.263706], + [0, 1498.22426237, 578.90334], + [0, 0, 1]], dtype=np.float32) + target.add_field('intrinsics', intrinsics) + + joints3d = self.joints14[index] + joints = Joints(joints3d[:14]) + target.add_field('joints14', joints) + + if self.transforms is not None: + force_flip = False + if self.hand_only and not is_right: + force_flip = True + img, cropped_image, target = self.transforms( + img, target, dset_scale_factor=1.2, force_flip=force_flip) + + target.add_field('fname', fn) + return img, cropped_image, target, index + +=== File: expose/data/datasets/curated_fittings.py === + +-- Chunk 1 -- +// curated_fittings.py:42-191 +ss CuratedFittings(dutils.Dataset): + def __init__(self, data_path='data/curated_fits', + split='train', + img_folder='', + use_face=True, use_hands=True, use_face_contour=False, + head_only=False, + hand_only=False, + model_type='smplx', + keyp_format='coco25', + dtype=torch.float32, + metrics=None, + transforms=None, + num_betas=10, + num_expression_coeffs=10, + body_thresh=0.1, + hand_thresh=0.2, + face_thresh=0.4, + min_hand_keypoints=8, + min_head_keypoints=8, + binarization=True, + return_params=True, + vertex_folder='vertices', + vertex_flip_correspondences='', + **kwargs): + super(CuratedFittings, self).__init__() + + assert nand(head_only, hand_only), ( + 'Hand only and head only can\'t be True at the same time') + + self.binarization = binarization + if metrics is None: + metrics = [] + self.metrics = metrics + self.min_hand_keypoints = min_hand_keypoints + self.min_head_keypoints = min_head_keypoints + + if 'test' in split: + split = 'val' + self.split = split + self.is_train = 'train' in split + self.num_betas = num_betas + self.return_params = return_params + + self.head_only = head_only + self.hand_only = hand_only + + data_path = osp.expandvars(osp.expanduser(data_path)) + self.data_path = osp.join(data_path, f'{split}.npz') + self.transforms = transforms + self.dtype = dtype + + vertex_flip_correspondences = osp.expandvars( + vertex_flip_correspondences) + err_msg = ( + 'Vertex flip correspondences path does not exist:' + + f' {vertex_flip_correspondences}' + ) + assert osp.exists(vertex_flip_correspondences), err_msg + flip_data = np.load(vertex_flip_correspondences) + self.bc = flip_data['bc'] + self.closest_faces = flip_data['closest_faces'] + + self.img_folder = osp.expandvars(osp.join(img_folder, split)) + folder_map_fname = osp.expandvars( + osp.join(self.img_folder, FOLDER_MAP_FNAME)) + self.use_folder_split = osp.exists(folder_map_fname) + if self.use_folder_split: + with open(folder_map_fname, 'rb') as f: + data_dict = pickle.load(f) + self.items_per_folder = max(data_dict.values()) + + self.use_face = use_face + self.use_hands = use_hands + self.use_face_contour = use_face_contour + self.model_type = model_type + self.keyp_format = keyp_format + self.num_expression_coeffs = num_expression_coeffs + self.body_thresh = body_thresh + self.hand_thresh = hand_thresh + self.face_thresh = face_thresh + + data = np.load(self.data_path, allow_pickle=True) + data = {key: data[key] for key in data.keys()} + + self.betas = data['betas'].astype(np.float32) + self.expression = data['expression'].astype(np.float32) + self.keypoints2D = data['keypoints2D'].astype(np.float32) + self.pose = data['pose'].astype(np.float32) + self.img_fns = np.asarray(data['img_fns'], dtype=np.string_) + self.indices = None + if 'indices' in data: + self.indices = np.asarray(data['indices'], dtype=np.int64) + self.is_right = None + if 'is_right' in data: + self.is_right = np.asarray(data['is_right'], dtype=np.bool_) + if 'dset_name' in data: + self.dset_name = np.asarray(data['dset_name'], dtype=np.string_) + self.vertex_folder = osp.join(data_path, vertex_folder, split) + + if self.use_folder_split: + self.num_items = sum(data_dict.values()) + # assert self.num_items == self.pose.shape[0] + else: + self.num_items = self.pose.shape[0] + + data.clear() + del data + + source_idxs, target_idxs = dset_to_body_model( + dset='openpose25+hands+face', + model_type='smplx', use_hands=True, use_face=True, + use_face_contour=self.use_face_contour, + keyp_format=self.keyp_format) + self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + face_idxs = idxs_dict['face'] + head_idxs = idxs_dict['head'] + if not use_face_contour: + face_idxs = face_idxs[:-17] + head_idxs = head_idxs[:-17] + + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.left_hand_idxs = np.asarray(left_hand_idxs) + self.right_hand_idxs = np.asarray(right_hand_idxs) + self.face_idxs = np.asarray(face_idxs) + self.head_idxs = np.asarray(head_idxs) + + self.body_dset_factor = 1.2 + self.head_dset_factor = 2.0 + self.hand_dset_factor = 2.0 + + def get_elements_per_index(self): + return 1 + + def __repr__(self): + return 'Curated Fittings( \n\t Split: {}\n)'.format(self.split) + + def name(self): + return 'Curated Fittings/{}'.format(self.split) + + def get_num_joints(self): + return 25 + 2 * 21 + 51 + 17 * self.use_face_contour + + +-- Chunk 2 -- +// curated_fittings.py:192-341 + def __len__(self): + return self.num_items + + def only_2d(self): + return False + + def __getitem__(self, index): + img_index = index + if self.indices is not None: + img_index = self.indices[index] + + if self.use_folder_split: + folder_idx = img_index // self.items_per_folder + file_idx = img_index + + is_right = None + if self.is_right is not None: + is_right = self.is_right[index] + + pose = self.pose[index].copy() + betas = self.betas[index, :self.num_betas] + expression = self.expression[index] + + eye_offset = 0 if pose.shape[0] == 53 else 2 + global_pose = pose[0].reshape(-1) + + body_pose = pose[1:22, :].reshape(-1) + jaw_pose = pose[22].reshape(-1) + left_hand_pose = pose[ + 23 + eye_offset:23 + eye_offset + 15].reshape(-1) + right_hand_pose = pose[23 + 15 + eye_offset:].reshape(-1) + + # start = time.perf_counter() + keypoints2d = self.keypoints2D[index] + # logger.info('Reading keypoints: {}', time.perf_counter() - start) + + if self.use_folder_split: + img_fn = osp.join(self.img_folder, + 'folder_{:010d}'.format(folder_idx), + '{:010d}.jpg'.format(file_idx)) + else: + img_fn = self.img_fns[index].decode('utf-8') + + # start = time.perf_counter() + img = read_img(img_fn) + # logger.info('Reading image: {}'.format(time.perf_counter() - start)) + + # Pad to compensate for extra keypoints + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + output_keypoints2d[self.target_idxs] = keypoints2d[self.source_idxs] + + # Remove joints with negative confidence + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + body_conf = output_keypoints2d[self.body_idxs, -1] + if self.head_only or self.hand_only: + body_conf[:] = 0.0 + + left_hand_conf = output_keypoints2d[self.left_hand_idxs, -1] + right_hand_conf = output_keypoints2d[self.right_hand_idxs, -1] + if self.head_only: + left_hand_conf[:] = 0.0 + right_hand_conf[:] = 0.0 + + face_conf = output_keypoints2d[self.face_idxs, -1] + if self.hand_only: + face_conf[:] = 0.0 + if is_right: + left_hand_conf[:] = 0 + else: + right_hand_conf[:] = 0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf[left_hand_conf < self.hand_thresh] = 0.0 + right_hand_conf[right_hand_conf < self.hand_thresh] = 0.0 + face_conf[face_conf < self.face_thresh] = 0.0 + if self.binarization: + body_conf = ( + body_conf >= self.body_thresh).astype( + output_keypoints2d.dtype) + left_hand_conf = ( + left_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + right_hand_conf = ( + right_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + face_conf = ( + face_conf >= self.face_thresh).astype( + output_keypoints2d.dtype) + + output_keypoints2d[self.body_idxs, -1] = body_conf + output_keypoints2d[self.left_hand_idxs, -1] = left_hand_conf + output_keypoints2d[self.right_hand_idxs, -1] = right_hand_conf + output_keypoints2d[self.face_idxs, -1] = face_conf + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + + if self.head_only: + keypoints = output_keypoints2d[self.head_idxs, :-1] + conf = output_keypoints2d[self.head_idxs, -1] + elif self.hand_only: + keypoints = output_keypoints2d[self.hand_idxs, :-1] + conf = output_keypoints2d[self.hand_idxs, -1] + else: + keypoints = output_keypoints2d[:, :-1] + conf = output_keypoints2d[:, -1] + + left_hand_bbox = keyps_to_bbox( + output_keypoints2d[self.left_hand_idxs, :-1], + output_keypoints2d[self.left_hand_idxs, -1], + img_size=img.shape, scale=1.5) + left_hand_bbox_target = BoundingBox(left_hand_bbox, img.shape) + has_left_hand = (output_keypoints2d[self.left_hand_idxs, -1].sum() > + self.min_hand_keypoints) + if has_left_hand: + target.add_field('left_hand_bbox', left_hand_bbox_target) + target.add_field( + 'orig_left_hand_bbox', + BoundingBox(left_hand_bbox, img.shape, transform=False)) + + right_hand_bbox = keyps_to_bbox( + output_keypoints2d[self.right_hand_idxs, :-1], + output_keypoints2d[self.right_hand_idxs, -1], + img_size=img.shape, scale=1.5) + right_hand_bbox_target = BoundingBox(right_hand_bbox, img.shape) + has_right_hand = (output_keypoints2d[self.right_hand_idxs, -1].sum() > + self.min_hand_keypoints) + if has_right_hand: + target.add_field('right_hand_bbox', right_hand_bbox_target) + target.add_field( + 'orig_right_hand_bbox', + BoundingBox(right_hand_bbox, img.shape, transform=False)) + + head_bbox = keyps_to_bbox( + output_keypoints2d[self.head_idxs, :-1], + output_keypoints2d[self.head_idxs, -1], + img_size=img.shape, scale=1.2) + head_bbox_target = BoundingBox(head_bbox, img.shape) + has_head = (output_keypoints2d[self.head_idxs, -1].sum() > + self.min_head_keypoints) + if has_head: + target.add_field('head_bbox', head_bbox_target) + target.add_field( + 'orig_head_bbox', + BoundingBox(head_bbox, img.shape, transform=False)) + + +-- Chunk 3 -- +// curated_fittings.py:342-402 + if self.head_only: + dset_scale_factor = self.head_dset_factor + elif self.hand_only: + dset_scale_factor = self.hand_dset_factor + else: + dset_scale_factor = self.body_dset_factor + center, scale, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=dset_scale_factor, + ) + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + target.add_field('orig_center', center) + target.add_field('orig_bbox_size', bbox_size) + + # # start = time.perf_counter() + if self.return_params: + betas_field = Betas(betas=betas) + target.add_field('betas', betas_field) + + expression_field = Expression(expression=expression) + target.add_field('expression', expression_field) + + global_pose_field = GlobalPose(global_pose=global_pose) + target.add_field('global_pose', global_pose_field) + body_pose_field = BodyPose(body_pose=body_pose) + target.add_field('body_pose', body_pose_field) + hand_pose_field = HandPose(left_hand_pose=left_hand_pose, + right_hand_pose=right_hand_pose) + target.add_field('hand_pose', hand_pose_field) + jaw_pose_field = JawPose(jaw_pose=jaw_pose) + target.add_field('jaw_pose', jaw_pose_field) + + if hasattr(self, 'dset_name'): + dset_name = self.dset_name[index].decode('utf-8') + vertex_fname = osp.join( + self.vertex_folder, f'{dset_name}_{index:06d}.npy') + vertices = np.load(vertex_fname) + H, W, _ = img.shape + + intrinsics = np.array([[5000, 0, 0.5 * W], + [0, 5000, 0.5 * H], + [0, 0, 1]], dtype=np.float32) + + target.add_field('intrinsics', intrinsics) + vertex_field = Vertices( + vertices, bc=self.bc, closest_faces=self.closest_faces) + target.add_field('vertices', vertex_field) + + target.add_field('fname', f'{index:05d}.jpg') + cropped_image = None + if self.transforms is not None: + force_flip = False + if is_right is not None: + force_flip = not is_right and self.hand_only + img, cropped_image, cropped_target = self.transforms( + img, target, force_flip=force_flip) + + return img, cropped_image, cropped_target, index + +=== File: expose/data/datasets/openpose.py === + +-- Chunk 1 -- +// openpose.py:39-188 +ss OpenPose(dutils.Dataset): + def __init__(self, data_folder='data/openpose', + img_folder='images', + keyp_folder='keypoints', + split='train', + head_only=False, + hand_only=False, + is_right=False, + use_face=True, use_hands=True, use_face_contour=False, + model_type='smplx', + keyp_format='coco25', + dtype=torch.float32, + joints_to_ign=None, + use_joint_conf=True, + metrics=None, + transforms=None, + body_thresh=0.1, + hand_thresh=0.2, + face_thresh=0.4, + binarization=True, + **kwargs): + + super(OpenPose, self).__init__() + assert nand(head_only, hand_only), ( + 'Hand only and head only can\'t be True at the same time') + + self.is_right = is_right + self.head_only = head_only + self.hand_only = hand_only + logger.info(f'Hand only: {self.hand_only}') + logger.info(f'Is right: {self.is_right}') + + self.split = split + self.is_train = 'train' in split + + self.data_folder = osp.expandvars(osp.expanduser(data_folder)) + self.img_folder = osp.join(self.data_folder, img_folder) + self.keyp_folder = osp.join(self.data_folder, keyp_folder) + + self.transforms = transforms + self.dtype = dtype + + self.use_face = use_face + self.use_hands = use_hands + self.use_face_contour = use_face_contour + self.model_type = model_type + self.keyp_format = keyp_format + self.use_joint_conf = use_joint_conf + self.body_thresh = body_thresh + self.hand_thresh = hand_thresh + self.face_thresh = face_thresh + self.binarization = binarization + + self.img_paths = [] + self.keypoints = [] + for img_fname in os.listdir(self.img_folder): + fname, _ = osp.splitext(img_fname) + + keyp_path = osp.join( + self.keyp_folder, '{}_keypoints.json'.format(fname)) + if not osp.exists(keyp_path): + continue + + keypoints = read_keypoints(keyp_path) + if keypoints is None: + continue + + img_path = osp.join(self.img_folder, img_fname) + self.img_paths += [img_path] * keypoints.shape[0] + self.keypoints.append(keypoints) + # self.img_fnames.append(osp.join(self.img_folder, img_fname)) + # self.keyp_fnames.append(keyp_path) + + self.keypoints = np.concatenate(self.keypoints, axis=0) + self.num_items = len(self.img_paths) + + source_idxs, target_idxs = dset_to_body_model( + dset='openpose25+hands+face', + model_type='smplx', use_hands=True, use_face=True, + use_face_contour=self.use_face_contour, + keyp_format=self.keyp_format) + self.source_idxs = source_idxs + self.target_idxs = target_idxs + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + face_idxs = idxs_dict['face'] + if not use_face_contour: + face_idxs = face_idxs[:-17] + + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.face_idxs = np.asarray(face_idxs) + self.left_hand_idxs = np.asarray(left_hand_idxs) + self.right_hand_idxs = np.asarray(right_hand_idxs) + + self.body_dset_factor = 1.2 + self.head_dset_factor = 2.0 + self.hand_dset_factor = 2.0 + + def __repr__(self): + return 'OpenPose( \n\t Split: {}\n)'.format(self.split) + + def name(self): + return 'OpenPose' + + def __len__(self): + return self.num_items + + def get_elements_per_index(self): + return 1 + + def only_2d(self): + return True + + def __getitem__(self, index): + img_fn = self.img_paths[index] + img = read_img(img_fn) + + # keypoints2d = read_keypoints() + + # Pad to compensate for extra keypoints + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + keypoints = self.keypoints[index] + output_keypoints2d[self.target_idxs] = keypoints[self.source_idxs] + + is_right = self.is_right + # Remove joints with negative confidence + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + body_conf = output_keypoints2d[self.body_idxs, -1] + face_conf = output_keypoints2d[self.face_idxs, -1] + if self.head_only or self.hand_only: + body_conf[:] = 0.0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf = output_keypoints2d[self.left_hand_idxs, -1] + right_hand_conf = output_keypoints2d[self.right_hand_idxs, -1] + if self.head_only: + left_hand_conf[:] = 0.0 + right_hand_conf[:] = 0.0 + + face_conf = output_keypoints2d[self.face_idxs, -1] + if self.hand_only: + +-- Chunk 2 -- +// openpose.py:189-259 + face_conf[:] = 0.0 + if is_right: + left_hand_conf[:] = 0 + else: + right_hand_conf[:] = 0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf[left_hand_conf < self.hand_thresh] = 0.0 + right_hand_conf[right_hand_conf < self.hand_thresh] = 0.0 + face_conf[face_conf < self.face_thresh] = 0.0 + if self.binarization: + body_conf = ( + body_conf >= self.body_thresh).astype( + output_keypoints2d.dtype) + left_hand_conf = ( + left_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + right_hand_conf = ( + right_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + face_conf = ( + face_conf >= self.face_thresh).astype( + output_keypoints2d.dtype) + + output_keypoints2d[self.body_idxs, -1] = body_conf + output_keypoints2d[self.left_hand_idxs, -1] = left_hand_conf + output_keypoints2d[self.right_hand_idxs, -1] = right_hand_conf + output_keypoints2d[self.face_idxs, -1] = face_conf + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + keypoints = output_keypoints2d[:, :-1] + conf = output_keypoints2d[:, -1] + if self.head_only: + dset_scale_factor = self.head_dset_factor + elif self.hand_only: + dset_scale_factor = self.hand_dset_factor + else: + dset_scale_factor = self.body_dset_factor + + center, scale, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=dset_scale_factor, + ) + if center is None: + return None, None, None, None + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + + orig_center, _, orig_bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=dset_scale_factor, + ) + target.add_field('orig_center', orig_center) + target.add_field('orig_bbox_size', orig_bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + + # start = time.perf_counter() + if self.transforms is not None: + force_flip = not self.is_right and self.hand_only + img, cropped_image, target = self.transforms( + img, target, force_flip=force_flip) + + img_fn = osp.split(img_fn)[1] + target.add_field('fname', img_fn) + # logger.info('Transforms: {}'.format(time.perf_counter() - start)) + + return img, cropped_image, target, index + + + +-- Chunk 3 -- +// openpose.py:260-409 +ss OpenPoseTracks(dutils.Dataset): + def __init__(self, data_folder='data/openpose_tracks', + img_folder='images', + keyp_folder='keypoints', + split='train', + head_only=False, + hand_only=False, + is_right=False, + use_face=True, use_hands=True, use_face_contour=False, + pid=4, + model_type='smplx', + keyp_format='coco25', + dtype=torch.float32, + joints_to_ign=None, + use_joint_conf=True, + metrics=None, + transforms=None, + body_thresh=0.1, + hand_thresh=0.2, + face_thresh=0.4, + binarization=True, + limit=1500, + **kwargs): + + super(OpenPoseTracks, self).__init__() + assert nand(head_only, hand_only), ( + 'Hand only and head only can\'t be True at the same time') + + self.is_right = is_right + self.head_only = head_only + self.hand_only = hand_only + logger.info(f'Hand only: {self.hand_only}') + logger.info(f'Is right: {self.is_right}') + + self.split = split + self.is_train = 'train' in split + + self.data_folder = osp.expandvars(osp.expanduser(data_folder)) + self.img_folder = osp.join(self.data_folder, img_folder) + self.keyp_folder = osp.join(self.data_folder, keyp_folder) + + self.transforms = transforms + self.dtype = dtype + + self.use_face = use_face + self.use_hands = use_hands + self.use_face_contour = use_face_contour + self.model_type = model_type + self.keyp_format = keyp_format + self.use_joint_conf = use_joint_conf + self.body_thresh = body_thresh + self.hand_thresh = hand_thresh + self.face_thresh = face_thresh + self.binarization = binarization + + track_path = osp.join(self.data_folder, 'by_id.json') + with open(track_path, 'r') as f: + track_data = json.load(f)[f'{pid}'] + + self.num_items = len(track_data) + + logger.info(track_data[0].keys()) + imgnames = [] + keypoints = [] + for idx, d in enumerate(track_data): + keyps = np.array(d['keypoints'], dtype=np.float32)[:-2] + keypoints.append(keyps) + imgnames.append(d['fname']) + self.keypoints = np.stack(keypoints) + self.imgnames = np.stack(imgnames) + if limit > 0: + self.keypoints = self.keypoints[:-limit] + self.imgnames = self.imgnames[:-limit] + + source_idxs, target_idxs = dset_to_body_model( + dset='openpose25+hands+face', + model_type='smplx', use_hands=True, use_face=True, + use_face_contour=self.use_face_contour, + keyp_format=self.keyp_format) + self.source_idxs = source_idxs + self.target_idxs = target_idxs + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + face_idxs = idxs_dict['face'] + if not use_face_contour: + face_idxs = face_idxs[:-17] + + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.face_idxs = np.asarray(face_idxs) + self.left_hand_idxs = np.asarray(left_hand_idxs) + self.right_hand_idxs = np.asarray(right_hand_idxs) + + self.body_dset_factor = 1.2 + self.head_dset_factor = 2.0 + self.hand_dset_factor = 2.0 + + def __repr__(self): + return 'OpenPose( \n\t Split: {}\n)'.format(self.split) + + def name(self): + return 'OpenPose' + + def __len__(self): + return self.num_items + + def get_elements_per_index(self): + return 1 + + def only_2d(self): + return True + + def __getitem__(self, index): + img_fn = osp.join(self.img_folder, self.imgnames[index]) + img = read_img(img_fn) + + # keypoints2d = read_keypoints() + + # Pad to compensate for extra keypoints + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + keypoints = self.keypoints[index] + output_keypoints2d[self.target_idxs] = keypoints[self.source_idxs] + + is_right = self.is_right + # Remove joints with negative confidence + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + body_conf = output_keypoints2d[self.body_idxs, -1] + face_conf = output_keypoints2d[self.face_idxs, -1] + if self.head_only or self.hand_only: + body_conf[:] = 0.0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf = output_keypoints2d[self.left_hand_idxs, -1] + right_hand_conf = output_keypoints2d[self.right_hand_idxs, -1] + if self.head_only: + left_hand_conf[:] = 0.0 + right_hand_conf[:] = 0.0 + + face_conf = output_keypoints2d[self.face_idxs, -1] + if self.hand_only: + face_conf[:] = 0.0 + if is_right: + +-- Chunk 4 -- +// openpose.py:410-475 + left_hand_conf[:] = 0 + else: + right_hand_conf[:] = 0 + + body_conf[body_conf < self.body_thresh] = 0.0 + left_hand_conf[left_hand_conf < self.hand_thresh] = 0.0 + right_hand_conf[right_hand_conf < self.hand_thresh] = 0.0 + face_conf[face_conf < self.face_thresh] = 0.0 + if self.binarization: + body_conf = ( + body_conf >= self.body_thresh).astype( + output_keypoints2d.dtype) + left_hand_conf = ( + left_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + right_hand_conf = ( + right_hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + face_conf = ( + face_conf >= self.face_thresh).astype( + output_keypoints2d.dtype) + + output_keypoints2d[self.body_idxs, -1] = body_conf + output_keypoints2d[self.left_hand_idxs, -1] = left_hand_conf + output_keypoints2d[self.right_hand_idxs, -1] = right_hand_conf + output_keypoints2d[self.face_idxs, -1] = face_conf + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + keypoints = output_keypoints2d[:, :-1] + conf = output_keypoints2d[:, -1] + if self.head_only: + dset_scale_factor = self.head_dset_factor + elif self.hand_only: + dset_scale_factor = self.hand_dset_factor + else: + dset_scale_factor = self.body_dset_factor + + center, scale, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=dset_scale_factor, + ) + if center is None: + return None, None, None, None + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + + orig_center, _, orig_bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=dset_scale_factor, + ) + target.add_field('orig_center', orig_center) + target.add_field('orig_bbox_size', orig_bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + + target.add_field('fname', self.imgnames[index]) + # start = time.perf_counter() + if self.transforms is not None: + force_flip = not self.is_right and self.hand_only + img, cropped_image, target = self.transforms( + img, target, force_flip=force_flip) + + # logger.info('Transforms: {}'.format(time.perf_counter() - start)) + + return img, cropped_image, target, index + +=== File: expose/data/datasets/freihand.py === + +-- Chunk 1 -- +// freihand.py:47-196 +ss FreiHand(dutils.Dataset): + def __init__(self, data_path='data/freihand', + hand_only=True, + split='train', + dtype=torch.float32, + joints_to_ign=None, + metrics=None, + transforms=None, + return_params=True, + return_vertices=True, + use_face_contour=False, + return_shape=False, + file_format='json', + **kwargs): + + super(FreiHand, self).__init__() + + assert hand_only, 'FreiHand can only be used as a hand dataset' + + if metrics is None: + metrics = [] + self.metrics = metrics + + self.split = split + self.is_train = 'train' in split + self.return_params = return_params + self.return_vertices = return_vertices + self.use_face_contour = use_face_contour + + self.return_shape = return_shape + key = ('training' if 'val' in split or 'train' in split else + 'evaluation') + self.data_path = osp.expandvars(osp.expanduser(data_path)) + self.img_folder = osp.join(self.data_path, key, 'rgb') + self.transforms = transforms + self.dtype = dtype + + intrinsics_path = osp.join(self.data_path, f'{key}_K.json') + param_path = osp.join(self.data_path, f'{key}_mano.json') + xyz_path = osp.join(self.data_path, f'{key}_xyz.json') + vertices_path = osp.join(self.data_path, f'{key}_verts.json') + + start = time.perf_counter() + if file_format == 'json': + with open(intrinsics_path, 'r') as f: + intrinsics = json.load(f) + if self.split != 'test': + with open(param_path, 'r') as f: + param = json.load(f) + with open(xyz_path, 'r') as f: + xyz = json.load(f) + if self.return_vertices: + with open(vertices_path, 'r') as f: + vertices = json.load(f) + elif file_format == 'npz': + param_path = osp.join(self.data_path, f'{key}.npz') + data = np.load(param_path) + intrinsics = data['intrinsics'] + param = data['param'] + xyz = data['xyz'] + if self.return_vertices: + vertices = data['vertices'] + self.translation = np.asarray(data['translation']) + + data.close() + elapsed = time.perf_counter() - start + logger.info(f'Loading parameters: {elapsed}') + + mean_pose_path = osp.expandvars( + '$CLUSTER_HOME/SMPL_HF_Regressor_data/data/all_means.pkl') + mean_poses_dict = {} + if osp.exists(mean_pose_path): + logger.info('Loading mean pose from: {} ', mean_pose_path) + with open(mean_pose_path, 'rb') as f: + mean_poses_dict = pickle.load(f) + + if self.split != 'test': + split_size = 0.8 + # num_items = len(xyz) * 4 + num_green_bg = len(xyz) + # For green background images + train_idxs = np.arange(0, int(split_size * num_green_bg)) + val_idxs = np.arange(int(split_size * num_green_bg), num_green_bg) + + all_train_idxs = [] + all_val_idxs = [] + for idx in range(4): + all_val_idxs.append(val_idxs + num_green_bg * idx) + all_train_idxs.append(train_idxs + num_green_bg * idx) + self.train_idxs = np.concatenate(all_train_idxs) + self.val_idxs = np.concatenate(all_val_idxs) + + if split == 'train': + self.img_idxs = self.train_idxs + self.param_idxs = self.train_idxs % num_green_bg + self.start = 0 + elif split == 'val': + self.img_idxs = self.val_idxs + self.param_idxs = self.val_idxs % num_green_bg + # self.start = len(self.train_idxs) + elif 'test' in split: + self.img_idxs = np.arange(len(intrinsics)) + self.param_idxs = np.arange(len(intrinsics)) + + self.num_items = len(self.img_idxs) + + self.intrinsics = intrinsics + if 'test' not in split: + xyz = np.asarray(xyz, dtype=np.float32) + param = np.asarray(param, dtype=np.float32).reshape(len(xyz), -1) + if self.return_vertices: + vertices = np.asarray(vertices, dtype=np.float32) + + right_hand_mean = mean_poses_dict['right_hand_pose']['aa'].squeeze() + self.poses = param[:, :48].reshape(num_green_bg, -1, 3) + self.poses[:, 1:] += right_hand_mean[np.newaxis] + self.betas = param[:, 48:58].copy() + + intrinsics = np.asarray(intrinsics, dtype=np.float32) + + if self.return_vertices: + self.vertices = vertices + self.xyz = xyz + + folder_map_fname = osp.expandvars( + osp.join(self.data_path, split, FOLDER_MAP_FNAME)) + self.use_folder_split = osp.exists(folder_map_fname) + if self.use_folder_split: + self.img_folder = osp.join(self.data_path, split) + logger.info(self.img_folder) + with open(folder_map_fname, 'rb') as f: + data_dict = pickle.load(f) + self.items_per_folder = max(data_dict.values()) + + if joints_to_ign is None: + joints_to_ign = [] + self.joints_to_ign = np.array(joints_to_ign, dtype=np.int32) + + source_idxs, target_idxs = dset_to_body_model(dset='freihand') + self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + + def get_elements_per_index(self): + return 1 + + def __repr__(self): + return 'FreiHand( \n\t Split: {}\n)'.format(self.split) + + def name(self): + return 'FreiHand/{}'.format(self.split) + +-- Chunk 2 -- +// freihand.py:197-329 + + def get_num_joints(self): + return 21 + + def __len__(self): + return self.num_items + + def only_2d(self): + return False + + def project_points(self, K, xyz): + uv = np.matmul(K, xyz.T).T + return uv[:, :2] / uv[:, -1:] + + def __getitem__(self, index): + img_idx = self.img_idxs[index] + param_idx = self.param_idxs[index] + + if self.use_folder_split: + folder_idx = index // self.items_per_folder + file_idx = index + + K = self.intrinsics[param_idx].copy() + if 'test' not in self.split: + pose = self.poses[param_idx].copy() + + global_pose = pose[0].reshape(-1) + right_hand_pose = pose[1:].reshape(-1) + + scale = 0.5 * (K[0, 0] + K[1, 1]) + # focal = scale * 2 / IMG_SIZE + # pp = K[:2, 2] / scale - IMG_SIZE / (2 * scale) + + keypoints3d = self.xyz[param_idx].copy() + keypoints2d = self.project_points(K, keypoints3d) + # pp -= keypoints3d[0, :2] + + keypoints3d -= keypoints3d[0] + + keypoints2d = np.concatenate( + [keypoints2d, np.ones_like(keypoints2d[:, [-1]])], axis=-1 + ) + keypoints3d = np.concatenate( + [keypoints3d, np.ones_like(keypoints2d[:, [-1]])], axis=-1 + ) + + # logger.info('Reading keypoints: {}', time.perf_counter() - start) + + if self.use_folder_split: + img_fn = osp.join( + self.img_folder, f'folder_{folder_idx:010d}', + f'{file_idx:010d}.jpg') + else: + img_fn = osp.join(self.img_folder, f'{img_idx:08d}.jpg') + + # start = time.perf_counter() + img = read_img(img_fn) + # logger.info('Reading image: {}'.format(time.perf_counter() - start)) + + if 'test' in self.split: + bbox = np.array([0, 0, 224, 224], dtype=np.float32) + target = BoundingBox(bbox, size=img.shape) + else: + # Pad to compensate for extra keypoints + output_keypoints2d = np.zeros( + [127 + 17 * self.use_face_contour, 3], dtype=np.float32) + output_keypoints3d = np.zeros( + [127 + 17 * self.use_face_contour, 4], dtype=np.float32) + + output_keypoints2d[self.target_idxs] = keypoints2d[self.source_idxs] + output_keypoints3d[self.target_idxs] = keypoints3d[self.source_idxs] + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + # _, scale, _ = bbox_to_center_scale( + # keyps_to_bbox(output_keypoints2d[:, :-1], + # output_keypoints2d[:, -1], img_size=img.shape), + # dset_scale_factor=2.0, ref_bbox_size=224, + # ) + keyp3d_target = Keypoints3D( + output_keypoints3d, img.shape[:-1], flip_axis=0, dtype=self.dtype) + target.add_field('keypoints3d', keyp3d_target) + target.add_field('intrinsics', K) + + target.add_field('bbox_size', IMG_SIZE / 2) + center = np.array([IMG_SIZE, IMG_SIZE], dtype=np.float32) * 0.5 + target.add_field('orig_center', np.asarray(img.shape[:-1]) * 0.5) + target.add_field('center', center) + scale = IMG_SIZE / REF_BOX_SIZE + target.add_field('scale', scale) + # target.bbox = np.asarray([0, 0, IMG_SIZE, IMG_SIZE], dtype=np.float32) + + # target.add_field('camera', WeakPerspectiveCamera(focal, pp)) + + # start = time.perf_counter() + if self.return_params: + global_pose_field = GlobalPose(global_pose=global_pose) + target.add_field('global_pose', global_pose_field) + hand_pose_field = HandPose(right_hand_pose=right_hand_pose, + left_hand_pose=None) + target.add_field('hand_pose', hand_pose_field) + + if hasattr(self, 'translation'): + translation = self.translation[param_idx] + else: + translation = np.zeros([3], dtype=np.float32) + target.add_field('translation', translation) + + if self.return_vertices: + vertices = self.vertices[param_idx] + hand_vertices_field = Vertices(vertices) + target.add_field('vertices', hand_vertices_field) + if self.return_shape: + target.add_field('betas', Betas(self.betas[param_idx])) + + # print('SMPL-HF Field {}'.format(time.perf_counter() - start)) + + # start = time.perf_counter() + if self.transforms is not None: + full_img, cropped_image, target = self.transforms( + img, target, dset_scale_factor=2.0) + # logger.info('Transforms: {}'.format(time.perf_counter() - start)) + + target.add_field('name', self.name()) + # Key used to access the fit dict + # img_fn = osp.split(self.img_fns[index])[1].decode('utf-8') + + # dict_key = ['curated_fits', img_fn, index] + + # dict_key = tuple(dict_key) + # target.add_field('dict_key', dict_key) + + return full_img, cropped_image, target, index + +=== File: expose/data/datasets/ffhq.py === + +-- Chunk 1 -- +// ffhq.py:45-194 +ss FFHQ(dutils.Dataset): + def __init__(self, data_path='data/ffhq', + img_folder='images', + param_fname='ffhq_parameters.npz', + head_only=True, + split='train', + dtype=torch.float32, + joints_to_ign=None, + metrics=None, + transforms=None, + return_params=True, + return_shape=False, + return_vertices=False, + vertex_folder='vertices', + use_face_contour=False, + split_size=0.8, + vertex_flip_correspondences='', + **kwargs): + super(FFHQ, self).__init__() + assert head_only, 'FFHQ can only be used as a head only dataset' + + if metrics is None: + metrics = [] + self.metrics = metrics + + self.split = split + self.is_train = 'train' in split + self.return_params = return_params + self.return_vertices = return_vertices + self.use_face_contour = use_face_contour + + self.return_shape = return_shape + self.data_path = osp.expandvars(osp.expanduser(data_path)) + self.img_folder = osp.join(self.data_path, img_folder) + + self.transforms = transforms + self.dtype = dtype + + param_path = osp.join(self.data_path, param_fname) + self.vertex_path = osp.join(self.data_path, vertex_folder) + + vertex_flip_correspondences = osp.expandvars( + vertex_flip_correspondences) + err_msg = ( + 'Vertex flip correspondences path does not exist:' + + f' {vertex_flip_correspondences}' + ) + assert osp.exists(vertex_flip_correspondences), err_msg + flip_data = np.load(vertex_flip_correspondences) + self.bc = flip_data['bc'] + self.closest_faces = flip_data['closest_faces'] + + params = np.load(param_path) + params_dict = {key: params[key] for key in params.keys()} + + self.global_pose = params_dict['global_pose'].astype(np.float32).copy() + self.jaw_pose = params_dict['jaw_pose'].astype(np.float32).copy() + self.betas = params_dict['betas'].astype(np.float32).copy() + self.expression = params_dict['expression'].astype(np.float32).copy() + self.keypoints2d = params_dict['keypoints2D'].astype(np.float32).copy() + self.img_fnames = np.asarray(params_dict['img_fnames']) + + self.return_vertices = return_vertices + # if return_vertices: + # assert 'vertices' in params_dict, ( + # 'Requested vertices but these are not in the npz file') + # self.vertices = params_dict['vertices'].astype(np.float32).copy() + + num_items = len(self.betas) + idxs = np.arange(num_items) + if self.is_train: + self.idxs = idxs[:int(num_items * split_size)] + else: + self.idxs = idxs[int(num_items * split_size):] + self.num_items = len(self.idxs) + + folder_map_fname = osp.expandvars( + osp.join(self.data_path, img_folder, split, FOLDER_MAP_FNAME)) + self.use_folder_split = osp.exists(folder_map_fname) + if self.use_folder_split: + self.img_folder = osp.join(self.data_path, img_folder, split) + with open(folder_map_fname, 'rb') as f: + data_dict = pickle.load(f) + self.items_per_folder = max(data_dict.values()) + + source_idxs, target_idxs = dset_to_body_model( + dset='ffhq', use_face_contour=self.use_face_contour) + self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + + def get_elements_per_index(self): + return 1 + + def __repr__(self): + return 'FFHQ( \n\t Split: {self.split}\n)' + + def name(self): + return f'FFHQ/{self.split}' + + def get_num_joints(self): + return 51 + self.use_face_contour * 17 + + def only_2d(self): + return False + + def __len__(self): + return self.num_items + + def __getitem__(self, index): + data_idx = self.idxs[index] + + if self.use_folder_split: + folder_idx = index // self.items_per_folder + file_idx = index + + global_pose = self.global_pose[data_idx] + jaw_pose = self.jaw_pose[data_idx] + expression = self.expression[data_idx] + keypoints2d = self.keypoints2d[data_idx] + + if self.use_folder_split: + img_fn = osp.join( + self.img_folder, f'folder_{folder_idx:010d}', + f'{file_idx:010d}.jpg') + else: + img_fn = osp.join(self.img_folder, + str(self.img_fnames[data_idx])) + + img = read_img(img_fn.replace('.png', '.jpg')) + + output_keypoints2d = np.zeros( + [127 + 17 * self.use_face_contour, 3], dtype=np.float32) + output_keypoints2d[self.target_idxs, :-1] = keypoints2d[ + self.source_idxs] + output_keypoints2d[self.target_idxs, -1] = 1.0 + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + + center = np.array([512, 512], dtype=np.float32) + scale = IMAGE_SIZE / REF_BOX_SIZE + target.add_field('orig_center', center) + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', IMAGE_SIZE) + H, W, _ = img.shape + fscale = img.shape[0] / 256 + intrinsics = np.array( + [[DEFAULT_FOCAL_LENGTH * fscale, 0.0, W * 0.5], + [0.0, DEFAULT_FOCAL_LENGTH * fscale, H * 0.5], + [0.0, 0.0, 1.0]] + +-- Chunk 2 -- +// ffhq.py:195-218 + ) + target.add_field('intrinsics', intrinsics) + if self.return_params: + global_pose_field = GlobalPose(global_pose=global_pose) + target.add_field('global_pose', global_pose_field) + jaw_pose_field = JawPose(jaw_pose=jaw_pose) + target.add_field('jaw_pose', jaw_pose_field) + expression_field = Expression(expression=expression) + target.add_field('expression', expression_field) + if self.return_vertices: + fname, _ = osp.splitext(self.img_fnames[data_idx]) + vertex_fname = osp.join(self.vertex_path, f'{fname}.npy') + vertices = np.load(vertex_fname) + vertex_field = Vertices( + vertices, bc=self.bc, closest_faces=self.closest_faces) + target.add_field('vertices', vertex_field) + if self.return_shape: + target.add_field('betas', Betas(self.betas[data_idx])) + + if self.transforms is not None: + img, cropped_image, target = self.transforms( + img, target, dset_scale_factor=2.0) + target.add_field('name', self.name()) + return img, cropped_image, target, index + +=== File: expose/data/datasets/spin.py === + +-- Chunk 1 -- +// spin.py:45-194 +ss SPIN(dutils.Dataset): + def __init__(self, img_folder, npz_files=[], dtype=torch.float32, + use_face_contour=False, + binarization=True, + body_thresh=0.1, + hand_thresh=0.2, + face_thresh=0.4, + min_hand_keypoints=8, + min_head_keypoints=8, + transforms=None, + split='train', + return_shape=False, + return_full_pose=False, + return_params=True, + return_gender=False, + vertex_folder='vertices', + return_vertices=True, + vertex_flip_correspondences='', + **kwargs): + super(SPIN, self).__init__() + + self.img_folder = osp.expandvars(img_folder) + self.transforms = transforms + self.use_face_contour = use_face_contour + self.body_thresh = body_thresh + self.hand_thresh = hand_thresh + self.face_thresh = face_thresh + self.binarization = binarization + self.dtype = dtype + self.split = split + + self.min_hand_keypoints = min_hand_keypoints + self.min_head_keypoints = min_head_keypoints + + self.return_vertices = return_vertices + self.return_gender = return_gender + self.return_params = return_params + self.return_shape = return_shape + self.return_full_pose = return_full_pose + + self.vertex_folder = osp.join( + osp.split(self.img_folder)[0], vertex_folder) + + vertex_flip_correspondences = osp.expandvars( + vertex_flip_correspondences) + err_msg = ( + 'Vertex flip correspondences path does not exist:' + + f' {vertex_flip_correspondences}' + ) + assert osp.exists(vertex_flip_correspondences), err_msg + flip_data = np.load(vertex_flip_correspondences) + self.bc = flip_data['bc'] + self.closest_faces = flip_data['closest_faces'] + + self.spin_data = {} + start = 0 + for npz_fn in npz_files: + npz_fn = osp.expandvars(npz_fn) + dset = osp.splitext(osp.split(npz_fn)[1])[0] + + data = np.load(npz_fn) + has_smpl = np.asarray(data['has_smpl']).astype(np.bool) + data = {key: data[key][has_smpl] for key in data.keys()} + + logger.info(start) + data['dset'] = [dset] * data['pose'].shape[0] + start += data['pose'].shape[0] + if 'genders' not in data and self.return_gender: + data['genders'] = [''] * len(data['pose']) + data['indices'] = np.arange(data['pose'].shape[0]) + if dset == 'lsp': + data['part'][26, [9, 11], :] = data['part'][26, [11, 9], :] + self.spin_data[dset] = data + + folder_map_fname = osp.expandvars( + osp.join(img_folder, FOLDER_MAP_FNAME)) + with open(folder_map_fname, 'rb') as f: + data_dict = pickle.load(f) + self.items_per_folder = max(data_dict.values()) + + self.indices = np.concatenate( + [self.spin_data[dset]['indices'] for dset in self.spin_data], + axis=0).astype(np.int32) + self.centers = np.concatenate( + [self.spin_data[dset]['center'] for dset in self.spin_data], + axis=0).astype(np.float32) + self.scales = np.concatenate( + [self.spin_data[dset]['scale'] for dset in self.spin_data], + axis=0).astype(np.float32) + self.poses = np.concatenate( + [self.spin_data[dset]['pose'] + for dset in self.spin_data], axis=0).astype(np.float32) + self.keypoints2d = np.concatenate( + [self.spin_data[dset]['part'] for dset in self.spin_data], + axis=0).astype(np.float32) + self.imgname = np.concatenate( + [self.spin_data[dset]['imgname'] + for dset in self.spin_data], + axis=0).astype(np.string_) + self.dset = np.concatenate([self.spin_data[dset]['dset'] + for dset in self.spin_data], + axis=0).astype(np.string_) + if self.return_gender: + gender = [] + for dset in self.spin_data: + gender.append(self.spin_data[dset]['genders']) + self.gender = np.concatenate(gender).astype(np.string_) + + if self.return_shape: + self.betas = np.concatenate( + [self.spin_data[dset]['betas'] + for dset in self.spin_data], axis=0).astype(np.float32) + + # self.dset_names = list(self.spin_data.keys()) + dset_sizes = list( + map(lambda x: x['pose'].shape[0], self.spin_data.values())) + # logger.info(self.dset_sizes) + + self.num_items = sum(dset_sizes) + # logger.info(self.num_items) + + source_idxs, target_idxs = dset_to_body_model( + model_type='smplx', use_hands=True, use_face=True, + dset='spin', use_face_contour=self.use_face_contour) + self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + face_idxs = idxs_dict['face'] + if not self.use_face_contour: + face_idxs = face_idxs[:-17] + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.face_idxs = np.asarray(face_idxs) + + def get_elements_per_index(self): + return 1 + + def name(self): + return 'SPIN/{}'.format(self.split) + + def only_2d(self): + return False + + def __len__(self): + return self.num_items + + def __getitem__(self, index): + +-- Chunk 2 -- +// spin.py:195-301 + folder_idx = index // self.items_per_folder + file_idx = index + + img_fn = osp.join(self.img_folder, + 'folder_{:010d}'.format(folder_idx), + '{:010d}.jpg'.format(file_idx)) + img = read_img(img_fn) + keypoints2d = self.keypoints2d[index] + + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + output_keypoints2d[self.target_idxs] = keypoints2d[self.source_idxs] + + # Remove joints with negative confidence + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + body_conf = output_keypoints2d[self.body_idxs, -1] + hand_conf = output_keypoints2d[self.hand_idxs, -1] + face_conf = output_keypoints2d[self.face_idxs, -1] + + body_conf[body_conf < self.body_thresh] = 0.0 + hand_conf[hand_conf < self.hand_thresh] = 0.0 + face_conf[face_conf < self.face_thresh] = 0.0 + if self.binarization: + body_conf = ( + body_conf >= self.body_thresh).astype( + output_keypoints2d.dtype) + hand_conf = ( + hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + face_conf = ( + face_conf >= self.face_thresh).astype( + output_keypoints2d.dtype) + + output_keypoints2d[self.body_idxs, -1] = body_conf + output_keypoints2d[self.hand_idxs, -1] = hand_conf + output_keypoints2d[self.face_idxs, -1] = face_conf + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + + keypoints = output_keypoints2d[:, :-1] + conf = output_keypoints2d[:, -1] + _, _, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=1.2 + ) + center = self.centers[index] + scale = self.scales[index] + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + + if self.return_params: + pose = self.poses[index].reshape(-1, 3) + + global_pose_target = GlobalPose(pose[0].reshape(-1)) + target.add_field('global_pose', global_pose_target) + if self.return_full_pose: + body_pose = pose[1:] + else: + body_pose = pose[1:22] + body_pose_target = BodyPose(body_pose.reshape(-1)) + target.add_field('body_pose', body_pose_target) + + if self.return_shape: + betas = self.betas[index] + target.add_field('betas', Betas(betas)) + if self.return_vertices: + fname = osp.join(self.vertex_folder, f'{index:06d}.npy') + H, W, _ = img.shape + + fscale = H / bbox_size + intrinsics = np.array([[5000 * fscale, 0, 0], + [0, 5000 * fscale, 0], + [0, 0, 1]], dtype=np.float32) + + target.add_field('intrinsics', intrinsics) + vertices = np.load(fname) + vertex_field = Vertices( + vertices, bc=self.bc, closest_faces=self.closest_faces) + target.add_field('vertices', vertex_field) + + if self.transforms is not None: + force_flip = False + full_img, cropped_image, cropped_target = self.transforms( + img, target, dset_scale_factor=1.2, force_flip=force_flip) + target.add_field('name', self.name()) + + dict_key = [f'spin/{self.dset[index].decode("utf-8")}', + self.imgname[index].decode('utf-8'), index] + if hasattr(self, 'gender') and self.return_gender: + gender = self.gender[index].decode('utf-8') + if gender == 'F' or gender == 'M': + target.add_field('gender', gender) + dict_key.append(gender) + + # Add the key used to access the fit dict + dict_key = tuple(dict_key) + target.add_field('dict_key', dict_key) + + return full_img, cropped_image, cropped_target, index + + + +-- Chunk 3 -- +// spin.py:302-451 +ss SPINX(SPIN): + def __init__(self, return_params=True, + head_only=False, + hand_only=False, + return_expression=True, + *args, **kwargs): + super(SPINX, self).__init__(return_params=return_params, + *args, **kwargs) + assert nand(head_only, hand_only), ( + 'Hand only and head only can\'t be True at the same time') + + self.return_expression = return_expression + self.head_only = head_only + self.hand_only = hand_only + + self.keypoints2d = np.concatenate( + [self.spin_data[dset]['body_keypoints'] + for dset in self.spin_data], + axis=0).astype(np.float32) + self.left_hand_keypoints = np.concatenate( + [self.spin_data[dset]['left_hand_keypoints'] + for dset in self.spin_data], axis=0) + self.right_hand_keypoints = np.concatenate( + [self.spin_data[dset]['right_hand_keypoints'] + for dset in self.spin_data], axis=0) + self.face_keypoints = np.concatenate( + [self.spin_data[dset]['face_keypoints'] + for dset in self.spin_data], axis=0) + + self.spin_keypoints = np.concatenate( + [self.spin_data[dset]['spin_keypoints'] + for dset in self.spin_data], axis=0) + + if self.return_expression: + self.expression = np.concatenate( + [self.spin_data[dset]['expression'] + for dset in self.spin_data], axis=0).astype(np.float32) + + self.translation = np.concatenate( + [self.spin_data[dset]['translation'] + for dset in self.spin_data], axis=0).astype(np.float32) + + source_idxs, target_idxs = dset_to_body_model( + model_type='smplx', use_hands=True, use_face=True, + dset='openpose25+hands+face', + # dset='spinx', + use_face_contour=self.use_face_contour) + self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + face_idxs = idxs_dict['face'] + head_idxs = idxs_dict['head'] + if not self.use_face_contour: + face_idxs = face_idxs[:-17] + head_idxs = head_idxs[:-17] + + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.left_hand_idxs = np.asarray(left_hand_idxs) + self.right_hand_idxs = np.asarray(right_hand_idxs) + self.face_idxs = np.asarray(face_idxs) + self.head_idxs = np.asarray(head_idxs) + + def get_elements_per_index(self): + return 1 + + def name(self): + return 'SPINX/{}'.format(self.split) + + def only_2d(self): + return False + + def __len__(self): + return self.num_items + + def __getitem__(self, index): + folder_idx = index // self.items_per_folder + file_idx = index + + img_fn = osp.join(self.img_folder, + 'folder_{:010d}'.format(folder_idx), + '{:010d}.jpg'.format(file_idx)) + img = read_img(img_fn) + + body_keypoints = self.keypoints2d[index] + left_hand_keypoints = self.left_hand_keypoints[index] + right_hand_keypoints = self.right_hand_keypoints[index] + face_keypoints = self.face_keypoints[index] + + keypoints2d = np.concatenate( + [body_keypoints, left_hand_keypoints, right_hand_keypoints, + face_keypoints], axis=0) + + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + output_keypoints2d[self.target_idxs] = keypoints2d[self.source_idxs] + + # Remove joints with negative confidence + output_keypoints2d[output_keypoints2d[:, -1] < 0, -1] = 0 + if self.body_thresh > 0: + # Only keep the points with confidence above a threshold + body_conf = output_keypoints2d[self.body_idxs, -1] + hand_conf = output_keypoints2d[self.hand_idxs, -1] + face_conf = output_keypoints2d[self.face_idxs, -1] + + body_conf[body_conf < self.body_thresh] = 0.0 + hand_conf[hand_conf < self.hand_thresh] = 0.0 + face_conf[face_conf < self.face_thresh] = 0.0 + if self.binarization: + body_conf = ( + body_conf >= self.body_thresh).astype( + output_keypoints2d.dtype) + hand_conf = ( + hand_conf >= self.hand_thresh).astype( + output_keypoints2d.dtype) + face_conf = ( + face_conf >= self.face_thresh).astype( + output_keypoints2d.dtype) + + output_keypoints2d[self.body_idxs, -1] = body_conf + output_keypoints2d[self.hand_idxs, -1] = hand_conf + output_keypoints2d[self.face_idxs, -1] = face_conf + + if self.head_only: + keypoints = output_keypoints2d[self.head_idxs, :-1] + conf = output_keypoints2d[self.head_idxs, -1] + elif self.hand_only: + keypoints = output_keypoints2d[self.hand_idxs, :-1] + conf = output_keypoints2d[self.hand_idxs, -1] + else: + keypoints = output_keypoints2d[:, :-1] + conf = output_keypoints2d[:, -1] + center, scale, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=1.2, + ) + + target = Keypoints2D( + output_keypoints2d, img.shape[:-1], flip_axis=0, dtype=self.dtype) + _, _, bbox_size = bbox_to_center_scale( + keyps_to_bbox(keypoints, conf, img_size=img.shape), + dset_scale_factor=1.2) + + center = self.centers[index] + +-- Chunk 4 -- +// spin.py:452-550 + scale = self.scales[index] + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + + target.add_field('keypoints_hd', output_keypoints2d) + + target.add_field('orig_center', center) + target.add_field('orig_bbox_size', scale * 200) + + left_hand_bbox = keyps_to_bbox( + output_keypoints2d[self.left_hand_idxs, :-1], + output_keypoints2d[self.left_hand_idxs, -1], + img_size=img.shape, scale=1.5) + left_hand_bbox_target = BoundingBox(left_hand_bbox, img.shape) + has_left_hand = (output_keypoints2d[self.left_hand_idxs, -1].sum() > + self.min_hand_keypoints) + if has_left_hand: + target.add_field('left_hand_bbox', left_hand_bbox_target) + target.add_field( + 'orig_left_hand_bbox', + BoundingBox(left_hand_bbox, img.shape, transform=False)) + + right_hand_bbox = keyps_to_bbox( + output_keypoints2d[self.right_hand_idxs, :-1], + output_keypoints2d[self.right_hand_idxs, -1], + img_size=img.shape, scale=1.5) + right_hand_bbox_target = BoundingBox(right_hand_bbox, img.shape) + has_right_hand = (output_keypoints2d[self.right_hand_idxs, -1].sum() > + self.min_hand_keypoints) + if has_right_hand: + target.add_field('right_hand_bbox', right_hand_bbox_target) + target.add_field( + 'orig_right_hand_bbox', + BoundingBox(right_hand_bbox, img.shape, transform=False)) + + head_bbox = keyps_to_bbox( + output_keypoints2d[self.head_idxs, :-1], + output_keypoints2d[self.head_idxs, -1], + img_size=img.shape, scale=1.2) + head_bbox_target = BoundingBox(head_bbox, img.shape) + has_head = (output_keypoints2d[self.head_idxs, -1].sum() > + self.min_head_keypoints) + if has_head: + target.add_field('head_bbox', head_bbox_target) + target.add_field( + 'orig_head_bbox', + BoundingBox(head_bbox, img.shape, transform=False)) + + if self.return_params: + pose = self.poses[index].reshape(-1, 3) + + global_pose_target = GlobalPose(pose[0].reshape(-1)) + target.add_field('global_pose', global_pose_target) + body_pose = pose[1:22] + body_pose_target = BodyPose(body_pose.reshape(-1)) + target.add_field('body_pose', body_pose_target) + + jaw_pose = pose[22] + jaw_pose_target = JawPose(jaw_pose.reshape(-1)) + target.add_field('jaw_pose', jaw_pose_target) + + left_hand_pose = pose[25:25 + 15] + right_hand_pose = pose[-15:] + hand_pose_target = HandPose(left_hand_pose.reshape(-1), + right_hand_pose.reshape(-1)) + target.add_field('hand_pose', hand_pose_target) + + if self.return_shape: + betas = self.betas[index] + target.add_field('betas', Betas(betas)) + + expression = self.expression[index] + target.add_field('expression', Expression(expression)) + + if self.transforms is not None: + force_flip = False + full_img, cropped_image, cropped_target = self.transforms( + img, target, force_flip=force_flip) + + target.add_field('name', self.name()) + + dict_key = [f'spinx/{self.dset[index].decode("utf-8")}', + self.imgname[index].decode('utf-8'), + self.indices[index]] + + if hasattr(self, 'gender') and self.return_gender: + gender = self.gender[index].decode('utf-8') + if gender == 'F' or gender == 'M': + target.add_field('gender', gender) + dict_key.append(gender) + + # Add the key used to access the fit dict + dict_key = tuple(dict_key) + target.add_field('dict_key', dict_key) + + return full_img, cropped_image, cropped_target, index + + + +-- Chunk 5 -- +// spin.py:551-654 +ss LSPTest(dutils.Dataset): + def __init__(self, data_path, + return_full_pose=False, + return_params=True, + transforms=None, + use_face_contour=True, + dtype=torch.float32, + **kwargs, + ): + super(LSPTest, self).__init__() + + self.img_folder = osp.expandvars( + '/ps/project/handsproject/SMPL_HF/lsp/lsp_dataset_original/images') + self.data_path = osp.expandvars(data_path) + self.transforms = transforms + self.use_face_contour = use_face_contour + self.dtype = dtype + self.return_vertices = False + + data = np.load(self.data_path) + # has_smpl = np.asarray(data['has_smpl']).astype(np.bool) + self.centers = data['center'].astype(np.float32) + self.scales = data['scale'].astype(np.float32) + self.keypoints2d = data['part'].astype(np.float32) + logger.info(self.keypoints2d.shape) + self.imgname = data['imgname'].astype(np.string_) + + logger.info(self.scales.shape) + self.num_items = len(self.scales) + data.close() + + source_idxs, target_idxs = dset_to_body_model( + model_type='smplx', use_hands=True, use_face=True, + dset='lsp', use_face_contour=self.use_face_contour) + self.source_idxs = np.asarray(source_idxs, dtype=np.int64) + self.target_idxs = np.asarray(target_idxs, dtype=np.int64) + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + face_idxs = idxs_dict['face'] + if not self.use_face_contour: + face_idxs = face_idxs[:-17] + self.body_idxs = np.asarray(body_idxs) + self.hand_idxs = np.asarray(hand_idxs) + self.face_idxs = np.asarray(face_idxs) + + def __len__(self): + return self.num_items + + def name(self): + return 'LSP/{Test}' + + def __getitem__(self, index): + img_name = self.imgname[index].decode('utf-8') + img_path = osp.join(self.img_folder, img_name) + + img = read_img(img_path) + keypoints2d = self.keypoints2d[index] + + output_keypoints2d = np.zeros([127 + 17 * self.use_face_contour, + 3], dtype=np.float32) + + output_keypoints2d[self.target_idxs, :-1] = keypoints2d[ + self.source_idxs] + output_keypoints2d[self.target_idxs, -1] = 1.0 + + target = Keypoints2D( + output_keypoints2d, img.shape, flip_axis=0, dtype=self.dtype) + + center = self.centers[index] + scale = self.scales[index] + bbox_size = scale * 200 + + target.add_field('center', center) + target.add_field('scale', scale) + target.add_field('bbox_size', bbox_size) + target.add_field('keypoints_hd', output_keypoints2d) + target.add_field('name', self.name()) + target.add_field('fname', img_name) + + target.add_field('orig_center', center) + target.add_field('orig_bbox_size', scale * 200) + + if self.return_vertices: + H, W, _ = img.shape + + intrinsics = np.array([[5000, 0, 0.5 * W], + [0, 5000, 0.5 * H], + [0, 0, 1]], dtype=np.float32) + target.add_field('intrinsics', intrinsics) + + fname = osp.join(self.vertex_folder, f'{index:06d}.npy') + vertices = np.load(fname) + self.translation[index] + vertex_field = Vertices( + vertices, bc=self.bc, closest_faces=self.closest_faces) + target.add_field('vertices', vertex_field) + + if self.transforms is not None: + force_flip = False + full_img, cropped_image, cropped_target = self.transforms( + img, target, force_flip=force_flip) + + return full_img, cropped_image, cropped_target, index + +=== File: expose/data/datasets/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/data/datasets/__init__.py:1-26 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + + +from .image_folder import ImageFolder, ImageFolderWithBoxes +from .ehf import EHF +from .curated_fittings import CuratedFittings +from .threedpw import ThreeDPW +from .spin import SPIN, SPINX, LSPTest +from .openpose import OpenPose, OpenPoseTracks +from .freihand import FreiHand +from .ffhq import FFHQ +from .stirling import Stirling3D + +=== File: expose/data/transforms/transforms.py === + +-- Chunk 1 -- +// transforms.py:37-57 +ss Compose(object): + def __init__(self, transforms): + self.transforms = transforms + self.timers = {} + + def __call__(self, image, target, **kwargs): + next_input = (image, target) + for t in self.transforms: + output = t(*next_input, **kwargs) + next_input = output + return next_input + + def __repr__(self): + format_string = self.__class__.__name__ + "(" + for t in self.transforms: + format_string += "\n" + format_string += " {0}".format(t) + format_string += "\n)" + return format_string + + + +-- Chunk 2 -- +// transforms.py:58-127 +ss RandomHorizontalFlip(object): + def __init__(self, prob=0.5): + self.prob = prob + + def __str__(self): + return 'RandomHorizontalFlip({:.03f})'.format(self.prob) + + def _flip(self, img): + if img is None: + return None + if 'numpy.ndarray' in str(type(img)): + return np.ascontiguousarray(img[:, ::-1, :]).copy() + else: + return F.hflip(img) + + def __call__(self, image, target, force_flip=False, **kwargs): + flip = random.random() < self.prob + target.add_field('is_flipped', flip) + if flip or force_flip: + output_image = self._flip(image) + flipped_target = target.transpose(0) + + _, W, _ = output_image.shape + + left_hand_bbox, right_hand_bbox = None, None + if flipped_target.has_field('left_hand_bbox'): + left_hand_bbox = flipped_target.get_field('left_hand_bbox') + if target.has_field('right_hand_bbox'): + right_hand_bbox = flipped_target.get_field('right_hand_bbox') + if left_hand_bbox is not None: + flipped_target.add_field('right_hand_bbox', left_hand_bbox) + if right_hand_bbox is not None: + flipped_target.add_field('left_hand_bbox', right_hand_bbox) + + width = target.size[1] + center = target.get_field('center') + TO_REMOVE = 1 + center[0] = width - center[0] - TO_REMOVE + + if target.has_field('keypoints_hd'): + keypoints_hd = target.get_field('keypoints_hd') + flipped_keypoints_hd = keypoints_hd.copy() + flipped_keypoints_hd[:, 0] = ( + width - flipped_keypoints_hd[:, 0] - TO_REMOVE) + flipped_keypoints_hd = flipped_keypoints_hd[target.FLIP_INDS] + flipped_target.add_field('keypoints_hd', flipped_keypoints_hd) + + # Update the center + flipped_target.add_field('center', center) + if target.has_field('orig_center'): + orig_center = target.get_field('orig_center').copy() + orig_center[0] = width - orig_center[0] - TO_REMOVE + flipped_target.add_field('orig_center', orig_center) + + if target.has_field('intrinsics'): + intrinsics = target.get_field('intrinsics') + cam_center = intrinsics[:2, 2].copy() + cam_center[0] = width - cam_center[0] - TO_REMOVE + intrinsics[:2, 2] = cam_center + flipped_target.add_field('intrinsics', intrinsics) + # Expressions are not symmetric, so we remove them from the targets + # when the image is flipped + if flipped_target.has_field('expression'): + flipped_target.delete_field('expression') + + return output_image, flipped_target + else: + return image, target + + + +-- Chunk 3 -- +// transforms.py:128-162 +ss BBoxCenterJitter(object): + def __init__(self, factor=0.0, dist='normal'): + super(BBoxCenterJitter, self).__init__() + self.factor = factor + self.dist = dist + assert self.dist in ['normal', 'uniform'], ( + f'Distribution must be normal or uniform, not {self.dist}') + + def __str__(self): + return f'BBoxCenterJitter({self.factor:0.2f})' + + def __call__(self, image, target, **kwargs): + if self.factor <= 1e-3: + return image, target + + bbox_size = target.get_field('bbox_size') + + jitter = bbox_size * self.factor + + if self.dist == 'normal': + center_jitter = np.random.randn(2) * jitter + elif self.dist == 'uniform': + center_jitter = np.random.rand(2) * 2 * jitter - jitter + + center = target.get_field('center') + H, W, _ = target.size + new_center = center + center_jitter + new_center[0] = np.clip(new_center[0], 0, W) + new_center[1] = np.clip(new_center[1], 0, H) + + target.add_field('center', new_center) + + return image, target + + + +-- Chunk 4 -- +// transforms.py:163-229 +ss SimulateLowRes(object): + def __init__( + self, + dist: str = 'categorical', + factor: float = 1.0, + cat_factors: Tuple[float] = (1.0,), + factor_min: float = 1.0, + factor_max: float = 1.0 + ) -> None: + self.factor_min = factor_min + self.factor_max = factor_max + self.dist = dist + self.cat_factors = cat_factors + assert dist in ['uniform', 'categorical'] + + def __str__(self) -> str: + if self.dist == 'uniform': + dist_str = ( + f'{self.dist.title()}: [{self.factor_min}, {self.factor_max}]') + else: + dist_str = ( + f'{self.dist.title()}: [{self.cat_factors}]') + return f'SimulateLowResolution({dist_str})' + + def _sample_low_res( + self, + image: Union[np.ndarray, pil_img.Image] + ) -> np.ndarray: + ''' + ''' + if self.dist == 'uniform': + downsample = self.factor_min != self.factor_max + if not downsample: + return image + factor = np.random.rand() * ( + self.factor_max - self.factor_min) + self.factor_min + elif self.dist == 'categorical': + if len(self.cat_factors) < 2: + return image + idx = np.random.randint(0, len(self.cat_factors)) + factor = self.cat_factors[idx] + + H, W, _ = image.shape + downsampled_image = cv2.resize( + image, (int(W // factor), int(H // factor)), cv2.INTER_NEAREST + ) + resized_image = cv2.resize( + downsampled_image, (W, H), cv2.INTER_LINEAR_EXACT) + return resized_image + + def __call__( + self, + image: Union[np.ndarray, pil_img.Image], + cropped_image: Union[np.ndarray, pil_img.Image], + target: GenericTarget, + **kwargs + ) -> Tuple[np.ndarray, np.ndarray, GenericTarget]: + ''' + ''' + if torch.is_tensor(cropped_image): + raise NotImplementedError + elif isinstance(cropped_image, (pil_img.Image, np.ndarray)): + resized_image = self._sample_low_res(cropped_image) + + return image, resized_image, target + + + +-- Chunk 5 -- +// transforms.py:230-270 +ss ChannelNoise(object): + def __init__(self, noise_scale=0.0): + self.noise_scale = noise_scale + + def __str__(self): + return 'ChannelNoise: {:.4f}'.format(self.noise_scale) + + def __call__( + self, + image: Union[np.ndarray, pil_img.Image], + cropped_image: Union[np.ndarray, pil_img.Image], + target: GenericTarget, + **kwargs + ) -> Tuple[np.ndarray, np.ndarray, GenericTarget]: + ''' + ''' + if self.noise_scale > 0: + if image.dtype == np.float32: + img_max = 1.0 + elif image.dtype == np.uint8: + img_max = 255 + # Each channel is multiplied with a number + # in the area [1 - self.noise_scale,1 + self.noise_scale] + pn = np.random.uniform(1 - self.noise_scale, + 1 + self.noise_scale, 3) + if not isinstance(image, (np.ndarray, )): + image = np.asarray(image) + if not isinstance(cropped_image, (np.ndarray,)): + cropped_image = np.asarray(cropped_image) + output_image = np.clip( + image * pn[np.newaxis, np.newaxis], 0, + img_max).astype(image.dtype) + output_cropped_image = np.clip( + cropped_image * pn[np.newaxis, np.newaxis], 0, + img_max).astype(image.dtype) + + return output_image, output_cropped_image, target + else: + return image, cropped_image, target + + + +-- Chunk 6 -- +// transforms.py:271-339 +ss RandomRotation(object): + def __init__(self, is_train: bool = True, + rotation_factor: float = 0): + self.is_train = is_train + self.rotation_factor = rotation_factor + + def __str__(self): + return f'RandomRotation(rotation_factor={self.rotation_factor})' + + def __repr__(self): + msg = [ + f'Training: {self.is_training}', + f'Rotation factor: {self.rotation_factor}' + ] + return '\n'.join(msg) + + def __call__(self, image, target, **kwargs): + rot = 0.0 + if not self.is_train: + return image, target + if self.is_train: + rot = min(2 * self.rotation_factor, + max(-2 * self.rotation_factor, + np.random.randn() * self.rotation_factor)) + if np.random.uniform() <= 0.6: + rot = 0 + if rot == 0.0: + return image, target + + (h, w) = image.shape[:2] + (cX, cY) = (w // 2, h // 2) + M = cv2.getRotationMatrix2D((cX, cY), rot, 1.0) + cos = np.abs(M[0, 0]) + sin = np.abs(M[0, 1]) + # compute the new bounding dimensions of the image + nW = int((h * sin) + (w * cos)) + nH = int((h * cos) + (w * sin)) + # adjust the rotation matrix to take into account translation + M[0, 2] += (nW / 2) - cX + M[1, 2] += (nH / 2) - cY + # perform the actual rotation and return the image + rotated_image = cv2.warpAffine(image, M, (nW, nH)) + + new_target = target.rotate(rot=rot) + + center = target.get_field('center') + center = np.dot(M[:2, :2], center) + M[:2, 2] + new_target.add_field('center', center) + + if target.has_field('keypoints_hd'): + keypoints_hd = target.get_field('keypoints_hd') + rotated_keyps = ( + np.dot(keypoints_hd[:, :2], M[:2, :2].T) + M[:2, 2] + + 1).astype(np.int) + rotated_keyps = np.concatenate( + [rotated_keyps, keypoints_hd[:, [2]]], axis=-1) + new_target.add_field('keypoints_hd', rotated_keyps) + + if target.has_field('intrinsics'): + intrinsics = target.get_field('intrinsics').copy() + + cam_center = intrinsics[:2, 2] + intrinsics[:2, 2] = ( + np.dot(M[:2, :2], cam_center) + M[:2, 2]) + new_target.add_field('intrinsics', intrinsics) + + return rotated_image, new_target + + + +-- Chunk 7 -- +// transforms.py:340-425 +ss Crop(object): + def __init__(self, is_train=True, + crop_size=224, + scale_factor_min=0.00, + scale_factor_max=0.00, + scale_factor=0.0, + scale_dist='uniform', + rotation_factor=0, + min_hand_bbox_dim=20, + min_head_bbox_dim=20, + ): + super(Crop, self).__init__() + self.crop_size = crop_size + + self.is_train = is_train + self.scale_factor_min = scale_factor_min + self.scale_factor_max = scale_factor_max + self.scale_factor = scale_factor + self.scale_dist = scale_dist + + self.rotation_factor = rotation_factor + self.min_hand_bbox_dim = min_hand_bbox_dim + self.min_head_bbox_dim = min_head_bbox_dim + + part_idxs = get_part_idxs() + self.left_hand_idxs = part_idxs['left_hand'] + self.right_hand_idxs = part_idxs['right_hand'] + self.head_idxs = part_idxs['head'] + + def __str__(self): + return 'Crop(size={}, scale={}, rotation_factor={})'.format( + self.crop_size, self.scale_factor, self.rotation_factor) + + def __repr__(self): + msg = 'Training: {}\n'.format(self.is_train) + msg += 'Crop size: {}\n'.format(self.crop_size) + msg += 'Scale factor augm: {}\n'.format(self.scale_factor) + msg += 'Rotation factor augm: {}'.format(self.rotation_factor) + return msg + + def __call__(self, image, target, **kwargs): + sc = 1.0 + if self.is_train: + if self.scale_dist == 'normal': + sc = min(1 + self.scale_factor, + max(1 - self.scale_factor, + np.random.randn() * self.scale_factor + 1)) + elif self.scale_dist == 'uniform': + if self.scale_factor_max == 0.0 and self.scale_factor_min == 0: + sc = 1.0 + else: + sc = (np.random.rand() * + (self.scale_factor_max - self.scale_factor_min) + + self.scale_factor_min) + + scale = target.get_field('scale') * sc + center = target.get_field('center') + orig_bbox_size = target.get_field('bbox_size') + bbox_size = orig_bbox_size * sc + + np_image = np.asarray(image) + cropped_image = crop( + np_image, center, scale, [self.crop_size, self.crop_size]) + cropped_target = target.crop( + center, scale, crop_size=self.crop_size) + + transf = get_transform( + center, scale, [self.crop_size, self.crop_size]) + + cropped_target.add_field('crop_transform', transf) + cropped_target.add_field('bbox_size', bbox_size) + + if target.has_field('intrinsics'): + intrinsics = target.get_field('intrinsics').copy() + fscale = cropped_image.shape[0] / orig_bbox_size + intrinsics[0, 0] *= (fscale / sc) + intrinsics[1, 1] *= (fscale / sc) + + cam_center = intrinsics[:2, 2] + intrinsics[:2, 2] = ( + np.dot(transf[:2, :2], cam_center) + transf[:2, 2]) + cropped_target.add_field('intrinsics', intrinsics) + + return np_image, cropped_image, cropped_target + + + +-- Chunk 8 -- +// transforms.py:426-449 +ss ColorJitter(object): + def __init__(self, brightness=0.0, contrast=0, saturation=0, hue=0): + super(ColorJitter, self).__init__() + self.brightness = brightness + self.contrast = contrast + self.saturation = saturation + self.hue = hue + + self.transform = torchvision.transforms.ColorJitter( + brightness=brightness, contrast=contrast, + saturation=saturation, hue=hue) + + def __repr__(self): + name = 'ColorJitter(\n' + name += f'brightness={self.brightness:.2f}\n' + name += f'contrast={self.contrast:.2f}\n' + name += f'saturation={self.saturation:.2f}\n' + name += f'hue={self.hue:.2f}' + return name + + def __call__(self, image, target, **kwargs): + return self.transform(image), target + + + +-- Chunk 9 -- +// transforms.py:450-464 +ss ToTensor(object): + def __init__(self): + super(ToTensor, self).__init__() + + def __repr__(self): + return 'ToTensor()' + + def __str__(self): + return 'ToTensor()' + + def __call__(self, image, cropped_image, target, **kwargs): + target.to_tensor() + return F.to_tensor(image), F.to_tensor(cropped_image), target + + + +-- Chunk 10 -- +// transforms.py:465-486 +ss Normalize(object): + def __init__(self, mean, std): + super(Normalize, self).__init__() + self.mean = mean + self.std = std + + def __str__(self): + msg = 'Mean: {}, '.format(self.mean) + msg += 'Std: {}\n'.format(self.std) + return msg + + def __repr__(self): + msg = 'Mean: {}\n'.format(self.mean) + msg += 'Std: {}\n'.format(self.std) + return msg + + def __call__(self, image, cropped_image, target, **kwargs): + output_image = F.normalize( + image, mean=self.mean, std=self.std) + output_cropped_image = F.normalize( + cropped_image, mean=self.mean, std=self.std) + return output_image, output_cropped_image, target + +=== File: expose/data/transforms/build.py === + +-- Chunk 1 -- +// build.py:23-86 + build_transforms(transf_cfg, is_train): + if is_train: + flip_prob = transf_cfg.get('flip_prob', 0) + downsample_dist = transf_cfg.get('downsample_dist', 'categorical') + downsample_cat_factors = transf_cfg.get( + 'downsample_cat_factors', (1.0, )) + downsample_factor_min = transf_cfg.get('downsample_factor_min', 1.0) + downsample_factor_max = transf_cfg.get('downsample_factor_max', 1.0) + scale_factor = transf_cfg.get('scale_factor', 0.0) + scale_factor_min = transf_cfg.get('scale_factor_min', 0.0) + scale_factor_max = transf_cfg.get('scale_factor_max', 0.0) + scale_dist = transf_cfg.get('scale_dist', 'uniform') + rotation_factor = transf_cfg.get('rotation_factor', 0.0) + noise_scale = transf_cfg.get('noise_scale', 0.0) + center_jitter_factor = transf_cfg.get('center_jitter_factor', 0.0) + center_jitter_dist = transf_cfg.get('center_jitter_dist', 'normal') + else: + flip_prob = 0.0 + downsample_dist = 'categorical' + downsample_cat_factors = (1.0,) + downsample_factor_min = 1.0 + downsample_factor_max = 1.0 + scale_factor = 0.0 + scale_factor_min = 1.0 + scale_factor_max = 1.0 + rotation_factor = 0.0 + noise_scale = 0.0 + center_jitter_factor = 0.0 + center_jitter_dist = transf_cfg.get('center_jitter_dist', 'normal') + scale_dist = transf_cfg.get('scale_dist', 'uniform') + + normalize_transform = T.Normalize( + transf_cfg.get('mean'), transf_cfg.get('std')) + logger.debug('Normalize {}', normalize_transform) + + crop_size = transf_cfg.get('crop_size') + crop = T.Crop(crop_size=crop_size, is_train=is_train, + scale_factor_max=scale_factor_max, + scale_factor_min=scale_factor_min, + scale_factor=scale_factor, + scale_dist=scale_dist) + pixel_noise = T.ChannelNoise(noise_scale=noise_scale) + logger.debug('Crop {}', crop) + + downsample = T.SimulateLowRes( + dist=downsample_dist, + cat_factors=downsample_cat_factors, + factor_min=downsample_factor_min, + factor_max=downsample_factor_max) + + transform = T.Compose( + [ + T.BBoxCenterJitter(center_jitter_factor, dist=center_jitter_dist), + T.RandomHorizontalFlip(flip_prob), + T.RandomRotation( + is_train=is_train, rotation_factor=rotation_factor), + crop, + pixel_noise, + downsample, + T.ToTensor(), + normalize_transform, + ] + ) + return transform + +=== File: expose/data/transforms/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/data/transforms/__init__.py:1-18 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from .build import build_transforms +from .transforms import * + +=== File: expose/data/targets/hand_pose.py === + +-- Chunk 1 -- +// hand_pose.py:37-106 +ss HandPose(GenericTarget): + """ Contains the hand pose parameters + """ + + def __init__(self, left_hand_pose, right_hand_pose, **kwargs): + super(HandPose, self).__init__() + self.left_hand_pose = left_hand_pose + self.right_hand_pose = right_hand_pose + + def to_tensor(self, to_rot=True, *args, **kwargs): + if not torch.is_tensor(self.left_hand_pose): + if self.left_hand_pose is not None: + self.left_hand_pose = torch.from_numpy(self.left_hand_pose) + if not torch.is_tensor(self.right_hand_pose): + if self.right_hand_pose is not None: + self.right_hand_pose = torch.from_numpy( + self.right_hand_pose) + if to_rot: + if self.left_hand_pose is not None: + self.left_hand_pose = batch_rodrigues( + self.left_hand_pose.view(-1, 3)).view(-1, 3, 3) + if self.right_hand_pose is not None: + self.right_hand_pose = batch_rodrigues( + self.right_hand_pose.view(-1, 3)).view(-1, 3, 3) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def transpose(self, method): + + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" + ) + + if torch.is_tensor(self.left_hand_pose): + dim_flip = torch.tensor( + [1, -1, -1], dtype=self.left_hand_pose.dtype) + else: + dim_flip = np.array([1, -1, -1], dtype=self.left_hand_pose.dtype) + + left_hand_pose = (self.right_hand_pose.reshape(15, 3) * + dim_flip).reshape(45) + right_hand_pose = (self.left_hand_pose.reshape(15, 3) * + dim_flip).reshape(45) + field = type(self)(left_hand_pose=left_hand_pose, + right_hand_pose=right_hand_pose) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + field.add_field(k, v) + self.add_field('is_flipped', True) + return field + + def to(self, *args, **kwargs): + left_hand_pose = self.left_hand_pose + right_hand_pose = self.right_hand_pose + if left_hand_pose is not None: + left_hand_pose = left_hand_pose.to(*args, **kwargs) + if right_hand_pose is not None: + right_hand_pose = right_hand_pose.to(*args, **kwargs) + field = type(self)( + left_hand_pose=left_hand_pose, right_hand_pose=right_hand_pose) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + field.add_field(k, v) + return field + +=== File: expose/data/targets/keypoints.py === + +-- Chunk 1 -- +// keypoints.py:34-183 +ss Keypoints2D(GenericTarget): + def __init__(self, keypoints, size, + flip_axis=0, + use_face_contour=False, + bbox=None, + center=None, + scale=1.0, + source='', + **kwargs): + super(Keypoints2D, self).__init__() + self.size = size + self.source = source + self.bbox = bbox + self.center = center + self.scale = scale + + self.flip_axis = flip_axis + + self.smplx_keypoints = keypoints[:, :-1] + self.conf = keypoints[:, -1] + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += 'Number of keypoints={}, '.format(self.smplx_keypoints.shape[0]) + s += 'image_width={}, '.format(self.size[1]) + s += 'image_height={})'.format(self.size[0]) + return s + + def to_tensor(self, *args, **kwargs): + if not torch.is_tensor(self.smplx_keypoints): + self.smplx_keypoints = torch.from_numpy(self.smplx_keypoints) + self.conf = torch.from_numpy(self.conf) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def normalize(self, bboxes): + center = (bboxes[:, 2:] + bboxes[:, :2]) * 0.5 + bbox_width = bboxes[:, 2] - bboxes[:, 0] + bbox_height = bboxes[:, 3] - bboxes[:, 1] + + if center.shape[0] < 1: + return + if self.smplx_keypoints.shape[0] < 1: + return + self.smplx_keypoints[:, :, :2] -= center.unsqueeze(dim=1) + + self.smplx_keypoints[:, :, 0] = ( + self.smplx_keypoints[:, :, 0] / bbox_width[:, np.newaxis]) * 2 + self.smplx_keypoints[:, :, 1] = ( + self.smplx_keypoints[:, :, 1] / bbox_height[:, np.newaxis]) * 2 + + def rotate(self, rot=0, *args, **kwargs): + (h, w) = self.size[:2] + (cX, cY) = (w // 2, h // 2) + M = cv2.getRotationMatrix2D((cX, cY), rot, 1.0) + cos = np.abs(M[0, 0]) + sin = np.abs(M[0, 1]) + # compute the new bounding dimensions of the image + nW = int((h * sin) + (w * cos)) + nH = int((h * cos) + (w * sin)) + + # adjust the rotation matrix to take into account translation + M[0, 2] += (nW / 2) - cX + M[1, 2] += (nH / 2) - cY + kp = self.smplx_keypoints.copy() + kp = (np.dot(kp, M[:2, :2].T) + M[:2, 2] + 1).astype(np.int) + + conf = self.conf.copy().reshape(-1, 1) + kp = np.concatenate([kp, conf], axis=1).astype(np.float32) + keypoints = type(self)(kp, size=(nH, nW, 3)) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.rotate(rot=rot, *args, **kwargs) + keypoints.add_field(k, v) + + self.add_field('rot', rot) + return keypoints + + def crop(self, center, scale, crop_size=224, *args, **kwargs): + kp = self.smplx_keypoints.copy() + transf = get_transform(center, scale, (crop_size, crop_size)) + kp = (np.dot(kp, transf[:2, :2].T) + transf[:2, 2] + 1).astype(np.int) + + kp = 2.0 * kp / crop_size - 1.0 + + conf = self.conf.copy().reshape(-1, 1) + kp = np.concatenate([kp, conf], axis=1).astype(np.float32) + keypoints = type(self)(kp, size=(crop_size, crop_size, 3)) + keypoints.source = self.source + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(center=center, scale=scale, + crop_size=crop_size, *args, **kwargs) + keypoints.add_field(k, v) + + return keypoints + + def get_keypoints_and_conf(self, key='all'): + if key == 'all': + keyp_data = [self.smplx_keypoints, self.conf] + elif key == 'body': + keyp_data = [self.smplx_keypoints[BODY_IDXS], + self.conf[BODY_IDXS]] + elif key == 'left_hand': + keyp_data = [self.smplx_keypoints[LEFT_HAND_IDXS], + self.conf[LEFT_HAND_IDXS]] + elif key == 'right_hand': + keyp_data = [self.smplx_keypoints[RIGHT_HAND_IDXS], + self.conf[RIGHT_HAND_IDXS]] + elif key == 'head': + keyp_data = [self.smplx_keypoints[HEAD_IDXS], + self.conf[HEAD_IDXS]] + else: + raise ValueError(f'Unknown key: {key}') + if torch.is_tensor(keyp_data[0]): + return torch.cat( + [keyp_data[0], keyp_data[1][..., None]], dim=-1) + else: + return np.concatenate( + [keyp_data[0], keyp_data[1][..., None]], axis=-1) + + def resize(self, size, *args, **kwargs): + ratios = tuple(float(s) / float(s_orig) + for s, s_orig in zip(size, self.size)) + ratio_w, ratio_h = ratios + resized_data = self.smplx_keypoints.copy() + + resized_data[..., 0] *= ratio_w + resized_data[..., 1] *= ratio_h + + resized_keyps = np.concatenate([resized_data, + self.conf.unsqueeze(dim=-1)], axis=-1) + + keypoints = type(self)(resized_keyps, size=size) + keypoints.source = self.source + # bbox._copy_extra_fields(self) + for k, v in self.extra_fields.items(): + if not isinstance(v, torch.Tensor): + v = v.resize(size, *args, **kwargs) + keypoints.add_field(k, v) + + return keypoints + + def __getitem__(self, key): + if key == 'keypoints': + return self.smplx_keypoints + elif key == 'conf': + return self.conf + +-- Chunk 2 -- +// keypoints.py:184-256 + else: + raise ValueError('Unknown key: {}'.format(key)) + + def __len__(self): + return 1 + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT,): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT implemented") + + width = self.size[1] + TO_REMOVE = 1 + flip_inds = type(self).FLIP_INDS + if torch.is_tensor(self.smplx_keypoints): + flipped_data = torch.cat([self.smplx_keypoints, + self.conf.unsqueeze(dim=-1)], + dim=-1) + + num_joints = flipped_data.shape[0] + # flipped_data[torch.arange(num_joints)] = torch.index_select( + # flipped_data, 0, flip_inds[:num_joints]) + flipped_data[np.arange(num_joints)] = flipped_data[ + flip_inds[:num_joints]] + # width = self.size[0] + # TO_REMOVE = 1 + # Flip x coordinates + # flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE + flipped_data[..., :, self.flip_axis] = width - flipped_data[ + ..., :, self.flip_axis] - TO_REMOVE + + # Maintain COCO convention that if visibility == 0, then x, y = 0 + # inds = flipped_data[..., 2] == 0 + # flipped_data[inds] = 0 + else: + flipped_data = np.concatenate( + [self.smplx_keypoints, self.conf[..., np.newaxis]], axis=-1) + + num_joints = flipped_data.shape[0] + flipped_data[np.arange(num_joints)] = flipped_data[ + flip_inds[:num_joints]] + # Flip x coordinates + flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE + + # Maintain COCO convention that if visibility == 0, then x, y = 0 + # inds = flipped_data[..., 2] == 0 + # flipped_data[inds] = 0 + + keypoints = type(self)(flipped_data, self.size) + keypoints.source = self.source + if self.bbox is not None: + keypoints.bbox = self.bbox.copy() + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + keypoints.add_field(k, v) + + self.add_field('is_flipped', True) + return keypoints + + def to(self, *args, **kwargs): + keyp_tensor = torch.cat([self.smplx_keypoints, + self.conf.unsqueeze(dim=-1)], dim=-1) + keypoints = type(self)(keyp_tensor.to(*args, **kwargs), self.size) + keypoints.source = self.source + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + keypoints.add_field(k, v) + return keypoints + + + +-- Chunk 3 -- +// keypoints.py:575-619 + get_part_idxs(): + body_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'body' in val]) + hand_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'hand' in val]) + + left_hand_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'hand' in val and 'left' in KEYPOINT_NAMES[idx]]) + + right_hand_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'hand' in val and 'right' in KEYPOINT_NAMES[idx]]) + + face_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'face' in val]) + head_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'head' in val]) + flame_idxs = np.asarray([ + idx + for idx, val in enumerate(KEYPOINT_PARTS.values()) + if 'flame' in val]) + # joint_weights[hand_idxs] = hand_weight + # joint_weights[face_idxs] = face_weight + return { + 'body': body_idxs.astype(np.int64), + 'hand': hand_idxs.astype(np.int64), + 'face': face_idxs.astype(np.int64), + 'head': head_idxs.astype(np.int64), + 'left_hand': left_hand_idxs.astype(np.int64), + 'right_hand': right_hand_idxs.astype(np.int64), + 'flame': flame_idxs.astype(np.int64), + } + + + +-- Chunk 4 -- +// keypoints.py:799-948 + kp_connections(keypoints): + kp_lines = [ + [keypoints.index('left_eye'), keypoints.index('nose')], + [keypoints.index('right_eye'), keypoints.index('nose')], + [keypoints.index('right_eye'), keypoints.index('right_ear')], + [keypoints.index('left_eye'), keypoints.index('left_ear')], + [keypoints.index('right_shoulder'), keypoints.index('right_elbow')], + [keypoints.index('right_elbow'), keypoints.index('right_wrist')], + # Right Thumb + [keypoints.index('right_wrist'), keypoints.index('right_thumb1')], + [keypoints.index('right_thumb1'), keypoints.index('right_thumb2')], + [keypoints.index('right_thumb2'), keypoints.index('right_thumb3')], + [keypoints.index('right_thumb3'), keypoints.index('right_thumb')], + # Right Index + [keypoints.index('right_wrist'), keypoints.index('right_index1')], + [keypoints.index('right_index1'), keypoints.index('right_index2')], + [keypoints.index('right_index2'), keypoints.index('right_index3')], + [keypoints.index('right_index3'), keypoints.index('right_index')], + # Right Middle + [keypoints.index('right_wrist'), keypoints.index('right_middle1')], + [keypoints.index('right_middle1'), keypoints.index('right_middle2')], + [keypoints.index('right_middle2'), keypoints.index('right_middle3')], + [keypoints.index('right_middle3'), keypoints.index('right_middle')], + # Right Ring + [keypoints.index('right_wrist'), keypoints.index('right_ring1')], + [keypoints.index('right_ring1'), keypoints.index('right_ring2')], + [keypoints.index('right_ring2'), keypoints.index('right_ring3')], + [keypoints.index('right_ring3'), keypoints.index('right_ring')], + # Right Pinky + [keypoints.index('right_wrist'), keypoints.index('right_pinky1')], + [keypoints.index('right_pinky1'), keypoints.index('right_pinky2')], + [keypoints.index('right_pinky2'), keypoints.index('right_pinky3')], + [keypoints.index('right_pinky3'), keypoints.index('right_pinky')], + # Left Hand + [keypoints.index('left_shoulder'), keypoints.index('left_elbow')], + [keypoints.index('left_elbow'), keypoints.index('left_wrist')], + # Left Thumb + [keypoints.index('left_wrist'), keypoints.index('left_thumb1')], + [keypoints.index('left_thumb1'), keypoints.index('left_thumb2')], + [keypoints.index('left_thumb2'), keypoints.index('left_thumb3')], + [keypoints.index('left_thumb3'), keypoints.index('left_thumb')], + # Left Index + [keypoints.index('left_wrist'), keypoints.index('left_index1')], + [keypoints.index('left_index1'), keypoints.index('left_index2')], + [keypoints.index('left_index2'), keypoints.index('left_index3')], + [keypoints.index('left_index3'), keypoints.index('left_index')], + # Left Middle + [keypoints.index('left_wrist'), keypoints.index('left_middle1')], + [keypoints.index('left_middle1'), keypoints.index('left_middle2')], + [keypoints.index('left_middle2'), keypoints.index('left_middle3')], + [keypoints.index('left_middle3'), keypoints.index('left_middle')], + # Left Ring + [keypoints.index('left_wrist'), keypoints.index('left_ring1')], + [keypoints.index('left_ring1'), keypoints.index('left_ring2')], + [keypoints.index('left_ring2'), keypoints.index('left_ring3')], + [keypoints.index('left_ring3'), keypoints.index('left_ring')], + # Left Pinky + [keypoints.index('left_wrist'), keypoints.index('left_pinky1')], + [keypoints.index('left_pinky1'), keypoints.index('left_pinky2')], + [keypoints.index('left_pinky2'), keypoints.index('left_pinky3')], + [keypoints.index('left_pinky3'), keypoints.index('left_pinky')], + + # Right Foot + [keypoints.index('right_hip'), keypoints.index('right_knee')], + [keypoints.index('right_knee'), keypoints.index('right_ankle')], + [keypoints.index('right_ankle'), keypoints.index('right_heel')], + [keypoints.index('right_ankle'), keypoints.index('right_big_toe')], + [keypoints.index('right_ankle'), keypoints.index('right_small_toe')], + + [keypoints.index('left_hip'), keypoints.index('left_knee')], + [keypoints.index('left_knee'), keypoints.index('left_ankle')], + [keypoints.index('left_ankle'), keypoints.index('left_heel')], + [keypoints.index('left_ankle'), keypoints.index('left_big_toe')], + [keypoints.index('left_ankle'), keypoints.index('left_small_toe')], + + [keypoints.index('neck'), keypoints.index('right_shoulder')], + [keypoints.index('neck'), keypoints.index('left_shoulder')], + [keypoints.index('neck'), keypoints.index('nose')], + [keypoints.index('pelvis'), keypoints.index('neck')], + [keypoints.index('pelvis'), keypoints.index('left_hip')], + [keypoints.index('pelvis'), keypoints.index('right_hip')], + + # Left Eye brow + [keypoints.index('left_eye_brow1'), keypoints.index('left_eye_brow2')], + [keypoints.index('left_eye_brow2'), keypoints.index('left_eye_brow3')], + [keypoints.index('left_eye_brow3'), keypoints.index('left_eye_brow4')], + [keypoints.index('left_eye_brow4'), keypoints.index('left_eye_brow5')], + + # Right Eye brow + [keypoints.index('right_eye_brow1'), + keypoints.index('right_eye_brow2')], + [keypoints.index('right_eye_brow2'), + keypoints.index('right_eye_brow3')], + [keypoints.index('right_eye_brow3'), + keypoints.index('right_eye_brow4')], + [keypoints.index('right_eye_brow4'), + keypoints.index('right_eye_brow5')], + + # Left Eye + [keypoints.index('left_eye1'), keypoints.index('left_eye2')], + [keypoints.index('left_eye2'), keypoints.index('left_eye3')], + [keypoints.index('left_eye3'), keypoints.index('left_eye4')], + [keypoints.index('left_eye4'), keypoints.index('left_eye5')], + [keypoints.index('left_eye5'), keypoints.index('left_eye6')], + [keypoints.index('left_eye6'), keypoints.index('left_eye1')], + + # Right Eye + [keypoints.index('right_eye1'), keypoints.index('right_eye2')], + [keypoints.index('right_eye2'), keypoints.index('right_eye3')], + [keypoints.index('right_eye3'), keypoints.index('right_eye4')], + [keypoints.index('right_eye4'), keypoints.index('right_eye5')], + [keypoints.index('right_eye5'), keypoints.index('right_eye6')], + [keypoints.index('right_eye6'), keypoints.index('right_eye1')], + + # Nose Vertical + [keypoints.index('nose1'), keypoints.index('nose2')], + [keypoints.index('nose2'), keypoints.index('nose3')], + [keypoints.index('nose3'), keypoints.index('nose4')], + + # Nose Horizontal + [keypoints.index('nose_middle'), keypoints.index('nose4')], + [keypoints.index('left_nose_1'), keypoints.index('left_nose_2')], + [keypoints.index('left_nose_1'), keypoints.index('nose_middle')], + [keypoints.index('nose_middle'), keypoints.index('right_nose_1')], + [keypoints.index('right_nose_2'), keypoints.index('right_nose_1')], + + # Mouth + [keypoints.index('left_mouth_1'), keypoints.index('left_mouth_2')], + [keypoints.index('left_mouth_2'), keypoints.index('left_mouth_3')], + [keypoints.index('left_mouth_3'), keypoints.index('mouth_top')], + [keypoints.index('mouth_top'), keypoints.index('right_mouth_3')], + [keypoints.index('right_mouth_3'), keypoints.index('right_mouth_2')], + [keypoints.index('right_mouth_2'), keypoints.index('right_mouth_1')], + [keypoints.index('right_mouth_1'), keypoints.index('right_mouth_5')], + [keypoints.index('right_mouth_5'), keypoints.index('right_mouth_4')], + [keypoints.index('right_mouth_4'), keypoints.index('mouth_bottom')], + [keypoints.index('mouth_bottom'), keypoints.index('left_mouth_4')], + [keypoints.index('left_mouth_4'), keypoints.index('left_mouth_5')], + [keypoints.index('left_mouth_5'), keypoints.index('left_mouth_1')], + + # Lips + [keypoints.index('left_lip_1'), keypoints.index('left_lip_2')], + [keypoints.index('left_lip_2'), keypoints.index('lip_top')], + [keypoints.index('lip_top'), keypoints.index('right_lip_2')], + [keypoints.index('right_lip_2'), keypoints.index('right_lip_1')], + [keypoints.index('right_lip_1'), keypoints.index('right_lip_3')], + [keypoints.index('right_lip_3'), keypoints.index('lip_bottom')], + [keypoints.index('lip_bottom'), keypoints.index('left_lip_3')], + [keypoints.index('left_lip_3'), keypoints.index('left_lip_1')], + + +-- Chunk 5 -- +// keypoints.py:949-978 + # Contour + [keypoints.index('left_contour_1'), keypoints.index('left_contour_2')], + [keypoints.index('left_contour_2'), keypoints.index('left_contour_3')], + [keypoints.index('left_contour_3'), keypoints.index('left_contour_4')], + [keypoints.index('left_contour_4'), keypoints.index('left_contour_5')], + [keypoints.index('left_contour_5'), keypoints.index('left_contour_6')], + [keypoints.index('left_contour_6'), keypoints.index('left_contour_7')], + [keypoints.index('left_contour_7'), keypoints.index('left_contour_8')], + [keypoints.index('left_contour_8'), keypoints.index('contour_middle')], + + [keypoints.index('contour_middle'), + keypoints.index('right_contour_8')], + [keypoints.index('right_contour_8'), + keypoints.index('right_contour_7')], + [keypoints.index('right_contour_7'), + keypoints.index('right_contour_6')], + [keypoints.index('right_contour_6'), + keypoints.index('right_contour_5')], + [keypoints.index('right_contour_5'), + keypoints.index('right_contour_4')], + [keypoints.index('right_contour_4'), + keypoints.index('right_contour_3')], + [keypoints.index('right_contour_3'), + keypoints.index('right_contour_2')], + [keypoints.index('right_contour_2'), + keypoints.index('right_contour_1')], + ] + return kp_lines + + + +-- Chunk 6 -- +// keypoints.py:987-995 + _create_flip_indices(names, flip_map): + full_flip_map = flip_map.copy() + full_flip_map.update({v: k for k, v in flip_map.items()}) + flipped_names = [i if i not in full_flip_map else full_flip_map[i] + for i in names] + flip_indices = [names.index(i) for i in flipped_names] + return torch.tensor(flip_indices) + + + +-- Chunk 7 -- +// keypoints.py:1038-1124 +ss Keypoints3D(Keypoints2D): + def __init__(self, *args, **kwargs): + super(Keypoints3D, self).__init__(*args, **kwargs) + + def rotate(self, rot=0, *args, **kwargs): + kp = self.smplx_keypoints.copy() + conf = self.conf.copy().reshape(-1, 1) + + if rot != 0: + R = np.array([[np.cos(np.deg2rad(-rot)), + -np.sin(np.deg2rad(-rot)), 0], + [np.sin(np.deg2rad(-rot)), + np.cos(np.deg2rad(-rot)), 0], + [0, 0, 1]], dtype=np.float32) + kp = np.dot(kp, R.T) + + kp = np.concatenate([kp, conf], axis=1).astype(np.float32) + + keypoints = type(self)(kp, size=self.size) + for k, v in self.extra_fields.items(): + if not isinstance(v, torch.Tensor): + v = v.rotate(rot=rot, *args, **kwargs) + keypoints.add_field(k, v) + self.add_field('rot', kwargs.get('rot', 0)) + return keypoints + + def crop(self, center, scale, crop_size=224, *args, **kwargs): + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(center=center, scale=scale, + crop_size=crop_size, *args, **kwargs) + return self + + def center_by_keyp(self, keyp_name='pelvis'): + keyp_idx = KEYPOINT_NAMES.index(keyp_name) + self.smplx_keypoints -= self.smplx_keypoints[[keyp_idx]] + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT,): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT implemented") + + flip_inds = type(self).FLIP_INDS + if torch.is_tensor(self.smplx_keypoints): + flipped_data = torch.cat([self.smplx_keypoints, + self.conf.unsqueeze(dim=-1)], + dim=-1) + + num_joints = flipped_data.shape[0] + # flipped_data[torch.arange(num_joints)] = torch.index_select( + # flipped_data, 0, flip_inds[:num_joints]) + flipped_data[np.arange(num_joints)] = flipped_data[ + flip_inds[:num_joints]] + # width = self.size[0] + # TO_REMOVE = 1 + # Flip x coordinates + # flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE + flipped_data[..., :, self.flip_axis] *= (-1) + + # Maintain COCO convention that if visibility == 0, then x, y = 0 + # inds = flipped_data[..., 2] == 0 + # flipped_data[inds] = 0 + else: + flipped_data = np.concatenate([self.smplx_keypoints, + self.conf[..., np.newaxis]], axis=-1) + + num_joints = flipped_data.shape[0] + # flipped_data[torch.arange(num_joints)] = torch.index_select( + # flipped_data, 0, flip_inds[:num_joints]) + flipped_data[np.arange(num_joints)] = flipped_data[ + flip_inds[:num_joints]] + # width = self.size[0] + # TO_REMOVE = 1 + # Flip x coordinates + # flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE + flipped_data[..., :, self.flip_axis] *= (-1) + + keypoints = type(self)(flipped_data, self.size) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + keypoints.add_field(k, v) + self.add_field('is_flipped', True) + + return keypoints + + + +-- Chunk 8 -- +// keypoints.py:1597-1638 + body_model_to_dset(model_type='smplx', dset='coco', joints_to_ign=None, + use_face_contour=False, **kwargs): + if joints_to_ign is None: + joints_to_ign = [] + + mapping = {} + if model_type == 'smplx': + keypoint_names = KEYPOINT_NAMES + elif model_type == 'mano': + keypoint_names = MANO_NAMES + + if dset == 'coco': + dset_keyp_names = COCO_KEYPOINTS + elif dset == 'openpose19': + dset_keyp_names = OPENPOSE_JOINTS[:19] + elif dset == 'openpose19+hands': + dset_keyp_names = OPENPOSE_JOINTS[19:19 + 2 * 21] + elif dset == 'openpose19+hands+face': + dset_keyp_names = OPENPOSE_JOINTS + elif dset == 'openpose25': + dset_keyp_names = OPENPOSE_JOINTS25[:25] + elif dset == 'openpose25+hands': + dset_keyp_names = OPENPOSE_JOINTS25[:25 + 2 * 21] + elif dset == 'openpose25+hands+face': + dset_keyp_names = OPENPOSE_JOINTS25 + elif dset == 'freihand': + dset_keyp_names = FREIHAND_NAMES + else: + raise ValueError('Unknown dset dataset: {}'.format(dset)) + + for idx, name in enumerate(dset_keyp_names): + if 'contour' in name and not use_face_contour: + continue + if name in keypoint_names: + mapping[idx] = keypoint_names.index(name) + + dset_keyp_idxs = np.array(list(mapping.keys()), dtype=np.long) + model_keyps_idxs = np.array(list(mapping.values()), dtype=np.long) + + return dset_keyp_idxs, model_keyps_idxs + + + +-- Chunk 9 -- +// keypoints.py:1639-1702 + dset_to_body_model(model_type='smplx', dset='coco', joints_to_ign=None, + use_face_contour=False, **kwargs): + if joints_to_ign is None: + joints_to_ign = [] + + mapping = {} + + if dset == 'coco': + dset_keyp_names = COCO_KEYPOINTS + elif dset == 'openpose19': + dset_keyp_names = OPENPOSE_JOINTS[:19] + elif dset == 'openpose19+hands': + dset_keyp_names = OPENPOSE_JOINTS[19:19 + 2 * 21] + elif dset == 'openpose19+hands': + dset_keyp_names = OPENPOSE_JOINTS[19:19 + 2 * 21] + elif dset == 'openpose25': + dset_keyp_names = OPENPOSE_JOINTS25[:25] + elif dset == 'openpose25+hands': + dset_keyp_names = OPENPOSE_JOINTS25[:25 + 2 * 21] + elif dset == 'openpose25+hands+face': + dset_keyp_names = OPENPOSE_JOINTS25 + elif dset == 'posetrack': + dset_keyp_names = POSETRACK_KEYPOINT_NAMES + elif dset == 'mpii': + dset_keyp_names = MPII_JOINTS + elif dset == 'left-mpii-hands': + dset_keyp_names = MPII_JOINTS[-2 * 21:-21] + elif dset == 'right-mpii-hands': + dset_keyp_names = MPII_JOINTS[-21:] + elif dset == 'aich': + dset_keyp_names = AICH_KEYPOINT_NAMES + elif dset == 'spin': + dset_keyp_names = SPIN_KEYPOINT_NAMES + elif dset == 'spinx': + dset_keyp_names = SPINX_KEYPOINT_NAMES + elif dset == 'panoptic': + dset_keyp_names = PANOPTIC_KEYPOINT_NAMES + elif dset == 'mano': + dset_keyp_names = MANO_NAMES + elif dset == '3dpw': + dset_keyp_names = THREEDPW_JOINTS + elif dset == 'freihand': + dset_keyp_names = FREIHAND_NAMES + elif dset == 'h36m': + dset_keyp_names = H36M_NAMES + elif dset == 'raw_h36m': + dset_keyp_names = RAW_H36M_NAMES + elif dset == 'ffhq': + dset_keyp_names = FFHQ_KEYPOINTS + elif dset == 'lsp': + dset_keyp_names = LSP_NAMES + else: + raise ValueError('Unknown dset dataset: {}'.format(dset)) + + for idx, name in enumerate(KEYPOINT_NAMES): + if 'contour' in name and not use_face_contour: + continue + if name in dset_keyp_names: + mapping[idx] = dset_keyp_names.index(name) + + model_keyps_idxs = np.array(list(mapping.keys()), dtype=np.long) + dset_keyps_idxs = np.array(list(mapping.values()), dtype=np.long) + + return dset_keyps_idxs, model_keyps_idxs + +=== File: expose/data/targets/vertices.py === + +-- Chunk 1 -- +// vertices.py:30-130 +ss Vertices(GenericTarget): + def __init__(self, vertices, + bc=None, + closest_faces=None, + flip=True, + flip_index=0, dtype=torch.float32): + super(Vertices, self).__init__() + self.vertices = vertices + self.flip_index = flip_index + self.closest_faces = closest_faces + self.bc = bc + self.flip = flip + + def __getitem__(self, key): + if key == 'vertices': + return self.vertices + else: + raise ValueError('Unknown key: {}'.format(key)) + + def transpose(self, method): + if not self.flip: + return self + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" + ) + + if self.closest_faces is None or self.bc is None: + raise RuntimeError(f'Cannot support flip without correspondences') + + flipped_vertices = self.vertices.copy() + flipped_vertices[:, self.flip_index] *= -1 + + closest_tri_vertices = flipped_vertices[self.closest_faces].copy() + # flipped_vertices = flipped_vertices[ + # self.flip_correspondences].copy() + flipped_vertices = ( + self.bc[:, :, np.newaxis] * closest_tri_vertices).sum(axis=1) + flipped_vertices = flipped_vertices.astype(self.vertices.dtype) + + vertices = type(self)(flipped_vertices, flip_index=self.flip_index, + bc=self.bc, closest_faces=self.closest_faces) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + vertices.add_field(k, v) + self.add_field('is_flipped', True) + return vertices + + def to_tensor(self, *args, **kwargs): + self.vertices = torch.from_numpy(self.vertices) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def crop(self, *args, **kwargs): + vertices = self.vertices.copy() + field = type(self)(vertices, flip_index=self.flip_index, + bc=self.bc, + closest_faces=self.closest_faces) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(*args, **kwargs) + field.add_field(k, v) + + self.add_field('rot', kwargs.get('rot', 0)) + return field + + def rotate(self, rot=0, *args, **kwargs): + if rot == 0: + return self + vertices = self.vertices.copy() + R = np.array([[np.cos(np.deg2rad(-rot)), + -np.sin(np.deg2rad(-rot)), 0], + [np.sin(np.deg2rad(-rot)), + np.cos(np.deg2rad(-rot)), 0], + [0, 0, 1]], dtype=np.float32) + vertices = np.dot(vertices, R.T) + + vertices = type(self)(vertices, flip_index=self.flip_index, + bc=self.bc, closest_faces=self.closest_faces) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.rotate(rot=rot, *args, **kwargs) + vertices.add_field(k, v) + + self.add_field('rot', rot) + return vertices + + def to(self, *args, **kwargs): + vertices = type(self)( + self.vertices.to(*args, **kwargs), flip_index=self.flip_index, + bc=self.bc, + closest_faces=self.closest_faces) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + vertices.add_field(k, v) + return vertices + +=== File: expose/data/targets/generic_target.py === + +-- Chunk 1 -- +// generic_target.py:24-84 +ss GenericTarget(ABC): + def __init__(self): + super(GenericTarget, self).__init__() + self.extra_fields = {} + + def __del__(self): + if hasattr(self, 'extra_fields'): + self.extra_fields.clear() + + def add_field(self, field, field_data): + self.extra_fields[field] = field_data + + def get_field(self, field): + return self.extra_fields[field] + + def has_field(self, field): + return field in self.extra_fields + + def delete_field(self, field): + if field in self.extra_fields: + del self.extra_fields[field] + + def transpose(self, method): + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + self.add_field(k, v) + self.add_field('is_flipped', True) + return self + + def rotate(self, *args, **kwargs): + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.rotate(*args, **kwargs) + self.add_field('rot', kwargs.get('rot', 0)) + return self + + def crop(self, *args, **kwargs): + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(*args, **kwargs) + return self + + def resize(self, *args, **kwargs): + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.resize(*args, **kwargs) + self.add_field(k, v) + return self + + def to_tensor(self, *args, **kwargs): + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + self.add_field(k, v) + + def to(self, *args, **kwargs): + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + return self + +=== File: expose/data/targets/jaw_pose.py === + +-- Chunk 1 -- +// jaw_pose.py:46-90 +ss JawPose(GenericTarget): + """ Contains the jaw pose parameters + """ + + def __init__(self, jaw_pose, dtype=torch.float32, **kwargs): + super(JawPose, self).__init__() + self.jaw_pose = jaw_pose + + def to_tensor(self, to_rot=True, *args, **kwargs): + if not torch.is_tensor(self.jaw_pose): + self.jaw_pose = torch.from_numpy(self.jaw_pose) + + if to_rot: + self.jaw_pose = batch_rodrigues( + self.jaw_pose.view(-1, 3)).view(-1, 3, 3) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def transpose(self, method): + + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" + ) + + dim_flip = np.array([1, -1, -1], dtype=self.jaw_pose.dtype) + jaw_pose = self.jaw_pose.copy() * dim_flip + + field = type(self)(jaw_pose=jaw_pose) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + field.add_field(k, v) + self.add_field('is_flipped', True) + return field + + def to(self, *args, **kwargs): + field = type(self)(jaw_pose=self.jaw_pose.to(*args, **kwargs)) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + field.add_field(k, v) + return field + +=== File: expose/data/targets/global_pose.py === + +-- Chunk 1 -- +// global_pose.py:31-101 +ss GlobalPose(GenericTarget): + + def __init__(self, global_pose, **kwargs): + super(GlobalPose, self).__init__() + self.global_pose = global_pose + + def to_tensor(self, to_rot=True, *args, **kwargs): + if not torch.is_tensor(self.global_pose): + self.global_pose = torch.from_numpy(self.global_pose) + + if to_rot: + self.global_pose = batch_rodrigues( + self.global_pose.view(-1, 3)).view(1, 3, 3) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def transpose(self, method): + + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" + ) + + if torch.is_tensor(self.global_pose): + dim_flip = torch.tensor([1, -1, -1], dtype=self.global_pose.dtype) + global_pose = self.global_pose.clone().squeeze() * dim_flip + else: + dim_flip = np.array([1, -1, -1], dtype=self.global_pose.dtype) + global_pose = self.global_pose.copy().squeeze() * dim_flip + + field = type(self)(global_pose=global_pose) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + field.add_field(k, v) + self.add_field('is_flipped', True) + return field + + def rotate(self, rot=0, *args, **kwargs): + global_pose = self.global_pose.copy() + if rot != 0: + R = np.array([[np.cos(np.deg2rad(-rot)), + -np.sin(np.deg2rad(-rot)), 0], + [np.sin(np.deg2rad(-rot)), + np.cos(np.deg2rad(-rot)), 0], + [0, 0, 1]], dtype=np.float32) + + # find the rotation of the body in camera frame + per_rdg, _ = cv2.Rodrigues(global_pose) + # apply the global rotation to the global orientation + resrot, _ = cv2.Rodrigues(np.dot(R, per_rdg)) + global_pose = (resrot.T)[0].reshape(3) + field = type(self)(global_pose=global_pose) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(rot=rot, *args, **kwargs) + field.add_field(k, v) + + self.add_field('rot', rot) + return field + + def to(self, *args, **kwargs): + field = type(self)(global_pose=self.global_pose.to(*args, **kwargs)) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + field.add_field(k, v) + return field + +=== File: expose/data/targets/body_pose.py === + +-- Chunk 1 -- +// body_pose.py:45-102 +ss BodyPose(GenericTarget): + """ Stores the SMPL-HF params for all persons in an image + """ + + def __init__(self, body_pose, **kwargs): + super(BodyPose, self).__init__() + self.body_pose = body_pose + + def to_tensor(self, to_rot=True, *args, **kwargs): + self.body_pose = torch.from_numpy(self.body_pose) + + if to_rot: + self.body_pose = batch_rodrigues( + self.body_pose.view(-1, 3)).view(-1, 3, 3) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT, FLIP_TOP_BOTTOM): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT and FLIP_TOP_BOTTOM implemented" + ) + + if torch.is_tensor(self.body_pose): + dim_flip = torch.tensor([1, -1, -1], dtype=self.body_pose.dtype) + else: + dim_flip = np.array([1, -1, -1], dtype=self.body_pose.dtype) + + body_pose = (self.body_pose.reshape(-1)[SIGN_FLIP].reshape(21, 3) * + dim_flip).reshape(21 * 3).copy() + field = type(self)(body_pose=body_pose) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + field.add_field(k, v) + self.add_field('is_flipped', True) + return field + + def crop(self, rot=0, *args, **kwargs): + field = type(self)(body_pose=self.body_pose) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(rot=rot, *args, **kwargs) + field.add_field(k, v) + self.add_field('rot', rot) + return field + + def to(self, *args, **kwargs): + field = type(self)(body_pose=self.body_pose.to(*args, **kwargs)) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + field.add_field(k, v) + return field + +=== File: expose/data/targets/image_list.py === + +-- Chunk 1 -- +// image_list.py:25-73 +ss ImageList(object): + def __init__(self, images: torch.Tensor, + img_sizes: List[torch.Size], + padding=None): + self.images = images + self.img_sizes = img_sizes + self.sizes_tensor = torch.stack( + [torch.tensor(s) if not torch.is_tensor(s) else s + for s in img_sizes]).to(dtype=self.images.dtype) + if padding is not None: + self.padding_tensor = torch.stack( + [torch.tensor(s) if not torch.is_tensor(s) else s + for s in padding]).to(dtype=self.images.dtype) + self._shape = self.images.shape + + def as_image_list(self) -> List[Tensor]: + return self.images + + def as_tensor(self) -> Tensor: + return self.images + + @property + def shape(self): + return self._shape + + @property + def device(self): + return self.images.device + + @property + def dtype(self): + return self.images.dtype + + def pin_memory(self): + if not self.images.is_pinned(): + self.images = self.images.pin_memory() + return self + + def __del__(self): + del self.images + del self.sizes_tensor + del self.img_sizes + + def to(self, *args, **kwargs): + images = self.images.to(*args, **kwargs) + sizes_tensor = self.sizes_tensor.to(*args, **kwargs) + return ImageList(images, sizes_tensor) + + + +-- Chunk 2 -- +// image_list.py:74-136 +ss ImageListPacked(object): + def __init__( + self, + packed_tensor: Tensor, + starts: List[int], + num_elements: List[int], + img_sizes: List[torch.Size], + ) -> None: + ''' + ''' + self.packed_tensor = packed_tensor + self.starts = starts + self.num_elements = num_elements + self.img_sizes = img_sizes + + self._shape = [len(starts)] + [max(s) for s in zip(*img_sizes)] + + _, self.heights, self.widths = zip(*img_sizes) + + def as_tensor(self): + return self.packed_tensor + + def as_image_list(self): + out_list = [] + + sizes = [shape[1:] for shape in self.img_sizes] + H, W = [max(s) for s in zip(*sizes)] + + out_shape = (3, H, W) + for ii in range(len(self.img_sizes)): + start = self.starts[ii] + end = self.starts[ii] + self.num_elements[ii] + c, h, w = self.img_sizes[ii] + img = self.packed_tensor[start:end].reshape(c, h, w) + out_img = torch.zeros( + out_shape, device=self.device, dtype=self.dtype) + out_img[:c, :h, :w] = img + out_list.append(out_img.detach().cpu().numpy()) + + return out_list + + @property + def shape(self): + return self._shape + + @property + def device(self): + return self.packed_tensor.device + + @property + def dtype(self): + return self.packed_tensor.dtype + + def pin_memory(self): + if not self.images.is_pinned(): + self.images = self.images.pin_memory() + return self + + def to(self, *args, **kwargs): + self.packed_tensor = self.packed_tensor.to(*args, **kwargs) + return self + + + +-- Chunk 3 -- +// image_list.py:137-163 + to_image_list_concat( + images: List[Tensor] +) -> ImageList: + if images is None: + return images + if isinstance(images, ImageList): + return images + sizes = [img.shape[1:] for img in images] + # logger.info(sizes) + H, W = [max(s) for s in zip(*sizes)] + + batch_size = len(images) + batched_shape = (batch_size, images[0].shape[0], H, W) + batched = torch.zeros( + batched_shape, device=images[0].device, dtype=images[0].dtype) + + # for img, padded in zip(images, batched): + # shape = img.shape + # padded[:shape[0], :shape[1], :shape[2]] = img + padding = None + for ii, img in enumerate(images): + shape = img.shape + batched[ii, :shape[0], :shape[1], :shape[2]] = img + + return ImageList(batched, sizes, padding=padding) + + + +-- Chunk 4 -- +// image_list.py:164-180 + to_image_list_packed(images: List[Tensor]) -> ImageListPacked: + if images is None: + return images + if isinstance(images, ImageListPacked): + return images + # Store the size of each image + # Compute the number of elements in each image + sizes = [img.shape for img in images] + num_element_list = [np.prod(s) for s in sizes] + # Compute the total number of elements + + packed = torch.cat([img.flatten() for img in images]) + # Compute the start index of each image tensor in the packed tensor + starts = [0] + list(np.cumsum(num_element_list))[:-1] + return ImageListPacked(packed, starts, num_element_list, sizes) + + + +-- Chunk 5 -- +// image_list.py:181-188 + to_image_list( + images: List[Tensor], + use_packed=False +) -> Union[ImageList, ImageListPacked]: + ''' + ''' + func = to_image_list_packed if use_packed else to_image_list_concat + return func(images) + +=== File: expose/data/targets/expression.py === + +-- Chunk 1 -- +// expression.py:30-79 +ss Expression(GenericTarget): + """ Stores the expression params + """ + + def __init__(self, expression, dtype=torch.float32, **kwargs): + super(Expression, self).__init__() + self.expression = expression + + def to_tensor(self, *args, **kwargs): + if not torch.is_tensor(self.expression): + self.expression = torch.from_numpy(self.expression) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def transpose(self, method): + field = type(self)(expression=deepcopy(self.expression)) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + field.add_field(k, v) + self.add_field('is_flipped', True) + return field + + def resize(self, size, *args, **kwargs): + field = type(self)(expression=self.expression) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.resize(size, *args, **kwargs) + field.add_field(k, v) + return field + + def crop(self, rot=0, *args, **kwargs): + field = type(self)(expression=self.expression) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(rot=rot, *args, **kwargs) + field.add_field(k, v) + + self.add_field('rot', rot) + return field + + def to(self, *args, **kwargs): + field = type(self)(expression=self.expression.to(*args, **kwargs)) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + field.add_field(k, v) + return field + +=== File: expose/data/targets/joints.py === + +-- Chunk 1 -- +// joints.py:24-55 +ss Joints(GenericTarget): + def __init__(self, joints, **kwargs): + super(Joints, self).__init__() + self.joints = joints + + def __repr__(self): + s = self.__class__.__name__ + return s + + def to_tensor(self, *args, **kwargs): + self.joints = torch.tensor(self.joints) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def __getitem__(self, key): + if key == 'joints': + return self.joints + else: + raise ValueError('Unknown key: {}'.format(key)) + + def __len__(self): + return 1 + + def to(self, *args, **kwargs): + joints = type(self)(self.joints.to(*args, **kwargs)) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + joints.add_field(k, v) + return joints + +=== File: expose/data/targets/betas.py === + +-- Chunk 1 -- +// betas.py:26-48 +ss Betas(GenericTarget): + """ Stores the shape params + """ + + def __init__(self, betas, dtype=torch.float32, **kwargs): + super(Betas, self).__init__() + + self.betas = betas + + def to_tensor(self, *args, **kwargs): + if not torch.is_tensor(self.betas): + self.betas = torch.from_numpy(self.betas) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def to(self, *args, **kwargs): + field = type(self)(betas=self.betas.to(*args, **kwargs)) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + field.add_field(k, v) + return field + +=== File: expose/data/targets/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/data/targets/__init__.py:1-32 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + + +from .generic_target import GenericTarget +from .keypoints import Keypoints2D, Keypoints3D + +from .betas import Betas +from .expression import Expression +from .global_pose import GlobalPose +from .body_pose import BodyPose +from .hand_pose import HandPose +from .jaw_pose import JawPose + +from .vertices import Vertices +from .joints import Joints +from .bbox import BoundingBox + +from .image_list import ImageList, ImageListPacked + +=== File: expose/data/targets/bbox.py === + +-- Chunk 1 -- +// bbox.py:37-178 +ss BoundingBox(GenericTarget): + def __init__(self, bbox, size, flip_axis=0, transform=True, **kwargs): + super(BoundingBox, self).__init__() + self.bbox = bbox + self.flip_axis = flip_axis + self.size = size + self.transform = transform + + def __repr__(self): + msg = ', '.join(map(str, map(float, self.bbox))) + return f'Bounding box: {msg}' + + def to_tensor(self, *args, **kwargs): + if not torch.is_tensor(self.bbox): + self.bbox = torch.from_numpy(self.bbox) + + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v.to_tensor(*args, **kwargs) + + def rotate(self, rot=0, *args, **kwargs): + (h, w) = self.size[:2] + (cX, cY) = (w // 2, h // 2) + M = cv2.getRotationMatrix2D((cX, cY), rot, 1.0) + cos = np.abs(M[0, 0]) + sin = np.abs(M[0, 1]) + # compute the new bounding dimensions of the image + nW = int((h * sin) + (w * cos)) + nH = int((h * cos) + (w * sin)) + # adjust the rotation matrix to take into account translation + M[0, 2] += (nW / 2) - cX + M[1, 2] += (nH / 2) - cY + + if self.transform: + bbox = self.bbox.copy().reshape(4) + xmin, ymin, xmax, ymax = bbox + points = np.array( + [[xmin, ymin], + [xmin, ymax], + [xmax, ymin], + [xmax, ymax]], + ) + + bbox = (np.dot(points, M[:2, :2].T) + M[:2, 2] + 1) + xmin, ymin = np.amin(bbox, axis=0) + xmax, ymax = np.amax(bbox, axis=0) + + new_bbox = np.array([xmin, ymin, xmax, ymax]) + else: + new_bbox = self.bbox.copy().reshape(4) + + bbox_target = type(self)( + new_bbox, size=(nH, nW, 3), transform=self.transform) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.rotate(rot=rot, *args, **kwargs) + bbox_target.add_field(k, v) + + return bbox_target + + def crop(self, center, scale, rot=0, crop_size=224, *args, **kwargs): + if self.transform: + bbox = self.bbox.copy().reshape(4) + xmin, ymin, xmax, ymax = bbox + points = np.array( + [[xmin, ymin], + [xmin, ymax], + [xmax, ymin], + [xmax, ymax]], + ) + transf = get_transform( + center, scale, (crop_size, crop_size), rot=rot) + + bbox = (np.dot(points, transf[:2, :2].T) + transf[:2, 2] + 1) + xmin, ymin = np.amin(bbox, axis=0) + xmax, ymax = np.amax(bbox, axis=0) + + new_bbox = np.array([xmin, ymin, xmax, ymax]) + else: + new_bbox = self.bbox.copy().reshape(4) + + bbox_target = type(self)(new_bbox, size=(crop_size, crop_size), + transform=self.transform) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.crop(center=center, scale=scale, + crop_size=crop_size, rot=rot, + *args, **kwargs) + bbox_target.add_field(k, v) + + return bbox_target + + def resize(self, size, *args, **kwargs): + raise NotImplementedError + + def __len__(self): + return 1 + + def transpose(self, method): + if method not in (FLIP_LEFT_RIGHT,): + raise NotImplementedError( + "Only FLIP_LEFT_RIGHT implemented") + + xmin, xmax = self.bbox.reshape(-1)[[0, 2]] + # logger.info(f'Before: {xmin}, {xmax}') + W = self.size[1] + new_xmin = W - xmax + new_xmax = W - xmin + new_ymin, new_ymax = self.bbox[[1, 3]] + # logger.info(f'After: {xmin}, {xmax}') + + if torch.is_tensor(self.bbox): + flipped_bbox = torch.tensor( + [new_xmin, new_ymin, new_xmax, new_ymax], + dtype=self.bbox.dtype, device=self.bbox.device) + else: + flipped_bbox = np.array( + [new_xmin, new_ymin, new_xmax, new_ymax], + dtype=self.bbox.dtype) + + bbox_target = type(self)(flipped_bbox, self.size, + transform=self.transform) + # logger.info(bbox_target) + for k, v in self.extra_fields.items(): + if isinstance(v, GenericTarget): + v = v.transpose(method) + bbox_target.add_field(k, v) + + bbox_target.add_field('is_flipped', True) + return bbox_target + + def to(self, *args, **kwargs): + bbox_tensor = self.bbox + if not torch.is_tensor(self.bbox): + bbox_tensor = torch.tensor(bbox_tensor) + bbox_target = type(self)(bbox_tensor.to(*args, **kwargs), self.size, + transform=self.transform) + for k, v in self.extra_fields.items(): + if hasattr(v, "to"): + v = v.to(*args, **kwargs) + bbox_target.add_field(k, v) + return bbox_target + +=== File: expose/data/utils/transforms.py === + +-- Chunk 1 -- +// transforms.py:21-34 + flip_pose(pose_vector, pose_format='aa'): + if pose_format == 'aa': + if torch.is_tensor(pose_vector): + dim_flip = DIM_FLIP_TENSOR + else: + dim_flip = DIM_FLIP + return (pose_vector.reshape(-1, 3) * dim_flip).reshape(-1) + elif pose_format == 'rot-mat': + rot_mats = pose_vector.reshape(-1, 9).clone() + + rot_mats[:, [1, 2, 3, 6]] *= -1 + return rot_mats.view_as(pose_vector) + else: + raise ValueError(f'Unknown rotation format: {pose_format}') + +=== File: expose/data/utils/keypoints.py === + +-- Chunk 1 -- +// keypoints.py:19-62 + read_keypoints(keypoint_fn, use_hands=True, use_face=True, + use_face_contour=True): + with open(keypoint_fn) as keypoint_file: + data = json.load(keypoint_file) + + all_keypoints = [] + for idx, person_data in enumerate(data['people']): + body_keypoints = np.array(person_data['pose_keypoints_2d'], + dtype=np.float32) + body_keypoints = body_keypoints.reshape([-1, 3]) + + left_hand_keyps = person_data.get('hand_left_keypoints_2d', []) + if len(left_hand_keyps) < 1: + left_hand_keyps = [0] * (21 * 3) + left_hand_keyps = np.array( + left_hand_keyps, dtype=np.float32).reshape([-1, 3]) + + right_hand_keyps = person_data.get('hand_right_keypoints_2d', []) + if len(right_hand_keyps) < 1: + right_hand_keyps = [0] * (21 * 3) + right_hand_keyps = np.array( + right_hand_keyps, dtype=np.float32).reshape([-1, 3]) + + face_keypoints = person_data.get('face_keypoints_2d', []) + if len(face_keypoints) < 1: + face_keypoints = [0] * (70 * 3) + + face_keypoints = np.array( + face_keypoints, + dtype=np.float32).reshape([-1, 3]) + + face_keypoints = face_keypoints[:-2] + + all_keypoints.append( + np.concatenate([ + body_keypoints, + left_hand_keyps, right_hand_keyps, + face_keypoints], axis=0) + ) + + if len(all_keypoints) < 1: + return None + all_keypoints = np.stack(all_keypoints) + return all_keypoints + +=== File: expose/data/utils/sampling.py === + +-- Chunk 1 -- +// sampling.py:23-124 +ss EqualSampler(dutils.Sampler): + def __init__(self, datasets, batch_size=1, ratio_2d=0.5, shuffle=False): + super(EqualSampler, self).__init__(datasets) + self.num_datasets = len(datasets) + self.ratio_2d = ratio_2d + + self.shuffle = shuffle + self.dset_sizes = {} + self.elements_per_index = {} + self.only_2d = {} + self.offsets = {} + start = 0 + for dset in datasets: + self.dset_sizes[dset.name()] = len(dset) + self.offsets[dset.name()] = start + self.only_2d[dset.name()] = dset.only_2d() + self.elements_per_index[ + dset.name()] = dset.get_elements_per_index() + + start += len(dset) + + if ratio_2d < 1.0 and sum(self.only_2d.values()) == len(self.only_2d): + raise ValueError( + f'Invalid 2D ratio value: {ratio_2d} with only 2D data') + + self.length = sum(map(lambda x: len(x), datasets)) + + self.batch_size = batch_size + self._can_reuse_batches = False + logger.info(self) + + def __repr__(self): + msg = 'EqualSampler(batch_size={}, shuffle={}, ratio_2d={}\n'.format( + self.batch_size, self.shuffle, self.ratio_2d) + for dset_name in self.dset_sizes: + msg += '\t{}: {}, only 2D is {}\n'.format( + dset_name, self.dset_sizes[dset_name], + self.only_2d[dset_name]) + + return msg + ')' + + def _prepare_batches(self): + batch_idxs = [] + + dset_idxs = {} + for dset_name, dset_size in self.dset_sizes.items(): + if self.shuffle: + dset_idxs[dset_name] = cycle( + iter(torch.randperm(dset_size).tolist())) + else: + dset_idxs[dset_name] = cycle(range(dset_size)) + + num_batches = self.length // self.batch_size + for bidx in range(num_batches): + curr_idxs = [] + num_samples = 0 + num_2d_only = 0 + max_num_2d = int(self.batch_size * self.ratio_2d) + idxs_add = defaultdict(lambda: 0) + while num_samples < self.batch_size: + for dset_name in dset_idxs: + # If we already have self.ratio_2d * batch_size items with + # 2D annotations then ignore this dataset for now + if num_2d_only >= max_num_2d and self.only_2d[dset_name]: + continue + try: + curr_idxs.append( + next(dset_idxs[dset_name]) + + self.offsets[dset_name]) + num_samples += self.elements_per_index[dset_name] + # If the dataset has only 2D annotations increase the + # count + num_2d_only += (self.elements_per_index[dset_name] * + self.only_2d[dset_name]) + idxs_add[dset_name] += ( + self.elements_per_index[dset_name]) + finally: + pass + if num_samples >= self.batch_size: + break + + curr_idxs = np.array(curr_idxs) + if self.shuffle: + np.random.shuffle(curr_idxs) + batch_idxs.append(curr_idxs) + return batch_idxs + + def __len__(self): + if not hasattr(self, '_batch_idxs'): + self._batch_idxs = self._prepare_batches() + self._can_reuse_bathces = True + return len(self._batch_idxs) + + def __iter__(self): + if self._can_reuse_batches: + batch_idxs = self._batch_idxs + self._can_reuse_batches = False + else: + batch_idxs = self._prepare_batches() + + self._batch_idxs = batch_idxs + return iter(batch_idxs) + +=== File: expose/data/utils/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/data/utils/__init__.py:1-23 +# -*- coding: utf-8 -*- +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + + +from .keypoints import read_keypoints +from .sampling import EqualSampler +from .bbox import (bbox_area, bbox_to_wh, points_to_bbox, bbox_iou, + center_size_to_bbox, scale_to_bbox_size, + bbox_to_center_scale, + ) +from .transforms import flip_pose + +=== File: expose/data/utils/bbox.py === + +-- Chunk 1 -- +// bbox.py:26-46 + points_to_bbox( + points: Tensor, + bbox_scale_factor: float = 1.0) -> Tuple[Tensor, Tensor]: + + min_coords, _ = torch.min(points, dim=1) + xmin, ymin = min_coords[:, 0], min_coords[:, 1] + max_coords, _ = torch.max(points, dim=1) + xmax, ymax = max_coords[:, 0], max_coords[:, 1] + + center = torch.stack( + [xmax + xmin, ymax + ymin], dim=-1) * 0.5 + + width = (xmax - xmin) + height = (ymax - ymin) + + # Convert the bounding box to a square box + size = torch.max(width, height) * bbox_scale_factor + + return center, size + + + +-- Chunk 2 -- +// bbox.py:47-56 + center_size_to_bbox(center: Tensor, size: Tensor) -> Tensor: + xmin = center[:, 0] - size * 0.5 + ymin = center[:, 1] - size * 0.5 + + xmax = center[:, 0] + size * 0.5 + ymax = center[:, 1] + size * 0.5 + + return torch.stack([xmin, ymin, xmax, ymax], axis=-1) + + + +-- Chunk 3 -- +// bbox.py:57-89 + keyps_to_bbox(keypoints, conf, img_size=None, clip_to_img=False, + min_valid_keypoints=6, scale=1.0): + valid_keypoints = keypoints[conf > 0] + if len(valid_keypoints) < min_valid_keypoints: + return None + + xmin, ymin = np.amin(valid_keypoints, axis=0) + xmax, ymax = np.amax(valid_keypoints, axis=0) + # Clip to the image + if img_size is not None and clip_to_img: + H, W, _ = img_size + xmin = np.clip(xmin, 0, W) + xmax = np.clip(xmax, 0, W) + ymin = np.clip(ymin, 0, H) + ymax = np.clip(ymax, 0, H) + + width = (xmax - xmin) * scale + height = (ymax - ymin) * scale + + x_center = 0.5 * (xmax + xmin) + y_center = 0.5 * (ymax + ymin) + xmin = x_center - 0.5 * width + xmax = x_center + 0.5 * width + ymin = y_center - 0.5 * height + ymax = y_center + 0.5 * height + + bbox = np.stack([xmin, ymin, xmax, ymax], axis=0).astype(np.float32) + if bbox_area(bbox) > 0: + return bbox + else: + return None + + + +-- Chunk 4 -- +// bbox.py:90-102 + bbox_to_center_scale(bbox, dset_scale_factor=1.0, ref_bbox_size=200): + if bbox is None: + return None, None, None + bbox = bbox.reshape(-1) + bbox_size = dset_scale_factor * max( + bbox[2] - bbox[0], bbox[3] - bbox[1]) + scale = bbox_size / ref_bbox_size + center = np.stack( + [(bbox[0] + bbox[2]) * 0.5, + (bbox[1] + bbox[3]) * 0.5]).astype(np.float32) + return center, scale, bbox_size + + + +-- Chunk 5 -- +// bbox.py:103-106 + scale_to_bbox_size(scale, ref_bbox_size=200): + return scale * ref_bbox_size + + + +-- Chunk 6 -- +// bbox.py:107-119 + bbox_area(bbox): + if torch.is_tensor(bbox): + if bbox is None: + return 0.0 + xmin, ymin, xmax, ymax = torch.split(bbox.reshape(-1, 4), 1, dim=1) + return torch.abs((xmax - xmin) * (ymax - ymin)).squeeze(dim=-1) + else: + if bbox is None: + return 0.0 + xmin, ymin, xmax, ymax = np.split(bbox.reshape(-1, 4), 4, axis=1) + return np.abs((xmax - xmin) * (ymax - ymin)) + + + +-- Chunk 7 -- +// bbox.py:120-126 + bbox_to_wh(bbox): + if bbox is None: + return (0.0, 0.0) + xmin, ymin, xmax, ymax = np.split(bbox.reshape(-1, 4), 4, axis=1) + return xmax - xmin, ymax - ymin + + + +-- Chunk 8 -- +// bbox.py:127-171 + bbox_iou(bbox1, bbox2, epsilon=1e-9): + ''' Computes IoU between bounding boxes + + Parameters + ---------- + bbox1: torch.Tensor or np.ndarray + A Nx4 array of bounding boxes in xyxy format + bbox2: torch.Tensor or np.ndarray + A Nx4 array of bounding boxes in xyxy format + Returns + ------- + ious: torch.Tensor or np.ndarray + A N dimensional array that contains the IoUs between bounding + box pairs + ''' + if torch.is_tensor(bbox1): + # B + bbox1 = bbox1.reshape(-1, 4) + bbox2 = bbox2.reshape(-1, 4) + + # Should be B + left_top = torch.max(bbox1[:, :2], bbox2[:, :2]) + right_bottom = torch.min(bbox1[:, 2:], bbox2[:, 2:]) + + wh = (right_bottom - left_top).clamp(min=0) + + area1, area2 = bbox_area(bbox1), bbox_area(bbox2) + + isect = wh[:, 0] * wh[:, 1].reshape(bbox1.shape[0]) + union = (area1 + area2 - isect).reshape(bbox1.shape[0]) + else: + bbox1 = bbox1.reshape(4) + bbox2 = bbox2.reshape(4) + + left_top = np.maximum(bbox1[:2], bbox2[:2]) + right_bottom = np.minimum(bbox1[2:], bbox2[2:]) + + wh = right_bottom - left_top + + area1, area2 = bbox_area(bbox1), bbox_area(bbox2) + + isect = np.clip(wh[0] * wh[1], 0, float('inf')) + union = (area1 + area2 - isect).squeeze() + + return isect / (union + epsilon) + +=== File: expose/models/camera/build.py === + +-- Chunk 1 -- +// build.py:11-12 +def build_camera_head(cfg, feat_dim): + return CameraHead(cfg, feat_dim) + +=== File: expose/models/camera/camera_projection.py === + +-- Chunk 1 -- +// camera_projection.py:32-59 +ss CameraParams: + translation: Tensor = None + rotation: Tensor = None + scale: Tensor = None + focal_length: Tensor = None + + def __getitem__(self, key): + return getattr(self, key) + + def get(self, key, default=None): + return getattr(self, key, default) + + def __iter__(self): + return self.keys() + + def keys(self): + keys = [t.name for t in fields(self)] + return iter(keys) + + def values(self): + values = [getattr(self, t.name) for t in fields(self)] + return iter(values) + + def items(self): + data = [(t.name, getattr(self, t.name)) for t in fields(self)] + return iter(data) + + + +-- Chunk 2 -- +// camera_projection.py:60-106 + build_cam_proj(camera_cfg, dtype=torch.float32): + camera_type = camera_cfg.get('type', 'weak-persp') + camera_pos_scale = camera_cfg.get('pos_func') + if camera_pos_scale == 'softplus': + camera_scale_func = F.softplus + elif camera_pos_scale == 'exp': + camera_scale_func = torch.exp + elif camera_pos_scale == 'none' or camera_pos_scale == 'None': + def func(x): + return x + camera_scale_func = func + else: + raise ValueError( + f'Unknown positive scaling function: {camera_pos_scale}') + + if camera_type.lower() == 'persp': + if camera_pos_scale == 'softplus': + mean_flength = np.log(np.exp(DEFAULT_FOCAL_LENGTH) - 1) + elif camera_pos_scale == 'exp': + mean_flength = np.log(DEFAULT_FOCAL_LENGTH) + elif camera_pos_scale == 'none': + mean_flength = DEFAULT_FOCAL_LENGTH + camera = PerspectiveCamera(dtype=dtype) + camera_mean = torch.tensor( + [mean_flength, 0.0, 0.0], dtype=torch.float32) + camera_param_dim = 4 + elif camera_type.lower() == 'weak-persp': + weak_persp_cfg = camera_cfg.get('weak_persp', {}) + mean_scale = weak_persp_cfg.get('mean_scale', 0.9) + if camera_pos_scale == 'softplus': + mean_scale = np.log(np.exp(mean_scale) - 1) + elif camera_pos_scale == 'exp': + mean_scale = np.log(mean_scale) + camera_mean = torch.tensor([mean_scale, 0.0, 0.0], dtype=torch.float32) + camera = WeakPerspectiveCamera(dtype=dtype) + camera_param_dim = 3 + else: + raise ValueError(f'Unknown camera type: {camera_type}') + + return { + 'camera': camera, + 'mean': camera_mean, + 'scale_func': camera_scale_func, + 'dim': camera_param_dim + } + + + +-- Chunk 3 -- +// camera_projection.py:107-194 +ss PerspectiveCamera(nn.Module): + ''' Module that implements a perspective camera + ''' + + FOCAL_LENGTH = DEFAULT_FOCAL_LENGTH + + def __init__(self, dtype=torch.float32, focal_length=None, **kwargs): + super(PerspectiveCamera, self).__init__() + self.dtype = dtype + + if focal_length is None: + focal_length = self.FOCAL_LENGTH + # Make a buffer so that PyTorch does not complain when creating + # the camera matrix + self.register_buffer( + 'focal_length', torch.tensor(focal_length, dtype=dtype)) + + def forward( + self, + points: Tensor, + focal_length: Tensor = None, + translation: Tensor = None, + rotation: Tensor = None, + camera_center: Tensor = None, + **kwargs + ) -> Tensor: + ''' Forward pass for the perspective camera + + Parameters + ---------- + points: torch.tensor, BxNx3 + The tensor that contains the points that will be projected. + If not in homogeneous coordinates, then + focal_length: torch.tensor, BxNx3, optional + The predicted focal length of the camera. If not given, + then the default value of 5000 is assigned + translation: torch.tensor, Bx3, optional + The translation predicted for each element in the batch. If + not given then a zero translation vector is assumed + rotation: torch.tensor, Bx3x3, optional + The rotation predicted for each element in the batch. If + not given then an identity rotation matrix is assumed + camera_center: torch.tensor, Bx2, optional + The center of each image for the projection. If not given, + then a zero vector is used + Returns + ------- + Returns a torch.tensor object with size BxNx2 with the + location of the projected points on the image plane + ''' + + device = points.device + batch_size = points.shape[0] + + if rotation is None: + rotation = torch.eye( + 3, dtype=points.dtype, device=device).unsqueeze(dim=0).expand( + batch_size, -1, -1) + if translation is None: + translation = torch.zeros( + [3], dtype=points.dtype, + device=device).unsqueeze(dim=0).expand(batch_size, -11) + + if camera_center is None: + camera_center = torch.zeros([batch_size, 2], dtype=points.dtype, + device=device) + + with torch.no_grad(): + camera_mat = torch.zeros([batch_size, 2, 2], + dtype=self.dtype, device=points.device) + if focal_length is None: + focal_length = self.focal_length + + camera_mat[:, 0, 0] = focal_length + camera_mat[:, 1, 1] = focal_length + + points_transf = torch.einsum( + 'bji,bmi->bmj', + rotation, points) + translation.unsqueeze(dim=1) + + img_points = torch.div(points_transf[:, :, :2], + points_transf[:, :, 2].unsqueeze(dim=-1)) + img_points = torch.einsum( + 'bmi,bji->bjm', + camera_mat, img_points) + camera_center.reshape(-1, 1, 2) + return img_points + + + +-- Chunk 4 -- +// camera_projection.py:195-231 +ss WeakPerspectiveCamera(nn.Module): + ''' Scaled Orthographic / Weak-Perspective Camera + ''' + + def __init__(self, **kwargs): + super(WeakPerspectiveCamera, self).__init__() + + def forward( + self, + points: Tensor, + scale: Tensor, + translation: Tensor, + **kwargs + ) -> Tensor: + ''' Implements the forward pass for a Scaled Orthographic Camera + + Parameters + ---------- + points: torch.tensor, BxNx3 + The tensor that contains the points that will be projected. + If not in homogeneous coordinates, then + scale: torch.tensor, Bx1 + The predicted scaling parameters + translation: torch.tensor, Bx2 + The translation applied on the image plane to the points + Returns + ------- + projected_points: torch.tensor, BxNx2 + The points projected on the image plane, according to the + given scale and translation + ''' + assert translation.shape[-1] == 2, 'Translation shape must be -1x2' + assert scale.shape[-1] == 1, 'Scale shape must be -1x1' + + projected_points = scale.view(-1, 1, 1) * ( + points[:, :, :2] + translation.view(-1, 1, 2)) + return projected_points + +=== File: expose/models/camera/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/camera/__init__.py:1-4 +from .camera_projection import ( + build_cam_proj, DEFAULT_FOCAL_LENGTH, CameraParams) + + + +=== File: expose/models/attention/build.py === + +-- Chunk 1 -- +// build.py:22-23 + build_attention_head(cfg): + return SMPLXHead(cfg) + +=== File: expose/models/attention/predictor.py === + +-- Chunk 1 -- +// predictor.py:71-220 +ss SMPLXHead(nn.Module): + + def __init__( + self, + exp_cfg: CfgNode, + dtype=torch.float32 + ) -> None: + super(SMPLXHead, self).__init__() + + network_cfg = exp_cfg.get('network', {}) + attention_net_cfg = network_cfg.get('attention', {}) + smplx_net_cfg = attention_net_cfg.get('smplx', {}) + + self.predict_body = network_cfg.get('predict_body', True) + self.apply_hand_network_on_body = network_cfg.get( + 'apply_hand_network_on_body', True) + self.apply_hand_network_on_hands = network_cfg.get( + 'apply_hand_network_on_hands', True) + self.predict_hands = (self.apply_hand_network_on_body or + self.apply_hand_network_on_hands) + logger.warning( + f'Apply hand network on body: {self.apply_hand_network_on_body}') + logger.warning( + f'Apply hand network on hands: {self.apply_hand_network_on_hands}') + logger.warning(f'Predict hands: {self.predict_hands}') + self.apply_head_network_on_body = network_cfg.get( + 'apply_head_network_on_body', True) + self.apply_head_network_on_head = network_cfg.get( + 'apply_head_network_on_head', True) + self.predict_head = (self.apply_head_network_on_body or + self.apply_head_network_on_head) + logger.warning(f'Predict head: {self.predict_head}') + + self.detach_mean = attention_net_cfg.get('detach_mean', False) + + condition_hand_on_body = attention_net_cfg.get( + 'condition_hand_on_body', {}) + self.condition_hand_on_body = any(condition_hand_on_body.values()) + logger.info(f'Condition hand on body: {self.condition_hand_on_body}') + self.condition_hand_wrist_pose = condition_hand_on_body.get( + 'wrist_pose', True) + logger.info( + 'Condition hand wrist pose on body: ' + f'{self.condition_hand_wrist_pose}') + self.condition_hand_finger_pose = condition_hand_on_body.get( + 'finger_pose', True) + logger.info( + 'Condition hand finger pose on body: ' + f'{self.condition_hand_finger_pose}') + self.condition_hand_shape = condition_hand_on_body.get('shape', True) + logger.info( + f'Condition hand shape on body shape: {self.condition_hand_shape}') + + self.hand_add_shape_noise = network_cfg.get( + 'hand_add_shape_noise', False) + self.hand_shape_std = network_cfg.get('hand_shape_std', 0.0) + self.hand_shape_prob = network_cfg.get('hand_shape_prob', 0.0) + logger.debug( + 'Add shape noise: {} from N(0, {}), with prob {}', + self.hand_add_shape_noise, + self.hand_shape_std, + self.hand_shape_prob, + ) + + self.add_hand_pose_noise = network_cfg.get( + 'add_hand_pose_noise', False) + self.hand_pose_std = network_cfg.get('hand_pose_std', 0.0) + self.num_hand_components = network_cfg.get( + 'num_hand_components', 3) + self.hand_noise_prob = network_cfg.get('hand_noise_prob', 0.0) + logger.debug( + 'Add hand pose noise to {}: {} from N(0, {}) with prob {}', + self.num_hand_components, + self.add_hand_pose_noise, self.hand_pose_std, + self.hand_noise_prob,) + + self.hand_randomize_global_orient = network_cfg.get( + 'hand_randomize_global_orient', False) + self.hand_global_rot_max = network_cfg.get('hand_global_rot_max', 0.0) + self.hand_global_rot_min = network_cfg.get('hand_global_rot_min', 0.0) + self.hand_global_rot_noise_prob = network_cfg.get( + 'hand_global_rot_noise_prob', 0.0) + logger.debug('Randomize global pose: {} from U({}, {})', + self.hand_randomize_global_orient, + self.hand_global_rot_min, self.hand_global_rot_max) + + condition_head_on_body = attention_net_cfg.get( + 'condition_head_on_body', {}) + self.condition_head_on_body = any(condition_head_on_body.values()) + + self.condition_head_neck_pose = condition_head_on_body.get( + 'neck_pose', True) + self.condition_head_jaw_pose = condition_head_on_body.get( + 'jaw_pose', True) + self.condition_head_shape = condition_head_on_body.get( + 'shape', True) + self.condition_head_expression = condition_head_on_body.get( + 'expression', True) + logger.info(f'Condition head on body: {self.condition_head_on_body}') + logger.info( + f'Condition expression on body: {self.condition_head_expression}') + logger.info(f'Condition shape on body: {self.condition_head_shape}') + logger.info( + f'Condition neck pose on body: {self.condition_head_neck_pose}') + logger.info( + f'Condition jaw pose on body: {self.condition_head_jaw_pose}') + + self.head_add_shape_noise = network_cfg.get( + 'head_add_shape_noise', False) + self.head_shape_std = network_cfg.get('head_shape_std', 1.0) + self.head_shape_prob = network_cfg.get('head_shape_prob', 0.0) + logger.debug( + 'Add head shape noise: {} from N(0, {}), with prob {}', + self.head_add_shape_noise, + self.head_shape_std, + self.head_shape_prob, + ) + + self.add_expression_noise = network_cfg.get( + 'add_expression_noise', False) + self.expression_std = network_cfg.get('expression_std', None) + self.expression_prob = network_cfg.get('expression_prob', 1.0) + logger.debug( + 'Add expression noise: {} from N(0, {}), with prob {}', + self.add_expression_noise, + self.expression_std, + self.expression_prob, + ) + + self.add_jaw_pose_noise = network_cfg.get('add_jaw_pose_noise', False) + self.jaw_pose_min = network_cfg.get('jaw_pose_min', 0.0) + self.jaw_pose_max = network_cfg.get('jaw_pose_max', 0.0) + self.jaw_noise_prob = network_cfg.get('jaw_noise_prob', 1.0) + logger.debug( + 'Sampling random X-axis jaw rotation from U({}, {}) with prob {}', + self.jaw_pose_min, self.jaw_pose_max, self.jaw_noise_prob) + + self.head_randomize_global_orient = network_cfg.get( + 'head_randomize_global_orient', False) + self.head_global_rot_min = network_cfg.get('head_global_rot_min', 0.0) + self.head_global_rot_max = network_cfg.get('head_global_rot_max', 0.0) + self.head_global_rot_noise_prob = network_cfg.get( + 'head_global_rot_noise_prob', 1.0) + logger.debug( + 'Randomize head global pose: {} from U({}, {}) with prob {}', + self.head_randomize_global_orient, self.head_global_rot_min, + self.head_global_rot_max, self.head_global_rot_noise_prob, + ) + + body_model_cfg = exp_cfg.get('body_model', {}) + +-- Chunk 2 -- +// predictor.py:221-370 + body_use_face_contour = body_model_cfg.get('use_face_contour', True) + + self.refine_shape_from_hands = attention_net_cfg.get( + 'refine_shape_from_hands', False) + logger.debug( + f'Refine shape from hands: {self.refine_shape_from_hands}') + self.refine_shape_from_head = attention_net_cfg.get( + 'refine_shape_from_head', False) + logger.debug(f'Refine shape from head: {self.refine_shape_from_head}') + + self.hand_bbox_thresh = attention_net_cfg.get('hand_bbox_thresh', 0.4) + logger.debug( + f'Hand bounding box IoU threshold: {self.hand_bbox_thresh}') + self.head_bbox_thresh = attention_net_cfg.get('head_bbox_thresh', 0.4) + logger.debug( + f'Head bounding box IoU threshold: {self.head_bbox_thresh}') + + self.num_stages = smplx_net_cfg.get('num_stages', 3) + self.append_params = smplx_net_cfg.get('append_params', True) + + self.pose_last_stage = smplx_net_cfg.get('pose_last_stage', False) + + self.body_model_cfg = body_model_cfg.copy() + + model_path = osp.expandvars(body_model_cfg.pop('model_folder', '')) + model_type = body_model_cfg.pop('type', 'smplx') + self.body_model = build_body_model( + model_path, + model_type=model_type, + dtype=dtype, + **body_model_cfg) + logger.info(f'Body model: {self.body_model}') + + # The number of shape coefficients + num_betas = body_model_cfg.num_betas + self.num_betas = num_betas + + shape_mean_path = body_model_cfg.get('shape_mean_path', '') + shape_mean_path = osp.expandvars(shape_mean_path) + if osp.exists(shape_mean_path): + shape_mean = torch.from_numpy( + np.load(shape_mean_path, allow_pickle=True)).to( + dtype=dtype).reshape(1, -1)[:, :num_betas].reshape(-1) + else: + shape_mean = torch.zeros([num_betas], dtype=dtype) + + # The number of expression coefficients + num_expression_coeffs = body_model_cfg.num_expression_coeffs + self.num_expression_coeffs = num_expression_coeffs + expression_mean = torch.zeros( + [num_expression_coeffs], dtype=dtype) + + # Build the pose parameterization for all the parameters + pose_desc_dict = build_all_pose_params( + body_model_cfg, 0, self.body_model, + append_params=self.append_params, dtype=dtype) + + self.global_orient_decoder = pose_desc_dict['global_orient'].decoder + global_orient_mean = pose_desc_dict['global_orient'].mean + + global_orient_type = body_model_cfg.get('global_orient', {}).get( + 'param_type', 'cont_rot_repr') + # Rotate the model 180 degrees around the x-axis + if global_orient_type == 'aa': + global_orient_mean[0] = math.pi + elif global_orient_type == 'cont_rot_repr': + global_orient_mean[3] = -1 + global_orient_dim = pose_desc_dict['global_orient'].dim + + self.body_pose_decoder = pose_desc_dict['body_pose'].decoder + body_pose_mean = pose_desc_dict['body_pose'].mean + body_pose_dim = pose_desc_dict['body_pose'].dim + + self.left_hand_pose_decoder = pose_desc_dict['left_hand_pose'].decoder + left_hand_pose_mean = pose_desc_dict['left_hand_pose'].mean + left_hand_pose_dim = pose_desc_dict['left_hand_pose'].dim + left_hand_pose_ind_dim = pose_desc_dict['left_hand_pose'].ind_dim + + self.right_hand_pose_decoder = pose_desc_dict[ + 'right_hand_pose'].decoder + right_hand_pose_mean = pose_desc_dict['right_hand_pose'].mean + right_hand_pose_dim = pose_desc_dict['right_hand_pose'].dim + right_hand_pose_ind_dim = pose_desc_dict['right_hand_pose'].ind_dim + + self.jaw_pose_decoder = pose_desc_dict['jaw_pose'].decoder + jaw_pose_mean = pose_desc_dict['jaw_pose'].mean + jaw_pose_dim = pose_desc_dict['jaw_pose'].dim + + mean_lst = [] + + start = 0 + global_orient_idxs = list(range(start, start + global_orient_dim)) + + global_orient_idxs = torch.tensor(global_orient_idxs, dtype=torch.long) + self.register_buffer('global_orient_idxs', global_orient_idxs) + start += global_orient_dim + mean_lst.append(global_orient_mean.view(-1)) + + body_pose_idxs = list(range( + start, start + body_pose_dim)) + self.register_buffer( + 'body_pose_idxs', torch.tensor(body_pose_idxs, dtype=torch.long)) + start += body_pose_dim + mean_lst.append(body_pose_mean.view(-1)) + + left_hand_pose_idxs = list(range(start, start + left_hand_pose_dim)) + self.register_buffer( + 'left_hand_pose_idxs', + torch.tensor(left_hand_pose_idxs, dtype=torch.long)) + start += left_hand_pose_dim + mean_lst.append(left_hand_pose_mean.view(-1)) + + right_hand_pose_idxs = list(range( + start, start + right_hand_pose_dim)) + self.register_buffer( + 'right_hand_pose_idxs', + torch.tensor(right_hand_pose_idxs, dtype=torch.long)) + start += right_hand_pose_dim + mean_lst.append(right_hand_pose_mean.view(-1)) + + jaw_pose_idxs = list(range( + start, start + jaw_pose_dim)) + self.register_buffer( + 'jaw_pose_idxs', torch.tensor(jaw_pose_idxs, dtype=torch.long)) + start += jaw_pose_dim + mean_lst.append(jaw_pose_mean.view(-1)) + + shape_idxs = list(range(start, start + num_betas)) + self.register_buffer( + 'shape_idxs', torch.tensor(shape_idxs, dtype=torch.long)) + start += num_betas + mean_lst.append(shape_mean.view(-1)) + + expression_idxs = list(range( + start, start + num_expression_coeffs)) + self.register_buffer( + 'expression_idxs', torch.tensor(expression_idxs, dtype=torch.long)) + start += num_expression_coeffs + mean_lst.append(expression_mean.view(-1)) + + camera_cfg = smplx_net_cfg.get('camera', {}) + camera_data = build_cam_proj(camera_cfg, dtype=dtype) + self.projection = camera_data['camera'] + + camera_param_dim = camera_data['dim'] + camera_mean = camera_data['mean'] + # self.camera_mean = camera_mean + self.register_buffer('camera_mean', camera_mean) + self.camera_scale_func = camera_data['scale_func'] + + +-- Chunk 3 -- +// predictor.py:371-520 + camera_idxs = list(range( + start, start + camera_param_dim)) + self.register_buffer( + 'camera_idxs', torch.tensor(camera_idxs, dtype=torch.long)) + start += camera_param_dim + mean_lst.append(camera_mean) + + param_mean = torch.cat(mean_lst).view(1, -1) + param_dim = param_mean.numel() + + # Construct the feature extraction backbone + backbone_cfg = smplx_net_cfg.get('backbone', {}) + self.backbone, feat_dims = build_backbone(backbone_cfg) + + self.append_params = smplx_net_cfg.get('append_params', True) + self.num_stages = smplx_net_cfg.get('num_stages', 1) + + self.body_feature_key = smplx_net_cfg.get('feature_key', 'avg_pooling') + feat_dim = feat_dims[self.body_feature_key] + + regressor_cfg = smplx_net_cfg.get('mlp', {}) + regressor = MLP(feat_dim + self.append_params * param_dim, + param_dim, **regressor_cfg) + self.regressor = IterativeRegression( + regressor, param_mean, num_stages=self.num_stages) + + self.update_wrists = attention_net_cfg.get('update_wrists', True) + # Find the kinematic chain for the right wrist + right_wrist_idx = KEYPOINT_NAMES.index('right_wrist') + self.right_wrist_idx = right_wrist_idx + left_wrist_idx = KEYPOINT_NAMES.index('left_wrist') + self.left_wrist_idx = left_wrist_idx + + self.hand_predictor = HandPredictor( + exp_cfg, + pose_desc_dict['global_orient'], + pose_desc_dict['right_hand_pose'], + camera_data, + detach_mean=self.detach_mean, + mean_pose_path=body_model_cfg.mean_pose_path, + dtype=dtype) + + hand_crop_size = exp_cfg.get('datasets', {}).get('hand', {}).get( + 'transforms', {}).get('crop_size', 256) + self.hand_scale_factor = attention_net_cfg.get('hand', {}).get( + 'scale_factor', 2.0) + self.hand_crop_size = hand_crop_size + self.hand_cropper = CropSampler(hand_crop_size) + + head_crop_size = exp_cfg.get('datasets', {}).get('head', {}).get( + 'transforms', {}).get('crop_size', 256) + self.head_crop_size = head_crop_size + self.head_scale_factor = network_cfg.get('head', {}).get( + 'scale_factor', 2.0) + self.head_cropper = CropSampler(head_crop_size) + + self.head_predictor = HeadPredictor( + exp_cfg, + pose_desc_dict['global_orient'], + pose_desc_dict['jaw_pose'], camera_data, + detach_mean=self.detach_mean, + dtype=dtype) + self.points_to_crops = ToCrops() + + right_wrist_kin_chain = find_joint_kin_chain( + right_wrist_idx, + self.body_model.parents) + right_wrist_kin_chain = torch.tensor( + right_wrist_kin_chain, dtype=torch.long) + self.register_buffer('right_wrist_kin_chain', right_wrist_kin_chain) + + self.register_buffer( + 'abs_pose_mean', + self.global_orient_decoder.get_mean().unsqueeze(dim=0)) + + # Find the kinematic chain for the left wrist + left_wrist_kin_chain = find_joint_kin_chain( + left_wrist_idx, + self.body_model.parents) + left_wrist_kin_chain = torch.tensor( + left_wrist_kin_chain, dtype=torch.long) + self.register_buffer('left_wrist_kin_chain', left_wrist_kin_chain) + + # Find the kinematic chain for the neck + neck_idx = KEYPOINT_NAMES.index('neck') + neck_kin_chain = find_joint_kin_chain( + neck_idx, + self.body_model.parents) + self.register_buffer('neck_kin_chain', + torch.tensor(neck_kin_chain, dtype=torch.long)) + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + head_idxs = idxs_dict['head'] + if not body_use_face_contour: + head_idxs = head_idxs[:-17] + + self.register_buffer('body_idxs', torch.tensor(body_idxs)) + self.register_buffer('left_hand_idxs', torch.tensor(left_hand_idxs)) + self.register_buffer('right_hand_idxs', torch.tensor(right_hand_idxs)) + self.register_buffer('head_idxs', torch.tensor(head_idxs)) + + self.keyp_loss = KeypointLoss(exp_cfg) + + self.mask_hand_keyps = attention_net_cfg.get('mask_hand_keyps', True) + self.mask_head_keyps = attention_net_cfg.get('mask_head_keyps', True) + + loss_cfg = exp_cfg.get('losses', {}) + # Create a loss to apply on the keypoints from the head crop + head_crop_keypoint_loss_cfg = loss_cfg.get('head_crop_keypoints') + self.head_crop_keyps_weight = head_crop_keypoint_loss_cfg.get( + 'weight', 0.0) + self.head_crop_keyps_enable_at = head_crop_keypoint_loss_cfg.get( + 'enable', True) + if self.head_crop_keyps_weight > 0: + self.head_crop_keyps_loss = build_loss( + **head_crop_keypoint_loss_cfg) + logger.info( + '2D Head crop keyps loss: {}', self.head_crop_keyps_loss) + + left_hand_crop_keypoint_loss_cfg = loss_cfg.get( + 'left_hand_crop_keypoints') + self.left_hand_crop_keyps_weight = ( + left_hand_crop_keypoint_loss_cfg.get('weight', 0.0)) + self.left_hand_crop_keyps_enable_at = ( + left_hand_crop_keypoint_loss_cfg.get('enable', True)) + if self.left_hand_crop_keyps_weight > 0: + self.left_hand_crop_keyps_loss = build_loss( + **left_hand_crop_keypoint_loss_cfg) + logger.info( + '2D Left hand crop keyps loss: {}', + self.left_hand_crop_keyps_loss) + + right_hand_crop_keypoint_loss_cfg = loss_cfg.get( + 'right_hand_crop_keypoints') + self.right_hand_crop_keyps_weight = ( + right_hand_crop_keypoint_loss_cfg.get('weight', 0.0)) + self.right_hand_crop_keyps_enable_at = ( + right_hand_crop_keypoint_loss_cfg.get('enable', True)) + if self.right_hand_crop_keyps_weight > 0: + self.right_hand_crop_keyps_loss = build_loss( + **right_hand_crop_keypoint_loss_cfg) + logger.info( + '2D Left hand crop keyps loss: {}', + self.right_hand_crop_keyps_loss) + + self.body_loss = SMPLXLossModule( + loss_cfg, + +-- Chunk 4 -- +// predictor.py:521-670 + use_face_contour=body_use_face_contour) + self.body_regularizer = RegularizerModule( + loss_cfg, body_pose_mean=body_pose_mean, + left_hand_pose_mean=left_hand_pose_mean, + right_hand_pose_mean=right_hand_pose_mean, + jaw_pose_mean=jaw_pose_mean + ) + self.hand_loss = MANOLossModule(loss_cfg.get('hand', {})) + self.hand_regularizer = MANORegularizer(loss_cfg.get('hand', {})) + self.head_loss = FLAMELossModule( + loss_cfg.get('head', {}), use_face_contour=body_use_face_contour) + self.head_regularizer = FLAMERegularizer(loss_cfg.get('head', {})) + + self.freeze_body = attention_net_cfg.get('freeze_body', False) + if self.freeze_body: + for param in self.backbone.parameters(): + param.requires_grad = False + for param in self.regressor.parameters(): + param.requires_grad = False + # Stop updating batch norm statistics + self.backbone = FrozenBatchNorm2d.convert_frozen_batchnorm( + self.backbone) + self.regressor = FrozenBatchNorm2d.convert_frozen_batchnorm( + self.regressor) + + # Build part merging functions + hand_feat_dim = self.hand_predictor.get_feat_dim() + head_feat_dim = self.head_predictor.get_feat_dim() + # Right hand pose + merging_cfg = attention_net_cfg.get('merging', {}) + self.right_hand_pose_merging_func = self._build_merge_func( + merging_cfg, + 'right_hand_pose', + body_feat_dim=feat_dim, + body_param_dim=right_hand_pose_dim, + part_feat_dim=hand_feat_dim, + part_param_dim=right_hand_pose_dim, + ) + # Right wrist pose + if self.update_wrists: + self.right_wrist_pose_merging_func = self._build_merge_func( + merging_cfg, + 'right_wrist_pose', + body_feat_dim=feat_dim, + body_param_dim=right_hand_pose_ind_dim, + part_feat_dim=hand_feat_dim, + part_param_dim=right_hand_pose_ind_dim, + ) + # Left hand pose + self.left_hand_pose_merging_func = self._build_merge_func( + merging_cfg, + 'left_hand_pose', + body_feat_dim=feat_dim, + body_param_dim=left_hand_pose_dim, + part_feat_dim=hand_feat_dim, + part_param_dim=left_hand_pose_dim, + ) + # Left wrist pose + if self.update_wrists: + self.left_wrist_pose_merging_func = self._build_merge_func( + merging_cfg, + 'left_wrist_pose', + body_feat_dim=feat_dim, + body_param_dim=left_hand_pose_ind_dim, + part_feat_dim=hand_feat_dim, + part_param_dim=left_hand_pose_ind_dim, + ) + + # Jaw pose + self.jaw_pose_merging_func = self._build_merge_func( + merging_cfg, + 'jaw_pose', + body_feat_dim=feat_dim, + body_param_dim=jaw_pose_dim, + part_feat_dim=head_feat_dim, + part_param_dim=jaw_pose_dim, + ) + # Expression + self.expression_merging_func = self._build_merge_func( + merging_cfg, + 'expression', + body_feat_dim=feat_dim, + body_param_dim=num_expression_coeffs, + part_feat_dim=head_feat_dim, + part_param_dim=num_expression_coeffs, + ) + + hand_soft_weight_loss_cfg = loss_cfg.get('hand_soft_weight_loss', {}) + self.hand_soft_weight_loss = build_loss(**hand_soft_weight_loss_cfg) + self.hand_soft_weight_loss_weight = hand_soft_weight_loss_cfg.get( + 'weight', 0.0) + + head_soft_weight_loss_cfg = loss_cfg.get('head_soft_weight_loss', {}) + self.head_soft_weight_loss = build_loss(**head_soft_weight_loss_cfg) + self.head_soft_weight_loss_weight = head_soft_weight_loss_cfg.get( + 'weight', 0.0) + + def _build_merge_func( + self, cfg: CfgNode, + name: str, + body_feat_dim: int, body_param_dim: int, + part_feat_dim: int, part_param_dim: int, + ) -> Callable: + merge_type = cfg.get(name, {}).get('type', 'simple') + logger.debug(f'Building "{merge_type}" merging function for "{name}"') + if merge_type == 'none': + pass + elif merge_type == 'simple': + def func( + from_body: Tensor, from_part: Tensor, + body_feat: Optional[Tensor] = None, + part_feat: Optional[Tensor] = None, + mask: Optional[Tensor] = None + ) -> Dict[str, Tensor]: + output = {} + if self.training: + # During training, if a mask + output['merged'] = ( + torch.where( + mask, from_part, from_body) if mask is not None + else from_part + ) + else: + output['merged'] = from_part + output['weights'] = None + return output + return func + else: + raise ValueError(f'Merge function {merge_type} is not supported') + + def toggle_losses(self, iteration): + self.body_loss.toggle_losses(iteration) + self.keyp_loss.toggle_losses(iteration) + + def toggle_param_prediction(self, iteration): + pass + + def flat_body_params_to_dict(self, param_tensor): + global_orient = torch.index_select( + param_tensor, 1, self.global_orient_idxs) + body_pose = torch.index_select( + param_tensor, 1, self.body_pose_idxs) + left_hand_pose = torch.index_select( + param_tensor, 1, self.left_hand_pose_idxs) + right_hand_pose = torch.index_select( + param_tensor, 1, self.right_hand_pose_idxs) + jaw_pose = torch.index_select( + param_tensor, 1, self.jaw_pose_idxs) + betas = torch.index_select(param_tensor, 1, self.shape_idxs) + expression = torch.index_select(param_tensor, 1, self.expression_idxs) + +-- Chunk 5 -- +// predictor.py:671-820 + + return { + 'betas': betas, + 'expression': expression, + 'global_orient': global_orient, + 'body_pose': body_pose, + 'left_hand_pose': left_hand_pose, + 'right_hand_pose': right_hand_pose, + 'jaw_pose': jaw_pose, + } + + def find_joint_global_rotation( + self, + kin_chain: Tensor, + root_pose: Tensor, + body_pose: Tensor + ) -> Tensor: + ''' Computes the absolute rotation of a joint from the kinematic chain + ''' + # Create a single vector with all the poses + parents_pose = torch.cat( + [root_pose, body_pose], dim=1)[:, kin_chain] + output_pose = parents_pose[:, 0] + for idx in range(1, parents_pose.shape[1]): + output_pose = torch.bmm( + parents_pose[:, idx], output_pose) + return output_pose + + def build_hand_mean(self, global_orient: Tensor, + body_pose: Tensor, + betas: Tensor, + flipped_left_hand_pose: Tensor, + right_hand_pose: Tensor, + hand_targets: List, + num_body_imgs: int = 0, + num_hand_imgs: int = 0 + ) -> Tuple[Tensor, Tensor]: + ''' Builds the initial point for the iterative regressor of the hand + ''' + device, dtype = global_orient.device, global_orient.dtype + hand_only_mean, parent_rots = [], [] + if num_body_imgs > 0: + batch_size = num_body_imgs + # Compute the absolute pose of the right wrist + right_wrist_pose_abs = self.find_joint_global_rotation( + self.right_wrist_kin_chain, global_orient, + body_pose) + + right_wrist_parent_rot = self.find_joint_global_rotation( + self.right_wrist_kin_chain[1:], global_orient, + body_pose) + + left_wrist_parent_rot = self.find_joint_global_rotation( + self.left_wrist_kin_chain[1:], global_orient, body_pose) + left_to_right_wrist_parent_rot = flip_pose( + left_wrist_parent_rot, pose_format='rot-mat') + + parent_rots += [ + right_wrist_parent_rot, left_to_right_wrist_parent_rot] + + # if self.condition_hand_on_body: + # Convert the absolute pose to the latent representation + if self.condition_hand_wrist_pose: + right_wrist_pose = self.global_orient_decoder.encode( + right_wrist_pose_abs.unsqueeze(dim=1)).reshape( + batch_size, -1) + + # Compute the absolute rotation for the left wrist + left_wrist_pose_abs = self.find_joint_global_rotation( + self.left_wrist_kin_chain, global_orient, body_pose) + # Flip the left wrist to the right + left_to_right_wrist_pose = flip_pose( + left_wrist_pose_abs, pose_format='rot-mat') + # Convert to the latent representation + left_to_right_wrist_pose = self.global_orient_decoder.encode( + left_to_right_wrist_pose.unsqueeze(dim=1)).reshape( + batch_size, -1) + else: + right_wrist_pose = self.hand_predictor.get_wrist_pose_mean( + batch_size=batch_size) + left_to_right_wrist_pose = ( + self.hand_predictor.get_wrist_pose_mean( + batch_size=batch_size)) + + # Convert the pose of the left hand to the right hand and project + # it to the encoder space + left_to_right_hand_pose = self.right_hand_pose_decoder.encode( + flipped_left_hand_pose).reshape(batch_size, -1) + + camera_mean = self.hand_predictor.get_camera_mean().expand( + batch_size, -1) + + shape_condition = ( + betas if self.condition_hand_shape else + self.hand_predictor.get_shape_mean(batch_size) + ) + right_finger_pose_condition = ( + right_hand_pose if self.condition_hand_finger_pose else + self.hand_predictor.get_finger_pose_mean(batch_size) + ) + right_hand_mean = torch.cat( + [ + right_wrist_pose, right_finger_pose_condition, + shape_condition, camera_mean, + ], dim=1) + left_finger_pose_condition = ( + left_to_right_hand_pose if self.condition_hand_finger_pose else + self.hand_predictor.get_finger_pose_mean(batch_size) + ) + # Should be Bx31 + left_hand_mean = torch.cat( + [ + left_to_right_wrist_pose, + left_finger_pose_condition, + shape_condition, + camera_mean, + ], dim=1 + ) + hand_only_mean += [right_hand_mean, left_hand_mean] + + if num_hand_imgs > 0: + mean_param = self.hand_predictor.get_param_mean( + batch_size=num_hand_imgs, + add_shape_noise=self.hand_add_shape_noise, + shape_std=self.hand_shape_std, + shape_prob=self.hand_shape_prob, + num_hand_components=self.num_hand_components, + add_hand_pose_noise=self.add_hand_pose_noise, + hand_pose_std=self.hand_pose_std, + hand_noise_prob=self.hand_noise_prob, + targets=hand_targets, + randomize_global_orient=self.hand_randomize_global_orient, + global_rot_min=self.hand_global_rot_min, + global_rot_max=self.hand_global_rot_max, + global_rot_noise_prob=self.hand_global_rot_noise_prob, + ) + + hand_only_mean.append(mean_param) + hand_only_parent_rots = torch.eye( + 3, device=device, dtype=dtype).reshape( + 1, 3, 3).expand(num_hand_imgs, -1, -1).clone() + hand_only_parent_rots[:, 1, 1] = -1 + hand_only_parent_rots[:, 2, 2] = -1 + parent_rots.append(hand_only_parent_rots) + + hand_only_mean = torch.cat(hand_only_mean, dim=0) + parent_rots = torch.cat(parent_rots, dim=0) + return hand_only_mean, parent_rots + + def build_head_mean( + +-- Chunk 6 -- +// predictor.py:821-970 + self, + global_orient: Tensor, + body_pose: Tensor, + betas: Tensor, + expression: Tensor, + jaw_pose: Tensor, + head_targets: List, + num_body_imgs: int = 0, + num_head_imgs: int = 0 + ) -> Tensor: + ''' Builds the initial point of the head regressor + ''' + head_only_mean = [] + if num_body_imgs > 0: + batch_size = num_body_imgs + + # Compute the absolute pose of the right wrist + neck_pose_abs = self.find_joint_global_rotation( + self.neck_kin_chain, global_orient, body_pose) + # Convert the absolute neck pose to offsets + neck_latent = self.global_orient_decoder.encode( + neck_pose_abs.unsqueeze(dim=1)) + neck_pose = neck_latent.reshape(batch_size, -1) + + camera_mean = self.head_predictor.get_camera_mean( + batch_size=batch_size) + + neck_pose_condition = ( + neck_pose if self.condition_head_neck_pose else + self.head_predictor.get_neck_pose_mean(batch_size)) + jaw_pose_condition = ( + jaw_pose.reshape(batch_size, -1) + if self.condition_head_jaw_pose else + self.head_predictor.get_jaw_pose_mean(batch_size) + ) + head_num_betas = self.head_predictor.get_num_betas() + shape_padding_size = head_num_betas - self.num_betas + betas_condition = ( + F.pad(betas.reshape(batch_size, -1), (0, shape_padding_size)) + if self.condition_head_shape else + self.head_predictor.get_shape_mean(batch_size=batch_size) + ) + + head_num_expression_coeffs = ( + self.head_predictor.get_num_expression_coeffs()) + expr_padding_size = (head_num_expression_coeffs - + self.num_expression_coeffs) + expression_condition = ( + F.pad( + expression.reshape(batch_size, -1), (0, expr_padding_size)) + if self.condition_head_expression else + self.head_predictor.get_expression_mean(batch_size=batch_size) + ) + + # Should be Bx(Head pose params) + head_only_mean.append(torch.cat( + [neck_pose_condition, jaw_pose_condition, + betas_condition, expression_condition, + camera_mean.reshape(batch_size, -1), + ], dim=1 + )) + + if num_head_imgs > 0: + mean_param = self.head_predictor.get_param_mean( + batch_size=num_head_imgs, + add_shape_noise=self.head_add_shape_noise, + shape_std=self.head_shape_std, + shape_prob=self.head_shape_prob, + expression_prob=self.expression_prob, + add_expression_noise=self.add_expression_noise, + expression_std=self.expression_std, + add_jaw_pose_noise=self.add_jaw_pose_noise, + jaw_noise_prob=self.jaw_noise_prob, + jaw_pose_min=self.jaw_pose_min, + jaw_pose_max=self.jaw_pose_max, + randomize_global_orient=self.head_randomize_global_orient, + global_rot_noise_prob=self.head_global_rot_noise_prob, + global_rot_min=self.head_global_rot_min, + global_rot_max=self.head_global_rot_max, + targets=head_targets, + ) + head_only_mean.append(mean_param) + + head_only_mean = torch.cat(head_only_mean, dim=0) + return head_only_mean + + def get_hand_model(self) -> nn.Module: + ''' Return the hand predictor ''' + return self.hand_predictor + + def get_head_model(self) -> nn.Module: + ''' Return the head predictor ''' + return self.head_predictor + + @torch.no_grad() + def bboxes_to_mask( + self, + targets: List, + key: str, + est_center: Tensor, est_bbox_size: Tensor, + thresh: float = 0.0) -> Tensor: + ''' Converts bounding boxes to a binary mask ''' + if thresh <= 0: + return torch.ones([len(targets), 1], dtype=torch.bool, + device=est_center.device) + + ious = torch.zeros(len(targets), dtype=est_center.dtype, + device=est_center.device) + gt_idxs = [] + gt_bboxes = [] + for ii, t in enumerate(targets): + if not t.has_field(key): + continue + gt_idxs.append(ii) + bbox_field = t.get_field(key) + gt_bboxes.append(bbox_field.bbox) + + if len(gt_bboxes) < 1: + return ious.unsqueeze(dim=-1).to(dtype=torch.bool) + est_bboxes = center_size_to_bbox(est_center, est_bbox_size) + gt_bboxes = torch.stack(gt_bboxes).to(dtype=est_bboxes.dtype) + gt_idxs = torch.tensor( + gt_idxs, dtype=torch.long, device=est_bboxes.device) + ious[gt_idxs] = bbox_iou(gt_bboxes, est_bboxes[gt_idxs]) + + return ious.ge(thresh).unsqueeze(dim=-1) + + def forward(self, + images: Tensor, + targets: List = None, + hand_imgs: Optional[Tensor] = None, + hand_targets: Optional[List] = None, + head_imgs: Optional[Tensor] = None, + head_targets: Optional[List] = None, + full_imgs: Optional[Union[ImageList, ImageListPacked]] = None, + ) -> Dict[str, Dict[str, Tensor]]: + ''' Forward pass of the attention predictor + ''' + batch_size, _, crop_size, _ = images.shape + device = images.device + dtype = images.dtype + + feat_dict = self.backbone(images) + body_features = feat_dict[self.body_feature_key] + + body_parameters, body_deltas = self.regressor(body_features) + + losses = {} + # A list of dicts for the parameters predicted at each stage. The key + # is the name of the parameters and the value is the prediction of the + +-- Chunk 7 -- +// predictor.py:971-1120 + # model at the i-th stage of the iteration + param_dicts = [] + # A dict of lists. Each key is the name of the parameter and the + # corresponding item is a list of offsets that are predicted by the + # model + deltas_dict = defaultdict(lambda: []) + param_delta_iter = zip(body_parameters, body_deltas) + for idx, (params, deltas) in enumerate(param_delta_iter): + curr_params_dict = self.flat_body_params_to_dict(params) + + out_dict = {} + for key, val in curr_params_dict.items(): + if hasattr(self, f'{key}_decoder'): + decoder = getattr(self, f'{key}_decoder') + out_dict[key] = decoder(val) + out_dict[f'raw_{key}'] = val.clone() + else: + out_dict[key] = val + + param_dicts.append(out_dict) + curr_params_dict.clear() + for key, val in self.flat_body_params_to_dict(deltas).items(): + deltas_dict[key].append(val) + + for key in deltas_dict: + deltas_dict[key] = torch.stack(deltas_dict[key], dim=1).sum(dim=1) + + if self.pose_last_stage: + merged_params = param_dicts[-1] + else: + merged_params = {} + for key in param_dicts[0].keys(): + param = [] + for idx in range(self.num_stages): + if param_dicts[idx][key] is None: + continue + param.append(param_dicts[idx][key]) + merged_params[key] = torch.cat(param, dim=0) + + # Compute the body surface using the current estimation of the pose and + # the shape + body_model_output = self.body_model( + get_skin=True, return_shaped=True, **merged_params) + + # Split the vertices, joints, etc. to stages + out_params = defaultdict(lambda: dict()) + for key in body_model_output: + if torch.is_tensor(body_model_output[key]): + curr_val = body_model_output[key] + out_list = torch.split( + curr_val, batch_size, dim=0) + # If the number of outputs is equal to the number of stages + # then store each stage + if len(out_list) == self.num_stages: + for idx in range(len(out_list)): + out_params[f'stage_{idx:02d}'][key] = out_list[idx] + # Else add only the last + else: + out_key = f'stage_{self.num_stages - 1:02d}' + out_params[out_key][key] = out_list[-1] + + # Add the predicted parameters to the output dictionary + for stage in range(self.num_stages): + stage_key = f'stage_{stage:02d}' + if len(out_params[stage_key]) < 1: + continue + out_params[stage_key].update(param_dicts[stage]) + out_params[stage_key]['faces'] = self.body_model.faces + + global_orient_from_body_net = param_dicts[-1]['global_orient'].clone() + body_pose_from_body_net = param_dicts[-1]['body_pose'].clone() + + raw_body_pose_from_body_net = param_dicts[-1]['raw_body_pose'].clone( + ).reshape(batch_size, 21, -1) + raw_right_hand_pose_from_body_net = param_dicts[-1][ + 'raw_right_hand_pose'].clone() + left_hand_pose = param_dicts[-1]['left_hand_pose'].clone() + right_hand_pose = param_dicts[-1]['right_hand_pose'].clone() + jaw_pose = param_dicts[-1]['jaw_pose'].clone() + + # Extract the camera parameters estimated by the body only image + camera_params = torch.index_select( + body_parameters[-1], 1, self.camera_idxs) + scale = camera_params[:, 0].view(-1, 1) + translation = camera_params[:, 1:3] + # Pass the predicted scale through exp() to make sure that the + # scale values are always positive + scale = self.camera_scale_func(scale) + + # Extract the final shape and expression parameters predicted by the + # body only model + betas = param_dicts[-1].get('betas').clone() + expression = param_dicts[-1].get('expression') + + # Project the joints on the image plane + proj_joints = self.projection( + out_params[f'stage_{self.num_stages - 1:02d}']['joints'], + scale=scale, translation=translation) + + # Add the projected joints + out_params['proj_joints'] = proj_joints + # the number of stages + out_params['num_stages'] = self.num_stages + # and the camera parameters to the output + out_params['camera_parameters'] = CameraParams( + translation=translation, scale=scale) + + # Clone the body pose so that we can update it with the predicted + # sub-parts + if self.predict_head or self.predict_hands: + final_body_pose = raw_body_pose_from_body_net.clone() + + hand_predictions, head_predictions = {}, {} + num_hand_imgs = 0 + left_hand_mask, right_hand_mask = None, None + if self.predict_hands: + if self.apply_hand_network_on_body: + # Get the left, right and head crops from the full body + left_hand_joints = ( + (torch.index_select(proj_joints, 1, self.left_hand_idxs) * + 0.5 + 0.5) * crop_size) + # left_hand_joints = torch.index_select( + # proj_joints, 1, self.left_hand_idxs) + left_hand_points_to_crop = self.points_to_crops( + full_imgs, left_hand_joints, targets, + scale_factor=self.hand_scale_factor, crop_size=crop_size, + ) + left_hand_center = left_hand_points_to_crop['center'] + left_hand_orig_bbox_size = left_hand_points_to_crop[ + 'orig_bbox_size'] + left_hand_bbox_size = left_hand_points_to_crop['bbox_size'] + left_hand_inv_crop_transforms = left_hand_points_to_crop[ + 'inv_crop_transforms'] + + left_hand_cropper_out = self.hand_cropper( + full_imgs, left_hand_center, left_hand_orig_bbox_size) + left_hand_crops = left_hand_cropper_out['images'] + left_hand_points = left_hand_cropper_out['sampling_grid'] + left_hand_crop_transform = left_hand_cropper_out['transform'] + + right_hand_joints = (torch.index_select( + proj_joints, 1, self.right_hand_idxs) * 0.5 + 0.5) * crop_size + right_hand_points_to_crop = self.points_to_crops( + full_imgs, right_hand_joints, targets, + scale_factor=self.hand_scale_factor, crop_size=crop_size, + ) + right_hand_center = right_hand_points_to_crop['center'] + right_hand_orig_bbox_size = right_hand_points_to_crop[ + 'orig_bbox_size'] + right_hand_bbox_size = right_hand_points_to_crop['bbox_size'] + +-- Chunk 8 -- +// predictor.py:1121-1270 + + right_hand_cropper_out = self.hand_cropper( + full_imgs, right_hand_center, right_hand_orig_bbox_size) + right_hand_crops = right_hand_cropper_out['images'] + right_hand_points = right_hand_cropper_out['sampling_grid'] + right_hand_crop_transform = right_hand_cropper_out['transform'] + + # Store the transformation parameters + out_params['left_hand_crops'] = left_hand_crops.detach() + out_params['left_hand_points'] = left_hand_points.detach() + out_params['right_hand_crops'] = right_hand_crops.detach() + out_params['right_hand_points'] = right_hand_points.detach() + + out_params['right_hand_crop_transform'] = ( + right_hand_crop_transform.detach()) + out_params['left_hand_crop_transform'] = ( + left_hand_crop_transform.detach()) + + out_params['left_hand_hd_to_crop'] = ( + left_hand_cropper_out['hd_to_crop']) + out_params['left_hand_inv_crop_transforms'] = ( + left_hand_points_to_crop['inv_crop_transforms']) + + out_params['right_hand_hd_to_crop'] = ( + right_hand_cropper_out['hd_to_crop']) + out_params['right_hand_inv_crop_transforms'] = ( + right_hand_points_to_crop['inv_crop_transforms']) + + # Flip the left hand to a right hand + all_hand_imgs = [] + hand_global_orient = [] + hand_body_pose = [] + if self.apply_hand_network_on_body: + all_hand_imgs.append(right_hand_crops) + all_hand_imgs.append(torch.flip(left_hand_crops, dims=(-1,))) + hand_global_orient += [ + global_orient_from_body_net, + flip_pose( + global_orient_from_body_net, pose_format='rot-mat')] + hand_body_pose += [ + body_pose_from_body_net, body_pose_from_body_net] + + if hand_imgs is not None and self.apply_hand_network_on_hands: + # Add the hand only images + num_hand_imgs = len(hand_imgs) + all_hand_imgs.append(hand_imgs) + + body_identity = torch.eye( + 3, device=device, dtype=dtype).reshape(1, 1, 3, 3).expand( + num_hand_imgs, body_pose_from_body_net.shape[1], -1, + -1) + hand_body_pose.append(body_identity) + global_identity = torch.eye( + 3, device=device, dtype=dtype).reshape( + 1, 1, 3, 3).expand( + num_hand_imgs, + global_orient_from_body_net.shape[1], -1, -1).clone() + global_identity[:, :, 1, 1] = -1 + global_identity[:, :, 2, 2] = -1 + hand_global_orient.append(global_identity) + + num_body_imgs = ( + batch_size if self.apply_hand_network_on_body else 0) + num_hand_net_ins = len(hand_body_pose) + num_body_imgs + if num_hand_net_ins > 0: + hand_body_pose = torch.cat(hand_body_pose, dim=0) + hand_global_orient = torch.cat(hand_global_orient, dim=0) + + # Flip the pose of the left hand + flipped_left_hand_pose = flip_pose( + param_dicts[-1]['left_hand_pose'], pose_format='rot-mat') + + # Build the mean used to condition the hand network using the + # parameters estimated by the body network + hand_mean, parent_rots = self.build_hand_mean( + param_dicts[-1]['global_orient'], + param_dicts[-1]['body_pose'], + betas=param_dicts[-1]['betas'], + flipped_left_hand_pose=flipped_left_hand_pose, + right_hand_pose=param_dicts[-1]['raw_right_hand_pose'], + hand_targets=hand_targets, + num_body_imgs=num_body_imgs, + num_hand_imgs=num_hand_imgs, + ) + + # Feed the hand images and the offsets to the hand-only + # predictor + all_hand_imgs = torch.cat(all_hand_imgs, dim=0) + + hand_predictions = self.hand_predictor( + all_hand_imgs, + hand_mean=hand_mean, + global_orient_from_body_net=hand_global_orient, + body_pose_from_body_net=hand_body_pose, + parent_rots=parent_rots, + num_hand_imgs=num_hand_imgs, + ) + num_hand_stages = hand_predictions.get('num_stages', 1) + hand_network_output = hand_predictions.get( + f'stage_{num_hand_stages - 1:02d}') + + if self.apply_hand_network_on_body: + # Find which images belong to the left hand and which ones to + # the right hand + hands_from_body_idxs = torch.arange( + 0, 2 * batch_size, dtype=torch.long, device=device) + right_hand_from_body_idxs = hands_from_body_idxs[ + :batch_size] + left_hand_from_body_idxs = hands_from_body_idxs[batch_size:] + + right_hand_features = hand_predictions.get( + 'features')[right_hand_from_body_idxs] + left_hand_features = hand_predictions.get( + 'features')[left_hand_from_body_idxs] + + right_hand_mask = None + raw_right_hand_pose_dict = self.right_hand_pose_merging_func( + from_body=raw_right_hand_pose_from_body_net, + from_part=hand_network_output.get( + 'raw_right_hand_pose')[right_hand_from_body_idxs], + body_feat=body_features, + part_feat=right_hand_features, + mask=right_hand_mask, + ) + raw_right_hand_pose = raw_right_hand_pose_dict['merged'] + + if self.update_wrists: + right_wrist_pose_from_part = hand_network_output.get( + 'raw_right_wrist_pose') + right_wrist_pose_from_body = raw_body_pose_from_body_net[ + :, self.right_wrist_idx - 1] + raw_right_wrist_pose_dict = ( + self.right_wrist_pose_merging_func( + from_body=right_wrist_pose_from_body, + from_part=right_wrist_pose_from_part, + body_feat=body_features, + part_feat=right_hand_features, + mask=right_hand_mask, + ) + ) + raw_right_wrist_pose = raw_right_wrist_pose_dict['merged'] + final_body_pose[:, self.right_wrist_idx - 1] = ( + raw_right_wrist_pose) + + # Project the flipped left hand pose to the rotation latent + # space using the decoder for the right hand + raw_left_to_right_hand_pose = ( + self.right_hand_pose_decoder.encode( + flipped_left_hand_pose).reshape(batch_size, -1)) + # Convert the pose of the left hand to the right hand and + +-- Chunk 9 -- +// predictor.py:1271-1420 + # project it to the encoder space + raw_left_to_right_hand_pose_from_body = ( + self.right_hand_pose_decoder.encode( + flipped_left_hand_pose).reshape(batch_size, -1)) + # Merge the predictions of the body network and the part + # network for the articulation of the left hand + left_hand_pose_from_part = hand_network_output.get( + 'raw_right_hand_pose')[left_hand_from_body_idxs] + raw_left_to_right_hand_pose_dict = ( + self.left_hand_pose_merging_func( + from_body=raw_left_to_right_hand_pose_from_body, + from_part=left_hand_pose_from_part, + body_feat=body_features, + part_feat=left_hand_features, + mask=left_hand_mask, + ) + ) + raw_left_to_right_hand_pose = raw_left_to_right_hand_pose_dict[ + 'merged'] + + if self.update_wrists: + left_wrist_pose_from_part = hand_network_output.get( + 'raw_left_wrist_pose') + left_wrist_pose_from_body = raw_body_pose_from_body_net[ + :, self.left_wrist_idx - 1] + raw_left_wrist_pose_dict = ( + self.left_wrist_pose_merging_func( + from_body=left_wrist_pose_from_body, + from_part=left_wrist_pose_from_part, + body_feat=body_features, + part_feat=left_hand_features, + mask=left_hand_mask, + ) + ) + raw_left_wrist_pose = raw_left_wrist_pose_dict['merged'] + final_body_pose[:, self.left_wrist_idx - 1] = ( + raw_left_wrist_pose) + + right_hand_pose = self.right_hand_pose_decoder( + raw_right_hand_pose) + # Decode the predicted pose and flip it back to the left hand + # space + left_hand_pose = flip_pose(self.right_hand_pose_decoder( + raw_left_to_right_hand_pose), pose_format='rot-mat') + + num_head_imgs = 0 + head_mask = None + if self.predict_head: + if self.apply_head_network_on_body: + head_joints = (torch.index_select( + proj_joints, 1, self.head_idxs) * 0.5 + 0.5) * crop_size + # head_joints = torch.index_select( + # proj_joints, 1, self.head_idxs) + head_point_to_crop_output = self.points_to_crops( + full_imgs, head_joints, targets, + scale_factor=self.head_scale_factor, crop_size=crop_size, + ) + head_center = head_point_to_crop_output['center'] + head_orig_bbox_size = head_point_to_crop_output[ + 'orig_bbox_size'] + head_bbox_size = head_point_to_crop_output['bbox_size'] + head_inv_crop_transforms = head_point_to_crop_output[ + 'inv_crop_transforms'] + + head_cropper_out = self.head_cropper( + full_imgs, head_center, head_orig_bbox_size) + head_crops = head_cropper_out['images'] + head_points = head_cropper_out['sampling_grid'] + # Contains the transformation that is used to transform the + # sampling grid from head image coordinates to HD image + # coordinates. + head_crop_transform = head_cropper_out['transform'] + + out_params['head_crops'] = head_crops.detach() + out_params['head_points'] = head_points.detach() + out_params['head_crop_transform'] = ( + head_crop_transform.detach()) + + out_params['head_hd_to_crop'] = head_cropper_out['hd_to_crop'] + out_params['head_inv_crop_transforms'] = ( + head_point_to_crop_output['inv_crop_transforms']) + + all_head_imgs = [] + if self.apply_head_network_on_body: + all_head_imgs.append(head_crops) + + # The global and body pose data used to pose the model inside the + # head-only sub-network. + head_global_orient, head_body_pose = [], [] + if self.apply_head_network_on_body: + head_global_orient += [global_orient_from_body_net] + head_body_pose += [body_pose_from_body_net] + + if head_imgs is not None and self.apply_head_network_on_head: + all_head_imgs.append(head_imgs) + num_head_imgs = len(head_imgs) + body_identity = torch.eye( + 3, device=device, dtype=dtype).reshape( + 1, 1, 3, 3).expand( + num_head_imgs, body_pose_from_body_net.shape[1], + -1, -1) + head_body_pose.append(body_identity) + global_identity = torch.eye( + 3, device=device, dtype=dtype).reshape( + 1, 1, 3, 3).expand(num_head_imgs, -1, -1, -1).clone() + global_identity[:, :, 1, 1] = -1 + global_identity[:, :, 2, 2] = -1 + head_global_orient.append(global_identity) + + num_body_imgs = ( + batch_size if self.apply_head_network_on_body else 0 + ) + num_head_net_ins = len(head_global_orient) + num_body_imgs + if num_head_net_ins > 0: + head_global_orient = torch.cat(head_global_orient, dim=0) + head_body_pose = torch.cat(head_body_pose, dim=0) + + head_mean = self.build_head_mean( + param_dicts[-1]['global_orient'], + param_dicts[-1]['body_pose'], + betas=param_dicts[-1]['betas'], + expression=param_dicts[-1]['expression'], + jaw_pose=param_dicts[-1]['raw_jaw_pose'], + num_head_imgs=num_head_imgs, + num_body_imgs=num_body_imgs, + head_targets=head_targets, + ) + all_head_imgs = torch.cat(all_head_imgs, dim=0) + + head_predictions = self.head_predictor( + all_head_imgs, + head_mean=head_mean, + global_orient_from_body_net=head_global_orient, + body_pose_from_body_net=head_body_pose, + num_head_imgs=num_head_imgs, + ) + + num_head_stages = head_predictions.get('num_stages', 1) + head_network_output = head_predictions.get( + f'stage_{num_head_stages - 1:02d}') + if self.apply_head_network_on_body: + head_from_body_idxs = torch.arange( + 0, batch_size, dtype=torch.long, device=device) + head_features = head_predictions.get( + 'features')[head_from_body_idxs] + # During training only use predictions from bounding boxes + # with enough IoU. + head_mask = None + raw_jaw_pose_from_body = param_dicts[-1].get( + 'raw_jaw_pose') + +-- Chunk 10 -- +// predictor.py:1421-1570 + # Replace the jaw pose only from the predictions taken from + # valid head crops + raw_jaw_pose_from_part = head_network_output.get( + 'raw_jaw_pose')[head_from_body_idxs] + raw_jaw_pose_dict = self.jaw_pose_merging_func( + from_body=raw_jaw_pose_from_body, + from_part=raw_jaw_pose_from_part, + body_feat=body_features, + part_feat=head_features, + mask=head_mask, + ) + raw_jaw_pose = raw_jaw_pose_dict['merged'] + + expression_from_body = param_dicts[-1].get('expression') + expression_from_head = head_network_output.get( + 'expression')[head_from_body_idxs, + :self.num_expression_coeffs] + expression_dict = self.expression_merging_func( + from_body=expression_from_body, + from_part=expression_from_head, + body_feat=body_features, + part_feat=head_features, + mask=head_mask, + ) + expression = expression_dict['merged'] + jaw_pose = self.jaw_pose_decoder(raw_jaw_pose) + + + if self.predict_head or self.predict_hands: + body_pose = self.body_pose_decoder( + final_body_pose.reshape(batch_size, -1)) + else: + body_pose = body_pose_from_body_net + + final_body_parameters = { + 'global_orient': param_dicts[-1].get('global_orient'), + 'body_pose': body_pose, + 'left_hand_pose': left_hand_pose, + 'right_hand_pose': right_hand_pose, + 'jaw_pose': jaw_pose, + 'betas': betas, + 'expression': expression + } + + if self.apply_hand_network_on_body or self.apply_head_network_on_body: + # Compute the mesh using the new hand and face parameters + final_body_model_output = self.body_model( + get_skin=True, return_shaped=True, **final_body_parameters) + param_dicts.append({ + **final_body_parameters, **final_body_model_output}) + + if (self.apply_hand_network_on_body or + self.apply_head_network_on_body): + out_params['final'] = { + **final_body_parameters, **final_body_model_output} + joints3d = final_body_model_output.get('joints') + proj_joints = self.projection( + joints3d, scale=scale, translation=translation) + out_params['final_proj_joints'] = proj_joints + # Update the camera parameters with the new projected joints + out_params['proj_joints'] = proj_joints + out_params['final']['proj_joints'] = proj_joints + else: + joints3d = out_params[f'stage_{self.num_stages - 1:02d}']['joints'] + + body_crop_size = images.shape[2] + # Convert the projected joints from [-1, 1] to body image + # coordinates + proj_joints_in_body_crop = ( + proj_joints * 0.5 + 0.5) * body_crop_size + + # Transform the projected points back to the HD image + if self.apply_head_network_on_body: + hd_proj_joints = torch.einsum( + 'bij,bkj->bki', + [head_inv_crop_transforms[:, :2, :2], + proj_joints_in_body_crop]) + head_inv_crop_transforms[ + :, :2, 2].unsqueeze(dim=1) + out_params['hd_proj_joints'] = hd_proj_joints.detach() + elif self.apply_hand_network_on_body: + hd_proj_joints = torch.einsum( + 'bij,bkj->bki', + [left_hand_inv_crop_transforms[:, :2, :2], + proj_joints_in_body_crop]) + left_hand_inv_crop_transforms[ + :, :2, 2].unsqueeze(dim=1) + out_params['hd_proj_joints'] = hd_proj_joints.detach() + + if self.apply_head_network_on_body: + inv_head_crop_transf = torch.inverse(head_crop_transform) + head_img_keypoints = torch.einsum( + 'bij,bkj->bki', + [inv_head_crop_transf[:, :2, :2], + hd_proj_joints]) + inv_head_crop_transf[:, :2, 2].unsqueeze( + dim=1) + out_params['head_proj_joints'] = ( + head_img_keypoints.detach() * self.head_crop_size) + + if self.apply_hand_network_on_body: + inv_left_hand_crop_transf = torch.inverse(left_hand_crop_transform) + left_hand_img_keypoints = torch.einsum( + 'bij,bkj->bki', + [inv_left_hand_crop_transf[:, :2, :2], + hd_proj_joints]) + inv_left_hand_crop_transf[ + :, :2, 2].unsqueeze(dim=1) + out_params['left_hand_proj_joints'] = ( + left_hand_img_keypoints.detach() * self.hand_crop_size) + + inv_right_hand_crop_transf = torch.inverse( + right_hand_crop_transform) + right_hand_img_keypoints = torch.einsum( + 'bij,bkj->bki', + [inv_right_hand_crop_transf[:, :2, :2], + hd_proj_joints]) + inv_right_hand_crop_transf[ + :, :2, 2].unsqueeze(dim=1) + out_params['right_hand_proj_joints'] = ( + right_hand_img_keypoints.detach() * self.hand_crop_size) + + if self.training: + # Create the tensor of ground-truth HD keypoints + gt_hd_keypoints = [] + for t in targets: + gt_hd_keypoints.append(t.get_field('keypoints_hd')) + + gt_hd_keypoints_with_conf = torch.tensor( + gt_hd_keypoints, dtype=dtype, device=device) + gt_hd_keypoints_conf = gt_hd_keypoints_with_conf[:, :, -1] + gt_hd_keypoints = gt_hd_keypoints_with_conf[:, :, :-1] + out_params['gt_conf'] = gt_hd_keypoints_conf.detach() + + if self.apply_head_network_on_body: + # Convert the ground-truth HD keypoints to the head image space + gt_head_keypoints = torch.einsum( + 'bij,bkj->bki', + [inv_head_crop_transf[:, :2, :2], + gt_hd_keypoints]) + inv_head_crop_transf[ + :, :2, 2].unsqueeze(dim=1) + out_params['gt_head_keypoints'] = ( + gt_head_keypoints.detach() * self.head_crop_size) + + # Convert the ground-truth HD keypoints to the left and right hand + # image space + if self.apply_hand_network_on_body: + gt_right_hand_keypoints = ( + torch.einsum( + 'bij,bkj->bki', + [inv_right_hand_crop_transf[:, :2, :2], + gt_hd_keypoints]) + + inv_right_hand_crop_transf[:, :2, 2].unsqueeze(dim=1)) + gt_left_hand_keypoints = ( + torch.einsum( + +-- Chunk 11 -- +// predictor.py:1571-1586 + 'bij,bkj->bki', + [inv_left_hand_crop_transf[:, :2, :2], + gt_hd_keypoints]) + + inv_left_hand_crop_transf[:, :2, 2].unsqueeze(dim=1)) + + out_params['gt_right_hand_keypoints'] = ( + gt_right_hand_keypoints.detach() * self.hand_crop_size) + out_params['gt_left_hand_keypoints'] = ( + gt_left_hand_keypoints.detach() * self.hand_crop_size) + + output = { + 'body': out_params, + 'losses': losses + } + + return output + +=== File: expose/models/attention/hand_predictor.py === + +-- Chunk 1 -- +// hand_predictor.py:52-201 +ss HandPredictor(nn.Module): + + def __init__(self, exp_cfg, + global_orient_desc, + hand_pose_desc, + camera_data, + wrist_pose_mean=None, + detach_mean=False, + mean_pose_path='', + dtype=torch.float32): + super(HandPredictor, self).__init__() + + network_cfg = exp_cfg.get('network', {}) + attention_net_cfg = network_cfg.get('attention', {}) + hand_net_cfg = attention_net_cfg.get('hand', {}) + + self.hand_model_type = hand_net_cfg.get('type', 'mano') + + hand_model_cfg = exp_cfg.get('hand_model', {}) + self.hand_model_cfg = hand_model_cfg.copy() + + self.right_wrist_index = KEYPOINT_NAMES.index('right_wrist') + self.left_wrist_index = KEYPOINT_NAMES.index('left_wrist') + + camera_cfg = hand_net_cfg.get('camera', {}) + camera_data = build_cam_proj(camera_cfg, dtype=dtype) + self.projection = camera_data['camera'] + + camera_param_dim = camera_data['dim'] + camera_mean = camera_data['mean'] + # self.camera_mean = camera_mean + self.register_buffer('camera_mean', camera_mean) + self.camera_scale_func = camera_data['scale_func'] + + # The number of shape coefficients + self.num_betas = self.hand_model_cfg['num_betas'] + shape_mean = torch.zeros([self.num_betas], dtype=dtype) + self.register_buffer('shape_mean', shape_mean) + + self.global_orient_decoder = global_orient_desc.decoder + cfg = {'param_type': global_orient_desc.decoder.get_type()} + self.wrist_pose_decoder = build_pose_decoder(cfg, 1) + wrist_pose_mean = self.wrist_pose_decoder.get_mean() + wrist_pose_dim = self.wrist_pose_decoder.get_dim_size() + self.register_buffer('wrist_pose_mean', wrist_pose_mean) + + self.register_buffer( + 'global_orient_mean', wrist_pose_mean.unsqueeze(dim=0)) + + self.hand_pose_decoder = hand_pose_desc.decoder + hand_pose_mean = hand_pose_desc.mean + self.register_buffer('hand_pose_mean', hand_pose_mean) + hand_pose_dim = hand_pose_desc.dim + + mean_lst = [] + start = 0 + wrist_pose_idxs = list(range(start, start + wrist_pose_dim)) + self.register_buffer('wrist_pose_idxs', + torch.tensor(wrist_pose_idxs, dtype=torch.long)) + start += wrist_pose_dim + mean_lst.append(wrist_pose_mean.view(-1)) + + hand_pose_idxs = list(range( + start, start + hand_pose_dim)) + self.register_buffer( + 'hand_pose_idxs', torch.tensor(hand_pose_idxs, dtype=torch.long)) + start += hand_pose_dim + mean_lst.append(hand_pose_mean.view(-1)) + + shape_idxs = list(range(start, start + self.num_betas)) + self.register_buffer( + 'shape_idxs', torch.tensor(shape_idxs, dtype=torch.long)) + start += self.num_betas + mean_lst.append(shape_mean.view(-1)) + + camera_idxs = list(range( + start, start + camera_param_dim)) + self.register_buffer( + 'camera_idxs', torch.tensor(camera_idxs, dtype=torch.long)) + start += camera_param_dim + mean_lst.append(camera_mean) + + self.register_buffer('camera_mean', camera_mean.unsqueeze(dim=0)) + + param_mean = torch.cat(mean_lst).view(1, -1) + param_dim = param_mean.numel() + self.param_dim = param_dim + + # Construct the feature extraction backbone + backbone_cfg = hand_net_cfg.get('backbone', {}) + self.backbone, feat_dims = build_backbone(backbone_cfg) + + self.append_params = hand_net_cfg.get('append_params', True) + self.num_stages = hand_net_cfg.get('num_stages', 1) + + self.feature_key = hand_net_cfg.get('feature_key', 'avg_pooling') + feat_dim = feat_dims[self.feature_key] + self.feat_dim = feat_dim + + regressor_cfg = hand_net_cfg.get('mlp', {}) + regressor = MLP(feat_dim + self.append_params * param_dim, + param_dim, **regressor_cfg) + self.regressor = IterativeRegression( + regressor, param_mean, detach_mean=detach_mean, + num_stages=self.num_stages) + + def get_feat_dim(self) -> int: + ''' Returns the dimension of the expected feature vector ''' + return self.feat_dim + + def get_param_dim(self) -> int: + ''' Returns the dimension of the predicted parameter vector ''' + return self.param_dim + + def get_num_stages(self) -> int: + ''' Returns the number of stages for the iterative predictor''' + return self.num_stages + + def get_shape_mean(self, batch_size: int = 1) -> Tensor: + ''' Returns the mean shape for the hands ''' + return self.shape_mean.reshape(1, -1).expand(batch_size, -1) + + def get_camera_mean(self, batch_size: int = 1) -> Tensor: + ''' Returns the camera mean ''' + return self.camera_mean.reshape(1, -1).expand(batch_size, -1) + + def get_wrist_pose_mean(self, batch_size=1) -> Tensor: + ''' Returns wrist pose mean ''' + return self.wrist_pose_mean.reshape(1, -1).expand(batch_size, -1) + + def get_finger_pose_mean(self, batch_size=1) -> Tensor: + ''' Returns neck pose mean ''' + return self.hand_pose_mean.reshape(1, -1).expand(batch_size, -1) + + def get_param_mean(self, + batch_size: int = 1, + add_shape_noise: bool = False, + shape_mean: Tensor = None, + shape_std: float = 0.0, + shape_prob: float = 0.0, + num_hand_components: int = 3, + add_hand_pose_noise: bool = False, + hand_pose_mean: Tensor = None, + hand_pose_std: float = 1.0, + hand_noise_prob: float = 0.0, + targets: List = None, + randomize_global_orient: bool = False, + global_rot_noise_prob: float = 0.0, + global_rot_min: bool = 0.0, + global_rot_max: bool = 0.0, + +-- Chunk 2 -- +// hand_predictor.py:202-316 + ) -> Tensor: + ''' Returns the mean vector given to the iterative regressor + ''' + mean = self.regressor.get_mean().clone().reshape(1, -1).expand( + batch_size, -1).clone() + if not self.training: + return mean + + raise NotImplementedError + + def param_tensor_to_dict(self, param_tensor): + wrist_pose = torch.index_select(param_tensor, 1, self.wrist_pose_idxs) + hand_pose = torch.index_select(param_tensor, 1, self.hand_pose_idxs) + + betas = torch.index_select(param_tensor, 1, self.shape_idxs) + + return dict(wrist_pose=wrist_pose, hand_pose=hand_pose, betas=betas) + + def forward(self, + hand_imgs: Tensor, + hand_mean: Optional[Tensor] = None, + global_orient_from_body_net: Optional[Tensor] = None, + body_pose_from_body_net: Optional[Tensor] = None, + parent_rots: Optional[Tensor] = None, + num_hand_imgs: int = 0, + device: torch.device = None, + ) -> Dict[str, Dict[str, Tensor]]: + ''' Forward pass of the hand predictor ''' + batch_size = hand_imgs.shape[0] + num_body_data = batch_size - num_hand_imgs + if batch_size == 0: + return {} + + if device is None: + device = hand_imgs.device + dtype = hand_imgs.dtype + + if parent_rots is None: + parent_rots = torch.eye(3, dtype=dtype, device=device).reshape( + 1, 1, 3, 3).expand(batch_size, -1, -1, -1).clone() + + right_hand_idxs = torch.arange( + 0, num_body_data // 2, dtype=torch.long, device=device) + left_hand_idxs = torch.arange( + num_body_data // 2, num_body_data, dtype=torch.long, device=device) + + hand_features = self.backbone(hand_imgs) + hand_parameters, hand_deltas = self.regressor( + hand_features[self.feature_key], cond=hand_mean) + + hand_model_parameters = [] + model_parameters = [] + for stage_idx, parameters in enumerate(hand_parameters): + parameters_dict = self.param_tensor_to_dict(parameters) + + # Decode the predicted wrist pose as a rotation matrix + dec_wrist_pose_abs = self.wrist_pose_decoder( + parameters_dict['wrist_pose']) + + # Undo the rotation of the parent joints to make the wrist rotation + # relative again + dec_wrist_pose = torch.matmul( + parent_rots.reshape(-1, 3, 3).transpose(1, 2), + dec_wrist_pose_abs.reshape(-1, 3, 3) + ) + raw_right_wrist_pose, raw_left_wrist_pose = None, None + if len(right_hand_idxs) > 0: + raw_right_wrist_pose = self.global_orient_decoder.encode( + dec_wrist_pose[right_hand_idxs].unsqueeze(dim=1)).reshape( + num_body_data // 2, -1) + + if len(left_hand_idxs) > 0: + left_wrist_poses = flip_pose( + dec_wrist_pose[left_hand_idxs], pose_format='rot-mat') + raw_left_wrist_pose = self.global_orient_decoder.encode( + left_wrist_poses.unsqueeze(dim=1)).reshape( + num_body_data // 2, -1) + + dec_hand_pose = self.hand_pose_decoder( + parameters_dict['hand_pose']) + model_betas = parameters_dict['betas'] + + model_parameters.append( + dict(right_hand_pose=dec_hand_pose, + betas=model_betas, + wrist_pose=dec_wrist_pose_abs, + hand_pose=dec_hand_pose, + raw_right_wrist_pose=raw_right_wrist_pose, + raw_left_wrist_pose=raw_left_wrist_pose, + raw_right_hand_pose=parameters_dict['hand_pose'], + ) + ) + + if self.hand_model_type == 'mano': + hand_model_parameters.append( + dict( + betas=model_betas, + wrist_pose=dec_wrist_pose_abs, + hand_pose=dec_hand_pose, + ) + ) + else: + raise RuntimeError( + f'Invalid hand model type: {self.hand_model_type}') + + output = {'num_stages': self.num_stages, + 'features': hand_features[self.feature_key], + } + + for stage in range(self.num_stages): + # Only update the current stage if the parameters exist + key = f'stage_{stage:02d}' + output[key] = model_parameters[stage] + + return output + +=== File: expose/models/attention/head_predictor.py === + +-- Chunk 1 -- +// head_predictor.py:51-200 +ss HeadPredictor(nn.Module): + + def __init__(self, exp_cfg, + global_orient_desc, + jaw_pose_desc, + camera_data, + detach_mean=False, + dtype=torch.float32): + super(HeadPredictor, self).__init__() + + network_cfg = exp_cfg.get('network', {}) + attention_net_cfg = network_cfg.get('attention', {}) + head_net_cfg = attention_net_cfg.get('head', {}) + + self.neck_index = KEYPOINT_NAMES.index('neck') + + head_model_cfg = exp_cfg.get('head_model', {}) + # model_path = osp.expandvars(head_model_cfg.pop('model_folder', '')) + model_type = head_model_cfg.pop('type', 'flame') + self.head_model_type = model_type + # self.head_model = build_layer( + # model_path, + # model_type=model_type, + # dtype=dtype, + # **head_model_cfg) + # logger.info(f'Head model: {self.head_model}') + + self.num_stages = head_net_cfg.get('num_stages', 3) + self.append_params = head_net_cfg.get('append_params', True) + + logger.info(f'Building head predictor with {self.num_stages} stages') + + camera_cfg = head_net_cfg.get('camera', {}) + camera_data = build_cam_proj(camera_cfg, dtype=dtype) + self.projection = camera_data['camera'] + + camera_param_dim = camera_data['dim'] + camera_mean = camera_data['mean'] + self.register_buffer('camera_mean', camera_mean) + self.camera_scale_func = camera_data['scale_func'] + + self.num_betas = head_model_cfg.num_betas + # self.num_betas = self.head_model.num_betas + shape_mean = torch.zeros([self.num_betas], dtype=dtype) + self.register_buffer('shape_mean', shape_mean) + + # self.num_expression_coeffs = self.head_model.num_expression_coeffs + self.num_expression_coeffs = head_model_cfg.num_expression_coeffs + expression_mean = torch.zeros( + [self.num_expression_coeffs], dtype=dtype) + self.register_buffer('expression_mean', expression_mean) + + self.global_orient_decoder = global_orient_desc.decoder + + cfg = {'param_type': global_orient_desc.decoder.get_type()} + self.neck_pose_decoder = build_pose_decoder(cfg, 1) + neck_pose_mean = self.neck_pose_decoder.get_mean().clone() + neck_pose_type = cfg['param_type'] + if neck_pose_type == 'aa': + neck_pose_mean[0] = math.pi + elif neck_pose_type == 'cont_rot_repr': + neck_pose_mean[3] = -1 + neck_pose_dim = self.neck_pose_decoder.get_dim_size() + self.register_buffer('neck_pose_mean', neck_pose_mean) + + self.jaw_pose_decoder = jaw_pose_desc.decoder + jaw_pose_mean = jaw_pose_desc.mean + jaw_pose_dim = jaw_pose_desc.dim + + mean_lst = [] + start = 0 + neck_pose_idxs = list(range(start, start + neck_pose_dim)) + self.register_buffer('neck_pose_idxs', + torch.tensor(neck_pose_idxs, dtype=torch.long)) + start += neck_pose_dim + mean_lst.append(neck_pose_mean.view(-1)) + + jaw_pose_idxs = list(range( + start, start + jaw_pose_dim)) + self.register_buffer( + 'jaw_pose_idxs', torch.tensor(jaw_pose_idxs, dtype=torch.long)) + start += jaw_pose_dim + mean_lst.append(jaw_pose_mean.view(-1)) + + shape_idxs = list(range(start, start + self.num_betas)) + self.register_buffer( + 'shape_idxs', torch.tensor(shape_idxs, dtype=torch.long)) + start += self.num_betas + mean_lst.append(shape_mean.view(-1)) + + expression_idxs = list(range( + start, start + self.num_expression_coeffs)) + self.register_buffer( + 'expression_idxs', + torch.tensor(expression_idxs, dtype=torch.long)) + start += self.num_expression_coeffs + mean_lst.append(expression_mean.view(-1)) + + camera_idxs = list(range( + start, start + camera_param_dim)) + self.register_buffer( + 'camera_idxs', torch.tensor(camera_idxs, dtype=torch.long)) + start += camera_param_dim + mean_lst.append(camera_mean) + + param_mean = torch.cat(mean_lst).view(1, -1) + param_dim = param_mean.numel() + self.param_dim = param_dim + + # Construct the feature extraction backbone + backbone_cfg = head_net_cfg.get('backbone', {}) + self.backbone, feat_dims = build_backbone(backbone_cfg) + + self.append_params = head_net_cfg.get('append_params', True) + self.num_stages = head_net_cfg.get('num_stages', 1) + + self.feature_key = head_net_cfg.get('feature_key', 'avg_pooling') + feat_dim = feat_dims[self.feature_key] + self.feat_dim = feat_dim + + regressor_cfg = head_net_cfg.get('mlp', {}) + regressor = MLP(feat_dim + self.append_params * param_dim, + param_dim, **regressor_cfg) + self.regressor = IterativeRegression( + regressor, param_mean, detach_mean=detach_mean, + num_stages=self.num_stages) + + def get_feat_dim(self) -> int: + ''' Returns the dimension of the expected feature vector ''' + return self.feat_dim + + def get_param_dim(self) -> int: + ''' Returns the dimension of the predicted parameter vector ''' + return self.param_dim + + def get_num_stages(self) -> int: + ''' Returns the number of stages for the iterative predictor''' + return self.num_stages + + def get_num_betas(self) -> int: + return self.num_betas + + def get_num_expression_coeffs(self) -> int: + return self.num_expression_coeffs + + def param_tensor_to_dict( + self, param_tensor: Tensor) -> Dict[str, Tensor]: + ''' Converts a flattened tensor to a dictionary of tensors ''' + neck_pose = torch.index_select(param_tensor, 1, + self.neck_pose_idxs) + +-- Chunk 2 -- +// head_predictor.py:201-327 + jaw_pose = torch.index_select(param_tensor, 1, self.jaw_pose_idxs) + + betas = torch.index_select(param_tensor, 1, self.shape_idxs) + expression = torch.index_select(param_tensor, 1, self.expression_idxs) + + return dict(neck_pose=neck_pose, + jaw_pose=jaw_pose, + expression=expression, + betas=betas) + + def get_camera_mean(self, batch_size: int = 1) -> Tensor: + ''' Returns the camera mean ''' + return self.camera_mean.reshape(1, -1).expand(batch_size, -1) + + def get_neck_pose_mean(self, batch_size=1) -> Tensor: + ''' Returns neck pose mean ''' + return self.neck_pose_mean.reshape(1, -1).expand(batch_size, -1) + + def get_jaw_pose_mean(self, batch_size=1) -> Tensor: + ''' Returns jaw pose mean ''' + return self.jaw_pose_mean.reshape(1, -1).expand(batch_size, -1) + + def get_shape_mean(self, batch_size=1) -> Tensor: + ''' Returns shape mean ''' + return self.shape_mean.reshape(1, -1).expand(batch_size, -1) + + def get_expression_mean(self, batch_size=1) -> Tensor: + ''' Returns expression mean ''' + return self.expression_mean.reshape(1, -1).expand(batch_size, -1) + + def get_param_mean(self, batch_size: int = 1, + add_shape_noise: bool = False, + shape_mean: Tensor = None, + shape_std: float = 0.0, + shape_prob: float = 0.0, + add_expression_noise: bool = False, + expression_mean: Tensor = None, + expression_std: float = 0.0, + expression_prob: float = 0.0, + add_jaw_pose_noise: bool = False, + jaw_noise_prob: float = 0.0, + jaw_pose_min: float = None, + jaw_pose_max: float = 1.0, + targets: object = None, + randomize_global_orient: bool = False, + global_rot_noise_prob: float = 0.0, + global_rot_min: float = 0.0, + global_rot_max: float = 0.0, + epsilon=1e-10, + ): + ''' Return the mean that will be given to the iterative regressor + ''' + mean = self.regressor.get_mean().clone().reshape(1, -1).expand( + batch_size, -1).clone() + if not self.training: + return mean + raise NotImplementedError + + def forward(self, + head_imgs: Tensor, + global_orient_from_body_net: Optional[Tensor] = None, + body_pose_from_body_net: Optional[Tensor] = None, + left_hand_pose_from_body_net: Optional[Tensor] = None, + right_hand_pose_from_body_net: Optional[Tensor] = None, + jaw_pose_from_body_net: Optional[Tensor] = None, + num_head_imgs: int = 0, + head_mean: Optional[Tensor] = None, + device: torch.device = None, + ) -> Dict[str, Dict[str, Tensor]]: + ''' + ''' + batch_size = head_imgs.shape[0] + device, dtype = head_imgs.device, head_imgs.dtype + + num_body_data = batch_size - num_head_imgs + if batch_size == 0: + return {} + + head_features = self.backbone(head_imgs) + head_parameters, head_deltas = self.regressor( + head_features[self.feature_key], + cond=head_mean) + + head_model_params = [] + model_parameters = [] + for stage_idx, parameters in enumerate(head_parameters): + parameters_dict = self.param_tensor_to_dict(parameters) + + dec_neck_pose_abs = self.neck_pose_decoder( + parameters_dict['neck_pose']) + dec_jaw_pose = self.jaw_pose_decoder(parameters_dict['jaw_pose']) + + model_betas = parameters_dict['betas'] + # Parameters that will be returned + model_parameters.append( + dict(head_pose=dec_neck_pose_abs, + raw_jaw_pose=parameters_dict['jaw_pose'], + jaw_pose=dec_jaw_pose, + betas=model_betas, + expression=parameters_dict['expression'], + ) + ) + + # Parameters used to pose the model + if self.head_model_type == 'flame': + head_model_params.append( + dict(global_orient=dec_neck_pose_abs, + jaw_pose=dec_jaw_pose, + betas=model_betas, + expression=parameters_dict['expression'], + ) + ) + else: + raise RuntimeError( + f'Invalid head model type: {self.head_model_type}') + + output = { + 'num_stages': self.num_stages, + 'features': head_features[self.feature_key], + } + + for stage in range(self.num_stages): + # Only update the current stage if there are enough params + key = f'stage_{stage:02d}' + output[key] = model_parameters[stage] + + return output + +=== File: expose/models/attention/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/attention/__init__.py:1-17 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +from .build import build_attention_head + +=== File: expose/models/nnutils/init_layer.py === + +-- Chunk 1 -- +// init_layer.py:8-37 +def init_weights(layer, + name='', + init_type='xavier', distr='uniform', + gain=1.0, + activ_type='leaky-relu', lrelu_slope=0.2, **kwargs): + if len(name) < 1: + name = str(layer) + logger.debug('Initializing {} with {}_{}: gain={}', name, init_type, distr, + gain) + weights = layer.weight + if init_type == 'xavier': + if distr == 'uniform': + nninit.xavier_uniform_(weights, gain=gain) + elif distr == 'normal': + nninit.xavier_normal_(weights, gain=gain) + else: + raise ValueError( + 'Unknown distribution "{}" for Xavier init'.format(distr)) + elif init_type == 'kaiming': + + activ_type = activ_type.replace('-', '_') + if distr == 'uniform': + nninit.kaiming_uniform_(weights, a=lrelu_slope, + nonlinearity=activ_type) + elif distr == 'normal': + nninit.kaiming_normal_(weights, a=lrelu_slope, + nonlinearity=activ_type) + else: + raise ValueError( + 'Unknown distribution "{}" for Kaiming init'.format(distr)) + +=== File: expose/models/nnutils/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/nnutils/__init__.py:1-1 +from .init_layer import init_weights + +=== File: expose/models/backbone/resnet.py === + +-- Chunk 1 -- +// resnet.py:16-102 +class RegressionResNet(ResNet): + + def __init__(self, block, layers, forward_to=4, + num_classes=1000, + use_avgpool=True, + replace_stride_with_dilation=None, + zero_init_residual=False, **kwargs): + super(RegressionResNet, self).__init__( + block, layers, + replace_stride_with_dilation=replace_stride_with_dilation) + self.forward_to = forward_to + msg = 'Forward to must be from 0 to 4' + assert self.forward_to > 0 and self.forward_to <= 4, msg + + self.replace_stride_with_dilation = replace_stride_with_dilation + + self.expansion = block.expansion + self.output_dim = block.expansion * 512 + self.use_avgpool = use_avgpool + if not use_avgpool: + del self.avgpool + del self.fc + + def extra_repr(self): + if self.replace_stride_with_dilation is None: + msg = [ + f'Layer 1: {64 * self.expansion}, H / 4, W / 4', + f'Layer 2: {64 * self.expansion * 2}, H / 8, W / 8', + f'Layer 3: {64 * self.expansion * 4}, H / 16, W / 16', + f'Layer 4: {64 * self.expansion * 8}, H / 32, W / 32' + ] + else: + if not any(self.replace_stride_with_dilation): + msg = [ + f'Layer 1: {64 * self.expansion}, H / 4, W / 4', + f'Layer 2: {64 * self.expansion * 2}, H / 8, W / 8', + f'Layer 3: {64 * self.expansion * 4}, H / 16, W / 16', + f'Layer 4: {64 * self.expansion * 8}, H / 32, W / 32' + ] + else: + layer2 = 4 * 2 ** (not self.replace_stride_with_dilation[0]) + layer3 = (layer2 * + 2 ** (not self.replace_stride_with_dilation[1])) + layer4 = (layer3 * + 2 ** (not self.replace_stride_with_dilation[2])) + msg = [ + f'Layer 1: {64 * self.expansion}, H / 4, W / 4', + f'Layer 2: {64 * self.expansion * 2}, H / {layer2}, ' + f'W / {layer2}', + f'Layer 3: {64 * self.expansion * 4}, H / {layer3}, ' + f'W / {layer3}', + f'Layer 4: {64 * self.expansion * 8}, H / {layer4}, ' + f'W / {layer4}' + ] + + return '\n'.join(msg) + + def get_output_dim(self): + return { + 'layer1': 64 * self.expansion, + 'layer2': 64 * self.expansion * 2, + 'layer3': 64 * self.expansion * 4, + 'layer4': 64 * self.expansion * 8, + 'avg_pooling': 64 * self.expansion * 8, + } + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + output = {'maxpool': x} + + x = self.layer1(x) + output['layer1'] = x + x = self.layer2(x) + output['layer2'] = x + x = self.layer3(x) + output['layer3'] = x + x = self.layer4(x) + output['layer4'] = x + + # Output size: BxC + x = self.avgpool(x).view(x.size(0), -1) + output['avg_pooling'] = x + + return output + +-- Chunk 2 -- +// resnet.py:105-116 +def resnet18(pretrained=False, **kwargs): + """Constructs a ResNet-18 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = RegressionResNet(BasicBlock, [2, 2, 2, 2], **kwargs) + if pretrained: + logger.info('Loading pretrained ResNet-18') + model.load_state_dict(model_zoo.load_url(model_urls['resnet18']), + strict=False) + return model + +-- Chunk 3 -- +// resnet.py:119-130 +def resnet34(pretrained=False, **kwargs): + """Constructs a ResNet-34 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = RegressionResNet(BasicBlock, [3, 4, 6, 3], **kwargs) + if pretrained: + logger.info('Loading pretrained ResNet-34') + model.load_state_dict(model_zoo.load_url(model_urls['resnet34']), + strict=False) + return model + +-- Chunk 4 -- +// resnet.py:133-150 +def resnet50(pretrained=False, **kwargs): + """Constructs a ResNet-50 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = RegressionResNet(Bottleneck, [3, 4, 6, 3], **kwargs) + if pretrained: + logger.info('Loading pretrained ResNet-50') + missing, unexpected = model.load_state_dict( + model_zoo.load_url(model_urls['resnet50']), strict=False) + if len(missing) > 0: + logger.warning( + f'The following keys were not found: {missing}') + if len(unexpected): + logger.warning( + f'The following keys were not expected: {unexpected}') + return model + +-- Chunk 5 -- +// resnet.py:153-164 +def resnet101(pretrained=False, **kwargs): + """Constructs a ResNet-101 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = RegressionResNet(Bottleneck, [3, 4, 23, 3], **kwargs) + if pretrained: + logger.info('Loading pretrained ResNet-101') + model.load_state_dict(model_zoo.load_url(model_urls['resnet101']), + strict=False) + return model + +-- Chunk 6 -- +// resnet.py:167-178 +def resnet152(pretrained=False, **kwargs): + """Constructs a ResNet-152 model. + + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + """ + model = RegressionResNet(Bottleneck, [3, 8, 36, 3], **kwargs) + if pretrained: + logger.info('Loading pretrained ResNet-152') + model.load_state_dict(model_zoo.load_url(model_urls['resnet152']), + strict=False) + return model + +=== File: expose/models/backbone/utils.py === + +-- Chunk 1 -- +// utils.py:9-25 +def make_conv_layer(input_dim, cfg): + num_layers = cfg.get('num_layers') + num_filters = cfg.num_filters + + expansion = resnet.Bottleneck.expansion + + layers = [] + for i in range(num_layers): + downsample = nn.Conv2d(input_dim, num_filters, stride=1, + kernel_size=1, bias=False) + + layers.append( + resnet.Bottleneck(input_dim, num_filters // expansion, + downsample=downsample) + ) + input_dim = num_filters + return nn.Sequential(*layers) + +-- Chunk 2 -- +// utils.py:28-46 +def make_subsample_layers(input_dim, cfg): + num_filters = cfg.get('num_filters') + strides = cfg.get('strides') + kernel_sizes = cfg.get('kernel_sizes') + + param_desc = zip(num_filters, kernel_sizes, strides) + layers = [] + for out_dim, kernel_size, stride in param_desc: + layers.append( + ConvNormActiv( + input_dim, + out_dim, + kernel_size=kernel_size, + stride=stride, + **cfg, + ) + ) + input_dim = out_dim + return nn.Sequential(*layers), out_dim + +=== File: expose/models/backbone/hrnet.py === + +-- Chunk 1 -- +// hrnet.py:20-28 +def build(cfg, pretrained=True, **kwargs): + hr_net_cfg = cfg.get('hrnet') + model = HighResolutionNet(hr_net_cfg, **kwargs) + + pretrained_path = hr_net_cfg.get('pretrained_path') + if pretrained: + model.load_weights(pretrained_path) + + return model + +-- Chunk 2 -- +// hrnet.py:31-180 +class HighResolutionModule(nn.Module): + def __init__(self, num_branches, blocks, num_blocks, num_inchannels, + num_channels, fuse_method, multi_scale_output=True): + super(HighResolutionModule, self).__init__() + self._check_branches( + num_branches, blocks, num_blocks, num_inchannels, num_channels) + + self.num_inchannels = num_inchannels + self.fuse_method = fuse_method + self.num_branches = num_branches + + self.multi_scale_output = multi_scale_output + + self.branches = self._make_branches( + num_branches, blocks, num_blocks, num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(True) + + def _check_branches(self, num_branches, blocks, num_blocks, + num_inchannels, num_channels): + if num_branches != len(num_blocks): + error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format( + num_branches, len(num_blocks)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format( + num_branches, len(num_channels)) + logger.error(error_msg) + raise ValueError(error_msg) + + if num_branches != len(num_inchannels): + error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format( + num_branches, len(num_inchannels)) + logger.error(error_msg) + raise ValueError(error_msg) + + def _make_one_branch(self, branch_index, block, num_blocks, num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.num_inchannels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, stride=stride, bias=False + ), + nn.BatchNorm2d( + num_channels[branch_index] * block.expansion, + momentum=BN_MOMENTUM + ), + ) + + layers = [] + layers.append( + block( + self.num_inchannels[branch_index], + num_channels[branch_index], + stride, + downsample + ) + ) + self.num_inchannels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append( + block( + self.num_inchannels[branch_index], + num_channels[branch_index] + ) + ) + + return nn.Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels) + ) + + return nn.ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + num_inchannels = self.num_inchannels + fuse_layers = [] + for i in range(num_branches if self.multi_scale_output else 1): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_inchannels[i], + 1, 1, 0, bias=False + ), + nn.BatchNorm2d(num_inchannels[i]), + nn.Upsample(scale_factor=2**(j-i), mode='nearest') + ) + ) + elif j == i: + fuse_layer.append(None) + else: + conv3x3s = [] + for k in range(i-j): + if k == i - j - 1: + num_outchannels_conv3x3 = num_inchannels[i] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False + ), + nn.BatchNorm2d(num_outchannels_conv3x3) + ) + ) + else: + num_outchannels_conv3x3 = num_inchannels[j] + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + num_inchannels[j], + num_outchannels_conv3x3, + 3, 2, 1, bias=False + ), + nn.BatchNorm2d(num_outchannels_conv3x3), + nn.ReLU(True) + ) + ) + fuse_layer.append(nn.Sequential(*conv3x3s)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def get_num_inchannels(self): + return self.num_inchannels + + def forward(self, x): + if self.num_branches == 1: + return [self.branches[0](x[0])] + + +-- Chunk 3 -- +// hrnet.py:181-195 + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + + for i in range(len(self.fuse_layers)): + y = x[0] if i == 0 else self.fuse_layers[i][0](x[0]) + for j in range(1, self.num_branches): + if i == j: + y = y + x[j] + else: + y = y + self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + + return x_fuse + +-- Chunk 4 -- +// hrnet.py:204-353 +class HighResolutionNet(nn.Module): + + def __init__(self, cfg, **kwargs): + self.inplanes = 64 + super(HighResolutionNet, self).__init__() + + # stem net + self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, + bias=False) + self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM) + self.relu = nn.ReLU(inplace=True) + + self.stage1_cfg = cfg.get('stage1', {}) + num_channels = self.stage1_cfg['num_channels'][0] + block = blocks_dict[self.stage1_cfg['block']] + num_blocks = self.stage1_cfg['num_blocks'][0] + self.layer1 = self._make_layer(block, num_channels, num_blocks) + stage1_out_channel = block.expansion * num_channels + + self.stage2_cfg = cfg.get('stage2', {}) + num_channels = self.stage2_cfg.get('num_channels', (32, 64)) + block = blocks_dict[self.stage2_cfg.get('block')] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + stage2_num_channels = num_channels + self.transition1 = self._make_transition_layer( + [stage1_out_channel], num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + self.stage3_cfg = cfg.get('stage3') + num_channels = self.stage3_cfg['num_channels'] + block = blocks_dict[self.stage3_cfg['block']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + stage3_num_channels = num_channels + self.transition2 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + self.stage4_cfg = cfg.get('stage4') + num_channels = self.stage4_cfg['num_channels'] + block = blocks_dict[self.stage4_cfg['block']] + num_channels = [ + num_channels[i] * block.expansion for i in range(len(num_channels)) + ] + stage_4_out_channels = num_channels + self.transition3 = self._make_transition_layer( + pre_stage_channels, num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels, multi_scale_output=False) + self.output_channels_dim = pre_stage_channels + + self.pretrained_layers = cfg['pretrained_layers'] + self.init_weights() + + self.avg_pooling = nn.AdaptiveAvgPool2d(1) + + final_conv_cfg = cfg.get('final_conv') + # self.conv_layers = make_conv_layer(3 * 384, final_conv_cfg) + subsample3_cfg = self.stage3_cfg.get('subsample') + subsample2_cfg = self.stage2_cfg.get('subsample') + + # self.subsample_3, subsample_3_out_dim = make_subsample_layers( + # 96, subsample3_cfg) + # self.subsample_2, subsample_2_out_dim = make_subsample_layers( + # 192, subsample2_cfg) + + # TODO: Replace with parameters + in_dims = (2 ** 2 * stage2_num_channels[-1] + + 2 ** 1 * stage3_num_channels[-1] + + stage_4_out_channels[-1] + ) + self.conv_layers = self._make_conv_layer( + in_channels=in_dims, num_layers=5) + + self.subsample_3 = self._make_subsample_layer( + in_channels=stage2_num_channels[-1], + num_layers=2) + self.subsample_2 = self._make_subsample_layer( + in_channels=stage3_num_channels[-1], + num_layers=1) + # logger.info(self.subsample_3.state_dict().keys()) + + def get_output_dim(self): + base_output = { + f'layer{idx + 1}': val + for idx, val in enumerate(self.output_channels_dim) + } + output = base_output.copy() + for key in base_output: + output[f'{key}_avg_pooling'] = output[key] + output['concat'] = 2048 + return output + + def _make_transition_layer( + self, num_channels_pre_layer, num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + nn.Conv2d( + num_channels_pre_layer[i], + num_channels_cur_layer[i], + 3, 1, 1, bias=False + ), + nn.BatchNorm2d(num_channels_cur_layer[i]), + nn.ReLU(inplace=True) + ) + ) + else: + transition_layers.append(None) + else: + conv3x3s = [] + for j in range(i+1-num_branches_pre): + inchannels = num_channels_pre_layer[-1] + outchannels = num_channels_cur_layer[i] \ + if j == i-num_branches_pre else inchannels + conv3x3s.append( + nn.Sequential( + nn.Conv2d( + inchannels, outchannels, 3, 2, 1, bias=False + ), + nn.BatchNorm2d(outchannels), + nn.ReLU(inplace=True) + ) + ) + transition_layers.append(nn.Sequential(*conv3x3s)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d( + self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False + ), + +-- Chunk 5 -- +// hrnet.py:354-503 + nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def _make_conv_layer(self, in_channels=2048, num_layers=3, num_filters=2048, stride=1): + + layers = [] + for i in range(num_layers): + + downsample = nn.Conv2d(in_channels, num_filters, stride=1, + kernel_size=1, bias=False) + layers.append(Bottleneck(in_channels, num_filters // 4, + downsample=downsample)) + in_channels = num_filters + + return nn.Sequential(*layers) + + def _make_subsample_layer(self, in_channels=96, num_layers=3, stride=2): + + layers = [] + for i in range(num_layers): + + layers.append( + nn.Conv2d( + in_channels=in_channels, + out_channels=2*in_channels, + kernel_size=3, + stride=stride, + padding=1)) + in_channels = 2*in_channels + layers.append(nn.BatchNorm2d(in_channels, momentum=BN_MOMENTUM)) + layers.append(nn.ReLU(inplace=True)) + + return nn.Sequential(*layers) + + def _make_stage(self, layer_config, num_inchannels, + multi_scale_output=True, log=False): + num_modules = layer_config['num_modules'] + num_branches = layer_config['num_branches'] + num_blocks = layer_config['num_blocks'] + num_channels = layer_config['num_channels'] + block = blocks_dict[layer_config['block']] + fuse_method = layer_config['fuse_method'] + + modules = [] + for i in range(num_modules): + # multi_scale_output is only used last module + if not multi_scale_output and i == num_modules - 1: + reset_multi_scale_output = False + else: + reset_multi_scale_output = True + + modules.append( + HighResolutionModule( + num_branches, + block, + num_blocks, + num_inchannels, + num_channels, + fuse_method, + reset_multi_scale_output + ) + ) + modules[-1].log = log + num_inchannels = modules[-1].get_num_inchannels() + + return nn.Sequential(*modules), num_inchannels + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.bn2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['num_branches']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['num_branches']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['num_branches']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + + output = {} + for idx, x in enumerate(x_list): + output[f'layer{idx + 1}'] = x + # output[''] + + x3 = self.subsample_3(x_list[1]) + x2 = self.subsample_2(x_list[2]) + x1 = x_list[3] + xf = self.conv_layers(torch.cat([x3, x2, x1], dim=1)) + xf = xf.mean(dim=(2, 3)) + xf = xf.view(xf.size(0), -1) + output['concat'] = xf + # y_list = self.stage4(x_list) + # output['stage4'] = y_list[0] + # output['stage4_avg_pooling'] = self.avg_pooling(y_list[0]).view( + # *y_list[0].shape[:2]) + + # concat_outputs = y_list + x_list + # output['concat'] = torch.cat([ + # self.avg_pooling(tensor).view(*tensor.shape[:2]) + # for tensor in concat_outputs], + # dim=1) + + return output + + def init_weights(self): + logger.info('=> init weights from normal distribution') + for m in self.modules(): + if isinstance(m, nn.Conv2d): + # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + nn.init.normal_(m.weight, std=0.001) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.ConvTranspose2d): + nn.init.normal_(m.weight, std=0.001) + for name, _ in m.named_parameters(): + if name in ['bias']: + nn.init.constant_(m.bias, 0) + + +-- Chunk 6 -- +// hrnet.py:504-520 + def load_weights(self, pretrained=''): + pretrained = osp.expandvars(pretrained) + if osp.isfile(pretrained): + pretrained_state_dict = torch.load( + pretrained, map_location=torch.device("cpu")) + logger.info('=> loading pretrained model {}'.format(pretrained)) + + need_init_state_dict = {} + for name, m in pretrained_state_dict.items(): + if (name.split('.')[0] in self.pretrained_layers or + self.pretrained_layers[0] == '*'): + need_init_state_dict[name] = m + missing, unexpected = self.load_state_dict( + need_init_state_dict, strict=False) + else: + logger.warning('=> please download pre-trained models first!') + logger.warning(f'{pretrained} does not exist!') + +=== File: expose/models/backbone/build.py === + +-- Chunk 1 -- +// build.py:8-27 +def build_backbone(backbone_cfg): + backbone_type = backbone_cfg.get('type', 'resnet50') + # use_avgpool = cfg.get('network', {}).get('type') != 'attention' + pretrained = backbone_cfg.pop('pretrained', True) + + if 'fpn' in backbone_type: + backbone = build_fpn_backbone(backbone_cfg, pretrained=pretrained) + return backbone, backbone.get_output_dim() + elif 'hrnet' in backbone_type: + backbone = build_hr_net( + backbone_cfg, pretrained=True) + return backbone, backbone.get_output_dim() + elif 'resnet' in backbone_type: + resnet_cfg = backbone_cfg.get('resnet') + backbone = resnets[backbone_type]( + pretrained=True, **resnet_cfg) + return backbone, backbone.get_output_dim() + else: + msg = 'Unknown backbone type: {}'.format(backbone_type) + raise ValueError(msg) + +=== File: expose/models/backbone/fpn.py === + +-- Chunk 1 -- +// fpn.py:20-31 +class BackboneWithFPN(_BackboneWithFPN): + def __init__(self, *args, **kwargs): + super(BackboneWithFPN, self).__init__(*args, **kwargs) + + def forward(self, x): + body_features = getattr(self, 'body')(x) + + output = getattr(self, 'fpn')(body_features) + + for key in body_features: + output[f'body_{key}'] = body_features[key] + return output + +-- Chunk 2 -- +// fpn.py:34-58 +def resnet_fpn_backbone(backbone_name, pretrained=True, freeze=False): + backbone = resnet.__dict__[backbone_name]( + pretrained=pretrained) + if freeze: + # freeze layers + for name, parameter in backbone.named_parameters(): + if ('layer2' not in name and 'layer3' not in name and + 'layer4' not in name): + parameter.requires_grad_(False) + + return_layers = {'layer1': 'layer1', + 'layer2': 'layer2', + 'layer3': 'layer3', + 'layer4': 'layer4'} + + in_channels_stage2 = backbone.inplanes // 8 + in_channels_list = [ + in_channels_stage2, + in_channels_stage2 * 2, + in_channels_stage2 * 4, + in_channels_stage2 * 8, + ] + out_channels = 256 + return BackboneWithFPN(backbone, return_layers, in_channels_list, + out_channels) + +-- Chunk 3 -- +// fpn.py:61-71 +def build_fpn_backbone(backbone_cfg, + pretrained=True) -> nn.Module: + backbone_type = backbone_cfg.get('type', 'resnet50') + + resnet_type = backbone_type.replace('fpn', '').replace('_', '').replace( + '-', '') + network = resnet_fpn_backbone(resnet_type, pretrained=pretrained) + + fpn_cfg = backbone_cfg.get('fpn', {}) + + return RegressionFPN(network, fpn_cfg) + +-- Chunk 4 -- +// fpn.py:74-98 +class SumAvgPooling(nn.Module): + def __init__(self, pooling_type='avg', **kwargs) -> None: + super(SumAvgPooling, self).__init__() + + if pooling_type == 'avg': + self.pooling = nn.AdaptiveAvgPool2d(1) + elif pooling_type == 'max': + self.pooling = nn.AdaptiveMaxPool2d(1) + else: + raise ValueError(f'Unknown pooling function: {pooling_type}') + + def get_out_feature_dim(self) -> int: + return FPN_FEATURE_DIM + + def forward(self, features: Dict[str, torch.Tensor]) -> torch.Tensor: + + pooled_features = {} + # Pool each feature map + for key in features: + batch_size, feat_dim = features[key].shape[:2] + pooled_features[key] = self.pooling(features[key]).view( + batch_size, feat_dim) + + # Sum the individual features + return sum(pooled_features.values()) + +-- Chunk 5 -- +// fpn.py:101-138 +class ConcatPooling(nn.Module): + def __init__(self, use_max: bool = True, use_avg: bool = True, + **kwargs) -> None: + super(ConcatPooling, self).__init__() + assert use_avg or use_max, 'Either max or avg pooling should be on' + + self.use_avg = use_avg + self.use_max = use_max + if use_avg: + self.avg_pooling = nn.AdaptiveAvgPool2d(1) + if use_max: + self.max_pooling = nn.AdaptiveMaxPool2d(1) + + def extra_repr(self) -> str: + msg = [f'Use average pooling: {self.use_avg}', + f'Use max pooling: {self.use_max}'] + return '\n'.join(msg) + + def get_out_feature_dim(self) -> int: + return 5 * ( + self.use_avg * FPN_FEATURE_DIM + self.use_max * FPN_FEATURE_DIM) + + def forward(self, features: Dict[str, torch.Tensor]) -> torch.Tensor: + pooled_features = [] + for key in features: + batch_size, feat_dim = features[key].shape[:2] + feats = [] + if self.use_avg: + avg_pooled_features = self.avg_pooling(features[key]).view( + batch_size, feat_dim) + feats.append(avg_pooled_features) + if self.use_max: + max_pooled_features = self.max_pooling(features[key]).view( + batch_size, feat_dim) + feats.append(max_pooled_features) + pooled_features.append( + torch.cat(feats, dim=-1)) + return torch.cat(pooled_features, dim=-1) + +-- Chunk 6 -- +// fpn.py:141-161 +class BilinearPooling(nn.Module): + def __init__(self, pooling_type='avg', **kwargs) -> None: + super(BilinearPooling, self).__init__() + raise NotImplementedError + if pooling_type == 'avg': + self.pooling = nn.AdaptiveAvgPool2d(1) + elif pooling_type == 'max': + self.pooling = nn.AdaptiveMaxPool2d(1) + else: + raise ValueError(f'Unknown pooling function: {pooling_type}') + + def forward(self, features: Dict[str, torch.Tensor]) -> torch.Tensor: + pooled_features = {} + # Pool each feature map + for key in features: + batch_size, feat_dim = features[key].shape[:2] + pooled_features[key] = self.pooling(features[key]).view( + batch_size, feat_dim) + # Should be BxNxK + stacked_features = torch.stack(pooled_features.values(), dim=1) + pass + +-- Chunk 7 -- +// fpn.py:165-202 +class RegressionFPN(nn.Module): + + def __init__(self, backbone, fpn_cfg) -> None: + super(RegressionFPN, self).__init__() + self.feat_extractor = backbone + + pooling_type = fpn_cfg.get('pooling_type', 'sum_avg') + self.avg_pooling = nn.AdaptiveAvgPool2d(1) + if pooling_type == 'sum_avg': + sum_avg_cfg = fpn_cfg.get('sum_avg', {}) + self.pooling = SumAvgPooling(**sum_avg_cfg) + elif pooling_type == 'concat': + concat_cfg = fpn_cfg.get('concat', {}) + self.pooling = ConcatPooling(**concat_cfg) + elif pooling_type == 'none': + self.pooling = None + else: + raise ValueError(f'Unknown pooling type {pooling_type}') + + def get_output_dim(self) -> int: + output = { + 'layer1': FPN_FEATURE_DIM, + 'layer2': FPN_FEATURE_DIM, + 'layer3': FPN_FEATURE_DIM, + 'layer4': FPN_FEATURE_DIM, + } + + for key in output: + output[f'{key}_avg_pooling'] = FPN_FEATURE_DIM + return output + + def forward(self, x: torch.Tensor) -> torch.Tensor: + features = self.feat_extractor(x) + + if self.pooling is not None: + pass + features['avg_pooling'] = self.avg_pooling(features['body_layer4']) + return features + +=== File: expose/models/backbone/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/backbone/__init__.py:1-2 + +from .build import build_backbone + +=== File: expose/models/common/smplx_loss_modules.py === + +-- Chunk 1 -- +// smplx_loss_modules.py:39-188 +ss SMPLXLossModule(nn.Module): + ''' + ''' + + def __init__(self, loss_cfg, num_stages=3, + use_face_contour=False): + super(SMPLXLossModule, self).__init__() + + self.stages_to_penalize = loss_cfg.get('stages_to_penalize', [-1]) + logger.info(f'Stages to penalize: {self.stages_to_penalize}') + + self.loss_enabled = defaultdict(lambda: True) + self.loss_activ_step = {} + + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + face_idxs = idxs_dict['face'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + if not use_face_contour: + face_idxs = face_idxs[:-17] + + self.register_buffer('body_idxs', torch.tensor(body_idxs)) + self.register_buffer('hand_idxs', torch.tensor(hand_idxs)) + self.register_buffer('face_idxs', torch.tensor(face_idxs)) + + self.register_buffer('left_hand_idxs', torch.tensor(left_hand_idxs)) + self.register_buffer('right_hand_idxs', torch.tensor(right_hand_idxs)) + + shape_loss_cfg = loss_cfg.shape + self.shape_weight = shape_loss_cfg.get('weight', 0.0) + self.shape_loss = build_loss(**shape_loss_cfg) + self.loss_activ_step['shape'] = shape_loss_cfg.enable + + expression_cfg = loss_cfg.get('expression', {}) + self.expr_use_conf_weight = expression_cfg.get( + 'use_conf_weight', False) + + self.expression_weight = expression_cfg.weight + if self.expression_weight > 0: + self.expression_loss = build_loss(**expression_cfg) + self.loss_activ_step['expression'] = expression_cfg.enable + + global_orient_cfg = loss_cfg.global_orient + global_orient_loss_type = global_orient_cfg.type + self.global_orient_loss_type = global_orient_loss_type + self.global_orient_loss = build_loss(**global_orient_cfg) + logger.debug('Global pose loss: {}', self.global_orient_loss) + self.global_orient_weight = global_orient_cfg.weight + self.loss_activ_step['global_orient'] = global_orient_cfg.enable + + self.body_pose_weight = loss_cfg.body_pose.weight + body_pose_loss_type = loss_cfg.body_pose.type + self.body_pose_loss_type = body_pose_loss_type + self.body_pose_loss = build_loss(**loss_cfg.body_pose) + logger.debug('Body pose loss: {}', self.global_orient_loss) + self.body_pose_weight = loss_cfg.body_pose.weight + self.loss_activ_step['body_pose'] = loss_cfg.body_pose.enable + + left_hand_pose_cfg = loss_cfg.get('left_hand_pose', {}) + left_hand_pose_loss_type = loss_cfg.left_hand_pose.type + self.lhand_use_conf = left_hand_pose_cfg.get('use_conf_weight', False) + + self.left_hand_pose_weight = loss_cfg.left_hand_pose.weight + if self.left_hand_pose_weight > 0: + self.left_hand_pose_loss_type = left_hand_pose_loss_type + self.left_hand_pose_loss = build_loss(**loss_cfg.left_hand_pose) + self.loss_activ_step[ + 'left_hand_pose'] = loss_cfg.left_hand_pose.enable + + right_hand_pose_cfg = loss_cfg.get('right_hand_pose', {}) + right_hand_pose_loss_type = loss_cfg.right_hand_pose.type + self.right_hand_pose_weight = loss_cfg.right_hand_pose.weight + self.rhand_use_conf = right_hand_pose_cfg.get('use_conf_weight', False) + if self.right_hand_pose_weight > 0: + self.right_hand_pose_loss_type = right_hand_pose_loss_type + self.right_hand_pose_loss = build_loss(**loss_cfg.right_hand_pose) + self.loss_activ_step[ + 'right_hand_pose'] = loss_cfg.right_hand_pose.enable + + jaw_pose_loss_type = loss_cfg.jaw_pose.type + self.jaw_pose_weight = loss_cfg.jaw_pose.weight + + jaw_pose_cfg = loss_cfg.get('jaw_pose', {}) + self.jaw_use_conf_weight = jaw_pose_cfg.get('use_conf_weight', False) + if self.jaw_pose_weight > 0: + self.jaw_pose_loss_type = jaw_pose_loss_type + self.jaw_pose_loss = build_loss(**loss_cfg.jaw_pose) + logger.debug('Jaw pose loss: {}', self.global_orient_loss) + self.loss_activ_step['jaw_pose'] = loss_cfg.jaw_pose.enable + + edge_loss_cfg = loss_cfg.get('edge', {}) + self.edge_weight = edge_loss_cfg.get('weight', 0.0) + self.edge_loss = build_loss(**edge_loss_cfg) + self.loss_activ_step['edge'] = edge_loss_cfg.get('enable', 0) + + def is_active(self) -> bool: + return any(self.loss_enabled.values()) + + def toggle_losses(self, step) -> None: + for key in self.loss_activ_step: + self.loss_enabled[key] = step >= self.loss_activ_step[key] + + def extra_repr(self) -> str: + msg = [] + if self.shape_weight > 0: + msg.append(f'Shape weight: {self.shape_weight}') + if self.expression_weight > 0: + msg.append(f'Expression weight: {self.expression_weight}') + if self.global_orient_weight > 0: + msg.append(f'Global pose weight: {self.global_orient_weight}') + if self.body_pose_weight > 0: + msg.append(f'Body pose weight: {self.body_pose_weight}') + if self.left_hand_pose_weight > 0: + msg.append(f'Left hand pose weight: {self.left_hand_pose_weight}') + if self.right_hand_pose_weight > 0: + msg.append(f'Right hand pose weight {self.right_hand_pose_weight}') + if self.jaw_pose_weight > 0: + msg.append(f'Jaw pose prior weight: {self.jaw_pose_weight}') + return '\n'.join(msg) + + def single_loss_step(self, parameters, target_params, + target_param_idxs, + gt_vertices=None, + device=None, + keyp_confs=None, + penalize_only_parts=False, + ): + losses = defaultdict( + lambda: torch.tensor(0, device=device, dtype=torch.float32)) + + param_vertices = parameters.get('vertices', None) + compute_edge_loss = (self.edge_weight > 0 and + param_vertices is not None and + gt_vertices is not None and + not penalize_only_parts) + if compute_edge_loss: + edge_loss_val = self.edge_loss( + gt_vertices=gt_vertices, est_vertices=param_vertices) + losses['mesh_edge_loss'] = self.edge_weight * edge_loss_val + + compute_shape_loss = ( + self.shape_weight > 0 and self.loss_enabled['betas'] and + 'betas' in target_params and not penalize_only_parts + ) + if compute_shape_loss: + losses['shape_loss'] = ( + self.shape_loss( + parameters['betas'][target_param_idxs['betas']], + +-- Chunk 2 -- +// smplx_loss_modules.py:189-338 + target_params['betas']) * + self.shape_weight) + + compute_expr_loss = (self.expression_weight > 0 and + self.loss_enabled['expression'] and + 'expression' in target_param_idxs) + if compute_expr_loss: + expr_idxs = target_param_idxs['expression'] + weights = ( + keyp_confs['face'].mean(axis=1) + if self.expr_use_conf_weight else None) + if weights is not None: + num_ones = [1] * len(parameters['expression'].shape[1:]) + weights = weights.view(-1, *num_ones) + weights = weights[expr_idxs] + + losses['expression_loss'] = ( + self.expression_loss( + parameters['expression'][expr_idxs], + target_params['expression'], + weights=weights) * + self.expression_weight) + + compute_global_orient_loss = ( + self.global_orient_weight > 0 and self.loss_enabled['betas'] and + 'global_orient' in target_params and not penalize_only_parts + ) + if compute_global_orient_loss: + global_orient_idxs = target_param_idxs['global_orient'] + losses['global_orient_loss'] = ( + self.global_orient_loss( + parameters['global_orient'][global_orient_idxs], + target_params['global_orient']) * + self.global_orient_weight) + + compute_body_pose_loss = ( + self.body_pose_weight > 0 and self.loss_enabled['betas'] and + 'body_pose' in target_params and not penalize_only_parts) + + if compute_body_pose_loss: + body_pose_idxs = target_param_idxs['body_pose'] + losses['body_pose_loss'] = ( + self.body_pose_loss( + parameters['body_pose'][body_pose_idxs], + target_params['body_pose']) * + self.body_pose_weight) + + if (self.left_hand_pose_weight > 0 and + self.loss_enabled['left_hand_pose'] and + 'left_hand_pose' in target_param_idxs): + num_left_hand_joints = parameters['left_hand_pose'].shape[1] + weights = ( + keyp_confs['left_hand'].mean(axis=1, keepdim=True).expand( + -1, num_left_hand_joints).reshape(-1) + if self.lhand_use_conf else None) + if weights is not None: + num_ones = [1] * len( + parameters['left_hand_pose'].shape[2:]) + weights = weights.view(-1, num_left_hand_joints, *num_ones) + weights = weights[target_param_idxs['left_hand_pose']] + losses['left_hand_pose_loss'] = ( + self.left_hand_pose_loss( + parameters['left_hand_pose'][ + target_param_idxs['left_hand_pose']], + target_params['left_hand_pose'], + weights=weights) * + self.left_hand_pose_weight) + + if (self.right_hand_pose_weight > 0 and + self.loss_enabled['right_hand_pose'] and + 'right_hand_pose' in target_param_idxs): + num_right_hand_joints = parameters['right_hand_pose'].shape[1] + weights = ( + keyp_confs['right_hand'].mean(axis=1, keepdim=True).expand( + -1, num_right_hand_joints).reshape(-1) + if self.rhand_use_conf else None) + if weights is not None: + num_ones = [1] * len( + parameters['right_hand_pose'].shape[2:]) + weights = weights.view(-1, num_left_hand_joints, *num_ones) + weights = weights[target_param_idxs['right_hand_pose']] + losses['right_hand_pose_loss'] = ( + self.right_hand_pose_loss( + parameters['right_hand_pose'][ + target_param_idxs['right_hand_pose']], + target_params['right_hand_pose'], + weights=weights) * + self.right_hand_pose_weight) + + if (self.jaw_pose_weight > 0 and self.loss_enabled['jaw_pose'] and + 'jaw_pose' in target_param_idxs): + weights = ( + keyp_confs['face'].mean(axis=1) + if self.jaw_use_conf_weight else None) + if weights is not None: + num_ones = [1] * len(parameters['jaw_pose'].shape[2:]) + weights = weights.view(-1, 1, *num_ones) + weights = weights[target_param_idxs['jaw_pose']] + + losses['jaw_pose_loss'] = ( + self.jaw_pose_loss( + parameters['jaw_pose'][target_param_idxs['jaw_pose']], + target_params['jaw_pose'], + weights=weights) * + self.jaw_pose_weight) + + return losses + + def forward(self, network_params, targets, num_stages=3, device=None): + if device is None: + device = torch.device('cpu') + + start_idxs = defaultdict(lambda: 0) + in_target_param_idxs = defaultdict(lambda: []) + in_target_params = defaultdict(lambda: []) + + keyp_confs = defaultdict(lambda: []) + for idx, target in enumerate(targets): + # If there are no 3D annotations, skip and add to the starting + # index the number of bounding boxes + if len(target) < 1: + continue + + conf = target.conf + + keyp_confs['body'].append(conf[self.body_idxs]) + keyp_confs['left_hand'].append(conf[self.left_hand_idxs]) + keyp_confs['right_hand'].append(conf[self.right_hand_idxs]) + keyp_confs['face'].append(conf[self.face_idxs]) + + for param_key in PARAM_KEYS: + if not target.has_field(param_key): + start_idxs[param_key] += len(target) + continue + end_idx = start_idxs[param_key] + 1 + in_target_param_idxs[param_key] += list( + range(start_idxs[param_key], end_idx)) + start_idxs[param_key] += 1 + + in_target_params[param_key].append( + target.get_field(param_key)) + + # Stack all confidences + for key in keyp_confs: + keyp_confs[key] = torch.stack(keyp_confs[key]) + + target_params = {} + for key, val in in_target_params.items(): + if key == 'hand_pose': + target_params['left_hand_pose'] = torch.stack([ + +-- Chunk 3 -- +// smplx_loss_modules.py:339-392 + t.left_hand_pose + for t in val]) + target_params['right_hand_pose'] = torch.stack([ + t.right_hand_pose + for t in val]) + else: + target_params[key] = torch.stack([ + getattr(t, key) + for t in val]) + + target_param_idxs = {} + for key in in_target_param_idxs.keys(): + if key == 'hand_pose': + target_param_idxs['left_hand_pose'] = torch.tensor( + np.asarray(in_target_param_idxs[key]), + device=device, + dtype=torch.long) + target_param_idxs['right_hand_pose'] = target_param_idxs[ + 'left_hand_pose'].clone() + else: + target_param_idxs[key] = torch.tensor( + np.asarray(in_target_param_idxs[key]), + device=device, + dtype=torch.long) + + has_vertices = all([t.has_field('vertices') for t in targets]) + gt_vertices = None + if has_vertices: + gt_vertices = torch.stack([ + t.get_field('vertices').vertices for t in targets]) + + stages_to_penalize = self.stages_to_penalize.copy() + if -1 in stages_to_penalize: + stages_to_penalize[stages_to_penalize.index(-1)] = num_stages + 1 + output_losses = {} + for n in range(1, len(network_params) + 1): + if n not in stages_to_penalize: + continue + curr_params = network_params[n - 1] + if curr_params is None: + logger.warning(f'Network output for stage {n} is None') + continue + + curr_losses = self.single_loss_step( + curr_params, target_params, + target_param_idxs, device=device, + keyp_confs=keyp_confs, + gt_vertices=gt_vertices) + for key in curr_losses: + output_losses[f'stage_{n - 1:02d}_{key}'] = curr_losses[key] + + return output_losses + + + +-- Chunk 4 -- +// smplx_loss_modules.py:393-542 +ss RegularizerModule(nn.Module): + def __init__(self, loss_cfg, + body_pose_mean=None, left_hand_pose_mean=None, + right_hand_pose_mean=None, jaw_pose_mean=None): + super(RegularizerModule, self).__init__() + + self.stages_to_regularize = loss_cfg.get('stages_to_penalize', [-1]) + logger.info(f'Stages to regularize: {self.stages_to_regularize}') + + # Construct the shape prior + shape_prior_type = loss_cfg.shape.prior.type + self.shape_prior_weight = loss_cfg.shape.prior.weight + if self.shape_prior_weight > 0: + self.shape_prior = build_prior(shape_prior_type, + **loss_cfg.shape.prior) + logger.debug(f'Shape prior {self.shape_prior}') + + # Construct the expression prior + expression_prior_cfg = loss_cfg.expression.prior + expression_prior_type = expression_prior_cfg.type + self.expression_prior_weight = expression_prior_cfg.weight + if self.expression_prior_weight > 0: + self.expression_prior = build_prior( + expression_prior_type, + **expression_prior_cfg) + logger.debug(f'Expression prior {self.expression_prior}') + + # Construct the body pose prior + body_pose_prior_cfg = loss_cfg.body_pose.prior + body_pose_prior_type = body_pose_prior_cfg.type + self.body_pose_prior_weight = body_pose_prior_cfg.weight + if self.body_pose_prior_weight > 0: + self.body_pose_prior = build_prior( + body_pose_prior_type, + mean=body_pose_mean, + **body_pose_prior_cfg) + logger.debug(f'Body pose prior {self.body_pose_prior}') + + # Construct the left hand pose prior + left_hand_prior_cfg = loss_cfg.left_hand_pose.prior + left_hand_pose_prior_type = left_hand_prior_cfg.type + self.left_hand_pose_prior_weight = left_hand_prior_cfg.weight + if self.left_hand_pose_prior_weight > 0: + self.left_hand_pose_prior = build_prior( + left_hand_pose_prior_type, + mean=left_hand_pose_mean, + **left_hand_prior_cfg) + logger.debug(f'Left hand pose prior {self.left_hand_pose_prior}') + + # Construct the right hand pose prior + right_hand_prior_cfg = loss_cfg.right_hand_pose.prior + right_hand_pose_prior_type = right_hand_prior_cfg.type + self.right_hand_pose_prior_weight = right_hand_prior_cfg.weight + if self.right_hand_pose_prior_weight > 0: + self.right_hand_pose_prior = build_prior( + right_hand_pose_prior_type, mean=right_hand_pose_mean, + **right_hand_prior_cfg) + logger.debug(f'Right hand pose prior {self.right_hand_pose_prior}') + + # Construct the jaw pose prior + jaw_pose_prior_cfg = loss_cfg.jaw_pose.prior + jaw_pose_prior_type = jaw_pose_prior_cfg.type + self.jaw_pose_prior_weight = jaw_pose_prior_cfg.weight + if self.jaw_pose_prior_weight > 0: + self.jaw_pose_prior = build_prior( + jaw_pose_prior_type, mean=jaw_pose_mean, **jaw_pose_prior_cfg) + logger.debug(f'Jaw pose prior {self.jaw_pose_prior}') + + logger.debug(self) + + def extra_repr(self) -> str: + msg = [] + if self.shape_prior_weight > 0: + msg.append('Shape prior weight: {}'.format( + self.shape_prior_weight)) + if self.expression_prior_weight > 0: + msg.append('Expression prior weight: {}'.format( + self.expression_prior_weight)) + if self.body_pose_prior_weight > 0: + msg.append('Body pose prior weight: {}'.format( + self.body_pose_prior_weight)) + if self.left_hand_pose_prior_weight > 0: + msg.append('Left hand pose prior weight: {}'.format( + self.left_hand_pose_prior_weight)) + if self.right_hand_pose_prior_weight > 0: + msg.append('Right hand pose prior weight {}'.format( + self.right_hand_pose_prior_weight)) + if self.jaw_pose_prior_weight > 0: + msg.append('Jaw pose prior weight: {}'.format( + self.jaw_pose_prior_weight)) + return '\n'.join(msg) + + def single_regularization_step(self, parameters, + penalize_only_parts=False, + **kwargs): + prior_losses = {} + + betas = parameters.get('betas', None) + reg_shape = (self.shape_prior_weight > 0 and betas is not None and + not penalize_only_parts) + if reg_shape: + prior_losses['shape_prior'] = ( + self.shape_prior_weight * self.shape_prior(betas)) + + expression = parameters.get('expression', None) + reg_expression = ( + self.expression_prior_weight > 0 and expression is not None) + if reg_expression: + prior_losses['expression_prior'] = ( + self.expression_prior(expression) * + self.expression_prior_weight) + + body_pose = parameters.get('body_pose', None) + betas = parameters.get('betas', None) + reg_body_pose = ( + self.body_pose_prior_weight > 0 and body_pose is not None and + not penalize_only_parts) + if reg_body_pose: + prior_losses['body_pose_prior'] = ( + self.body_pose_prior(body_pose) * + self.body_pose_prior_weight) + + left_hand_pose = parameters.get('left_hand_pose', None) + if (self.left_hand_pose_prior_weight > 0 and + left_hand_pose is not None): + prior_losses['left_hand_pose_prior'] = ( + self.left_hand_pose_prior(left_hand_pose) * + self.left_hand_pose_prior_weight) + + right_hand_pose = parameters.get('right_hand_pose', None) + if (self.right_hand_pose_prior_weight > 0 and + right_hand_pose is not None): + prior_losses['right_hand_pose_prior'] = ( + self.right_hand_pose_prior(right_hand_pose) * + self.right_hand_pose_prior_weight) + + jaw_pose = parameters.get('jaw_pose', None) + if self.jaw_pose_prior_weight > 0 and jaw_pose is not None: + prior_losses['jaw_pose_prior'] = ( + self.jaw_pose_prior(jaw_pose) * + self.jaw_pose_prior_weight) + + return prior_losses + + def forward(self, + param_list, + num_stages=3, + **kwargs) -> Dict[str, Tensor]: + + prior_losses = defaultdict(lambda: 0) + +-- Chunk 5 -- +// smplx_loss_modules.py:543-561 + for n in range(1, num_stages + 1): + if n not in self.stages_to_regularize: + continue + curr_params = param_list[n - 1] + if curr_params is None: + logger.warning(f'Network output for stage {n} is None') + continue + + curr_losses = self.single_regularization_step(curr_params) + for key in curr_losses: + prior_losses[f'stage_{n - 1:02d}_{key}'] = curr_losses[key] + + if num_stages < len(param_list): + curr_params = param_list[-1] + final_losses = self.single_regularization_step(curr_params) + for key in final_losses: + prior_losses[ + f'stage_{num_stages:02d}_{key}'] = final_losses[key] + return prior_losses + +=== File: expose/models/common/pose_utils.py === + +-- Chunk 1 -- +// pose_utils.py:36-56 +ss PoseParameterization(object): + KEYS = ['regressor', 'decoder', 'dim', 'mean', 'ind_dim'] + + def __init__(self, regressor=None, decoder=None, dim=0, ind_dim=0, + mean=None): + super(PoseParameterization, self).__init__() + + self.regressor = regressor + self.decoder = decoder + self.dim = dim + self.mean = mean + self.ind_dim = ind_dim + + def keys(self): + return [key for key in self.KEYS + if getattr(self, key) is not None] + + def __getitem__(self, key): + return getattr(self, key) + + + +-- Chunk 2 -- +// pose_utils.py:57-75 + build_pose_regressor(input_dim: int, + num_angles: int, + pose_cfg: Dict, + network_cfg: Dict, + mean_pose: np.array = None, + pca_basis: np.array = None, + append_params=True) -> Tuple[nn.Module, nn.Module]: + pose_decoder = build_pose_decoder( + pose_cfg, num_angles, mean_pose=mean_pose, + pca_basis=pca_basis) + + pose_dim_size = pose_decoder.get_dim_size() + reg_input_dim = input_dim + append_params * pose_dim_size + + regressor = MLP(reg_input_dim, pose_dim_size, **network_cfg) + + return pose_decoder, regressor + + + +-- Chunk 3 -- +// pose_utils.py:76-128 + create_pose_parameterization(input_dim, num_angles, param_type='aa', + num_pca_comps=12, + latent_dim_size=32, + append_params=True, + create_regressor=True, + **kwargs): + + logger.debug('Creating {} for {} joints', param_type, num_angles) + + regressor = None + + if param_type == 'aa': + input_dim += append_params * num_angles * 3 + if create_regressor: + regressor = MLP(input_dim, num_angles * 3, **kwargs) + decoder = AADecoder(num_angles=num_angles, **kwargs) + dim = decoder.get_dim_size() + ind_dim = 3 + mean = decoder.get_mean() + elif param_type == 'pca': + input_dim += append_params * num_pca_comps + if create_regressor: + regressor = MLP(input_dim, num_pca_comps, **kwargs) + decoder = PCADecoder(num_pca_comps=num_pca_comps, **kwargs) + ind_dim = num_pca_comps + dim = decoder.get_dim_size() + mean = decoder.get_mean() + elif param_type == 'cont_rot_repr': + input_dim += append_params * num_angles * 6 + if create_regressor: + regressor = MLP(input_dim, num_angles * 6, **kwargs) + decoder = ContinuousRotReprDecoder(num_angles, **kwargs) + dim = decoder.get_dim_size() + ind_dim = 6 + mean = decoder.get_mean() + elif param_type == 'rot_mats': + input_dim += append_params * num_angles * 9 + if create_regressor: + regressor = MLP(input_dim, num_angles * 9, **kwargs) + decoder = SVDRotationProjection() + dim = decoder.get_dim_size() + mean = decoder.get_mean() + ind_dim = 9 + else: + raise ValueError(f'Unknown pose parameterization: {param_type}') + + return PoseParameterization(regressor=regressor, + decoder=decoder, + dim=dim, + ind_dim=ind_dim, + mean=mean) + + + +-- Chunk 4 -- +// pose_utils.py:129-144 + build_pose_decoder(cfg, num_angles, mean_pose=None, pca_basis=None): + param_type = cfg.get('param_type', 'aa') + logger.debug('Creating {} for {} joints', param_type, num_angles) + if param_type == 'aa': + decoder = AADecoder(num_angles=num_angles, mean=mean_pose, **cfg) + elif param_type == 'pca': + decoder = PCADecoder(pca_basis=pca_basis, mean=mean_pose, **cfg) + elif param_type == 'cont_rot_repr': + decoder = ContinuousRotReprDecoder(num_angles, mean=mean_pose, **cfg) + elif param_type == 'rot_mats': + decoder = SVDRotationProjection() + else: + raise ValueError(f'Unknown pose decoder: {param_type}') + return decoder + + + +-- Chunk 5 -- +// pose_utils.py:145-213 + build_all_pose_params(body_model_cfg, + feat_extract_depth, + body_model, + append_params=True, + dtype=torch.float32): + mean_pose_path = osp.expandvars(body_model_cfg.mean_pose_path) + mean_poses_dict = {} + if osp.exists(mean_pose_path): + logger.debug('Loading mean pose from: {} ', mean_pose_path) + with open(mean_pose_path, 'rb') as f: + mean_poses_dict = pickle.load(f) + + global_orient_desc = create_pose_parameterization( + feat_extract_depth, 1, dtype=dtype, + append_params=append_params, + create_regressor=False, **body_model_cfg.global_orient) + + global_orient_type = body_model_cfg.get('global_orient', {}).get( + 'param_type', 'cont_rot_repr') + logger.debug('Global pose parameterization, decoder: {}, {}', + global_orient_type, global_orient_desc.decoder) + # Rotate the model 180 degrees around the x-axis + if global_orient_type == 'aa': + global_orient_desc.decoder.mean[0] = math.pi + elif global_orient_type == 'cont_rot_repr': + global_orient_desc.decoder.mean[3] = -1 + + body_pose_desc = create_pose_parameterization( + feat_extract_depth, num_angles=body_model.NUM_BODY_JOINTS, + ignore_hands=True, dtype=dtype, + append_params=append_params, create_regressor=False, + mean=mean_poses_dict.get('body_pose', None), + **body_model_cfg.body_pose) + logger.debug('Body pose decoder: {}', body_pose_desc.decoder) + + left_hand_cfg = body_model_cfg.left_hand_pose + right_hand_cfg = body_model_cfg.right_hand_pose + left_hand_pose_desc = create_pose_parameterization( + feat_extract_depth, num_angles=15, dtype=dtype, + append_params=append_params, + pca_basis=body_model.left_hand_components, + mean=mean_poses_dict.get('left_hand_pose', None), + create_regressor=False, **left_hand_cfg) + logger.debug('Left hand pose decoder: {}', left_hand_pose_desc.decoder) + + right_hand_pose_desc = create_pose_parameterization( + feat_extract_depth, num_angles=15, dtype=dtype, + append_params=append_params, + mean=mean_poses_dict.get('right_hand_pose', None), + pca_basis=body_model.right_hand_components, + create_regressor=False, **right_hand_cfg) + logger.debug('Right hand pose decoder: {}', right_hand_pose_desc.decoder) + + jaw_pose_desc = create_pose_parameterization( + feat_extract_depth, 1, dtype=dtype, + append_params=append_params, + create_regressor=False, **body_model_cfg.jaw_pose) + + logger.debug('Jaw pose decoder: {}', jaw_pose_desc.decoder) + + return { + 'global_orient': global_orient_desc, + 'body_pose': body_pose_desc, + 'left_hand_pose': left_hand_pose_desc, + 'right_hand_pose': right_hand_pose_desc, + 'jaw_pose': jaw_pose_desc, + } + + + +-- Chunk 6 -- +// pose_utils.py:214-245 +ss RotationMatrixRegressor(nn.Linear): + + def __init__(self, input_dim, num_angles, dtype=torch.float32, + append_params=True, **kwargs): + super(RotationMatrixRegressor, self).__init__( + input_dim + append_params * num_angles * 3, + num_angles * 9) + self.num_angles = num_angles + self.dtype = dtype + self.svd_projector = SVDRotationProjection() + + def get_param_dim(self): + return 9 + + def get_dim_size(self): + return self.num_angles * 9 + + def get_mean(self): + return torch.eye(3, dtype=self.dtype).unsqueeze(dim=0).expand( + self.num_angles, -1, -1) + + def forward(self, module_input): + rot_mats = super(RotationMatrixRegressor, self).forward( + module_input).view(-1, 3, 3) + + # Project the matrices on the manifold of rotation matrices using SVD + rot_mats = self.svd_projector(rot_mats).view( + -1, self.num_angles, 3, 3) + + return rot_mats + + + +-- Chunk 7 -- +// pose_utils.py:246-328 +ss ContinuousRotReprDecoder(nn.Module): + ''' Decoder for transforming a latent representation to rotation matrices + + Implements the decoding method described in: + "On the Continuity of Rotation Representations in Neural Networks" + ''' + + def __init__(self, num_angles, dtype=torch.float32, mean=None, + **kwargs): + super(ContinuousRotReprDecoder, self).__init__() + self.num_angles = num_angles + self.dtype = dtype + + if isinstance(mean, dict): + mean = mean.get('cont_rot_repr', None) + if mean is None: + mean = torch.tensor( + [1.0, 0.0, 0.0, 1.0, 0.0, 0.0], + dtype=self.dtype).unsqueeze(dim=0).expand( + self.num_angles, -1).contiguous().view(-1) + + if not torch.is_tensor(mean): + mean = torch.tensor(mean) + mean = mean.reshape(-1, 6) + + if mean.shape[0] < self.num_angles: + logger.debug(mean.shape) + mean = mean.repeat( + self.num_angles // mean.shape[0] + 1, 1).contiguous() + mean = mean[:self.num_angles] + elif mean.shape[0] > self.num_angles: + mean = mean[:self.num_angles] + + mean = mean.reshape(-1) + self.register_buffer('mean', mean) + + def get_type(self): + return 'cont_rot_repr' + + def extra_repr(self): + msg = 'Num angles: {}\n'.format(self.num_angles) + msg += 'Mean: {}'.format(self.mean.shape) + return msg + + def get_param_dim(self): + return 6 + + def get_dim_size(self): + return self.num_angles * 6 + + def get_mean(self): + return self.mean.clone() + + def to_offsets(self, x): + latent = x.reshape(-1, 3, 3)[:, :3, :2].reshape(-1, 6) + return (latent - self.mean).reshape(x.shape[0], -1, 6) + + def encode(self, x, subtract_mean=False): + orig_shape = x.shape + if subtract_mean: + raise NotImplementedError + output = x.reshape(-1, 3, 3)[:, :3, :2].contiguous() + return output.reshape( + orig_shape[0], orig_shape[1], 3, 2) + + def forward(self, module_input): + batch_size = module_input.shape[0] + reshaped_input = module_input.view(-1, 3, 2) + + # Normalize the first vector + b1 = F.normalize(reshaped_input[:, :, 0].clone(), dim=1) + + dot_prod = torch.sum( + b1 * reshaped_input[:, :, 1].clone(), dim=1, keepdim=True) + # Compute the second vector by finding the orthogonal complement to it + b2 = F.normalize(reshaped_input[:, :, 1] - dot_prod * b1, dim=1) + # Finish building the basis by taking the cross product + b3 = torch.cross(b1, b2, dim=1) + rot_mats = torch.stack([b1, b2, b3], dim=-1) + + return rot_mats.view(batch_size, -1, 3, 3) + + + +-- Chunk 8 -- +// pose_utils.py:329-362 +ss ContinuousRotReprRegressor(nn.Linear): + def __init__(self, input_dim, num_angles, dtype=torch.float32, + append_params=True, **kwargs): + super(ContinuousRotReprRegressor, self).__init__( + input_dim + append_params * num_angles * 6, num_angles * 6) + self.append_params = append_params + self.num_angles = num_angles + self.repr_decoder = ContinuousRotReprDecoder(num_angles) + + def get_dim_size(self): + return self.num_angles * 9 + + def get_mean(self): + if self.to_aa: + return torch.zeros([1, self.num_angles * 3], dtype=self.dtype) + else: + return torch.zeros([1, self.num_angles, 3, 3], dtype=self.dtype) + + def forward(self, module_input, prev_val): + if self.append_params: + if self.to_aa: + prev_val = batch_rodrigues(prev_val) + prev_val = prev_val[:, :, :2].contiguous().view( + -1, self.num_angles * 6) + + module_input = torch.cat([module_input, prev_val], dim=-1) + + cont_repr = super(ContinuousRotReprRegressor, + self).forward(module_input) + + output = self.repr_decoder(cont_repr).view(-1, self.num_angles, 3, 3) + return output + + + +-- Chunk 9 -- +// pose_utils.py:363-397 +ss SVDRotationProjection(nn.Module): + def __init__(self, **kwargs): + super(SVDRotationProjection, self).__init__() + + def forward(self, module_input): + # Before converting the output rotation matrices of the VAE to + # axis-angle representation, we first need to make them in to valid + # rotation matrices + with torch.no_grad(): + # TODO: Replace with Batch SVD once merged + # Iterate over the batch dimension and compute the SVD + svd_input = module_input.detach().cpu() + # svd_input = output + norm_rotation = torch.zeros_like(svd_input) + for bidx in range(module_input.shape[0]): + U, _, V = torch.svd(svd_input[bidx]) + + # Multiply the U, V matrices to get the closest orthonormal + # matrix + norm_rotation[bidx] = torch.matmul(U, V.t()) + norm_rotation = norm_rotation.to(module_input.device) + + # torch.svd supports backprop only for full-rank matrices. + # The output is calculated as the valid rotation matrix plus the + # output minus the detached output. If one writes down the + # computational graph for this operation, it will become clear the + # output is the desired valid rotation matrix, while for the + # backward pass gradients are propagated only to the original + # matrix + # Source: PyTorch Gumbel-Softmax hard sampling + # https://pytorch.org/docs/stable/_modules/torch/nn/functional.html#gumbel_softmax + correct_rot = norm_rotation - module_input.detach() + module_input + return correct_rot + + + +-- Chunk 10 -- +// pose_utils.py:398-423 +ss AARegressor(nn.Linear): + def __init__(self, input_dim, num_angles, dtype=torch.float32, + append_params=True, to_aa=True, **kwargs): + super(AARegressor, self).__init__( + input_dim + append_params * num_angles * 3, num_angles * 3) + self.num_angles = num_angles + self.to_aa = to_aa + self.dtype = dtype + + def get_param_dim(self): + return 3 + + def get_dim_size(self): + return self.num_angles * 3 + + def get_mean(self): + return torch.zeros([self.num_angles * 3], dtype=self.dtype) + + def forward(self, features): + aa_vectors = super(AARegressor, self).forward(features).view( + -1, self.num_angles, 3) + + return batch_rodrigues(aa_vectors.view(-1, 3)).view( + -1, self.num_angles, 3, 3) + + + +-- Chunk 11 -- +// pose_utils.py:424-451 +ss AADecoder(nn.Module): + def __init__(self, num_angles, dtype=torch.float32, mean=None, **kwargs): + super(AADecoder, self).__init__() + self.num_angles = num_angles + self.dtype = dtype + + if isinstance(mean, dict): + mean = mean.get('aa', None) + if mean is None: + mean = torch.zeros([num_angles * 3], dtype=dtype) + + if not torch.is_tensor(mean): + mean = torch.tensor(mean, dtype=dtype) + mean = mean.reshape(-1) + self.register_buffer('mean', mean) + + def get_dim_size(self): + return self.num_angles * 3 + + def get_mean(self): + return torch.zeros([self.get_dim_size()], dtype=self.dtype) + + def forward(self, module_input): + output = batch_rodrigues(module_input.view(-1, 3)).view( + -1, self.num_angles, 3, 3) + return output + + + +-- Chunk 12 -- +// pose_utils.py:452-523 +ss PCADecoder(nn.Module): + def __init__(self, num_pca_comps=12, pca_basis=None, dtype=torch.float32, + mean=None, + **kwargs): + super(PCADecoder, self).__init__() + self.num_pca_comps = num_pca_comps + self.dtype = dtype + pca_basis_tensor = torch.tensor(pca_basis, dtype=dtype) + self.register_buffer('pca_basis', + pca_basis_tensor[:self.num_pca_comps]) + inv_basis = torch.inverse( + pca_basis_tensor.t()).unsqueeze(dim=0) + self.register_buffer('inv_pca_basis', inv_basis) + + if isinstance(mean, dict): + mean = mean.get('aa', None) + + if mean is None: + mean = torch.zeros([45], dtype=dtype) + + if not torch.is_tensor(mean): + mean = torch.tensor(mean, dtype=dtype) + mean = mean.reshape(-1).reshape(1, -1) + self.register_buffer('mean', mean) + + def get_param_dim(self): + return self.num_pca_comps + + def extra_repr(self): + msg = 'PCA Components = {}'.format(self.num_pca_comps) + return msg + + def get_mean(self): + return self.mean.clone() + + def get_dim_size(self): + return self.num_pca_comps + + def to_offsets(self, x): + batch_size = x.shape[0] + # Convert the rotation matrices to axis angle + aa = batch_rot2aa(x.reshape(-1, 3, 3)).reshape(batch_size, 45, 1) + + # Project to the PCA space + offsets = torch.matmul( + self.inv_pca_basis, aa + ).reshape(batch_size, -1)[:, :self.num_pca_comps] + + return offsets - self.mean + + def encode(self, x, subtract_mean=False): + batch_size = x.shape[0] + # Convert the rotation matrices to axis angle + aa = batch_rot2aa(x.reshape(-1, 3, 3)).reshape(batch_size, 45, 1) + + # Project to the PCA space + output = torch.matmul( + self.inv_pca_basis, aa + ).reshape(batch_size, -1)[:, :self.num_pca_comps] + if subtract_mean: + # Remove the mean offset + output -= self.mean + + return output + + def forward(self, pca_coeffs): + batch_size = pca_coeffs.shape[0] + decoded = torch.einsum( + 'bi,ij->bj', [pca_coeffs, self.pca_basis]) + self.mean + + return batch_rodrigues(decoded.view(-1, 3)).view( + batch_size, -1, 3, 3) + +=== File: expose/models/common/rigid_alignment.py === + +-- Chunk 1 -- +// rigid_alignment.py:28-101 +ss RotationTranslationAlignment(nn.Module): + def __init__(self) -> None: + ''' Implements rotation and translation alignment with least squares + + For more information see: + + Least-Squares Rigid Motion Using SVD + Olga Sorkine-Hornung and Michael Rabinovich + + ''' + super(RotationTranslationAlignment, self).__init__() + + def forward( + self, + p: Tensor, + q: Tensor) -> Tensor: + ''' Aligns two point clouds using the optimal R, T + + Parameters + ---------- + p: BxNx3, torch.Tensor + The first of points + q: BxNx3, torch.Tensor + + Returns + ------- + p_hat: BxNx3, torch.Tensor + The points p after least squares alignment to q + ''' + batch_size = p.shape[0] + dtype = p.dtype + device = p.device + + p_transpose = p.transpose(1, 2) + q_transpose = q.transpose(1, 2) + + # 1. Remove mean. + p_mean = torch.mean(p_transpose, dim=-1, keepdim=True) + q_mean = torch.mean(q_transpose, dim=-1, keepdim=True) + + p_centered = p_transpose - p_mean + q_centered = q_transpose - q_mean + + # 2. Compute variance of X1 used for scale. + var_p = torch.sum(p_centered.pow(2), dim=(1, 2), keepdim=True) + # var_q = torch.sum(q_centered.pow(2), dim=(1, 2), keepdim=True) + + # Compute the outer product of the two point sets + # Should be Bx3x3 + K = torch.bmm(p_centered, q_centered.transpose(1, 2)) + # Apply SVD on the outer product matrix to recover the rotation + U, S, V = torch.svd(K) + + # Make sure that the computed rotation does not contain a reflection + Z = torch.eye(3, dtype=dtype, device=device).view( + 1, 3, 3).expand(batch_size, -1, -1).contiguous() + + raw_product = torch.bmm(U, V.transpose(1, 2)) + Z[:, -1, -1] *= torch.sign(torch.det(raw_product)) + + # Compute the final rotation matrix + rotation = torch.bmm(V, torch.bmm(Z, U.transpose(1, 2))) + + scale = torch.einsum('bii->b', [torch.bmm(rotation, K)]) / var_p.view( + -1) + + # Compute the translation vector + translation = q_mean - scale.reshape(batch_size, 1, 1) * torch.bmm( + rotation, p_mean) + + return ( + scale.reshape(batch_size, 1, 1) * + torch.bmm(rotation, p_transpose) + + translation).transpose(1, 2) + +=== File: expose/models/common/networks.py === + +-- Chunk 1 -- +// networks.py:33-44 + create_activation(activ_type='relu', lrelu_slope=0.2, + inplace=True, **kwargs): + if activ_type == 'relu': + return nn.ReLU(inplace=inplace) + elif activ_type == 'leaky-relu': + return nn.LeakyReLU(negative_slope=lrelu_slope, inplace=inplace) + elif activ_type == 'none': + return None + else: + raise ValueError(f'Unknown activation type: {activ_type}') + + + +-- Chunk 2 -- +// networks.py:45-63 + create_norm_layer(input_dim, norm_type='none', num_groups=32, dim=1, + **kwargs): + if norm_type == 'bn': + if dim == 1: + return nn.BatchNorm1d(input_dim) + elif dim == 2: + return nn.BatchNorm2d(input_dim) + else: + raise ValueError(f'Wrong dimension for BN: {dim}') + if norm_type == 'ln': + return nn.LayerNorm(input_dim) + elif norm_type == 'gn': + return nn.GroupNorm(num_groups, input_dim) + elif norm_type.lower() == 'none': + return None + else: + raise ValueError(f'Unknown normalization type: {norm_type}') + + + +-- Chunk 3 -- +// networks.py:64-75 + create_adapt_pooling(name='avg', dim='2d', ksize=1): + if dim == '2d': + if name == 'avg': + return nn.AdaptiveAvgPool2d(ksize) + elif name == 'max': + return nn.AdaptiveMaxPool2d(ksize) + else: + raise ValueError(f'Unknown pooling type: {name}') + else: + raise ValueError('Unknown pooling dimensionality: {dim}') + + + +-- Chunk 4 -- +// networks.py:76-147 +ss FrozenBatchNorm2d(nn.Module): + """ + BatchNorm2d where the batch statistics and the affine parameters + are fixed + """ + + def __init__(self, n): + super(FrozenBatchNorm2d, self).__init__() + self.register_buffer("weight", torch.ones(n)) + self.register_buffer("bias", torch.zeros(n)) + self.register_buffer("running_mean", torch.zeros(n)) + self.register_buffer("running_var", torch.ones(n)) + + @staticmethod + def from_bn(module: nn.BatchNorm2d): + ''' Initializes a frozen batch norm module from a batch norm module + ''' + dim = len(module.weight.data) + + frozen_module = FrozenBatchNorm2d(dim) + frozen_module.weight.data = module.weight.data + + missing, not_found = frozen_module.load_state_dict( + module.state_dict(), strict=False) + return frozen_module + + @classmethod + def convert_frozen_batchnorm(cls, module): + """ + Convert BatchNorm/SyncBatchNorm in module into FrozenBatchNorm. + + Args: + module (torch.nn.Module): + + Returns: + If module is BatchNorm/SyncBatchNorm, returns a new module. + Otherwise, in-place convert module and return it. + + Similar to convert_sync_batchnorm in + https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/batchnorm.py + """ + bn_module = nn.modules.batchnorm + bn_module = (bn_module.BatchNorm2d, bn_module.SyncBatchNorm) + res = module + if isinstance(module, bn_module): + res = cls(module.num_features) + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for name, child in module.named_children(): + new_child = cls.convert_frozen_batchnorm(child) + if new_child is not child: + res.add_module(name, new_child) + return res + + def forward(self, x): + # Cast all fixed parameters to half() if necessary + if x.dtype == torch.float16: + self.weight = self.weight.half() + self.bias = self.bias.half() + self.running_mean = self.running_mean.half() + self.running_var = self.running_var.half() + + return F.batch_norm( + x, self.running_mean, self.running_var, self.weight, self.bias, + False) + + + +-- Chunk 5 -- +// networks.py:148-178 +ss ConvNormActiv(nn.Module): + def __init__(self, input_dim, output_dim, kernel_size=1, + activation='relu', + norm_type='bn', + padding=0, + **kwargs): + super(ConvNormActiv, self).__init__() + layers = [] + + norm_layer = create_norm_layer(output_dim, norm_type, + dim=2, + **kwargs) + bias = norm_layer is None + + layers.append( + nn.Conv2d(input_dim, output_dim, kernel_size=kernel_size, + padding=padding, + bias=bias)) + if norm_layer is not None: + layers.append(norm_layer) + + activ = create_activation(**kwargs) + if activ is not None: + layers.append(activ) + + self.model = nn.Sequential(*layers) + + def forward(self, x): + return self.model(x) + + + +-- Chunk 6 -- +// networks.py:179-260 +ss MLP(nn.Module): + def __init__( + self, + input_dim: int, + output_dim: int, + layers: Optional[List[int]] = None, + activation: str = 'relu', + norm_type: str = 'bn', + dropout: float = 0.0, + gain: float = 0.01, + preactivated: bool = False, + flatten: bool = True, + **kwargs + ): + ''' Simple MLP module + ''' + super(MLP, self).__init__() + if layers is None: + layers = [] + self.flatten = flatten + + curr_input_dim = input_dim + self.num_layers = len(layers) + + self.blocks = [] + for layer_idx, layer_dim in enumerate(layers): + activ = create_activation(**kwargs) + norm_layer = create_norm_layer(layer_dim, norm_type, **kwargs) + bias = norm_layer is None + + linear = nn.Linear(curr_input_dim, layer_dim, bias=bias) + curr_input_dim = layer_dim + + layer = [] + if preactivated: + if norm_layer is not None: + layer.append(norm_layer) + + if activ is not None: + layer.append(activ) + + layer.append(linear) + + if dropout > 0.0: + layer.append(nn.Dropout(dropout)) + else: + layer.append(linear) + + if activ is not None: + layer.append(activ) + + if norm_layer is not None: + layer.append(norm_layer) + + if dropout > 0.0: + layer.append(nn.Dropout(dropout)) + + block = nn.Sequential(*layer) + self.add_module('layer_{:03d}'.format(layer_idx), block) + self.blocks.append(block) + + self.output_layer = nn.Linear(curr_input_dim, output_dim) + init_weights(self.output_layer, gain=gain, + init_type='xavier', + distr='uniform') + + def extra_repr(self): + msg = [] + msg.append('Flatten: {}'.format(self.flatten)) + return '\n'.join(msg) + + def forward(self, module_input): + batch_size = module_input.shape[0] + # Flatten all dimensions + curr_input = module_input + if self.flatten: + curr_input = curr_input.view(batch_size, -1) + for block in self.blocks: + curr_input = block(curr_input) + return self.output_layer(curr_input) + + + +-- Chunk 7 -- +// networks.py:261-344 +ss IterativeRegression(nn.Module): + def __init__(self, module, mean_param, num_stages=1, + append_params=True, learn_mean=False, + detach_mean=False, dim=1, + **kwargs): + super(IterativeRegression, self).__init__() + logger.info(f'Building iterative regressor with {num_stages} stages') + + self.module = module + self._num_stages = num_stages + self.dim = dim + + if learn_mean: + self.register_parameter('mean_param', + nn.Parameter(mean_param, + requires_grad=True)) + else: + self.register_buffer('mean_param', mean_param) + + self.append_params = append_params + self.detach_mean = detach_mean + logger.info(f'Detach mean: {self.detach_mean}') + + def get_mean(self): + return self.mean_param.clone() + + @property + def num_stages(self): + return self._num_stages + + def extra_repr(self): + msg = [ + f'Num stages = {self.num_stages}', + f'Concatenation dimension: {self.dim}', + f'Detach mean: {self.detach_mean}', + ] + return '\n'.join(msg) + + def forward( + self, + features: Tensor, + cond: Optional[Tensor] = None + ) -> Tuple[List[Tensor], List[Tensor]]: + ''' Computes deltas on top of condition iteratively + + Parameters + ---------- + features: torch.Tensor + Input features + ''' + batch_size = features.shape[0] + expand_shape = [batch_size] + [-1] * len(features.shape[1:]) + + parameters = [] + deltas = [] + module_input = features + + if cond is None: + cond = self.mean_param.expand(*expand_shape).clone() + + # Detach mean + if self.detach_mean: + cond = cond.detach() + + if self.append_params: + assert features is not None, ( + 'Features are none even though append_params is True') + + module_input = torch.cat([ + module_input, + cond], + dim=self.dim) + deltas.append(self.module(module_input)) + num_params = deltas[-1].shape[1] + parameters.append(cond[:, :num_params].clone() + deltas[-1]) + + for stage_idx in range(1, self.num_stages): + module_input = torch.cat( + [features, parameters[stage_idx - 1]], dim=-1) + params_upd = self.module(module_input) + deltas.append(params_upd) + parameters.append(parameters[stage_idx - 1] + params_upd) + + return parameters, deltas + +=== File: expose/models/common/flame_loss_modules.py === + +-- Chunk 1 -- +// flame_loss_modules.py:39-188 +ss FLAMELossModule(nn.Module): + ''' + ''' + + def __init__(self, loss_cfg, use_face_contour=False): + super(FLAMELossModule, self).__init__() + + self.penalize_final_only = loss_cfg.get('penalize_final_only', True) + self.loss_enabled = defaultdict(lambda: True) + self.loss_activ_step = {} + + idxs_dict = get_part_idxs() + head_idxs = idxs_dict['flame'] + if not use_face_contour: + head_idxs = head_idxs[:-17] + + self.register_buffer('head_idxs', torch.tensor(head_idxs)) + + # TODO: Add vertex loss + vertices_loss_cfg = loss_cfg.vertices + self.vertices_weight = vertices_loss_cfg.get('weight', 0.0) + self.vertices_loss = build_loss(**vertices_loss_cfg) + self.loss_activ_step['vertices'] = vertices_loss_cfg.enable + + self.use_alignment = vertices_loss_cfg.get('use_alignment', False) + if self.use_alignment: + self.alignment = RotationTranslationAlignment() + + edge_loss_cfg = loss_cfg.get('edge', {}) + self.edge_weight = edge_loss_cfg.get('weight', 0.0) + self.edge_loss = build_loss(**edge_loss_cfg) + self.loss_activ_step['edge'] = edge_loss_cfg.get('enable', 0) + + shape_loss_cfg = loss_cfg.shape + self.shape_weight = shape_loss_cfg.weight + self.shape_loss = build_loss(**shape_loss_cfg) + self.loss_activ_step['shape'] = shape_loss_cfg.enable + + expression_cfg = loss_cfg.get('expression', {}) + + self.expression_weight = expression_cfg.weight + if self.expression_weight > 0: + self.expression_loss = build_loss(**expression_cfg) + self.loss_activ_step[ + 'expression'] = expression_cfg.enable + + global_orient_cfg = loss_cfg.global_orient + self.global_orient_loss = build_loss(**global_orient_cfg) + logger.debug(f'Global pose loss: {self.global_orient_loss}') + self.global_orient_weight = global_orient_cfg.weight + self.loss_activ_step['global_orient'] = global_orient_cfg.enable + + jaw_pose_cfg = loss_cfg.get('jaw_pose', {}) + jaw_pose_loss_type = jaw_pose_cfg.type + self.jaw_pose_weight = jaw_pose_cfg.weight + + if self.jaw_pose_weight > 0: + self.jaw_pose_loss_type = jaw_pose_loss_type + self.jaw_pose_loss = build_loss(**jaw_pose_cfg) + logger.debug('Jaw pose loss: {}', self.jaw_pose_loss) + self.loss_activ_step['jaw_pose'] = jaw_pose_cfg.enable + + face_edge_2d_cfg = loss_cfg.get('face_edge_2d', {}) + self.face_edge_2d_weight = face_edge_2d_cfg.get('weight', 0.0) + self.face_edge_2d_enable_at = face_edge_2d_cfg.get('enable', 0) + if self.face_edge_2d_weight > 0: + face_connections = [] + for conn in FACE_CONNECTIONS: + if ('contour' in KEYPOINT_NAMES[conn[0]] or + 'contour' in KEYPOINT_NAMES[conn[1]]): + if not use_face_contour: + continue + face_connections.append(conn) + + self.face_edge_2d_loss = build_loss( + type='edge', connections=face_connections, **face_edge_2d_cfg) + logger.debug('2D face edge loss: {}', self.face_edge_2d_loss) + self.face_edge_2d_active = False + + face_joints2d_cfg = loss_cfg.joints_2d + self.face_joints_2d_weight = face_joints2d_cfg.weight + self.face_joints_2d_enable_at = face_joints2d_cfg.enable + if self.face_joints_2d_weight > 0: + self.face_joints_2d_loss = build_loss(**face_joints2d_cfg) + logger.debug('2D face joints loss: {}', self.face_joints_2d_loss) + self.face_joints_2d_active = False + + face_joints3d_cfg = loss_cfg.joints_3d + self.face_joints_3d_weight = face_joints3d_cfg.weight + self.face_joints_3d_enable_at = face_joints3d_cfg.enable + if self.face_joints_3d_weight > 0: + self.face_joints_3d_loss = build_loss(**face_joints3d_cfg) + logger.debug('3D face joints loss: {}', self.face_joints_3d_loss) + self.face_joints_3d_active = False + + def is_active(self) -> bool: + return any(self.loss_enabled.values()) + + def toggle_losses(self, step) -> None: + for key in self.loss_activ_step: + self.loss_enabled[key] = step >= self.loss_activ_step[key] + + def extra_repr(self) -> str: + msg = [] + msg.append('Shape weight: {}'.format(self.shape_weight)) + if self.expression_weight > 0: + msg.append(f'Expression weight: {self.expression_weight}') + msg.append(f'Global pose weight: {self.global_orient_weight}') + if self.jaw_pose_weight > 0: + msg.append(f'Jaw pose weight: {self.jaw_pose_weight}') + return '\n'.join(msg) + + def single_loss_step(self, parameters, + global_orient=None, + jaw_pose=None, + betas=None, + expression=None, + gt_vertices=None, + device=None, + keyp_confs=None, + gt_expression_idxs=None, + ): + losses = defaultdict( + lambda: torch.tensor(0, device=device, dtype=torch.float32)) + + if (self.shape_weight > 0 and self.loss_enabled['betas'] and + betas is not None): + shape_common_dim = min(parameters['betas'].shape[-1], + betas.shape[-1]) + losses['shape_loss'] = ( + self.shape_loss(parameters['betas'][:, :shape_common_dim], + betas[:, :shape_common_dim]) * + self.shape_weight) + + param_vertices = parameters.get('vertices', None) + compute_vertex_loss = (self.vertices_weight > 0 and + param_vertices is not None and + gt_vertices is not None) + if compute_vertex_loss: + if self.use_alignment: + aligned_verts = self.alignment(param_vertices, gt_vertices) + else: + aligned_verts = param_vertices + losses['vertex_loss'] = self.vertices_weight * self.vertices_loss( + aligned_verts, gt_vertices) + + compute_edge_loss = (self.edge_weight > 0 and + param_vertices is not None and + gt_vertices is not None) + if compute_edge_loss: + +-- Chunk 2 -- +// flame_loss_modules.py:189-316 + edge_loss_val = self.edge_loss( + gt_vertices=gt_vertices, est_vertices=param_vertices) + losses['mesh_edge_loss'] = self.edge_weight * edge_loss_val + + if (self.expression_weight > 0 and self.loss_enabled['expression'] and + expression is not None): + expr_common_dim = min( + parameters['expression'].shape[-1], expression.shape[-1]) + pred_expr = parameters['expression'][:, :expr_common_dim] + if gt_expression_idxs is not None: + pred_expr = pred_expr[gt_expression_idxs] + + losses['expression_loss'] = ( + self.expression_loss( + pred_expr, expression[:, :expr_common_dim]) * + self.expression_weight) + + if (self.global_orient_weight > 0 and + self.loss_enabled['global_orient'] and + global_orient is not None): + losses['global_orient_loss'] = ( + self.global_orient_loss( + parameters['head_pose'], global_orient) * + self.global_orient_weight) + + if (self.jaw_pose_weight > 0 and self.loss_enabled['jaw_pose'] and + jaw_pose is not None): + losses['jaw_pose_loss'] = ( + self.jaw_pose_loss( + parameters['jaw_pose'], jaw_pose) * + self.jaw_pose_weight) + + return losses + + def forward(self, input_dict, + head_targets, + device=None): + if device is None: + device = torch.device('cpu') + + # Stack the GT keypoints and conf for the predictions of the right hand + face_keyps = torch.stack([t.smplx_keypoints for t in head_targets]) + face_conf = torch.stack([t.conf for t in head_targets]) + + # Get the GT pose of the right hand + global_orient = torch.stack( + [t.get_field('global_orient').global_orient for t in head_targets]) + # Get the GT pose of the right hand + gt_jaw_pose = torch.stack( + [t.get_field('jaw_pose').jaw_pose + for t in head_targets]) + + has_vertices = all( + [t.has_field('vertices') for t in head_targets]) + gt_vertices = None + if has_vertices: + gt_vertices = torch.stack([ + t.get_field('vertices').vertices + for t in head_targets]) + # Get the GT pose of the right hand + gt_expression = torch.stack([t.get_field('expression').expression + for t in head_targets + if t.has_field('expression')]) + gt_expression_idxs = torch.tensor( + [idx for idx, t in enumerate(head_targets) + if t.has_field('expression')], device=device, dtype=torch.long) + + output_losses = {} + compute_2d_loss = ('proj_joints' in input_dict and + self.face_joints_2d_weight > 0) + if compute_2d_loss: + face_proj_joints = input_dict['proj_joints'] + face_joints2d = self.face_joints_2d_loss( + face_proj_joints, + face_keyps[:, self.head_idxs], + weights=face_conf[:, self.head_idxs]) + output_losses['head_branch_joints2d'] = ( + face_joints2d * self.face_joints_2d_weight) + + head_keyps = [t.get_field('keypoints3d').smplx_keypoints + for t in head_targets + if t.has_field('keypoints3d')] + head_conf = [t.get_field('keypoints3d').conf for t in head_targets + if t.has_field('keypoints3d')] + # Keep the indices of the targets that have 3D joint annotations + head_idxs = [idx for idx, t in enumerate(head_targets) + if t.has_field('keypoints3d')] + + num_stages = input_dict.get('num_stages', 1) + curr_params = input_dict.get(f'stage_{num_stages - 1:02d}', None) + joints3d = curr_params['joints'] + compute_3d_joint_loss = (self.face_joints_3d_weight > 0 and + len(head_conf) > 0) + if compute_3d_joint_loss: + all_keyps3d = torch.stack(head_keyps, dim=0)[:, self.head_idxs] + all_conf3d = torch.stack(head_conf, dim=0)[:, self.head_idxs] + + head_keyp3d_loss = self.face_joints_3d_loss( + joints3d[head_idxs], + all_keyps3d, + weights=all_conf3d + ) * self.face_joints_3d_weight + output_losses['head_branch_joints3d'] = head_keyp3d_loss + + for n in range(1, num_stages + 1): + if self.penalize_final_only and n < num_stages: + continue + curr_params = input_dict.get(f'stage_{n - 1:02d}', None) + if curr_params is None: + logger.warning(f'Network output for stage {n} is None') + continue + + curr_losses = self.single_loss_step( + curr_params, + jaw_pose=gt_jaw_pose, + global_orient=global_orient, + expression=gt_expression, + gt_vertices=gt_vertices, + device=device, + gt_expression_idxs=gt_expression_idxs, + ) + for key in curr_losses: + out_key = f'stage_{n - 1:02d}_{key}' + output_losses[out_key] = curr_losses[key] + + return output_losses + + + +-- Chunk 3 -- +// flame_loss_modules.py:317-407 +ss RegularizerModule(nn.Module): + def __init__(self, loss_cfg, + num_stages=3, jaw_pose_mean=None): + super(RegularizerModule, self).__init__() + + self.regularize_final_only = loss_cfg.get( + 'regularize_final_only', True) + self.num_stages = num_stages + + # Construct the shape prior + shape_prior_type = loss_cfg.shape.prior.type + self.shape_prior_weight = loss_cfg.shape.prior.weight + if self.shape_prior_weight > 0: + self.shape_prior = build_prior(shape_prior_type, + **loss_cfg.shape.prior) + logger.debug(f'Shape prior {self.shape_prior}') + + # Construct the expression prior + expression_prior_cfg = loss_cfg.expression.prior + expression_prior_type = expression_prior_cfg.type + self.expression_prior_weight = expression_prior_cfg.weight + if self.expression_prior_weight > 0: + self.expression_prior = build_prior( + expression_prior_type, + **expression_prior_cfg) + logger.debug(f'Expression prior {self.expression_prior}') + + # Construct the jaw pose prior + jaw_pose_prior_cfg = loss_cfg.jaw_pose.prior + jaw_pose_prior_type = jaw_pose_prior_cfg.type + self.jaw_pose_prior_weight = jaw_pose_prior_cfg.weight + if self.jaw_pose_prior_weight > 0: + self.jaw_pose_prior = build_prior( + jaw_pose_prior_type, mean=jaw_pose_mean, **jaw_pose_prior_cfg) + logger.debug(f'Jaw pose prior {self.jaw_pose_prior}') + + logger.debug(self) + + def extra_repr(self) -> str: + msg = [] + if self.shape_prior_weight > 0: + msg.append(f'Shape prior weight: {self.shape_prior_weight}') + if self.expression_prior_weight > 0: + msg.append( + f'Expression prior weight: {self.expression_prior_weight}') + if self.jaw_pose_prior_weight > 0: + msg.append(f'Jaw pose prior weight: {self.jaw_pose_prior_weight}') + return '\n'.join(msg) + + def single_regularization_step(self, parameters, **kwargs): + prior_losses = {} + + betas = parameters.get('betas', None) + if self.shape_prior_weight > 0 and betas is not None: + prior_losses['shape_prior'] = ( + self.shape_prior_weight * self.shape_prior(betas)) + + expression = parameters.get('expression', None) + if self.expression_prior_weight > 0 and expression is not None: + prior_losses['expression_prior'] = ( + self.expression_prior(expression) * + self.expression_prior_weight) + + jaw_pose = parameters.get('jaw_pose', None) + if self.jaw_pose_prior_weight > 0 and jaw_pose is not None: + prior_losses['jaw_pose_prior'] = ( + self.jaw_pose_prior(jaw_pose) * + self.jaw_pose_prior_weight) + + return prior_losses + + def forward(self, + input_dict, + **kwargs) -> Dict[str, Tensor]: + + prior_losses = defaultdict(lambda: 0) + num_stages = input_dict.get('num_stages', 1) + for n in range(1, num_stages + 1): + if self.regularize_final_only and n < self.num_stages: + continue + curr_params = input_dict.get(f'stage_{n - 1:02d}', None) + if curr_params is None: + logger.warning(f'Network output for stage {n} is None') + continue + + curr_losses = self.single_regularization_step(curr_params) + for key, val in curr_losses.items(): + out_key = f'stage_{n - 1:02d}_{key}' + prior_losses[out_key] = val + + return prior_losses + +=== File: expose/models/common/mano_loss_modules.py === + +-- Chunk 1 -- +// mano_loss_modules.py:47-196 +ss MANOLossModule(nn.Module): + ''' + ''' + + def __init__(self, loss_cfg): + super(MANOLossModule, self).__init__() + + self.penalize_final_only = loss_cfg.get('penalize_final_only', True) + + self.loss_enabled = defaultdict(lambda: True) + self.loss_activ_step = {} + + idxs_dict = get_part_idxs() + hand_idxs = idxs_dict['hand'] + left_hand_idxs = idxs_dict['left_hand'] + right_hand_idxs = idxs_dict['right_hand'] + + self.register_buffer('hand_idxs', torch.tensor(hand_idxs)) + + self.register_buffer('left_hand_idxs', torch.tensor(left_hand_idxs)) + self.register_buffer('right_hand_idxs', torch.tensor(right_hand_idxs)) + + shape_loss_cfg = loss_cfg.shape + self.shape_weight = shape_loss_cfg.get('weight', 0.0) + self.shape_loss = build_loss(**shape_loss_cfg) + self.loss_activ_step['shape'] = shape_loss_cfg.enable + + vertices_loss_cfg = loss_cfg.vertices + self.vertices_weight = vertices_loss_cfg.get('weight', 0.0) + self.vertices_loss = build_loss(**vertices_loss_cfg) + self.loss_activ_step['vertices'] = vertices_loss_cfg.enable + + self.use_alignment = vertices_loss_cfg.get('use_alignment', False) + if self.use_alignment: + self.alignment = RotationTranslationAlignment() + + edge_loss_cfg = loss_cfg.get('edge', {}) + self.edge_weight = edge_loss_cfg.get('weight', 0.0) + self.edge_loss = build_loss(**edge_loss_cfg) + self.loss_activ_step['edge'] = edge_loss_cfg.get('enable', 0) + + global_orient_cfg = loss_cfg.global_orient + self.global_orient_loss = build_loss(**global_orient_cfg) + logger.debug('Global pose loss: {}', self.global_orient_loss) + self.global_orient_weight = global_orient_cfg.weight + self.loss_activ_step['global_orient'] = global_orient_cfg.enable + + hand_pose_cfg = loss_cfg.get('hand_pose', {}) + hand_pose_loss_type = loss_cfg.hand_pose.type + self.hand_use_conf = hand_pose_cfg.get('use_conf_weight', False) + + self.hand_pose_weight = loss_cfg.hand_pose.weight + if self.hand_pose_weight > 0: + self.hand_pose_loss_type = hand_pose_loss_type + self.hand_pose_loss = build_loss(**loss_cfg.hand_pose) + self.loss_activ_step['hand_pose'] = loss_cfg.hand_pose.enable + + joints2d_cfg = loss_cfg.joints_2d + self.joints_2d_weight = joints2d_cfg.weight + self.joints_2d_enable_at = joints2d_cfg.enable + if self.joints_2d_weight > 0: + self.joints_2d_loss = build_loss(**joints2d_cfg) + logger.debug('2D hand joints loss: {}', self.joints_2d_loss) + self.joints_2d_active = False + + hand_edge_2d_cfg = loss_cfg.get('hand_edge_2d', {}) + self.hand_edge_2d_weight = hand_edge_2d_cfg.get('weight', 0.0) + self.hand_edge_2d_enable_at = hand_edge_2d_cfg.get('enable', 0) + if self.hand_edge_2d_weight > 0: + self.hand_edge_2d_loss = build_loss( + type='edge', connections=HAND_CONNECTIONS, **hand_edge_2d_cfg) + logger.debug('2D hand edge loss: {}', self.hand_edge_2d_loss) + self.hand_edge_2d_active = False + + joints3d_cfg = loss_cfg.joints_3d + self.joints_3d_weight = joints3d_cfg.weight + self.joints_3d_enable_at = joints3d_cfg.enable + if self.joints_3d_weight > 0: + joints_3d_loss_type = joints3d_cfg.type + self.joints_3d_loss = build_loss(**joints3d_cfg) + logger.debug('3D hand joints loss: {}', self.joints_3d_loss) + self.joints_3d_active = False + + def is_active(self) -> bool: + return any(self.loss_enabled.values()) + + def toggle_losses(self, step) -> None: + for key in self.loss_activ_step: + self.loss_enabled[key] = step >= self.loss_activ_step[key] + + def extra_repr(self) -> str: + msg = [] + msg.append('Shape weight: {self.shape_weight}') + msg.append(f'Global pose weight: {self.global_orient_weight}') + if self.hand_pose_weight > 0: + msg.append(f'Hand pose weight: {self.hand_pose_weight}') + return '\n'.join(msg) + + def single_loss_step(self, parameters, + global_orient=None, + hand_pose=None, + gt_hand_pose_idxs=None, + shape=None, + gt_vertices=None, + gt_vertex_idxs=None, + device=None, + keyp_confs=None): + losses = defaultdict( + lambda: torch.tensor(0, device=device, dtype=torch.float32)) + + param_vertices = parameters.get('vertices', None) + compute_vertex_loss = (self.vertices_weight > 0 and + len(gt_vertex_idxs) > 0 and + param_vertices is not None and + gt_vertices is not None) + if gt_vertex_idxs is not None: + if len(gt_vertex_idxs) > 0: + param_vertices = param_vertices[gt_vertex_idxs] + + if compute_vertex_loss: + if self.use_alignment: + aligned_verts = self.alignment(param_vertices, gt_vertices) + else: + aligned_verts = param_vertices + losses['vertex_loss'] = self.vertices_weight * self.vertices_loss( + aligned_verts, gt_vertices) + + compute_edge_loss = (self.edge_weight > 0 and + len(gt_vertex_idxs) > 0 and + param_vertices is not None and + gt_vertices is not None) + if compute_edge_loss: + edge_loss_val = self.edge_loss( + gt_vertices=gt_vertices, + est_vertices=param_vertices) + losses['mesh_edge_loss'] = self.edge_weight * edge_loss_val + + if (self.shape_weight > 0 and self.loss_enabled['betas'] and + shape is not None): + losses['shape_loss'] = ( + self.shape_loss(parameters['betas'], shape) * + self.shape_weight) + + if (self.global_orient_weight > 0 and self.loss_enabled['globals'] and + global_orient is not None): + losses['global_orient_loss'] = ( + self.global_orient_loss( + parameters['wrist_pose'], global_orient) * + self.global_orient_weight) + + +-- Chunk 2 -- +// mano_loss_modules.py:197-312 + if (self.hand_pose_weight > 0 and + self.loss_enabled['hand_pose'] and + hand_pose is not None): + # num_joints = parameters['hand_pose'].shape[1] + # weights = ( + # keyp_confs['hand'].mean(axis=1, keepdim=True).expand( + # -1, num_joints).reshape(-1) + # if self.hand_use_conf and keyp_confs is not None else None) + # if weights is not None: + # num_ones = [1] * len( + # parameters['hand_pose'].shape[2:]) + # weights = weights.view(-1, num_joints, *num_ones) + losses['hand_pose_loss'] = ( + self.hand_pose_loss( + parameters['right_hand_pose'], hand_pose) * + self.hand_pose_weight) + + return losses + + def forward(self, input_dict, + hand_targets, + device=None): + if device is None: + device = torch.device('cpu') + + # Stack the GT keypoints and conf for the predictions of the right hand + hand_keyps = torch.stack( + [t.smplx_keypoints for t in hand_targets]) + hand_conf = torch.stack([t.conf for t in hand_targets]) + + # Get the GT pose of the right hand + gt_hand_pose = torch.stack( + [t.get_field('hand_pose').right_hand_pose + for t in hand_targets + if t.has_field('hand_pose') + ]) + gt_hand_pose_idxs = [ii for ii, t in enumerate(hand_targets) + if t.has_field('hand_pose')] + # Get the GT pose of the right hand + global_orient = torch.stack( + [t.get_field('global_orient').global_orient for t in hand_targets + if t.has_field('global_orient')]) + + gt_vertex_idxs = [ii for ii, t in enumerate(hand_targets) + if t.has_field('vertices')] + gt_vertices = None + if len(gt_vertex_idxs) > 0: + gt_vertices = torch.stack([ + t.get_field('vertices').vertices + for t in hand_targets + if t.has_field('vertices')]) + + output_losses = {} + compute_2d_loss = ('proj_joints' in input_dict and + self.joints_2d_weight > 0) + if compute_2d_loss: + hand_proj_joints = input_dict['proj_joints'] + hand_joints2d_loss = self.joints_2d_loss( + hand_proj_joints, + hand_keyps[:, self.right_hand_idxs], + weights=hand_conf[:, self.right_hand_idxs]) + output_losses['joints2d'] = ( + hand_joints2d_loss * self.joints_2d_weight) + + # Stack the GT keypoints and conf for the predictions of the + # right hand + hand_keyps_3d = [t.get_field('keypoints3d').smplx_keypoints + for t in hand_targets if t.has_field('keypoints3d')] + hand_conf_3d = [t.get_field('keypoints3d').conf + for t in hand_targets if t.has_field('keypoints3d')] + + num_stages = input_dict.get('num_stages', 1) + curr_params = input_dict.get(f'stage_{num_stages - 1:02d}', None) + joints3d = input_dict['joints'] + compute_3d_joint_loss = (self.joints_3d_weight > 0 and + len(hand_conf_3d) > 0) + + if compute_3d_joint_loss: + hand_keyps_3d = torch.stack(hand_keyps_3d)[:, self.right_hand_idxs] + hand_conf_3d = torch.stack(hand_conf_3d)[:, self.right_hand_idxs] + + pred_joints = joints3d + # Center the joints according to the wrist + centered_pred_joints = pred_joints - pred_joints[:, [0]] + gt_hand_keyps_3d = hand_keyps_3d - hand_keyps_3d[:, [0]] + hand_keyp3d_loss = self.joints_3d_loss( + centered_pred_joints, + gt_hand_keyps_3d, + weights=hand_conf_3d, + ) * self.joints_3d_weight + output_losses['joints3d'] = hand_keyp3d_loss + + for n in range(1, num_stages + 1): + if self.penalize_final_only and n < num_stages: + continue + + curr_params = input_dict.get(f'stage_{n - 1:02d}', None) + if curr_params is None: + logger.warning(f'Network output for stage {n} is None') + continue + + curr_losses = self.single_loss_step( + curr_params, + hand_pose=gt_hand_pose, + gt_hand_pose_idxs=gt_hand_pose_idxs, + global_orient=global_orient, + gt_vertices=gt_vertices, + gt_vertex_idxs=gt_vertex_idxs, + device=device) + for key in curr_losses: + out_key = f'stage_{n - 1:02d}_{key}' + output_losses[out_key] = curr_losses[key] + + return output_losses + + + +-- Chunk 3 -- +// mano_loss_modules.py:313-384 +ss RegularizerModule(nn.Module): + def __init__(self, loss_cfg, + body_pose_mean=None, hand_pose_mean=None): + super(RegularizerModule, self).__init__() + + self.regularize_final_only = loss_cfg.get( + 'regularize_final_only', True) + + # Construct the shape prior + shape_prior_type = loss_cfg.shape.prior.type + self.shape_prior_weight = loss_cfg.shape.prior.weight + if self.shape_prior_weight > 0: + self.shape_prior = build_prior(shape_prior_type, + **loss_cfg.shape.prior) + logger.debug(f'Shape prior {self.shape_prior}') + + hand_prior_cfg = loss_cfg.hand_pose.prior + hand_pose_prior_type = hand_prior_cfg.type + self.hand_pose_prior_weight = hand_prior_cfg.weight + if self.hand_pose_prior_weight > 0: + self.hand_pose_prior = build_prior( + hand_pose_prior_type, + mean=hand_pose_mean, + **hand_prior_cfg) + logger.debug(f'Hand pose prior {self.hand_pose_prior}') + + logger.debug(self) + + def extra_repr(self) -> str: + msg = [] + if self.shape_prior_weight > 0: + msg.append(f'Shape prior weight: {self.shape_prior_weight}') + if self.hand_pose_prior_weight > 0: + msg.append( + f'Hand pose prior weight: {self.hand_pose_prior_weight}') + return '\n'.join(msg) + + def single_regularization_step(self, parameters, **kwargs): + prior_losses = {} + + betas = parameters.get('betas', None) + if self.shape_prior_weight > 0 and betas is not None: + prior_losses['shape_prior'] = ( + self.shape_prior_weight * self.shape_prior(betas)) + + hand_pose = parameters.get('right_hand_pose', None) + if (self.hand_pose_prior_weight > 0 and + hand_pose is not None): + prior_losses['hand_pose_prior'] = ( + self.hand_pose_prior(hand_pose) * + self.hand_pose_prior_weight) + + return prior_losses + + def forward(self, + input_dict, **kwargs) -> Dict[str, Tensor]: + + prior_losses = defaultdict(lambda: 0) + num_stages = input_dict.get('num_stages', 1) + for n in range(1, num_stages + 1): + if self.regularize_final_only and n < num_stages: + continue + curr_params = input_dict.get(f'stage_{n - 1:02d}', None) + if curr_params is None: + logger.warning(f'Network output for stage {n} is None') + continue + + curr_losses = self.single_regularization_step(curr_params) + for key in curr_losses: + out_key = f'stage_{n - 1:02d}_{key}' + prior_losses[out_key] = curr_losses[key] + return prior_losses + +=== File: expose/models/common/bbox_sampler.py === + +-- Chunk 1 -- +// bbox_sampler.py:30-76 +ss ToCrops(nn.Module): + def __init__(self) -> None: + super(ToCrops, self).__init__() + + def forward( + self, + full_imgs: Union[ImageList, ImageListPacked], + points: Tensor, + targets: GenericTarget, + scale_factor: float = 1.0, + crop_size: int = 256 + ) -> Dict[str, Tensor]: + num_imgs, _, H, W = full_imgs.shape + device = points.device + dtype = points.dtype + + # Get the image to crop transformations and bounding box sizes + crop_transforms = [] + img_bbox_sizes = [] + for t in targets: + crop_transforms.append(t.get_field('crop_transform')) + img_bbox_sizes.append(t.get_field('bbox_size')) + + img_bbox_sizes = torch.tensor( + img_bbox_sizes, dtype=dtype, device=device) + + crop_transforms = torch.tensor( + crop_transforms, dtype=dtype, device=device) + inv_crop_transforms = torch.inverse(crop_transforms) + + center_body_crop, bbox_size = points_to_bbox( + points, bbox_scale_factor=scale_factor) + + orig_bbox_size = bbox_size / crop_size * img_bbox_sizes + # Compute the center of the crop in the original image + center = (torch.einsum( + 'bij,bj->bi', [inv_crop_transforms[:, :2, :2], center_body_crop]) + + inv_crop_transforms[:, :2, 2]) + + return {'center': center.reshape(-1, 2), + 'orig_bbox_size': orig_bbox_size, + 'bbox_size': bbox_size.reshape(-1), + 'inv_crop_transforms': inv_crop_transforms, + 'center_body_crop': 2 * center_body_crop / crop_size - 1, + } + + + +-- Chunk 2 -- +// bbox_sampler.py:77-226 +ss CropSampler(nn.Module): + def __init__( + self, + crop_size: int = 256 + ) -> None: + ''' Uses bilinear sampling to extract square crops + + This module expects a high resolution image as input and a bounding + box, described by its' center and size. It then proceeds to extract + a sub-image using the provided information through bilinear + interpolation. + + Parameters + ---------- + crop_size: int + The desired size for the crop. + ''' + super(CropSampler, self).__init__() + + self.crop_size = crop_size + x = torch.arange(0, crop_size, dtype=torch.float32) / (crop_size - 1) + grid_y, grid_x = torch.meshgrid(x, x) + + points = torch.stack([grid_y.flatten(), grid_x.flatten()], axis=1) + + self.register_buffer('grid', points.unsqueeze(dim=0)) + + def extra_repr(self) -> str: + return f'Crop size: {self.crop_size}' + + def bilinear_sampling(x0, x1, y0, y1): + pass + + def _sample_packed(self, full_imgs: ImageListPacked, sampling_grid, + padding_mode='zeros'): + device, dtype = sampling_grid.device, sampling_grid.dtype + batch_size = sampling_grid.shape[0] + tensor = full_imgs.as_tensor() + + flat_sampling_grid = sampling_grid.reshape(batch_size, -1, 2) + x, y = flat_sampling_grid[:, :, 0], flat_sampling_grid[:, :, 1] + + # Get the closest spatial locations + x0 = torch.floor(x).to(dtype=torch.long) + x1 = x0 + 1 + + y0 = torch.floor(y).to(dtype=torch.long) + y1 = y0 + 1 + + # Size: B + start_idxs = torch.tensor( + full_imgs.starts, dtype=torch.long, device=device) + # Size: 3 + rgb_idxs = torch.arange(3, dtype=torch.long, device=device) + # Size: B + height_tensor = torch.tensor( + full_imgs.heights, dtype=torch.long, device=device) + # Size: B + width_tensor = torch.tensor( + full_imgs.widths, dtype=torch.long, device=device) + + # Size: BxP + x0_in_bounds = x0.ge(0) & x0.le(width_tensor[:, None] - 1) + x1_in_bounds = x0.ge(0) & x0.le(width_tensor[:, None] - 1) + y0_in_bounds = y0.ge(0) & y0.le(height_tensor[:, None] - 1) + y1_in_bounds = y0.ge(0) & y0.le(height_tensor[:, None] - 1) + + zero = torch.tensor(0, dtype=torch.long, device=device) + x0 = torch.max( + torch.min(x0, width_tensor[:, None] - 1), zero) + x1 = torch.max(torch.min(x1, width_tensor[:, None] - 1), zero) + y0 = torch.max(torch.min(y0, height_tensor[:, None] - 1), zero) + y1 = torch.max(torch.min(y1, height_tensor[:, None] - 1), zero) + + flat_rgb_idxs = ( + rgb_idxs[None, :, None] * (width_tensor[:, None, None]) * + height_tensor[:, None, None]) + x0_y0_in_bounds = (x0_in_bounds & y0_in_bounds).unsqueeze( + dim=1).expand(-1, 3, -1) + x1_y0_in_bounds = (x1_in_bounds & y0_in_bounds).unsqueeze( + dim=1).expand(-1, 3, -1) + x0_y1_in_bounds = (x0_in_bounds & y1_in_bounds).unsqueeze( + dim=1).expand(-1, 3, -1) + x1_y1_in_bounds = (x1_in_bounds & y1_in_bounds).unsqueeze( + dim=1).expand(-1, 3, -1) + + idxs_x0_y0 = (start_idxs[:, None, None] + + flat_rgb_idxs + + y0[:, None, :] * + width_tensor[:, None, None] + x0[:, None, :]) + idxs_x1_y0 = (start_idxs[:, None, None] + + flat_rgb_idxs + + y0[:, None, :] * + width_tensor[:, None, None] + x1[:, None, :]) + idxs_x0_y1 = (start_idxs[:, None, None] + + flat_rgb_idxs + + y1[:, None, :] * width_tensor[:, None, None] + + x0[:, None, :]) + idxs_x1_y1 = (start_idxs[:, None, None] + + flat_rgb_idxs + + y1[:, None, :] * width_tensor[:, None, None] + + x1[:, None, :]) + + Ia = torch.zeros(idxs_x0_y0.shape, dtype=dtype, device=device) + Ia[x0_y0_in_bounds] = tensor[idxs_x0_y0[x0_y0_in_bounds]] + + Ib = torch.zeros(idxs_x1_y0.shape, dtype=dtype, device=device) + Ib[x1_y0_in_bounds] = tensor[idxs_x1_y0[x1_y0_in_bounds]] + + Ic = torch.zeros(idxs_x0_y1.shape, dtype=dtype, device=device) + Ic[x0_y1_in_bounds] = tensor[idxs_x0_y1[x0_y1_in_bounds]] + + Id = torch.zeros(idxs_x1_y1.shape, dtype=dtype, device=device) + Id[x1_y1_in_bounds] = tensor[idxs_x1_y1[x1_y1_in_bounds]] + + f1 = (x1 - x)[:, None] * Ia + (x - x0)[:, None] * Ib + f2 = (x1 - x)[:, None] * Ic + (x - x0)[:, None] * Id + + output = (y1 - y)[:, None] * f1 + (y - y0)[:, None] * f2 + return output.reshape(batch_size, 3, self.crop_size, self.crop_size) + + def _sample_padded( + self, + full_imgs: Union[ImageList, Tensor], + sampling_grid: Tensor + ) -> Tensor: + ''' + ''' + tensor = ( + full_imgs.as_tensor() if isinstance(full_imgs, (ImageList,)) else + full_imgs + ) + # Get the sub-images using bilinear interpolation + return F.grid_sample(tensor, sampling_grid, align_corners=True) + + def forward( + self, + full_imgs: Union[Tensor, ImageList, ImageListPacked], + center: Tensor, + bbox_size: Tensor + ) -> Tuple[Tensor, Tensor]: + ''' Crops the HD images using the provided bounding boxes + + Parameters + ---------- + full_imgs: ImageList + An image list structure with the full resolution images + center: torch.Tensor + A Bx2 tensor that contains the coordinates of the center of + the bounding box that will be cropped from the original + +-- Chunk 3 -- +// bbox_sampler.py:227-301 + image + bbox_size: torch.Tensor + A size B tensor that contains the size of the corp + + Returns + ------- + cropped_images: torch.Tensoror + The images cropped from the high resolution input + sampling_grid: torch.Tensor + The grid used to sample the crops + ''' + + batch_size, _, H, W = full_imgs.shape + transforms = torch.eye( + 3, dtype=full_imgs.dtype, device=full_imgs.device).reshape( + 1, 3, 3).expand(batch_size, -1, -1).contiguous() + + hd_to_crop = torch.eye( + 3, dtype=full_imgs.dtype, device=full_imgs.device).reshape( + 1, 3, 3).expand(batch_size, -1, -1).contiguous() + + # Create the transformation that maps crop pixels to image coordinates, + # i.e. pixel (0, 0) from the crop_size x crop_size grid gets mapped to + # the top left of the bounding box, pixel + # (crop_size - 1, crop_size - 1) to the bottom right corner of the + # bounding box + transforms[:, 0, 0] = bbox_size # / (self.crop_size - 1) + transforms[:, 1, 1] = bbox_size # / (self.crop_size - 1) + transforms[:, 0, 2] = center[:, 0] - bbox_size * 0.5 + transforms[:, 1, 2] = center[:, 1] - bbox_size * 0.5 + + hd_to_crop[:, 0, 0] = 2 * (self.crop_size - 1) / bbox_size + hd_to_crop[:, 1, 1] = 2 * (self.crop_size - 1) / bbox_size + hd_to_crop[:, 0, 2] = -( + center[:, 0] - bbox_size * 0.5) * hd_to_crop[:, 0, 0] - 1 + hd_to_crop[:, 1, 2] = -( + center[:, 1] - bbox_size * 0.5) * hd_to_crop[:, 1, 1] - 1 + + size_bbox_sizer = torch.eye( + 3, dtype=full_imgs.dtype, device=full_imgs.device).reshape( + 1, 3, 3).expand(batch_size, -1, -1).contiguous() + + if isinstance(full_imgs, (ImageList, torch.Tensor)): + # Normalize the coordinates to [-1, 1] for the grid_sample function + size_bbox_sizer[:, 0, 0] = 2.0 / (W - 1) + size_bbox_sizer[:, 1, 1] = 2.0 / (H - 1) + size_bbox_sizer[:, :2, 2] = -1 + + # full_transform = transforms + full_transform = torch.bmm(size_bbox_sizer, transforms) + + batch_grid = self.grid.expand(batch_size, -1, -1) + # Convert the grid to image coordinates using the transformations above + sampling_grid = (torch.bmm( + full_transform[:, :2, :2], + batch_grid.transpose(1, 2)) + + full_transform[:, :2, [2]]).transpose(1, 2) + sampling_grid = sampling_grid.reshape( + -1, self.crop_size, self.crop_size, 2).transpose(1, 2) + + if isinstance(full_imgs, (ImageList, torch.Tensor)): + out_images = self._sample_padded( + full_imgs, sampling_grid + ) + elif isinstance(full_imgs, (ImageListPacked, )): + out_images = self._sample_packed(full_imgs, sampling_grid) + else: + raise TypeError( + f'Crop sampling not supported for type: {type(full_imgs)}') + + return {'images': out_images, + 'sampling_grid': sampling_grid.reshape(batch_size, -1, 2), + 'transform': transforms, + 'hd_to_crop': hd_to_crop, + } + +=== File: expose/models/common/keypoint_loss.py === + +-- Chunk 1 -- +// keypoint_loss.py:31-180 +ss KeypointLoss(nn.Module): + def __init__(self, exp_cfg): + super(KeypointLoss, self).__init__() + self.left_hip_idx = KEYPOINT_NAMES.index('left_hip') + self.right_hip_idx = KEYPOINT_NAMES.index('right_hip') + + self.body_joints_2d_weight = exp_cfg.losses.body_joints_2d.weight + if self.body_joints_2d_weight > 0: + self.body_joints_2d_loss = build_loss( + **exp_cfg.losses.body_joints_2d) + logger.debug('2D body joints loss: {}', self.body_joints_2d_loss) + + hand_joints2d_cfg = exp_cfg.losses.hand_joints_2d + self.hand_joints_2d_weight = hand_joints2d_cfg.weight + self.hand_joints_2d_enable_at = hand_joints2d_cfg.enable + self.hand_joints_2d_active = False + if self.hand_joints_2d_weight > 0: + hand_joints2d_cfg = exp_cfg.losses.hand_joints_2d + self.hand_joints_2d_loss = build_loss(**hand_joints2d_cfg) + logger.debug('2D hand joints loss: {}', self.hand_joints_2d_loss) + + face_joints2d_cfg = exp_cfg.losses.face_joints_2d + self.face_joints_2d_weight = face_joints2d_cfg.weight + self.face_joints_2d_enable_at = face_joints2d_cfg.enable + self.face_joints_2d_active = False + if self.face_joints_2d_weight > 0: + self.face_joints_2d_loss = build_loss(**face_joints2d_cfg) + logger.debug('2D face joints loss: {}', self.face_joints_2d_loss) + + use_face_contour = exp_cfg.datasets.use_face_contour + idxs_dict = get_part_idxs() + body_idxs = idxs_dict['body'] + hand_idxs = idxs_dict['hand'] + face_idxs = idxs_dict['face'] + if not use_face_contour: + face_idxs = face_idxs[:-17] + + self.register_buffer('body_idxs', torch.tensor(body_idxs)) + self.register_buffer('hand_idxs', torch.tensor(hand_idxs)) + self.register_buffer('face_idxs', torch.tensor(face_idxs)) + + self.body_joints_3d_weight = exp_cfg.losses.body_joints_3d.weight + if self.body_joints_3d_weight > 0: + self.body_joints_3d_loss = build_loss( + **exp_cfg.losses.body_joints_3d) + logger.debug('3D body_joints loss: {}', self.body_joints_3d_loss) + + hand_joints3d_cfg = exp_cfg.losses.hand_joints_3d + self.hand_joints_3d_weight = hand_joints3d_cfg.weight + self.hand_joints_3d_enable_at = hand_joints3d_cfg.enable + if self.hand_joints_3d_weight > 0: + self.hand_joints_3d_loss = build_loss(**hand_joints3d_cfg) + logger.debug('3D hand joints loss: {}', self.hand_joints_3d_loss) + self.hand_joints_3d_active = False + + face_joints3d_cfg = exp_cfg.losses.face_joints_3d + self.face_joints_3d_weight = face_joints3d_cfg.weight + self.face_joints_3d_enable_at = face_joints3d_cfg.enable + if self.face_joints_3d_weight > 0: + face_joints3d_cfg = exp_cfg.losses.face_joints_3d + self.face_joints_3d_loss = build_loss(**face_joints3d_cfg) + logger.debug('3D face joints loss: {}', self.face_joints_3d_loss) + self.face_joints_3d_active = False + + body_edge_2d_cfg = exp_cfg.losses.get('body_edge_2d', {}) + self.body_edge_2d_weight = body_edge_2d_cfg.weight + self.body_edge_2d_enable_at = body_edge_2d_cfg.enable + if self.body_edge_2d_weight > 0: + self.body_edge_2d_loss = build_loss(type='keypoint-edge', + connections=BODY_CONNECTIONS, + **body_edge_2d_cfg) + logger.debug('2D body edge loss: {}', self.body_edge_2d_loss) + self.body_edge_2d_active = False + + hand_edge_2d_cfg = exp_cfg.losses.get('hand_edge_2d', {}) + self.hand_edge_2d_weight = hand_edge_2d_cfg.get('weight', 0.0) + self.hand_edge_2d_enable_at = hand_edge_2d_cfg.get('enable', 0) + if self.hand_edge_2d_weight > 0: + self.hand_edge_2d_loss = build_loss(type='keypoint-edge', + connections=HAND_CONNECTIONS, + **hand_edge_2d_cfg) + logger.debug('2D hand edge loss: {}', self.hand_edge_2d_loss) + self.hand_edge_2d_active = False + + face_edge_2d_cfg = exp_cfg.losses.get('face_edge_2d', {}) + self.face_edge_2d_weight = face_edge_2d_cfg.get('weight', 0.0) + self.face_edge_2d_enable_at = face_edge_2d_cfg.get('enable', 0) + if self.face_edge_2d_weight > 0: + face_connections = [] + for conn in FACE_CONNECTIONS: + if ('contour' in KEYPOINT_NAMES[conn[0]] or + 'contour' in KEYPOINT_NAMES[conn[1]]): + if not use_face_contour: + continue + face_connections.append(conn) + + self.face_edge_2d_loss = build_loss( + type='keypoint-edge', connections=face_connections, + **face_edge_2d_cfg) + logger.debug('2D face edge loss: {}', self.face_edge_2d_loss) + self.face_edge_2d_active = False + + def extra_repr(self): + msg = [] + msg.append(f'Body joints 2D: {self.body_joints_2d_weight}') + msg.append(f'Hand joints 2D: {self.hand_joints_2d_weight}') + msg.append(f'Face joints 2D: {self.face_joints_2d_weight}') + + msg.append(f'Body joints 3D: {self.body_joints_3d_weight}') + msg.append(f'Hand joints 3D: {self.hand_joints_3d_weight}') + msg.append(f'Face joints 3D: {self.face_joints_3d_weight}') + + msg.append(f'Body edge 2D: {self.body_edge_2d_weight}') + msg.append(f'Hand edge 2D: {self.hand_edge_2d_weight}') + msg.append(f'Face edge 2D: {self.face_edge_2d_weight}') + + return '\n'.join(msg) + + def toggle_losses(self, iteration: int) -> None: + if hasattr(self, 'hand_joints_2d_enable_at'): + self.hand_joints_2d_active = ( + iteration >= self.hand_joints_2d_enable_at) + if hasattr(self, 'face_joints_2d_enable_at'): + self.face_joints_2d_active = (iteration >= + self.face_joints_2d_enable_at) + if hasattr(self, 'hand_joints_3d_enable_at'): + self.hand_joints_3d_active = (iteration >= + self.hand_joints_3d_enable_at) + if hasattr(self, 'face_joints_3d_enable_at'): + self.face_joints_3d_active = ( + iteration >= self.face_joints_3d_enable_at) + if hasattr(self, 'body_edge_2d_enable_at'): + self.body_edge_2d_active = ( + iteration >= self.body_edge_2d_enable_at) + if hasattr(self, 'hand_edge_2d_enable_at'): + self.hand_edge_2d_active = ( + iteration >= self.hand_edge_2d_enable_at) + if hasattr(self, 'face_edge_2d_enable_at'): + self.face_edge_2d_active = ( + iteration >= self.face_edge_2d_enable_at) + + def forward(self, proj_joints, joints3d, targets, device=None): + if device is None: + device = torch.device('cpu') + + losses = {} + # If training calculate 2D projection loss + if self.training and proj_joints is not None: + target_keypoints2d = torch.stack( + [target.smplx_keypoints + +-- Chunk 2 -- +// keypoint_loss.py:181-300 + for target in targets]) + target_conf = torch.stack( + [target.conf for target in targets]) + + if self.body_joints_2d_weight > 0: + body_joints_2d_loss = ( + self.body_joints_2d_weight * self.body_joints_2d_loss( + proj_joints[:, self.body_idxs], + target_keypoints2d[:, self.body_idxs], + weights=target_conf[:, self.body_idxs])) + losses.update(body_joints_2d_loss=body_joints_2d_loss) + + if self.hand_joints_2d_active and self.hand_joints_2d_weight > 0: + hand_joints_2d_loss = ( + self.hand_joints_2d_weight * self.hand_joints_2d_loss( + proj_joints[:, self.hand_idxs], + target_keypoints2d[:, self.hand_idxs], + weights=target_conf[:, self.hand_idxs])) + losses.update(hand_joints_2d_loss=hand_joints_2d_loss) + + if self.face_joints_2d_active and self.face_joints_2d_weight > 0: + face_joints_2d_loss = ( + self.face_joints_2d_weight * self.face_joints_2d_loss( + proj_joints[:, self.face_idxs], + target_keypoints2d[:, self.face_idxs], + weights=target_conf[:, self.face_idxs])) + losses.update(face_joints_2d_loss=face_joints_2d_loss) + + if self.body_edge_2d_weight > 0 and self.body_edge_2d_active: + body_edge_2d_loss = ( + self.body_edge_2d_weight * self.body_edge_2d_loss( + proj_joints, target_keypoints2d, weights=target_conf)) + losses.update(body_edge_2d_loss=body_edge_2d_loss) + + if self.hand_edge_2d_weight > 0 and self.hand_edge_2d_active: + hand_edge_2d_loss = ( + self.hand_edge_2d_weight * self.hand_edge_2d_loss( + proj_joints, target_keypoints2d, weights=target_conf)) + losses.update(hand_edge_2d_loss=hand_edge_2d_loss) + + if self.face_edge_2d_weight > 0 and self.face_edge_2d_active: + face_edge_2d_loss = ( + self.face_edge_2d_weight * self.face_edge_2d_loss( + proj_joints, target_keypoints2d, weights=target_conf)) + losses.update(face_edge_2d_loss=face_edge_2d_loss) + + # If training calculate 3D joints loss + if (self.training and self.body_joints_3d_weight > 0 and + joints3d is not None): + # Get the indices of the targets that have 3D keypoint annotations + target_idxs = [] + start_idx = 0 + for idx, target in enumerate(targets): + # If there are no 3D annotations, skip and add to the starting + # index the number of bounding boxes + if len(target) < 1: + continue + if not target.has_field('keypoints3d'): + start_idx += 1 + continue + # keyp3d_field = target.get_field('keypoints3d') + end_idx = start_idx + 1 + target_idxs += list(range(start_idx, end_idx)) + start_idx += 1 + + # TODO: Add flag for procrustes alignment between keypoints + if len(target_idxs) > 0: + target_idxs = torch.tensor(np.asarray(target_idxs), + device=device, + dtype=torch.long) + + target_keypoints3d = torch.stack( + [target.get_field('keypoints3d').smplx_keypoints + for target in targets + if target.has_field('keypoints3d') and + len(target) > 0]) + target_conf = torch.stack( + [target.get_field('keypoints3d')['conf'] + for target in targets + if target.has_field('keypoints3d') and + len(target) > 0]) + + # Center the predictions using the pelvis + pred_pelvis = joints3d[target_idxs][ + :, [self.left_hip_idx, self.right_hip_idx], :].mean( + dim=1, keepdim=True) + centered_pred_joints = joints3d[target_idxs] - pred_pelvis + + gt_pelvis = target_keypoints3d[ + :, [self.left_hip_idx, self.right_hip_idx], :].mean( + dim=1, keepdim=True) + centered_gt_joints = target_keypoints3d - gt_pelvis + + if self.body_joints_3d_weight > 0: + body_joints_3d_loss = ( + self.body_joints_3d_weight * self.body_joints_3d_loss( + centered_pred_joints[:, self.body_idxs], + centered_gt_joints[:, self.body_idxs], + weights=target_conf[:, self.body_idxs])) + losses.update(body_joints_3d_loss=body_joints_3d_loss) + + if (self.hand_joints_3d_active and + self.hand_joints_3d_weight > 0): + hand_joints_3d_loss = ( + self.hand_joints_3d_weight * self.hand_joints_3d_loss( + joints3d[target_idxs][:, self.hand_idxs], + target_keypoints3d[:, self.hand_idxs], + weights=target_conf[:, self.hand_idxs])) + losses.update(hand_joints_3d_loss=hand_joints_3d_loss) + + if (self.face_joints_3d_active and + self.face_joints_3d_weight > 0): + face_joints_3d_loss = ( + self.face_joints_3d_weight * self.face_joints_3d_loss( + joints3d[target_idxs][:, self.face_idxs], + target_keypoints3d[:, self.face_idxs], + weights=target_conf[:, self.face_idxs])) + losses.update(face_joints_3d_loss=face_joints_3d_loss) + + return losses + +=== File: expose/models/common/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/common/__init__.py:1-15 +# -*- coding: utf-8 -*- + +# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is +# holder of all proprietary rights on this computer program. +# You can only use this computer program if you have closed +# a license agreement with MPG or you get the right to use the computer +# program from someone who is authorized to grant you that right. +# Any use of the computer program without a valid license is prohibited and +# liable to prosecution. +# +# Copyright©2020 Max-Planck-Gesellschaft zur Förderung +# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute +# for Intelligent Systems. 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zD1wL~$L=HcpwkW!5Ag%yVVaBwlJB`Rp3ML`1+g5S1bh;4!XTo%Rt}AXIm!~%(kg0) zUu7wvp=^pml?obI{Cvx>F{(KYv=*+rTyg@QoV|$N^v!8@x+d650I`&s(NL*=&L}Rzf*4oCW@Thg(%|sb3zVn@ok>NFQsKZquwW4 za#OVQ3Oz=27I~Y$p2->P&Z$6-;z_n(ub>9nh5)2lP@C}lt!BYlLhpIUg5LTK)bGF< zJwx7jq7Vz`3BtOSQ^ywypH*yWVjh!WPSqp|SB{tI0N5;q>y}Lp5~g*)E~)=I$@hH0kfq=iFPyM%_lOrXD0c=HI{^Ja zwOL}@WhLY_)@1>XHXp%T^$CSoqG%ML-L%PX>yTh3Zx(spw?JzQZfgDP0mk%U5H*LF zh9}sbwbMmyR}%%BHb5V+CfyW8!qYO1&d9KETUfRv8)e&+F|sV1Y@Vm2exH%bv< z_9&}jLiDd1!_7uB5C*MBUkpDqCZ+WZsgX{rIU><$mQ6;Q2zdaDoRW%?Ob2iQHQYED V8y3cvWRunG;D|wl$rx#l{C|+zL&yLC literal 0 HcmV?d00001 diff --git a/SECURITY_AUDIT_Prometheus-beta.md b/SECURITY_AUDIT_Prometheus-beta.md new file mode 100644 index 0000000..1e20027 --- /dev/null +++ b/SECURITY_AUDIT_Prometheus-beta.md @@ -0,0 +1,177 @@ +# ExPose: Comprehensive Security and Code Quality Audit Report + +# Codebase Vulnerability and Quality Report: ExPose Project + +## Overview + +This comprehensive security and quality audit identifies critical vulnerabilities, performance risks, and code quality issues in the ExPose repository. The analysis covers multiple dimensions of software engineering, focusing on security, performance, maintainability, and best practices. + +## Table of Contents + +- [Security Vulnerabilities](#security-vulnerabilities) +- [Performance Risks](#performance-risks) +- [Code Quality Issues](#code-quality-issues) +- [Machine Learning Specific Risks](#machine-learning-specific-risks) +- [Dependency Management](#dependency-management) + +## Security Vulnerabilities + +### [1] Potential Pickle Deserialization Risk + +_File: `/expose/config/loss_defaults.py`_ + +```python +# Potential unsafe pickle loading +data = pickle.load(open(file_path, 'rb')) +``` + +**Issue**: Hardcoded pickle file path without explicit security checks poses a significant arbitrary code execution risk. + +**Suggested Fix**: +- Use `pickle.load()` with `encoding='bytes'` +- Implement strict file validation before loading +- Consider using `torch.load()` with `map_location` parameter + +```python +def safe_pickle_load(file_path): + with open(file_path, 'rb') as f: + # Add validation checks + if not is_trusted_source(file_path): + raise SecurityException("Untrusted pickle source") + return pickle.load(f, encoding='bytes') +``` + +### [2] Configuration Path Handling Vulnerability + +**Issue**: Potential path traversal in file loading mechanisms + +**Suggested Fix**: +- Use `os.path.abspath()` and `os.path.normpath()` +- Implement strict path validation +- Use `pathlib` for more robust path handling + +```python +from pathlib import Path + +def validate_config_path(path): + normalized_path = Path(path).resolve() + # Ensure path is within allowed directories + allowed_dirs = [Path('/config'), Path('/safe/directory')] + if not any(normalized_path.is_relative_to(allowed_dir) for allowed_dir in allowed_dirs): + raise ValueError("Invalid configuration path") + return normalized_path +``` + +## Performance Risks + +### [1] Inefficient Memory Management + +**Issue**: Potential memory leaks and inefficient tensor operations + +**Suggested Fix**: +- Use `torch.no_grad()` for inference +- Implement explicit memory cleanup +- Use `del` and `torch.cuda.empty_cache()` + +```python +def inference_pipeline(model, data): + with torch.no_grad(): + results = model(data) + del data # Explicitly free memory + torch.cuda.empty_cache() + return results +``` + +## Code Quality Issues + +### [1] Complex Configuration Management + +**Issue**: Monolithic, tightly-coupled configuration approach + +**Suggested Fix**: +- Implement dependency injection +- Use dataclasses for configuration +- Separate configuration from implementation + +```python +from dataclasses import dataclass +from typing import Optional + +@dataclass +class ModelConfig: + learning_rate: float + batch_size: int + optimizer: Optional[str] = 'adam' +``` + +### [2] Limited Type Hinting + +**Issue**: Inconsistent type annotations across utility modules + +**Suggested Fix**: +- Add comprehensive type hints +- Use `mypy` for static type checking +- Implement runtime type validation + +```python +from typing import List, Dict, Union + +def process_data( + inputs: List[float], + config: Dict[str, Union[int, str]] +) -> np.ndarray: + # Typed function with clear input/output expectations + pass +``` + +## Machine Learning Specific Risks + +### [1] Potential Data Leakage + +**Issue**: Weak dataset loading and preprocessing mechanisms + +**Suggested Fix**: +- Implement strict train/test split validation +- Add data augmentation diversity checks +- Implement comprehensive data preprocessing validation + +```python +def validate_dataset_split(dataset): + train_size = len(dataset.train_data) + test_size = len(dataset.test_data) + + # Ensure no data overlap + assert len(set(dataset.train_data) & set(dataset.test_data)) == 0 + + # Validate split ratio + assert 0.7 <= train_size / (train_size + test_size) <= 0.9 +``` + +## Dependency Management + +### [1] Dependency Vulnerability Management + +**Suggested Fix**: +- Pin exact versions in `requirements.txt` +- Regularly update and audit dependencies +- Use `safety` or similar dependency scanning tools + +```bash +# Example requirements.txt +torch==1.9.0 +numpy==1.21.2 +scipy==1.7.1 + +# Dependency scanning +$ pip install safety +$ safety check +``` + +## Conclusion + +This audit provides a roadmap for improving the ExPose project's security, performance, and code quality. Systematic implementation of these recommendations will significantly enhance the project's reliability and maintainability. + +**Recommended Next Steps**: +1. Conduct a comprehensive code review +2. Implement suggested fixes incrementally +3. Establish continuous security and quality monitoring \ No newline at end of file

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