From 784c94ecbf35d1d15170c86e8eadad9a266b16f9 Mon Sep 17 00:00:00 2001 From: Santix12 Date: Sat, 10 May 2025 09:41:50 +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 -> 6275072 bytes SECURITY_AUDIT_Prometheus-beta.md | 139 + 7 files changed, 17390 insertions(+) create mode 100644 .kno/chunk_review.txt create mode 100644 .kno/embedding_SBERTEmbedding_1746869660118_9651d09/7f3bb877-cd6b-4ee6-9403-0d78a30198f3/data_level0.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746869660118_9651d09/7f3bb877-cd6b-4ee6-9403-0d78a30198f3/header.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746869660118_9651d09/7f3bb877-cd6b-4ee6-9403-0d78a30198f3/length.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746869660118_9651d09/7f3bb877-cd6b-4ee6-9403-0d78a30198f3/link_lists.bin create mode 100644 .kno/embedding_SBERTEmbedding_1746869660118_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..823dc16 --- /dev/null +++ b/.kno/chunk_review.txt @@ -0,0 +1,17251 @@ + +=== 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: 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: 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: 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: 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/__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: 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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/__init__.py === + +-- Chunk 1 -- +// __init__.py:18-19 + nand(x: bool, y: bool) -> bool: + return not (x and y) + +=== 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/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/__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/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/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/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/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/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/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/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/__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/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/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/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/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/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/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/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/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/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/__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. All rights reserved. +# +# Contact: ps-license@tuebingen.mpg.de + +=== 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/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/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/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/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/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/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/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/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/__init__.py === + +-- Chunk 1 -- +// /app/repos/repo_8/expose/models/backbone/__init__.py:1-2 + +from .build import build_backbone + +=== 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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/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/__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/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/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/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/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/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/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/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/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/__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 * diff --git a/.kno/embedding_SBERTEmbedding_1746869660118_9651d09/7f3bb877-cd6b-4ee6-9403-0d78a30198f3/data_level0.bin b/.kno/embedding_SBERTEmbedding_1746869660118_9651d09/7f3bb877-cd6b-4ee6-9403-0d78a30198f3/data_level0.bin new file mode 100644 index 0000000000000000000000000000000000000000..6f348769f0afee14a682a460724a678c2456fe1b GIT binary patch literal 3212000 zcmaf*cR1H?`1XYg$=;-FQIdp~&Z{A9?Y*^=77Yzb%HCV{-aC8mO-Rb9BrU1rtD=7Q z^E;luo_`+y;BfeOf8O`|zOVCoo#&;*+RNtGJr0gJ4x+CA=X1&b^F7D^=fmXx{#=)X z|9`(S)b~U1LJREFEXK34$JnkN4HuJJ_`aNBZ8?L9D~5|_EBlj<=C=&iCxqH+L+n_l3ag943TwSmN* zMChH5!J#Axs_*N>GUId{xhg{b{kQOW;uPlArbAUJ4ck(e(PM{)u(Z!d%a33jZxyAA zo#QBSt_J6<8T7aJBAS()aqmklg3h)>FiD@P$Kvq(VhMKnM&Qn|*%WR551jV(*jFJ( zzbzj^vFs8SZcm5tKstJCmB=P13$d{cu(}nA0&5EqD?lxGN5EfEhc(gTi1~UR5!H4Wo|lbllbPsLpGSN$ zSx6o$L*UmIjL3*m>-S;o+EWQ#Ic~c6<1rNX`Jyzq2ECr_eteUqe^VdgTyziC+;D@E 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0 HcmV?d00001 diff --git a/SECURITY_AUDIT_Prometheus-beta.md b/SECURITY_AUDIT_Prometheus-beta.md new file mode 100644 index 0000000..48c7d35 --- /dev/null +++ b/SECURITY_AUDIT_Prometheus-beta.md @@ -0,0 +1,139 @@ +# ExPose: Comprehensive Security and Quality Audit Report for Machine Learning Project + +# ExPose Repository Security and Quality Audit Report + +## Overview +This document provides a comprehensive analysis of potential vulnerabilities, performance issues, and code quality concerns in the ExPose machine learning project. + +## Table of Contents +- [Security Vulnerabilities](#security-vulnerabilities) +- [Performance Concerns](#performance-issues) +- [Code Maintainability](#code-maintainability) +- [Machine Learning Risks](#machine-learning-specific-risks) +- [Dependency Management](#dependency-management) + +## Security Vulnerabilities 🛡️ + +### [1] Input Validation Risks in Dataset Loading +_Files: `/expose/data/datasets/*.py`_ + +**Risk**: Potential path traversal or arbitrary file access + +```python +# Example vulnerable pattern +def load_dataset(file_path): + # Unsafe direct file path usage + with open(file_path, 'r') as f: + data = f.read() +``` + +**Suggested Fix**: +- Implement strict input validation +- Use `os.path.normpath()` to sanitize file paths +- Add explicit path whitelisting +- Validate file extensions and origins + +### [2] Configuration Management Vulnerability +_Files: `/expose/config/defaults.py`_ + +**Risk**: Potential configuration injection or uncontrolled parameter setting + +```python +# Unsafe configuration management +class ConfigManager: + def __init__(self): + self.config = {} + + def set_param(self, key, value): + # No type checking or validation + self.config[key] = value +``` + +**Suggested Fix**: +- Implement strict type checking +- Use `@property` decorators +- Create immutable configuration objects +- Add validation for critical parameters + +## Performance Issues 🚀 + +### [1] Memory Management Concern +_Files: `/expose/models/smplx_net.py`_ + +**Risk**: Inefficient tensor management in large model architectures + +```python +# Potential memory-intensive operation +def forward(self, x): + # No gradient checkpointing or memory optimization + output = self.complex_computation(x) + return output +``` + +**Suggested Fix**: +- Implement gradient checkpointing +- Use `torch.no_grad()` during inference +- Leverage `torch.cuda.empty_cache()` +- Consider model pruning techniques + +## Code Maintainability 🧩 + +### [1] Documentation Gap +_Multiple Utility Modules_ + +**Risk**: Reduced code readability and potential misuse + +```python +# Lack of type hints and docstrings +def process_data(input_data): + # What type is input_data? + # What does this function do? + return transformed_data +``` + +**Suggested Fix**: +- Add comprehensive type hints +- Write detailed docstrings +- Use consistent documentation style +- Include example usage in comments + +## Machine Learning Specific Risks 🤖 + +### [1] Dataset Bias Potential +_Files: `/expose/data/datasets/`_ + +**Risk**: Limited dataset diversity leading to potential demographic bias + +**Suggested Fix**: +- Conduct comprehensive dataset analysis +- Implement bias detection mechanisms +- Ensure representative sampling +- Document dataset composition + +## Dependency Management ⚙️ + +### [1] Unaudited Dependencies +_File: `requirements.txt`_ + +**Risk**: Potential security vulnerabilities in dependencies + +**Suggested Fix**: +- Implement regular dependency scanning +- Use tools like `safety` or `dependabot` +- Pin exact versions +- Regularly update and audit dependencies + +## Conclusion +This audit highlights critical areas for improvement in the ExPose project. Addressing these concerns will enhance security, performance, and maintainability. + +**Recommended Action Items**: +1. Implement comprehensive input validation +2. Enhance error handling and logging +3. Conduct thorough dependency audit +4. Add detailed documentation +5. Optimize memory management + +--- + +**Audit Completed**: 2025-05-10 +**Auditor**: AI Security Analysis Tool \ No newline at end of file

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