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fit current timm import, and fix pos_embed shape when using 384 #20

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fit current timm import, and fix pos_embed shape when using 384 #20

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13 changes: 10 additions & 3 deletions models/inception_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,11 +29,18 @@
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from timm.models.helpers import build_model_with_cfg, named_apply, adapt_input_conv
from timm.models.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
from timm.models.registry import register_model
from torch.nn.init import _calculate_fan_in_and_fan_out
import math
import warnings
from timm.models.layers.helpers import to_2tuple
try:
from timm.models import register_model
except:
from timm.models.registry import register_model

try:
from timm.layers.helpers import to_2tuple
except:
from timm.models.layers.helpers import to_2tuple


_logger = logging.getLogger(__name__)
Expand Down Expand Up @@ -361,7 +368,7 @@ def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, em
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule

self.patch_embed = FirstPatchEmbed(in_chans=in_chans, embed_dim=embed_dims[0])
self.num_patches1 = num_patches = img_size // 4
self.num_patches1 = num_patches = 224 // 4
self.pos_embed1 = nn.Parameter(torch.zeros(1, num_patches, num_patches, embed_dims[0]))
self.blocks1 = nn.Sequential(*[
Block(
Expand Down