mirror of
https://github.com/huggingface/pytorch-image-models.git
synced 2025-06-03 15:01:08 +08:00
Updated tnt model weights on hub, add back legacy model in case bwd compat
This commit is contained in:
Ross Wightman
parent
69b1fbcdc1
commit
74ad32a67e
@ -22,13 +22,13 @@ from ._features import feature_take_indices
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from ._manipulate import checkpoint
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from ._registry import generate_default_cfgs, register_model
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__all__ = ['TNT'] # model_registry will add each entrypoint fn to this
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class Attention(nn.Module):
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""" Multi-Head Attention
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"""
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def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
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super().__init__()
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self.hidden_dim = hidden_dim
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@ -62,6 +62,7 @@ class Attention(nn.Module):
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class Block(nn.Module):
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""" TNT Block
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"""
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def __init__(
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self,
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dim,
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@ -136,13 +137,13 @@ class Block(nn.Module):
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B, N, C = patch_embed.size()
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if self.legacy:
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patch_embed = torch.cat([
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patch_embed[:, 0:1], patch_embed[:, 1:] + \
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self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1)),
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patch_embed[:, 0:1],
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patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1)),
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], dim=1)
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else:
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patch_embed = torch.cat([
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patch_embed[:, 0:1], patch_embed[:, 1:] + \
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self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, N - 1, -1)))),
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patch_embed[:, 0:1],
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patch_embed[:, 1:] + self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, N - 1, -1)))),
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], dim=1)
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patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed)))
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patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed)))
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@ -152,7 +153,16 @@ class Block(nn.Module):
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class PixelEmbed(nn.Module):
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""" Image to Pixel Embedding
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"""
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def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4, legacy=False):
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def __init__(
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self,
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img_size=224,
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patch_size=16,
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in_chans=3,
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in_dim=48,
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stride=4,
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legacy=False,
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):
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super().__init__()
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img_size = to_2tuple(img_size)
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patch_size = to_2tuple(patch_size)
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@ -184,14 +194,17 @@ class PixelEmbed(nn.Module):
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def forward(self, x: torch.Tensor, pixel_pos: torch.Tensor) -> torch.Tensor:
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B, C, H, W = x.shape
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_assert(H == self.img_size[0],
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_assert(
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H == self.img_size[0],
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
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_assert(W == self.img_size[1],
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_assert(
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W == self.img_size[1],
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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
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if self.legacy:
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x = self.proj(x)
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x = self.unfold(x)
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x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size[0], self.new_patch_size[1])
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x = x.transpose(1, 2).reshape(
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B * self.num_patches, self.in_dim, self.new_patch_size[0], self.new_patch_size[1])
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else:
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x = self.unfold(x)
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x = x.transpose(1, 2).reshape(B * self.num_patches, C, self.patch_size[0], self.patch_size[1])
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@ -204,6 +217,7 @@ class PixelEmbed(nn.Module):
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class TNT(nn.Module):
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""" Transformer in Transformer - https://arxiv.org/abs/2103.00112
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"""
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def __init__(
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self,
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img_size=224,
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@ -458,21 +472,26 @@ def _cfg(url='', **kwargs):
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default_cfgs = generate_default_cfgs({
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'tnt_s_legacy_patch16_224.in1k': _cfg(
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hf_hub_id='timm/',
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#url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
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),
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'tnt_s_patch16_224.in1k': _cfg(
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# hf_hub_id='timm/',
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# url='https://github.com/contrastive/pytorch-image-models/releases/download/TNT/tnt_s_patch16_224.pth.tar',
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url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_s_81.5.pth.tar',
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hf_hub_id='timm/',
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#url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_s_81.5.pth.tar',
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),
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'tnt_b_patch16_224.in1k': _cfg(
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# hf_hub_id='timm/',
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url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_b_82.9.pth.tar',
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hf_hub_id='timm/',
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#url='https://github.com/huawei-noah/Efficient-AI-Backbones/releases/download/tnt/tnt_b_82.9.pth.tar',
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),
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})
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def checkpoint_filter_fn(state_dict, model):
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state_dict.pop('outer_tokens', None)
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if 'patch_pos' in state_dict:
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out_dict = state_dict
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else:
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out_dict = {}
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for k, v in state_dict.items():
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k = k.replace('outer_pos', 'patch_pos')
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@ -515,6 +534,15 @@ def _create_tnt(variant, pretrained=False, **kwargs):
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return model
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@register_model
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def tnt_s_legacy_patch16_224(pretrained=False, **kwargs) -> TNT:
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model_cfg = dict(
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patch_size=16, embed_dim=384, inner_dim=24, depth=12, num_heads_outer=6,
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qkv_bias=False, legacy=True)
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model = _create_tnt('tnt_s_legacy_patch16_224', pretrained=pretrained, **dict(model_cfg, **kwargs))
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return model
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@register_model
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def tnt_s_patch16_224(pretrained=False, **kwargs) -> TNT:
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model_cfg = dict(
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