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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@ -46,7 +46,7 @@ class Attention(nn.Module):
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def forward(self, x):
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B, N, C = x.shape
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qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
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q, k = qk.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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q, k = qk.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3)
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attn = (q @ k.transpose(-2, -1)) * self.scale
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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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@ -89,7 +90,7 @@ class Block(nn.Module):
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attn_drop=attn_drop,
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proj_drop=proj_drop,
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)
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self.norm_mlp_in = norm_layer(dim)
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self.mlp_in = Mlp(
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in_features=dim,
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@ -118,7 +119,7 @@ class Block(nn.Module):
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proj_drop=proj_drop,
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)
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self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
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self.norm_mlp = norm_layer(dim_out)
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self.mlp = Mlp(
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in_features=dim_out,
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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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@ -248,7 +262,7 @@ class TNT(nn.Module):
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self.num_patches = num_patches
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new_patch_size = self.pixel_embed.new_patch_size
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num_pixel = new_patch_size[0] * new_patch_size[1]
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self.norm1_proj = norm_layer(num_pixel * inner_dim)
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self.proj = nn.Linear(num_pixel * inner_dim, embed_dim)
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self.norm2_proj = norm_layer(embed_dim)
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@ -278,7 +292,7 @@ class TNT(nn.Module):
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self.blocks = nn.ModuleList(blocks)
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self.feature_info = [
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dict(module=f'blocks.{i}', num_chs=embed_dim, reduction=r) for i in range(depth)]
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self.norm = norm_layer(embed_dim)
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self.head_drop = nn.Dropout(drop_rate)
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self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
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@ -359,7 +373,7 @@ class TNT(nn.Module):
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B, _, height, width = x.shape
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pixel_embed = self.pixel_embed(x, self.pixel_pos)
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patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1))))
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patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1)
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patch_embed = patch_embed + self.patch_pos
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@ -381,7 +395,7 @@ class TNT(nn.Module):
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# split prefix (e.g. class, distill) and spatial feature tokens
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prefix_tokens = [y[:, 0:self.num_prefix_tokens] for y in intermediates]
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intermediates = [y[:, self.num_prefix_tokens:] for y in intermediates]
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if reshape:
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# reshape to BCHW output format
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H, W = self.pixel_embed.dynamic_feat_size((height, width))
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@ -416,7 +430,7 @@ class TNT(nn.Module):
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def forward_features(self, x):
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B = x.shape[0]
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pixel_embed = self.pixel_embed(x, self.pixel_pos)
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patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1))))
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patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1)
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patch_embed = patch_embed + self.patch_pos
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@ -458,42 +472,47 @@ 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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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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k = k.replace('inner_pos', 'pixel_pos')
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k = k.replace('patch_embed', 'pixel_embed')
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k = k.replace('proj_norm1', 'norm1_proj')
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k = k.replace('proj_norm2', 'norm2_proj')
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k = k.replace('inner_norm1', 'norm_in')
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k = k.replace('inner_attn', 'attn_in')
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k = k.replace('inner_norm2', 'norm_mlp_in')
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k = k.replace('inner_mlp', 'mlp_in')
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k = k.replace('outer_norm1', 'norm_out')
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k = k.replace('outer_attn', 'attn_out')
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k = k.replace('outer_norm2', 'norm_mlp')
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k = k.replace('outer_mlp', 'mlp')
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if k == 'pixel_pos' and model.pixel_embed.legacy == False:
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B, N, C = v.shape
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H = W = int(N ** 0.5)
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assert H * W == N
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v = v.permute(0, 2, 1).reshape(B, C, H, W)
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out_dict[k] = v
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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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k = k.replace('inner_pos', 'pixel_pos')
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k = k.replace('patch_embed', 'pixel_embed')
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k = k.replace('proj_norm1', 'norm1_proj')
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k = k.replace('proj_norm2', 'norm2_proj')
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k = k.replace('inner_norm1', 'norm_in')
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k = k.replace('inner_attn', 'attn_in')
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k = k.replace('inner_norm2', 'norm_mlp_in')
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k = k.replace('inner_mlp', 'mlp_in')
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k = k.replace('outer_norm1', 'norm_out')
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k = k.replace('outer_attn', 'attn_out')
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k = k.replace('outer_norm2', 'norm_mlp')
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k = k.replace('outer_mlp', 'mlp')
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if k == 'pixel_pos' and model.pixel_embed.legacy == False:
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B, N, C = v.shape
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H = W = int(N ** 0.5)
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assert H * W == N
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v = v.permute(0, 2, 1).reshape(B, C, H, W)
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out_dict[k] = v
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""" convert patch embedding weight from manual patchify + linear proj to conv"""
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if out_dict['patch_pos'].shape != model.patch_pos.shape:
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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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