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Update tnt.py
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Ryan
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@ -5,6 +5,9 @@ A PyTorch implement of TNT as described in
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The official mindspore code is released and available at
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https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT
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The official pytorch code is released and available at
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https://github.com/huawei-noah/Efficient-AI-Backbones/tree/master/tnt_pytorch
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"""
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import math
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from typing import Optional
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@ -12,7 +15,7 @@ from typing import Optional
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import torch
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import torch.nn as nn
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from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
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from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
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from timm.layers import Mlp, DropPath, trunc_normal_, _assert, to_2tuple, resample_abs_pos_embed
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from ._builder import build_model_with_cfg
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from ._manipulate import checkpoint
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@ -22,28 +25,6 @@ from ._registry import register_model
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__all__ = ['TNT'] # model_registry will add each entrypoint fn to this
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
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'first_conv': 'pixel_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'tnt_s_patch16_224': _cfg(
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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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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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),
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'tnt_b_patch16_224': _cfg(
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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),
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}
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class Attention(nn.Module):
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""" Multi-Head Attention
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"""
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@ -94,6 +75,7 @@ class Block(nn.Module):
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drop_path=0.,
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act_layer=nn.GELU,
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norm_layer=nn.LayerNorm,
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legacy=False,
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):
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super().__init__()
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# Inner transformer
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@ -115,9 +97,14 @@ class Block(nn.Module):
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act_layer=act_layer,
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drop=proj_drop,
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)
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self.norm1_proj = norm_layer(dim)
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self.proj = nn.Linear(dim * num_pixel, dim_out, bias=True)
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self.legacy = legacy
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if self.legacy:
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self.norm1_proj = norm_layer(dim)
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self.proj = nn.Linear(dim * num_pixel, dim_out, bias=True)
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else:
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self.norm1_proj = norm_layer(dim * num_pixel)
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self.proj = nn.Linear(dim * num_pixel, dim_out, bias=False)
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self.norm2_proj = norm_layer(dim_out)
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# Outer transformer
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self.norm_out = norm_layer(dim_out)
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@ -146,9 +133,16 @@ class Block(nn.Module):
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pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed)))
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# outer
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B, N, C = patch_embed.size()
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patch_embed = torch.cat(
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[patch_embed[:, 0:1], 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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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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], 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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], 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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return pixel_embed, patch_embed
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@ -157,7 +151,7 @@ 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):
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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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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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@ -165,13 +159,18 @@ class PixelEmbed(nn.Module):
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self.grid_size = (img_size[0] // patch_size[0], img_size[1] // patch_size[1])
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num_patches = (self.grid_size[0]) * (self.grid_size[1])
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self.img_size = img_size
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self.patch_size = patch_size
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self.legacy = legacy
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self.num_patches = num_patches
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self.in_dim = in_dim
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new_patch_size = [math.ceil(ps / stride) for ps in patch_size]
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self.new_patch_size = new_patch_size
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self.proj = nn.Conv2d(in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride)
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self.unfold = nn.Unfold(kernel_size=new_patch_size, stride=new_patch_size)
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if self.legacy:
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self.unfold = nn.Unfold(kernel_size=new_patch_size, stride=new_patch_size)
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else:
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self.unfold = nn.Unfold(kernel_size=patch_size, stride=patch_size)
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def forward(self, x, pixel_pos):
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B, C, H, W = x.shape
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@ -179,9 +178,14 @@ class PixelEmbed(nn.Module):
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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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f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]}).")
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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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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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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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x = self.proj(x)
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x = x + pixel_pos
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x = x.reshape(B * self.num_patches, self.in_dim, -1).transpose(1, 2)
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return x
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@ -211,6 +215,7 @@ class TNT(nn.Module):
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drop_path_rate=0.,
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norm_layer=nn.LayerNorm,
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first_stride=4,
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legacy=False,
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):
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super().__init__()
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assert global_pool in ('', 'token', 'avg')
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@ -225,6 +230,7 @@ class TNT(nn.Module):
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in_chans=in_chans,
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in_dim=inner_dim,
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stride=first_stride,
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legacy=legacy,
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)
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num_patches = self.pixel_embed.num_patches
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self.num_patches = num_patches
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@ -255,6 +261,7 @@ class TNT(nn.Module):
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attn_drop=attn_drop_rate,
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drop_path=dpr[i],
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norm_layer=norm_layer,
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legacy=legacy,
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))
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self.blocks = nn.ModuleList(blocks)
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self.norm = norm_layer(embed_dim)
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@ -338,14 +345,38 @@ class TNT(nn.Module):
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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':
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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 state_dict['patch_pos'].shape != model.patch_pos.shape:
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state_dict['patch_pos'] = resample_abs_pos_embed(
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state_dict['patch_pos'],
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if out_dict['patch_pos'].shape != model.patch_pos.shape:
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out_dict['patch_pos'] = resample_abs_pos_embed(
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out_dict['patch_pos'],
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new_size=model.pixel_embed.grid_size,
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num_prefix_tokens=1,
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)
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return state_dict
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return out_dict
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def _create_tnt(variant, pretrained=False, **kwargs):
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@ -359,6 +390,30 @@ def _create_tnt(variant, pretrained=False, **kwargs):
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return model
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def _cfg(url='', **kwargs):
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return {
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'url': url,
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'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
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'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
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'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
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'first_conv': 'pixel_embed.proj', 'classifier': 'head',
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**kwargs
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}
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default_cfgs = {
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'tnt_s_patch16_224': _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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),
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'tnt_b_patch16_224': _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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),
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}
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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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