diff --git a/timm/models/__init__.py b/timm/models/__init__.py index aa13bbcc..08e2b59a 100644 --- a/timm/models/__init__.py +++ b/timm/models/__init__.py @@ -30,6 +30,7 @@ from .vovnet import * from .xception import * from .xception_aligned import * from .hardcorenas import * +from .tnt import * from .factory import create_model, split_model_name, safe_model_name from .helpers import load_checkpoint, resume_checkpoint, model_parameters diff --git a/timm/models/tnt.py b/timm/models/tnt.py new file mode 100644 index 00000000..13589a7d --- /dev/null +++ b/timm/models/tnt.py @@ -0,0 +1,247 @@ +""" Transformer in Transformer (TNT) in PyTorch + +A PyTorch implement of TNT as described in +'Transformer in Transformer' - https://arxiv.org/abs/2103.00112 + +The official mindspore code is released and available at +https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT +""" +import math +import torch +import torch.nn as nn +from functools import partial + +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD +from timm.models.helpers import load_pretrained +from timm.models.layers import DropPath, trunc_normal_ +from timm.models.vision_transformer import Mlp +from timm.models.registry import register_model + + +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'classifier': 'head', + **kwargs + } + + +default_cfgs = { + 'tnt_s_patch16_224': _cfg( + url='https://sourceforge.net/projects/transformer-in-transformer/files/release_v1/tnt_s_patch16_224.pth', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + ), + 'tnt_b_patch16_224': _cfg( + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + ), +} + + +class Attention(nn.Module): + """ Multi-Head Attention + """ + def __init__(self, dim, hidden_dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.): + super().__init__() + self.hidden_dim = hidden_dim + self.num_heads = num_heads + head_dim = hidden_dim // num_heads + self.head_dim = head_dim + self.scale = head_dim ** -0.5 + + self.qk = nn.Linear(dim, hidden_dim * 2, bias=qkv_bias) + self.v = nn.Linear(dim, dim, bias=qkv_bias) + self.attn_drop = nn.Dropout(attn_drop, inplace=True) + self.proj = nn.Linear(dim, dim) + self.proj_drop = nn.Dropout(proj_drop, inplace=True) + + def forward(self, x): + B, N, C = x.shape + qk = self.qk(x).reshape(B, N, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4) + q, k = qk[0], qk[1] # make torchscript happy (cannot use tensor as tuple) + v = self.v(x).reshape(B, N, self.num_heads, -1).permute(0, 2, 1, 3) + + attn = (q @ k.transpose(-2, -1)) * self.scale + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + """ TNT Block + """ + def __init__(self, dim, in_dim, num_pixel, num_heads=12, in_num_head=4, mlp_ratio=4., + qkv_bias=False, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm): + super().__init__() + # Inner transformer + self.norm_in = norm_layer(in_dim) + self.attn_in = Attention( + in_dim, in_dim, num_heads=in_num_head, qkv_bias=qkv_bias, + attn_drop=attn_drop, proj_drop=drop) + + self.norm_mlp_in = norm_layer(in_dim) + self.mlp_in = Mlp(in_features=in_dim, hidden_features=int(in_dim * 4), + out_features=in_dim, act_layer=act_layer, drop=drop) + + self.norm1_proj = norm_layer(in_dim) + self.proj = nn.Linear(in_dim * num_pixel, dim, bias=True) + # Outer transformer + self.norm_out = norm_layer(dim) + self.attn_out = Attention( + dim, dim, num_heads=num_heads, qkv_bias=qkv_bias, + attn_drop=attn_drop, proj_drop=drop) + self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() + + self.norm_mlp = norm_layer(dim) + self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), + out_features=dim, act_layer=act_layer, drop=drop) + + def forward(self, pixel_embed, patch_embed): + # inner + pixel_embed = pixel_embed + self.drop_path(self.attn_in(self.norm_in(pixel_embed))) + pixel_embed = pixel_embed + self.drop_path(self.mlp_in(self.norm_mlp_in(pixel_embed))) + # outer + B, N, C = patch_embed.size() + patch_embed[:, 1:] = patch_embed[:, 1:] + self.proj(self.norm1_proj(pixel_embed).reshape(B, N - 1, -1)) + patch_embed = patch_embed + self.drop_path(self.attn_out(self.norm_out(patch_embed))) + patch_embed = patch_embed + self.drop_path(self.mlp(self.norm_mlp(patch_embed))) + return pixel_embed, patch_embed + + +class PixelEmbed(nn.Module): + """ Image to Pixel Embedding + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, in_dim=48, stride=4): + super().__init__() + num_patches = (img_size // patch_size) ** 2 + self.img_size = img_size + self.num_patches = num_patches + self.in_dim = in_dim + new_patch_size = math.ceil(patch_size / stride) + self.new_patch_size = new_patch_size + + self.proj = nn.Conv2d(in_chans, self.in_dim, kernel_size=7, padding=3, stride=stride) + self.unfold = nn.Unfold(kernel_size=new_patch_size, stride=new_patch_size) + + def forward(self, x, pixel_pos): + B, C, H, W = x.shape + assert H == self.img_size and W == self.img_size, \ + f"Input image size ({H}*{W}) doesn't match model ({self.img_size}*{self.img_size})." + x = self.proj(x) + x = self.unfold(x) + x = x.transpose(1, 2).reshape(B * self.num_patches, self.in_dim, self.new_patch_size, self.new_patch_size) + x = x + pixel_pos + x = x.reshape(B * self.num_patches, self.in_dim, -1).transpose(1, 2) + return x + + +class TNT(nn.Module): + """ Transformer in Transformer - https://arxiv.org/abs/2103.00112 + """ + def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, in_dim=48, depth=12, + num_heads=12, in_num_head=4, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0., + drop_path_rate=0., norm_layer=nn.LayerNorm, first_stride=4): + super().__init__() + self.num_classes = num_classes + self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models + + self.pixel_embed = PixelEmbed( + img_size=img_size, patch_size=patch_size, in_chans=in_chans, in_dim=in_dim, stride=first_stride) + num_patches = self.pixel_embed.num_patches + self.num_patches = num_patches + new_patch_size = self.pixel_embed.new_patch_size + num_pixel = new_patch_size ** 2 + + self.norm1_proj = norm_layer(num_pixel * in_dim) + self.proj = nn.Linear(num_pixel * in_dim, embed_dim) + self.norm2_proj = norm_layer(embed_dim) + + self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim)) + self.patch_pos = nn.Parameter(torch.zeros(1, num_patches + 1, embed_dim)) + self.pixel_pos = nn.Parameter(torch.zeros(1, in_dim, new_patch_size, new_patch_size)) + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + blocks = [] + for i in range(depth): + blocks.append(Block( + dim=embed_dim, in_dim=in_dim, num_pixel=num_pixel, num_heads=num_heads, in_num_head=in_num_head, + mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate, attn_drop=attn_drop_rate, + drop_path=dpr[i], norm_layer=norm_layer)) + self.blocks = nn.ModuleList(blocks) + self.norm = norm_layer(embed_dim) + + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + trunc_normal_(self.cls_token, std=.02) + trunc_normal_(self.patch_pos, std=.02) + trunc_normal_(self.pixel_pos, std=.02) + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + @torch.jit.ignore + def no_weight_decay(self): + return {'patch_pos', 'pixel_pos', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x): + B = x.shape[0] + pixel_embed = self.pixel_embed(x, self.pixel_pos) + + patch_embed = self.norm2_proj(self.proj(self.norm1_proj(pixel_embed.reshape(B, self.num_patches, -1)))) + patch_embed = torch.cat((self.cls_token.expand(B, -1, -1), patch_embed), dim=1) + patch_embed = patch_embed + self.patch_pos + patch_embed = self.pos_drop(patch_embed) + + for blk in self.blocks: + pixel_embed, patch_embed = blk(pixel_embed, patch_embed) + + patch_embed = self.norm(patch_embed) + return patch_embed[:, 0] + + def forward(self, x): + x = self.forward_features(x) + x = self.head(x) + return x + + +@register_model +def tnt_s_patch16_224(pretrained=False, **kwargs): + model = TNT(patch_size=16, embed_dim=384, in_dim=24, depth=12, num_heads=6, in_num_head=4, + qkv_bias=False, **kwargs) + model.default_cfg = default_cfgs['tnt_s_patch16_224'] + if pretrained: + load_pretrained( + model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3)) + return model + + +@register_model +def tnt_b_patch16_224(pretrained=False, **kwargs): + model = TNT(patch_size=16, embed_dim=640, in_dim=40, depth=12, num_heads=10, in_num_head=4, + qkv_bias=False, **kwargs) + model.default_cfg = default_cfgs['tnt_b_patch16_224'] + if pretrained: + load_pretrained( + model, num_classes=model.num_classes, in_chans=kwargs.get('in_chans', 3)) + return model