Add TNT model

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

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