Initial Cait commit. Still some cleanup to do.

tests/test_models.py

@@ -15,7 +15,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
     torch._C._jit_set_profiling_mode(False)
 
 # transformer models don't support many of the spatial / feature based model functionalities
-NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*']
+NON_STD_FILTERS = ['vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*']
 NUM_NON_STD = len(NON_STD_FILTERS)
 
 # exclude models that cause specific test failures

timm/models/__init__.py

@@ -1,5 +1,6 @@
 from .byoanet import *
 from .byobnet import *
+from .cait import *
 from .coat import *
 from .cspnet import *
 from .densenet import *

timm/models/cait.py

@@ -0,0 +1,447 @@
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+
+import torch
+import torch.nn as nn
+from functools import partial
+
+from .layers import trunc_normal_, DropPath
+from .vision_transformer import Mlp, PatchEmbed, _cfg
+from .registry import register_model
+
+
+__all__ = ['Cait', 'Class_Attention', 'LayerScale_Block_CA', 'LayerScale_Block', 'Attention_talking_head']
+
+
+class Class_Attention(nn.Module):
+    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    # with slight modifications to do CA 
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.q = nn.Linear(dim, dim, bias=qkv_bias)
+        self.k = nn.Linear(dim, dim, bias=qkv_bias)
+        self.v = nn.Linear(dim, dim, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        q = self.q(x[:, 0]).unsqueeze(1).reshape(B, 1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+        k = self.k(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+
+        q = q * self.scale
+        v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+
+        attn = (q @ k.transpose(-2, -1))
+        attn = attn.softmax(dim=-1)
+        attn = self.attn_drop(attn)
+
+        x_cls = (attn @ v).transpose(1, 2).reshape(B, 1, C)
+        x_cls = self.proj(x_cls)
+        x_cls = self.proj_drop(x_cls)
+
+        return x_cls
+
+
+class LayerScale_Block_CA(nn.Module):
+    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    # with slight modifications to add CA and LayerScale
+    def __init__(
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+            drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=Class_Attention,
+            mlp_block=Mlp, init_values=1e-4):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_block(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+
+    def forward(self, x, x_cls):
+        u = torch.cat((x_cls, x), dim=1)
+
+        x_cls = x_cls + self.drop_path(self.gamma_1 * self.attn(self.norm1(u)))
+
+        x_cls = x_cls + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x_cls)))
+
+        return x_cls
+
+
+class Attention_talking_head(nn.Module):
+    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    # with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
+    def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+        super().__init__()
+
+        self.num_heads = num_heads
+
+        head_dim = dim // num_heads
+
+        self.scale = qk_scale or head_dim ** -0.5
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+
+        self.proj = nn.Linear(dim, dim)
+
+        self.proj_l = nn.Linear(num_heads, num_heads)
+        self.proj_w = nn.Linear(num_heads, num_heads)
+
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x):
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0] * self.scale, qkv[1], qkv[2]
+
+        attn = (q @ k.transpose(-2, -1))
+
+        attn = self.proj_l(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+
+        attn = attn.softmax(dim=-1)
+
+        attn = self.proj_w(attn.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class LayerScale_Block(nn.Module):
+    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    # with slight modifications to add layerScale
+    def __init__(
+            self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+            drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=Attention_talking_head,
+            mlp_block=Mlp, init_values=1e-4):
+        super().__init__()
+        self.norm1 = norm_layer(dim)
+        self.attn = attn_block(
+            dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = mlp_block(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+        self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+        self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True)
+
+    def forward(self, x):
+        x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class Cait(nn.Module):
+    # taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+    # with slight modifications to adapt to our cait models
+    def __init__(
+            self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
+            num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
+            drop_path_rate=0., norm_layer=nn.LayerNorm, global_pool=None,
+            block_layers=LayerScale_Block,
+            block_layers_token=LayerScale_Block_CA,
+            patch_layer=PatchEmbed, act_layer=nn.GELU,
+            attn_block=Attention_talking_head, mlp_block=Mlp,
+            init_scale=1e-4,
+            attn_block_token_only=Class_Attention,
+            mlp_block_token_only=Mlp,
+            depth_token_only=2,
+            mlp_ratio_clstk=4.0):
+        super().__init__()
+
+        self.num_classes = num_classes
+        self.num_features = self.embed_dim = embed_dim
+
+        self.patch_embed = patch_layer(
+            img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
+
+        num_patches = self.patch_embed.num_patches
+
+        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
+        self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        dpr = [drop_path_rate for i in range(depth)]
+        self.blocks = nn.ModuleList([
+            block_layers(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
+                act_layer=act_layer, attn_block=attn_block, mlp_block=mlp_block, init_values=init_scale)
+            for i in range(depth)])
+
+        self.blocks_token_only = nn.ModuleList([
+            block_layers_token(
+                dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio_clstk, qkv_bias=qkv_bias, qk_scale=qk_scale,
+                drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=norm_layer,
+                act_layer=act_layer, attn_block=attn_block_token_only,
+                mlp_block=mlp_block_token_only, init_values=init_scale)
+            for i in range(depth_token_only)])
+
+        self.norm = norm_layer(embed_dim)
+
+        self.feature_info = [dict(num_chs=embed_dim, reduction=0, module='head')]
+        self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
+
+        trunc_normal_(self.pos_embed, std=.02)
+        trunc_normal_(self.cls_token, 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 {'pos_embed', 'cls_token'}
+
+    def forward_features(self, x):
+        B = x.shape[0]
+        x = self.patch_embed(x)
+
+        cls_tokens = self.cls_token.expand(B, -1, -1)
+
+        x = x + self.pos_embed
+        x = self.pos_drop(x)
+
+        for i, blk in enumerate(self.blocks):
+            x = blk(x)
+
+        for i, blk in enumerate(self.blocks_token_only):
+            cls_tokens = blk(x, cls_tokens)
+
+        x = torch.cat((cls_tokens, x), dim=1)
+
+        x = self.norm(x)
+        return x[:, 0]
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+
+        return x
+
+
+@register_model
+def cait_xxs24_224(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=224, patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/XXS24_224.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_xxs24(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=384, patch_size=16, embed_dim=192, depth=24, num_heads=4, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/XXS24_384.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_xxs36_224(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=224, patch_size=16, embed_dim=192, depth=36, num_heads=4, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/XXS36_224.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_xxs36(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=384, patch_size=16, embed_dim=192, depth=36, num_heads=4, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/XXS36_384.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_xs24(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=384, patch_size=16, embed_dim=288, depth=24, num_heads=6, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/XS24_384.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_s24_224(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=224, patch_size=16, embed_dim=384, depth=24, num_heads=8, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/S24_224.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_s24(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=384, patch_size=16, embed_dim=384, depth=24, num_heads=8, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-5, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/S24_384.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_s36(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=384, patch_size=16, embed_dim=384, depth=36, num_heads=8, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-6, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/S36_384.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_m36(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=384, patch_size=16, embed_dim=768, depth=36, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-6, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/M36_384.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model
+
+
+@register_model
+def cait_m48(pretrained=False, **kwargs):
+    model = Cait(
+        img_size=448, patch_size=16, embed_dim=768, depth=48, num_heads=16, mlp_ratio=4, qkv_bias=True,
+        norm_layer=partial(nn.LayerNorm, eps=1e-6), init_scale=1e-6, depth_token_only=2, **kwargs)
+
+    model.default_cfg = _cfg()
+    if pretrained:
+        checkpoint = torch.hub.load_state_dict_from_url(
+            url="https://dl.fbaipublicfiles.com/deit/M48_448.pth",
+            map_location="cpu", check_hash=True
+        )
+        checkpoint_no_module = {}
+        for k in model.state_dict().keys():
+            checkpoint_no_module[k] = checkpoint["model"]['module.' + k]
+
+        model.load_state_dict(checkpoint_no_module)
+
+    return model