ConViT cleanup, fix torchscript, bit of reformatting, reuse existing layers.

timm/models/convit.py

-"""These modules are adapted from those of timm, see
-https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+""" ConViT Model
+
+@article{d2021convit,
+  title={ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases},
+  author={d'Ascoli, St{\'e}phane and Touvron, Hugo and Leavitt, Matthew and Morcos, Ari and Biroli, Giulio and Sagun, Levent},
+  journal={arXiv preprint arXiv:2103.10697},
+  year={2021}
+}
+
+Paper link: https://arxiv.org/abs/2103.10697
+Original code: https://github.com/facebookresearch/convit, original copyright below
 """
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the CC-by-NC license found in the
+# LICENSE file in the root directory of this source tree.
+#
+'''These modules are adapted from those of timm, see
+https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+'''
 
 import torch
 import torch.nn as nn
@@ -9,8 +27,9 @@ import torch.nn.functional as F
 
 from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from .helpers import build_model_with_cfg
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
-from timm.models.registry import register_model
+from .layers import DropPath, to_2tuple, trunc_normal_, PatchEmbed, Mlp
+from .registry import register_model
+from .vision_transformer_hybrid import HybridEmbed
 
 import torch
 import torch.nn as nn
@@ -29,7 +48,7 @@ def _cfg(url='', **kwargs):
 default_cfgs = {
     # ConViT
     'convit_tiny': _cfg(
-        url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"), 
+        url="https://dl.fbaipublicfiles.com/convit/convit_tiny.pth"),
     'convit_small': _cfg(
         url="https://dl.fbaipublicfiles.com/convit/convit_small.pth"),
     'convit_base': _cfg(
@@ -37,71 +56,31 @@ default_cfgs = {
 }
 
 
-class Mlp(nn.Module):
-    def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-        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)
-            
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
 class GPSA(nn.Module):
     def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
-                 locality_strength=1., use_local_init=True):
+                 locality_strength=1.):
         super().__init__()
         self.num_heads = num_heads
         self.dim = dim
         head_dim = dim // num_heads
         self.scale = qk_scale or head_dim ** -0.5
+        self.locality_strength = locality_strength
+
+        self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
+        self.v = nn.Linear(dim, dim, bias=qkv_bias)
 
-        self.qk = nn.Linear(dim, dim * 2, 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.pos_proj = nn.Linear(3, num_heads)
         self.proj_drop = nn.Dropout(proj_drop)
         self.locality_strength = locality_strength
         self.gating_param = nn.Parameter(torch.ones(self.num_heads))
-        self.apply(self._init_weights)
-        if use_local_init:
-            self.local_init(locality_strength=locality_strength)
+        self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3)  # silly torchscript hack, won't work with None
 
-    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)
-        
     def forward(self, x):
         B, N, C = x.shape
-        if not hasattr(self, 'rel_indices') or self.rel_indices.size(1)!=N:
-            self.get_rel_indices(N)
-
+        if self.rel_indices is None or self.rel_indices.shape[1] != N:
+            self.rel_indices = self.get_rel_indices(N)
         attn = self.get_attention(x)
         v = self.v(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
         x = (attn @ v).transpose(1, 2).reshape(B, N, C)
@@ -110,61 +89,58 @@ class GPSA(nn.Module):
         return x
 
     def get_attention(self, x):
-        B, N, C = x.shape        
+        B, N, C = x.shape
         qk = self.qk(x).reshape(B, N, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
         q, k = qk[0], qk[1]
-        pos_score = self.rel_indices.expand(B, -1, -1,-1)
-        pos_score = self.pos_proj(pos_score).permute(0,3,1,2) 
+        pos_score = self.rel_indices.expand(B, -1, -1, -1)
+        pos_score = self.pos_proj(pos_score).permute(0, 3, 1, 2)
         patch_score = (q @ k.transpose(-2, -1)) * self.scale
         patch_score = patch_score.softmax(dim=-1)
         pos_score = pos_score.softmax(dim=-1)
 
-        gating = self.gating_param.view(1,-1,1,1)
-        attn = (1.-torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
+        gating = self.gating_param.view(1, -1, 1, 1)
+        attn = (1. - torch.sigmoid(gating)) * patch_score + torch.sigmoid(gating) * pos_score
         attn /= attn.sum(dim=-1).unsqueeze(-1)
         attn = self.attn_drop(attn)
         return attn
 
-    def get_attention_map(self, x, return_map = False):
-
-        attn_map = self.get_attention(x).mean(0) # average over batch
-        distances = self.rel_indices.squeeze()[:,:,-1]**.5
-        dist = torch.einsum('nm,hnm->h', (distances, attn_map))
-        dist /= distances.size(0)
+    def get_attention_map(self, x, return_map=False):
+        attn_map = self.get_attention(x).mean(0)  # average over batch
+        distances = self.rel_indices.squeeze()[:, :, -1] ** .5
+        dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / distances.size(0)
         if return_map:
             return dist, attn_map
         else:
             return dist
-    
-    def local_init(self, locality_strength=1.):
-        
+
+    def local_init(self):
         self.v.weight.data.copy_(torch.eye(self.dim))
-        locality_distance = 1 #max(1,1/locality_strength**.5)
-        
-        kernel_size = int(self.num_heads**.5)
-        center = (kernel_size-1)/2 if kernel_size%2==0 else kernel_size//2
+        locality_distance = 1  # max(1,1/locality_strength**.5)
+
+        kernel_size = int(self.num_heads ** .5)
+        center = (kernel_size - 1) / 2 if kernel_size % 2 == 0 else kernel_size // 2
         for h1 in range(kernel_size):
             for h2 in range(kernel_size):
-                position = h1+kernel_size*h2
-                self.pos_proj.weight.data[position,2] = -1
-                self.pos_proj.weight.data[position,1] = 2*(h1-center)*locality_distance
-                self.pos_proj.weight.data[position,0] = 2*(h2-center)*locality_distance
-        self.pos_proj.weight.data *= locality_strength
-
-    def get_rel_indices(self, num_patches):
-        img_size = int(num_patches**.5)
-        rel_indices   = torch.zeros(1, num_patches, num_patches, 3)
-        ind = torch.arange(img_size).view(1,-1) - torch.arange(img_size).view(-1, 1)
-        indx = ind.repeat(img_size,img_size)
-        indy = ind.repeat_interleave(img_size,dim=0).repeat_interleave(img_size,dim=1)
-        indd = indx**2 + indy**2
-        rel_indices[:,:,:,2] = indd.unsqueeze(0)
-        rel_indices[:,:,:,1] = indy.unsqueeze(0)
-        rel_indices[:,:,:,0] = indx.unsqueeze(0)
+                position = h1 + kernel_size * h2
+                self.pos_proj.weight.data[position, 2] = -1
+                self.pos_proj.weight.data[position, 1] = 2 * (h1 - center) * locality_distance
+                self.pos_proj.weight.data[position, 0] = 2 * (h2 - center) * locality_distance
+        self.pos_proj.weight.data *= self.locality_strength
+
+    def get_rel_indices(self, num_patches: int) -> torch.Tensor:
+        img_size = int(num_patches ** .5)
+        rel_indices = torch.zeros(1, num_patches, num_patches, 3)
+        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
+        indx = ind.repeat(img_size, img_size)
+        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
+        indd = indx ** 2 + indy ** 2
+        rel_indices[:, :, :, 2] = indd.unsqueeze(0)
+        rel_indices[:, :, :, 1] = indy.unsqueeze(0)
+        rel_indices[:, :, :, 0] = indx.unsqueeze(0)
         device = self.qk.weight.device
-        self.rel_indices = rel_indices.to(device)
+        return rel_indices.to(device)
+
 
- 
 class MHSA(nn.Module):
     def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
         super().__init__()
@@ -176,41 +152,28 @@ class MHSA(nn.Module):
         self.attn_drop = nn.Dropout(attn_drop)
         self.proj = nn.Linear(dim, dim)
         self.proj_drop = nn.Dropout(proj_drop)
-        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)
 
-    def get_attention_map(self, x, return_map = False):
+    def get_attention_map(self, x, return_map=False):
         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], qkv[1], qkv[2]
         attn_map = (q @ k.transpose(-2, -1)) * self.scale
         attn_map = attn_map.softmax(dim=-1).mean(0)
 
-        img_size = int(N**.5)
-        ind = torch.arange(img_size).view(1,-1) - torch.arange(img_size).view(-1, 1)
-        indx = ind.repeat(img_size,img_size)
-        indy = ind.repeat_interleave(img_size,dim=0).repeat_interleave(img_size,dim=1)
-        indd = indx**2 + indy**2
-        distances = indd**.5
+        img_size = int(N ** .5)
+        ind = torch.arange(img_size).view(1, -1) - torch.arange(img_size).view(-1, 1)
+        indx = ind.repeat(img_size, img_size)
+        indy = ind.repeat_interleave(img_size, dim=0).repeat_interleave(img_size, dim=1)
+        indd = indx ** 2 + indy ** 2
+        distances = indd ** .5
         distances = distances.to('cuda')
 
-        dist = torch.einsum('nm,hnm->h', (distances, attn_map))
-        dist /= N
-        
+        dist = torch.einsum('nm,hnm->h', (distances, attn_map)) / N
         if return_map:
             return dist, attn_map
         else:
             return dist
 
-            
     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)
@@ -228,15 +191,19 @@ class MHSA(nn.Module):
 
 class Block(nn.Module):
 
-    def __init__(self, dim, num_heads,  mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
+    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, use_gpsa=True, **kwargs):
         super().__init__()
         self.norm1 = norm_layer(dim)
         self.use_gpsa = use_gpsa
         if self.use_gpsa:
-            self.attn = GPSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, **kwargs)
+            self.attn = GPSA(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
+                proj_drop=drop, **kwargs)
         else:
-            self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop, **kwargs)
+            self.attn = MHSA(
+                dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
+                proj_drop=drop, **kwargs)
         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)
@@ -246,75 +213,12 @@ class Block(nn.Module):
         x = x + self.drop_path(self.attn(self.norm1(x)))
         x = x + self.drop_path(self.mlp(self.norm2(x)))
         return x
-    
-
-class PatchEmbed(nn.Module):
-    """ Image to Patch Embedding, from timm
-    """
-    def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        num_patches = (img_size[1] // patch_size[1]) * (img_size[0] // patch_size[0])
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.num_patches = num_patches
-
-        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
-        self.apply(self._init_weights)
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        assert H == self.img_size[0] and W == self.img_size[1], \
-            f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)
-        return x
-
-    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)
-
-
-class HybridEmbed(nn.Module):
-    """ CNN Feature Map Embedding, from timm
-    """
-    def __init__(self, backbone, img_size=224, feature_size=None, in_chans=3, embed_dim=768):
-        super().__init__()
-        assert isinstance(backbone, nn.Module)
-        img_size = to_2tuple(img_size)
-        self.img_size = img_size
-        self.backbone = backbone
-        if feature_size is None:
-            with torch.no_grad():
-                training = backbone.training
-                if training:
-                    backbone.eval()
-                o = self.backbone(torch.zeros(1, in_chans, img_size[0], img_size[1]))[-1]
-                feature_size = o.shape[-2:]
-                feature_dim = o.shape[1]
-                backbone.train(training)
-        else:
-            feature_size = to_2tuple(feature_size)
-            feature_dim = self.backbone.feature_info.channels()[-1]
-        self.num_patches = feature_size[0] * feature_size[1]
-        self.proj = nn.Linear(feature_dim, embed_dim)
-        self.apply(self._init_weights)
-
-    def forward(self, x):
-        x = self.backbone(x)[-1]
-        x = x.flatten(2).transpose(1, 2)
-        x = self.proj(x)
-        return x
 
 
 class ConViT(nn.Module):
     """ Vision Transformer with support for patch or hybrid CNN input stage
     """
+
     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., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
@@ -335,7 +239,7 @@ class ConViT(nn.Module):
                 img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=embed_dim)
         num_patches = self.patch_embed.num_patches
         self.num_patches = num_patches
-        
+
         self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))
         self.pos_drop = nn.Dropout(p=drop_rate)
 
@@ -350,7 +254,7 @@ class ConViT(nn.Module):
                 drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
                 use_gpsa=True,
                 locality_strength=locality_strength)
-            if i<local_up_to_layer else
+            if i < local_up_to_layer else
             Block(
                 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,
@@ -363,7 +267,10 @@ class ConViT(nn.Module):
         self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity()
 
         trunc_normal_(self.cls_token, std=.02)
-        self.head.apply(self._init_weights)
+        self.apply(self._init_weights)
+        for n, m in self.named_modules():
+            if hasattr(m, 'local_init'):
+                m.local_init()
 
     def _init_weights(self, m):
         if isinstance(m, nn.Linear):
@@ -395,8 +302,8 @@ class ConViT(nn.Module):
             x = x + self.pos_embed
         x = self.pos_drop(x)
 
-        for u,blk in enumerate(self.blocks):
-            if u == self.local_up_to_layer :
+        for u, blk in enumerate(self.blocks):
+            if u == self.local_up_to_layer:
                 x = torch.cat((cls_tokens, x), dim=1)
             x = blk(x)
 
@@ -415,30 +322,29 @@ def _create_convit(variant, pretrained=False, **kwargs):
         default_cfg=default_cfgs[variant],
         **kwargs)
 
-    
+
 @register_model
 def convit_tiny(pretrained=False, **kwargs):
     model_args = dict(
-        local_up_to_layer=10, locality_strength=1.0, embed_dim=48, 
+        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
         num_heads=4, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model = _create_convit(
-        variant='convit_tiny', pretrained=pretrained, **model_args)
+    model = _create_convit(variant='convit_tiny', pretrained=pretrained, **model_args)
     return model
 
+
 @register_model
 def convit_small(pretrained=False, **kwargs):
     model_args = dict(
-        local_up_to_layer=10, locality_strength=1.0, embed_dim=48, 
+        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
         num_heads=9, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model = _create_convit(
-        variant='convit_small', pretrained=pretrained, **model_args)
+    model = _create_convit(variant='convit_small', pretrained=pretrained, **model_args)
     return model
 
+
 @register_model
 def convit_base(pretrained=False, **kwargs):
     model_args = dict(
-        local_up_to_layer=10, locality_strength=1.0, embed_dim=48, 
+        local_up_to_layer=10, locality_strength=1.0, embed_dim=48,
         num_heads=16, norm_layer=partial(nn.LayerNorm, eps=1e-6), **kwargs)
-    model = _create_convit(
-        variant='convit_base', pretrained=pretrained, **model_args)
+    model = _create_convit(variant='convit_base', pretrained=pretrained, **model_args)
     return model