A few more crossvit tweaks, fix training w/ no_weight_decay names, add crop option for scaling, adjust default crop_pct for large img size to 1.0 for better results

Author: rwightman

timm/models/crossvit.py

@@ -40,7 +40,7 @@
 def _cfg(url='', **kwargs):
     return {
         'url': url,
-        'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None,
+        'num_classes': 1000, 'input_size': (3, 240, 240), 'pool_size': None, 'crop_pct': 0.875,
         'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'fixed_input_size': True,
         'first_conv': ('patch_embed.0.proj', 'patch_embed.1.proj'),
         'classifier': ('head.0', 'head.1'),
@@ -56,7 +56,7 @@ def _cfg(url='', **kwargs):
     ),
     'crossvit_15_dagger_408': _cfg(
         url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_15_dagger_384.pth',
-        input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
+        input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
     ),
     'crossvit_18_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_224.pth'),
     'crossvit_18_dagger_240': _cfg(
@@ -65,7 +65,7 @@ def _cfg(url='', **kwargs):
     ),
     'crossvit_18_dagger_408': _cfg(
         url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_18_dagger_384.pth',
-        input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'),
+        input_size=(3, 408, 408), first_conv=('patch_embed.0.proj.0', 'patch_embed.1.proj.0'), crop_pct=1.0,
     ),
     'crossvit_9_240': _cfg(url='https://github.com/IBM/CrossViT/releases/download/weights-0.1/crossvit_9_224.pth'),
     'crossvit_9_dagger_240': _cfg(
@@ -263,14 +263,15 @@ def __init__(
             self, img_size=224, img_scale=(1.0, 1.0), patch_size=(8, 16), in_chans=3, num_classes=1000,
             embed_dim=(192, 384), depth=((1, 3, 1), (1, 3, 1), (1, 3, 1)), num_heads=(6, 12), mlp_ratio=(2., 2., 4.),
             qkv_bias=True, drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
-            norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=False
+            norm_layer=partial(nn.LayerNorm, eps=1e-6), multi_conv=False, crop_scale=False,
     ):
         super().__init__()
 
         self.num_classes = num_classes
         self.img_size = to_2tuple(img_size)
         img_scale = to_2tuple(img_scale)
         self.img_size_scaled = [tuple([int(sj * si) for sj in self.img_size]) for si in img_scale]
+        self.crop_scale = crop_scale  # crop instead of interpolate for scale
         num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
         self.num_branches = len(patch_size)
         self.embed_dim = embed_dim
@@ -307,8 +308,7 @@ def __init__(
             for i in range(self.num_branches)])
 
         for i in range(self.num_branches):
-            if hasattr(self, f'pos_embed_{i}'):
-                trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
+            trunc_normal_(getattr(self, f'pos_embed_{i}'), std=.02)
             trunc_normal_(getattr(self, f'cls_token_{i}'), std=.02)
 
         self.apply(self._init_weights)
@@ -324,9 +324,12 @@ def _init_weights(self, m):
 
     @torch.jit.ignore
     def no_weight_decay(self):
-        out = {'cls_token'}
-        if self.pos_embed[0].requires_grad:
-            out.add('pos_embed')
+        out = set()
+        for i in range(self.num_branches):
+            out.add(f'cls_token_{i}')
+            pe = getattr(self, f'pos_embed_{i}', None)
+            if pe is not None and pe.requires_grad:
+                out.add(f'pos_embed_{i}')
         return out
 
     def get_classifier(self):
@@ -342,23 +345,29 @@ def forward_features(self, x):
         B, C, H, W = x.shape
         xs = []
         for i, patch_embed in enumerate(self.patch_embed):
+            x_ = x
             ss = self.img_size_scaled[i]
-            x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic', align_corners=False) if H != ss[0] else x
-            tmp = patch_embed(x_)
+            if H != ss[0] or W != ss[1]:
+                if self.crop_scale and ss[0] <= H and ss[1] <= W:
+                    cu, cl = int(round((H - ss[0]) / 2.)), int(round((W - ss[1]) / 2.))
+                    x_ = x_[:, :, cu:cu + ss[0], cl:cl + ss[1]]
+                else:
+                    x_ = torch.nn.functional.interpolate(x_, size=ss, mode='bicubic', align_corners=False)
+            x_ = patch_embed(x_)
             cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1  # hard-coded for torch jit script
             cls_tokens = cls_tokens.expand(B, -1, -1)
-            tmp = torch.cat((cls_tokens, tmp), dim=1)
+            x_ = torch.cat((cls_tokens, x_), dim=1)
             pos_embed = self.pos_embed_0 if i == 0 else self.pos_embed_1  # hard-coded for torch jit script
-            tmp = tmp + pos_embed
-            tmp = self.pos_drop(tmp)
-            xs.append(tmp)
+            x_ = x_ + pos_embed
+            x_ = self.pos_drop(x_)
+            xs.append(x_)
 
         for i, blk in enumerate(self.blocks):
             xs = blk(xs)
 
         # NOTE: was before branch token section, move to here to assure all branch token are before layer norm
         xs = [norm(xs[i]) for i, norm in enumerate(self.norm)]
-        return [x[:, 0] for x in xs]
+        return [xo[:, 0] for xo in xs]
 
     def forward(self, x):
         xs = self.forward_features(x)