Better handling of crossvit for tests / forward_features, fix torchscript regression in my changes

Author: rwightman

tests/test_models.py

@@ -188,25 +188,22 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
 
     input_tensor = torch.randn((batch_size, *input_size))
 
-    # test forward_features (always unpooled)
-    if 'crossvit' not in model_name:
-        # FIXME remove crossvit exception
-        outputs = model.forward_features(input_tensor)
-        if isinstance(outputs, tuple):
-            outputs = outputs[0]
-        assert outputs.shape[1] == model.num_features
+    outputs = model.forward_features(input_tensor)
+    if isinstance(outputs, (tuple, list)):
+        outputs = outputs[0]
+    assert outputs.shape[1] == model.num_features
 
     # test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
     model.reset_classifier(0)
     outputs = model.forward(input_tensor)
-    if isinstance(outputs, tuple):
+    if isinstance(outputs,  (tuple, list)):
         outputs = outputs[0]
     assert len(outputs.shape) == 2
     assert outputs.shape[1] == model.num_features
 
     model = create_model(model_name, pretrained=False, num_classes=0).eval()
     outputs = model.forward(input_tensor)
-    if isinstance(outputs, tuple):
+    if isinstance(outputs, (tuple, list)):
         outputs = outputs[0]
     assert len(outputs.shape) == 2
     assert outputs.shape[1] == model.num_features

timm/models/crossvit.py

@@ -268,12 +268,9 @@ def __init__(
         super().__init__()
 
         self.num_classes = num_classes
-        if not isinstance(img_size, (tuple, list)):
-            img_size = to_2tuple(img_size)
-        self.img_size = img_size
-        if not isinstance(img_scale, (tuple, list)):
-            img_scale = to_2tuple(img_scale)
-        self.img_size_scaled = [tuple([int(sj * si) for sj in img_size]) for si in img_scale]
+        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]
         num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
         self.num_branches = len(patch_size)
         self.embed_dim = embed_dim
@@ -346,7 +343,7 @@ def forward_features(self, x):
         xs = []
         for i, patch_embed in enumerate(self.patch_embed):
             ss = self.img_size_scaled[i]
-            x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic') if H != ss[0] else x
+            x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic', align_corners=False) if H != ss[0] else x
             tmp = 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)
@@ -361,15 +358,12 @@ def forward_features(self, x):
 
         # 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 tuple([x[:, 0] for x in xs])
+        return [x[:, 0] for x in xs]
 
     def forward(self, x):
         xs = self.forward_features(x)
         ce_logits = [head(xs[i]) for i, head in enumerate(self.head)]
-        if isinstance(self.head[0], nn.Identity):
-            # FIXME to pass current passthrough features tests, could use better approach
-            ce_logits = tuple(ce_logits)
-        else:
+        if not isinstance(self.head[0], nn.Identity):
             ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
         return ce_logits