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https://github.com/huggingface/pytorch-image-models.git
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Better handling of crossvit for tests / forward_features, fix torchscript regression in my changes
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@ -188,25 +188,22 @@ def test_model_default_cfgs_non_std(model_name, batch_size):
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input_tensor = torch.randn((batch_size, *input_size))
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# test forward_features (always unpooled)
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if 'crossvit' not in model_name:
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# FIXME remove crossvit exception
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outputs = model.forward_features(input_tensor)
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if isinstance(outputs, tuple):
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outputs = outputs[0]
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assert outputs.shape[1] == model.num_features
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outputs = model.forward_features(input_tensor)
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if isinstance(outputs, (tuple, list)):
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outputs = outputs[0]
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assert outputs.shape[1] == model.num_features
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# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
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model.reset_classifier(0)
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outputs = model.forward(input_tensor)
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if isinstance(outputs, tuple):
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if isinstance(outputs, (tuple, list)):
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outputs = outputs[0]
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assert len(outputs.shape) == 2
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assert outputs.shape[1] == model.num_features
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model = create_model(model_name, pretrained=False, num_classes=0).eval()
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outputs = model.forward(input_tensor)
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if isinstance(outputs, tuple):
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if isinstance(outputs, (tuple, list)):
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outputs = outputs[0]
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assert len(outputs.shape) == 2
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assert outputs.shape[1] == model.num_features
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@ -268,12 +268,9 @@ class CrossViT(nn.Module):
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super().__init__()
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self.num_classes = num_classes
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if not isinstance(img_size, (tuple, list)):
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img_size = to_2tuple(img_size)
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self.img_size = img_size
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if not isinstance(img_scale, (tuple, list)):
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img_scale = to_2tuple(img_scale)
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self.img_size_scaled = [tuple([int(sj * si) for sj in img_size]) for si in img_scale]
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self.img_size = to_2tuple(img_size)
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img_scale = to_2tuple(img_scale)
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self.img_size_scaled = [tuple([int(sj * si) for sj in self.img_size]) for si in img_scale]
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num_patches = _compute_num_patches(self.img_size_scaled, patch_size)
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self.num_branches = len(patch_size)
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self.embed_dim = embed_dim
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@ -346,7 +343,7 @@ class CrossViT(nn.Module):
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xs = []
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for i, patch_embed in enumerate(self.patch_embed):
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ss = self.img_size_scaled[i]
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x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic') if H != ss[0] else x
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x_ = torch.nn.functional.interpolate(x, size=ss, mode='bicubic', align_corners=False) if H != ss[0] else x
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tmp = patch_embed(x_)
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cls_tokens = self.cls_token_0 if i == 0 else self.cls_token_1 # hard-coded for torch jit script
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cls_tokens = cls_tokens.expand(B, -1, -1)
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@ -361,15 +358,12 @@ class CrossViT(nn.Module):
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# NOTE: was before branch token section, move to here to assure all branch token are before layer norm
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xs = [norm(xs[i]) for i, norm in enumerate(self.norm)]
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return tuple([x[:, 0] for x in xs])
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return [x[:, 0] for x in xs]
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def forward(self, x):
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xs = self.forward_features(x)
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ce_logits = [head(xs[i]) for i, head in enumerate(self.head)]
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if isinstance(self.head[0], nn.Identity):
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# FIXME to pass current passthrough features tests, could use better approach
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ce_logits = tuple(ce_logits)
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else:
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if not isinstance(self.head[0], nn.Identity):
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ce_logits = torch.mean(torch.stack(ce_logits, dim=0), dim=0)
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return ce_logits
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