126 lines
3.7 KiB
Python
126 lines
3.7 KiB
Python
import pytest
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import torch
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from mmseg.models.backbones.vit import VisionTransformer
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from .utils import check_norm_state
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def test_vit_backbone():
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with pytest.raises(TypeError):
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# pretrained must be a string path
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model = VisionTransformer()
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model.init_weights(pretrained=0)
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with pytest.raises(TypeError):
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# img_size must be int or tuple
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model = VisionTransformer(img_size=512.0)
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with pytest.raises(TypeError):
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# out_indices must be int ,list or tuple
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model = VisionTransformer(out_indices=1.)
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with pytest.raises(TypeError):
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# test upsample_pos_embed function
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x = torch.randn(1, 196)
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VisionTransformer.resize_pos_embed(x, 512, 512, 224, 224, 'bilinear')
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with pytest.raises(IndexError):
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# forward inputs must be [N, C, H, W]
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x = torch.randn(3, 30, 30)
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model = VisionTransformer()
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model(x)
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with pytest.raises(AssertionError):
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# The length of img_size tuple must be lower than 3.
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VisionTransformer(img_size=(224, 224, 224))
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with pytest.raises(TypeError):
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# Pretrained must be None or Str.
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VisionTransformer(pretrained=123)
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with pytest.raises(AssertionError):
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# with_cls_token must be True when output_cls_token == True
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VisionTransformer(with_cls_token=False, output_cls_token=True)
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# Test img_size isinstance tuple
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imgs = torch.randn(1, 3, 224, 224)
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model = VisionTransformer(img_size=(224, ))
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model.init_weights()
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model(imgs)
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# Test img_size isinstance tuple
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imgs = torch.randn(1, 3, 224, 224)
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model = VisionTransformer(img_size=(224, 224))
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model(imgs)
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# Test norm_eval = True
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model = VisionTransformer(norm_eval=True)
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model.train()
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# Test ViT backbone with input size of 224 and patch size of 16
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model = VisionTransformer()
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), True)
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# Test normal size input image
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 14, 14)
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# Test large size input image
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imgs = torch.randn(1, 3, 256, 256)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 16, 16)
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# Test small size input image
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imgs = torch.randn(1, 3, 32, 32)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 2, 2)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 14, 14)
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# Test unbalanced size input image
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imgs = torch.randn(1, 3, 112, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 7, 14)
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# Test irregular input image
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imgs = torch.randn(1, 3, 234, 345)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 15, 22)
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# Test with_cp=True
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model = VisionTransformer(with_cp=True)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 14, 14)
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# Test with_cls_token=False
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model = VisionTransformer(with_cls_token=False)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 14, 14)
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# Test final norm
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model = VisionTransformer(final_norm=True)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 14, 14)
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# Test patch norm
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model = VisionTransformer(patch_norm=True)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[-1].shape == (1, 768, 14, 14)
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# Test output_cls_token
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model = VisionTransformer(with_cls_token=True, output_cls_token=True)
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imgs = torch.randn(1, 3, 224, 224)
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feat = model(imgs)
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assert feat[0][0].shape == (1, 768, 14, 14)
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assert feat[0][1].shape == (1, 768)
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