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- # Copyright (c) OpenMMLab. All rights reserved.
- from unittest import TestCase
- import torch
- from mmpose.models.backbones.swin import SwinBlock, SwinTransformer
- class TestSwin(TestCase):
- def test_swin_block(self):
- # test SwinBlock structure and forward
- block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
- self.assertEqual(block.ffn.embed_dims, 64)
- self.assertEqual(block.attn.w_msa.num_heads, 4)
- self.assertEqual(block.ffn.feedforward_channels, 256)
- x = torch.randn(1, 56 * 56, 64)
- x_out = block(x, (56, 56))
- self.assertEqual(x_out.shape, torch.Size([1, 56 * 56, 64]))
- # Test BasicBlock with checkpoint forward
- block = SwinBlock(
- embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True)
- self.assertTrue(block.with_cp)
- x = torch.randn(1, 56 * 56, 64)
- x_out = block(x, (56, 56))
- self.assertEqual(x_out.shape, torch.Size([1, 56 * 56, 64]))
- def test_swin_transformer(self):
- """Test Swin Transformer backbone."""
- with self.assertRaises(AssertionError):
- # Because swin uses non-overlapping patch embed, so the stride of
- # patch embed must be equal to patch size.
- SwinTransformer(strides=(2, 2, 2, 2), patch_size=4)
- # test pretrained image size
- with self.assertRaises(AssertionError):
- SwinTransformer(pretrain_img_size=(224, 224, 224))
- # Test absolute position embedding
- temp = torch.randn((1, 3, 224, 224))
- model = SwinTransformer(pretrain_img_size=224, use_abs_pos_embed=True)
- model.init_weights()
- model(temp)
- # Test patch norm
- model = SwinTransformer(patch_norm=False)
- model(temp)
- # Test normal inference
- temp = torch.randn((1, 3, 32, 32))
- model = SwinTransformer()
- outs = model(temp)
- self.assertEqual(outs[0].shape, (1, 96, 8, 8))
- self.assertEqual(outs[1].shape, (1, 192, 4, 4))
- self.assertEqual(outs[2].shape, (1, 384, 2, 2))
- self.assertEqual(outs[3].shape, (1, 768, 1, 1))
- # Test abnormal inference size
- temp = torch.randn((1, 3, 31, 31))
- model = SwinTransformer()
- outs = model(temp)
- self.assertEqual(outs[0].shape, (1, 96, 8, 8))
- self.assertEqual(outs[1].shape, (1, 192, 4, 4))
- self.assertEqual(outs[2].shape, (1, 384, 2, 2))
- self.assertEqual(outs[3].shape, (1, 768, 1, 1))
- # Test abnormal inference size
- temp = torch.randn((1, 3, 112, 137))
- model = SwinTransformer()
- outs = model(temp)
- self.assertEqual(outs[0].shape, (1, 96, 28, 35))
- self.assertEqual(outs[1].shape, (1, 192, 14, 18))
- self.assertEqual(outs[2].shape, (1, 384, 7, 9))
- self.assertEqual(outs[3].shape, (1, 768, 4, 5))
- model = SwinTransformer(frozen_stages=4)
- model.train()
- for p in model.parameters():
- self.assertFalse(p.requires_grad)
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