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