111 lines
3.5 KiB
Python
111 lines
3.5 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from copy import deepcopy
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from unittest import TestCase
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import torch
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from mmcls.models.backbones import DaViT
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from mmcls.models.backbones.davit import SpatialBlock
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class TestDaViT(TestCase):
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def setUp(self):
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self.cfg = dict(arch='t', patch_size=4, drop_path_rate=0.1)
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def test_structure(self):
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# Test invalid default arch
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with self.assertRaisesRegex(AssertionError, 'not in default archs'):
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cfg = deepcopy(self.cfg)
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cfg['arch'] = 'unknown'
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DaViT(**cfg)
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# Test invalid custom arch
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with self.assertRaisesRegex(AssertionError, 'Custom arch needs'):
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cfg = deepcopy(self.cfg)
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cfg['arch'] = {
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'num_layers': 24,
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'num_heads': 16,
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'feedforward_channels': 4096
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}
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DaViT(**cfg)
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# Test custom arch
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cfg = deepcopy(self.cfg)
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cfg['arch'] = {
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'embed_dims': 64,
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'num_heads': [3, 3, 3, 3],
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'depths': [1, 1, 2, 1]
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}
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model = DaViT(**cfg)
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self.assertEqual(model.embed_dims, 64)
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self.assertEqual(model.num_layers, 4)
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for layer in model.stages:
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self.assertEqual(
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layer.blocks[0].spatial_block.attn.w_msa.num_heads, 3)
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def test_init_weights(self):
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# test weight init cfg
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cfg = deepcopy(self.cfg)
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cfg['init_cfg'] = [
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dict(
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type='Kaiming',
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layer='Conv2d',
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mode='fan_in',
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nonlinearity='linear')
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]
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model = DaViT(**cfg)
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ori_weight = model.patch_embed.projection.weight.clone().detach()
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model.init_weights()
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initialized_weight = model.patch_embed.projection.weight
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self.assertFalse(torch.allclose(ori_weight, initialized_weight))
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def test_forward(self):
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imgs = torch.randn(1, 3, 224, 224)
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cfg = deepcopy(self.cfg)
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model = DaViT(**cfg)
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 1)
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self.assertEqual(outs[0].shape, (1, 768, 7, 7))
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# Test forward with multi out indices
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cfg = deepcopy(self.cfg)
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cfg['out_indices'] = [2, 3]
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model = DaViT(**cfg)
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 2)
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self.assertEqual(outs[0].shape, (1, 384, 14, 14))
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self.assertEqual(outs[1].shape, (1, 768, 7, 7))
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# test with checkpoint forward
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cfg = deepcopy(self.cfg)
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cfg['with_cp'] = True
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model = DaViT(**cfg)
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for m in model.modules():
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if isinstance(m, SpatialBlock):
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self.assertTrue(m.with_cp)
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model.init_weights()
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model.train()
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 1)
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self.assertEqual(outs[0].shape, (1, 768, 7, 7))
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# Test forward with dynamic input size
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imgs1 = torch.randn(1, 3, 224, 224)
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imgs2 = torch.randn(1, 3, 256, 256)
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imgs3 = torch.randn(1, 3, 256, 309)
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cfg = deepcopy(self.cfg)
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model = DaViT(**cfg)
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for imgs in [imgs1, imgs2, imgs3]:
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 1)
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expect_feat_shape = (imgs.shape[2] // 32, imgs.shape[3] // 32)
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self.assertEqual(outs[0].shape, (1, 768, *expect_feat_shape))
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