186 lines
6.5 KiB
Python
186 lines
6.5 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 MViT
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class TestMViT(TestCase):
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def setUp(self):
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self.cfg = dict(arch='tiny', img_size=224, drop_path_rate=0.1)
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def test_arch(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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MViT(**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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'embed_dims': 96,
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'num_layers': 10,
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}
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MViT(**cfg)
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# Test custom arch
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cfg = deepcopy(self.cfg)
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embed_dims = 96
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num_layers = 10
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num_heads = 1
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downscale_indices = (2, 5, 7)
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cfg['arch'] = {
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'embed_dims': embed_dims,
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'num_layers': num_layers,
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'num_heads': num_heads,
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'downscale_indices': downscale_indices
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}
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model = MViT(**cfg)
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self.assertEqual(len(model.blocks), num_layers)
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for i, block in enumerate(model.blocks):
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if i in downscale_indices:
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num_heads *= 2
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embed_dims *= 2
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self.assertEqual(block.out_dims, embed_dims)
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self.assertEqual(block.attn.num_heads, num_heads)
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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['use_abs_pos_embed'] = True
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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 = MViT(**cfg)
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ori_weight = model.patch_embed.projection.weight.clone().detach()
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# The pos_embed is all zero before initialize
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self.assertTrue(torch.allclose(model.pos_embed, torch.tensor(0.)))
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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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self.assertFalse(torch.allclose(model.pos_embed, torch.tensor(0.)))
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self.assertFalse(
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torch.allclose(model.blocks[0].attn.rel_pos_h, torch.tensor(0.)))
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self.assertFalse(
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torch.allclose(model.blocks[0].attn.rel_pos_w, torch.tensor(0.)))
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# test rel_pos_zero_init
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cfg = deepcopy(self.cfg)
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cfg['rel_pos_zero_init'] = True
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model = MViT(**cfg)
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model.init_weights()
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self.assertTrue(
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torch.allclose(model.blocks[0].attn.rel_pos_h, torch.tensor(0.)))
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self.assertTrue(
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torch.allclose(model.blocks[0].attn.rel_pos_w, torch.tensor(0.)))
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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 = MViT(**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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feat = outs[-1]
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self.assertEqual(feat.shape, (1, 768, 7, 7))
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# test multiple output indices
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cfg = deepcopy(self.cfg)
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cfg['out_scales'] = (0, 1, 2, 3)
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model = MViT(**cfg)
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model.init_weights()
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 4)
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for stride, out in zip([1, 2, 4, 8], outs):
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self.assertEqual(out.shape,
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(1, 96 * stride, 56 // stride, 56 // stride))
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# test dim_mul_in_attention = False
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cfg = deepcopy(self.cfg)
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cfg['out_scales'] = (0, 1, 2, 3)
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cfg['dim_mul_in_attention'] = False
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model = MViT(**cfg)
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 4)
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for dim_mul, stride, out in zip([2, 4, 8, 8], [1, 2, 4, 8], outs):
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self.assertEqual(out.shape,
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(1, 96 * dim_mul, 56 // stride, 56 // stride))
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# test rel_pos_spatial = False
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cfg = deepcopy(self.cfg)
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cfg['out_scales'] = (0, 1, 2, 3)
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cfg['rel_pos_spatial'] = False
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cfg['img_size'] = None
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model = MViT(**cfg)
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 4)
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for stride, out in zip([1, 2, 4, 8], outs):
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self.assertEqual(out.shape,
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(1, 96 * stride, 56 // stride, 56 // stride))
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# test residual_pooling = False
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cfg = deepcopy(self.cfg)
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cfg['out_scales'] = (0, 1, 2, 3)
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cfg['residual_pooling'] = False
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model = MViT(**cfg)
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 4)
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for stride, out in zip([1, 2, 4, 8], outs):
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self.assertEqual(out.shape,
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(1, 96 * stride, 56 // stride, 56 // stride))
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# test use_abs_pos_embed = True
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cfg = deepcopy(self.cfg)
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cfg['out_scales'] = (0, 1, 2, 3)
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cfg['use_abs_pos_embed'] = True
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model = MViT(**cfg)
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model.init_weights()
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 4)
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for stride, out in zip([1, 2, 4, 8], outs):
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self.assertEqual(out.shape,
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(1, 96 * stride, 56 // stride, 56 // stride))
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# test dynamic inputs shape
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cfg = deepcopy(self.cfg)
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cfg['out_scales'] = (0, 1, 2, 3)
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model = MViT(**cfg)
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imgs = torch.randn(1, 3, 352, 260)
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h_resolution = (352 + 2 * 3 - 7) // 4 + 1
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w_resolution = (260 + 2 * 3 - 7) // 4 + 1
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 4)
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expect_h = h_resolution
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expect_w = w_resolution
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for i, out in enumerate(outs):
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self.assertEqual(out.shape, (1, 96 * 2**i, expect_h, expect_w))
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expect_h = (expect_h + 2 * 1 - 3) // 2 + 1
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expect_w = (expect_w + 2 * 1 - 3) // 2 + 1
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def test_structure(self):
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# test drop_path_rate decay
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cfg = deepcopy(self.cfg)
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cfg['drop_path_rate'] = 0.2
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model = MViT(**cfg)
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for i, block in enumerate(model.blocks):
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expect_prob = 0.2 / (model.num_layers - 1) * i
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if expect_prob > 0:
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self.assertAlmostEqual(block.drop_path.drop_prob, expect_prob)
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