180 lines
6.2 KiB
Python
180 lines
6.2 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import math
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import os
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import tempfile
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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 mmengine.runner import load_checkpoint, save_checkpoint
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from mmcls.models.backbones import DeiT3
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class TestDeiT3(TestCase):
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def setUp(self):
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self.cfg = dict(
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arch='b', img_size=224, patch_size=16, 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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DeiT3(**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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DeiT3(**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': 128,
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'num_layers': 24,
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'num_heads': 16,
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'feedforward_channels': 1024
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}
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model = DeiT3(**cfg)
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self.assertEqual(model.embed_dims, 128)
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self.assertEqual(model.num_layers, 24)
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for layer in model.layers:
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self.assertEqual(layer.attn.num_heads, 16)
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self.assertEqual(layer.ffn.feedforward_channels, 1024)
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# Test out_indices
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cfg = deepcopy(self.cfg)
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cfg['out_indices'] = {1: 1}
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with self.assertRaisesRegex(AssertionError, "get <class 'dict'>"):
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DeiT3(**cfg)
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cfg['out_indices'] = [0, 13]
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with self.assertRaisesRegex(AssertionError, 'Invalid out_indices 13'):
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DeiT3(**cfg)
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# Test model structure
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cfg = deepcopy(self.cfg)
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model = DeiT3(**cfg)
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self.assertEqual(len(model.layers), 12)
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dpr_inc = 0.1 / (12 - 1)
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dpr = 0
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for layer in model.layers:
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self.assertEqual(layer.attn.embed_dims, 768)
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self.assertEqual(layer.attn.num_heads, 12)
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self.assertEqual(layer.ffn.feedforward_channels, 3072)
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self.assertAlmostEqual(layer.attn.out_drop.drop_prob, dpr)
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self.assertAlmostEqual(layer.ffn.dropout_layer.drop_prob, dpr)
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dpr += dpr_inc
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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 = DeiT3(**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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# test load checkpoint
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pretrain_pos_embed = model.pos_embed.clone().detach()
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tmpdir = tempfile.gettempdir()
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checkpoint = os.path.join(tmpdir, 'test.pth')
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save_checkpoint(model.state_dict(), checkpoint)
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cfg = deepcopy(self.cfg)
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model = DeiT3(**cfg)
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load_checkpoint(model, checkpoint, strict=True)
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self.assertTrue(torch.allclose(model.pos_embed, pretrain_pos_embed))
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# test load checkpoint with different img_size
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cfg = deepcopy(self.cfg)
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cfg['img_size'] = 384
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model = DeiT3(**cfg)
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load_checkpoint(model, checkpoint, strict=True)
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os.remove(checkpoint)
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def test_forward(self):
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imgs = torch.randn(1, 3, 224, 224)
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# test with_cls_token=False
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cfg = deepcopy(self.cfg)
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cfg['with_cls_token'] = False
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cfg['output_cls_token'] = True
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with self.assertRaisesRegex(AssertionError, 'but got False'):
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DeiT3(**cfg)
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cfg = deepcopy(self.cfg)
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cfg['with_cls_token'] = False
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cfg['output_cls_token'] = False
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model = DeiT3(**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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patch_token = outs[-1]
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self.assertEqual(patch_token.shape, (1, 768, 14, 14))
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# test with output_cls_token
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cfg = deepcopy(self.cfg)
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model = DeiT3(**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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patch_token, cls_token = outs[-1]
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self.assertEqual(patch_token.shape, (1, 768, 14, 14))
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self.assertEqual(cls_token.shape, (1, 768))
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# test without output_cls_token
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cfg = deepcopy(self.cfg)
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cfg['output_cls_token'] = False
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model = DeiT3(**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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patch_token = outs[-1]
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self.assertEqual(patch_token.shape, (1, 768, 14, 14))
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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'] = [-3, -2, -1]
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model = DeiT3(**cfg)
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outs = model(imgs)
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self.assertIsInstance(outs, tuple)
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self.assertEqual(len(outs), 3)
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for out in outs:
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patch_token, cls_token = out
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self.assertEqual(patch_token.shape, (1, 768, 14, 14))
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self.assertEqual(cls_token.shape, (1, 768))
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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 = DeiT3(**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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patch_token, cls_token = outs[-1]
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expect_feat_shape = (math.ceil(imgs.shape[2] / 16),
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math.ceil(imgs.shape[3] / 16))
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self.assertEqual(patch_token.shape, (1, 768, *expect_feat_shape))
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self.assertEqual(cls_token.shape, (1, 768))
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