120 lines
3.6 KiB
Python
120 lines
3.6 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 torch.nn.modules import GroupNorm
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from torch.nn.modules.batchnorm import _BatchNorm
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from mmcls.models.backbones import MlpMixer
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def is_norm(modules):
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"""Check if is one of the norms."""
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if isinstance(modules, (GroupNorm, _BatchNorm)):
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return True
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return False
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def check_norm_state(modules, train_state):
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"""Check if norm layer is in correct train state."""
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for mod in modules:
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if isinstance(mod, _BatchNorm):
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if mod.training != train_state:
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return False
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return True
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class TestMLPMixer(TestCase):
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def setUp(self):
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self.cfg = dict(
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arch='b',
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img_size=224,
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patch_size=16,
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drop_rate=0.1,
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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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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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MlpMixer(**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': 24,
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'num_layers': 16,
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'tokens_mlp_dims': 4096
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}
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MlpMixer(**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': 6,
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'tokens_mlp_dims': 256,
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'channels_mlp_dims': 1024
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}
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model = MlpMixer(**cfg)
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self.assertEqual(model.embed_dims, 128)
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self.assertEqual(model.num_layers, 6)
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for layer in model.layers:
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self.assertEqual(layer.token_mix.feedforward_channels, 256)
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self.assertEqual(layer.channel_mix.feedforward_channels, 1024)
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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 = MlpMixer(**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(3, 3, 224, 224)
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# test forward with single out indices
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cfg = deepcopy(self.cfg)
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model = MlpMixer(**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, (3, 768, 196))
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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 = MlpMixer(**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 feat in outs:
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self.assertEqual(feat.shape, (3, 768, 196))
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# test with invalid input shape
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imgs2 = torch.randn(3, 3, 256, 256)
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cfg = deepcopy(self.cfg)
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model = MlpMixer(**cfg)
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with self.assertRaisesRegex(AssertionError, 'dynamic input shape.'):
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model(imgs2)
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