76 lines
1.9 KiB
Python
76 lines
1.9 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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from copy import deepcopy
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import pytest
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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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def test_mlp_mixer_backbone():
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cfg_ori = 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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with pytest.raises(AssertionError):
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# test invalid arch
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cfg = deepcopy(cfg_ori)
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cfg['arch'] = 'unknown'
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MlpMixer(**cfg)
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with pytest.raises(AssertionError):
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# test arch without essential keys
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cfg = deepcopy(cfg_ori)
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cfg['arch'] = {
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'num_layers': 24,
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'tokens_mlp_dims': 384,
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'channels_mlp_dims': 3072,
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}
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MlpMixer(**cfg)
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# Test MlpMixer base model with input size of 224
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# and patch size of 16
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model = MlpMixer(**cfg_ori)
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model.init_weights()
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model.train()
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assert check_norm_state(model.modules(), True)
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imgs = torch.randn(3, 3, 224, 224)
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feat = model(imgs)[-1]
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assert feat.shape == (3, 768, 196)
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# Test MlpMixer with multi out indices
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cfg = deepcopy(cfg_ori)
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cfg['out_indices'] = [-3, -2, -1]
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model = MlpMixer(**cfg)
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for out in model(imgs):
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assert out.shape == (3, 768, 196)
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