93 lines
2.6 KiB
Python
93 lines
2.6 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 Conformer
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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_conformer_backbone():
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cfg_ori = dict(
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arch='T',
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drop_path_rate=0.1,
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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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Conformer(**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'] = {'embed_dims': 24, 'channel_ratio': 6, 'num_heads': 9}
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Conformer(**cfg)
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# Test Conformer small model with patch size of 16
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model = Conformer(**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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conv_feature, transformer_feature = model(imgs)[-1]
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assert conv_feature.shape == (3, 64 * 1 * 4
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) # base_channels * channel_ratio * 4
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assert transformer_feature.shape == (3, 384)
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# Test custom arch Conformer without output cls token
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cfg = deepcopy(cfg_ori)
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cfg['arch'] = {
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'embed_dims': 128,
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'depths': 15,
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'num_heads': 16,
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'channel_ratio': 3,
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}
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cfg['with_cls_token'] = False
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cfg['base_channels'] = 32
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model = Conformer(**cfg)
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conv_feature, transformer_feature = model(imgs)[-1]
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assert conv_feature.shape == (3, 32 * 3 * 4)
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assert transformer_feature.shape == (3, 128)
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# Test ViT with multi out indices
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cfg = deepcopy(cfg_ori)
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cfg['out_indices'] = [4, 8, 12]
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model = Conformer(**cfg)
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outs = model(imgs)
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assert len(outs) == 3
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# stage 1
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conv_feature, transformer_feature = outs[0]
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assert conv_feature.shape == (3, 64 * 1)
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assert transformer_feature.shape == (3, 384)
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# stage 2
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conv_feature, transformer_feature = outs[1]
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assert conv_feature.shape == (3, 64 * 1 * 2)
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assert transformer_feature.shape == (3, 384)
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# stage 3
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conv_feature, transformer_feature = outs[2]
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assert conv_feature.shape == (3, 64 * 1 * 4)
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assert transformer_feature.shape == (3, 384)
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