94 lines
2.6 KiB
Python
94 lines
2.6 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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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 HRNet
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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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@pytest.mark.parametrize('base_channels', [18, 30, 32, 40, 44, 48, 64])
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def test_hrnet_arch_zoo(base_channels):
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cfg_ori = dict(arch=f'w{base_channels}')
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# Test HRNet model with input size of 224
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model = HRNet(**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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outs = model(imgs)
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out_channels = base_channels
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out_size = 56
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assert isinstance(outs, tuple)
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for out in outs:
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assert out.shape == (3, out_channels, out_size, out_size)
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out_channels = out_channels * 2
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out_size = out_size // 2
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def test_hrnet_custom_arch():
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cfg_ori = dict(
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extra=dict(
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stage1=dict(
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num_modules=1,
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num_branches=1,
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block='BOTTLENECK',
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num_blocks=(4, ),
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num_channels=(64, )),
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stage2=dict(
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num_modules=1,
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num_branches=2,
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block='BASIC',
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num_blocks=(4, 4),
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num_channels=(32, 64)),
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stage3=dict(
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num_modules=4,
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num_branches=3,
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block='BOTTLENECK',
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num_blocks=(4, 4, 2),
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num_channels=(32, 64, 128)),
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stage4=dict(
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num_modules=3,
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num_branches=4,
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block='BASIC',
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num_blocks=(4, 3, 4, 4),
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num_channels=(32, 64, 152, 256)),
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), )
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# Test HRNet model with input size of 224
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model = HRNet(**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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outs = model(imgs)
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out_channels = (32, 64, 152, 256)
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out_size = 56
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assert isinstance(outs, tuple)
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for out, out_channel in zip(outs, out_channels):
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assert out.shape == (3, out_channel, out_size, out_size)
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out_size = out_size // 2
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