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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmpose.models.backbones.hrformer import (HRFomerModule, HRFormer,
HRFormerBlock)
def test_hrformer_module():
norm_cfg = dict(type='BN')
block = HRFormerBlock
# Test multiscale forward
num_channles = (32, 64)
num_inchannels = [c * block.expansion for c in num_channles]
hrmodule = HRFomerModule(
num_branches=2,
block=block,
num_blocks=(2, 2),
num_inchannels=num_inchannels,
num_channels=num_channles,
num_heads=(1, 2),
num_window_sizes=(7, 7),
num_mlp_ratios=(4, 4),
drop_paths=(0., 0.),
norm_cfg=norm_cfg)
feats = [
torch.randn(1, num_inchannels[0], 64, 64),
torch.randn(1, num_inchannels[1], 32, 32)
]
feats = hrmodule(feats)
assert len(str(hrmodule)) > 0
assert len(feats) == 2
assert feats[0].shape == torch.Size([1, num_inchannels[0], 64, 64])
assert feats[1].shape == torch.Size([1, num_inchannels[1], 32, 32])
# Test single scale forward
num_channles = (32, 64)
in_channels = [c * block.expansion for c in num_channles]
hrmodule = HRFomerModule(
num_branches=2,
block=block,
num_blocks=(2, 2),
num_inchannels=num_inchannels,
num_channels=num_channles,
num_heads=(1, 2),
num_window_sizes=(7, 7),
num_mlp_ratios=(4, 4),
drop_paths=(0., 0.),
norm_cfg=norm_cfg,
multiscale_output=False,
)
feats = [
torch.randn(1, in_channels[0], 64, 64),
torch.randn(1, in_channels[1], 32, 32)
]
feats = hrmodule(feats)
assert len(feats) == 1
assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
# Test single branch HRFormer module
hrmodule = HRFomerModule(
num_branches=1,
block=block,
num_blocks=(1, ),
num_inchannels=[num_inchannels[0]],
num_channels=[num_channles[0]],
num_heads=(1, ),
num_window_sizes=(7, ),
num_mlp_ratios=(4, ),
drop_paths=(0.1, ),
norm_cfg=norm_cfg,
)
feats = [
torch.randn(1, in_channels[0], 64, 64),
]
feats = hrmodule(feats)
assert len(feats) == 1
assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
# Value tests
kwargs = dict(
num_branches=2,
block=block,
num_blocks=(2, 2),
num_inchannels=num_inchannels,
num_channels=num_channles,
num_heads=(1, 2),
num_window_sizes=(7, 7),
num_mlp_ratios=(4, 4),
drop_paths=(0.1, 0.1),
norm_cfg=norm_cfg,
)
with pytest.raises(ValueError):
# len(num_blocks) should equal num_branches
kwargs['num_blocks'] = [2, 2, 2]
HRFomerModule(**kwargs)
kwargs['num_blocks'] = [2, 2]
with pytest.raises(ValueError):
# len(num_blocks) should equal num_branches
kwargs['num_channels'] = [2]
HRFomerModule(**kwargs)
kwargs['num_channels'] = [2, 2]
with pytest.raises(ValueError):
# len(num_blocks) should equal num_branches
kwargs['num_inchannels'] = [2]
HRFomerModule(**kwargs)
kwargs['num_inchannels'] = [2, 2]
def test_hrformer_backbone():
norm_cfg = dict(type='BN')
# only have 3 stages
extra = dict(
drop_path_rate=0.2,
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(2, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='HRFORMERBLOCK',
window_sizes=(7, 7),
num_heads=(1, 2),
mlp_ratios=(4, 4),
num_blocks=(2, 2),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='HRFORMERBLOCK',
window_sizes=(7, 7, 7),
num_heads=(1, 2, 4),
mlp_ratios=(4, 4, 4),
num_blocks=(2, 2, 2),
num_channels=(32, 64, 128)),
stage4=dict(
num_modules=3,
num_branches=4,
block='HRFORMERBLOCK',
window_sizes=(7, 7, 7, 7),
num_heads=(1, 2, 4, 8),
mlp_ratios=(4, 4, 4, 4),
num_blocks=(2, 2, 2, 2),
num_channels=(32, 64, 128, 256),
multiscale_output=True))
with pytest.raises(ValueError):
# len(num_blocks) should equal num_branches
extra['stage4']['num_branches'] = 3
HRFormer(extra=extra)
extra['stage4']['num_branches'] = 4
# Test HRFormer-S
model = HRFormer(extra=extra, norm_cfg=norm_cfg)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 64, 64)
feats = model(imgs)
assert len(feats) == 4
assert feats[0].shape == torch.Size([1, 32, 16, 16])
assert feats[3].shape == torch.Size([1, 256, 2, 2])
# Test single scale output and model
# without relative position bias
extra['stage4']['multiscale_output'] = False
extra['with_rpe'] = False
model = HRFormer(extra=extra, norm_cfg=norm_cfg)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 64, 64)
feats = model(imgs)
assert len(feats) == 1
assert feats[0].shape == torch.Size([1, 32, 16, 16])
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