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# Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmdet.models.backbones.hrnet import HRModule, HRNet
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
@pytest.mark.parametrize('block', [BasicBlock, Bottleneck])
def test_hrmodule(block):
# Test multiscale forward
num_channles = (32, 64)
in_channels = [c * block.expansion for c in num_channles]
hrmodule = HRModule(
num_branches=2,
blocks=block,
in_channels=in_channels,
num_blocks=(4, 4),
num_channels=num_channles,
)
feats = [
torch.randn(1, in_channels[0], 64, 64),
torch.randn(1, in_channels[1], 32, 32)
]
feats = hrmodule(feats)
assert len(feats) == 2
assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
assert feats[1].shape == torch.Size([1, in_channels[1], 32, 32])
# Test single scale forward
num_channles = (32, 64)
in_channels = [c * block.expansion for c in num_channles]
hrmodule = HRModule(
num_branches=2,
blocks=block,
in_channels=in_channels,
num_blocks=(4, 4),
num_channels=num_channles,
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])
def test_hrnet_backbone():
# only have 3 stages
extra = dict(
stage1=dict(
num_modules=1,
num_branches=1,
block='BOTTLENECK',
num_blocks=(4, ),
num_channels=(64, )),
stage2=dict(
num_modules=1,
num_branches=2,
block='BASIC',
num_blocks=(4, 4),
num_channels=(32, 64)),
stage3=dict(
num_modules=4,
num_branches=3,
block='BASIC',
num_blocks=(4, 4, 4),
num_channels=(32, 64, 128)))
with pytest.raises(AssertionError):
# HRNet now only support 4 stages
HRNet(extra=extra)
extra['stage4'] = dict(
num_modules=3,
num_branches=3, # should be 4
block='BASIC',
num_blocks=(4, 4, 4, 4),
num_channels=(32, 64, 128, 256))
with pytest.raises(AssertionError):
# len(num_blocks) should equal num_branches
HRNet(extra=extra)
extra['stage4']['num_branches'] = 4
# Test hrnetv2p_w32
model = HRNet(extra=extra)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 256, 256)
feats = model(imgs)
assert len(feats) == 4
assert feats[0].shape == torch.Size([1, 32, 64, 64])
assert feats[3].shape == torch.Size([1, 256, 8, 8])
# Test single scale output
model = HRNet(extra=extra, multiscale_output=False)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 256, 256)
feats = model(imgs)
assert len(feats) == 1
assert feats[0].shape == torch.Size([1, 32, 64, 64])
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