def test_resnet_half_channel():
model = ResNet(50, base_channels=32, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, 128, 56, 56)
assert feat[1].shape == (1, 256, 28, 28)
assert feat[2].shape == (1, 512, 14, 14)
assert feat[3].shape == (1, 1024, 7, 7)
def test_resnet():
"""Test resnet backbone."""
with pytest.raises(KeyError):
# ResNet depth should be in [18, 34, 50, 101, 152]
ResNet(20)
with pytest.raises(AssertionError):
# In ResNet: 1 <= num_stages <= 4
ResNet(50, num_stages=0)
with pytest.raises(AssertionError):
# In ResNet: 1 <= num_stages <= 4
ResNet(50, num_stages=5)
with pytest.raises(AssertionError):
# len(strides) == len(dilations) == num_stages
ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
with pytest.raises(TypeError):
# pretrained must be a string path
model = ResNet(50)
model.init_weights(pretrained=0)
with pytest.raises(AssertionError):
# Style must be in ['pytorch', 'caffe']
ResNet(50, style='tensorflow')
# Test ResNet50 norm_eval=True
model = ResNet(50, norm_eval=True)
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test ResNet50 with torchvision pretrained weight
model = ResNet(depth=50,
norm_eval=True,
init_cfg=dict(type='Pretrained',
checkpoint='torchvision://resnet50'))
model.init_weights()
model.train()
assert check_norm_state(model.modules(), False)
# Test ResNet50 with first stage frozen
frozen_stages = 1
model = ResNet(50, frozen_stages=frozen_stages)
model.init_weights()
model.train()
assert model.norm1.training is False
for layer in [model.conv1, model.norm1]:
for param in layer.parameters():
assert param.requires_grad is False
for i in range(1, frozen_stages + 1):
layer = getattr(model, f'layer{i}')
for mod in layer.modules():
if isinstance(mod, _BatchNorm):
assert mod.training is False
for param in layer.parameters():
assert param.requires_grad is False
# Test ResNet18 forward
model = ResNet(18, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, 64, 56, 56)
assert feat[1].shape == (1, 128, 28, 28)
assert feat[2].shape == (1, 256, 14, 14)
assert feat[3].shape == (1, 512, 7, 7)
# Test ResNet50 with BatchNorm forward
model = ResNet(50, out_indices=(0, 1, 2, 3))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, 256, 56, 56)
assert feat[1].shape == (1, 512, 28, 28)
assert feat[2].shape == (1, 1024, 14, 14)
assert feat[3].shape == (1, 2048, 7, 7)
# Test ResNet50 with DropPath forward
model = ResNet(50, out_indices=(0, 1, 2, 3), drop_path_rate=0.5)
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, 256, 56, 56)
assert feat[1].shape == (1, 512, 28, 28)
assert feat[2].shape == (1, 1024, 14, 14)
assert feat[3].shape == (1, 2048, 7, 7)
# Test ResNet50 with layers 1, 2, 3 out forward
model = ResNet(50, out_indices=(0, 1, 2))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 3
assert feat[0].shape == (1, 256, 56, 56)
assert feat[1].shape == (1, 512, 28, 28)
assert feat[2].shape == (1, 1024, 14, 14)
# Test ResNet50 with layers 3 (top feature maps) out forward
model = ResNet(50, out_indices=(3, ))
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 1
assert feat[0].shape == (1, 2048, 7, 7)
# Test ResNet50 with checkpoint forward
model = ResNet(50, out_indices=(0, 1, 2, 3), with_cp=True)
for m in model.modules():
if is_block(m):
assert m.with_cp
model.init_weights()
model.train()
imgs = torch.randn(1, 3, 224, 224)
feat = model(imgs)
assert len(feat) == 4
assert feat[0].shape == (1, 256, 56, 56)
assert feat[1].shape == (1, 512, 28, 28)
assert feat[2].shape == (1, 1024, 14, 14)
assert feat[3].shape == (1, 2048, 7, 7)
# zero initialization of residual blocks
model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=True)
model.init_weights()
for m in model.modules():
if isinstance(m, Bottleneck):
assert all_zeros(m.norm3)
elif isinstance(m, BasicBlock):
assert all_zeros(m.norm2)
# non-zero initialization of residual blocks
model = ResNet(50, out_indices=(0, 1, 2, 3), zero_init_residual=False)
model.init_weights()
for m in model.modules():
if isinstance(m, Bottleneck):
assert not all_zeros(m.norm3)
elif isinstance(m, BasicBlock):
assert not all_zeros(m.norm2)