def __init__(self,
inplanes,
planes,
stride=1,
downsample=None,
conv_cfg=None,
norm_cfg=None):
spconv.SparseModule.__init__(self)
BasicBlock.__init__(self,
inplanes,
planes,
stride=stride,
downsample=downsample,
conv_cfg=conv_cfg,
norm_cfg=norm_cfg)
def test_resnet_basic_block():
with pytest.raises(AssertionError):
# Not implemented yet.
dcn = dict(type='DCN', deformable_groups=1, fallback_on_stride=False)
BasicBlock(64, 64, dcn=dcn)
with pytest.raises(AssertionError):
# Not implemented yet.
plugins = [
dict(
cfg=dict(type='ContextBlock', ratio=1. / 16),
position='after_conv3')
]
BasicBlock(64, 64, plugins=plugins)
with pytest.raises(AssertionError):
# Not implemented yet
plugins = [
dict(
cfg=dict(
type='GeneralizedAttention',
spatial_range=-1,
num_heads=8,
attention_type='0010',
kv_stride=2),
position='after_conv2')
]
BasicBlock(64, 64, plugins=plugins)
# test BasicBlock structure and forward
block = BasicBlock(64, 64)
assert block.conv1.in_channels == 64
assert block.conv1.out_channels == 64
assert block.conv1.kernel_size == (3, 3)
assert block.conv2.in_channels == 64
assert block.conv2.out_channels == 64
assert block.conv2.kernel_size == (3, 3)
x = torch.randn(1, 64, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])
# Test BasicBlock with checkpoint forward
block = BasicBlock(64, 64, with_cp=True)
assert block.with_cp
x = torch.randn(1, 64, 56, 56)
x_out = block(x)
assert x_out.shape == torch.Size([1, 64, 56, 56])