def __init__(self,
spatial_dims: int,
in_channels: int,
out_channels: int,
kernel_size: Union[Sequence[int], int],
stride: Union[Sequence[int], int],
num_groups: int,
norm_name: str,
is_prunable: bool = False):
super(UnetResBlockEx, self).__init__(
spatial_dims=spatial_dims,
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=stride,
norm_name=norm_name,
)
self.conv1 = get_conv_layer(
spatial_dims,
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
conv_only=True,
num_groups=num_groups,
is_prunable=is_prunable,
)
self.conv2 = get_conv_layer(
spatial_dims,
out_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
conv_only=True,
num_groups=num_groups,
is_prunable=is_prunable,
)
self.conv3 = get_conv_layer(
spatial_dims,
in_channels,
out_channels,
kernel_size=1,
stride=stride,
conv_only=True,
num_groups=num_groups,
is_prunable=is_prunable,
)
self.lrelu = get_act_layer(("leakyrelu", {
"inplace": True,
"negative_slope": 0.01
}))
self.norm1 = get_norm_layer(spatial_dims, out_channels, norm_name)
self.norm2 = get_norm_layer(spatial_dims, out_channels, norm_name)
self.norm3 = get_norm_layer(spatial_dims, out_channels, norm_name)
self.downsample = in_channels != out_channels
stride_np = np.atleast_1d(stride)
if not np.all(stride_np == 1):
self.downsample = True
def test_suggested(self):
with self.assertRaisesRegex(ValueError, "did you mean 'GROUP'?"):
get_norm_layer(name="grop", spatial_dims=2)
def test_norm_layer(self, input_param, expected):
layer = get_norm_layer(**input_param)
self.assertEqual(f"{layer}", expected)