def get_conv_layer(
spatial_dims: int,
in_channels: int,
out_channels: int,
kernel_size: Union[Sequence[int], int] = 3,
stride: Union[Sequence[int], int] = 1,
act: Optional[Union[Tuple, str]] = Act.PRELU,
norm: Union[Tuple, str] = Norm.INSTANCE,
bias: bool = False,
conv_only: bool = True,
is_transposed: bool = False,
num_groups: int = 1,
is_prunable: bool = False,
):
padding = get_padding(kernel_size, stride)
output_padding = None
if is_transposed:
output_padding = get_output_padding(kernel_size, stride, padding)
return Convolution(
spatial_dims,
in_channels,
out_channels,
strides=stride,
kernel_size=kernel_size,
act=act,
norm=norm,
groups=num_groups,
bias=bias,
conv_only=conv_only,
is_transposed=is_transposed,
is_prunable=is_prunable,
padding=padding,
output_padding=output_padding,
)
from monai.networks import eval_mode
from monai.networks.blocks.dynunet_block import UnetBasicBlock, UnetResBlock, UnetUpBlock, get_padding
from tests.utils import test_script_save
TEST_CASE_RES_BASIC_BLOCK = []
for spatial_dims in range(2, 4):
for kernel_size in [1, 3]:
for stride in [1, 2]:
for norm_name in [("GROUP", {
"num_groups": 16
}), ("batch", {
"track_running_stats": False
}), "instance"]:
for in_size in [15, 16]:
padding = get_padding(kernel_size, stride)
if not isinstance(padding, int):
padding = padding[0]
out_size = int(
(in_size + 2 * padding - kernel_size) / stride) + 1
test_case = [
{
"spatial_dims":
spatial_dims,
"in_channels":
16,
"out_channels":
16,
"kernel_size":
kernel_size,
"norm_name":