208 lines
6 KiB
Python
208 lines
6 KiB
Python
import torch
|
|
from append_block import append_block
|
|
from L1NormLayer import L1NormLayer
|
|
from append_parameter import append_parameter
|
|
|
|
|
|
def make_network(
|
|
input_dim_x: int,
|
|
input_dim_y: int,
|
|
input_number_of_channel: int,
|
|
iterations: int,
|
|
torch_device: torch.device,
|
|
epsilon: bool | None = None,
|
|
positive_function_type: int = 0,
|
|
beta: float | None = None,
|
|
# Conv:
|
|
number_of_output_channels: list[int] = [32 * 8, 64 * 8, 96 * 8, 10],
|
|
kernel_size_conv: list[tuple[int, int]] = [
|
|
(5, 5),
|
|
(5, 5),
|
|
(-1, -1), # Take the whole input image x and y size
|
|
(1, 1),
|
|
],
|
|
stride_conv: list[tuple[int, int]] = [
|
|
(1, 1),
|
|
(1, 1),
|
|
(1, 1),
|
|
(1, 1),
|
|
],
|
|
padding_conv: list[tuple[int, int]] = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(0, 0),
|
|
(0, 0),
|
|
],
|
|
dilation_conv: list[tuple[int, int]] = [
|
|
(1, 1),
|
|
(1, 1),
|
|
(1, 1),
|
|
(1, 1),
|
|
],
|
|
# Pool:
|
|
kernel_size_pool: list[tuple[int, int]] = [
|
|
(2, 2),
|
|
(2, 2),
|
|
(-1, -1), # No pooling layer
|
|
(-1, -1), # No pooling layer
|
|
],
|
|
stride_pool: list[tuple[int, int]] = [
|
|
(2, 2),
|
|
(2, 2),
|
|
(-1, -1),
|
|
(-1, -1),
|
|
],
|
|
padding_pool: list[tuple[int, int]] = [
|
|
(0, 0),
|
|
(0, 0),
|
|
(0, 0),
|
|
(0, 0),
|
|
],
|
|
dilation_pool: list[tuple[int, int]] = [
|
|
(1, 1),
|
|
(1, 1),
|
|
(1, 1),
|
|
(1, 1),
|
|
],
|
|
enable_onoff: bool = False,
|
|
) -> tuple[
|
|
torch.nn.Sequential,
|
|
list[list[torch.nn.parameter.Parameter]],
|
|
list[str],
|
|
]:
|
|
|
|
assert len(number_of_output_channels) == len(kernel_size_conv)
|
|
assert len(number_of_output_channels) == len(stride_conv)
|
|
assert len(number_of_output_channels) == len(padding_conv)
|
|
assert len(number_of_output_channels) == len(dilation_conv)
|
|
assert len(number_of_output_channels) == len(kernel_size_pool)
|
|
assert len(number_of_output_channels) == len(stride_pool)
|
|
assert len(number_of_output_channels) == len(padding_pool)
|
|
assert len(number_of_output_channels) == len(dilation_pool)
|
|
|
|
if enable_onoff:
|
|
input_number_of_channel *= 2
|
|
|
|
parameter_cnn_top: list[torch.nn.parameter.Parameter] = []
|
|
parameter_nnmf: list[torch.nn.parameter.Parameter] = []
|
|
parameter_norm: list[torch.nn.parameter.Parameter] = []
|
|
|
|
test_image = torch.ones(
|
|
(1, input_number_of_channel, input_dim_x, input_dim_y), device=torch_device
|
|
)
|
|
|
|
network = torch.nn.Sequential()
|
|
network = network.to(torch_device)
|
|
|
|
for block_id in range(0, len(number_of_output_channels)):
|
|
|
|
test_image = append_block(
|
|
network=network,
|
|
out_channels=number_of_output_channels[block_id],
|
|
test_image=test_image,
|
|
dilation=dilation_conv[block_id],
|
|
padding=padding_conv[block_id],
|
|
stride=stride_conv[block_id],
|
|
kernel_size=kernel_size_conv[block_id],
|
|
epsilon=epsilon,
|
|
positive_function_type=positive_function_type,
|
|
beta=beta,
|
|
iterations=iterations,
|
|
torch_device=torch_device,
|
|
parameter_cnn_top=parameter_cnn_top,
|
|
parameter_nnmf=parameter_nnmf,
|
|
parameter_norm=parameter_norm,
|
|
last_layer = block_id == len(number_of_output_channels)-1,
|
|
)
|
|
|
|
if (kernel_size_pool[block_id][0] > 0) and (kernel_size_pool[block_id][1] > 0):
|
|
network.append(torch.nn.ReLU())
|
|
test_image = network[-1](test_image)
|
|
|
|
mock_output = (
|
|
torch.nn.functional.conv2d(
|
|
torch.zeros(
|
|
1,
|
|
1,
|
|
test_image.shape[2],
|
|
test_image.shape[3],
|
|
),
|
|
torch.zeros((1, 1, 2, 2)),
|
|
stride=(2, 2),
|
|
padding=(0, 0),
|
|
dilation=(1, 1),
|
|
)
|
|
.squeeze(0)
|
|
.squeeze(0)
|
|
)
|
|
|
|
network.append(
|
|
torch.nn.Unfold(
|
|
kernel_size=(2, 2),
|
|
stride=(2, 2),
|
|
padding=(0, 0),
|
|
dilation=(1, 1),
|
|
)
|
|
)
|
|
test_image = network[-1](test_image)
|
|
|
|
network.append(
|
|
torch.nn.Fold(
|
|
output_size=mock_output.shape,
|
|
kernel_size=(1, 1),
|
|
dilation=1,
|
|
padding=0,
|
|
stride=1,
|
|
)
|
|
)
|
|
test_image = network[-1](test_image)
|
|
|
|
network.append(L1NormLayer())
|
|
test_image = network[-1](test_image)
|
|
|
|
network.append(
|
|
torch.nn.Conv2d(
|
|
in_channels=test_image.shape[1],
|
|
out_channels=test_image.shape[1] // 4,
|
|
kernel_size=(1, 1),
|
|
bias=False,
|
|
).to(torch_device)
|
|
)
|
|
|
|
test_image = network[-1](test_image)
|
|
append_parameter(module=network[-1], parameter_list=parameter_nnmf)
|
|
|
|
network.append(
|
|
torch.nn.BatchNorm2d(
|
|
num_features=test_image.shape[1],
|
|
device=torch_device,
|
|
momentum=0.1,
|
|
track_running_stats=False,
|
|
)
|
|
)
|
|
test_image = network[-1](test_image)
|
|
append_parameter(module=network[-1], parameter_list=parameter_norm)
|
|
|
|
network.append(torch.nn.Softmax(dim=1))
|
|
test_image = network[-1](test_image)
|
|
|
|
network.append(torch.nn.Flatten())
|
|
test_image = network[-1](test_image)
|
|
|
|
parameters: list[list[torch.nn.parameter.Parameter]] = [
|
|
parameter_cnn_top,
|
|
parameter_nnmf,
|
|
parameter_norm,
|
|
]
|
|
|
|
name_list: list[str] = [
|
|
"cnn_top",
|
|
"nnmf",
|
|
"batchnorm2d",
|
|
]
|
|
|
|
return (
|
|
network,
|
|
parameters,
|
|
name_list,
|
|
)
|