Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Change default values #267

Merged
merged 1 commit into from
Feb 17, 2023
Merged

Change default values #267

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
33 changes: 17 additions & 16 deletions generative/networks/nets/patchgan_discriminator.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@
import torch
import torch.nn as nn
from monai.networks.blocks import Convolution
from monai.networks.layers import Act


class MultiScalePatchDiscriminator(nn.Sequential):
Expand All @@ -31,8 +32,8 @@ class MultiScalePatchDiscriminator(nn.Sequential):
Args:
num_d: number of discriminators
num_layers_d: number of Convolution layers (Conv + activation + normalisation + [dropout]) in each
of the discriminators. In each layer, the number of channels are doubled and the spatial size is
divided by 2.
of the discriminators. In each layer, the number of channels are doubled and the spatial size is
divided by 2.
spatial_dims: number of spatial dimensions (1D, 2D etc.)
num_channels: number of filters in the first convolutional layer (double of the value is taken from then on)
in_channels: number of input channels
Expand All @@ -43,7 +44,7 @@ class MultiScalePatchDiscriminator(nn.Sequential):
bias: introduction of layer bias
dropout: proportion of dropout applied, defaults to 0.
minimum_size_im: minimum spatial size of the input image. Introduced to make sure the architecture
requested isn't going to downsample the input image beyond value of 1.
requested isn't going to downsample the input image beyond value of 1.
last_conv_kernel_size: kernel size of the last convolutional layer.
"""

Expand All @@ -54,10 +55,10 @@ def __init__(
spatial_dims: int,
num_channels: int,
in_channels: int,
out_channels: int,
kernel_size: int,
activation: str | tuple = "PRELU",
norm: str | tuple = "INSTANCE",
out_channels: int = 1,
kernel_size: int = 4,
activation: str | tuple = (Act.LEAKYRELU, {"negative_slope": 0.2}),
norm: str | tuple = "BATCH",
bias: bool = False,
dropout: float | tuple = 0.0,
minimum_size_im: int = 256,
Expand All @@ -77,11 +78,11 @@ def __init__(
"Please reduce num_layers, reduce num_D or enter bigger images." % (i_, num_layers_d_i)
)
subnet_d = PatchDiscriminator(
num_layers_d_i,
spatial_dims=spatial_dims,
num_channels=self.num_channels,
in_channels=in_channels,
out_channels=out_channels,
num_layers_d=num_layers_d_i,
kernel_size=kernel_size,
activation=activation,
norm=norm,
Expand Down Expand Up @@ -122,13 +123,13 @@ class PatchDiscriminator(nn.Sequential):
In CVPR 2018.

Args:
num_layers_d: number of Convolution layers (Conv + activation + normalisation + [dropout]) in each
of the discriminators. In each layer, the number of channels are doubled and the spatial size is
divided by 2.
spatial_dims: number of spatial dimensions (1D, 2D etc.)
num_channels: number of filters in the first convolutional layer (double of the value is taken from then on)
in_channels: number of input channels
out_channels: number of output channels in each discriminator
num_layers_d: number of Convolution layers (Conv + activation + normalisation + [dropout]) in each
of the discriminators. In each layer, the number of channels are doubled and the spatial size is
divided by 2.
kernel_size: kernel size of the convolution layers
activation: activation layer type
norm: normalisation type
Expand All @@ -140,14 +141,14 @@ class PatchDiscriminator(nn.Sequential):

def __init__(
self,
num_layers_d: int,
spatial_dims: int,
num_channels: int,
in_channels: int,
out_channels: int,
kernel_size: int,
activation: str | tuple = "PRELU",
norm: str | tuple = "INSTANCE",
out_channels: int = 1,
num_layers_d: int = 3,
kernel_size: int = 4,
activation: str | tuple = (Act.LEAKYRELU, {"negative_slope": 0.2}),
norm: str | tuple = "BATCH",
bias: bool = False,
padding: int | Sequence[int] = 1,
dropout: float | tuple = 0.0,
Expand Down