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Added bias=False

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Bias term already included in the BN layers; can be set to False as it is redundant
This commit is contained in:
ankandrew
2021-03-12 15:11:39 -03:00
committed by GitHub
parent dc7f4f4ee7
commit 40d9b0432d
1 changed files with 5 additions and 2 deletions
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7
ML/Pytorch/CNN_architectures/pytorch_resnet.py
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@@ -23,7 +23,7 @@ class block(nn.Module):
super(block, self).__init__() super(block, self).__init__()
self.expansion = 4 self.expansion = 4
self.conv1 = nn.Conv2d( self.conv1 = nn.Conv2d(
in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0 in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0, bias=False
) )
self.bn1 = nn.BatchNorm2d(intermediate_channels) self.bn1 = nn.BatchNorm2d(intermediate_channels)
self.conv2 = nn.Conv2d( self.conv2 = nn.Conv2d(
@@ -32,6 +32,7 @@ class block(nn.Module):
kernel_size=3, kernel_size=3,
stride=stride, stride=stride,
padding=1, padding=1,
bias=False
) )
self.bn2 = nn.BatchNorm2d(intermediate_channels) self.bn2 = nn.BatchNorm2d(intermediate_channels)
self.conv3 = nn.Conv2d( self.conv3 = nn.Conv2d(
@@ -40,6 +41,7 @@ class block(nn.Module):
kernel_size=1, kernel_size=1,
stride=1, stride=1,
padding=0, padding=0,
bias=False
) )
self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion) self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)
self.relu = nn.ReLU() self.relu = nn.ReLU()
@@ -70,7 +72,7 @@ class ResNet(nn.Module):
def __init__(self, block, layers, image_channels, num_classes): def __init__(self, block, layers, image_channels, num_classes):
super(ResNet, self).__init__() super(ResNet, self).__init__()
self.in_channels = 64 self.in_channels = 64
self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3) self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64) self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU() self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
@@ -122,6 +124,7 @@ class ResNet(nn.Module):
intermediate_channels * 4, intermediate_channels * 4,
kernel_size=1, kernel_size=1,
stride=stride, stride=stride,
bias=False
), ),
nn.BatchNorm2d(intermediate_channels * 4), nn.BatchNorm2d(intermediate_channels * 4),
) )