From 40d9b0432d07d284c0b96f3b2084abb4f4a27c3b Mon Sep 17 00:00:00 2001 From: ankandrew <61120139+ankandrew@users.noreply.github.com> Date: Fri, 12 Mar 2021 15:11:39 -0300 Subject: [PATCH] Added bias=False Bias term already included in the BN layers; can be set to False as it is redundant --- ML/Pytorch/CNN_architectures/pytorch_resnet.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/ML/Pytorch/CNN_architectures/pytorch_resnet.py b/ML/Pytorch/CNN_architectures/pytorch_resnet.py index 4822d12..1279179 100644 --- a/ML/Pytorch/CNN_architectures/pytorch_resnet.py +++ b/ML/Pytorch/CNN_architectures/pytorch_resnet.py @@ -23,7 +23,7 @@ class block(nn.Module): super(block, self).__init__() self.expansion = 4 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.conv2 = nn.Conv2d( @@ -32,6 +32,7 @@ class block(nn.Module): kernel_size=3, stride=stride, padding=1, + bias=False ) self.bn2 = nn.BatchNorm2d(intermediate_channels) self.conv3 = nn.Conv2d( @@ -40,6 +41,7 @@ class block(nn.Module): kernel_size=1, stride=1, padding=0, + bias=False ) self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion) self.relu = nn.ReLU() @@ -70,7 +72,7 @@ class ResNet(nn.Module): def __init__(self, block, layers, image_channels, num_classes): super(ResNet, self).__init__() 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.relu = nn.ReLU() self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) @@ -122,6 +124,7 @@ class ResNet(nn.Module): intermediate_channels * 4, kernel_size=1, stride=stride, + bias=False ), nn.BatchNorm2d(intermediate_channels * 4), )