The implementation of ResNet is different from official implementation in Caffe #191
Description
The downsample part in each block/layer (not the skip connection part), the PyTorch do it in conv3x3 using stride=2, but official caffe version in conv1x1 with stride=2
conv1x1 -> caffe do it in here
conv3x3 -> pytorch do it in here
conv1x1
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
padding=1, bias=False)
(layer2): Sequential (
(0): Bottleneck (
(conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
(bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
...
layer {
bottom: "res2c"
top: "res3a_branch2a"
name: "res3a_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
}