jianghaojun
commented
4 years ago I found another two mistakes:
- self.conv2 is not implemented as groupconv.
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2g = conv3x3(planes, planes, stride, padding=2, dilation=2, groups = 32)
self.bn2g = nn.BatchNorm2d(planes)And I checked the sknet50.prototxt in original caffe repo, both of the conv3x3s' groups are 32: conv2_1/3x3g32 conv2_1/3x3g32d2
- As paper said, the output channel d for fc1 is follow this equation:
d = max(C/r, L), where L denotes the minimal value of d (L=32 is a typical setting our experiments).
However, the code is implemented as d = C/r, where r=16.
- The following code is corrected:
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride, padding=1, groups=32)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2g = conv3x3(planes, planes, stride, padding=2, dilation=2, groups = 32)
self.bn2g = nn.BatchNorm2d(planes)
self.conv3 = conv1x1(planes, planes * self.expansion)
self.bn3 = nn.BatchNorm2d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.avg_pool = nn.AdaptiveAvgPool2d(1)
reduction_planes = planes // reduction if planes // reduction > 32 else 32
self.conv_fc1 = nn.Conv2d(planes, reduction_planes, 1, bias=False)
self.bn_fc1 = nn.BatchNorm2d(reduction_planes)
self.conv_fc2 = nn.Conv2d(reduction_planes, 2 * planes, 1, bias=False)
self.D = planes
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
d1 = self.conv2(out)
d1 = self.bn2(d1)
d1 = self.relu(d1)
d2 = self.conv2g(out)
d2 = self.bn2g(d2)
d2 = self.relu(d2)
d = self.avg_pool(d1) + self.avg_pool(d2)
d = F.relu(self.bn_fc1(self.conv_fc1(d)))
d = self.conv_fc2(d)
d = torch.unsqueeze(d, 1).view(-1, 2, self.D, 1, 1)
d = F.softmax(d, 1)
d1 = d1 * d[:, 0, :, :, :].squeeze(1)
d2 = d2 * d[:, 1, :, :, :].squeeze(1)
d = d1 + d2
out = self.conv3(d)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
As paper(Selective Kernel Networks) said，the conventional convolution with a 5×5 kernel is replaced with the dilated convolution with a 3×3 kernel and dilation size 2. But the code is implemented as: " self.conv2g = conv3x3(planes, planes, stride, groups = 32) self.bn2g = nn.BatchNorm2d(planes) " I can't understand why. Could you tell me? thank you.