Your implementation of da-faster-rcnn seems very different from the original paper, right? about scl HOT 3 CLOSED

class netD_img(nn.Module):
def init(self, beta=1, ch_in=1024, ch_out=1024, W=38, H=75, stride_1=1, padding_1=1, kernel=3):
super(netD_img, self).init()
self.conv_image = nn.Conv2d(ch_in, ch_out, stride=stride_1, padding=padding_1, kernel_size=kernel)
self.bn_image = nn.BatchNorm2d(ch_out)
self.fc_1_image = nn.Linear(1, 2)
self.ch_out = ch_out
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=2)
self.bn_2 = nn.BatchNorm2d(ch_out)
#self.softmax = nn.Softmax()
#self.logsoftmax = nn.LogSoftmax()

def forward(self, x):
x = self.conv_image(x)
x = self.relu(x)
x = self.bn_image(x)
x = self.maxpool(x)
x = self.bn_2(x)
# convert to 1024WH x 1.
x = flatten(x)
x = torch.transpose(x, 0, 1)
x = self.fc_1_image(x)
# 1 x n vector
#y = self.softmax(x)
#x = self.logsoftmax(x)
#return x, y
return x