pointnet.py 文件源码

def forward(self, x):
        # x (bz x 3 x 2048) -> conv(3, 64) -> conv(64, 128) -> conv(128, 1024) -> max_pool(2048) -> 1024 -> fc(1024, 512)
        #   -> fc(512, 256) -> fc(256, 9)

        batchsize = x.size()[0]
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.relu(self.bn3(self.conv3(x)))
        x = self.mp1(x)
        x = x.view(-1, 1024)

        x = F.relu(self.bn4(self.fc1(x)))
        x = F.relu(self.bn5(self.fc2(x)))
        x = self.fc3(x) # bz x 9 
        # identity transform
        # bz x 9 
        iden = Variable(torch.from_numpy(np.array([1,0,0,0,1,0,0,0,1]).astype(np.float32))).view(1,9).repeat(batchsize,1)
        if x.is_cuda:
            iden = iden.cuda()
        x = x + iden
        x = x.view(-1, 3, 3) # bz x 3 x 3 
        return x

# 128 x 128 transform