9963f2ded56fb471cd0597a3a64e99eaeb0d64ca,pytorch/pytorchcv/models/others/_linknet.py,BasicBlock,__init__,#BasicBlock#Any#Any#Any#Any#Any#Any#Any#,17

Before Change

        self.conv2 = nn.Conv2d(out_planes, out_planes, kernel_size, 1, padding, groups=groups, bias=bias)
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.downsample = None
        if stride > 1:
            self.downsample = nn.Sequential(nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False),
                                            nn.BatchNorm2d(out_planes),)

    def forward(self, x):
        residual = x

        out = self.conv1(x)

After Change

                bias=bias),
            nn.BatchNorm2d(in_planes//4),
            nn.ReLU(inplace=True))
        self.tp_conv = nn.Sequential(
            nn.ConvTranspose2d(
                in_planes//4,
                in_planes//4,
                kernel_size,
                stride,
                padding,
                output_padding,
                bias=bias),
            nn.BatchNorm2d(in_planes//4),
            nn.ReLU(inplace=True))
        self.conv2 = nn.Sequential(
            nn.Conv2d(
                in_planes//4,
                out_planes,

Instances