9d2cd4feba3fc1031c6b33ac179ec20ee49a5d36,models/mobilenet.py,InvertedResidual,__init__,#InvertedResidual#Any#Any#Any#Any#,43

Before Change

        self.use_res_connect = self.stride == 1 and inp == oup

        if expand_ratio == 1:
            self.conv = nn.Sequential(
                // dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                // pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                // pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                // dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                nn.BatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                // pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                nn.BatchNorm2d(oup),
            )

    def forward(self, x):

After Change

        self.use_res_connect = self.stride == 1 and inp == oup

        if expand_ratio == 1:
            self.conv = nn.Sequential(
                // dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                SynchronizedBatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                // pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                SynchronizedBatchNorm2d(oup),
            )
        else:
            self.conv = nn.Sequential(
                // pw
                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),
                SynchronizedBatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                // dw
                nn.Conv2d(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False),
                SynchronizedBatchNorm2d(hidden_dim),
                nn.ReLU6(inplace=True),
                // pw-linear
                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),
                SynchronizedBatchNorm2d(oup),
            )

    def forward(self, x):

Instances