e3fe7fca004b2fd3eab4e69cfa91c032863b8031,pytorch/models/nasnet.py,NormalCell,__init__,#NormalCell#Any#Any#Any#Any#,415

Before Change

        super(NormalCell, self).__init__()
        self.conv_prev_1x1 = nn.Sequential()
        self.conv_prev_1x1.add_module("relu", nn.ReLU())
        self.conv_prev_1x1.add_module("conv", nn.Conv2d(in_channels_left, out_channels_left, 1, stride=1, bias=False))
        self.conv_prev_1x1.add_module("bn", nn.BatchNorm2d(out_channels_left, eps=0.001, momentum=0.1, affine=True))

        self.conv_1x1 = nn.Sequential()
        self.conv_1x1.add_module("relu", nn.ReLU())
        self.conv_1x1.add_module("conv", nn.Conv2d(in_channels_right, out_channels_right, 1, stride=1, bias=False))
        self.conv_1x1.add_module("bn", nn.BatchNorm2d(out_channels_right, eps=0.001, momentum=0.1, affine=True))

        self.comb_iter_0_left = BranchSeparables(out_channels_right, out_channels_right, 5, 1, 2, bias=False)
        self.comb_iter_0_right = BranchSeparables(out_channels_left, out_channels_left, 3, 1, 1, bias=False)

        self.comb_iter_1_left = BranchSeparables(out_channels_left, out_channels_left, 5, 1, 2, bias=False)
        self.comb_iter_1_right = BranchSeparables(out_channels_left, out_channels_left, 3, 1, 1, bias=False)

        self.comb_iter_2_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_3_left = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)
        self.comb_iter_3_right = nn.AvgPool2d(3, stride=1, padding=1, count_include_pad=False)

        self.comb_iter_4_left = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)

    def forward(self, x, x_prev):

After Change

        self.comb_iter_2_left = nasnet_avgpool3x3_s1()

        self.comb_iter_3_left = nasnet_avgpool3x3_s1()
        self.comb_iter_3_right = nasnet_avgpool3x3_s1()

        self.comb_iter_4_left = BranchSeparables(out_channels_right, out_channels_right, 3, 1, 1, bias=False)

    def forward(self, x, x_prev):

Instances