lib/backbones/resnext.py [65:81]:
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    def __init__(self, block, num_blocks, cardinality, bottleneck_width, strides):
        super().__init__()
        self.cardinality = cardinality
        self.bottleneck_width = bottleneck_width
        self.in_channels = 64

        # temporally for small patches
        self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)

        self.layer1 = self.make_layer(block, num_blocks[0], strides[0])
        self.layer2 = self.make_layer(block, num_blocks[1], strides[1])
        self.layer3 = self.make_layer(block, num_blocks[2], strides[2])
        self.layer4 = self.make_layer(block, num_blocks[3], strides[3])

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
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lib/backbones/resnext.py [101:117]:
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    def __init__(self, block, num_blocks, cardinality, bottleneck_width, strides):
        super().__init__()
        self.cardinality = cardinality
        self.bottleneck_width = bottleneck_width
        self.in_channels = 64

        # temporally for small patches
        self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)

        self.layer1 = self.make_layer(block, num_blocks[0], strides[0])
        self.layer2 = self.make_layer(block, num_blocks[1], strides[1])
        self.layer3 = self.make_layer(block, num_blocks[2], strides[2])
        self.layer4 = self.make_layer(block, num_blocks[3], strides[3])

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
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