def __init__()

in Dassl.pytorch/dassl/modeling/backbone/efficientnet/model.py [0:0]

• 65 lines of code
• 7 McCabe index (conditional complexity)

    def __init__(self, blocks_args=None, global_params=None):
        super().__init__()
        assert isinstance(blocks_args, list), "blocks_args should be a list"
        assert len(blocks_args) > 0, "block args must be greater than 0"
        self._global_params = global_params
        self._blocks_args = blocks_args

        # Batch norm parameters
        bn_mom = 1 - self._global_params.batch_norm_momentum
        bn_eps = self._global_params.batch_norm_epsilon

        # Get stem static or dynamic convolution depending on image size
        image_size = global_params.image_size
        Conv2d = get_same_padding_conv2d(image_size=global_params.image_size)

        # Stem
        in_channels = 3  # rgb
        out_channels = round_filters(
            32, self._global_params
        )  # number of output channels
        self._conv_stem = Conv2d(
            in_channels, out_channels, kernel_size=3, stride=2, bias=False
        )
        self._bn0 = nn.BatchNorm2d(
            num_features=out_channels, momentum=bn_mom, eps=bn_eps
        )
        image_size = calculate_output_image_size(image_size, 2)

        # Build blocks
        self._blocks = nn.ModuleList([])
        for block_args in self._blocks_args:

            # Update block input and output filters based on depth multiplier.
            block_args = block_args._replace(
                input_filters=round_filters(
                    block_args.input_filters, self._global_params
                ),
                output_filters=round_filters(
                    block_args.output_filters, self._global_params
                ),
                num_repeat=round_repeats(
                    block_args.num_repeat, self._global_params
                ),
            )

            # The first block needs to take care of stride and filter size increase.
            self._blocks.append(
                MBConvBlock(
                    block_args, self._global_params, image_size=image_size
                )
            )
            image_size = calculate_output_image_size(
                image_size, block_args.stride
            )
            if block_args.num_repeat > 1:
                block_args = block_args._replace(
                    input_filters=block_args.output_filters, stride=1
                )
            for _ in range(block_args.num_repeat - 1):
                self._blocks.append(
                    MBConvBlock(
                        block_args, self._global_params, image_size=image_size
                    )
                )
                # image_size = calculate_output_image_size(image_size, block_args.stride) # ?

        # Head
        in_channels = block_args.output_filters  # output of final block
        out_channels = round_filters(1280, self._global_params)
        Conv2d = get_same_padding_conv2d(image_size=image_size)
        self._conv_head = Conv2d(
            in_channels, out_channels, kernel_size=1, bias=False
        )
        self._bn1 = nn.BatchNorm2d(
            num_features=out_channels, momentum=bn_mom, eps=bn_eps
        )

        # Final linear layer
        self._avg_pooling = nn.AdaptiveAvgPool2d(1)
        self._dropout = nn.Dropout(self._global_params.dropout_rate)
        # self._fc = nn.Linear(out_channels, self._global_params.num_classes)
        self._swish = MemoryEfficientSwish()

        self._out_features = out_channels