def __define_temporal_convolutional_block()

in anticipation/anticipation/models/backbone/timeception.py [0:0]

• 61 lines of code
• 2 McCabe index (conditional complexity)

    def __define_temporal_convolutional_block(self, input_shape, n_channels_per_branch_out, kernel_sizes, dilation_rates, layer_num, group_num):
        """
        Define 5 branches of convolutions that operate of channels of each group.
        """

        n_channels_in = input_shape[1]

        dw_input_shape = list(input_shape)
        dw_input_shape[1] = n_channels_per_branch_out

        # branch 1: dimension reduction only and no temporal conv
        layer_name = 'conv_b1_g%d_tc%d' % (group_num, layer_num)
        layer = Conv3d(n_channels_in, n_channels_per_branch_out, kernel_size=(1, 1, 1))
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'bn_b1_g%d_tc%d' % (group_num, layer_num)
        layer = BatchNorm3d(n_channels_per_branch_out)
        layer._name = layer_name
        setattr(self, layer_name, layer)

        # branch 2: dimension reduction followed by depth-wise temp conv (kernel-size 3)
        layer_name = 'conv_b2_g%d_tc%d' % (group_num, layer_num)
        layer = Conv3d(n_channels_in, n_channels_per_branch_out, kernel_size=(1, 1, 1))
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'convdw_b2_g%d_tc%d' % (group_num, layer_num)
        layer = DepthwiseConv1DLayer(dw_input_shape, kernel_sizes[0], dilation_rates[0], layer_name)
        setattr(self, layer_name, layer)
        layer_name = 'bn_b2_g%d_tc%d' % (group_num, layer_num)
        layer = BatchNorm3d(n_channels_per_branch_out)
        layer._name = layer_name
        setattr(self, layer_name, layer)

        # branch 3: dimension reduction followed by depth-wise temp conv (kernel-size 5)
        layer_name = 'conv_b3_g%d_tc%d' % (group_num, layer_num)
        layer = Conv3d(n_channels_in, n_channels_per_branch_out, kernel_size=(1, 1, 1))
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'convdw_b3_g%d_tc%d' % (group_num, layer_num)
        layer = DepthwiseConv1DLayer(dw_input_shape, kernel_sizes[1], dilation_rates[1], layer_name)
        setattr(self, layer_name, layer)
        layer_name = 'bn_b3_g%d_tc%d' % (group_num, layer_num)
        layer = BatchNorm3d(n_channels_per_branch_out)
        layer._name = layer_name
        setattr(self, layer_name, layer)

        # branch 4: dimension reduction followed by depth-wise temp conv (kernel-size 7)
        layer_name = 'conv_b4_g%d_tc%d' % (group_num, layer_num)
        layer = Conv3d(n_channels_in, n_channels_per_branch_out, kernel_size=(1, 1, 1))
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'convdw_b4_g%d_tc%d' % (group_num, layer_num)
        layer = DepthwiseConv1DLayer(dw_input_shape, kernel_sizes[2], dilation_rates[2], layer_name)
        setattr(self, layer_name, layer)
        layer_name = 'bn_b4_g%d_tc%d' % (group_num, layer_num)
        layer = BatchNorm3d(n_channels_per_branch_out)
        layer._name = layer_name
        setattr(self, layer_name, layer)

        # branch 5: dimension reduction followed by temporal max pooling
        layer_name = 'conv_b5_g%d_tc%d' % (group_num, layer_num)
        layer = Conv3d(n_channels_in, n_channels_per_branch_out, kernel_size=(1, 1, 1))
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'maxpool_b5_g%d_tc%d' % (group_num, layer_num)
        layer = MaxPool3d(kernel_size=(2, 1, 1), stride=(1, 1, 1))
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'padding_b5_g%d_tc%d' % (group_num, layer_num)
        layer = torch.nn.ReplicationPad3d((0, 0, 0, 0, 1, 0))  # left, right, top, bottom, front, back
        layer._name = layer_name
        setattr(self, layer_name, layer)
        layer_name = 'bn_b5_g%d_tc%d' % (group_num, layer_num)
        layer = BatchNorm3d(n_channels_per_branch_out)
        layer._name = layer_name
        setattr(self, layer_name, layer)