4959d01620ad25a34915119a2c473c918f5cacb6,tflearn/layers/conv.py,,resnext_block,#,1439

Before Change

        for i in range(nb_blocks):

            identity = resnet

            card_branches = list()
            for i in range(cardinality):

                if not downsample:
                    downsample_strides = 1

                branch = conv_2d(resnet, bottleneck_size, 1,
                             downsample_strides, "valid",
                             "linear", bias, weights_init,
                             bias_init, regularizer, weight_decay,
                             trainable, restore)

                if batch_norm:
                    branch = batch_normalization(branch, trainable=trainable)
                branch = tflearn.activation(branch, activation)

                branch = conv_2d(branch, bottleneck_size, 3, 1, "same",
                             "linear", bias, weights_init,
                             bias_init, regularizer, weight_decay,
                             trainable, restore)
                if batch_norm:
                    branch = batch_normalization(branch, trainable=trainable)
                branch = tflearn.activation(branch, activation)

                branch = conv_2d(branch, out_channels, 1, 1, "valid",
                             activation, bias, weights_init,
                             bias_init, regularizer, weight_decay,
                             trainable, restore)

                if batch_norm:
                    branch = batch_normalization(branch, trainable=trainable)

                card_branches.append(branch)

            resnet = tf.add_n(card_branches)

            // Downsampling
            if downsample_strides > 1:
                identity = avg_pool_2d(identity, 1, downsample_strides)

            // Projection to new dimension
            if in_channels != out_channels:
                ch = (out_channels - in_channels) // 2
                identity = tf.pad(identity,
                                  [[0, 0], [0, 0], [0, 0], [ch, ch]])
                in_channels = out_channels

            resnet = resnet + identity
            resnet = tflearn.activation(resnet, activation)

        return resnet

After Change

    bottleneck_values = [64, 40, 24, 14, 4]
    bottleneck_size = bottleneck_values[card_values.index(cardinality)]
    // Group width for reference
    group_width = [64, 80, 96, 112, 128]

    assert cardinality in card_values, "cardinality must be in [1, 2, 4, 8, 32]"

    with tf.variable_scope(scope, default_name=name, values=[incoming],

Instances