ops.py 文件源码

def add_minibatch_features(image,df_dim):
    shape = image.get_shape().as_list()
    dim = np.prod(shape[1:])            # dim = prod(9,2) = 18
    h_mb0 = lrelu(conv2d(image, df_dim, name='d_mb0_conv'))
    h_mb1 = conv2d(h_mb0, df_dim, name='d_mbh1_conv')

    dims=h_mb1.get_shape().as_list()
    conv_dims=np.prod(dims[1:])

    image_ = tf.reshape(h_mb1, tf.stack([-1, conv_dims]))
    #image_ = tf.reshape(h_mb1, tf.stack([batch_size, -1]))

    n_kernels = 300
    dim_per_kernel = 50
    x = linear(image_, n_kernels * dim_per_kernel,'d_mbLinear')
    act = tf.reshape(x, (-1, n_kernels, dim_per_kernel))

    act= tf.reshape(x, (-1, n_kernels, dim_per_kernel))
    act_tp=tf.transpose(act, [1,2,0])
    #bs x n_ker x dim_ker x bs -> bs x n_ker x bs :
    abs_dif = tf.reduce_sum(tf.abs(tf.expand_dims(act, 3) - tf.expand_dims(act_tp, 0)), 2)
    eye=tf.expand_dims( tf.eye( tf.shape(abs_dif)[0] ), 1)#bs x 1 x bs
    masked=tf.exp(-abs_dif) - eye
    f1=tf.reduce_mean( masked, 2)
    mb_features = tf.reshape(f1, [-1, 1, 1, n_kernels])
    return conv_cond_concat(image, mb_features)

## following is from https://github.com/openai/improved-gan/blob/master/imagenet/discriminator.py#L88
#def add_minibatch_features(image,df_dim,batch_size):
#    shape = image.get_shape().as_list()
#    dim = np.prod(shape[1:])            # dim = prod(9,2) = 18
#    h_mb0 = lrelu(conv2d(image, df_dim, name='d_mb0_conv'))
#    h_mb1 = conv2d(h_mb0, df_dim, name='d_mbh1_conv')
#
#    dims=h_mb1.get_shape().as_list()
#    conv_dims=np.prod(dims[1:])
#
#    image_ = tf.reshape(h_mb1, tf.stack([-1, conv_dims]))
#    #image_ = tf.reshape(h_mb1, tf.stack([batch_size, -1]))
#
#    n_kernels = 300
#    dim_per_kernel = 50
#    x = linear(image_, n_kernels * dim_per_kernel,'d_mbLinear')
#    activation = tf.reshape(x, (batch_size, n_kernels, dim_per_kernel))
#    big = np.zeros((batch_size, batch_size), dtype='float32')
#    big += np.eye(batch_size)
#    big = tf.expand_dims(big, 1)
#    abs_dif = tf.reduce_sum(tf.abs(tf.expand_dims(activation, 3) - tf.expand_dims(tf.transpose(activation, [1, 2, 0]), 0)), 2)
#    mask = 1. - big
#    masked = tf.exp(-abs_dif) * mask
#    f1 = tf.reduce_sum(masked, 2) / tf.reduce_sum(mask)
#    mb_features = tf.reshape(f1, [batch_size, 1, 1, n_kernels])
#    return conv_cond_concat(image, mb_features)