visulize_help.py

import numpy
import tensorflow as tf

def batch_norm_func(y, out_size):
    # Batch Normalize
    fc_mean, fc_var = tf.nn.moments(
        y,
        axes=[0],  # the dimension you wanna normalize, here [0] for batch
        # for image, you wanna do [0, 1, 2] for [batch, height, width] but not channel
    )
    scale = tf.Variable(tf.ones([out_size]))
    shift = tf.Variable(tf.zeros([out_size]))
    epsilon = 0.001

    # apply moving average for mean and var when train on batch
    ema = tf.train.ExponentialMovingAverage(decay=0.5)

    def mean_var_with_update():
        ema_apply_op = ema.apply([fc_mean, fc_var])
        with tf.control_dependencies([ema_apply_op]):
            return tf.identity(fc_mean), tf.identity(fc_var)

    mean, var = mean_var_with_update()

    y = tf.nn.batch_normalization(y, mean, var, shift, scale, epsilon)
    return y

def scale_to_unit_interval(ndar, eps=1e-8):
    """ Scales all values in the ndarray ndar to be between 0 and 1 """
    ndar = ndar.copy()
    ndar -= ndar.min()
    ndar *= 1.0 / (ndar.max() + eps)
    return ndar


def tile_raster_images(X, img_shape, tile_shape, tile_spacing=(0, 0),
                       scale_rows_to_unit_interval=True,
                       output_pixel_vals=True):
    """
    Transform an array with one flattened image per row into an array in
    which images are reshaped and layed out like tiles on a floor.
    This function is useful for visualizing datasets whose rows are images,
    and also columns of matrices for transforming those rows
    (such as the first layer of a neural net).
    :type X: a 2-D ndarray or a tuple of 4 channels, elements of which can
    be 2-D ndarrays or None;
    :param X: a 2-D array in which every row is a flattened image.
    :type img_shape: tuple; (height, width)
    :param img_shape: the original shape of each image
    :type tile_shape: tuple; (rows, cols)
    :param tile_shape: the number of images to tile (rows, cols)，
     在每行每列上分别有多少个图像
    :param output_pixel_vals: if output should be pixel values (i.e. int8
    values) or floats 是否以图像的形式进行显示
    :param scale_rows_to_unit_interval: if the values need to be scaled before
    being plotted to [0,1] or not
    :returns: array suitable for viewing as an image.
    (See:`Image.fromarray`.)
    :rtype: a 2-d array with same dtype as X.
    """
    # 对参数进行断言，确保它们都是二维元组
    assert len(img_shape) == 2
    assert len(tile_shape) == 2
    assert len(tile_spacing) == 2

    # The expression below can be re-written in a more C style as
    # follows :
    #
    # out_shape    = [0,0]
    # out_shape[0] = (img_shape[0]+tile_spacing[0])*tile_shape[0] -
    #                tile_spacing[0]
    # out_shape[1] = (img_shape[1]+tile_spacing[1])*tile_shape[1] -
    #                tile_spacing[1]
    out_shape = [
        (ishp + tsp) * tshp - tsp
        for ishp, tshp, tsp in zip(img_shape, tile_shape, tile_spacing)
    ]

    if isinstance(X, tuple):
        assert len(X) == 4
        # Create an output numpy ndarray to store the image
        if output_pixel_vals:
            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
                                    dtype='uint8')
        else:
            out_array = numpy.zeros((out_shape[0], out_shape[1], 4),
                                    dtype=X.dtype)

        # colors default to 0, alpha defaults to 1 (opaque)
        if output_pixel_vals:
            channel_defaults = [0, 0, 0, 255]
        else:
            channel_defaults = [0., 0., 0., 1.]

        for i in range(4):
            if X[i] is None:
                # if channel is None, fill it with zeros of the correct
                # dtype
                dt = out_array.dtype
                if output_pixel_vals:
                    dt = 'uint8'
                out_array[:, :, i] = numpy.zeros(
                    out_shape,
                    dtype=dt
                ) + channel_defaults[i]
            else:
                # use a recurrent call to compute the channel and store it
                # in the output
                out_array[:, :, i] = tile_raster_images(
                    X[i], img_shape, tile_shape, tile_spacing,
                    scale_rows_to_unit_interval, output_pixel_vals)
        return out_array

    else:
        # if we are dealing with only one channel
        H, W = img_shape
        Hs, Ws = tile_spacing

        # generate a matrix to store the output
        dt = X.dtype
        if output_pixel_vals:
            dt = 'uint8'
        out_array = numpy.zeros(out_shape, dtype=dt)

        # begin to tile the images
        for tile_row in range(tile_shape[0]):
            for tile_col in range(tile_shape[1]):
                # a 2-D array in which every row is a flattened image
                if tile_row * tile_shape[1] + tile_col < X.shape[0]:
                    this_x = X[tile_row * tile_shape[1] + tile_col]
                    if scale_rows_to_unit_interval:
                        # if we should scale values to be between 0 and 1
                        # do this by calling the `scale_to_unit_interval`
                        # function
                        this_img = scale_to_unit_interval(
                            this_x.reshape(img_shape))
                    else:
                        this_img = this_x.reshape(img_shape)
                    # add the slice to the corresponding position in the
                    # output array
                    c = 1
                    if output_pixel_vals:
                        c = 255
                    out_array[
                        tile_row * (H + Hs): tile_row * (H + Hs) + H,
                        tile_col * (W + Ws): tile_col * (W + Ws) + W
                    ] = this_img * c
        return out_array