python类batch_to_space()的实例源码

def imageRearrange(self, image, block=4):
        image = tf.slice(image, [0, 0, 0, 0], [block * block, -1, -1, -1])
        x1 = tf.batch_to_space(image, [[0, 0], [0, 0]], block)
        image_r = tf.reshape(tf.transpose(tf.reshape(x1,
            [self.output_size, block, self.output_size, block, self.c_dim])
            , [1, 0, 3, 2, 4]),
            [1, self.output_size * block, self.output_size * block, self.c_dim])
        return image_r

def imageRearrange(self, image, block=4):
        image = tf.slice(image, [0, 0, 0, 0], [block * block, -1, -1, -1])
        x1 = tf.batch_to_space(image, [[0, 0], [0, 0]], block)
        image_r = tf.reshape(tf.transpose(tf.reshape(x1,
            [self.output_size, block, self.output_size, block, self.c_dim])
            , [1, 0, 3, 2, 4]),
            [1, self.output_size * block, self.output_size * block, self.c_dim])
        return image_r

def imageRearrange(self, image, block=4):
        image = tf.slice(image, [0, 0, 0, 0], [block * block, -1, -1, -1])
        x1 = tf.batch_to_space(image, [[0, 0], [0, 0]], block)
        image_r = tf.reshape(tf.transpose(tf.reshape(x1,
            [self.output_size, block, self.output_size, block, self.c_dim])
            , [1, 0, 3, 2, 4]),
            [1, self.output_size * block, self.output_size * block, self.c_dim])
        return image_r

def unboxn(vin, n):
    """vin = (batch, h, w, depth), returns vout = (batch, n*h, n*w, depth), each pixel is duplicated."""
    s = tf.shape(vin)
    vout = tf.concat(0, [vin] * (n ** 2))  # Poor man's replacement for tf.tile (required for Adversarial Training support).
    vout = tf.reshape(vout, [s[0] * (n ** 2), s[1], s[2], s[3]])
    vout = tf.batch_to_space(vout, [[0, 0], [0, 0]], n)
    return vout

def __call__(self, vin):
        # TODO: replace with atrous_2d
        vout = tf.space_to_batch(vin, [[0, 0], [0, 0]], self.dilation)
        vout = LayerConv.__call__(self, vout)
        vout = tf.batch_to_space(vout, [[0, 0], [0, 0]], self.dilation)
        return vout

def group_batch_images(x):
    sz = x.get_shape().as_list()
    num_cols = int(math.sqrt(sz[0]))
    img = tf.slice(x, [0,0,0,0],[num_cols ** 2, -1, -1, -1])
    img = tf.batch_to_space(img, [[0,0],[0,0]], num_cols)

    return img

def imageSummary(opt,image,tag,H,W):
    blockSize = opt.visBlockSize
    imageOne = tf.batch_to_space(image[:blockSize**2],crops=[[0,0],[0,0]],block_size=blockSize)
    imagePermute = tf.reshape(imageOne,[H,blockSize,W,blockSize,-1])
    imageTransp = tf.transpose(imagePermute,[1,0,3,2,4])
    imageBlocks = tf.reshape(imageTransp,[1,H*blockSize,W*blockSize,-1])
    imageBlocks = tf.cast(imageBlocks*255,tf.uint8)
    summary = tf.summary.image(tag,imageBlocks)
    return summary

# make image summary from image batch (mean/variance)

def imageSummary(opt,image,tag,H,W):
    blockSize = opt.visBlockSize
    imageOne = tf.batch_to_space(image[:blockSize**2],crops=[[0,0],[0,0]],block_size=blockSize)
    imagePermute = tf.reshape(imageOne,[H,blockSize,W,blockSize,-1])
    imageTransp = tf.transpose(imagePermute,[1,0,3,2,4])
    imageBlocks = tf.reshape(imageTransp,[1,H*blockSize,W*blockSize,-1])
    imageBlocks = tf.cast(imageBlocks*255,tf.uint8)
    summary = tf.summary.image(tag,imageBlocks)
    return summary

# make image summary from image batch (mean/variance)

def atrous_conv2d(value, filters, rate, name):
    """ Returns the result of a convolution with holes from value and filters.
    Do not use the tensorflow implementation because of issues with shape definition
    of the result. The semantic is the same.
    It uses only the "VALID" padding.

    Warning: this implementation is PGNet specific. It's used only to define the last
    convolutional layer and therefore depends on pgnet constants
    """

    pad_top = 0
    pad_bottom = 0
    pad_left = 0
    pad_right = 0

    in_height = value.get_shape()[1].value + pad_top + pad_bottom
    in_width = value.get_shape()[2].value + pad_left + pad_right

    # More padding so that rate divides the height and width of the input.
    pad_bottom_extra = (rate - in_height % rate) % rate
    pad_right_extra = (rate - in_width % rate) % rate

    # The paddings argument to space_to_batch includes both padding components.
    space_to_batch_pad = ((pad_top, pad_bottom + pad_bottom_extra),
                          (pad_left, pad_right + pad_right_extra))

    value = tf.space_to_batch(
        input=value, paddings=space_to_batch_pad, block_size=rate)

    value = tf.nn.conv2d(
        input=value,
        filter=filters,
        strides=(1, LAST_CONV_OUTPUT_STRIDE, LAST_CONV_OUTPUT_STRIDE, 1),
        padding="VALID",
        name=name)

    # The crops argument to batch_to_space is just the extra padding component.
    batch_to_space_crop = ((0, pad_bottom_extra), (0, pad_right_extra))

    value = tf.batch_to_space(
        input=value, crops=batch_to_space_crop, block_size=rate)

    return value