我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用tensorflow.batch_to_space()。
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 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
