本文整理汇总了Python中tensorflow.depth_to_space函数的典型用法代码示例。如果您正苦于以下问题：Python depth_to_space函数的具体用法？Python depth_to_space怎么用？Python depth_to_space使用的例子？那么恭喜您, 这里精选的函数代码示例或许可以为您提供帮助。
在下文中一共展示了depth_to_space函数的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: testDepthInterleavedDepth3
def testDepthInterleavedDepth3(self):
x_np = [[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]])示例2: SubpixelConv2D
def SubpixelConv2D(*args, **kwargs):
kwargs['output_dim'] = 4*kwargs['output_dim']
output = lib.ops.conv2d.Conv2D(*args, **kwargs)
output = tf.transpose(output, [0,2,3,1])
output = tf.depth_to_space(output, 2)
output = tf.transpose(output, [0,3,1,2])
return output示例3: testBlockSize0
def testBlockSize0(self):
x_np = [[[[1], [2]],
[[3], [4]]]]
block_size = 0
with self.assertRaises(ValueError):
out_tf = tf.depth_to_space(x_np, block_size)
out_tf.eval()示例4: depth_to_space
def depth_to_space(input, scale, data_format=None):
''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
data_format = 'NHWC'
data_format = data_format.lower()
out = tf.depth_to_space(input, scale, data_format=data_format)
return out示例5: testDepthInterleaved
def testDepthInterleaved(self):
x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1, 10], [2, 20]],
[[3, 30], [4, 40]]]])示例6: deconv2d
def deconv2d(cur, i):
thicker = conv(
cur,
output_filters * 4, (1, 1),
padding="SAME",
activation=tf.nn.relu,
name="deconv2d" + str(i))
return tf.depth_to_space(thicker, 2)示例7: UpsampleConv
def UpsampleConv(name, input_dim, output_dim, filter_size, inputs, he_init=True, biases=True):
output = inputs
output = tf.concat([output, output, output, output], axis=1)
output = tf.transpose(output, [0,2,3,1])
output = tf.depth_to_space(output, 2)
output = tf.transpose(output, [0,3,1,2])
output = lib.ops.conv2d.Conv2D(name, input_dim, output_dim, filter_size, output, he_init=he_init, biases=biases)
return output示例8: testBlockSizeOne
def testBlockSizeOne(self):
x_np = [[[[1, 1, 1, 1],
[2, 2, 2, 2]],
[[3, 3, 3, 3],
[4, 4, 4, 4]]]]
block_size = 1
with self.assertRaises(ValueError):
out_tf = tf.depth_to_space(x_np, block_size)
out_tf.eval()示例9: testBlockSize4FlatInput
def testBlockSize4FlatInput(self):
x_np = [[[[1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]]]]
block_size = 4
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(), [[[[1], [2], [5], [6]],
[[3], [4], [7], [8]],
[[9], [10], [13], [14]],
[[11], [12], [15], [16]]]])示例10: depth_to_space
def depth_to_space(cls, ipt, scale, data_format=None):
""" Uses phase shift algorithm to convert channels/depth
for spatial resolution """
if data_format is None:
data_format = K.image_data_format()
data_format = data_format.lower()
ipt = cls._preprocess_conv2d_input(ipt, data_format)
out = tf.depth_to_space(ipt, scale)
out = cls._postprocess_conv2d_output(out, data_format)
return out示例11: phase_shift
def phase_shift(x, upsampling_factor=2, data_format="NCHW", name="PhaseShift"):
if data_format == "NCHW":
x = tf.transpose(x, [0,2,3,1])
x = tf.depth_to_space(x, upsampling_factor, name=name)
if data_format == "NCHW":
x = tf.transpose(x, [0,3,1,2])
return x示例12: testDepthToSpaceTranspose
def testDepthToSpaceTranspose(self):
x = np.arange(20 * 5 * 8 * 7, dtype=np.float32).reshape([20, 5, 8, 7])
block_size = 2
crops = np.zeros((2, 2), dtype=np.int32)
y1 = tf.batch_to_space(x, crops, block_size=block_size)
y2 = tf.transpose(
tf.depth_to_space(
tf.transpose(x, [3, 1, 2, 0]), block_size=block_size),
[3, 1, 2, 0])
with self.test_session():
self.assertAllEqual(y1.eval(), y2.eval())示例13: testBlockSizeTooLarge
def testBlockSizeTooLarge(self):
x_np = [[[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[9, 10, 11, 12],
[13, 14, 15, 16]]]]
block_size = 4
# Raise an exception, since th depth is only 4 and needs to be
# divisible by 16.
with self.assertRaises(IndexError):
out_tf = tf.depth_to_space(x_np, block_size)
out_tf.eval()示例14: decompress_step
def decompress_step(source, hparams, first_relu, is_2d, name):
"""Decompression function."""
with tf.variable_scope(name):
shape = common_layers.shape_list(source)
multiplier = 4 if is_2d else 2
kernel = (1, 1) if is_2d else (1, 1)
thicker = common_layers.conv_block(
source, hparams.hidden_size * multiplier, [((1, 1), kernel)],
first_relu=first_relu, name="decompress_conv")
if is_2d:
return tf.depth_to_space(thicker, 2)
return tf.reshape(thicker, [shape[0], shape[1] * 2, 1, hparams.hidden_size])示例15: testDepthInterleavedLarger
def testDepthInterleavedLarger(self):
x_np = [[[[1, 10, 2, 20, 3, 30, 4, 40],
[5, 50, 6, 60, 7, 70, 8, 80]],
[[9, 90, 10, 100, 11, 110, 12, 120],
[13, 130, 14, 140, 15, 150, 16, 160]]]]
block_size = 2
with self.test_session(use_gpu=False):
x_tf = tf.depth_to_space(x_np, block_size)
self.assertAllEqual(x_tf.eval(),
[[[[1, 10], [2, 20], [5, 50], [6, 60]],
[[3, 30], [4, 40], [7, 70], [8, 80]],
[[9, 90], [10, 100], [13, 130], [14, 140]],
[[11, 110], [12, 120], [15, 150], [16, 160]]]])