本文整理汇总了Python中tensorflow.python.ops.array_ops.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: compareToTranspose
def compareToTranspose(self, batch_size, in_height, in_width, out_channels,
block_size, data_format, use_gpu):
in_channels = out_channels * block_size * block_size
nhwc_input_shape = [batch_size, in_height, in_width, in_channels]
nchw_input_shape = [batch_size, in_channels, in_height, in_width]
total_size = np.prod(nhwc_input_shape)
if data_format == "NCHW_VECT_C":
# Initialize the input tensor with qint8 values that circle -127..127.
x = [((f + 128) % 255) - 127 for f in range(total_size)]
t = constant_op.constant(x, shape=nhwc_input_shape, dtype=dtypes.float32)
expected = self.depthToSpaceUsingTranspose(t, block_size, "NHWC")
t = test_util.NHWCToNCHW_VECT_C(t)
t, _, _ = gen_array_ops.quantize_v2(t, -128.0, 127.0, dtypes.qint8)
t = array_ops.depth_to_space(t, block_size, data_format="NCHW_VECT_C")
t = gen_array_ops.dequantize(t, -128, 127)
actual = test_util.NCHW_VECT_CToNHWC(t)
else:
# Initialize the input tensor with ascending whole numbers as floats.
x = [f * 1.0 for f in range(total_size)]
shape = nchw_input_shape if data_format == "NCHW" else nhwc_input_shape
t = constant_op.constant(x, shape=shape, dtype=dtypes.float32)
expected = self.depthToSpaceUsingTranspose(t, block_size, data_format)
actual = array_ops.depth_to_space(t, block_size, data_format=data_format)
with self.test_session(use_gpu=use_gpu) as sess:
actual_vals, expected_vals = sess.run([actual, expected])
self.assertTrue(np.array_equal(actual_vals, expected_vals))
示例2: testBlockSize0
def testBlockSize0(self):
x_np = [[[[1], [2]],
[[3], [4]]]]
block_size = 0
with self.assertRaises(ValueError):
out_tf = array_ops.depth_to_space(x_np, block_size)
out_tf.eval()
示例3: _SpaceToDepthGrad
def _SpaceToDepthGrad(op, grad):
# Its gradient is the opposite op: DepthToSpace.
block_size = op.get_attr("block_size")
data_format = op.get_attr("data_format")
if data_format == "NCHW_VECT_C":
raise ValueError("Cannot compute SpaceToDepth gradient with NCHW_VECT_C. "
"NCHW_VECT_C requires qint8 data type.")
return array_ops.depth_to_space(grad, block_size, data_format=data_format)
示例4: _testOne
def _testOne(self, inputs, block_size, outputs):
input_nhwc = math_ops.to_float(inputs)
with self.test_session(use_gpu=False):
# test NHWC (default) on CPU
x_tf = array_ops.depth_to_space(input_nhwc, block_size)
self.assertAllEqual(x_tf.eval(), outputs)
if test.is_gpu_available():
with self.test_session(use_gpu=True):
# test NHWC (default) on GPU
x_tf = array_ops.depth_to_space(input_nhwc, block_size)
self.assertAllEqual(x_tf.eval(), outputs)
# test NCHW on GPU
input_nchw = test_util.NHWCToNCHW(input_nhwc)
output_nchw = array_ops.depth_to_space(
input_nchw, block_size, data_format="NCHW")
output_nhwc = test_util.NCHWToNHWC(output_nchw)
self.assertAllEqual(output_nhwc.eval(), outputs)
示例5: testBatchSize0
def testBatchSize0(self):
block_size = 2
batch_size = 0
input_nhwc = array_ops.ones([batch_size, 2, 3, 12])
x_out = array_ops.ones([batch_size, 4, 6, 3])
with self.cached_session(use_gpu=False):
# test NHWC (default) on CPU
x_tf = array_ops.depth_to_space(input_nhwc, block_size)
self.assertAllEqual(x_tf.shape, x_out.shape)
self.evaluate(x_tf)
if test.is_gpu_available():
with self.cached_session(use_gpu=True):
# test NHWC (default) on GPU
x_tf = array_ops.depth_to_space(input_nhwc, block_size)
self.assertAllEqual(x_tf.shape, x_out.shape)
self.evaluate(x_tf)
示例6: 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 = array_ops.depth_to_space(x_np, block_size)
out_tf.eval()
示例7: 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(ValueError):
out_tf = array_ops.depth_to_space(x_np, block_size)
out_tf.eval()
示例8: 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 = self.batch_to_space(x, crops, block_size=block_size)
y2 = array_ops.transpose(
array_ops.depth_to_space(
array_ops.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9: testDepthToSpace
def testDepthToSpace(self):
for dtype in self.numeric_types:
self._assertOpOutputMatchesExpected(
lambda x: array_ops.depth_to_space(x, block_size=2),
np.array([[[[1, 2, 3, 4]]]], dtype=dtype),
expected=np.array([[[[1], [2]],
[[3], [4]]]], dtype=dtype))
self._assertOpOutputMatchesExpected(
lambda x: array_ops.depth_to_space(x, block_size=2),
np.array([[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]], dtype=dtype),
expected=np.array([[[[1, 2, 3], [4, 5, 6]],
[[7, 8, 9], [10, 11, 12]]]], dtype=dtype))
self._assertOpOutputMatchesExpected(
lambda x: array_ops.depth_to_space(x, block_size=2),
np.array([[[[1, 2, 3, 4],
[5, 6, 7, 8]],
[[9, 10, 11, 12],
[13, 14, 15, 16]]]], dtype=dtype),
expected=np.array([[[[1], [2], [5], [6]],
[[3], [4], [7], [8]],
[[9], [10], [13], [14]],
[[11], [12], [15], [16]]]], dtype=dtype))
示例10: _checkGrad
def _checkGrad(self, x, block_size):
assert 4 == x.ndim
with self.test_session(use_gpu=True):
tf_x = ops.convert_to_tensor(x)
tf_y = array_ops.depth_to_space(tf_x, block_size)
epsilon = 1e-2
((x_jacob_t, x_jacob_n)) = gradient_checker.compute_gradient(
tf_x,
x.shape,
tf_y,
tf_y.get_shape().as_list(),
x_init_value=x,
delta=epsilon)
self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)
示例11: _checkGrad
def _checkGrad(self, x, block_size, data_format):
# NCHW is implemented for only GPU.
if data_format == "NCHW" and not test.is_gpu_available():
return
assert 4 == x.ndim
with self.cached_session(use_gpu=True):
tf_x = ops.convert_to_tensor(x)
tf_y = array_ops.depth_to_space(tf_x, block_size, data_format=data_format)
epsilon = 1e-2
((x_jacob_t, x_jacob_n)) = gradient_checker.compute_gradient(
tf_x,
x.shape,
tf_y,
tf_y.get_shape().as_list(),
x_init_value=x,
delta=epsilon)
self.assertAllClose(x_jacob_t, x_jacob_n, rtol=1e-2, atol=epsilon)
示例12: _SpaceToDepthGrad
def _SpaceToDepthGrad(op, grad):
# Its gradient is the opposite op: DepthToSpace.
block_size = op.get_attr("block_size")
return array_ops.depth_to_space(grad, block_size)
示例13: op
def op(x):
return array_ops.depth_to_space(x, block_size=2,
data_format=data_format)
示例14: testUnknownShape
def testUnknownShape(self):
t = array_ops.depth_to_space(
array_ops.placeholder(dtypes.float32), block_size=4)
self.assertEqual(4, t.get_shape().ndims)
示例15: _testOne
def _testOne(self, inputs, block_size, outputs):
with self.test_session(use_gpu=True):
x_tf = array_ops.depth_to_space(math_ops.to_float(inputs), block_size)
self.assertAllEqual(x_tf.eval(), outputs)