本文整理汇总了Python中tensorflow.python.ops.array_ops.meshgrid方法的典型用法代码示例。如果您正苦于以下问题:Python array_ops.meshgrid方法的具体用法?Python array_ops.meshgrid怎么用?Python array_ops.meshgrid使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.array_ops
的用法示例。
在下文中一共展示了array_ops.meshgrid方法的4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: _compareDiff
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import meshgrid [as 别名]
def _compareDiff(self, x, y, use_gpu):
for index in ('ij', 'xy'):
numpy_out = np.meshgrid(x, y, indexing=index)
tf_out = array_ops.meshgrid(x, y, indexing=index)
with self.test_session(use_gpu=use_gpu):
for xx, yy in zip(numpy_out, tf_out):
self.assertAllEqual(xx, yy.eval())
示例2: _compareDiffType
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import meshgrid [as 别名]
def _compareDiffType(self, n, np_dtype, use_gpu):
inputs = []
for index in ('ij', 'xy'):
for i in range(n):
x = np.linspace(-10, 10, 5).astype(np_dtype)
if np_dtype in (np.complex64, np.complex128):
x += 1j
inputs.append(x)
numpy_out = np.meshgrid(*inputs, indexing=index)
with self.test_session(use_gpu=use_gpu):
tf_out = array_ops.meshgrid(*inputs, indexing=index)
for X, _X in zip(numpy_out, tf_out):
self.assertAllEqual(X, _X.eval())
示例3: dense_image_warp
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import meshgrid [as 别名]
def dense_image_warp(image, flow, name='dense_image_warp'):
"""Image warping using per-pixel flow vectors.
Apply a non-linear warp to the image, where the warp is specified by a dense
flow field of offset vectors that define the correspondences of pixel values
in the output image back to locations in the source image. Specifically, the
pixel value at output[b, j, i, c] is
images[b, j - flow[b, j, i, 0], i - flow[b, j, i, 1], c].
The locations specified by this formula do not necessarily map to an int
index. Therefore, the pixel value is obtained by bilinear
interpolation of the 4 nearest pixels around
(b, j - flow[b, j, i, 0], i - flow[b, j, i, 1]). For locations outside
of the image, we use the nearest pixel values at the image boundary.
Args:
image: 4-D float `Tensor` with shape `[batch, height, width, channels]`.
flow: A 4-D float `Tensor` with shape `[batch, height, width, 2]`.
name: A name for the operation (optional).
Note that image and flow can be of type tf.half, tf.float32, or tf.float64,
and do not necessarily have to be the same type.
Returns:
A 4-D float `Tensor` with shape`[batch, height, width, channels]`
and same type as input image.
Raises:
ValueError: if height < 2 or width < 2 or the inputs have the wrong number
of dimensions.
"""
with ops.name_scope(name):
batch_size, height, width, channels = image.get_shape().as_list()
batch_size = tf.shape(image)[0]
# The flow is defined on the image grid. Turn the flow into a list of query
# points in the grid space.
grid_x, grid_y = array_ops.meshgrid(
math_ops.range(width), math_ops.range(height))
stacked_grid = math_ops.cast(
array_ops.stack([grid_y, grid_x], axis=2), flow.dtype)
batched_grid = array_ops.expand_dims(stacked_grid, axis=0)
query_points_on_grid = batched_grid - flow
query_points_flattened = array_ops.reshape(query_points_on_grid,
[batch_size, height * width, 2])
# Compute values at the query points, then reshape the result back to the
# image grid.
interpolated = _interpolate_bilinear(image, query_points_flattened)
interpolated = array_ops.reshape(interpolated,
[batch_size, height, width, channels])
return interpolated
示例4: dense_image_warp
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import meshgrid [as 别名]
def dense_image_warp(image, flow, name='dense_image_warp'):
"""Image warping using per-pixel flow vectors.
Apply a non-linear warp to the image, where the warp is specified by a dense
flow field of offset vectors that define the correspondences of pixel values
in the output image back to locations in the source image. Specifically, the
pixel value at output[b, j, i, c] is
images[b, j - flow[b, j, i, 0], i - flow[b, j, i, 1], c].
The locations specified by this formula do not necessarily map to an int
index. Therefore, the pixel value is obtained by bilinear
interpolation of the 4 nearest pixels around
(b, j - flow[b, j, i, 0], i - flow[b, j, i, 1]). For locations outside
of the image, we use the nearest pixel values at the image boundary.
Args:
image: 4-D float `Tensor` with shape `[batch, height, width, channels]`.
flow: A 4-D float `Tensor` with shape `[batch, height, width, 2]`.
name: A name for the operation (optional).
Note that image and flow can be of type tf.half, tf.float32, or tf.float64,
and do not necessarily have to be the same type.
Returns:
A 4-D float `Tensor` with shape`[batch, height, width, channels]`
and same type as input image.
Raises:
ValueError: if height < 2 or width < 2 or the inputs have the wrong number
of dimensions.
"""
with ops.name_scope(name):
batch_size, height, width, channels = array_ops.unstack(array_ops.shape(image))
# The flow is defined on the image grid. Turn the flow into a list of query
# points in the grid space.
grid_x, grid_y = array_ops.meshgrid(
math_ops.range(width), math_ops.range(height))
stacked_grid = math_ops.cast(
array_ops.stack([grid_y, grid_x], axis=2), flow.dtype)
batched_grid = array_ops.expand_dims(stacked_grid, axis=0)
query_points_on_grid = batched_grid - flow
query_points_flattened = array_ops.reshape(query_points_on_grid,
[batch_size, height * width, 2])
# Compute values at the query points, then reshape the result back to the
# image grid.
interpolated = _interpolate_bilinear(image, query_points_flattened)
interpolated = array_ops.reshape(interpolated,
[batch_size, height, width, channels])
return interpolated