本文整理汇总了Python中tensorflow.python.ops.array_ops.reverse_v2方法的典型用法代码示例。如果您正苦于以下问题:Python array_ops.reverse_v2方法的具体用法?Python array_ops.reverse_v2怎么用?Python array_ops.reverse_v2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.reverse_v2方法的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: random_flip_left_right
# 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 reverse_v2 [as 别名]
def random_flip_left_right(image, bboxes, seed=None):
"""Random flip left-right of an image and its bounding boxes.
"""
def flip_bboxes(bboxes):
"""Flip bounding boxes coordinates.
"""
bboxes = tf.stack([bboxes[:, 0], 1 - bboxes[:, 3],
bboxes[:, 2], 1 - bboxes[:, 1]], axis=-1)
return bboxes
# Random flip. Tensorflow implementation.
with tf.name_scope('random_flip_left_right'):
image = ops.convert_to_tensor(image, name='image')
_Check3DImage(image, require_static=False)
uniform_random = random_ops.random_uniform([], 0, 1.0, seed=seed)
mirror_cond = math_ops.less(uniform_random, .5)
# Flip image.
result = control_flow_ops.cond(mirror_cond,
lambda: array_ops.reverse_v2(image, [1]),
lambda: image)
# Flip bboxes.
bboxes = control_flow_ops.cond(mirror_cond,
lambda: flip_bboxes(bboxes),
lambda: bboxes)
return fix_image_flip_shape(image, result), bboxes
示例2: _reverse
# 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 reverse_v2 [as 别名]
def _reverse(self, t, lengths):
"""Time reverse the provided tensor or list of tensors.
Assumes the top dimension is the time dimension.
Args:
t: 3D tensor or list of 2D tensors to be reversed
lengths: 1D tensor of lengths, or `None`
Returns:
A reversed tensor or list of tensors
"""
if isinstance(t, list):
return list(reversed(t))
else:
if lengths is None:
return array_ops.reverse_v2(t, [0])
else:
return array_ops.reverse_sequence(t, lengths, 0, 1)
示例3: flip_up_down
# 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 reverse_v2 [as 别名]
def flip_up_down(image):
"""Flip an image horizontally (upside down).
Outputs the contents of `image` flipped along the first dimension, which is
`height`.
See also `reverse()`.
Args:
image: A 3-D tensor of shape `[height, width, channels].`
Returns:
A 3-D tensor of the same type and shape as `image`.
Raises:
ValueError: if the shape of `image` not supported.
"""
image = ops.convert_to_tensor(image, name='image')
_Check3DImage(image, require_static=False)
return fix_image_flip_shape(image, array_ops.reverse_v2(image, [0]))
示例4: _reverse2DimAuto
# 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 reverse_v2 [as 别名]
def _reverse2DimAuto(self, np_dtype):
x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np_dtype)
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
x_tf_1 = array_ops.reverse_v2(x_np, [0]).eval()
x_tf_2 = array_ops.reverse_v2(x_np, [-2]).eval()
x_tf_3 = array_ops.reverse_v2(x_np, [1]).eval()
x_tf_4 = array_ops.reverse_v2(x_np, [-1]).eval()
x_tf_5 = array_ops.reverse_v2(x_np, [1, 0]).eval()
self.assertAllEqual(x_tf_1, np.asarray(x_np)[::-1, :])
self.assertAllEqual(x_tf_2, np.asarray(x_np)[::-1, :])
self.assertAllEqual(x_tf_3, np.asarray(x_np)[:, ::-1])
self.assertAllEqual(x_tf_4, np.asarray(x_np)[:, ::-1])
self.assertAllEqual(x_tf_5, np.asarray(x_np)[::-1, ::-1])
# This is the version of reverse that uses axis indices rather than
# bool tensors
# TODO(b/32254538): Change this test to use array_ops.reverse
示例5: testUnknownDims
# 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 reverse_v2 [as 别名]
def testUnknownDims(self):
reverse_v2 = array_ops.reverse_v2
data_t = tf.placeholder(tf.float32)
axis_known_t = tf.placeholder(tf.int32, shape=[3])
reverse_known_t = reverse_v2(data_t, axis_known_t)
# Unlike V1 we cannot know this anymore
self.assertEqual(None, reverse_known_t.get_shape().ndims)
axis_unknown_t = tf.placeholder(tf.int32)
reverse_unknown_t = reverse_v2(data_t, axis_unknown_t)
self.assertIs(None, reverse_unknown_t.get_shape().ndims)
data_2d_t = tf.placeholder(tf.float32, shape=[None, None])
axis_2d_t = tf.placeholder(tf.int32, shape=[3])
reverse_2d_t = reverse_v2(data_2d_t, axis_2d_t)
self.assertEqual(2, reverse_2d_t.get_shape().ndims)
示例6: _ReverseV2Grad
# 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 reverse_v2 [as 别名]
def _ReverseV2Grad(op, grad):
axis = op.inputs[1]
return array_ops.reverse_v2(grad, axis), None
示例7: rot90
# 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 reverse_v2 [as 别名]
def rot90(image, k=1, name=None):
"""Rotate an image counter-clockwise by 90 degrees.
Args:
image: A 3-D tensor of shape `[height, width, channels]`.
k: A scalar integer. The number of times the image is rotated by 90 degrees.
name: A name for this operation (optional).
Returns:
A rotated 3-D tensor of the same type and shape as `image`.
"""
with ops.name_scope(name, 'rot90', [image, k]) as scope:
image = ops.convert_to_tensor(image, name='image')
image = control_flow_ops.with_dependencies(
_Check3DImage(image, require_static=False), image)
k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k')
k.get_shape().assert_has_rank(0)
k = math_ops.mod(k, 4)
def _rot90():
return array_ops.transpose(array_ops.reverse_v2(image, [1]),
[1, 0, 2])
def _rot180():
return array_ops.reverse_v2(image, [0, 1])
def _rot270():
return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]),
[1])
cases = [(math_ops.equal(k, 1), _rot90),
(math_ops.equal(k, 2), _rot180),
(math_ops.equal(k, 3), _rot270)]
ret = control_flow_ops.case(cases, default=lambda: image, exclusive=True,
name=scope)
ret.set_shape([None, None, image.get_shape()[2]])
return ret
示例8: rot90
# 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 reverse_v2 [as 别名]
def rot90(image, k=1, name=None):
"""Rotate an image counter-clockwise by 90 degrees.
Args:
image: A 3-D tensor of shape `[height, width, channels]`.
k: A scalar integer. The number of times the image is rotated by 90 degrees.
name: A name for this operation (optional).
Returns:
A rotated 3-D tensor of the same type and shape as `image`.
"""
with ops.name_scope(name, 'rot90', [image, k]) as scope:
image = ops.convert_to_tensor(image, name='image')
_Check3DImage(image, require_static=False)
k = ops.convert_to_tensor(k, dtype=dtypes.int32, name='k')
k.get_shape().assert_has_rank(0)
k = math_ops.mod(k, 4)
def _rot90():
return array_ops.transpose(array_ops.reverse_v2(image, [1]),
[1, 0, 2])
def _rot180():
return array_ops.reverse_v2(image, [0, 1])
def _rot270():
return array_ops.reverse_v2(array_ops.transpose(image, [1, 0, 2]),
[1])
cases = [(math_ops.equal(k, 1), _rot90),
(math_ops.equal(k, 2), _rot180),
(math_ops.equal(k, 3), _rot270)]
ret = control_flow_ops.case(cases, default=lambda: image, exclusive=True,
name=scope)
ret.set_shape([None, None, image.get_shape()[2]])
return ret
示例9: _FFTSizeForGrad
# 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 reverse_v2 [as 别名]
def _FFTSizeForGrad(grad, rank):
return math_ops.reduce_prod(
array_ops.slice(
array_ops.reverse_v2(array_ops.shape(grad), [0]), (0,), (rank,)))
示例10: ndlstm_base_dynamic
# 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 reverse_v2 [as 别名]
def ndlstm_base_dynamic(inputs, noutput, scope=None, reverse=False):
"""Run an LSTM, either forward or backward.
This is a 1D LSTM implementation using dynamic_rnn and
the TensorFlow LSTM op.
Args:
inputs: input sequence (length, batch_size, ninput)
noutput: depth of output
scope: optional scope name
reverse: run LSTM in reverse
Returns:
Output sequence (length, batch_size, noutput)
"""
with variable_scope.variable_scope(scope, "SeqLstm", [inputs]):
# TODO(tmb) make batch size, sequence_length dynamic
# example: sequence_length = tf.shape(inputs)[0]
_, batch_size, _ = _shape(inputs)
lstm_cell = core_rnn_cell_impl.BasicLSTMCell(noutput, state_is_tuple=False)
state = array_ops.zeros([batch_size, lstm_cell.state_size])
sequence_length = int(inputs.get_shape()[0])
sequence_lengths = math_ops.to_int64(
array_ops.fill([batch_size], sequence_length))
if reverse:
inputs = array_ops.reverse_v2(inputs, [0])
outputs, _ = rnn.dynamic_rnn(
lstm_cell, inputs, sequence_lengths, state, time_major=True)
if reverse:
outputs = array_ops.reverse_v2(outputs, [0])
return outputs
示例11: _event_shape
# 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 reverse_v2 [as 别名]
def _event_shape(self):
return array_ops.reverse_v2(array_ops.shape(self.alpha), [0])[0]
示例12: _auc_convert_hist_to_auc
# 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 reverse_v2 [as 别名]
def _auc_convert_hist_to_auc(hist_true_acc, hist_false_acc, nbins):
"""Convert histograms to auc.
Args:
hist_true_acc: `Tensor` holding accumulated histogram of scores for records
that were `True`.
hist_false_acc: `Tensor` holding accumulated histogram of scores for
records that were `False`.
nbins: Integer number of bins in the histograms.
Returns:
Scalar `Tensor` estimating AUC.
"""
# Note that this follows the "Approximating AUC" section in:
# Efficient AUC learning curve calculation, R. R. Bouckaert,
# AI'06 Proceedings of the 19th Australian joint conference on Artificial
# Intelligence: advances in Artificial Intelligence
# Pages 181-191.
# Note that the above paper has an error, and we need to re-order our bins to
# go from high to low score.
# Normalize histogram so we get fraction in each bin.
normed_hist_true = math_ops.truediv(hist_true_acc,
math_ops.reduce_sum(hist_true_acc))
normed_hist_false = math_ops.truediv(hist_false_acc,
math_ops.reduce_sum(hist_false_acc))
# These become delta x, delta y from the paper.
delta_y_t = array_ops.reverse_v2(normed_hist_true, [0], name='delta_y_t')
delta_x_t = array_ops.reverse_v2(normed_hist_false, [0], name='delta_x_t')
# strict_1d_cumsum requires float32 args.
delta_y_t = math_ops.cast(delta_y_t, dtypes.float32)
delta_x_t = math_ops.cast(delta_x_t, dtypes.float32)
# Trapezoidal integration, \int_0^1 0.5 * (y_t + y_{t-1}) dx_t
y_t = _strict_1d_cumsum(delta_y_t, nbins)
first_trap = delta_x_t[0] * y_t[0] / 2.0
other_traps = delta_x_t[1:] * (y_t[1:] + y_t[:nbins - 1]) / 2.0
return math_ops.add(first_trap, math_ops.reduce_sum(other_traps), name='auc')
示例13: testReverse0DimAuto
# 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 reverse_v2 [as 别名]
def testReverse0DimAuto(self):
x_np = 4
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
x_tf = array_ops.reverse_v2(x_np, []).eval()
self.assertAllEqual(x_tf, x_np)
示例14: _reverse1DimAuto
# 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 reverse_v2 [as 别名]
def _reverse1DimAuto(self, np_dtype):
x_np = np.array([1, 2, 3, 4, 5], dtype=np_dtype)
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
x_tf = array_ops.reverse_v2(x_np, [0]).eval()
self.assertAllEqual(x_tf, np.asarray(x_np)[::-1])
示例15: testInvalid
# 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 reverse_v2 [as 别名]
def testInvalid(self):
x_np = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.float32)
with self.test_session():
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"is out of valid range"):
array_ops.reverse_v2(x_np, [-30]).eval()
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"is out of valid range"):
array_ops.reverse_v2(x_np, [2]).eval()
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
"axis 0 specified more than once"):
array_ops.reverse_v2(x_np, [0, -2]).eval()