tensorflow/tensorflow/python/eager/ops_test.py at master · codeplaysoftware/tensorflow · GitHub

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# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ==============================================================================

"""Tests for operations in eager execution."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import threading

import numpy as np

from tensorflow.core.protobuf import config_pb2

from tensorflow.python.eager import context

from tensorflow.python.eager import execute

from tensorflow.python.eager import test

from tensorflow.python.framework import constant_op

from tensorflow.python.framework import dtypes

from tensorflow.python.framework import ops

from tensorflow.python.framework import tensor_shape

from tensorflow.python.framework import test_util

from tensorflow.python.layers import core

from tensorflow.python.ops import array_ops

from tensorflow.python.ops import control_flow_ops

from tensorflow.python.ops import math_ops

from tensorflow.python.ops import random_ops

from tensorflow.python.ops import resource_variable_ops

from tensorflow.python.ops import sparse_ops

class OpsTest(test_util.TensorFlowTestCase):

def testExecuteBasic(self):

three = constant_op.constant(3)

five = constant_op.constant(5)

product = three * five

self.assertAllEqual(15, product)

def testMatMulGPU(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

three = constant_op.constant([[3.]]).gpu()

five = constant_op.constant([[5.]]).gpu()

product = math_ops.matmul(three, five)

self.assertEqual([[15.0]], product.numpy())

def testExecuteStringAttr(self):

three = constant_op.constant(3.0)

checked_three = array_ops.check_numerics(three,

message='just checking')

self.assertEqual([[3]], checked_three.numpy())

def testExecuteFloatAttr(self):

three = constant_op.constant(3.0)

almost_three = constant_op.constant(2.8)

almost_equal = math_ops.approximate_equal(

three, almost_three, tolerance=0.3)

self.assertTrue(almost_equal)

def testExecuteIntAttr(self):

three = constant_op.constant(3)

four = constant_op.constant(4)

total = math_ops.add_n([three, four])

self.assertAllEqual(7, total)

def testExecuteBoolAttr(self):

three = constant_op.constant([[3]])

five = constant_op.constant([[5]])

product = math_ops.matmul(three, five, transpose_a=True)

self.assertAllEqual([[15]], product)

def testExecuteOneListOutput(self):

split_dim = constant_op.constant(1)

value = constant_op.constant([[0, 1, 2], [3, 4, 5]])

x1, x2, x3 = array_ops.split(value, 3, axis=split_dim)

self.assertAllEqual([[0], [3]], x1)

self.assertAllEqual([[1], [4]], x2)

self.assertAllEqual([[2], [5]], x3)

def testGraphMode(self):

graph = ops.Graph()

with graph.as_default(), context.graph_mode():

array_ops.placeholder(dtypes.int32)

self.assertEqual(1, len(graph.get_operations()))

# See comments on handling of int32 tensors on GPU in

# EagerTensor.__init__.

def testInt32CPUDefault(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

with context.device('/gpu:0'):

r = constant_op.constant(1) + constant_op.constant(2)

self.assertAllEqual(r, 3)

def testExecuteListOutputLen1(self):

split_dim = constant_op.constant(1)

value = constant_op.constant([[0, 1, 2], [3, 4, 5]])

result = array_ops.split(value, 1, axis=split_dim)

self.assertTrue(isinstance(result, list))

self.assertEqual(1, len(result))

self.assertAllEqual([[0, 1, 2], [3, 4, 5]], result[0])

def testExecuteListOutputLen0(self):

empty = constant_op.constant([], dtype=dtypes.int32)

result = array_ops.unstack(empty, 0)

self.assertTrue(isinstance(result, list))

self.assertEqual(0, len(result))

def testExecuteMultipleNonListOutput(self):

x = constant_op.constant([1, 2, 3, 4, 5, 6])

y = constant_op.constant([1, 3, 5])

result = array_ops.listdiff(x, y)

out, idx = result

self.assertTrue(out is result.out)

self.assertTrue(idx is result.idx)

self.assertAllEqual([2, 4, 6], out)

self.assertAllEqual([1, 3, 5], idx)

def testExecuteMultipleListOutput(self):

split_dim = constant_op.constant(1, dtype=dtypes.int64)

indices = constant_op.constant([[0, 2], [0, 4], [0, 5], [1, 0], [1, 1]],

dtype=dtypes.int64)

values = constant_op.constant([2, 3, 5, 7, 11])

shape = constant_op.constant([2, 7], dtype=dtypes.int64)

result = sparse_ops.gen_sparse_ops.sparse_split(

split_dim,

indices,

values,

shape,

num_split=2)

output_indices, output_values, output_shape = result

self.assertEqual(2, len(output_indices))

self.assertEqual(2, len(output_values))

self.assertEqual(2, len(output_shape))

self.assertEqual(output_indices, result.output_indices)

self.assertEqual(output_values, result.output_values)

self.assertEqual(output_shape, result.output_shape)

self.assertAllEqual([[0, 2], [1, 0], [1, 1]], output_indices[0])

self.assertAllEqual([[0, 0], [0, 1]], output_indices[1])

self.assertAllEqual([2, 7, 11], output_values[0])

self.assertAllEqual([3, 5], output_values[1])

self.assertAllEqual([2, 4], output_shape[0])

self.assertAllEqual([2, 3], output_shape[1])

# TODO(josh11b): Test an op that has multiple outputs, some but not

# all of which are lists. Examples: barrier_take_many (currently

# unsupported since it uses a type list) or sdca_optimizer (I don't

# have an example of legal inputs & outputs).

def testComposition(self):

x = constant_op.constant(1, dtype=dtypes.int32)

three_x = x + x + x

self.assertEquals(dtypes.int32, three_x.dtype)

self.assertAllEqual(3, three_x)

def testOperatorOverrides(self):

# TODO(henrytan): test with negative number.

a = constant_op.constant([1])

b = constant_op.constant([2])

self.assertAllEqual((-a), [-1])

self.assertAllEqual(abs(b), [2])

self.assertAllEqual((a + b), [3])

self.assertAllEqual((a - b), [-1])

self.assertAllEqual((a * b), [2])

self.assertAllEqual((a * a), [1])

self.assertAllEqual((a**b), [1])

self.assertAllEqual((a / b), [1 / 2])

self.assertAllEqual((a / a), [1])

self.assertAllEqual((a % b), [1])

self.assertAllEqual((a < b), [True])

self.assertAllEqual((a <= b), [True])

self.assertAllEqual((a > b), [False])

self.assertAllEqual((a >= b), [False])

self.assertAllEqual((a == b), False)

self.assertAllEqual((a != b), True)

self.assertAllEqual(1, a[constant_op.constant(0)])

def test_basic_slice(self):

npt = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)

t = constant_op.constant(npt)

self.assertAllEqual(npt[:, :, :], t[:, :, :])

self.assertAllEqual(npt[::, ::, ::], t[::, ::, ::])

self.assertAllEqual(npt[::1, ::1, ::1], t[::1, ::1, ::1])

self.assertAllEqual(npt[::1, ::5, ::2], t[::1, ::5, ::2])

self.assertAllEqual(npt[::-1, :, :], t[::-1, :, :])

self.assertAllEqual(npt[:, ::-1, :], t[:, ::-1, :])

self.assertAllEqual(npt[:, :, ::-1], t[:, :, ::-1])

self.assertAllEqual(npt[-2::-1, :, ::1], t[-2::-1, :, ::1])

self.assertAllEqual(npt[-2::-1, :, ::2], t[-2::-1, :, ::2])

def testDegenerateSlices(self):

npt = np.arange(1, 19, dtype=np.float32).reshape(3, 2, 3)

t = constant_op.constant(npt)

# degenerate by offering a forward interval with a negative stride

self.assertAllEqual(npt[0:-1:-1, :, :], t[0:-1:-1, :, :])

# degenerate with a reverse interval with a positive stride

self.assertAllEqual(npt[-1:0, :, :], t[-1:0, :, :])

# empty interval in every dimension

self.assertAllEqual(npt[-1:0, 2:2, 2:3:-1], t[-1:0, 2:2, 2:3:-1])

def testEllipsis(self):

npt = np.array(

[[[[[1, 2], [3, 4], [5, 6]]], [[[7, 8], [9, 10], [11, 12]]]]])

t = constant_op.constant(npt)

self.assertAllEqual(npt[0:], t[0:])

# implicit ellipsis

self.assertAllEqual(npt[0:, ...], t[0:, ...])

# ellipsis alone

self.assertAllEqual(npt[...], t[...])

# ellipsis at end

self.assertAllEqual(npt[0:1, ...], t[0:1, ...])

# ellipsis at begin

self.assertAllEqual(npt[..., 0:1], t[..., 0:1])

# ellipsis at middle

self.assertAllEqual(npt[0:1, ..., 0:1], t[0:1, ..., 0:1])

def testShrink(self):

npt = np.array([[[[[1, 2, 4, 5], [5, 6, 7, 8], [9, 10, 11, 12]]],

[[[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]]])

t = constant_op.constant(npt)

self.assertAllEqual(npt[:, :, :, :, 3], t[:, :, :, :, 3])

self.assertAllEqual(npt[..., 3], t[..., 3])

self.assertAllEqual(npt[:, 0], t[:, 0])

self.assertAllEqual(npt[:, :, 0], t[:, :, 0])

def testOpWithInputsOnDifferentDevices(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

# The GPU kernel for the Reshape op requires that the

# shape input be on CPU.

value = constant_op.constant([1., 2.]).gpu()

shape = constant_op.constant([2, 1])

reshaped = array_ops.reshape(value, shape)

self.assertAllEqual([[1], [2]], reshaped.cpu())

def testInt64(self):

# Fill requires the first input to be an int32 tensor.

self.assertAllEqual(

[1.0, 1.0],

array_ops.fill(constant_op.constant([2], dtype=dtypes.int64),

constant_op.constant(1)))

def testOutputOnHostMemory(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

# The Shape op kernel on GPU places the output in host memory.

value = constant_op.constant([1.]).gpu()

shape = array_ops.shape(value)

self.assertEqual([1], shape.numpy())

def testSilentCopy(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

# Temporarily replace the context

# pylint: disable=protected-access

del context._context

try:

context._context = context.Context(

device_policy=context.DEVICE_PLACEMENT_SILENT)

cpu_tensor = constant_op.constant(1.0)

gpu_tensor = cpu_tensor.gpu()

self.assertAllEqual(cpu_tensor + gpu_tensor, 2.0)

finally:

del context._context

context._context = context.Context()

# pylint: enable=protected-access

def testSoftPlacement(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

# Temporarily replace the context

# pylint: disable=protected-access

del context._context

try:

context._context = context.Context(

device_policy=context.DEVICE_PLACEMENT_SILENT,

config=config_pb2.ConfigProto(allow_soft_placement=True))

cpu_tensor = constant_op.constant(1.0)

result = cpu_tensor + cpu_tensor

self.assertEqual(result.device,

'/job:localhost/replica:0/task:0/device:GPU:0')

finally:

del context._context

context._context = context.Context()

# pylint: enable=protected-access

def testRandomUniform(self):

scalar_shape = constant_op.constant([], dtype=dtypes.int32)

x = random_ops.random_uniform(scalar_shape)

self.assertEquals(0, x.shape.ndims)

self.assertEquals(dtypes.float32, x.dtype)

x = random_ops.random_uniform(

scalar_shape, minval=constant_op.constant(5.),

maxval=constant_op.constant(6.))

self.assertLess(x, 6)

self.assertGreaterEqual(x, 5)

def testArgsToMatchingEagerDefault(self):

# Uses default

ctx = context.context()

t, r = execute.args_to_matching_eager([[3, 4]], ctx, dtypes.int32)

self.assertEquals(t, dtypes.int32)

self.assertEquals(r[0].dtype, dtypes.int32)

t, r = execute.args_to_matching_eager([[3, 4]], ctx, dtypes.int64)

self.assertEquals(t, dtypes.int64)

self.assertEquals(r[0].dtype, dtypes.int64)

# Doesn't use default

t, r = execute.args_to_matching_eager(

[['string', 'arg']], ctx, dtypes.int32)

self.assertEquals(t, dtypes.string)

self.assertEquals(r[0].dtype, dtypes.string)

def testFlattenLayer(self):

flatten_layer = core.Flatten()

x = constant_op.constant([[[-10, -20], [-30, -40]], [[10, 20], [30, 40]]])

y = flatten_layer(x)

self.assertAllEqual([[-10, -20, -30, -40], [10, 20, 30, 40]], y)

def testIdentity(self):

self.assertAllEqual(2, array_ops.identity(2))

def testIdentityOnVariable(self):

if not context.context().num_gpus():

self.skipTest('No GPUs found')

with context.device('/gpu:0'):

v = resource_variable_ops.ResourceVariable(True)

self.assertAllEqual(True, array_ops.identity(v))

def testIncompatibleSetShape(self):

x = constant_op.constant(1)

with self.assertRaises(ValueError):

x.set_shape((1, 2))

def testCompatibleSetShape(self):

x = constant_op.constant([[1, 2]])

x.set_shape(tensor_shape.TensorShape([None, 2]))

self.assertEqual(x.get_shape(), (1, 2))

def testCastScalarToPrimitiveTypes(self):

x = constant_op.constant(1.3)

self.assertIsInstance(int(x), int)

self.assertEqual(int(x), 1)

self.assertIsInstance(float(x), float)

self.assertAllClose(float(x), 1.3)

def testCastNonScalarToPrimitiveTypesFails(self):

x = constant_op.constant([1.3, 2])

with self.assertRaises(TypeError):

int(x)

with self.assertRaises(TypeError):

float(x)

def testFormatString(self):

x = constant_op.constant(3.1415)

self.assertEqual('3.14', '{:.2f}'.format(x))

def testNoOpIsNone(self):

self.assertTrue(control_flow_ops.no_op() is None)

def testEagerContextPreservedAcrossThreads(self):

def init_fn():

self.assertTrue(context.executing_eagerly())

with ops.init_scope():

self.assertTrue(context.executing_eagerly())

context_switches = context.context().context_switches

self.assertEqual(len(context_switches.stack), 1)

self.assertFalse(context_switches.stack[0].is_building_function)

self.assertEqual(context_switches.stack[0].enter_context_fn,

context.eager_mode)

self.assertTrue(context.executing_eagerly())

t1 = threading.Thread(target=init_fn)

t1.start()

t1.join()

if __name__ == '__main__':

test.main()

tensorflow/tensorflow/python/eager/ops_test.py at master · codeplaysoftware/tensorflow · GitHub

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