tensorflow/tensorflow/python/eager/tensor_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.

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

"""Unit tests for TensorFlow "Eager" Mode's Tensor class."""

from __future__ import absolute_import

from __future__ import division

from __future__ import print_function

import copy

import re

import numpy as np

from tensorflow.python import pywrap_tensorflow

from tensorflow.python.eager import context

from tensorflow.python.eager import core

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 test_util

def _create_tensor(value, device=None, dtype=None):

ctx = context.context()

if device is None:

device = ctx.device_name

if dtype is not None:

dtype = dtype.as_datatype_enum

try:

return ops.EagerTensor(

value, context=ctx._handle, device=device, dtype=dtype)

except core._NotOkStatusException as e: # pylint: disable=protected-access

raise core._status_to_exception(e.code, e.message)

class TFETensorTest(test_util.TensorFlowTestCase):

def testScalarTensor(self):

t = _create_tensor(3, dtype=dtypes.int32)

self.assertAllEqual(t, _create_tensor(np.array(3)))

self.assertEqual(dtypes.int32, t.dtype)

self.assertEqual(0, t.shape.ndims)

self.assertAllEqual([], t.shape.as_list())

self.assertIn("tf.Tensor", str(t))

self.assertIn("tf.Tensor", repr(t))

def testBadConstructorArgs(self):

ctx = context.context()

handle = ctx._handle

device = ctx.device_name

# Missing context.

with self.assertRaisesRegexp(

TypeError, r"Required argument 'context' \(pos 2\) not found"):

ops.EagerTensor(1, device=device)

# Missing device.

with self.assertRaisesRegexp(

TypeError, r"Required argument 'device' \(pos 3\) not found"):

ops.EagerTensor(1, context=handle)

# Bad dtype type.

with self.assertRaisesRegexp(TypeError,

"Expecting a DataType value for dtype. Got"):

ops.EagerTensor(1, context=handle, device=device, dtype="1")

# Following errors happen when trying to copy to GPU.

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

self.skipTest("No GPUs found")

with ops.device("/device:GPU:0"):

device = ctx.device_name

# Bad context.

with self.assertRaisesRegexp(

TypeError, "Expecting a PyCapsule encoded context handle. Got"):

ops.EagerTensor(1.0, context=1, device=device)

# Bad device.

with self.assertRaisesRegexp(

TypeError, "Error parsing device argument to CopyToDevice"):

ops.EagerTensor(1.0, context=handle, device=1)

def testNumpyValue(self):

values = np.array([3.0])

t = _create_tensor(values)

self.assertAllEqual(values, t)

def testNumpyValueWithCast(self):

values = np.array([3.0], dtype=np.float32)

t = _create_tensor(values, dtype=dtypes.float64)

self.assertAllEqual(values, t)

ctx = context.context()

# Bad dtype value.

with self.assertRaisesRegexp(TypeError, "Invalid dtype argument value"):

ops.EagerTensor(

values, context=ctx._handle, device=ctx.device_name, dtype=12345)

def testNumpyOrderHandling(self):

n = np.array([[1, 2], [3, 4]], order="F")

t = _create_tensor(n)

self.assertAllEqual([[1, 2], [3, 4]], t)

def testNumpyArrayDtype(self):

tensor = constant_op.constant([1.0, 2.0, 3.0])

numpy_tensor = np.asarray(tensor, dtype=np.int32)

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

def testNdimsAgreesWithNumpy(self):

numpy_tensor = np.asarray(1.0)

tensor = constant_op.constant(numpy_tensor)

self.assertAllEqual(numpy_tensor.ndim, tensor.ndim)

numpy_tensor = np.asarray([1.0, 2.0, 3.0])

tensor = constant_op.constant(numpy_tensor)

self.assertAllEqual(numpy_tensor.ndim, tensor.ndim)

numpy_tensor = np.asarray([[1.0, 2.0, 3.0], [1.0, 2.0, 3.0]])

tensor = constant_op.constant(numpy_tensor)

self.assertAllEqual(numpy_tensor.ndim, tensor.ndim)

def testCopy(self):

t = constant_op.constant(1.0)

tt = copy.copy(t)

self.assertAllEqual(tt, 1.0)

del tt

tt = copy.deepcopy(t)

self.assertAllEqual(tt, 1.0)

del tt

self.assertAllEqual(t, 1.0)

def testConstantDtype(self):

self.assertEqual(constant_op.constant(1.0, dtype=np.int64).dtype,

dtypes.int64)

def testTensorAndNumpyMatrix(self):

expected = np.array([[1.0, 2.0], [3.0, 4.0]], np.float32)

actual = _create_tensor([[1.0, 2.0], [3.0, 4.0]])

self.assertAllEqual(expected, actual)

self.assertEqual(np.float32, actual.dtype)

self.assertEqual(dtypes.float32, actual.dtype)

self.assertAllEqual([2, 2], actual.shape.as_list())

def testFloatDowncast(self):

# Unless explicitly specified, float64->float32

t = _create_tensor(3.0)

self.assertEqual(dtypes.float32, t.dtype)

t = _create_tensor(3.0, dtype=dtypes.float64)

self.assertEqual(dtypes.float64, t.dtype)

def testBool(self):

t = _create_tensor(False)

if t:

self.assertFalse(True)

def testIntDowncast(self):

t = _create_tensor(3)

self.assertEqual(dtypes.int32, t.dtype)

t = _create_tensor(3, dtype=dtypes.int64)

self.assertEqual(dtypes.int64, t.dtype)

t = _create_tensor(2**33)

self.assertEqual(dtypes.int64, t.dtype)

def testTensorCreationFailure(self):

with self.assertRaises(ValueError):

# Should fail because the each row of the Python object has a different

# number of columns.

self.assertEqual(None, _create_tensor([[1], [1, 2]]))

def testMultiLineTensorStr(self):

t = _create_tensor(np.eye(3))

tensor_str = str(t)

self.assertIn("shape=%s, dtype=%s" % (t.shape, t.dtype.name), tensor_str)

self.assertIn(str(t), tensor_str)

def testMultiLineTensorRepr(self):

t = _create_tensor(np.eye(3))

tensor_repr = repr(t)

self.assertTrue(tensor_repr.startswith("<"))

self.assertTrue(tensor_repr.endswith(">"))

self.assertIn("id=%d, shape=%s, dtype=%s, numpy=\n%r" %

(t._id, t.shape, t.dtype.name, t.numpy()), tensor_repr)

def testTensorStrReprObeyNumpyPrintOptions(self):

orig_threshold = np.get_printoptions()["threshold"]

orig_edgeitems = np.get_printoptions()["edgeitems"]

np.set_printoptions(threshold=2, edgeitems=1)

t = _create_tensor(np.arange(10, dtype=np.int32))

self.assertTrue(re.match(r".*\[.*0.*\.\.\..*9.*\]", str(t)))

self.assertTrue(re.match(r".*\[.*0.*\.\.\..*9.*\]", repr(t)))

# Clean up: reset to previous printoptions.

np.set_printoptions(threshold=orig_threshold, edgeitems=orig_edgeitems)

def testZeroDimTensorStr(self):

t = _create_tensor(42)

self.assertIn("42, shape=(), dtype=int32", str(t))

def testZeroDimTensorRepr(self):

t = _create_tensor(42)

self.assertTrue(repr(t).startswith("<"))

self.assertTrue(repr(t).endswith(">"))

self.assertIn("id=%d, shape=(), dtype=int32, numpy=42" % t._id, repr(t))

def testZeroSizeTensorStr(self):

t = _create_tensor(np.zeros(0, dtype=np.float32))

self.assertIn("[], shape=(0,), dtype=float32", str(t))

def testZeroSizeTensorRepr(self):

t = _create_tensor(np.zeros(0, dtype=np.float32))

self.assertTrue(repr(t).startswith("<"))

self.assertTrue(repr(t).endswith(">"))

self.assertIn("id=%d, shape=(0,), dtype=float32, numpy=%r" % (t._id,

t.numpy()),

repr(t))

def testStringTensor(self):

t_np_orig = np.array([[b"a", b"ab"], [b"abc", b"abcd"]])

t = _create_tensor(t_np_orig)

t_np = t.numpy()

self.assertTrue(np.all(t_np == t_np_orig), "%s vs %s" % (t_np, t_np_orig))

def testIterateOverTensor(self):

l = [[1, 2], [3, 4]]

t = _create_tensor(l)

for list_element, tensor_element in zip(l, t):

self.assertAllEqual(list_element, tensor_element.numpy())

def testStringTensorOnGPU(self):

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

self.skipTest("No GPUs found")

with ops.device("/device:GPU:0"):

with self.assertRaisesRegexp(

RuntimeError, "Can't copy Tensor with type string to device"):

_create_tensor("test string")

class TFETensorUtilTest(test_util.TensorFlowTestCase):

def testListOfThree(self):

t1 = _create_tensor([[1, 2], [3, 4], [5, 6]], dtype=dtypes.int32)

t2 = _create_tensor([[1, 2, 5], [3, 4, 5]], dtype=dtypes.int32)

t3 = _create_tensor([[1], [3], [5], [6]], dtype=dtypes.int32)

r = pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, t2, t3], 0)

self.assertAllEqual(np.array([3, 2, 4]), r.numpy())

r = pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, t2, t3], 1)

self.assertAllEqual(np.array([2, 3, 1]), r.numpy())

def testEmptyTensorList(self):

a = pywrap_tensorflow.TFE_Py_TensorShapeSlice([], 0)

self.assertTrue(isinstance(a, ops.EagerTensor))

self.assertEqual(0, a.numpy().size)

def testTensorListContainsNonTensors(self):

t1 = _create_tensor([1, 2], dtype=dtypes.int32)

with self.assertRaisesRegexp(

TypeError,

r"Expected a list of EagerTensors but element 1 has type \"str\""):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, "abc"], 0)

with self.assertRaisesRegexp(

TypeError,

r"Expected a list of EagerTensors but element 0 has type \"int\""):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([2, t1], 0)

def testTensorListNotList(self):

t1 = _create_tensor([1, 2], dtype=dtypes.int32)

with self.assertRaisesRegexp(

TypeError,

r"tensors argument must be a list or a tuple. Got \"EagerTensor\""):

pywrap_tensorflow.TFE_Py_TensorShapeSlice(t1, -2)

def testNegativeSliceDim(self):

t1 = _create_tensor([1, 2], dtype=dtypes.int32)

with self.assertRaisesRegexp(

ValueError,

r"Slice dimension must be non-negative. Got -2"):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1], -2)

def testUnicode(self):

self.assertEqual(constant_op.constant(u"asdf").numpy(), b"asdf")

def testFloatTensor(self):

self.assertEqual(dtypes.float64, _create_tensor(np.float64()).dtype)

self.assertEqual(dtypes.float32, _create_tensor(np.float32()).dtype)

self.assertEqual(dtypes.float32, _create_tensor(0.0).dtype)

def testSliceDimOutOfRange(self):

t1 = _create_tensor([[1, 2], [3, 4], [5, 6]], dtype=dtypes.int32)

t2 = _create_tensor([1, 2], dtype=dtypes.int32)

t3 = _create_tensor(2, dtype=dtypes.int32)

with self.assertRaisesRegexp(

IndexError,

r"Slice dimension \(2\) must be smaller than rank of all tensors, "

"but tensor at index 0 has rank 2"):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1], 2)

with self.assertRaisesRegexp(

IndexError,

r"Slice dimension \(1\) must be smaller than rank of all tensors, "

"but tensor at index 0 has rank 1"):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t2], 1)

with self.assertRaisesRegexp(

IndexError,

r"Slice dimension \(1\) must be smaller than rank of all tensors, "

"but tensor at index 1 has rank 1"):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t1, t2], 1)

with self.assertRaisesRegexp(

IndexError,

r"Slice dimension \(0\) must be smaller than rank of all tensors, "

"but tensor at index 0 has rank 0"):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t3], 0)

with self.assertRaisesRegexp(

IndexError,

r"Slice dimension \(0\) must be smaller than rank of all tensors, "

"but tensor at index 2 has rank 0"):

pywrap_tensorflow.TFE_Py_TensorShapeSlice([t2, t1, t3], 0)

if __name__ == "__main__":

test.main()

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

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