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tensor_test.py
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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 numpy as np
from tensorflow.python.eager import context
from tensorflow.python.eager import core
from tensorflow.python.eager import test
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.assertEqual(t.numpy(), _create_tensor(np.array(3)).numpy())
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.numpy())
def testNumpyValueWithCast(self):
values = np.array([3.0], dtype=np.float32)
t = _create_tensor(values, dtype=dtypes.float64)
self.assertAllEqual(values, t.numpy())
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.numpy())
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.numpy())
self.assertEqual(np.float32, actual.numpy().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.numpy()), 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.assertIn("[0 ..., 9]", str(t))
self.assertIn("[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 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")
if __name__ == "__main__":
test.main()