| # 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. |
| # ============================================================================== |
| """Tests for operations in eager execution.""" |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import gc |
| import threading |
| import weakref |
| |
| import numpy as np |
| |
| from tensorflow.python.eager import context |
| from tensorflow.python.eager import execute |
| from tensorflow.python.eager import test |
| from tensorflow.python.framework import config |
| 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) |
| |
| @test_util.run_gpu_only |
| def testMatMulGPU(self): |
| 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__. |
| @test_util.run_gpu_only |
| def testInt32CPUDefault(self): |
| 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): |
| |
| def ops_test(v1, v2): |
| a = constant_op.constant(v1) |
| b = constant_op.constant(v2) |
| |
| self.assertAllEqual((-a), np.negative(v1)) |
| self.assertAllEqual(abs(b), np.absolute(v2)) |
| |
| self.assertAllEqual((a + b), np.add(v1, v2)) |
| self.assertAllEqual((a - b), np.subtract(v1, v2)) |
| self.assertAllEqual((a * b), np.multiply(v1, v2)) |
| self.assertAllEqual((a * a), np.multiply(v1, v1)) |
| |
| if all(x >= 0 for x in v2): |
| self.assertAllEqual((a**b), np.power(v1, v2)) |
| self.assertAllEqual((a / b), np.true_divide(v1, v2)) |
| |
| self.assertAllEqual((a / a), np.true_divide(v1, v1)) |
| self.assertAllEqual((a % b), np.mod(v1, v2)) |
| |
| self.assertAllEqual((a < b), np.less(v1, v2)) |
| self.assertAllEqual((a <= b), np.less_equal(v1, v2)) |
| self.assertAllEqual((a > b), np.greater(v1, v2)) |
| self.assertAllEqual((a >= b), np.greater_equal(v1, v2)) |
| self.assertAllEqual((a == b), np.equal(v1, v2)[0]) |
| self.assertAllEqual((a != b), np.not_equal(v1, v2)[0]) |
| self.assertAllEqual(v1[0], a[constant_op.constant(0)]) |
| |
| ops_test([1, 4, 8], [2, 3, 5]) |
| ops_test([1, -4, -5], [-2, 3, -6]) |
| |
| 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]) |
| |
| @test_util.run_gpu_only |
| def testOpWithInputsOnDifferentDevices(self): |
| # 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))) |
| |
| @test_util.run_gpu_only |
| def testOutputOnHostMemory(self): |
| # 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()) |
| |
| @test_util.run_gpu_only |
| def testSilentCopy(self): |
| # Temporarily replace the context |
| # pylint: disable=protected-access |
| del context._context |
| context._context = context.Context() |
| try: |
| config.set_device_policy('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 |
| |
| @test_util.run_gpu_only |
| def testSoftPlacement(self): |
| # Temporarily replace the context |
| # pylint: disable=protected-access |
| del context._context |
| context._context = context.Context() |
| try: |
| config.set_device_policy('silent') |
| config.set_soft_device_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)) |
| |
| @test_util.run_gpu_only |
| def testIdentityOnVariable(self): |
| 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 testRange(self): |
| x = constant_op.constant(2) |
| self.assertEqual([0, 1], list(range(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() |
| |
| def testWeakrefEagerTensor(self): |
| x = constant_op.constant([[1.]]) |
| x.at1 = constant_op.constant([[2.]]) |
| x.at2 = 3. |
| weak_x = weakref.ref(x) |
| weak_xat1 = weakref.ref(x.at1) |
| del x |
| self.assertIs(weak_x(), None) |
| self.assertIs(weak_xat1(), None) |
| |
| def testWeakKeyDictionaryTensor(self): |
| weak_key_dict = weakref.WeakKeyDictionary() |
| strong_x = constant_op.constant([[1.]]) |
| strong_y = constant_op.constant([[2.]]) |
| weak_key_dict[strong_x] = constant_op.constant([[3.]]) |
| weak_key_dict[strong_y] = constant_op.constant([[4.]]) |
| strong_y.a = constant_op.constant([[5.]]) |
| weak_x = weakref.ref(strong_x) |
| del strong_x |
| self.assertIs(weak_x(), None) |
| self.assertEqual([strong_y], list(weak_key_dict)) |
| self.assertEqual(1, len(list(weak_key_dict))) |
| self.assertEqual(1, len(weak_key_dict)) |
| del strong_y |
| self.assertEqual([], list(weak_key_dict)) |
| |
| def testEagerTensorsCanBeGarbageCollected(self): |
| x = constant_op.constant([[1.]]) |
| y = constant_op.constant([[2.]]) |
| x.y = y |
| y.x = x |
| weak_x = weakref.ref(x) |
| weak_y = weakref.ref(y) |
| del x |
| del y |
| gc.collect() |
| self.assertIs(weak_x(), None) |
| self.assertIs(weak_y(), None) |
| |
| |
| if __name__ == '__main__': |
| test.main() |
