diff options
author | 2016-09-08 22:31:27 -0800 | |
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committer | 2016-09-08 23:47:31 -0700 | |
commit | c9b03c7266da529847eb8895e24cbe8ced9d0cd0 (patch) | |
tree | 1e93eb7d7207571e45cecc37ed587f81246cd25b | |
parent | f9b5a393ee3ede830de1fdea4f256e19b6da6f8e (diff) |
As a part of efforts to remove uses of use_gpu parameter to tf test session,
remove usages from sparse_tensor_dense_matmul_op_test.
Change: 132643587
-rw-r--r-- | tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py | 13 | ||||
-rw-r--r-- | tensorflow/python/kernel_tests/sparse_xent_op_test.py | 51 |
2 files changed, 23 insertions, 41 deletions
diff --git a/tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py b/tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py index 9b0871e41a..2a3f4702fd 100644 --- a/tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_tensor_dense_matmul_op_test.py @@ -36,7 +36,7 @@ def _maybe_complex(x): class SparseTensorDenseMatMulTest(tf.test.TestCase): - def _testMatmul(self, x, y, adjoint_a=False, adjoint_b=False, use_gpu=False): + def _testMatmul(self, x, y, adjoint_a=False, adjoint_b=False): x_mat = np.matrix(x) if adjoint_a: x_mat = x_mat.H @@ -50,7 +50,7 @@ class SparseTensorDenseMatMulTest(tf.test.TestCase): x_values = x[np.where(x)] x_shape = x.shape - with self.test_session(use_gpu=use_gpu): + with self.test_session(use_gpu=True): sp_x_value = tf.SparseTensorValue( indices=x_indices, values=x_values, shape=x_shape) tf_value_ans = sparse_ops.sparse_tensor_dense_matmul( @@ -77,8 +77,7 @@ class SparseTensorDenseMatMulTest(tf.test.TestCase): y = _maybe_complex(np.random.randn(10, 20).astype(np_dtype)) - self._testMatmul(x, y, use_gpu=True) - self._testMatmul(x, y, use_gpu=False) + self._testMatmul(x, y) def testBasic(self): np.random.seed(127) # Repeatable results @@ -102,8 +101,7 @@ class SparseTensorDenseMatMulTest(tf.test.TestCase): y = _maybe_complex(np.random.randn(k, n).astype(np_dtype)) - self._testMatmul(x, y, use_gpu=False) - self._testMatmul(x, y, use_gpu=True) + self._testMatmul(x, y) def testLarge(self): np.random.seed(127) # Repeatable results @@ -125,8 +123,7 @@ class SparseTensorDenseMatMulTest(tf.test.TestCase): y = np.random.randn(k, m).astype(np.float32) x = x.transpose() if adjoint_a else x y = y.transpose() if adjoint_b else y - self._testMatmul(x, y, adjoint_a, adjoint_b, use_gpu=False) - self._testMatmul(x, y, adjoint_a, adjoint_b, use_gpu=True) + self._testMatmul(x, y, adjoint_a, adjoint_b) def _sparse_tensor_dense_vs_dense_matmul_benchmark_dense( diff --git a/tensorflow/python/kernel_tests/sparse_xent_op_test.py b/tensorflow/python/kernel_tests/sparse_xent_op_test.py index 00c78282f0..d67d5b7f9f 100644 --- a/tensorflow/python/kernel_tests/sparse_xent_op_test.py +++ b/tensorflow/python/kernel_tests/sparse_xent_op_test.py @@ -46,22 +46,18 @@ class SparseXentTest(tf.test.TestCase): l = -np.sum(labels_mat * np.log(probs + 1.0e-20), axis=1) return l, bp - def _testXent(self, np_features, np_labels, use_gpu=False): + def _testXent(self, np_features, np_labels): np_loss, np_backprop = self._npXent(np_features, np_labels) - with self.test_session(use_gpu=use_gpu) as sess: + with self.test_session(use_gpu=True) as sess: loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( np_features, np_labels) tf_loss, tf_backprop = sess.run([loss, backprop]) self.assertAllCloseAccordingToType(np_loss, tf_loss) self.assertAllCloseAccordingToType(np_backprop, tf_backprop) - def _testAll(self, features, labels): - self._testXent(features, labels, use_gpu=False) - self._testXent(features, labels, use_gpu=True) - - def _testSingleClass(self, use_gpu=False): + def testSingleClass(self): for label_dtype in np.int32, np.int64: - with self.test_session(use_gpu=use_gpu) as sess: + with self.test_session(use_gpu=True) as sess: loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( np.array([[1.], [-1.], [0.]]).astype(np.float32), np.array([0, 0, 0]).astype(label_dtype)) @@ -69,18 +65,14 @@ class SparseXentTest(tf.test.TestCase): self.assertAllClose([0.0, 0.0, 0.0], tf_loss) self.assertAllClose([[0.0], [0.0], [0.0]], tf_backprop) - def testSingleClass(self): - self._testSingleClass(use_gpu=True) - self._testSingleClass(use_gpu=False) - - def _testInvalidLabel(self, use_gpu): + def testInvalidLabel(self): features = [ [1., 1., 1., 1.], [1., 1., 1., 1.], [1., 2., 3., 4.], [1., 2., 3., 4.]] labels = [4, 3, 0, -1] - with self.test_session(use_gpu=use_gpu) as sess: + with self.test_session(use_gpu=True) as sess: loss, backprop = gen_nn_ops._sparse_softmax_cross_entropy_with_logits( features, labels) tf_loss, tf_backprop = sess.run([loss, backprop]) @@ -93,10 +85,6 @@ class SparseXentTest(tf.test.TestCase): self.assertAllClose( [np.nan, 1.3862, 3.4420, np.nan], tf_loss, rtol=1e-3, atol=1e-3) - def testInvalidLabel(self): - self._testInvalidLabel(use_gpu=True) - self._testInvalidLabel(use_gpu=False) - def testNpXent(self): # We create 2 batches of logits for testing. # batch 0 is the boring uniform distribution: 1, 1, 1, 1, with target 3. @@ -131,33 +119,33 @@ class SparseXentTest(tf.test.TestCase): rtol=1.e-3, atol=1.e-3) def testShapeMismatch(self): - with self.test_session(): + with self.test_session(use_gpu=True): with self.assertRaisesRegexp(ValueError, ".*Rank mismatch:*"): tf.nn.sparse_softmax_cross_entropy_with_logits( [[0., 1.], [2., 3.], [2., 3.]], [[0, 2]]) def testScalar(self): - with self.test_session(): + with self.test_session(use_gpu=True): with self.assertRaisesRegexp(ValueError, ".*Logits cannot be scalars*"): tf.nn.sparse_softmax_cross_entropy_with_logits( tf.constant(1.0), tf.constant(0)) def testLabelsPlaceholderScalar(self): - with self.test_session(): + with self.test_session(use_gpu=True): labels = tf.placeholder(np.int32) y = tf.nn.sparse_softmax_cross_entropy_with_logits([[7.]], labels) with self.assertRaisesOpError("labels must be 1-D"): y.eval(feed_dict={labels: 0}) def testVector(self): - with self.test_session(): + with self.test_session(use_gpu=True): loss = tf.nn.sparse_softmax_cross_entropy_with_logits( tf.constant([1.0]), tf.constant(0)) self.assertAllClose(0.0, loss.eval()) def testFloat(self): for label_dtype in np.int32, np.int64: - self._testAll( + self._testXent( np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float32), np.array([3, 0]).astype(label_dtype)) @@ -165,12 +153,11 @@ class SparseXentTest(tf.test.TestCase): for label_dtype in np.int32, np.int64: self._testXent( np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float64), - np.array([0, 3]).astype(label_dtype), - use_gpu=False) + np.array([0, 3]).astype(label_dtype)) def testHalf(self): for label_dtype in np.int32, np.int64: - self._testAll( + self._testXent( np.array([[1., 1., 1., 1.], [1., 2., 3., 4.]]).astype(np.float16), np.array([3, 0]).astype(label_dtype)) @@ -178,7 +165,7 @@ class SparseXentTest(tf.test.TestCase): self._testXent(np.zeros((0, 3)), np.zeros((0,), dtype=np.int32)) def testGradient(self): - with self.test_session(): + with self.test_session(use_gpu=True): l = tf.constant([3, 0, 1], name="l") f = tf.constant([0.1, 0.2, 0.3, 0.4, 0.1, 0.4, 0.9, 1.6, @@ -189,11 +176,11 @@ class SparseXentTest(tf.test.TestCase): print("cross entropy gradient err = ", err) self.assertLess(err, 5e-8) - def _testHighDim(self, use_gpu, features, labels): + def _testHighDim(self, features, labels): np_loss, np_backprop = self._npXent(np.array(features), np.array(labels)) # manually reshape loss np_loss = np.reshape(np_loss, np.array(labels).shape) - with self.test_session(use_gpu=use_gpu) as sess: + with self.test_session(use_gpu=True) as sess: loss = tf.nn.sparse_softmax_cross_entropy_with_logits( features, labels) backprop = loss.op.inputs[0].op.outputs[1] @@ -204,15 +191,13 @@ class SparseXentTest(tf.test.TestCase): def testHighDim(self): features = [[[1., 1., 1., 1.]], [[1., 2., 3., 4.]]] labels = [[3], [0]] - self._testHighDim(True, features, labels) - self._testHighDim(False, features, labels) + self._testHighDim(features, labels) def testHighDim2(self): features = [[[1., 1., 1., 1.], [2., 2., 2., 2.]], [[1., 2., 3., 4.], [5., 6., 7., 8.]]] labels = [[3, 2], [0, 3]] - self._testHighDim(True, features, labels) - self._testHighDim(False, features, labels) + self._testHighDim(features, labels) def testScalarHandling(self): with self.test_session(use_gpu=False) as sess: |