| # Copyright 2015 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 miscellaneous functionality in tensorflow.ops.nn.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import math |
| |
| from absl.testing import parameterized |
| import numpy as np |
| from six.moves import xrange # pylint: disable=redefined-builtin |
| |
| from tensorflow.python.eager import def_function |
| 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_spec |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import gradient_checker |
| from tensorflow.python.ops import math_ops |
| from tensorflow.python.ops import nn_impl |
| from tensorflow.python.ops import nn_ops |
| from tensorflow.python.ops import partitioned_variables |
| from tensorflow.python.ops import variable_scope |
| from tensorflow.python.ops import variables |
| import tensorflow.python.ops.nn_grad # pylint: disable=unused-import |
| from tensorflow.python.ops.nn_impl import _compute_sampled_logits |
| from tensorflow.python.platform import test as test_lib |
| |
| |
| class ZeroFractionTest(test_lib.TestCase): |
| |
| def _ZeroFraction(self, x): |
| assert x.shape |
| total_elements = np.prod(x.shape) |
| nonzeros = np.count_nonzero(x.flatten()) |
| return 1.0 - nonzeros / total_elements |
| |
| @test_util.run_deprecated_v1 |
| def testZeroFraction(self): |
| x_shape = [5, 17] |
| x_np = np.random.randint(0, 2, size=x_shape).astype(np.float32) |
| y_np = self._ZeroFraction(x_np) |
| |
| x_tf = constant_op.constant(x_np) |
| x_tf.set_shape(x_shape) |
| y_tf = nn_impl.zero_fraction(x_tf) |
| y_tf_np = self.evaluate(y_tf) |
| |
| eps = 1e-8 |
| self.assertAllClose(y_tf_np, y_np, eps) |
| |
| @test_util.run_deprecated_v1 |
| def testZeroFractionEmpty(self): |
| x = np.zeros(0) |
| y = self.evaluate(nn_impl.zero_fraction(x)) |
| self.assertTrue(np.isnan(y)) |
| |
| @test_util.run_deprecated_v1 |
| def testZeroFraction2_27Zeros(self): |
| sparsity = nn_impl.zero_fraction( |
| array_ops.zeros([int(2**27 * 1.01)], dtype=dtypes.int8)) |
| self.assertAllClose(1.0, self.evaluate(sparsity)) |
| |
| @test_util.run_deprecated_v1 |
| def testZeroFraction2_27Ones(self): |
| sparsity = nn_impl.zero_fraction( |
| array_ops.ones([int(2**27 * 1.01)], dtype=dtypes.int8)) |
| self.assertAllClose(0.0, self.evaluate(sparsity)) |
| |
| @test_util.run_deprecated_v1 |
| def testUnknownSize(self): |
| value = array_ops.placeholder(dtype=dtypes.float32) |
| sparsity = nn_impl.zero_fraction(value) |
| with self.cached_session() as sess: |
| self.assertAllClose( |
| 0.25, |
| sess.run(sparsity, {value: [[0., 1.], [0.3, 2.]]})) |
| |
| |
| class SoftmaxTest(test_lib.TestCase, parameterized.TestCase): |
| |
| def _softmax(self, x): |
| assert len(x.shape) == 2 |
| m = x.max(1)[:, np.newaxis] |
| u = np.exp(x - m) |
| z = u.sum(1)[:, np.newaxis] |
| return u / z |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testSoftmax(self): |
| x_shape = [5, 10] |
| x_np = np.random.randn(*x_shape).astype(np.float32) |
| y_np = self._softmax(x_np) |
| x_tf = constant_op.constant(x_np) |
| y_tf = nn_ops.softmax_v2(x_tf) |
| y_tf_last_dim = nn_ops.softmax_v2(x_tf, 1) |
| y_tf_np = self.evaluate(y_tf) |
| y_tf_last_dim_np = self.evaluate(y_tf_last_dim) |
| eps = 1e-3 |
| self.assertAllClose(y_tf_np, y_np, eps) |
| self.assertAllClose(y_tf_last_dim_np, y_np, eps) |
| |
| def testSoftmaxAxes(self): |
| arr = np.linspace(0., 1, 12).reshape(3, 4) |
| x_neg_axis = nn_ops.softmax_v2(arr, axis=-2) |
| y_pos_axis = nn_ops.softmax_v2(arr, axis=0) |
| z_gt_axis = nn_ops.softmax_v2(arr, axis=0) |
| x_neg_axis_tf = self.evaluate(x_neg_axis) |
| y_pos_axis_tf = self.evaluate(y_pos_axis) |
| z_gt_axis_tf = self.evaluate(z_gt_axis) |
| eps = 1e-3 |
| self.assertAllClose(x_neg_axis_tf, y_pos_axis_tf, eps) |
| self.assertAllClose(y_pos_axis_tf, z_gt_axis_tf, eps) |
| |
| @parameterized.parameters(((5, 10),), ((2, 3, 4),)) |
| @test_util.run_deprecated_v1 |
| def testGradient(self, x_shape): |
| x_np = np.random.randn(*x_shape).astype(np.float64) |
| with self.cached_session(): |
| x_tf = constant_op.constant(x_np) |
| y_tf = nn_ops.softmax_v2(x_tf) |
| err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf, |
| x_shape) |
| eps = 2e-8 |
| self.assertLess(err, eps) |
| |
| |
| class LogPoissonLossTest(test_lib.TestCase): |
| |
| def _log_poisson_loss(self, x, z, compute_full_loss=False): |
| lpl = np.exp(x) - z * x |
| if compute_full_loss: |
| stirling_approx = z * np.log(z) - z + 0.5 * np.log(2. * np.pi * z) |
| lpl += np.ma.masked_array(stirling_approx, mask=(z <= 1)).filled(0.) |
| return lpl |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testLogPoissonLoss(self): |
| x_shape = [5, 10] |
| x_np = np.random.randn(*x_shape).astype(np.float32) |
| z_np = np.random.randint(0, 5, size=x_shape).astype(np.float32) |
| y_np = self._log_poisson_loss(x_np, z_np, compute_full_loss=False) |
| y_np_stirling = self._log_poisson_loss(x_np, z_np, compute_full_loss=True) |
| y_tf = nn_impl.log_poisson_loss(z_np, x_np, compute_full_loss=False) |
| y_tf_stirling = nn_impl.log_poisson_loss(z_np, x_np, compute_full_loss=True) |
| y_tf_np = self.evaluate(y_tf) |
| y_tf_np_stirling = self.evaluate(y_tf_stirling) |
| eps = 1e-3 |
| self.assertAllClose(y_tf_np, y_np, eps) |
| self.assertAllClose(y_tf_np_stirling, y_np_stirling, eps) |
| |
| @test_util.run_deprecated_v1 |
| def testGradient(self): |
| x_shape = [5, 10] |
| x_np = np.random.randn(*x_shape).astype(np.float64) |
| z_np = np.random.randint(0, 5, size=x_shape).astype(np.float64) |
| with self.cached_session(): |
| x_tf = constant_op.constant(x_np) |
| y_tf = nn_impl.log_poisson_loss(z_np, x_tf, compute_full_loss=False) |
| y_tf_stirling = nn_impl.log_poisson_loss( |
| z_np, x_tf, compute_full_loss=True) |
| err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf, |
| x_shape) |
| err_stirling = gradient_checker.compute_gradient_error( |
| x_tf, x_shape, y_tf_stirling, x_shape) |
| eps = 1e-6 |
| self.assertLess(err, eps) |
| self.assertLess(err_stirling, eps) |
| |
| |
| class LogSoftmaxTest(test_lib.TestCase, parameterized.TestCase): |
| |
| def _log_softmax(self, x): |
| assert len(x.shape) == 2 |
| m = x.max(1)[:, np.newaxis] |
| u = x - m |
| return u - np.log(np.sum(np.exp(u), 1, keepdims=True)) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testLogSoftmax(self): |
| x_shape = [5, 10] |
| x_np = np.random.randn(*x_shape).astype(np.float32) |
| y_np = self._log_softmax(x_np) |
| x_tf = constant_op.constant(x_np) |
| y_tf = nn_ops.log_softmax_v2(x_tf) |
| y_tf_np = self.evaluate(y_tf) |
| eps = 1e-3 |
| self.assertAllClose(y_tf_np, y_np, eps) |
| |
| def testLogSoftmaxAxes(self): |
| arr = np.linspace(0., 1, 12).reshape(3, 4) |
| x_neg_axis = nn_ops.log_softmax_v2(arr, axis=-2) |
| y_pos_axis = nn_ops.log_softmax_v2(arr, axis=0) |
| z_gt_axis = nn_ops.log_softmax_v2(arr, axis=0) |
| x_neg_axis_tf = self.evaluate(x_neg_axis) |
| y_pos_axis_tf = self.evaluate(y_pos_axis) |
| z_gt_axis_tf = self.evaluate(z_gt_axis) |
| eps = 1e-3 |
| self.assertAllClose(x_neg_axis_tf, y_pos_axis_tf, eps) |
| self.assertAllClose(y_pos_axis_tf, z_gt_axis_tf, eps) |
| |
| @parameterized.parameters(((5, 10),), ((2, 3, 4),)) |
| @test_util.run_deprecated_v1 |
| def testGradient(self, x_shape): |
| x_np = np.random.randn(*x_shape).astype(np.float64) |
| with self.cached_session(): |
| x_tf = constant_op.constant(x_np) |
| y_tf = nn_ops.log_softmax_v2(x_tf) |
| err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf, |
| x_shape) |
| eps = 1e-7 |
| self.assertLess(err, eps) |
| |
| |
| class L2LossTest(test_lib.TestCase): |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testL2Loss(self): |
| for dtype in [dtypes.float32, dtypes.float64]: |
| x = constant_op.constant( |
| [1.0, 0.0, 3.0, 2.0], shape=[2, 2], name="x", dtype=dtype) |
| l2loss = nn_ops.l2_loss(x) |
| value = self.evaluate(l2loss) |
| self.assertAllClose(7.0, value) |
| |
| @test_util.run_deprecated_v1 |
| def testGradient(self): |
| x_shape = [20, 7, 3] |
| np.random.seed(1) # Make it reproducible. |
| x_val = np.random.random_sample(x_shape).astype(np.float64) |
| with self.cached_session(): |
| x = constant_op.constant(x_val, name="x") |
| output = nn_ops.l2_loss(x) |
| err = gradient_checker.compute_gradient_error(x, x_shape, output, [1]) |
| print("L2Loss gradient err = %g " % err) |
| err_tolerance = 1e-10 |
| self.assertLess(err, err_tolerance) |
| |
| |
| class L2NormalizeTest(test_lib.TestCase): |
| |
| def _l2Normalize(self, x, dim): |
| if isinstance(dim, list): |
| norm = np.linalg.norm(x, axis=tuple(dim)) |
| for d in dim: |
| norm = np.expand_dims(norm, d) |
| return x / norm |
| else: |
| norm = np.apply_along_axis(np.linalg.norm, dim, x) |
| return x / np.expand_dims(norm, dim) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testL2Normalize(self): |
| x_shape = [20, 7, 3] |
| np.random.seed(1) |
| x_np = np.random.random_sample(x_shape).astype(np.float32) |
| for dim in range(len(x_shape)): |
| y_np = self._l2Normalize(x_np, dim) |
| x_tf = constant_op.constant(x_np, name="x") |
| y_tf = nn_impl.l2_normalize_v2(x_tf, dim) |
| self.assertAllClose(y_np, self.evaluate(y_tf)) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testL2NormalizeDimArray(self): |
| x_shape = [20, 7, 3] |
| np.random.seed(1) |
| x_np = np.random.random_sample(x_shape).astype(np.float32) |
| dim = [1, 2] |
| y_np = self._l2Normalize(x_np, dim) |
| x_tf = constant_op.constant(x_np, name="x") |
| y_tf = nn_impl.l2_normalize_v2(x_tf, dim) |
| self.assertAllClose(y_np, self.evaluate(y_tf)) |
| |
| @test_util.run_deprecated_v1 |
| def testL2NormalizeGradient(self): |
| x_shape = [20, 7, 3] |
| np.random.seed(1) |
| x_np = np.random.random_sample(x_shape).astype(np.float64) |
| for dim in range(len(x_shape)): |
| with self.cached_session(): |
| x_tf = constant_op.constant(x_np, name="x") |
| y_tf = nn_impl.l2_normalize_v2(x_tf, dim) |
| err = gradient_checker.compute_gradient_error(x_tf, x_shape, y_tf, |
| x_shape) |
| print("L2Normalize gradient err = %g " % err) |
| self.assertLess(err, 1e-4) |
| |
| |
| class DropoutTest(test_lib.TestCase): |
| |
| def testDropout(self): |
| # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate |
| # that it is producing approximately the right number of ones over a large |
| # number of samples, based on the keep probability. |
| x_dim = 40 |
| y_dim = 30 |
| num_iter = 10 |
| for keep_prob in [0.1, 0.5, 0.8]: |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| dropout = nn_ops.dropout(t, rate=(1 - keep_prob)) |
| final_count = 0 |
| self.assertEqual([x_dim, y_dim], dropout.get_shape()) |
| for _ in xrange(0, num_iter): |
| value = self.evaluate(dropout) |
| final_count += np.count_nonzero(value) |
| # Verifies that there are only two values: 0 and 1/keep_prob. |
| sorted_value = np.unique(np.sort(value)) |
| self.assertEqual(0, sorted_value[0]) |
| self.assertAllClose(1 / keep_prob, sorted_value[1]) |
| |
| # Check that we are in the 15% error range |
| expected_count = x_dim * y_dim * keep_prob * num_iter |
| rel_error = math.fabs(final_count - expected_count) / expected_count |
| print(rel_error) |
| self.assertTrue(rel_error < 0.15) |
| |
| def testShapedDropout(self): |
| # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate |
| # that it is producing approximately the right number of ones over a large |
| # number of samples, based on the keep probability. This time with shaped |
| # noise. |
| x_dim = 40 * 30 |
| y_dim = 3 |
| num_iter = 10 |
| for keep_prob in [0.1, 0.5, 0.8]: |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| dropout = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, 1]) |
| self.assertEqual([x_dim, y_dim], dropout.get_shape()) |
| final_count = 0 |
| for _ in xrange(0, num_iter): |
| value = self.evaluate(dropout) |
| final_count += np.count_nonzero(value) |
| # Verifies that there are only two values: 0 and 1/keep_prob. |
| sorted_value = np.unique(np.sort(value)) |
| self.assertEqual(0, sorted_value[0]) |
| self.assertAllClose(1 / keep_prob, sorted_value[1]) |
| |
| # Check that we are in the 15% error range |
| expected_count = x_dim * y_dim * keep_prob * num_iter |
| rel_error = math.fabs(final_count - expected_count) / expected_count |
| print(rel_error) |
| self.assertTrue(rel_error < 0.15) |
| |
| def testShapedDropoutCorrelation(self): |
| # Runs a shaped dropout and tests that the correlations are correct. |
| x_dim = 40 |
| y_dim = 30 |
| num_iter = 10 |
| for keep_prob in [0.1, 0.5, 0.8]: |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| dropout = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, 1]) |
| self.assertEqual([x_dim, y_dim], dropout.get_shape()) |
| for _ in xrange(0, num_iter): |
| value = self.evaluate(dropout) |
| # Verifies that each y column as only one type of activation. |
| for i in xrange(x_dim): |
| sorted_value = np.unique(np.sort(value[i, :])) |
| self.assertEqual(sorted_value.size, 1) |
| |
| @test_util.run_deprecated_v1 |
| def testDropoutPlaceholderKeepProb(self): |
| # Runs dropout with 0-1 tensor 10 times, sum the number of ones and validate |
| # that it is producing approximately the right number of ones over a large |
| # number of samples, based on the keep probability. |
| x_dim = 40 |
| y_dim = 30 |
| num_iter = 10 |
| for keep_prob in [0.1, 0.5, 0.8]: |
| with self.cached_session(): |
| t = constant_op.constant( |
| 1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| keep_prob_placeholder = array_ops.placeholder(dtypes.float32) |
| dropout = nn_ops.dropout(t, keep_prob_placeholder) |
| final_count = 0 |
| self.assertEqual([x_dim, y_dim], dropout.get_shape()) |
| for _ in xrange(0, num_iter): |
| value = dropout.eval(feed_dict={keep_prob_placeholder: keep_prob}) |
| final_count += np.count_nonzero(value) |
| # Verifies that there are only two values: 0 and 1/keep_prob. |
| sorted_value = np.unique(np.sort(value)) |
| self.assertEqual(0, sorted_value[0]) |
| self.assertAllClose(1 / keep_prob, sorted_value[1]) |
| # Check that we are in the 15% error range |
| expected_count = x_dim * y_dim * keep_prob * num_iter |
| rel_error = math.fabs(final_count - expected_count) / expected_count |
| print(rel_error) |
| self.assertTrue(rel_error < 0.15) |
| |
| @test_util.run_deprecated_v1 |
| def testShapedDropoutUnknownShape(self): |
| x_dim = 40 |
| y_dim = 30 |
| keep_prob = 0.5 |
| x = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| dropout_x = nn_ops.dropout( |
| x, |
| rate=(1 - keep_prob), |
| noise_shape=array_ops.placeholder(dtypes.int32)) |
| self.assertEqual(x.get_shape(), dropout_x.get_shape()) |
| |
| def testPartialShapedDropout(self): |
| x_dim = 40 * 30 |
| y_dim = 3 |
| num_iter = 10 |
| for keep_prob in [0.1, 0.5, 0.8]: |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| # Set noise_shape=[None, 1] which means [x_dim, 1]. |
| dropout = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[None, 1]) |
| self.assertEqual([x_dim, y_dim], dropout.get_shape()) |
| final_count = 0 |
| for _ in xrange(0, num_iter): |
| value = self.evaluate(dropout) |
| final_count += np.count_nonzero(value) |
| # Verifies that there are only two values: 0 and 1/keep_prob. |
| sorted_value = np.unique(np.sort(value)) |
| self.assertEqual(0, sorted_value[0]) |
| self.assertAllClose(1 / keep_prob, sorted_value[1]) |
| |
| # Check that we are in the 15% error range |
| expected_count = x_dim * y_dim * keep_prob * num_iter |
| rel_error = math.fabs(final_count - expected_count) / expected_count |
| print(rel_error) |
| self.assertTrue(rel_error < 0.15) |
| |
| @test_util.run_deprecated_v1 |
| def testInvalidKeepProb(self): |
| x_dim = 40 |
| y_dim = 30 |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout(t, -1.0) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout(t, 1.1) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout(t, [0.0, 1.0]) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout(t, array_ops.placeholder(dtypes.float64)) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout(t, array_ops.placeholder(dtypes.float32, shape=[2])) |
| |
| @test_util.run_deprecated_v1 |
| def testInvalidRate(self): |
| x_dim = 40 |
| y_dim = 30 |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout_v2(t, -1.0) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout_v2(t, 1.1) |
| with self.assertRaises(ValueError): |
| nn_ops.dropout_v2(t, [0.0, 1.0]) |
| |
| def testLargeRate(self): |
| x_dim = 40 |
| y_dim = 30 |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| _ = nn_ops.dropout_v2(t, 0.9) |
| |
| @test_util.run_deprecated_v1 |
| def testShapedDropoutShapeError(self): |
| # Runs shaped dropout and verifies an error is thrown on misshapen noise. |
| x_dim = 40 |
| y_dim = 30 |
| keep_prob = 0.5 |
| t = constant_op.constant(1.0, shape=[x_dim, y_dim], dtype=dtypes.float32) |
| with self.assertRaises(ValueError): |
| _ = nn_ops.dropout( |
| t, rate=(1 - keep_prob), noise_shape=[x_dim, y_dim + 10]) |
| with self.assertRaises(ValueError): |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, y_dim, 5]) |
| with self.assertRaises(ValueError): |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim + 3]) |
| with self.assertRaises(ValueError): |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim]) |
| # test that broadcasting proceeds |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[y_dim]) |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[1, y_dim]) |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[x_dim, 1]) |
| _ = nn_ops.dropout(t, rate=(1 - keep_prob), noise_shape=[1, 1]) |
| |
| def testNoDropoutFast(self): |
| x = array_ops.zeros((5,)) |
| y = nn_ops.dropout(x, rate=0) |
| self.assertTrue(x is y) |
| |
| y = nn_ops.dropout_v2(x, rate=0) |
| self.assertTrue(x is y) |
| |
| def testDropoutWithIntegerInputs(self): |
| x = constant_op.constant([1, 1, 1, 1, 1]) |
| with self.assertRaises(ValueError): |
| _ = nn_ops.dropout(x, 0.5) |
| |
| |
| class ComputeSampledLogitsTest(test_lib.TestCase): |
| |
| def setUp(self): |
| self._eps = 1e-3 |
| |
| def _GenerateTestData(self, num_classes, dim, batch_size, num_true, labels, |
| sampled, subtract_log_q): |
| """Randomly generates input/output data for a single test case. |
| |
| This function returns numpy constants for use in a test case. |
| |
| Args: |
| num_classes: An int. The number of embedding classes in the test case. |
| dim: An int. The dimension of the embedding. |
| batch_size: An int. The batch size. |
| num_true: An int. The number of target classes per training example. |
| labels: A list of batch_size * num_true ints. The target classes. |
| sampled: A list of indices in [0, num_classes). |
| subtract_log_q: A bool corresponding to the parameter in |
| _compute_sampled_logits(). |
| |
| Returns: |
| weights: Embedding weights to use as test input. It is a numpy array |
| of shape [num_classes, dim] |
| biases: Embedding biases to use as test input. It is a numpy array |
| of shape [num_classes]. |
| hidden_acts: Forward activations of the network to use as test input. |
| It is a numpy array of shape [batch_size, dim]. |
| sampled_vals: A tuple based on `sampled` to use as test input in the |
| format returned by a *_candidate_sampler function. |
| exp_logits: The output logits expected from _compute_sampled_logits(). |
| It is a numpy array of shape [batch_size, num_true + len(sampled)]. |
| exp_labels: The output labels expected from _compute_sampled_logits(). |
| It is a numpy array of shape [batch_size, num_true + len(sampled)]. |
| """ |
| weights = np.random.randn(num_classes, dim).astype(np.float32) |
| biases = np.random.randn(num_classes).astype(np.float32) |
| hidden_acts = np.random.randn(batch_size, dim).astype(np.float32) |
| |
| true_exp = np.full([batch_size, 1], fill_value=0.5, dtype=np.float32) |
| sampled_exp = np.full([len(sampled)], fill_value=0.5, dtype=np.float32) |
| sampled_vals = (sampled, true_exp, sampled_exp) |
| |
| sampled_w, sampled_b = weights[sampled], biases[sampled] |
| true_w, true_b = weights[labels], biases[labels] |
| |
| true_logits = np.sum( |
| hidden_acts.reshape((batch_size, 1, dim)) * true_w.reshape( |
| (batch_size, num_true, dim)), |
| axis=2) |
| true_b = true_b.reshape((batch_size, num_true)) |
| true_logits += true_b |
| sampled_logits = np.dot(hidden_acts, sampled_w.T) + sampled_b |
| |
| if subtract_log_q: |
| true_logits -= np.log(true_exp) |
| sampled_logits -= np.log(sampled_exp[np.newaxis, :]) |
| |
| exp_logits = np.concatenate([true_logits, sampled_logits], axis=1) |
| exp_labels = np.hstack((np.ones_like(true_logits) / num_true, |
| np.zeros_like(sampled_logits))) |
| |
| return weights, biases, hidden_acts, sampled_vals, exp_logits, exp_labels |
| |
| def _ShardTestEmbeddings(self, weights, biases, num_shards): |
| """Shards the weights and biases returned by _GenerateTestData. |
| |
| Args: |
| weights: The weights returned by _GenerateTestData. |
| biases: The biases returned by _GenerateTestData. |
| num_shards: The number of shards to create. |
| |
| Returns: |
| sharded_weights: A list of size `num_shards` containing all the weights. |
| sharded_biases: A list of size `num_shards` containing all the biases. |
| """ |
| with ops.Graph().as_default() as g: |
| sharded_weights = variable_scope.get_variable( |
| "w", |
| partitioner=partitioned_variables.fixed_size_partitioner(num_shards), |
| initializer=constant_op.constant(weights)) |
| sharded_biases = variable_scope.get_variable( |
| "b", |
| partitioner=partitioned_variables.fixed_size_partitioner(num_shards), |
| initializer=constant_op.constant(biases)) |
| with self.session(graph=g) as sess: |
| variables.global_variables_initializer().run() |
| return self.evaluate([list(sharded_weights), list(sharded_biases)]) |
| |
| def testShapes(self): |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| |
| for num_true in range(1, 5): |
| labels = np.random.randint( |
| low=0, high=num_classes, size=batch_size * num_true) |
| (weights, biases, hidden_acts, sampled_vals, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=num_true, |
| labels=labels, |
| sampled=[1, 0, 2, 3], |
| subtract_log_q=False) |
| logits_tensor, labels_tensor = _compute_sampled_logits( |
| weights=constant_op.constant(weights), |
| biases=constant_op.constant(biases), |
| labels=constant_op.constant( |
| labels, dtype=dtypes.int64, shape=(batch_size, num_true)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=num_true, |
| sampled_values=sampled_vals, |
| subtract_log_q=False, |
| remove_accidental_hits=False, |
| partition_strategy="div", |
| name="sampled_logits_basic_num_true_%d" % num_true) |
| got_logits, got_labels = self.evaluate([logits_tensor, labels_tensor]) |
| self.assertEqual(exp_logits.shape, got_logits.shape, self._eps) |
| self.assertEqual(exp_labels.shape, got_labels.shape, self._eps) |
| |
| def testBasic(self): |
| """Without accidental hit removal or subtract_log_q.""" |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| |
| for num_true in range(1, 5): |
| labels = np.random.randint( |
| low=0, high=num_classes, size=batch_size * num_true) |
| (weights, biases, hidden_acts, sampled_vals, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=num_true, |
| labels=labels, |
| sampled=[1, 0, 2, 3], |
| subtract_log_q=False) |
| logits_tensor, labels_tensor = _compute_sampled_logits( |
| weights=constant_op.constant(weights), |
| biases=constant_op.constant(biases), |
| labels=constant_op.constant( |
| labels, dtype=dtypes.int64, shape=(batch_size, num_true)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=num_true, |
| sampled_values=sampled_vals, |
| subtract_log_q=False, |
| remove_accidental_hits=False, |
| partition_strategy="div", |
| name="sampled_logits_basic_num_true_%d" % num_true) |
| got_logits, got_labels = self.evaluate([logits_tensor, labels_tensor]) |
| self.assertAllClose(exp_logits, got_logits, self._eps) |
| self.assertAllClose(exp_labels, got_labels, self._eps) |
| |
| def testAccidentalHitRemoval(self): |
| """With accidental hit removal, no subtract_log_q.""" |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| sampled = [1, 0, 2, 3] |
| |
| for num_true in range(1, 5): |
| labels = np.random.randint( |
| low=0, high=num_classes, size=batch_size * num_true) |
| (weights, biases, hidden_acts, sampled_vals, _, |
| _) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=num_true, |
| labels=labels, |
| sampled=sampled, |
| subtract_log_q=False) |
| logits_tensor, _ = _compute_sampled_logits( |
| weights=constant_op.constant(weights), |
| biases=constant_op.constant(biases), |
| labels=constant_op.constant( |
| labels, dtype=dtypes.int64, shape=(batch_size, num_true)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=len(sampled), |
| num_classes=num_classes, |
| num_true=num_true, |
| sampled_values=sampled_vals, |
| subtract_log_q=False, |
| remove_accidental_hits=True, |
| partition_strategy="div", |
| name="sampled_logits_accidental_hit_removal_num_true_%d" % num_true) |
| # Test that the exponentiated logits of accidental hits are near 0. |
| # First we need to find the hits in this random test run: |
| labels_reshape = labels.reshape((batch_size, num_true)) |
| got_logits = self.evaluate(logits_tensor) |
| for row in xrange(batch_size): |
| row_labels = labels_reshape[row, :] |
| for col in xrange(len(sampled)): |
| if sampled[col] in row_labels: |
| # We need to add the num_true_test offset into logits_* |
| self.assertNear( |
| np.exp(got_logits[row, col + num_true]), 0., self._eps) |
| |
| def testSubtractLogQ(self): |
| """With subtract_log_q, no accidental hit removal.""" |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| |
| for num_true in range(1, 5): |
| labels = np.random.randint( |
| low=0, high=num_classes, size=batch_size * num_true) |
| (weights, biases, hidden_acts, sampled_vals, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=num_true, |
| labels=labels, |
| sampled=[1, 0, 2, 3], |
| subtract_log_q=True) |
| logits_tensor, labels_tensor = _compute_sampled_logits( |
| weights=constant_op.constant(weights), |
| biases=constant_op.constant(biases), |
| labels=constant_op.constant( |
| labels, dtype=dtypes.int64, shape=(batch_size, num_true)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=num_true, |
| sampled_values=sampled_vals, |
| subtract_log_q=True, |
| remove_accidental_hits=False, |
| partition_strategy="div", |
| name="sampled_logits_subtract_log_q_num_true_%d" % num_true) |
| got_logits, got_labels = self.evaluate([logits_tensor, labels_tensor]) |
| self.assertAllClose(exp_logits, got_logits, self._eps) |
| self.assertAllClose(exp_labels, got_labels, self._eps) |
| |
| def testSharded(self): |
| """With sharded weights and sharded biases.""" |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| |
| for num_true in range(1, 5): |
| labels = np.random.randint( |
| low=0, high=num_classes, size=batch_size * num_true) |
| (weights, biases, hidden_acts, sampled_vals, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=num_true, |
| labels=labels, |
| sampled=[1, 0, 2, 3], |
| subtract_log_q=False) |
| weight_shards, bias_shards = self._ShardTestEmbeddings( |
| weights, biases, num_shards=3) |
| logits_tensor, labels_tensor = _compute_sampled_logits( |
| weights=[constant_op.constant(shard) for shard in weight_shards], |
| biases=[constant_op.constant(shard) for shard in bias_shards], |
| labels=constant_op.constant( |
| labels, dtype=dtypes.int64, shape=(batch_size, num_true)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=num_true, |
| sampled_values=sampled_vals, |
| subtract_log_q=False, |
| remove_accidental_hits=False, |
| partition_strategy="div", |
| name="sampled_logits_sharded_num_true_%d" % num_true) |
| got_logits, got_labels = self.evaluate([logits_tensor, labels_tensor]) |
| self.assertAllClose(exp_logits, got_logits, self._eps) |
| self.assertAllClose(exp_labels, got_labels, self._eps) |
| |
| def testNCELoss(self): |
| # A simple test to verify the numerics. |
| |
| def _SigmoidCrossEntropyWithLogits(logits, targets): |
| # logits, targets: float arrays of the same shape. |
| assert logits.shape == targets.shape |
| pred = 1. / (1. + np.exp(-logits)) |
| eps = 0.0001 |
| pred = np.minimum(np.maximum(pred, eps), 1 - eps) |
| return -targets * np.log(pred) - (1. - targets) * np.log(1. - pred) |
| |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| labels = [0, 1, 2] |
| (weights, biases, hidden_acts, sampled_vals, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=1, |
| labels=labels, |
| sampled=[1, 0, 2, 3], |
| subtract_log_q=True) |
| exp_nce_loss = np.sum( |
| _SigmoidCrossEntropyWithLogits(exp_logits, exp_labels), 1) |
| |
| got_nce_loss = nn_impl.nce_loss_v2( |
| weights=constant_op.constant(weights), |
| biases=constant_op.constant(biases), |
| labels=constant_op.constant(labels, shape=(batch_size, 1)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=1, |
| sampled_values=sampled_vals) |
| |
| self.assertAllClose(exp_nce_loss, self.evaluate(got_nce_loss), 1e-4) |
| |
| # Test with sharded weights and sharded biases. |
| weight_shards, bias_shards = self._ShardTestEmbeddings( |
| weights, biases, num_shards=3) |
| got_nce_loss = nn_impl.nce_loss_v2( |
| weights=[constant_op.constant(shard) for shard in weight_shards], |
| biases=[constant_op.constant(shard) for shard in bias_shards], |
| labels=constant_op.constant(labels, shape=(batch_size, 1)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=1, |
| sampled_values=sampled_vals) |
| |
| self.assertAllClose(exp_nce_loss, self.evaluate(got_nce_loss), 1e-4) |
| |
| def testSampledSoftmaxLoss(self): |
| # A simple test to verify the numerics. |
| |
| def _SoftmaxCrossEntropyWithLogits(logits, targets): |
| # logits, targets: float arrays of the same shape. |
| assert logits.shape == targets.shape |
| stable_exp_logits = np.exp( |
| logits - np.amax(logits, axis=1, keepdims=True)) |
| pred = stable_exp_logits / np.sum(stable_exp_logits, 1, keepdims=True) |
| return -np.sum(targets * np.log(pred + 1.0e-20), axis=1) |
| |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| labels = [0, 1, 2] |
| (weights, biases, hidden_acts, sampled_vals, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=1, |
| labels=labels, |
| sampled=[1, 0, 2, 3], |
| subtract_log_q=True) |
| exp_sampled_softmax_loss = _SoftmaxCrossEntropyWithLogits( |
| exp_logits, exp_labels) |
| |
| got_sampled_softmax_loss = nn_impl.sampled_softmax_loss_v2( |
| weights=constant_op.constant(weights), |
| biases=constant_op.constant(biases), |
| labels=constant_op.constant(labels, shape=(batch_size, 1)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=1, |
| sampled_values=sampled_vals, |
| remove_accidental_hits=False) |
| |
| self.assertAllClose(exp_sampled_softmax_loss, |
| self.evaluate(got_sampled_softmax_loss), 1e-4) |
| |
| # Test with sharded weights and sharded biases. |
| weight_shards, bias_shards = self._ShardTestEmbeddings( |
| weights, biases, num_shards=3) |
| got_sampled_softmax_loss = nn_impl.sampled_softmax_loss_v2( |
| weights=[constant_op.constant(shard) for shard in weight_shards], |
| biases=[constant_op.constant(shard) for shard in bias_shards], |
| labels=constant_op.constant(labels, shape=(batch_size, 1)), |
| inputs=constant_op.constant(hidden_acts), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=1, |
| sampled_values=sampled_vals, |
| remove_accidental_hits=False) |
| |
| self.assertAllClose(exp_sampled_softmax_loss, |
| self.evaluate(got_sampled_softmax_loss), 1e-4) |
| |
| def testSampledSoftmaxLossBf16(self): |
| # A simple test to verify the numerics for bfloat16. |
| def _SoftmaxCrossEntropyWithLogits(logits, targets): |
| # logits, targets: float arrays of the same shape. |
| assert logits.shape == targets.shape |
| stable_exp_logits = np.exp( |
| logits - np.amax(logits, axis=1, keepdims=True)) |
| pred = stable_exp_logits / np.sum(stable_exp_logits, 1, keepdims=True) |
| return -np.sum(targets * np.log(pred + 1.0e-20), axis=1) |
| |
| np.random.seed(0) |
| num_classes = 5 |
| batch_size = 3 |
| labels = [0, 1, 2] |
| sampled = [1, 0, 2, 3] |
| (weights, biases, hidden_acts, _, exp_logits, |
| exp_labels) = self._GenerateTestData( |
| num_classes=num_classes, |
| dim=10, |
| batch_size=batch_size, |
| num_true=1, |
| labels=labels, |
| sampled=sampled, |
| subtract_log_q=True) |
| exp_sampled_softmax_loss = _SoftmaxCrossEntropyWithLogits( |
| exp_logits, exp_labels) |
| |
| true_exp_bf16 = np.full([batch_size, 1], |
| fill_value=0.5, |
| dtype=dtypes.bfloat16.as_numpy_dtype) |
| sampled_exp_bf16 = np.full([len(sampled)], |
| fill_value=0.5, |
| dtype=dtypes.bfloat16.as_numpy_dtype) |
| sampled_vals_bf16 = (sampled, true_exp_bf16, sampled_exp_bf16) |
| |
| got_sampled_softmax_loss = math_ops.cast( |
| nn_impl.sampled_softmax_loss_v2( |
| weights=constant_op.constant(weights, dtype=dtypes.bfloat16), |
| biases=constant_op.constant(biases, dtype=dtypes.bfloat16), |
| labels=constant_op.constant( |
| labels, shape=(batch_size, 1), dtype=dtypes.bfloat16), |
| inputs=constant_op.constant(hidden_acts, dtype=dtypes.bfloat16), |
| num_sampled=4, |
| num_classes=num_classes, |
| num_true=1, |
| sampled_values=sampled_vals_bf16, |
| remove_accidental_hits=False), dtypes.float32) |
| |
| self.assertAllClose(exp_sampled_softmax_loss, |
| self.evaluate(got_sampled_softmax_loss), 1e-1) |
| |
| |
| class CReluTest(test_lib.TestCase): |
| |
| def test(self): |
| np.random.seed(1) # Make it reproducible. |
| x = np.random.randn(3, 4).astype(np.float32) |
| y = np.concatenate([x * (x > 0), -x * (x < 0)], axis=1) |
| |
| z = self.evaluate(nn_ops.crelu(constant_op.constant(x))) |
| self.assertAllClose(y, z, 1e-4) |
| |
| |
| class ReluTest(test_lib.TestCase): |
| |
| def test(self): |
| np.random.seed(1) # Make it reproducible. |
| x = np.random.randn(3, 4).astype(np.float32) |
| y = np.maximum(x, 0.0) |
| |
| z = self.evaluate(nn_ops.relu(constant_op.constant(x))) |
| self.assertAllEqual(y, z) |
| |
| @test_util.run_deprecated_v1 |
| def testNaNs(self): |
| # Test that relu(nan) = nan for various sizes. |
| for i in range(18): |
| x = np.zeros(i) + np.nan |
| with self.cached_session(): |
| z = nn_ops.relu(constant_op.constant(x)).eval() |
| self.assertTrue(np.isnan(z).all()) |
| |
| |
| class LeakyReluTest(test_lib.TestCase): |
| |
| def testRange(self): |
| batch_size = 3 |
| height, width = 4, 4 |
| np.random.seed(1) # Make it reproducible. |
| inputs = np.random.uniform(size=(batch_size, height, width, 3)).astype( |
| np.float32) |
| inputs = constant_op.constant(inputs) |
| |
| outputs = nn_ops.leaky_relu(inputs) |
| self.assertEquals(inputs.shape, outputs.shape) |
| |
| inputs, outputs = self.evaluate([inputs, outputs]) |
| |
| self.assertGreaterEqual(outputs.min(), 0.0) |
| self.assertLessEqual(outputs.max(), 1.0) |
| self.assertAllClose(inputs, outputs) |
| |
| @test_util.run_deprecated_v1 |
| def testValues(self): |
| for dtype in [np.int32, np.int64, np.float16, np.float32, np.float64]: |
| np_values = np.array([-2, -1, 0, 1, 2], dtype=dtype) |
| outputs = nn_ops.leaky_relu(constant_op.constant(np_values)) |
| |
| outputs = self.evaluate(outputs) |
| |
| tol = 2e-3 if dtype == np.float16 else 1e-6 |
| self.assertAllClose( |
| outputs, [-0.4, -0.2, 0.0, 1.0, 2.0], rtol=tol, atol=tol) |
| |
| @test_util.run_deprecated_v1 |
| def testName(self): |
| np_values = np.array([-2, -1, 0, 1, 2], dtype=np.float64) |
| outputs_with_name_set = nn_ops.leaky_relu( |
| constant_op.constant(np_values), |
| name='test_relu_op') |
| self.assertEqual(outputs_with_name_set.name, 'test_relu_op:0') |
| outputs_without_name_set = nn_ops.leaky_relu( |
| constant_op.constant(np_values)) |
| self.assertEqual(outputs_without_name_set.name, 'LeakyRelu:0') |
| |
| |
| class SwishTest(test_lib.TestCase): |
| |
| @test_util.run_deprecated_v1 |
| def testValues(self): |
| np_values = np.array( |
| [np.linspace(-7.0, 0.0, 100), |
| np.linspace(0.0, 7.0, 100)], |
| dtype=np.float32) |
| tf_values = constant_op.constant(np_values) |
| actual_tf_outputs = nn_impl.swish(tf_values) |
| expected_tf_outputs = tf_values * math_ops.sigmoid(tf_values) |
| |
| actual_outputs, expected_outputs = self.evaluate( |
| [actual_tf_outputs, expected_tf_outputs]) |
| |
| self.assertAllClose(actual_outputs, expected_outputs) |
| |
| @test_util.run_deprecated_v1 |
| def testGradients(self): |
| shape = [5, 3, 4] |
| sigma = 5 |
| input_values = np.random.randn(*shape) * sigma |
| x_tf = constant_op.constant(input_values) |
| y_tf = nn_impl.swish(x_tf) |
| with self.cached_session(): |
| err = gradient_checker.compute_gradient_error(x_tf, shape, y_tf, shape) |
| self.assertLess(err, 1e-4) |
| |
| |
| class MomentsTest(test_lib.TestCase): |
| |
| def doOutputTest(self, |
| input_shape, |
| moments_axes, |
| tol=1e-4, |
| check_gradients=False): |
| for mu in [0.0, 1.0, 1e3]: |
| for sigma in [1.0, 0.1]: |
| for keep_dims in [True, False]: |
| input_values = np.random.rand(*input_shape) * sigma + mu |
| expected_mean = np.mean( |
| input_values, axis=moments_axes, keepdims=keep_dims) |
| expected_var = np.var( |
| input_values, axis=moments_axes, keepdims=keep_dims) |
| with ops.Graph().as_default() as g: |
| with self.session(graph=g) as sess: |
| inputs = constant_op.constant( |
| input_values, shape=input_shape, dtype=dtypes.float32) |
| mean, variance = nn_impl.moments_v2( |
| inputs, moments_axes, keepdims=keep_dims) |
| |
| if check_gradients: |
| err = gradient_checker.compute_gradient_error( |
| inputs, input_shape, mean, mean.shape.as_list()) |
| self.assertLess(err, 1e-3) |
| err = gradient_checker.compute_gradient_error( |
| inputs, input_shape, variance, variance.shape.as_list()) |
| self.assertLess(err, 1e-3) |
| |
| # Evaluate. |
| [mean, variance] = self.evaluate([mean, variance]) |
| # Make sure that there are no NaNs |
| self.assertFalse(np.isnan(mean).any()) |
| self.assertFalse(np.isnan(variance).any()) |
| self.assertAllClose(mean, expected_mean, rtol=tol, atol=tol) |
| self.assertAllClose(variance, expected_var, rtol=tol, atol=tol) |
| |
| def testOutputAndGradient2DInput0(self): |
| self.doOutputTest((10, 10), (0,), check_gradients=True) |
| |
| def testOutputAndGradient2DInput01(self): |
| self.doOutputTest((10, 10), (0, 1), check_gradients=True) |
| |
| def testOutput2DInput0(self): |
| self.doOutputTest((10, 300), (0,)) |
| |
| def testOutput2DInput1(self): |
| self.doOutputTest((10, 300), (1,)) |
| |
| def testOutput2DInput01(self): |
| self.doOutputTest((10, 300), (0, 1)) |
| |
| def testOutput4DInput0(self): |
| self.doOutputTest((10, 10, 10, 30), (0,)) |
| |
| def testOutput4DInput1(self): |
| self.doOutputTest((10, 10, 10, 30), (1,)) |
| |
| def testOutput4DInput3(self): |
| self.doOutputTest((10, 10, 10, 30), (3,)) |
| |
| def testOutput4DInput012(self): |
| self.doOutputTest((10, 10, 10, 30), (0, 1, 2)) |
| |
| def testOutput4DInput123(self): |
| self.doOutputTest((10, 10, 10, 30), (1, 2, 3)) |
| |
| |
| class DataFormatDimMapTest(test_lib.TestCase): |
| |
| def _test(self, x_val, y_val_expected): |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_dim_map(x) |
| |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, y_val_expected) |
| |
| def test(self): |
| self._test(0, 0) |
| self._test(1, 2) |
| self._test(2, 3) |
| self._test(3, 1) |
| self._test(-1, 1) |
| self._test(-2, 3) |
| self._test(-3, 2) |
| self._test(-4, 0) |
| self._test([1, 3], [2, 1]) |
| self._test([1, 3, -2], [2, 1, 3]) |
| self._test([1, -3, -2], [2, 2, 3]) |
| self._test([[1, -3], [1, -1]], [[2, 2], [2, 1]]) |
| |
| def testNHWCtoNCHW(self): |
| x_val = [1, -3, -2] |
| y_val_expected = [2, 2, 3] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_dim_map(x, src_format="NHWC", dst_format="NCHW") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, y_val_expected) |
| |
| def testNHWCtoHWNC(self): |
| x_val = [-4, -3, -2, -1, 0, 1, 2, 3] |
| y_val_expected = [2, 0, 1, 3, 2, 0, 1, 3] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_dim_map(x, src_format="NHWC", dst_format="HWNC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, y_val_expected) |
| |
| def testNHWCtoWHCN(self): |
| x_val = [-4, -3, -2, -1, 0, 1, 2, 3] |
| y_val_expected = [3, 1, 0, 2, 3, 1, 0, 2] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_dim_map(x, src_format="NHWC", dst_format="WHCN") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, y_val_expected) |
| |
| def testArbitraryASCII(self): |
| x_val = [-4, -3, -2, -1, 0, 1, 2, 3] |
| y_val_expected = [3, 2, 1, 0, 3, 2, 1, 0] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_dim_map(x, src_format="qwer", dst_format="rewq") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, y_val_expected) |
| |
| |
| class DataFormatVectorPermuteTest(test_lib.TestCase): |
| |
| def testNHWCToNCHW(self): |
| x_val = [7, 4, 9, 3] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x) |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [7, 3, 4, 9]) |
| |
| def testNCHWToNHWC(self): |
| x_val = [7, 4, 9, 3] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x, src_format="NCHW", dst_format="NHWC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [7, 9, 3, 4]) |
| |
| def testNHWCToHWNC(self): |
| x_val = [7, 4, 9, 3] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x, src_format="NHWC", dst_format="HWNC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [4, 9, 7, 3]) |
| |
| def testHWNCToNHWC(self): |
| x_val = [7, 4, 9, 3] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x, src_format="HWNC", dst_format="NHWC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [9, 7, 4, 3]) |
| |
| def testNHWCToNCHW2D(self): |
| x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x) |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [[7, 4], [5, 1], [9, 3], [4, 5]]) |
| |
| def testNHWCToHWNC2D(self): |
| x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x, src_format="NHWC", dst_format="HWNC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [[9, 3], [4, 5], [7, 4], [5, 1]]) |
| |
| def testHWNCToNHWC2D(self): |
| x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x, src_format="HWNC", dst_format="NHWC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [[4, 5], [7, 4], [9, 3], [5, 1]]) |
| |
| def testNCHWToNHWC2D(self): |
| x_val = [[7, 4], [9, 3], [4, 5], [5, 1]] |
| x = constant_op.constant(x_val) |
| y = nn_ops.data_format_vec_permute(x, src_format="NCHW", dst_format="NHWC") |
| with test_util.use_gpu(): |
| y_val = self.evaluate(y) |
| self.assertAllEqual(y_val, [[7, 4], [4, 5], [5, 1], [9, 3]]) |
| |
| |
| @test_util.run_all_in_graph_and_eager_modes |
| class AvgPoolTest(test_lib.TestCase): |
| |
| def test1DTensor(self): |
| x = array_ops.ones([3, 6, 5]) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.avg_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.avg_pool1d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test1DNumpy(self): |
| # explicilty use float32 for ROCm, as MIOpen does not yet support float64 |
| # np.ones defaults to using float64 when dtype is not explicitly specified |
| dtype = np.float32 if test_lib.is_built_with_rocm() else np.float64 |
| x = np.ones([3, 6, 5], dtype=dtype) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.avg_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.avg_pool1d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test2DTensor(self): |
| x = array_ops.ones([3, 6, 6, 5]) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.avg_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.avg_pool(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test2DNumpy(self): |
| # explicilty use float32 for ROCm, as MIOpen does not yet support float64 |
| # np.ones defaults to using float64 when dtype is not explicitly specified |
| dtype = np.float32 if test_lib.is_built_with_rocm() else np.float64 |
| x = np.ones([3, 6, 6, 5], dtype=dtype) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.avg_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.avg_pool(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test3DTensor(self): |
| if test_lib.is_built_with_rocm(): |
| self.skipTest("Pooling with 3D tensors is not supported in ROCm") |
| x = array_ops.ones([3, 7, 6, 6, 5]) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.avg_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.avg_pool3d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test3DNumpy(self): |
| if test_lib.is_built_with_rocm(): |
| self.skipTest("Pooling with 3D tensors is not supported in ROCm") |
| x = np.ones([3, 7, 6, 6, 5], dtype=np.float32) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.avg_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.avg_pool3d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| |
| @test_util.run_all_in_graph_and_eager_modes |
| class MaxPoolTest(test_lib.TestCase): |
| |
| def test1DTensor(self): |
| x = array_ops.ones([3, 6, 5]) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.max_pool1d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test1DNumpy(self): |
| # explicilty use float32 for ROCm, as MIOpen does not yet support float64 |
| # np.ones defaults to using float64 when dtype is not explicitly specified |
| dtype = np.float32 if test_lib.is_built_with_rocm() else np.float64 |
| x = np.ones([3, 6, 5], dtype=dtype) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.max_pool1d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test2DTensor(self): |
| x = array_ops.ones([3, 6, 6, 5]) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.max_pool(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test2DNumpy(self): |
| # explicilty use float32 for ROCm, as MIOpen does not yet support float64 |
| # np.ones defaults to using float64 when dtype is not explicitly specified |
| dtype = np.float32 if test_lib.is_built_with_rocm() else np.float64 |
| x = np.ones([3, 6, 6, 5], dtype=dtype) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.max_pool(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test3DTensor(self): |
| if test_lib.is_built_with_rocm(): |
| self.skipTest("Pooling with 3D tensors is not supported in ROCm") |
| x = array_ops.ones([3, 7, 6, 6, 5]) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.max_pool3d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test3DNumpy(self): |
| if test_lib.is_built_with_rocm(): |
| self.skipTest("Pooling with 3D tensors is not supported in ROCm") |
| x = np.ones([3, 7, 6, 6, 5], dtype=np.float32) |
| ksize = 2 |
| strides = 2 |
| |
| y1 = nn_ops.max_pool_v2(x, ksize, strides, "SAME") |
| y2 = nn_ops.max_pool3d(x, ksize, strides, "SAME") |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def testIncorrectSizeInputSmall(self): |
| x = array_ops.ones([3, 4]) |
| with self.assertRaisesRegex( |
| ValueError, "Input tensor must be of rank 3, 4 or 5 but was 2."): |
| nn_ops.max_pool_v2(x, 2, 2, "SAME") |
| |
| def testIncorrectSizeInput(self): |
| x = array_ops.ones([3, 4, 1, 2, 1, 2]) |
| with self.assertRaisesRegex( |
| ValueError, "Input tensor must be of rank 3, 4 or 5 but was 6."): |
| nn_ops.max_pool_v2(x, 2, 2, "SAME") |
| |
| |
| @test_util.run_all_in_graph_and_eager_modes |
| class ConvolutionTest(test_lib.TestCase): |
| |
| def testUnknownSize(self): |
| # explicilty use float32 for ROCm, as MIOpen does not yet support float64 |
| # np.ones defaults to using float64 when dtype is not explicitly specified |
| dtype = np.float32 if test_lib.is_built_with_rocm() else np.float64 |
| x = tensor_spec.TensorSpec(None, dtypes.float32, name="x") |
| k = np.ones([3, 6, 6, 5], dtype=dtype) |
| |
| @def_function.function |
| def F(value): |
| return nn_ops.convolution(value, k, "SAME") |
| |
| F.get_concrete_function(x) |
| |
| |
| class ConvTransposeTest(test_lib.TestCase): |
| |
| def test1D(self): |
| t = array_ops.ones([2, 4, 3]) |
| v = array_ops.ones([2, 5, 3]) |
| strides = 2 |
| |
| y1 = nn_ops.conv1d_transpose(t, v, [2, 8, 5], strides) |
| y2 = nn_ops.conv_transpose(t, v, [2, 8, 5], strides) |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test1DTensor(self): |
| t = array_ops.ones([2, 4, 3]) |
| v = array_ops.ones([2, 5, 3]) |
| strides = 2 |
| |
| y1 = nn_ops.conv1d_transpose(t, v, [2, 8, 5], strides) |
| y2 = nn_ops.conv_transpose(t, v, constant_op.constant([2, 8, 5]), strides) |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test2D(self): |
| t = array_ops.ones([2, 4, 4, 3]) |
| v = array_ops.ones([2, 2, 5, 3]) |
| strides = 2 |
| |
| y1 = nn_ops.conv2d_transpose_v2(t, v, [2, 8, 8, 5], strides) |
| y2 = nn_ops.conv_transpose(t, v, [2, 8, 8, 5], strides) |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test2DTensor(self): |
| t = array_ops.ones([2, 4, 4, 3]) |
| v = array_ops.ones([2, 2, 5, 3]) |
| strides = 2 |
| |
| y1 = nn_ops.conv2d_transpose_v2(t, v, [2, 8, 8, 5], strides) |
| y2 = nn_ops.conv_transpose(t, v, constant_op.constant([2, 8, 8, 5]), |
| strides) |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test3D(self): |
| t = array_ops.ones([2, 4, 4, 4, 3]) |
| v = array_ops.ones([2, 2, 2, 5, 3]) |
| strides = 2 |
| |
| y1 = nn_ops.conv3d_transpose_v2(t, v, [2, 8, 8, 8, 5], strides) |
| y2 = nn_ops.conv_transpose(t, v, [2, 8, 8, 8, 5], strides) |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def test3DTensor(self): |
| t = array_ops.ones([2, 4, 4, 4, 3]) |
| v = array_ops.ones([2, 2, 2, 5, 3]) |
| strides = 2 |
| |
| y1 = nn_ops.conv3d_transpose_v2(t, v, [2, 8, 8, 8, 5], strides) |
| y2 = nn_ops.conv_transpose(t, v, constant_op.constant([2, 8, 8, 8, 5]), |
| strides) |
| |
| self.assertAllEqual(self.evaluate(y1), self.evaluate(y2)) |
| |
| def testIncorrectSizeInputSmall(self): |
| with self.assertRaisesRegex( |
| ValueError, "output_shape must be of length 3, 4 or 5 but was 2."): |
| nn_ops.conv_transpose(None, 2, [2, 3], "SAME") |
| |
| def testIncorrectSizeInput(self): |
| with self.assertRaisesRegex( |
| ValueError, "output_shape must be of length 3, 4 or 5 but was 6."): |
| nn_ops.conv_transpose(None, 2, [2, 3, 4, 2, 5, 1], "SAME") |
| |
| def testTensorsNoShape(self): |
| with self.assertRaisesRegex( |
| ValueError, |
| "output_shape must be a tensor or sized collection."): |
| nn_ops.conv_transpose(None, None, None, None) |
| |
| |
| if __name__ == "__main__": |
| test_lib.main() |
