5bfc37ef25
use_gpu is True by default in test utils starting CL 356906251 I will wait a bit before checking this in since once this is checked in, it would be harder to roll back CL 356906251 PiperOrigin-RevId: 357322055 Change-Id: Ibbeb900d93f9fb43c2dc61285ee38e582b29dcfc
644 lines
21 KiB
Python
644 lines
21 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for bincount_ops.bincount."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from absl.testing import parameterized
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import numpy as np
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import bincount_ops
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from tensorflow.python.ops import gen_math_ops
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from tensorflow.python.ops import sparse_ops
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from tensorflow.python.ops.ragged import ragged_factory_ops
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from tensorflow.python.ops.ragged import ragged_tensor
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from tensorflow.python.platform import googletest
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class BincountTest(test_util.TensorFlowTestCase):
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def test_empty(self):
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with self.session():
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([], minlength=5)),
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[0, 0, 0, 0, 0])
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([], minlength=1)), [0])
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([], minlength=0)), [])
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self.assertEqual(
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self.evaluate(
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bincount_ops.bincount([], minlength=0, dtype=np.float32)).dtype,
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np.float32)
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self.assertEqual(
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self.evaluate(
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bincount_ops.bincount([], minlength=3, dtype=np.float64)).dtype,
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np.float64)
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def test_values(self):
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with self.session():
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([1, 1, 1, 2, 2, 3])),
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[0, 3, 2, 1])
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arr = [1, 1, 2, 1, 2, 3, 1, 2, 3, 4, 1, 2, 3, 4, 5]
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount(arr)), [0, 5, 4, 3, 2, 1])
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arr += [0, 0, 0, 0, 0, 0]
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount(arr)), [6, 5, 4, 3, 2, 1])
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self.assertAllEqual(self.evaluate(bincount_ops.bincount([])), [])
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self.assertAllEqual(self.evaluate(bincount_ops.bincount([0, 0, 0])), [3])
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([5])), [0, 0, 0, 0, 0, 1])
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount(np.arange(10000))),
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np.ones(10000))
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def test_maxlength(self):
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with self.session():
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([5], maxlength=3)), [0, 0, 0])
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([1], maxlength=3)), [0, 1])
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount([], maxlength=3)), [])
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def test_random_with_weights(self):
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num_samples = 10000
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with self.session():
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np.random.seed(42)
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for dtype in [dtypes.int32, dtypes.int64, dtypes.float32, dtypes.float64]:
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arr = np.random.randint(0, 1000, num_samples)
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if dtype == dtypes.int32 or dtype == dtypes.int64:
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weights = np.random.randint(-100, 100, num_samples)
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else:
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weights = np.random.random(num_samples)
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self.assertAllClose(
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self.evaluate(bincount_ops.bincount(arr, weights)),
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np.bincount(arr, weights))
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def test_random_without_weights(self):
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num_samples = 10000
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with self.session():
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np.random.seed(42)
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for dtype in [np.int32, np.float32]:
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arr = np.random.randint(0, 1000, num_samples)
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weights = np.ones(num_samples).astype(dtype)
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self.assertAllClose(
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self.evaluate(bincount_ops.bincount(arr, None)),
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np.bincount(arr, weights))
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def test_zero_weights(self):
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with self.session():
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self.assertAllEqual(
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self.evaluate(bincount_ops.bincount(np.arange(1000), np.zeros(1000))),
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np.zeros(1000))
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def test_negative(self):
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# unsorted_segment_sum will only report InvalidArgumentError on CPU
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with self.cached_session(), ops.device("/CPU:0"):
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with self.assertRaises(errors.InvalidArgumentError):
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self.evaluate(bincount_ops.bincount([1, 2, 3, -1, 6, 8]))
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def test_shape_function(self):
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# size must be scalar.
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with self.assertRaisesRegex(
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(ValueError, errors.InvalidArgumentError),
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"Shape must be rank 0 but is rank 1 .*Bincount"):
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gen_math_ops.bincount([1, 2, 3, 1, 6, 8], [1], [])
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# size must be positive.
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"must be non-negative"):
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gen_math_ops.bincount([1, 2, 3, 1, 6, 8], -5, [])
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# if size is a constant then the shape is known.
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v1 = gen_math_ops.bincount([1, 2, 3, 1, 6, 8], 5, [])
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self.assertAllEqual(v1.get_shape().as_list(), [5])
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# if size is a placeholder then the shape is unknown.
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with ops.Graph().as_default():
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s = array_ops.placeholder(dtype=dtypes.int32)
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v2 = gen_math_ops.bincount([1, 2, 3, 1, 6, 8], s, [])
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self.assertAllEqual(v2.get_shape().as_list(), [None])
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class BincountOpTest(test_util.TensorFlowTestCase, parameterized.TestCase):
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_bincount_all_count(self, dtype):
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np.random.seed(42)
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size = 1000
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inp = np.random.randint(0, size, (4096), dtype=dtype)
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np_out = np.bincount(inp, minlength=size)
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(input=inp, weights=[], size=size)))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_bincount_all_count_with_weights(self, dtype):
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np.random.seed(42)
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size = 1000
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inp = np.random.randint(0, size, (4096,), dtype=dtype)
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np_weight = np.random.random((4096,))
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np_out = np.bincount(inp, minlength=size, weights=np_weight)
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(
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input=inp, weights=np_weight, size=size)))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_bincount_all_binary(self, dtype):
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np.random.seed(42)
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size = 10
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inp = np.random.randint(0, size, (4096), dtype=dtype)
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np_out = np.ones((size,))
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(
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input=inp, weights=[], size=size, binary_output=True)))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_bincount_all_binary_with_weights(self, dtype):
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np.random.seed(42)
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size = 10
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inp = np.random.randint(0, size, (4096,), dtype=dtype)
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np_weight = np.random.random((4096,))
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np_out = np.ones((size,))
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(
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input=inp, weights=np_weight, size=size, binary_output=True)))
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def _test_bincount_col_count(self, num_rows, num_cols, size, dtype):
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np.random.seed(42)
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inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
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np_out = np.reshape(
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np.concatenate(
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[np.bincount(inp[j, :], minlength=size) for j in range(num_rows)],
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axis=0), (num_rows, size))
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(input=inp, weights=[], size=size)))
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def _test_bincount_col_binary(self, num_rows, num_cols, size, dtype):
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np.random.seed(42)
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inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
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np_out = np.reshape(
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np.concatenate([
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np.where(np.bincount(inp[j, :], minlength=size) > 0, 1, 0)
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for j in range(num_rows)
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],
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axis=0), (num_rows, size))
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(
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input=inp, weights=[], size=size, binary_output=True)))
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def _test_bincount_col_count_with_weights(self, num_rows, num_cols, size,
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dtype):
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np.random.seed(42)
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inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
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np_weight = np.random.random((num_rows, num_cols))
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np_out = np.reshape(
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np.concatenate([
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np.bincount(inp[j, :], weights=np_weight[j, :], minlength=size)
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for j in range(num_rows)
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],
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axis=0), (num_rows, size))
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with test_util.use_gpu():
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.dense_bincount(
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input=inp, weights=np_weight, size=size)))
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def test_col_reduce_basic(self):
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with test_util.use_gpu():
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v = self.evaluate(
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gen_math_ops.dense_bincount(
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input=[[1, 2, 3], [0, 3, 2]], weights=[], size=4))
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expected_out = [[0., 1., 1., 1.], [1., 0., 1., 1.]]
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self.assertAllEqual(expected_out, v)
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_col_reduce_shared_memory(self, dtype):
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# num_rows * num_bins less than half of max shared memory.
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num_rows = 128
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num_cols = 27
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size = 10
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self._test_bincount_col_count(num_rows, num_cols, size, dtype)
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_col_reduce_global_memory(self, dtype):
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# num_rows * num_bins more than half of max shared memory.
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num_rows = 128
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num_cols = 27
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size = 1024
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self._test_bincount_col_count(num_rows, num_cols, size, dtype)
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_col_reduce_shared_memory_with_weights(self, dtype):
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# num_rows * num_bins less than half of max shared memory.
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num_rows = 128
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num_cols = 27
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size = 100
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self._test_bincount_col_count_with_weights(num_rows, num_cols, size, dtype)
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_col_reduce_global_memory_with_weights(self, dtype):
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# num_rows * num_bins more than half of max shared memory.
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num_rows = 128
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num_cols = 27
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size = 1024
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self._test_bincount_col_count_with_weights(num_rows, num_cols, size, dtype)
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_col_reduce_binary(self, dtype):
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num_rows = 128
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num_cols = 7
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size = 10
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self._test_bincount_col_binary(num_rows, num_cols, size, dtype)
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def test_invalid_rank(self):
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with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
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"at most rank 2"):
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with test_util.use_gpu():
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self.evaluate(
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gen_math_ops.dense_bincount(
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input=[[[1, 2, 3], [0, 3, 2]]], weights=[], size=10))
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class SparseBincountOpTest(test_util.TensorFlowTestCase,
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parameterized.TestCase):
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_sparse_bincount_all_count(self, dtype):
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np.random.seed(42)
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num_rows = 128
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size = 1000
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n_elems = 4096
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inp_indices = np.random.randint(0, num_rows, (n_elems,))
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inp_vals = np.random.randint(0, size, (n_elems,), dtype=dtype)
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np_out = np.bincount(inp_vals, minlength=size)
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.sparse_bincount(
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indices=inp_indices,
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values=inp_vals,
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dense_shape=[num_rows],
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size=size,
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weights=[])))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_sparse_bincount_all_count_with_weights(self, dtype):
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np.random.seed(42)
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num_rows = 128
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size = 1000
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n_elems = 4096
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inp_indices = np.random.randint(0, num_rows, (n_elems,))
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inp_vals = np.random.randint(0, size, (n_elems,), dtype=dtype)
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inp_weight = np.random.random((n_elems,))
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np_out = np.bincount(inp_vals, minlength=size, weights=inp_weight)
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.sparse_bincount(
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indices=inp_indices,
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values=inp_vals,
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dense_shape=[num_rows],
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size=size,
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weights=inp_weight)))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_sparse_bincount_all_binary(self, dtype):
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np.random.seed(42)
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num_rows = 128
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size = 10
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n_elems = 4096
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inp_indices = np.random.randint(0, num_rows, (n_elems,))
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inp_vals = np.random.randint(0, size, (n_elems,), dtype=dtype)
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np_out = np.ones((size,))
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.sparse_bincount(
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indices=inp_indices,
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values=inp_vals,
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dense_shape=[num_rows],
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size=size,
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weights=[],
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binary_output=True)))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_sparse_bincount_all_binary_weights(self, dtype):
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np.random.seed(42)
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num_rows = 128
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size = 10
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n_elems = 4096
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inp_indices = np.random.randint(0, num_rows, (n_elems,))
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inp_vals = np.random.randint(0, size, (n_elems,), dtype=dtype)
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inp_weight = np.random.random((n_elems,))
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np_out = np.ones((size,))
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self.assertAllEqual(
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np_out,
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self.evaluate(
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gen_math_ops.sparse_bincount(
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indices=inp_indices,
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values=inp_vals,
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dense_shape=[num_rows],
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size=size,
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weights=inp_weight,
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binary_output=True)))
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@parameterized.parameters([{
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"dtype": np.int32,
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}, {
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"dtype": np.int64,
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}])
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def test_sparse_bincount_col_reduce_count(self, dtype):
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num_rows = 128
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num_cols = 27
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size = 100
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np.random.seed(42)
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inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
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np_out = np.reshape(
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np.concatenate(
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[np.bincount(inp[j, :], minlength=size) for j in range(num_rows)],
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axis=0), (num_rows, size))
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# from_dense will filter out 0s.
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inp = inp + 1
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# from_dense will cause OOM in GPU.
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with ops.device("/CPU:0"):
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inp_sparse = sparse_ops.from_dense(inp)
|
|
self.assertAllEqual(
|
|
np_out,
|
|
self.evaluate(
|
|
gen_math_ops.sparse_bincount(
|
|
indices=inp_sparse.indices,
|
|
values=inp_sparse.values - 1,
|
|
dense_shape=inp_sparse.dense_shape,
|
|
size=size,
|
|
weights=[])))
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_sparse_bincount_col_reduce_binary(self, dtype):
|
|
num_rows = 128
|
|
num_cols = 27
|
|
size = 100
|
|
np.random.seed(42)
|
|
inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
|
|
np_out = np.reshape(
|
|
np.concatenate([
|
|
np.where(np.bincount(inp[j, :], minlength=size) > 0, 1, 0)
|
|
for j in range(num_rows)
|
|
],
|
|
axis=0), (num_rows, size))
|
|
# from_dense will filter out 0s.
|
|
inp = inp + 1
|
|
# from_dense will cause OOM in GPU.
|
|
with ops.device("/CPU:0"):
|
|
inp_sparse = sparse_ops.from_dense(inp)
|
|
self.assertAllEqual(
|
|
np_out,
|
|
self.evaluate(
|
|
gen_math_ops.sparse_bincount(
|
|
indices=inp_sparse.indices,
|
|
values=inp_sparse.values - 1,
|
|
dense_shape=inp_sparse.dense_shape,
|
|
size=size,
|
|
weights=[],
|
|
binary_output=True)))
|
|
|
|
|
|
class RaggedBincountOpTest(test_util.TensorFlowTestCase,
|
|
parameterized.TestCase):
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_ragged_bincount_count(self, dtype):
|
|
x = ragged_factory_ops.constant([[], [], [3, 0, 1], [], [5, 0, 4, 4]])
|
|
expected_output = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0,
|
|
0], [1, 1, 0, 1, 0, 0],
|
|
[0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 2, 1]]
|
|
self.assertAllEqual(
|
|
expected_output,
|
|
self.evaluate(
|
|
gen_math_ops.ragged_bincount(
|
|
splits=x.row_splits, values=x.values, weights=[], size=6)))
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_ragged_bincount_binary(self, dtype):
|
|
x = ragged_factory_ops.constant([[], [], [3, 0, 1], [], [5, 0, 4, 4]])
|
|
expected_output = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0,
|
|
0], [1, 1, 0, 1, 0, 0],
|
|
[0, 0, 0, 0, 0, 0], [1, 0, 0, 0, 1, 1]]
|
|
self.assertAllEqual(
|
|
expected_output,
|
|
self.evaluate(
|
|
gen_math_ops.ragged_bincount(
|
|
splits=x.row_splits,
|
|
values=x.values,
|
|
weights=[],
|
|
size=6,
|
|
binary_output=True)))
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_ragged_bincount_count_with_weights(self, dtype):
|
|
x = ragged_factory_ops.constant([[], [], [3, 0, 1], [], [5, 0, 4, 4]])
|
|
weights = ragged_factory_ops.constant([[], [], [.1, .2, .3], [],
|
|
[.2, .5, .6, .3]])
|
|
expected_output = [[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0],
|
|
[.2, .3, 0, .1, 0, 0], [0, 0, 0, 0, 0, 0],
|
|
[.5, 0, 0, 0, .9, .2]]
|
|
self.assertAllClose(
|
|
expected_output,
|
|
self.evaluate(
|
|
gen_math_ops.ragged_bincount(
|
|
splits=x.row_splits,
|
|
values=x.values,
|
|
weights=weights.values,
|
|
size=6)))
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_ragged_bincount_count_np(self, dtype):
|
|
np.random.seed(42)
|
|
num_rows = 128
|
|
num_cols = 27
|
|
size = 1000
|
|
inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
|
|
np_out = np.reshape(
|
|
np.concatenate(
|
|
[np.bincount(inp[j, :], minlength=size) for j in range(num_rows)],
|
|
axis=0), (num_rows, size))
|
|
x = ragged_tensor.RaggedTensor.from_tensor(inp)
|
|
self.assertAllEqual(
|
|
np_out,
|
|
self.evaluate(
|
|
gen_math_ops.ragged_bincount(
|
|
splits=x.row_splits, values=x.values, weights=[], size=size)))
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_ragged_bincount_count_np_with_weights(self, dtype):
|
|
np.random.seed(42)
|
|
num_rows = 128
|
|
num_cols = 27
|
|
size = 1000
|
|
inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
|
|
np_weight = np.random.random((num_rows, num_cols))
|
|
np_out = np.reshape(
|
|
np.concatenate([
|
|
np.bincount(inp[j, :], weights=np_weight[j, :], minlength=size)
|
|
for j in range(num_rows)
|
|
],
|
|
axis=0), (num_rows, size))
|
|
x = ragged_tensor.RaggedTensor.from_tensor(inp)
|
|
self.assertAllEqual(
|
|
np_out,
|
|
self.evaluate(
|
|
gen_math_ops.ragged_bincount(
|
|
splits=x.row_splits,
|
|
values=x.values,
|
|
weights=np_weight,
|
|
size=size)))
|
|
|
|
@parameterized.parameters([{
|
|
"dtype": np.int32,
|
|
}, {
|
|
"dtype": np.int64,
|
|
}])
|
|
def test_ragged_bincount_binary_np_with_weights(self, dtype):
|
|
np.random.seed(42)
|
|
num_rows = 128
|
|
num_cols = 27
|
|
size = 1000
|
|
inp = np.random.randint(0, size, (num_rows, num_cols), dtype=dtype)
|
|
np_out = np.reshape(
|
|
np.concatenate([
|
|
np.where(np.bincount(inp[j, :], minlength=size) > 0, 1, 0)
|
|
for j in range(num_rows)
|
|
],
|
|
axis=0), (num_rows, size))
|
|
x = ragged_tensor.RaggedTensor.from_tensor(inp)
|
|
self.assertAllEqual(
|
|
np_out,
|
|
self.evaluate(
|
|
gen_math_ops.ragged_bincount(
|
|
splits=x.row_splits,
|
|
values=x.values,
|
|
weights=[],
|
|
size=size,
|
|
binary_output=True)))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
googletest.main()
|