822b035fd1
Also added unit tests and enabled all related functional tests for pooling ops. PiperOrigin-RevId: 321433980 Change-Id: Ia175333d81398deadfcc9e18bb7eeabe8e0c67de
608 lines
20 KiB
Python
608 lines
20 KiB
Python
# Copyright 2017 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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"""Functional tests for pooling operations."""
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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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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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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 gen_nn_ops
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from tensorflow.python.ops import nn_ops
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from tensorflow.python.platform import googletest
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def NHWCToNCHW(input_tensor):
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"""Convert the input from NHWC format to NCHW.
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Args:
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input_tensor: a 4-D tensor, or a 4-element array representing the same.
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Returns:
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the converted tensor or a shape array
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"""
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if isinstance(input_tensor, ops.Tensor):
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return array_ops.transpose(input_tensor, [0, 3, 1, 2])
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else:
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return [input_tensor[0], input_tensor[3], input_tensor[1], input_tensor[2]]
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def NCHWToNHWC(input_tensor):
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"""Convert the input from NCHW format to NHWC.
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Args:
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input_tensor: a 4-D tensor, or a 4-element array representing the same.
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Returns:
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the converted tensor or a shape array
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"""
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if isinstance(input_tensor, ops.Tensor):
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return array_ops.transpose(input_tensor, [0, 2, 3, 1])
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else:
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return [input_tensor[0], input_tensor[2], input_tensor[3], input_tensor[1]]
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def GetTestConfigs():
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"""Get all the valid tests configs to run.
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Returns:
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all the valid test configs
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"""
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test_configs = ["NHWC", "NCHW"]
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return test_configs
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class PoolingTest(xla_test.XLATestCase):
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def _VerifyOneTest(self, pool_func, input_sizes, ksize, strides, padding,
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data_format, expected):
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"""Verifies the output values of the pooling function.
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Args:
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pool_func: Function to be called, currently only co.MaxPool.
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input_sizes: Input tensor dimensions.
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ksize: The kernel size dimensions
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strides: The stride dimensions
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padding: Padding type.
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data_format: The data format we use to run the pooling operation.
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expected: An array containing the expected operation outputs.
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"""
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total_size = np.prod(input_sizes)
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# Initializes the input tensor with array containing incrementing
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# numbers from 1.
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x = np.array([f * 1.0 for f in range(1, total_size + 1)], dtype=np.float32)
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x = x.reshape(input_sizes)
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with self.session() as sess:
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with self.test_scope():
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inputs = array_ops.placeholder(dtypes.float32)
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t = inputs
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if data_format == "NCHW":
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t = NHWCToNCHW(t)
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ksize = NHWCToNCHW(ksize)
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strides = NHWCToNCHW(strides)
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t = pool_func(t,
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ksize=ksize,
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strides=strides,
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padding=padding,
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data_format=data_format)
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if data_format == "NCHW":
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t = NCHWToNHWC(t)
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actual = sess.run(t, {inputs: x})
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self.assertAllClose(expected, actual.flatten(), rtol=1e-5, atol=1e-6)
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def _VerifyValues(self, pool_func, input_sizes, ksize, strides, padding,
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expected):
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"""Verifies the output values of the pooling function.
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Args:
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pool_func: Function to be called, co.MaxPool, co.AvgPool,
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or the Lua version.
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input_sizes: Input tensor dimensions.
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ksize: The kernel size dimensions
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strides: The stride dimensions
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padding: Padding type.
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expected: An array containing the expected operation outputs.
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"""
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for data_format in GetTestConfigs():
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self._VerifyOneTest(pool_func, input_sizes, ksize, strides, padding,
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data_format, expected)
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def testMaxPoolValidPadding(self):
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expected_output = [13.0, 14.0, 15.0]
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self._VerifyValues(nn_ops.max_pool,
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input_sizes=[1, 3, 3, 3],
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1],
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padding="VALID",
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expected=expected_output)
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def testMaxPoolSamePadding(self):
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expected_output = [13.0, 14.0, 15.0, 16.0, 17.0, 18.0]
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self._VerifyValues(nn_ops.max_pool,
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input_sizes=[1, 2, 3, 3],
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1],
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padding="SAME",
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expected=expected_output)
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def testMaxPoolSamePaddingNonSquareWindow(self):
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# input is:
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# [1.0, 2.0
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# 3.0 4.0]
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#
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# Window of [x, x] should do:
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#
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# [max(1.0, 2.0), max(2.0, padded0),
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# max(3.0, 4.0), max(4.0, padded0)]
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 2, 2, 1],
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ksize=[1, 1, 2, 1],
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strides=[1, 1, 1, 1],
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padding="SAME",
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expected=[2.0, 2.0, 4.0, 4.0])
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def testMaxPoolValidPaddingUnevenStride(self):
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 4, 4, 1],
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ksize=[1, 2, 2, 1],
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strides=[1, 1, 2, 1],
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padding="VALID",
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expected=[6.0, 8.0, 10.0, 12.0, 14.0, 16.0])
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 4, 4, 1],
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 1, 1],
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padding="VALID",
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expected=[6.0, 7.0, 8.0, 14.0, 15.0, 16.0])
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def testMaxPoolSamePaddingFilter4(self):
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expected_output = [
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21.0, 22.0, 23.0, 24.0, 29.0, 30.0, 31.0, 32.0, 53.0, 54.0, 55.0, 56.0,
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61.0, 62.0, 63.0, 64.0
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]
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 4, 4, 4],
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1],
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padding="SAME",
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expected=expected_output)
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def testMaxPoolSamePaddingFilter8(self):
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expected_output = [
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145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 161.0, 162.0,
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163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 177.0, 178.0, 179.0, 180.0,
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181.0, 182.0, 183.0, 184.0, 185.0, 186.0, 187.0, 188.0, 189.0, 190.0,
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191.0, 192.0, 273.0, 274.0, 275.0, 276.0, 277.0, 278.0, 279.0, 280.0,
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289.0, 290.0, 291.0, 292.0, 293.0, 294.0, 295.0, 296.0, 305.0, 306.0,
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307.0, 308.0, 309.0, 310.0, 311.0, 312.0, 313.0, 314.0, 315.0, 316.0,
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317.0, 318.0, 319.0, 320.0, 401.0, 402.0, 403.0, 404.0, 405.0, 406.0,
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407.0, 408.0, 417.0, 418.0, 419.0, 420.0, 421.0, 422.0, 423.0, 424.0,
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433.0, 434.0, 435.0, 436.0, 437.0, 438.0, 439.0, 440.0, 441.0, 442.0,
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443.0, 444.0, 445.0, 446.0, 447.0, 448.0, 465.0, 466.0, 467.0, 468.0,
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469.0, 470.0, 471.0, 472.0, 481.0, 482.0, 483.0, 484.0, 485.0, 486.0,
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487.0, 488.0, 497.0, 498.0, 499.0, 500.0, 501.0, 502.0, 503.0, 504.0,
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505.0, 506.0, 507.0, 508.0, 509.0, 510.0, 511.0, 512.0
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]
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 8, 8, 8],
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ksize=[1, 3, 3, 1],
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strides=[1, 2, 2, 1],
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padding="SAME",
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expected=expected_output)
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# Tests for DepthwiseMaxPooling on CPU only.
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def testDepthwiseMaxPool1x1DepthWindow1(self):
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# input is:
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# [1.0, ..., 10.0] along depth,
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#
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# We maxpool by depth in patches of 2.
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 1, 1, 10],
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ksize=[1, 1, 1, 2],
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strides=[1, 1, 1, 2],
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padding="SAME",
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expected=[2.0, 4.0, 6.0, 8.0, 10.0])
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def testDepthwiseMaxPool2x2DepthWindow3(self):
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# input is:
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#
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# a 2x2x6 cube, and we depthwise max across 3 to produce a 2x2x2
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# output. Each node has contiguous values, so the depthwise max
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# should be multiples of 3.0.
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 2, 2, 6],
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ksize=[1, 1, 1, 3],
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strides=[1, 1, 1, 3],
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padding="SAME",
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expected=[3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])
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def testKernelSmallerThanStrideValid(self):
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 7, 7, 1],
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ksize=[1, 2, 2, 1],
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strides=[1, 3, 3, 1],
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padding="VALID",
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expected=[9, 12, 30, 33])
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def testKernelSmallerThanStrideSame(self):
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 3, 3, 1],
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ksize=[1, 1, 1, 1],
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strides=[1, 2, 2, 1],
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padding="SAME",
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expected=[1, 3, 7, 9])
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self._VerifyValues(
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nn_ops.max_pool,
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input_sizes=[1, 4, 4, 1],
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ksize=[1, 1, 1, 1],
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strides=[1, 2, 2, 1],
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padding="SAME",
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expected=[1, 3, 9, 11])
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# Average pooling
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def testAvgPoolValidPadding(self):
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expected_output = [7, 8, 9]
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self._VerifyValues(
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nn_ops.avg_pool,
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input_sizes=[1, 3, 3, 3],
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1],
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padding="VALID",
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expected=expected_output)
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def testAvgPoolSamePadding(self):
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expected_output = [7., 8., 9., 11.5, 12.5, 13.5]
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self._VerifyValues(
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nn_ops.avg_pool,
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input_sizes=[1, 2, 3, 3],
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ksize=[1, 2, 2, 1],
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strides=[1, 2, 2, 1],
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padding="SAME",
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expected=expected_output)
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class PoolGradTest(xla_test.XLATestCase):
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CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
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def _VerifyOneTest(self,
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pool_func,
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pool_grad_func,
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input_sizes,
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ksize,
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strides,
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padding,
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data_format,
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pool_grad_grad_func=None):
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"""Verifies the output values of the pooling gradient function.
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Args:
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pool_func: Forward pooling function
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pool_grad_func: Pooling gradient function for pool_grad_func
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input_sizes: Input tensor dimensions.
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ksize: The kernel size dimensions
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strides: The stride dimensions
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padding: Padding type.
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data_format: The data format we use to run the pooling operation.
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pool_grad_grad_func: Second-order gradient function, if available.
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"""
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total_size = np.prod(input_sizes)
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# TODO(b/73062247): MaxPoolGradGrad can confuse gradients when x is equally
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# maximal at 16 bits. Switch to np.random.randn when resolved.
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x = np.arange(1, total_size + 1, dtype=np.float32)
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x *= (np.random.randint(2, size=total_size) * 2 - 1) # Flip signs randomly
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# Verify some specifically interesting values...
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x[np.random.choice(total_size)] = np.inf
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x[np.random.choice(total_size)] = -np.inf
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# TODO(b/74222344): Fix nan handling for max pool grad.
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# x[np.random.choice(total_size)] = np.nan
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x = x.reshape(input_sizes)
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with self.session() as sess:
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# Use the forward pool function to compute some corresponding outputs
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# (needed for the CPU device, and we need the shape in both cases).
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with ops.device(self.CPU_DEVICE):
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inputs = array_ops.placeholder(dtypes.float32, shape=input_sizes)
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outputs = pool_func(
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inputs,
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ksize=ksize,
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strides=strides,
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padding=padding,
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data_format="NHWC")
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output_vals = np.array(sess.run(outputs, {inputs: x}))
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output_gradient_vals = np.arange(
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1, output_vals.size + 1, dtype=np.float32)
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output_gradient_vals = output_gradient_vals.reshape(output_vals.shape)
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output_grad_grad_vals = np.arange(1, x.size + 1, dtype=np.float32)
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output_grad_grad_vals = output_grad_grad_vals.reshape(x.shape)
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# Use the Tensorflow CPU pooling gradient to compute the expected input
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# gradients.
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with ops.device(self.CPU_DEVICE):
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output_gradients = array_ops.placeholder(
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dtypes.float32, shape=output_vals.shape)
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expected_input_gradients = pool_grad_func(
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inputs,
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outputs,
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output_gradients,
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ksize=ksize,
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strides=strides,
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padding=padding,
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data_format="NHWC")
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expected_input_gradient_vals = sess.run(
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expected_input_gradients,
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{inputs: x,
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output_gradients: output_gradient_vals})
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output_grad_gradients = array_ops.placeholder(
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dtypes.float32, shape=expected_input_gradient_vals.shape)
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if pool_grad_grad_func is not None:
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expected_grad_gradients = pool_grad_grad_func(
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inputs,
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outputs,
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output_grad_gradients,
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ksize=ksize,
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strides=strides,
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padding=padding,
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data_format="NHWC")
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expected_grad_gradients_vals = sess.run(expected_grad_gradients, {
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inputs: x,
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output_grad_gradients: output_grad_grad_vals
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})
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# Run the gradient op on the XLA device
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with self.test_scope():
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outputs = array_ops.placeholder(dtypes.float32, shape=output_vals.shape)
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xla_inputs = inputs
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xla_outputs = outputs
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xla_output_gradients = output_gradients
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xla_output_grad_gradients = output_grad_gradients
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xla_ksize = ksize
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xla_strides = strides
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if data_format == "NCHW":
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xla_inputs = NHWCToNCHW(inputs)
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xla_outputs = NHWCToNCHW(outputs)
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xla_output_gradients = NHWCToNCHW(output_gradients)
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xla_output_grad_gradients = NHWCToNCHW(output_grad_gradients)
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xla_ksize = NHWCToNCHW(ksize)
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xla_strides = NHWCToNCHW(strides)
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actual_input_gradients = pool_grad_func(
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xla_inputs,
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xla_outputs,
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xla_output_gradients,
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ksize=xla_ksize,
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strides=xla_strides,
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padding=padding,
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data_format=data_format)
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if data_format == "NCHW":
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actual_input_gradients = NCHWToNHWC(actual_input_gradients)
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if pool_grad_grad_func is not None:
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actual_grad_gradients = pool_grad_grad_func(
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xla_inputs,
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xla_outputs,
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xla_output_grad_gradients,
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ksize=xla_ksize,
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strides=xla_strides,
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padding=padding,
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data_format=data_format)
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if data_format == "NCHW":
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actual_grad_gradients = NCHWToNHWC(actual_grad_gradients)
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actual_input_gradients_vals = sess.run(actual_input_gradients, {
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inputs: x,
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outputs: output_vals,
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output_gradients: output_gradient_vals
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})
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# Compare the Tensorflow and XLA results.
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self.assertAllClose(
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expected_input_gradient_vals,
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actual_input_gradients_vals,
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rtol=1e-4,
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atol=1e-6)
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self.assertShapeEqual(actual_input_gradients_vals, inputs)
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if pool_grad_grad_func is not None:
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actual_grad_gradients_vals = sess.run(
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actual_grad_gradients, {
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inputs: x,
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outputs: output_vals,
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output_grad_gradients: output_grad_grad_vals
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})
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# Compare the Tensorflow and XLA results.
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self.assertAllClose(
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expected_grad_gradients_vals,
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actual_grad_gradients_vals,
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rtol=1e-4,
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atol=1e-6)
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self.assertShapeEqual(actual_grad_gradients_vals, outputs)
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def _VerifyValues(self,
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pool_func,
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pool_grad_func,
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input_sizes,
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ksize,
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strides,
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padding,
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pool_grad_grad_func=None):
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"""Verifies the output values of the pooling function.
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Args:
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pool_func: Pooling function to be called, e.g., tf.nn.max_pool2d
|
|
pool_grad_func: Corresponding pooling gradient function.
|
|
input_sizes: Input tensor dimensions.
|
|
ksize: The kernel size dimensions
|
|
strides: The stride dimensions
|
|
padding: Padding type.
|
|
pool_grad_grad_func: Second-order gradient function, if available.
|
|
"""
|
|
for data_format in GetTestConfigs():
|
|
self._VerifyOneTest(
|
|
pool_func,
|
|
pool_grad_func,
|
|
input_sizes,
|
|
ksize,
|
|
strides,
|
|
padding,
|
|
data_format,
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
def _TestPooling(self, forward_op, backward_op, pool_grad_grad_func=None):
|
|
# VALID padding
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 3, 3, 3],
|
|
ksize=[1, 2, 2, 1],
|
|
strides=[1, 2, 2, 1],
|
|
padding="VALID",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
# SAME padding
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 2, 3, 3],
|
|
ksize=[1, 2, 2, 1],
|
|
strides=[1, 2, 2, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
# SAME padding, non square window
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 2, 2, 1],
|
|
ksize=[1, 1, 2, 1],
|
|
strides=[1, 1, 1, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
# VALID padding, uneven stride
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 4, 4, 1],
|
|
ksize=[1, 2, 2, 1],
|
|
strides=[1, 1, 2, 1],
|
|
padding="VALID",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 4, 4, 1],
|
|
ksize=[1, 2, 2, 1],
|
|
strides=[1, 2, 1, 1],
|
|
padding="VALID",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
# SAME padding, size 4 input
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 4, 4, 4],
|
|
ksize=[1, 2, 2, 1],
|
|
strides=[1, 2, 2, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
# SAME padding, size 8 input
|
|
self._VerifyValues(
|
|
forward_op,
|
|
backward_op,
|
|
input_sizes=[1, 8, 8, 8],
|
|
ksize=[1, 3, 3, 1],
|
|
strides=[1, 2, 2, 1],
|
|
padding="SAME",
|
|
pool_grad_grad_func=pool_grad_grad_func)
|
|
|
|
@test_util.disable_mlir_bridge("TODO(b/159845178): Implement support for "
|
|
"MaxPoolGradGrad op in MLIR-based bridge")
|
|
def testMaxPool(self):
|
|
self._TestPooling(
|
|
nn_ops.max_pool,
|
|
gen_nn_ops.max_pool_grad,
|
|
pool_grad_grad_func=gen_nn_ops.max_pool_grad_grad)
|
|
|
|
# TODO(b/159845178): Remove this once MLIR bridge supports MaxPoolGradGrad
|
|
# (then `testMaxPool` test will be sufficient)
|
|
def testMaxPoolNoGradGrad(self):
|
|
self._TestPooling(
|
|
nn_ops.max_pool, gen_nn_ops.max_pool_grad, pool_grad_grad_func=None)
|
|
|
|
def testAvgPool(self):
|
|
# Wrapper around AvgPoolGrad that ignores extra arguments needed by
|
|
# MaxPoolGrad.
|
|
def AvgPoolGrad(inputs, outputs, output_gradients, ksize, strides, padding,
|
|
data_format):
|
|
del outputs # Unused by average-pooling gradients.
|
|
return gen_nn_ops.avg_pool_grad(
|
|
inputs.get_shape().as_list(),
|
|
output_gradients,
|
|
ksize=ksize,
|
|
strides=strides,
|
|
padding=padding,
|
|
data_format=data_format)
|
|
|
|
self._TestPooling(nn_ops.avg_pool, AvgPoolGrad)
|
|
|
|
# The CPU implementation of AvgPoolGrad doesn't accept kernels smaller than
|
|
# the stride size, so we only run the following tests on MaxPoolGrad.
|
|
|
|
def testMaxPoolKernelSmallerThanStrideValid(self):
|
|
self._VerifyValues(
|
|
nn_ops.max_pool,
|
|
gen_nn_ops.max_pool_grad,
|
|
input_sizes=[1, 7, 7, 1],
|
|
ksize=[1, 2, 2, 1],
|
|
strides=[1, 3, 3, 1],
|
|
padding="VALID")
|
|
|
|
def testMaxPoolKernelSmallerThanStrideSame(self):
|
|
self._VerifyValues(
|
|
nn_ops.max_pool,
|
|
gen_nn_ops.max_pool_grad,
|
|
input_sizes=[1, 3, 3, 1],
|
|
ksize=[1, 1, 1, 1],
|
|
strides=[1, 2, 2, 1],
|
|
padding="SAME")
|
|
|
|
self._VerifyValues(
|
|
nn_ops.max_pool,
|
|
gen_nn_ops.max_pool_grad,
|
|
input_sizes=[1, 4, 4, 1],
|
|
ksize=[1, 1, 1, 1],
|
|
strides=[1, 2, 2, 1],
|
|
padding="SAME")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
googletest.main()
|