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+# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Functional tests for pooling operations."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.compiler.tests.xla_test import XLATestCase
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import gen_nn_ops
+from tensorflow.python.ops import nn_ops
+from tensorflow.python.platform import googletest
+
+
+def NHWCToNCHW(input_tensor):
+ """Convert the input from NHWC format to NCHW.
+
+ Args:
+ input_tensor: a 4-D tensor, or a 4-element array representing the same.
+
+ Returns:
+ the converted tensor or a shape array
+ """
+ if isinstance(input_tensor, ops.Tensor):
+ return array_ops.transpose(input_tensor, [0, 3, 1, 2])
+ else:
+ return [input_tensor[0], input_tensor[3], input_tensor[1], input_tensor[2]]
+
+
+def NCHWToNHWC(input_tensor):
+ """Convert the input from NCHW format to NHWC.
+
+ Args:
+ input_tensor: a 4-D tensor, or a 4-element array representing the same.
+
+ Returns:
+ the converted tensor or a shape array
+ """
+ if isinstance(input_tensor, ops.Tensor):
+ return array_ops.transpose(input_tensor, [0, 2, 3, 1])
+ else:
+ return [input_tensor[0], input_tensor[2], input_tensor[3], input_tensor[1]]
+
+
+def GetTestConfigs():
+ """Get all the valid tests configs to run.
+
+ Returns:
+ all the valid test configs
+ """
+ test_configs = ["NHWC", "NCHW"]
+ return test_configs
+
+
+class PoolingTest(XLATestCase):
+
+ def _VerifyOneTest(self, pool_func, input_sizes, ksize, strides, padding,
+ data_format, expected):
+ """Verifies the output values of the pooling function.
+
+ Args:
+ pool_func: Function to be called, currently only co.MaxPool.
+ input_sizes: Input tensor dimensions.
+ ksize: The kernel size dimensions
+ strides: The stride dimensions
+ padding: Padding type.
+ data_format: The data format we use to run the pooling operation.
+ expected: An array containing the expected operation outputs.
+ """
+ total_size = np.prod(input_sizes)
+ # Initializes the input tensor with array containing incrementing
+ # numbers from 1.
+ x = np.array([f * 1.0 for f in range(1, total_size + 1)], dtype=np.float32)
+ x = x.reshape(input_sizes)
+ with self.test_session() as sess:
+ with self.test_scope():
+ inputs = array_ops.placeholder(dtypes.float32)
+ t = inputs
+ if data_format == "NCHW":
+ t = NHWCToNCHW(t)
+ ksize = NHWCToNCHW(ksize)
+ strides = NHWCToNCHW(strides)
+ t = pool_func(t,
+ ksize=ksize,
+ strides=strides,
+ padding=padding,
+ data_format=data_format)
+ if data_format == "NCHW":
+ t = NCHWToNHWC(t)
+ actual = sess.run(t, {inputs: x})
+ self.assertAllClose(expected, actual.flatten(), rtol=1e-5, atol=1e-6)
+
+ def _VerifyValues(self, pool_func, input_sizes, ksize, strides, padding,
+ expected):
+ """Verifies the output values of the pooling function.
+
+ Args:
+ pool_func: Function to be called, co.MaxPool, co.AvgPool,
+ or the Lua version.
+ input_sizes: Input tensor dimensions.
+ ksize: The kernel size dimensions
+ strides: The stride dimensions
+ padding: Padding type.
+ expected: An array containing the expected operation outputs.
+ """
+ for data_format in GetTestConfigs():
+ self._VerifyOneTest(pool_func, input_sizes, ksize, strides, padding,
+ data_format, expected)
+
+ def testMaxPoolValidPadding(self):
+ expected_output = [13.0, 14.0, 15.0]
+ self._VerifyValues(nn_ops.max_pool,
+ input_sizes=[1, 3, 3, 3],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 2, 1],
+ padding="VALID",
+ expected=expected_output)
+
+ def testMaxPoolSamePadding(self):
+ expected_output = [13.0, 14.0, 15.0, 16.0, 17.0, 18.0]
+ self._VerifyValues(nn_ops.max_pool,
+ input_sizes=[1, 2, 3, 3],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 2, 1],
+ padding="SAME",
+ expected=expected_output)
+
+ def testMaxPoolSamePaddingNonSquareWindow(self):
+ # input is:
+ # [1.0, 2.0
+ # 3.0 4.0]
+ #
+ # Window of [x, x] should do:
+ #
+ # [max(1.0, 2.0), max(2.0, padded0),
+ # max(3.0, 4.0), max(4.0, padded0)]
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 2, 2, 1],
+ ksize=[1, 1, 2, 1],
+ strides=[1, 1, 1, 1],
+ padding="SAME",
+ expected=[2.0, 2.0, 4.0, 4.0])
+
+ def testMaxPoolValidPaddingUnevenStride(self):
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 4, 4, 1],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 1, 2, 1],
+ padding="VALID",
+ expected=[6.0, 8.0, 10.0, 12.0, 14.0, 16.0])
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 4, 4, 1],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 1, 1],
+ padding="VALID",
+ expected=[6.0, 7.0, 8.0, 14.0, 15.0, 16.0])
+
+ def testMaxPoolSamePaddingFilter4(self):
+ expected_output = [
+ 21.0, 22.0, 23.0, 24.0, 29.0, 30.0, 31.0, 32.0, 53.0, 54.0, 55.0, 56.0,
+ 61.0, 62.0, 63.0, 64.0
+ ]
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 4, 4, 4],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 2, 1],
+ padding="SAME",
+ expected=expected_output)
+
+ def testMaxPoolSamePaddingFilter8(self):
+ expected_output = [
+ 145.0, 146.0, 147.0, 148.0, 149.0, 150.0, 151.0, 152.0, 161.0, 162.0,
+ 163.0, 164.0, 165.0, 166.0, 167.0, 168.0, 177.0, 178.0, 179.0, 180.0,
+ 181.0, 182.0, 183.0, 184.0, 185.0, 186.0, 187.0, 188.0, 189.0, 190.0,
+ 191.0, 192.0, 273.0, 274.0, 275.0, 276.0, 277.0, 278.0, 279.0, 280.0,
+ 289.0, 290.0, 291.0, 292.0, 293.0, 294.0, 295.0, 296.0, 305.0, 306.0,
+ 307.0, 308.0, 309.0, 310.0, 311.0, 312.0, 313.0, 314.0, 315.0, 316.0,
+ 317.0, 318.0, 319.0, 320.0, 401.0, 402.0, 403.0, 404.0, 405.0, 406.0,
+ 407.0, 408.0, 417.0, 418.0, 419.0, 420.0, 421.0, 422.0, 423.0, 424.0,
+ 433.0, 434.0, 435.0, 436.0, 437.0, 438.0, 439.0, 440.0, 441.0, 442.0,
+ 443.0, 444.0, 445.0, 446.0, 447.0, 448.0, 465.0, 466.0, 467.0, 468.0,
+ 469.0, 470.0, 471.0, 472.0, 481.0, 482.0, 483.0, 484.0, 485.0, 486.0,
+ 487.0, 488.0, 497.0, 498.0, 499.0, 500.0, 501.0, 502.0, 503.0, 504.0,
+ 505.0, 506.0, 507.0, 508.0, 509.0, 510.0, 511.0, 512.0
+ ]
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 8, 8, 8],
+ ksize=[1, 3, 3, 1],
+ strides=[1, 2, 2, 1],
+ padding="SAME",
+ expected=expected_output)
+
+ # Tests for DepthwiseMaxPooling on CPU only.
+ def testDepthwiseMaxPool1x1DepthWindow1(self):
+ # input is:
+ # [1.0, ..., 10.0] along depth,
+ #
+ # We maxpool by depth in patches of 2.
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 1, 1, 10],
+ ksize=[1, 1, 1, 2],
+ strides=[1, 1, 1, 2],
+ padding="SAME",
+ expected=[2.0, 4.0, 6.0, 8.0, 10.0])
+
+ def testDepthwiseMaxPool2x2DepthWindow3(self):
+ # input is:
+ #
+ # a 2x2x6 cube, and we depthwise max across 3 to produce a 2x2x2
+ # output. Each node has contiguous values, so the depthwise max
+ # should be multiples of 3.0.
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 2, 2, 6],
+ ksize=[1, 1, 1, 3],
+ strides=[1, 1, 1, 3],
+ padding="SAME",
+ expected=[3.0, 6.0, 9.0, 12.0, 15.0, 18.0, 21.0, 24.0])
+
+ def testKernelSmallerThanStrideValid(self):
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 7, 7, 1],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 3, 3, 1],
+ padding="VALID",
+ expected=[9, 12, 30, 33])
+
+ def testKernelSmallerThanStrideSame(self):
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 3, 3, 1],
+ ksize=[1, 1, 1, 1],
+ strides=[1, 2, 2, 1],
+ padding="SAME",
+ expected=[1, 3, 7, 9])
+
+ self._VerifyValues(
+ nn_ops.max_pool,
+ input_sizes=[1, 4, 4, 1],
+ ksize=[1, 1, 1, 1],
+ strides=[1, 2, 2, 1],
+ padding="SAME",
+ expected=[1, 3, 9, 11])
+
+ # Average pooling
+ def testAvgPoolValidPadding(self):
+ expected_output = [7, 8, 9]
+ self._VerifyValues(
+ nn_ops.avg_pool,
+ input_sizes=[1, 3, 3, 3],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 2, 1],
+ padding="VALID",
+ expected=expected_output)
+
+ def testAvgPoolSamePadding(self):
+ expected_output = [7., 8., 9., 11.5, 12.5, 13.5]
+ self._VerifyValues(
+ nn_ops.avg_pool,
+ input_sizes=[1, 2, 3, 3],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 2, 1],
+ padding="SAME",
+ expected=expected_output)
+
+
+class PoolGradTest(XLATestCase):
+
+ CPU_DEVICE = "/job:localhost/replica:0/task:0/cpu:0"
+
+ def _VerifyOneTest(self, pool_func, pool_grad_func, input_sizes, ksize,
+ strides, padding, data_format):
+ """Verifies the output values of the pooling gradient function.
+
+ Args:
+ pool_func: Forward pooling function
+ pool_grad_func: Pooling gradient function for pool_grad_func
+ input_sizes: Input tensor dimensions.
+ ksize: The kernel size dimensions
+ strides: The stride dimensions
+ padding: Padding type.
+ data_format: The data format we use to run the pooling operation.
+ """
+ total_size = np.prod(input_sizes)
+ x = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_sizes)
+ with self.test_session() as sess:
+ # Use the forward pool function to compute some corresponding outputs
+ # (needed for the CPU device, and we need the shape in both cases).
+ with ops.device(self.CPU_DEVICE):
+ inputs = array_ops.placeholder(dtypes.float32, shape=input_sizes)
+ outputs = pool_func(
+ inputs,
+ ksize=ksize,
+ strides=strides,
+ padding=padding,
+ data_format="NHWC")
+
+ output_vals = np.array(sess.run(outputs, {inputs: x}))
+ output_gradient_vals = np.arange(
+ 1, output_vals.size + 1, dtype=np.float32)
+ output_gradient_vals = output_gradient_vals.reshape(output_vals.shape)
+
+ # Use the Tensorflow CPU pooling gradient to compute the expected input
+ # gradients.
+ with ops.device(self.CPU_DEVICE):
+ output_gradients = array_ops.placeholder(
+ dtypes.float32, shape=output_vals.shape)
+ expected_input_gradients = pool_grad_func(
+ inputs,
+ outputs,
+ output_gradients,
+ ksize=ksize,
+ strides=strides,
+ padding=padding,
+ data_format="NHWC")
+ expected_input_gradient_vals = sess.run(
+ expected_input_gradients,
+ {inputs: x,
+ output_gradients: output_gradient_vals})
+
+ # Run the gradient op on the XLA device
+ with self.test_scope():
+ outputs = array_ops.placeholder(dtypes.float32, shape=output_vals.shape)
+ xla_inputs = inputs
+ xla_outputs = outputs
+ xla_output_gradients = output_gradients
+ xla_ksize = ksize
+ xla_strides = strides
+ if data_format == "NCHW":
+ xla_inputs = NHWCToNCHW(inputs)
+ xla_outputs = NHWCToNCHW(outputs)
+ xla_output_gradients = NHWCToNCHW(output_gradients)
+ xla_ksize = NHWCToNCHW(ksize)
+ xla_strides = NHWCToNCHW(strides)
+ actual_input_gradients = pool_grad_func(
+ xla_inputs,
+ xla_outputs,
+ xla_output_gradients,
+ ksize=xla_ksize,
+ strides=xla_strides,
+ padding=padding,
+ data_format=data_format)
+ if data_format == "NCHW":
+ actual_input_gradients = NCHWToNHWC(actual_input_gradients)
+ actual = sess.run(actual_input_gradients, {
+ inputs: x,
+ outputs: output_vals,
+ output_gradients: output_gradient_vals
+ })
+
+ # Compare the Tensorflow and XLA results.
+ self.assertAllClose(
+ expected_input_gradient_vals.flatten(),
+ actual.flatten(),
+ rtol=1e-5,
+ atol=1e-6)
+ self.assertShapeEqual(actual, inputs)
+
+ def _VerifyValues(self, pool_func, pool_grad_func, input_sizes, ksize,
+ strides, padding):
+ """Verifies the output values of the pooling function.
+
+ Args:
+ pool_func: Pooling function to be called, e.g., tf.nn.max_pool
+ pool_grad_func: Corresponding pooling gradient function.
+ input_sizes: Input tensor dimensions.
+ ksize: The kernel size dimensions
+ strides: The stride dimensions
+ padding: Padding type.
+ """
+ for data_format in GetTestConfigs():
+ self._VerifyOneTest(pool_func, pool_grad_func, input_sizes, ksize,
+ strides, padding, data_format)
+
+ def _TestPooling(self, forward_op, backward_op):
+ # 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")
+
+ # 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")
+
+ # 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")
+
+ # 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")
+ self._VerifyValues(
+ forward_op,
+ backward_op,
+ input_sizes=[1, 4, 4, 1],
+ ksize=[1, 2, 2, 1],
+ strides=[1, 2, 1, 1],
+ padding="VALID")
+
+ # 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")
+
+ # 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")
+
+ def testMaxPool(self):
+ self._TestPooling(nn_ops.max_pool, gen_nn_ops._max_pool_grad)
+
+ 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()
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