| # 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.""" |
| |
| import numpy as np |
| |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import errors |
| from tensorflow.python.framework import ops |
| from tensorflow.python.framework import test_util |
| 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(xla_test.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.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(xla_test.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, |
| pool_grad_grad_func=None): |
| """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. |
| pool_grad_grad_func: Second-order gradient function, if available. |
| """ |
| total_size = np.prod(input_sizes) |
| # TODO(b/73062247): MaxPoolGradGrad can confuse gradients when x is equally |
| # maximal at 16 bits. Switch to np.random.randn when resolved. |
| x = np.arange(1, total_size + 1, dtype=np.float32) |
| x *= (np.random.randint(2, size=total_size) * 2 - 1) # Flip signs randomly |
| # Verify some specifically interesting values... |
| x[np.random.choice(total_size)] = np.inf |
| x[np.random.choice(total_size)] = -np.inf |
| # TODO(b/74222344): Fix nan handling for max pool grad. |
| # x[np.random.choice(total_size)] = np.nan |
| x = x.reshape(input_sizes) |
| with self.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) |
| output_grad_grad_vals = np.arange(1, x.size + 1, dtype=np.float32) |
| output_grad_grad_vals = output_grad_grad_vals.reshape(x.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}) |
| |
| output_grad_gradients = array_ops.placeholder( |
| dtypes.float32, shape=expected_input_gradient_vals.shape) |
| if pool_grad_grad_func is not None: |
| expected_grad_gradients = pool_grad_grad_func( |
| inputs, |
| outputs, |
| output_grad_gradients, |
| ksize=ksize, |
| strides=strides, |
| padding=padding, |
| data_format="NHWC") |
| expected_grad_gradients_vals = sess.run(expected_grad_gradients, { |
| inputs: x, |
| output_grad_gradients: output_grad_grad_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_output_grad_gradients = output_grad_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_output_grad_gradients = NHWCToNCHW(output_grad_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) |
| if pool_grad_grad_func is not None: |
| actual_grad_gradients = pool_grad_grad_func( |
| xla_inputs, |
| xla_outputs, |
| xla_output_grad_gradients, |
| ksize=xla_ksize, |
| strides=xla_strides, |
| padding=padding, |
| data_format=data_format) |
| if data_format == "NCHW": |
| actual_grad_gradients = NCHWToNHWC(actual_grad_gradients) |
| actual_input_gradients_vals = 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, |
| actual_input_gradients_vals, |
| rtol=1e-4, |
| atol=1e-6) |
| self.assertShapeEqual(actual_input_gradients_vals, inputs) |
| |
| if pool_grad_grad_func is not None: |
| actual_grad_gradients_vals = sess.run( |
| actual_grad_gradients, { |
| inputs: x, |
| outputs: output_vals, |
| output_grad_gradients: output_grad_grad_vals |
| }) |
| |
| # Compare the Tensorflow and XLA results. |
| self.assertAllClose( |
| expected_grad_gradients_vals, |
| actual_grad_gradients_vals, |
| rtol=1e-4, |
| atol=1e-6) |
| self.assertShapeEqual(actual_grad_gradients_vals, outputs) |
| |
| def _VerifyValues(self, |
| pool_func, |
| pool_grad_func, |
| input_sizes, |
| ksize, |
| strides, |
| padding, |
| pool_grad_grad_func=None): |
| """Verifies the output values of the pooling function. |
| |
| Args: |
| 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) |
| |
| 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) |
| |
| 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) |
| |
| @test_util.disable_mlir_bridge( |
| "TODO(b/266613412): investigate FPE in AvgPoolGrad for TPU" |
| ) |
| def testAvgPoolGradSamePaddingZeroStrideZeroSize(self): |
| output_gradient_vals = np.array([0.39117979], dtype=np.float32) |
| output_gradient_vals = output_gradient_vals.reshape([1, 1, 1, 1]) |
| with self.session() as sess: |
| with self.test_scope(): |
| output_gradients = array_ops.placeholder( |
| dtypes.float32, shape=output_gradient_vals.shape |
| ) |
| t = gen_nn_ops.avg_pool_grad( |
| orig_input_shape=[1, 0, 0, 0], |
| grad=output_gradients, |
| ksize=[1, 0, 0, 0], |
| strides=[1, 0, 0, 0], |
| padding="SAME", |
| data_format="NCHW", |
| ) |
| with self.assertRaisesRegex( |
| errors.InvalidArgumentError, |
| ( |
| "Sliding window ksize field for dimension 1 must be positive but" |
| " is 0" |
| ), |
| ): |
| sess.run(t, {output_gradients: output_gradient_vals}) |
| |
| # 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() |
