f4094a3abf
ROCm platform currently does not support Pooling Ops with 3D Tensors This commit skips subtests (within python unit-tests) that test this functionality. The "skip" is guarded by the call to "is_built_with_rocm()", and hence these unit-tests will not be affected in any way when running with TF which was not built with ROCm support (i.e. `--config=rocm`)
396 lines
14 KiB
Python
396 lines
14 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 unified pooling functionality in tensorflow.ops.nn."""
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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 math
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import gradient_checker
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from tensorflow.python.ops import nn_ops
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import tensorflow.python.ops.nn_grad # pylint: disable=unused-import
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from tensorflow.python.platform import test
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def pool_direct_single_axis(
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input, # pylint: disable=redefined-builtin
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axis,
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window_size,
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pooling_type,
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padding,
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dilation_rate,
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stride):
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"""Numpy implementation of pooling along a single axis.
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This is intended for testing only, and therefore isn't particularly efficient.
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See pool_direct below for the meaning of the arguments.
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Args:
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input: numpy array.
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axis: axis along which to perform pooling.
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window_size: int >= 1. Size of pooling window within axis.
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pooling_type: either "MAX" or "AVG".
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padding: either "SAME" or "VALID".
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dilation_rate: int >= 1. Dilation factor for window, i.e. stride at which
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to sample input.
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stride: int >= 1. Stride at which to generate output.
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Returns:
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pooling output array of rank N+2.
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Raises:
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ValueError: if arguments are invalid.
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"""
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effective_window_size = (window_size - 1) * dilation_rate + 1
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input_size = input.shape[axis]
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if padding == "SAME":
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output_size = int(math.ceil(input_size / stride))
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total_padding_amount = max(
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0, (output_size - 1) * stride + effective_window_size - input_size)
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before_padding = total_padding_amount // 2
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elif padding == "VALID":
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output_size = int(
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math.ceil((input_size - effective_window_size + 1) / stride))
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before_padding = 0
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else:
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raise ValueError("Unsupported padding type: %r" % (padding,))
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output_shape = input.shape[:axis] + (output_size,) + input.shape[axis + 1:]
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output = np.zeros(output_shape, input.dtype)
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initial_dim_selector = tuple(np.s_[:] for _ in range(axis))
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if pooling_type == "MAX":
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pooling_func = np.max
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elif pooling_type == "AVG":
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pooling_func = np.mean
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else:
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raise ValueError("Unsupported pooling type: %r" % (pooling_type,))
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for output_pos in range(output_size):
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input_start_pos = output_pos * stride - before_padding
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input_end_pos = min(input_start_pos + effective_window_size, input_size)
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if input_start_pos < 0:
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input_start_pos += dilation_rate
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input_slice = np.s_[input_start_pos:input_end_pos:dilation_rate]
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output[initial_dim_selector + (output_pos,)] = pooling_func(
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input[initial_dim_selector + (input_slice,)], axis=axis)
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return output
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def pool_direct(
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input, # pylint: disable=redefined-builtin
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window_shape,
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pooling_type,
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padding, # pylint: disable=redefined-builtin
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dilation_rate,
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strides,
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data_format=None):
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"""Numpy implementation of pooling.
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This is intended for testing only, and therefore isn't particularly efficient.
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See tensorflow.nn.pool.
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Args:
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input: numpy array of rank N+2.
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window_shape: Sequence of N ints >= 1.
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pooling_type: either "MAX" or "AVG".
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padding: either "SAME" or "VALID".
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dilation_rate: Sequence of N ints >= 1.
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strides: Sequence of N ints >= 1.
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data_format: If specified and starts with "NC", indicates that second
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dimension, rather than the last dimension, specifies the channel.
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Returns:
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pooling output array of rank N+2.
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Raises:
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ValueError: if arguments are invalid.
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"""
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if data_format is None or not data_format.startswith("NC"):
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spatial_start_dim = 1
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else:
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spatial_start_dim = 2
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output = input
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for i in range(len(window_shape)):
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output = pool_direct_single_axis(
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input=output,
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axis=i + spatial_start_dim,
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window_size=window_shape[i],
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pooling_type=pooling_type,
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padding=padding,
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dilation_rate=dilation_rate[i],
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stride=strides[i])
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return output
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class PoolingTest(test.TestCase):
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def _test(self, input_shape, **kwargs):
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# Use negative numbers to make sure there isn't any zero padding getting
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# used.
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x = -np.arange(
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np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
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y1 = pool_direct(input=x, **kwargs)
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y2 = nn_ops.pool(input=x, **kwargs)
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self.assertAllClose(y1, self.evaluate(y2), rtol=1e-2, atol=1e-2)
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def testPoolSimple(self):
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["MAX", "AVG"]:
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self._test(
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input_shape=[1, 1, 10, 1],
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window_shape=[1, 3],
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1, 1],
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strides=[1, 2])
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def testPool1D(self):
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["MAX", "AVG"]:
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for input_shape in [[2, 9, 2], [2, 10, 2]]:
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for window_shape in [[1], [2], [3]]:
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if padding != "SAME":
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for dilation_rate in [[1], [2], [3]]:
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=dilation_rate,
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strides=[1])
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for strides in [[1], [2], [3]]:
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if np.any(np.array(strides) > window_shape):
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continue
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1],
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strides=strides)
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def testPool2D(self):
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["MAX", "AVG"]:
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for input_shape in [[2, 9, 10, 2], [2, 10, 9, 2]]:
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for window_shape in [[1, 1], [2, 1], [2, 3]]:
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if padding != "SAME":
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for dilation_rate in [[1, 1], [2, 1], [1, 2], [2, 3]]:
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=dilation_rate,
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strides=[1, 1])
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for strides in [[1, 1], [2, 1], [1, 2], [2, 3]]:
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if np.any(np.array(strides) > window_shape):
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continue
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1, 1],
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strides=strides)
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def testPool3D(self):
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if test.is_built_with_rocm():
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self.skipTest("Pooling with 3D tensors is not supported in ROCm")
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["MAX", "AVG"]:
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for input_shape in [[2, 9, 10, 11, 2], [2, 10, 9, 11, 2]]:
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for window_shape in [[1, 1, 1], [2, 1, 2], [2, 3, 2]]:
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if padding != "SAME":
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for dilation_rate in [[1, 1, 1], [2, 1, 2], [1, 2, 2],
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[2, 3, 3]]:
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=dilation_rate,
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strides=[1, 1, 1])
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for strides in [[1, 1, 1], [2, 1, 2], [1, 2, 2], [2, 3, 3]]:
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if np.any(np.array(strides) > window_shape):
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continue
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1, 1, 1],
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strides=strides)
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def testPoolNC(self):
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if test.is_gpu_available(cuda_only=True):
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# "NC*" format is currently only supported on CUDA.
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with self.session(use_gpu=True):
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for padding in ["SAME", "VALID"]:
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self._test(
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input_shape=[2, 2, 9],
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window_shape=[2],
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padding=padding,
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pooling_type="MAX",
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strides=[1],
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dilation_rate=[1],
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data_format="NCW")
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self._test(
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input_shape=[2, 2, 9],
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window_shape=[2],
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padding=padding,
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pooling_type="MAX",
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strides=[2],
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dilation_rate=[1],
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data_format="NCW")
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self._test(
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input_shape=[2, 2, 7, 9],
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window_shape=[2, 2],
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padding=padding,
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pooling_type="MAX",
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strides=[1, 2],
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dilation_rate=[1, 1],
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data_format="NCHW")
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if test.is_built_with_rocm():
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# Pooling with 3D tensors is not supported in ROCm
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continue
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self._test(
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input_shape=[2, 2, 7, 5, 3],
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window_shape=[2, 2, 2],
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padding=padding,
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pooling_type="MAX",
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strides=[1, 2, 1],
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dilation_rate=[1, 1, 1],
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data_format="NCDHW")
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self._test(
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input_shape=[2, 2, 7, 9],
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window_shape=[2, 2],
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padding="VALID",
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pooling_type="MAX",
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strides=[1, 1],
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dilation_rate=[2, 2],
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data_format="NCHW")
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def _test_gradient(self, input_shape, **kwargs):
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x_val = -np.arange(
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np.prod(input_shape), dtype=np.float32).reshape(input_shape) - 1
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x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
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output = nn_ops.pool(input=x, **kwargs)
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y_shape = output.get_shape().as_list()
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err = gradient_checker.compute_gradient_error(
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[x], [input_shape], output, y_shape, x_init_value=[x_val])
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err_tolerance = 1e-2
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self.assertLess(err, err_tolerance)
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@test_util.run_deprecated_v1
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def testGradient1D(self):
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["AVG", "MAX"]:
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for input_shape in [[2, 5, 2], [1, 4, 1]]:
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for window_shape in [[1], [2]]:
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if padding != "SAME":
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for dilation_rate in [[1], [2]]:
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self._test_gradient(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=dilation_rate,
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strides=[1])
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for strides in [[1], [2]]:
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if np.any(np.array(strides) > window_shape):
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continue
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1],
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strides=strides)
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@test_util.run_deprecated_v1
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def testGradient2D(self):
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["AVG", "MAX"]:
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for input_shape in [[2, 4, 5, 2], [1, 5, 4, 1]]:
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for window_shape in [[1, 1], [2, 1], [2, 2]]:
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if padding != "SAME":
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for dilation_rate in [[1, 1], [2, 1], [2, 2]]:
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self._test_gradient(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=dilation_rate,
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strides=[1, 1])
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for strides in [[1, 1], [2, 1], [1, 2], [2, 2]]:
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if np.any(np.array(strides) > window_shape):
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continue
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1, 1],
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strides=strides)
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@test_util.run_deprecated_v1
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def testGradient3D(self):
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if test.is_built_with_rocm():
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self.skipTest("Pooling with 3D tensors is not supported in ROCm")
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with self.session(use_gpu=test.is_gpu_available()):
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for padding in ["SAME", "VALID"]:
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for pooling_type in ["AVG", "MAX"]:
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for input_shape in [[1, 3, 5, 4, 1], [1, 5, 4, 3, 1]]:
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for window_shape in [[1, 1, 1], [2, 1, 2], [2, 2, 2]]:
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if padding != "SAME":
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for dilation_rate in [[1, 1, 1], [2, 1, 2], [2, 2, 2]]:
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self._test_gradient(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=dilation_rate,
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strides=[1, 1, 1])
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for strides in [[1, 1, 1], [2, 1, 2], [2, 2, 2]]:
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if np.any(np.array(strides) > window_shape):
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continue
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self._test(
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input_shape=input_shape,
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window_shape=window_shape,
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padding=padding,
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pooling_type=pooling_type,
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dilation_rate=[1, 1, 1],
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strides=strides)
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if __name__ == "__main__":
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test.main()
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