750beea911
Also, fix a bug in IsFunctionCallOp found by this CL. Contrary to what it sounds like, `use_gpu` does not force the test to run on GPUs, it merely *allows* the test to run on GPUs (there is a separate `force_gpu` option for forcing). This means setting `use_gpu` to `True` means that the test will run on GPUs if one is available. Given that setting `use_gpu` to `True` by default makes sense, and there should be a good reason for a test to set it to `False` (which disallows GPU use, even when one is available). For this reason, this CL changes the default value of `use_gpu`. As you can see, this has already found a few real bugs. In a later CL I will remove instances that pass use_gpu=True explicitly as those should no longer be necessary. PiperOrigin-RevId: 356906251 Change-Id: Ibd0f785af0d2b1290dc40e84f928ff4291a58fe7
283 lines
11 KiB
Python
283 lines
11 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 atrous convolution 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 contextlib
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import numpy as np
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from tensorflow.python.eager import context
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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 array_ops
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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 upsample_filters(filters, rate):
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"""Upsamples the filters by a factor of rate along the spatial dimensions.
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Args:
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filters: spatial_shape + [in_channels, out_channels]
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Original filters.
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rate: A list of len(spatial_shape) positive ints, specifying the
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upsampling rate.
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Returns:
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filters_up: output_spatial_shape + [in_channels, out_channels].
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Upsampled filters with
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output_spatial_shape[i] = (spatial_shape[i] - 1) * rate[i] + 1
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containing (rate[i] - 1) zeros between consecutive filter values along
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spatial dimension i.
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"""
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num_spatial_dims = len(rate)
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spatial_shape = np.array(filters.shape[:num_spatial_dims])
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output_spatial_shape = (spatial_shape - 1) * rate + 1
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output = np.zeros(
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tuple(output_spatial_shape) + tuple(filters.shape[-2:]), filters.dtype)
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output[tuple(np.s_[::rate[i]] for i in range(num_spatial_dims))] = filters
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return output
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class AtrousConvolutionTest(test.TestCase):
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@contextlib.contextmanager
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def _delay_checks(self):
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"""Context manager for combining checks depending on tensor evaluations.
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Each call to Session.run has some overhead, and this overhead can easily
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account for the majority of the time spent in tests that call Session.run
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(or Tensor.eval) many times.
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This context manager provides a mechanism for registering callback functions
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and associated tensors. When the context is exited, all of the tensors
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associated with all of the registrations are evaluated with a single call to
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Session.run, and then each registered callback function is called with the
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values of its associated tensors.
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Yields:
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A function `add_check(check, *args, **kwargs)` where `check` is the
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callback function to be invoked, and `*args` and `**kwargs` specify the
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associated Tensors. When in EAGER mode, check is executed in add_check,
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otherwise, it's delayed after the context.
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"""
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checks = []
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def add_check(check, *args, **kwargs):
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if context.executing_eagerly():
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args_val, kwargs_val = self.evaluate([args, kwargs])
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check(*args_val, **kwargs_val)
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else:
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checks.append((check, args, kwargs))
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yield add_check
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if not context.executing_eagerly():
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all_values = self.evaluate([[args, kwargs] for _, args, kwargs in checks])
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for (check, _, _), (args, kwargs) in zip(checks, all_values):
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check(*args, **kwargs)
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def _test_atrous_convolution(self, add_check, input_shape, filter_shape,
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dilation_rate, **kwargs):
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filters = np.arange(
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np.prod(filter_shape), dtype=np.float32).reshape(filter_shape)
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filters_upsampled = upsample_filters(filters, dilation_rate)
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x = np.arange(np.prod(input_shape), dtype=np.float32).reshape(input_shape)
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y1 = nn_ops.convolution(
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input=x, filter=filters, dilation_rate=dilation_rate, **kwargs)
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y2 = nn_ops.convolution(input=x, filter=filters_upsampled, **kwargs)
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def check(y1_eval, y2_eval):
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self.assertAllClose(y1_eval, y2_eval, rtol=1e-2, atol=1e-2)
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add_check(check, y1, y2)
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@test_util.run_v1_only("b/120545219")
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def test_unknown_spatial_dims_for_channel_last_format(self):
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x = array_ops.placeholder(dtypes.float32, [1, None, None, 10])
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w = array_ops.zeros([3, 3, 10, 20])
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y = nn_ops.convolution(
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x, w, "VALID", dilation_rate=[2, 2], data_format="NHWC")
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self.assertEqual(y.shape.as_list(), [1, None, None, 20])
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@test_util.run_v1_only("b/120545219")
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def test_unknown_spatial_dims_for_channel_first_format(self):
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x = array_ops.placeholder(dtypes.float32, [1, 10, None, None])
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w = array_ops.zeros([3, 3, 10, 20])
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y = nn_ops.convolution(
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x, w, "VALID", dilation_rate=[2, 2], data_format="NCHW")
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self.assertEqual(y.shape.as_list(), [1, 20, None, None])
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@test_util.run_in_graph_and_eager_modes
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def testAtrousConvolution2D(self):
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with self._delay_checks() as add_check:
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for padding in ["SAME", "VALID"]:
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for height, width in [[9, 9], [9, 10]]:
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for kernel_height, kernel_width in [[1, 1], [2, 2], [2, 3]]:
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for dilation_rate in [[1, 1], [3, 2], [2, 1]]:
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self._test_atrous_convolution(
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add_check=add_check,
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input_shape=[2, height, width, 2],
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filter_shape=[kernel_height, kernel_width, 2, 2],
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padding=padding,
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dilation_rate=dilation_rate,
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)
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@test_util.run_in_graph_and_eager_modes
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def testAtrousConvolution3D(self):
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with self._delay_checks() as add_check:
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for padding in ["SAME", "VALID"]:
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for depth, height, width in [[9, 9, 10], [9, 10, 9]]:
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for kernel_depth, kernel_height, kernel_width in [[3, 3,
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3], [3, 2, 2],
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[2, 1, 3]]:
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for dilation_rate in [[1, 1, 1], [3, 3, 3], [3, 2, 3], [3, 1, 2]]:
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self._test_atrous_convolution(
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add_check=add_check,
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input_shape=[2, depth, height, width, 2],
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filter_shape=[
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kernel_depth, kernel_height, kernel_width, 2, 2
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],
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padding=padding,
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dilation_rate=dilation_rate,
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)
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@test_util.run_in_graph_and_eager_modes
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def testAtrousConvolution1D(self):
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with self._delay_checks() as add_check:
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for padding in ["SAME", "VALID"]:
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for width in [9, 10]:
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for kernel_width in range(1, 4):
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for rate in range(1, 4):
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self._test_atrous_convolution(
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add_check=add_check,
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input_shape=[2, width, 2],
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filter_shape=[kernel_width, 2, 2],
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padding=padding,
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dilation_rate=[rate],
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)
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@test_util.run_in_graph_and_eager_modes
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def testAtrousConvolutionNC(self):
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if test.is_gpu_available(cuda_only=True):
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# "NCW" and "NCHW" formats are currently supported only on CUDA.
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with test_util.device(use_gpu=True):
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with self._delay_checks() as add_check:
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for padding in ["SAME", "VALID"]:
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self._test_atrous_convolution(
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add_check=add_check,
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input_shape=[2, 2, 9],
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padding=padding,
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filter_shape=[3, 2, 2],
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dilation_rate=[2],
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data_format="NCW",
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)
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self._test_atrous_convolution(
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add_check=add_check,
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input_shape=[2, 2, 9, 5],
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padding=padding,
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filter_shape=[3, 3, 2, 2],
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dilation_rate=[2, 1],
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data_format="NCHW",
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)
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@test_util.run_in_graph_and_eager_modes
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def testAtrousSequence(self):
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"""Tests optimization of sequence of atrous convolutions.
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See the documentation of with_space_to_batch.
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"""
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with self._delay_checks() as add_check:
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for padding in ["SAME", "VALID"]:
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for height in range(15, 17):
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for width in range(15, 17):
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x_shape = [3, height, width, 2]
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x = np.random.random_sample(x_shape).astype(np.float32)
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kernel_sizes = [1, 3] if padding == "SAME" else range(1, 3)
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for kernel in kernel_sizes:
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f_shape = [kernel, kernel, 2, 2]
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f1 = 1e-2 * np.random.random_sample(f_shape).astype(np.float32)
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f2 = 1e-2 * np.random.random_sample(f_shape).astype(np.float32)
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def combined_op(converted_input, num_spatial_dims, padding_arg): # pylint: disable=unused-argument
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# pylint: disable=cell-var-from-loop
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result = nn_ops.convolution(
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input=converted_input, filter=f1, padding=padding)
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result = nn_ops.convolution(
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input=result, filter=f2, padding=padding)
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# pylint: enable=cell-var-from-loop
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return result
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for rate_height in range(2, 4):
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for rate_width in range(2, 4):
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dilation_rate = [rate_height, rate_width]
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y1 = nn_ops.convolution(
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input=x,
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filter=f1,
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padding=padding,
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dilation_rate=dilation_rate)
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y1 = nn_ops.convolution(
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input=y1,
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filter=f2,
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padding=padding,
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dilation_rate=dilation_rate)
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y2 = nn_ops.with_space_to_batch(
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input=x,
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dilation_rate=dilation_rate,
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op=combined_op,
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padding="VALID")
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def check(y1_eval, y2_eval):
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self.assertAllClose(y1_eval, y2_eval, rtol=1e-2, atol=1e-2)
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add_check(check, y1, y2)
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def _test_gradient(self, x_shape, f_shape, dilation_rate, padding):
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x_val = np.random.random_sample(x_shape).astype(np.float32)
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f_val = np.random.random_sample(f_shape).astype(np.float32)
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x = constant_op.constant(x_val, name="x", dtype=dtypes.float32)
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f = constant_op.constant(f_val, name="f", dtype=dtypes.float32)
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output = nn_ops.convolution(
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input=x, filter=f, dilation_rate=dilation_rate, padding=padding)
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y_shape = output.get_shape().as_list()
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err = gradient_checker.compute_gradient_error([x, f], [x_shape, f_shape],
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output, y_shape)
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err_tolerance = 1e-3
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self.assertLess(err, err_tolerance)
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@test_util.run_v1_only("b/120545219")
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@test_util.disable_xla("b/178665095")
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def testGradient(self):
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with self.cached_session():
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for padding in ["SAME", "VALID"]:
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for rate_width in range(1, 3):
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for rate_height in range(1, 3):
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self._test_gradient(
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x_shape=[2, 5, 6, 2],
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f_shape=[3, 3, 2, 2],
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dilation_rate=[rate_height, rate_width],
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padding=padding)
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if __name__ == "__main__":
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test.main()
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