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Speed up atrous_convolution_test by combining evaluations.
To make this test run faster (and prevent it from timing out under certain circumstances), this change combines all evaluations for each test method into a single call to Session.run, to eliminate overhead. This reduces the test time from about 40 seconds to 10 seconds. RELNOTES: n/a PiperOrigin-RevId: 158175227
This commit is contained in:
A. Unique TensorFlower
TensorFlower Gardener
parent
81cf61fdb6
commit
b65eb3f9b5
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@ -18,6 +18,8 @@ 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.framework import constant_op
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@ -53,124 +55,170 @@ def upsample_filters(filters, rate):
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return output
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@contextlib.contextmanager
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def delay_checks(sess):
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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 (or
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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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Args:
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sess: The session to use to evaluate the 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.
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"""
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checks = []
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def add_check(check, *args, **kwargs):
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checks.append((check, args, kwargs))
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yield add_check
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all_values = sess.run([[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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class AtrousConvolutionTest(test.TestCase):
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def _test_atrous_convolution(self, input_shape, filter_shape, dilation_rate,
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**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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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(np.prod(filter_shape),
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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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y1 = nn_ops.convolution(input=x, filter=filters,
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dilation_rate=dilation_rate, **kwargs)
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y2 = nn_ops.convolution(input=x, filter=filters_upsampled, **kwargs)
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self.assertAllClose(y1.eval(), y2.eval(), rtol=1e-2, atol=1e-2)
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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 testAtrousConvolution2D(self):
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with self.test_session():
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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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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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with self.test_session() as sess:
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with delay_checks(sess) 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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def testAtrousConvolution3D(self):
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with self.test_session():
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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, 3],
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[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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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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with self.test_session() as sess:
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with delay_checks(sess) 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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def testAtrousConvolution1D(self):
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with self.test_session():
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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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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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with self.test_session() as sess:
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with delay_checks(sess) 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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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 self.test_session(use_gpu=True):
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for padding in ["SAME", "VALID"]:
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self._test_atrous_convolution(
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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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self._test_atrous_convolution(
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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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with self.test_session(use_gpu=True) as sess:
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with delay_checks(sess) 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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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.test_session():
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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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with self.test_session() as sess:
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with delay_checks(sess) 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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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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result = nn_ops.convolution(
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input=converted_input, filter=f1,
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padding=padding) # pylint: disable=cell-var-from-loop
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result = nn_ops.convolution(
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input=result, filter=f2,
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padding=padding) # pylint: disable=cell-var-from-loop
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return result
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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(input=converted_input, filter=f1,
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padding=padding)
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result = nn_ops.convolution(input=result, filter=f2,
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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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self.assertAllClose(
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y1.eval(), y2.eval(), rtol=1e-2, atol=1e-2)
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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(input=x, filter=f1, padding=padding,
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dilation_rate=dilation_rate)
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y1 = nn_ops.convolution(input=y1, 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, dilation_rate=dilation_rate, 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,
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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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