diff options
Diffstat (limited to 'tensorflow/python/kernel_tests/conv_ops_3d_test.py')
| -rw-r--r-- | tensorflow/python/kernel_tests/conv_ops_3d_test.py | 267 |
1 files changed, 132 insertions, 135 deletions
diff --git a/tensorflow/python/kernel_tests/conv_ops_3d_test.py b/tensorflow/python/kernel_tests/conv_ops_3d_test.py index 116681fc4c..14622ab467 100644 --- a/tensorflow/python/kernel_tests/conv_ops_3d_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_3d_test.py @@ -21,8 +21,6 @@ from __future__ import print_function import collections import math -import numpy as np - from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import test_util @@ -47,19 +45,8 @@ def GetTestConfigs(): class Conv3DTest(test.TestCase): - def _DtypesToTest(self, use_gpu): - if use_gpu: - if not test_util.CudaSupportsHalfMatMulAndConv(): - return [dtypes.float32] - else: - # It is important that float32 comes before float16 here, - # as we will be using its gradients as reference for fp16 gradients. - return [dtypes.float32, dtypes.float16] - else: - return [dtypes.float64, dtypes.float32, dtypes.float16] - def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride, - padding, data_format, dtype, use_gpu): + padding, data_format, use_gpu): total_size_1 = 1 total_size_2 = 1 for s in tensor_in_sizes: @@ -67,14 +54,13 @@ class Conv3DTest(test.TestCase): for s in filter_in_sizes: total_size_2 *= s - # Initializes the input tensor with array containing numbers from 0 to 1. - # We keep the input tensor values fairly small to avoid overflowing a float16 - # tensor during the conv3d - x1 = [f * 1.0 / total_size_1 for f in range(1, total_size_1 + 1)] - x2 = [f * 1.0 / total_size_2 for f in range(1, total_size_2 + 1)] + # Initializes the input tensor with array containing incrementing + # numbers from 1. + x1 = [f * 1.0 for f in range(1, total_size_1 + 1)] + x2 = [f * 1.0 for f in range(1, total_size_2 + 1)] with self.test_session(use_gpu=use_gpu): - t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype) - t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype) + t1 = constant_op.constant(x1, shape=tensor_in_sizes) + t2 = constant_op.constant(x2, shape=filter_in_sizes) if isinstance(stride, collections.Iterable): strides = [1] + list(stride) + [1] @@ -95,35 +81,27 @@ class Conv3DTest(test.TestCase): expected): results = [] for data_format, use_gpu in GetTestConfigs(): - for dtype in self._DtypesToTest(use_gpu): - result = self._SetupValuesForDevice( - tensor_in_sizes, - filter_in_sizes, - stride, - padding, - data_format, - dtype, - use_gpu=use_gpu) - results.append(result) - + result = self._SetupValuesForDevice( + tensor_in_sizes, + filter_in_sizes, + stride, + padding, + data_format, + use_gpu=use_gpu) + results.append(result) + tolerance = 1e-2 if use_gpu else 1e-5 with self.test_session() as sess: values = sess.run(results) for value in values: print("expected = ", expected) print("actual = ", value) - tol = 1e-6 - if value.dtype == np.float16: - tol = 1e-3 - - self.assertAllClose(expected, value.flatten(), atol=tol, - rtol=tol) + self.assertAllClose(expected, value.flatten(), atol=tolerance, + rtol=1e-6) def testConv3D1x1x1Filter(self): expected_output = [ - 0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5 , - 0.59259259, 0.62962963, 0.77777778, 0.92592593, 0.85185185, - 1.05555556, 1.25925926, 1.07407407, 1.33333333, 1.59259259, - 1.2962963 , 1.61111111, 1.92592593 + 30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, + 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0 ] # These are equivalent to the Conv2D1x1 case. @@ -149,10 +127,8 @@ class Conv3DTest(test.TestCase): # Expected values computed using scipy's correlate function. def testConv3D2x2x2Filter(self): expected_output = [ - 3.77199074, 3.85069444, 3.92939815, 4.2650463 , 4.35763889, - 4.45023148, 6.73032407, 6.89236111, 7.05439815, 7.22337963, - 7.39930556, 7.57523148, 9.68865741, 9.93402778, 10.17939815, - 10.18171296, 10.44097222, 10.70023148 + 19554., 19962., 20370., 22110., 22590., 23070., 34890., 35730., 36570., + 37446., 38358., 39270., 50226., 51498., 52770., 52782., 54126., 55470. ] # expected_shape = [1, 3, 1, 2, 5] self._VerifyValues( @@ -164,19 +140,69 @@ class Conv3DTest(test.TestCase): def testConv3DStrides(self): expected_output = [ - 0.06071429, 0.08988095, 0.10238095, 0.11488095, 0.12738095, - 0.13988095, 0.08452381, 0.26071429, 0.35238095, 0.36488095, - 0.37738095, 0.38988095, 0.40238095, 0.23452381, 0.46071429, - 0.61488095, 0.62738095, 0.63988095, 0.65238095, 0.66488095, - 0.38452381, 1.12738095, 1.48988095, 1.50238095, 1.51488095, - 1.52738095, 1.53988095, 0.88452381, 1.32738095, 1.75238095, - 1.76488095, 1.77738095, 1.78988095, 1.80238095, 1.03452381, - 1.52738095, 2.01488095, 2.02738095, 2.03988095, 2.05238095, - 2.06488095, 1.18452381, 2.19404762, 2.88988095, 2.90238095, - 2.91488095, 2.92738095, 2.93988095, 1.68452381, 2.39404762, - 3.15238095, 3.16488095, 3.17738095, 3.18988095, 3.20238095, - 1.83452381, 2.59404762, 3.41488095, 3.42738095, 3.43988095, - 3.45238095, 3.46488095, 1.98452381 + 102., + 151., + 172., + 193., + 214., + 235., + 142., + 438., + 592., + 613., + 634., + 655., + 676., + 394., + 774., + 1033., + 1054., + 1075., + 1096., + 1117., + 646., + 1894., + 2503., + 2524., + 2545., + 2566., + 2587., + 1486., + 2230., + 2944., + 2965., + 2986., + 3007., + 3028., + 1738., + 2566., + 3385., + 3406., + 3427., + 3448., + 3469., + 1990., + 3686., + 4855., + 4876., + 4897., + 4918., + 4939., + 2830., + 4022., + 5296., + 5317., + 5338., + 5359., + 5380., + 3082., + 4358., + 5737., + 5758., + 5779., + 5800., + 5821., + 3334., ] self._VerifyValues( tensor_in_sizes=[1, 5, 8, 7, 1], @@ -186,10 +212,7 @@ class Conv3DTest(test.TestCase): expected=expected_output) def testConv3D2x2x2FilterStride2(self): - expected_output = [ - 3.77199074, 3.85069444, 3.92939815, 9.68865741, 9.93402778, - 10.17939815 - ] + expected_output = [19554., 19962., 20370., 50226., 51498., 52770.] self._VerifyValues( tensor_in_sizes=[1, 4, 2, 3, 3], filter_in_sizes=[2, 2, 2, 3, 3], @@ -199,14 +222,11 @@ class Conv3DTest(test.TestCase): def testConv3DStride3(self): expected_output = [ - 1.51140873, 1.57167659, 1.63194444, 1.56349206, 1.62673611, - 1.68998016, 1.6155754 , 1.68179563, 1.74801587, 1.9280754 , - 2.01215278, 2.09623016, 1.98015873, 2.0672123 , 2.15426587, - 2.03224206, 2.12227183, 2.21230159, 4.4280754 , 4.65500992, - 4.88194444, 4.48015873, 4.71006944, 4.93998016, 4.53224206, - 4.76512897, 4.99801587, 4.84474206, 5.09548611, 5.34623016, - 4.8968254 , 5.15054563, 5.40426587, 4.94890873, 5.20560516, - 5.46230159 + 36564., 38022., 39480., 37824., 39354., 40884., 39084., 40686., 42288., + 46644., 48678., 50712., 47904., 50010., 52116., 49164., 51342., 53520., + 107124., 112614., 118104., 108384., 113946., 119508., 109644., 115278., + 120912., 117204., 123270., 129336., 118464., 124602., 130740., 119724., + 125934., 132144. ] self._VerifyValues( tensor_in_sizes=[1, 6, 7, 8, 2], @@ -217,9 +237,8 @@ class Conv3DTest(test.TestCase): def testConv3D2x2x2FilterStride2Same(self): expected_output = [ - 3.77199074, 3.85069444, 3.92939815, 2.0162037 , 2.06597222, - 2.11574074, 9.68865741, 9.93402778, 10.17939815, 4.59953704, - 4.73263889, 4.86574074 + 19554., 19962., 20370., 10452., 10710., 10968., 50226., 51498., 52770., + 23844., 24534., 25224. ] self._VerifyValues( tensor_in_sizes=[1, 4, 2, 3, 3], @@ -229,10 +248,7 @@ class Conv3DTest(test.TestCase): expected=expected_output) def testKernelSmallerThanStride(self): - expected_output = [ - 0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037 , - 0.77777778, 0.92592593, 1. - ] + expected_output = [1., 3., 7., 9., 19., 21., 25., 27.] self._VerifyValues( tensor_in_sizes=[1, 3, 3, 3, 1], filter_in_sizes=[1, 1, 1, 1, 1], @@ -247,12 +263,9 @@ class Conv3DTest(test.TestCase): expected=expected_output) expected_output = [ - 0.54081633, 0.58017493, 0.28061224, 0.81632653, 0.85568513, - 0.40306122, 0.41873178, 0.4340379 , 0.19642857, 2.46938776, - 2.50874636, 1.1377551 , 2.74489796, 2.78425656, 1.26020408, - 1.16873178, 1.1840379 , 0.51785714, 1.09511662, 1.10604956, - 0.44642857, 1.17164723, 1.18258017, 0.47704082, 0.3691691 , - 0.37244898, 0.125 + 1484., 1592., 770., 2240., 2348., 1106., 1149., 1191., 539., 6776., + 6884., 3122., 7532., 7640., 3458., 3207., 3249., 1421., 3005., 3035., + 1225., 3215., 3245., 1309., 1013., 1022., 343. ] self._VerifyValues( tensor_in_sizes=[1, 7, 7, 7, 1], @@ -261,10 +274,7 @@ class Conv3DTest(test.TestCase): padding="SAME", expected=expected_output) - expected_output = [ - 0.540816, 0.580175, 0.816327, 0.855685, 2.469388, 2.508746, - 2.744898, 2.784257 - ] + expected_output = [1484., 1592., 2240., 2348., 6776., 6884., 7532., 7640.] self._VerifyValues( tensor_in_sizes=[1, 7, 7, 7, 1], filter_in_sizes=[2, 2, 2, 1, 1], @@ -278,7 +288,7 @@ class Conv3DTest(test.TestCase): filter_in_sizes=[2, 1, 2, 1, 2], stride=1, padding="VALID", - expected=[1.5625, 1.875]) + expected=[50, 60]) def _ConstructAndTestGradientForConfig( self, batch, input_shape, filter_shape, in_depth, out_depth, stride, @@ -318,63 +328,50 @@ class Conv3DTest(test.TestCase): input_data = [x * 1.0 / input_size for x in range(0, input_size)] filter_data = [x * 1.0 / filter_size for x in range(0, filter_size)] - - for data_type in self._DtypesToTest(use_gpu=use_gpu): + if test.is_gpu_available() and use_gpu: + data_type = dtypes.float32 # TODO(mjanusz): Modify gradient_checker to also provide max relative # error and synchronize the tolerance levels between the tests for forward # and backward computations. - if data_type == dtypes.float64: - tolerance = 1e-8 - elif data_type == dtypes.float32: + if test.is_gpu_available(): tolerance = 5e-3 - elif data_type == dtypes.float16: - tolerance = 1e-3 - - - with self.test_session(use_gpu=use_gpu): - orig_input_tensor = constant_op.constant( + else: + # As of Aug 2016, higher tolerance is needed for some CPU architectures. + # Runs on a single machine can also generate slightly different errors + # because of multithreading. + tolerance = 8e-3 + else: + data_type = dtypes.float64 + tolerance = 1e-8 + with self.test_session(use_gpu=use_gpu): + orig_input_tensor = constant_op.constant( input_data, shape=input_shape, dtype=data_type, name="input") - filter_tensor = constant_op.constant( + filter_tensor = constant_op.constant( filter_data, shape=filter_shape, dtype=data_type, name="filter") - if data_format == "NCDHW": - input_tensor = test_util.NHWCToNCHW(orig_input_tensor) - new_strides = test_util.NHWCToNCHW(strides) - else: - input_tensor = orig_input_tensor - new_strides = strides + if data_format == "NCDHW": + input_tensor = test_util.NHWCToNCHW(orig_input_tensor) + strides = test_util.NHWCToNCHW(strides) + else: + input_tensor = orig_input_tensor - conv = nn_ops.conv3d( - input_tensor, filter_tensor, new_strides, padding, + conv = nn_ops.conv3d( + input_tensor, filter_tensor, strides, padding, data_format=data_format, name="conv") - if data_format == "NCDHW": - conv = test_util.NCHWToNHWC(conv) - - - if test_input: - jacob_t, jacob_n = gradient_checker.compute_gradient(orig_input_tensor, - input_shape, - conv, - output_shape) - else: - jacob_t, jacob_n = gradient_checker.compute_gradient(filter_tensor, - filter_shape, - conv, - output_shape) - - - if data_type != dtypes.float16: - reference_jacob_t = jacob_t - err = np.fabs(jacob_t - jacob_n).max() - else: - # Compare fp16 theoretical gradients to fp32 theoretical gradients, - # since fp16 numerical gradients are too imprecise. - err = np.fabs(jacob_t - reference_jacob_t).max() - - print("conv3d gradient error = ", err) - self.assertLess(err, tolerance) + if data_format == "NCDHW": + conv = test_util.NCHWToNHWC(conv) + if test_input: + err = gradient_checker.compute_gradient_error(orig_input_tensor, + input_shape, + conv, output_shape) + else: + err = gradient_checker.compute_gradient_error(filter_tensor, + filter_shape, conv, + output_shape) + print("conv3d gradient error = ", err) + self.assertLess(err, tolerance) def ConstructAndTestGradient(self, **kwargs): for data_format, use_gpu in GetTestConfigs(): |