# Copyright 2016 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 3d convolutional operations.""" from __future__ import absolute_import from __future__ import division 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 tensor_shape from tensorflow.python.framework import test_util from tensorflow.python.ops import gradient_checker from tensorflow.python.ops import gradients_impl from tensorflow.python.ops import nn_ops import tensorflow.python.ops.nn_grad # pylint: disable=unused-import from tensorflow.python.platform import test def GetTestConfigs(): """Get all the valid tests configs to run. Returns: all the valid test configs as tuples of data_format and use_gpu. """ test_configs = [("NDHWC", False), ("NDHWC", True)] if test.is_gpu_available(cuda_only=True): # "NCDHW" format is only supported on CUDA. test_configs += [("NCDHW", True)] return test_configs 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): total_size_tensor = 1 total_size_filter = 1 for s in tensor_in_sizes: total_size_tensor *= s for s in filter_in_sizes: total_size_filter *= s # Initializes the input tensor with array containing numbers from 0 to 1. # We keep the input tensor values fairly small to avoid overflowing float16 # during the conv3d. x1 = [f * 1.0 / total_size_tensor for f in range(1, total_size_tensor + 1)] x2 = [f * 1.0 / total_size_filter for f in range(1, total_size_filter + 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) if isinstance(stride, collections.Iterable): strides = [1] + list(stride) + [1] else: strides = [1, stride, stride, stride, 1] if data_format == "NCDHW": t1 = test_util.NHWCToNCHW(t1) strides = test_util.NHWCToNCHW(strides) conv = nn_ops.conv3d(t1, t2, strides, padding=padding, data_format=data_format) if data_format == "NCDHW": conv = test_util.NCHWToNHWC(conv) return conv def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, stride, padding, 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) with self.cached_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) def _ComputeReferenceDilatedConv(self, tensor_in_sizes, filter_in_sizes, stride, dilation, padding, data_format, use_gpu): total_size_tensor = 1 total_size_filter = 1 for s in tensor_in_sizes: total_size_tensor *= s for s in filter_in_sizes: total_size_filter *= s # Initializes the input tensor with array containing incrementing # numbers from 1. x1 = [f * 1.0 for f in range(1, total_size_tensor + 1)] x2 = [f * 1.0 for f in range(1, total_size_filter + 1)] with self.test_session(use_gpu=use_gpu): t1 = constant_op.constant(x1, shape=tensor_in_sizes) t2 = constant_op.constant(x2, shape=filter_in_sizes) if isinstance(stride, collections.Iterable): strides = list(stride) else: strides = [stride, stride, stride] if data_format == "NCDHW": t1 = test_util.NHWCToNCHW(t1) full_strides = [1, 1] + strides full_dilation = [1, 1] + dilation else: full_strides = [1] + strides + [1] full_dilation = [1] + dilation + [1] expected = nn_ops.convolution( t1, t2, padding=padding, strides=strides, dilation_rate=dilation, data_format=data_format) computed = nn_ops.conv3d( t1, t2, strides=full_strides, dilations=full_dilation, padding=padding, data_format=data_format) if data_format == "NCDHW": expected = test_util.NCHWToNHWC(expected) computed = test_util.NCHWToNHWC(computed) return expected, computed def _VerifyDilatedConvValues(self, tensor_in_sizes, filter_in_sizes, stride, padding, dilations): expected_results = [] computed_results = [] default_dilations = ( dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1) for data_format, use_gpu in GetTestConfigs(): # If any dilation rate is larger than 1, only do test on the GPU # because we currently do not have a CPU implementation for arbitrary # dilation rates. if default_dilations or use_gpu: expected, computed = self._ComputeReferenceDilatedConv( tensor_in_sizes, filter_in_sizes, stride, dilations, padding, data_format, use_gpu) expected_results.append(expected) computed_results.append(computed) tolerance = 1e-2 if use_gpu else 1e-5 with self.cached_session() as sess: expected_values = sess.run(expected_results) computed_values = sess.run(computed_results) for e_value, c_value in zip(expected_values, computed_values): print("expected = ", e_value) print("actual = ", c_value) self.assertAllClose( e_value.flatten(), c_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 ] # These are equivalent to the Conv2D1x1 case. self._VerifyValues( tensor_in_sizes=[1, 2, 3, 1, 3], filter_in_sizes=[1, 1, 1, 3, 3], stride=1, padding="VALID", expected=expected_output) self._VerifyValues( tensor_in_sizes=[1, 2, 1, 3, 3], filter_in_sizes=[1, 1, 1, 3, 3], stride=1, padding="VALID", expected=expected_output) self._VerifyValues( tensor_in_sizes=[1, 1, 2, 3, 3], filter_in_sizes=[1, 1, 1, 3, 3], stride=1, padding="VALID", expected=expected_output) def testConv3D1x1x1Filter2x1x1Dilation(self): if test.is_gpu_available(cuda_only=True): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 3, 6, 1, 1], filter_in_sizes=[1, 1, 1, 1, 1], stride=1, padding="VALID", dilations=[2, 1, 1]) # 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 ] # expected_shape = [1, 3, 1, 2, 5] self._VerifyValues( tensor_in_sizes=[1, 4, 2, 3, 3], # b, z, y, x, fin filter_in_sizes=[2, 2, 2, 3, 3], # z, y, x, fin, fout stride=1, padding="VALID", expected=expected_output) def testConv3D2x2x2Filter1x2x1Dilation(self): if test.is_gpu_available(cuda_only=True): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 4, 6, 3, 1], filter_in_sizes=[2, 2, 2, 1, 1], stride=1, padding="VALID", dilations=[1, 2, 1]) 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 ] self._VerifyValues( tensor_in_sizes=[1, 5, 8, 7, 1], filter_in_sizes=[1, 2, 3, 1, 1], stride=[2, 3, 1], # different stride for each spatial dimension padding="SAME", expected=expected_output) def testConv3D2x2x2FilterStride2(self): expected_output = [ 3.77199074, 3.85069444, 3.92939815, 9.68865741, 9.93402778, 10.17939815 ] self._VerifyValues( tensor_in_sizes=[1, 4, 2, 3, 3], filter_in_sizes=[2, 2, 2, 3, 3], stride=2, padding="VALID", expected=expected_output) 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 ] self._VerifyValues( tensor_in_sizes=[1, 6, 7, 8, 2], filter_in_sizes=[3, 2, 1, 2, 3], stride=3, padding="VALID", expected=expected_output) 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 ] self._VerifyValues( tensor_in_sizes=[1, 4, 2, 3, 3], filter_in_sizes=[2, 2, 2, 3, 3], stride=2, padding="SAME", expected=expected_output) def testKernelSmallerThanStride(self): expected_output = [ 0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778, 0.92592593, 1. ] self._VerifyValues( tensor_in_sizes=[1, 3, 3, 3, 1], filter_in_sizes=[1, 1, 1, 1, 1], stride=2, padding="SAME", expected=expected_output) self._VerifyValues( tensor_in_sizes=[1, 3, 3, 3, 1], filter_in_sizes=[1, 1, 1, 1, 1], stride=2, padding="VALID", 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 ] self._VerifyValues( tensor_in_sizes=[1, 7, 7, 7, 1], filter_in_sizes=[2, 2, 2, 1, 1], stride=3, padding="SAME", expected=expected_output) expected_output = [ 0.540816, 0.580175, 0.816327, 0.855685, 2.469388, 2.508746, 2.744898, 2.784257 ] self._VerifyValues( tensor_in_sizes=[1, 7, 7, 7, 1], filter_in_sizes=[2, 2, 2, 1, 1], stride=3, padding="VALID", expected=expected_output) def testKernelSizeMatchesInputSize(self): self._VerifyValues( tensor_in_sizes=[1, 2, 1, 2, 1], filter_in_sizes=[2, 1, 2, 1, 2], stride=1, padding="VALID", expected=[1.5625, 1.875]) def _ConstructAndTestGradientForConfig( self, batch, input_shape, filter_shape, in_depth, out_depth, stride, padding, test_input, data_format, use_gpu): input_planes, input_rows, input_cols = input_shape filter_planes, filter_rows, filter_cols = filter_shape input_shape = [batch, input_planes, input_rows, input_cols, in_depth] filter_shape = [ filter_planes, filter_rows, filter_cols, in_depth, out_depth ] if isinstance(stride, collections.Iterable): strides = [1] + list(stride) + [1] else: strides = [1, stride, stride, stride, 1] if padding == "VALID": output_planes = int( math.ceil((input_planes - filter_planes + 1.0) / strides[1])) output_rows = int( math.ceil((input_rows - filter_rows + 1.0) / strides[2])) output_cols = int( math.ceil((input_cols - filter_cols + 1.0) / strides[3])) else: output_planes = int(math.ceil(float(input_planes) / strides[1])) output_rows = int(math.ceil(float(input_rows) / strides[2])) output_cols = int(math.ceil(float(input_cols) / strides[3])) output_shape = [batch, output_planes, output_rows, output_cols, out_depth] input_size = 1 for x in input_shape: input_size *= x filter_size = 1 for x in filter_shape: filter_size *= x 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): # 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: 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( input_data, shape=input_shape, dtype=data_type, name="input") 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 conv = nn_ops.conv3d( input_tensor, filter_tensor, new_strides, padding, data_format=data_format, name="conv") if data_format == "NCDHW": conv = test_util.NCHWToNHWC(conv) self.assertEqual(conv.shape, tensor_shape.TensorShape(output_shape)) 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) def ConstructAndTestGradient(self, **kwargs): for data_format, use_gpu in GetTestConfigs(): self._ConstructAndTestGradientForConfig(data_format=data_format, use_gpu=use_gpu, **kwargs) def testInputGradientValidPaddingStrideOne(self): self.ConstructAndTestGradient( batch=2, input_shape=(3, 5, 4), filter_shape=(3, 3, 3), in_depth=2, out_depth=3, stride=1, padding="VALID", test_input=True) def testFilterGradientValidPaddingStrideOne(self): self.ConstructAndTestGradient( batch=4, input_shape=(4, 6, 5), filter_shape=(2, 2, 2), in_depth=2, out_depth=3, stride=1, padding="VALID", test_input=False) def testInputGradientValidPaddingStrideTwo(self): self.ConstructAndTestGradient( batch=2, input_shape=(6, 3, 5), filter_shape=(3, 3, 3), in_depth=2, out_depth=3, stride=2, padding="VALID", test_input=True) def testFilterGradientValidPaddingStrideTwo(self): self.ConstructAndTestGradient( batch=2, input_shape=(7, 6, 5), filter_shape=(2, 2, 2), in_depth=2, out_depth=3, stride=2, padding="VALID", test_input=False) def testInputGradientValidPaddingStrideThree(self): self.ConstructAndTestGradient( batch=2, input_shape=(3, 7, 6), filter_shape=(3, 3, 3), in_depth=2, out_depth=3, stride=3, padding="VALID", test_input=True) def testFilterGradientValidPaddingStrideThree(self): self.ConstructAndTestGradient( batch=2, input_shape=(4, 4, 7), filter_shape=(4, 4, 4), in_depth=2, out_depth=3, stride=3, padding="VALID", test_input=False) def testInputGradientSamePaddingStrideOne(self): self.ConstructAndTestGradient( batch=2, input_shape=(3, 2, 2), filter_shape=(3, 2, 1), in_depth=2, out_depth=1, stride=1, padding="SAME", test_input=True) def testFilterGradientSamePaddingStrideOne(self): self.ConstructAndTestGradient( batch=2, input_shape=(3, 6, 5), filter_shape=(2, 2, 2), in_depth=2, out_depth=3, stride=1, padding="SAME", test_input=False) def testInputGradientSamePaddingStrideTwo(self): self.ConstructAndTestGradient( batch=2, input_shape=(6, 3, 4), filter_shape=(3, 3, 3), in_depth=2, out_depth=3, stride=2, padding="SAME", test_input=True) def testFilterGradientSamePaddingStrideTwo(self): self.ConstructAndTestGradient( batch=4, input_shape=(7, 3, 5), filter_shape=(2, 2, 2), in_depth=2, out_depth=3, stride=2, padding="SAME", test_input=False) def testInputGradientSamePaddingStrideThree(self): self.ConstructAndTestGradient( batch=2, input_shape=(9, 3, 6), filter_shape=(3, 3, 3), in_depth=2, out_depth=3, stride=3, padding="SAME", test_input=True) def testFilterGradientSamePaddingStrideThree(self): self.ConstructAndTestGradient( batch=2, input_shape=(9, 4, 7), filter_shape=(4, 4, 4), in_depth=2, out_depth=3, stride=3, padding="SAME", test_input=False) def testInputGradientSamePaddingDifferentStrides(self): self.ConstructAndTestGradient( batch=1, input_shape=(5, 8, 7), filter_shape=(1, 2, 3), in_depth=2, out_depth=3, stride=[2, 3, 1], padding="SAME", test_input=True) def testFilterGradientKernelSizeMatchesInputSize(self): self.ConstructAndTestGradient( batch=2, input_shape=(5, 4, 3), filter_shape=(5, 4, 3), in_depth=2, out_depth=3, stride=1, padding="VALID", test_input=False) def testInputGradientKernelSizeMatchesInputSize(self): self.ConstructAndTestGradient( batch=2, input_shape=(5, 4, 3), filter_shape=(5, 4, 3), in_depth=2, out_depth=3, stride=1, padding="VALID", test_input=True) def disabledtestFilterGradientSamePaddingDifferentStrides(self): self.ConstructAndTestGradient( batch=1, input_shape=(5, 8, 7), filter_shape=(1, 2, 3), in_depth=2, out_depth=3, stride=[2, 3, 1], padding="SAME", test_input=False) # Testing for backprops def _RunAndVerifyBackprop(self, input_sizes, filter_sizes, output_sizes, strides, dilations, padding, data_format, use_gpu, err, mode): total_input_size = 1 total_filter_size = 1 for s in input_sizes: total_input_size *= s for s in filter_sizes: total_filter_size *= s # Initializes the input tensor with array containing incrementing # numbers from 1. x1 = [f * 1.0 for f in range(1, total_input_size + 1)] x2 = [f * 1.0 for f in range(1, total_filter_size + 1)] default_dilations = ( dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1) # If any dilation rate is larger than 1, only do test on the GPU # because we currently do not have a CPU implementation for arbitrary # dilation rates. if default_dilations or use_gpu: with self.test_session(use_gpu=use_gpu) as sess: if data_format == "NCDHW": input_sizes = test_util.NHWCToNCHW(input_sizes) t1 = constant_op.constant(x1, shape=input_sizes) t2 = constant_op.constant(x2, shape=filter_sizes) full_strides = [1] + strides + [1] full_dilations = [1] + dilations + [1] if data_format == "NCDHW": full_strides = test_util.NHWCToNCHW(full_strides) full_dilations = test_util.NHWCToNCHW(full_dilations) actual = nn_ops.conv3d( t1, t2, strides=full_strides, dilations=full_dilations, padding=padding, data_format=data_format) expected = nn_ops.convolution( t1, t2, padding=padding, strides=strides, dilation_rate=dilations, data_format=data_format) if data_format == "NCDHW": actual = test_util.NCHWToNHWC(actual) expected = test_util.NCHWToNHWC(expected) actual_grad = gradients_impl.gradients(actual, t1 if mode == "input" else t2)[0] expected_grad = gradients_impl.gradients(expected, t1 if mode == "input" else t2)[0] # "values" consists of two tensors for two backprops actual_value = sess.run(actual_grad) expected_value = sess.run(expected_grad) self.assertShapeEqual(actual_value, actual_grad) self.assertShapeEqual(expected_value, expected_grad) print("expected = ", expected_value) print("actual = ", actual_value) self.assertArrayNear(expected_value.flatten(), actual_value.flatten(), err) def testConv3D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): if test.is_gpu_available(cuda_only=True): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackprop( input_sizes=[1, 3, 6, 1, 1], filter_sizes=[2, 2, 1, 1, 1], output_sizes=[1, 1, 5, 1, 1], strides=[1, 1, 1], dilations=[2, 1, 1], padding="VALID", data_format=data_format, use_gpu=use_gpu, err=1e-5, mode="filter") def testConv3D2x2Depth3ValidBackpropInputStride1x1Dilation2x1(self): if test.is_gpu_available(cuda_only=True): for (data_format, use_gpu) in GetTestConfigs(): self._RunAndVerifyBackprop( input_sizes=[1, 3, 6, 1, 1], filter_sizes=[2, 2, 1, 1, 1], output_sizes=[1, 1, 5, 1, 1], strides=[1, 1, 1], dilations=[2, 1, 1], padding="VALID", data_format=data_format, use_gpu=use_gpu, err=1e-5, mode="input") if __name__ == "__main__": test.main()