1 /*
2 * Copyright (C) 2017 The Android Open Source Project
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 #include "Operations.h"
18 #include "CpuOperationUtils.h"
19
20 #include "tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_float.h"
21 #include "tensorflow/contrib/lite/kernels/internal/optimized/depthwiseconv_uint8.h"
22
23 namespace android {
24 namespace nn {
25
26 #define ANDROID_NN_DEPTHWISE_CONV_PARAMETERS \
27 uint32_t height = getSizeOfDimension(inputShape, 1); \
28 uint32_t width = getSizeOfDimension(inputShape, 2); \
29 uint32_t filterHeight = getSizeOfDimension(filterShape, 1); \
30 uint32_t filterWidth = getSizeOfDimension(filterShape, 2); \
31 uint32_t outHeight = getSizeOfDimension(outputShape, 1); \
32 uint32_t outWidth = getSizeOfDimension(outputShape, 2); \
33 \
34 uint32_t paddingHeight = (uint32_t)padding_top; \
35 uint32_t paddingWidth = (uint32_t)padding_left;
36
depthwiseConvFloat32(const float * inputData,const Shape & inputShape,const float * filterData,const Shape & filterShape,const float * biasData,const Shape & biasShape,int32_t padding_left,int32_t padding_right,int32_t padding_top,int32_t padding_bottom,int32_t stride_width,int32_t stride_height,int32_t depth_multiplier,int32_t activation,float * outputData,const Shape & outputShape)37 bool depthwiseConvFloat32(const float* inputData, const Shape& inputShape,
38 const float* filterData, const Shape& filterShape,
39 const float* biasData, const Shape& biasShape,
40 int32_t padding_left, int32_t padding_right,
41 int32_t padding_top, int32_t padding_bottom,
42 int32_t stride_width, int32_t stride_height,
43 int32_t depth_multiplier, int32_t activation,
44 float* outputData, const Shape& outputShape) {
45
46 ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
47
48 float output_activation_min, output_activation_max;
49 CalculateActivationRangeFloat(activation, &output_activation_min,
50 &output_activation_max);
51
52 tflite::optimized_ops::DepthwiseConv(
53 inputData, convertShapeToDims(inputShape),
54 filterData, convertShapeToDims(filterShape),
55 biasData, convertShapeToDims(biasShape),
56 stride_width, stride_height,
57 paddingWidth, paddingHeight, depth_multiplier,
58 output_activation_min, output_activation_max,
59 outputData, convertShapeToDims(outputShape));
60
61 return true;
62 }
63
64
depthwiseConvQuant8(const uint8_t * inputData,const Shape & inputShape,const uint8_t * filterData,const Shape & filterShape,const int32_t * biasData,const Shape & biasShape,int32_t padding_left,int32_t padding_right,int32_t padding_top,int32_t padding_bottom,int32_t stride_width,int32_t stride_height,int32_t depth_multiplier,int32_t activation,uint8_t * outputData,const Shape & outputShape)65 bool depthwiseConvQuant8(const uint8_t* inputData, const Shape& inputShape,
66 const uint8_t* filterData, const Shape& filterShape,
67 const int32_t* biasData, const Shape& biasShape,
68 int32_t padding_left, int32_t padding_right,
69 int32_t padding_top, int32_t padding_bottom,
70 int32_t stride_width, int32_t stride_height,
71 int32_t depth_multiplier, int32_t activation,
72 uint8_t* outputData, const Shape& outputShape) {
73
74 ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
75
76 float real_multiplier = 0.0;
77 int32_t output_multiplier = 0;
78 int32_t output_shift = 0;
79 int32_t output_activation_min = 0;
80 int32_t output_activation_max = 0;
81
82
83 if (!GetQuantizedConvolutionMultipler(inputShape, filterShape, biasShape,
84 outputShape, &real_multiplier) ||
85 !QuantizeMultiplierSmallerThanOne(real_multiplier, &output_multiplier,
86 &output_shift)) {
87 return false;
88 }
89 CalculateActivationRangeUint8(activation, outputShape,
90 &output_activation_min,
91 &output_activation_max);
92
93 uint32_t inputOffset = -inputShape.offset;
94 uint32_t filterOffset = -filterShape.offset;
95 uint32_t outputOffset = outputShape.offset;
96
97 tflite::optimized_ops::DepthwiseConv(
98 inputData, convertShapeToDims(inputShape), inputOffset,
99 filterData, convertShapeToDims(filterShape), filterOffset,
100 biasData, convertShapeToDims(biasShape),
101 stride_width, stride_height,
102 paddingWidth, paddingHeight, depth_multiplier,
103 outputOffset, output_multiplier, output_shift,
104 output_activation_min, output_activation_max,
105 outputData, convertShapeToDims(outputShape));
106
107 return true;
108 }
109
110 #undef ANDROID_NN_DEPTHWISE_CONV_PARAMETERS
111 } // namespace nn
112 } // namespace android
113