1 /* Copyright 2019 The TensorFlow Authors. All Rights Reserved.
2 
3 Licensed under the Apache License, Version 2.0 (the "License");
4 you may not use this file except in compliance with the License.
5 You may obtain a copy of the License at
6 
7     http://www.apache.org/licenses/LICENSE-2.0
8 
9 Unless required by applicable law or agreed to in writing, software
10 distributed under the License is distributed on an "AS IS" BASIS,
11 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 See the License for the specific language governing permissions and
13 limitations under the License.
14 ==============================================================================*/
15 
16 #include "tensorflow/lite/micro/kernels/conv.h"
17 
18 #include "tensorflow/lite/c/builtin_op_data.h"
19 #include "tensorflow/lite/c/common.h"
20 #include "tensorflow/lite/kernels/internal/common.h"
21 #include "tensorflow/lite/kernels/internal/quantization_util.h"
22 #include "tensorflow/lite/kernels/internal/reference/conv.h"
23 #include "tensorflow/lite/kernels/internal/reference/integer_ops/conv.h"
24 #include "tensorflow/lite/kernels/internal/tensor_ctypes.h"
25 #include "tensorflow/lite/kernels/kernel_util.h"
26 #include "tensorflow/lite/kernels/padding.h"
27 #include "tensorflow/lite/micro/kernels/kernel_util.h"
28 
29 namespace tflite {
30 namespace {
31 
Init(TfLiteContext * context,const char * buffer,size_t length)32 void* Init(TfLiteContext* context, const char* buffer, size_t length) {
33   TFLITE_DCHECK(context->AllocatePersistentBuffer != nullptr);
34   return context->AllocatePersistentBuffer(context, sizeof(OpDataConv));
35 }
36 
Eval(TfLiteContext * context,TfLiteNode * node)37 TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
38   const TfLiteEvalTensor* input =
39       tflite::micro::GetEvalInput(context, node, kConvInputTensor);
40   const TfLiteEvalTensor* filter =
41       tflite::micro::GetEvalInput(context, node, kConvWeightsTensor);
42   const TfLiteEvalTensor* bias =
43       (NumInputs(node) == 3)
44           ? tflite::micro::GetEvalInput(context, node, kConvBiasTensor)
45           : nullptr;
46   TfLiteEvalTensor* output =
47       tflite::micro::GetEvalOutput(context, node, kConvOutputTensor);
48 
49   TFLITE_DCHECK(node->builtin_data != nullptr);
50   const auto& params =
51       *(reinterpret_cast<TfLiteConvParams*>(node->builtin_data));
52   TFLITE_DCHECK(node->user_data != nullptr);
53   const auto& data = *(static_cast<const OpDataConv*>(node->user_data));
54 
55   TF_LITE_ENSURE_EQ(context, input->type, output->type);
56   TF_LITE_ENSURE_MSG(
57       context,
58       input->type == filter->type ||
59           (input->type == kTfLiteInt16 && filter->type == kTfLiteInt8),
60       "Hybrid models are not supported on TFLite Micro.");
61 
62   switch (input->type) {  // Already know in/out types are same.
63     case kTfLiteFloat32: {
64       tflite::reference_ops::Conv(
65           ConvParamsFloat(params, data), tflite::micro::GetTensorShape(input),
66           tflite::micro::GetTensorData<float>(input),
67           tflite::micro::GetTensorShape(filter),
68           tflite::micro::GetTensorData<float>(filter),
69           tflite::micro::GetTensorShape(bias),
70           tflite::micro::GetTensorData<float>(bias),
71           tflite::micro::GetTensorShape(output),
72           tflite::micro::GetTensorData<float>(output),
73           tflite::micro::GetTensorShape(nullptr), nullptr);
74       break;
75     }
76     case kTfLiteInt16: {
77       reference_integer_ops::ConvPerChannel(
78           ConvParamsQuantized(params, data), data.per_channel_output_multiplier,
79           data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
80           tflite::micro::GetTensorData<int16_t>(input),
81           tflite::micro::GetTensorShape(filter),
82           tflite::micro::GetTensorData<int8_t>(filter),
83           tflite::micro::GetTensorShape(bias),
84           tflite::micro::GetTensorData<std::int64_t>(bias),
85           tflite::micro::GetTensorShape(output),
86           tflite::micro::GetTensorData<int16_t>(output));
87       break;
88     }
89     case kTfLiteInt8: {
90       reference_integer_ops::ConvPerChannel(
91           ConvParamsQuantized(params, data), data.per_channel_output_multiplier,
92           data.per_channel_output_shift, tflite::micro::GetTensorShape(input),
93           tflite::micro::GetTensorData<int8_t>(input),
94           tflite::micro::GetTensorShape(filter),
95           tflite::micro::GetTensorData<int8_t>(filter),
96           tflite::micro::GetTensorShape(bias),
97           tflite::micro::GetTensorData<int32_t>(bias),
98           tflite::micro::GetTensorShape(output),
99           tflite::micro::GetTensorData<int8_t>(output));
100       break;
101     }
102     default:
103       TF_LITE_KERNEL_LOG(context, "Type %s (%d) not supported.",
104                          TfLiteTypeGetName(input->type), input->type);
105       return kTfLiteError;
106   }
107   return kTfLiteOk;
108 }
109 
110 }  // namespace
111 
Register_CONV_2D()112 TfLiteRegistration Register_CONV_2D() {
113   return {/*init=*/Init,
114           /*free=*/nullptr,
115           /*prepare=*/ConvPrepare,
116           /*invoke=*/Eval,
117           /*profiling_string=*/nullptr,
118           /*builtin_code=*/0,
119           /*custom_name=*/nullptr,
120           /*version=*/0};
121 }
122 
123 }  // namespace tflite
124