1 /* Copyright 2018 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 #include <algorithm>
16 #include <memory>
17 #include <string>
18 #include <unordered_map>
19 #include <vector>
20
21 #include "tensorflow/lite/toco/graph_transformations/graph_transformations.h"
22 #include "tensorflow/lite/toco/model.h"
23 #include "tensorflow/lite/toco/tooling_util.h"
24 #include "tensorflow/core/platform/logging.h"
25
26 namespace toco {
27
Run(Model * model,std::size_t op_index,bool * modified)28 ::tensorflow::Status ResolveFakeQuantArgsFromVars::Run(Model* model,
29 std::size_t op_index,
30 bool* modified) {
31 *modified = false;
32 const auto fakequant_it = model->operators.begin() + op_index;
33 auto* fakequant_base_op = fakequant_it->get();
34 if (fakequant_base_op->type != OperatorType::kFakeQuant) {
35 return ::tensorflow::OkStatus();
36 }
37 auto* fakequant_op = static_cast<FakeQuantOperator*>(fakequant_base_op);
38
39 if (fakequant_op->minmax) {
40 // Already resolved.
41 return ::tensorflow::OkStatus();
42 }
43
44 CHECK_EQ(fakequant_op->inputs.size(), 3);
45 // We need to yield until the min and max parameters have been
46 // resolved to constant arrays.
47 for (int i = 1; i <= 2; i++) {
48 if (!IsConstantParameterArray(*model, fakequant_op->inputs[i])) {
49 return ::tensorflow::OkStatus();
50 }
51 }
52
53 // Obtain the final min/max values
54 const auto& min_array = model->GetArray(fakequant_op->inputs[1]);
55 const auto& max_array = model->GetArray(fakequant_op->inputs[2]);
56 CHECK_EQ(RequiredBufferSizeForShape(min_array.shape()), 1);
57 CHECK_EQ(RequiredBufferSizeForShape(max_array.shape()), 1);
58 fakequant_op->minmax = std::make_unique<MinMax>();
59 MinMax& minmax = *fakequant_op->minmax;
60 minmax.min = min_array.GetBuffer<ArrayDataType::kFloat>().data[0];
61 minmax.max = max_array.GetBuffer<ArrayDataType::kFloat>().data[0];
62 // We always want [min, max] to contain 0.
63 if (minmax.min > 0 || minmax.max < 0) {
64 LOG(WARNING) << "For " << LogName(*fakequant_op) << " the MinMax range "
65 << "[" << minmax.min << ", " << minmax.max
66 << "] does not contain 0. "
67 << "Proceeding by tweaking it to contain 0, which will result "
68 "in poor accuracy.";
69 }
70 minmax.min = std::min(minmax.min, 0.);
71 minmax.max = std::max(minmax.max, 0.);
72
73 // We won't use the input arrays that provided these min and max
74 // values, anymore. Delete them unless they are used by something
75 // else.
76 for (int i = 1; i <= 2; i++) {
77 DeleteArrayIfUnusedOutsideOfOp(fakequant_op->inputs[i], fakequant_op,
78 model);
79 }
80 fakequant_op->inputs.resize(1);
81 *modified = true;
82 return ::tensorflow::OkStatus();
83 }
84
85 } // namespace toco
86