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/* Copyright 2017 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.
==============================================================================*/
#include <algorithm>
#include <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h"
#include "tensorflow/contrib/lite/toco/model.h"
#include "tensorflow/contrib/lite/toco/tooling_util.h"
#include "tensorflow/core/platform/logging.h"
namespace toco {
namespace {
bool ApplyMinMaxToArray(GraphTransformation* transformation, Model* model,
const MinMax& minmax, const string& array_name) {
auto& annotated_array = model->GetArray(array_name);
if (annotated_array.minmax) {
return false;
}
annotated_array.GetOrCreateMinMax() = minmax;
transformation->AddMessageF(
"Read min/max annotation for array %s: min=%g, max=%g", array_name,
minmax.min, minmax.max);
return true;
}
} // end namespace
bool ReadFakeQuantMinMax::Run(Model* model, std::size_t op_index) {
const auto fakequant_it = model->operators.begin() + op_index;
auto* fakequant_base_op = fakequant_it->get();
if (fakequant_base_op->type != OperatorType::kFakeQuant) {
return false;
}
auto* fakequant_op = static_cast<FakeQuantOperator*>(fakequant_base_op);
bool changed = false;
if (!fakequant_op->minmax) {
CHECK_EQ(fakequant_op->inputs.size(), 3);
// We need to yield until the min and max parameters have been
// resolved to constant arrays.
for (int i = 1; i <= 2; i++) {
if (!IsConstantParameterArray(*model, fakequant_op->inputs[1])) {
return false;
}
}
// Obtain the final min/max values
const auto& min_array = model->GetArray(fakequant_op->inputs[1]);
const auto& max_array = model->GetArray(fakequant_op->inputs[2]);
CHECK_EQ(RequiredBufferSizeForShape(min_array.shape()), 1);
CHECK_EQ(RequiredBufferSizeForShape(max_array.shape()), 1);
fakequant_op->minmax.reset(new MinMax);
MinMax& minmax = *fakequant_op->minmax;
minmax.min = min_array.GetBuffer<ArrayDataType::kFloat>().data[0];
minmax.max = max_array.GetBuffer<ArrayDataType::kFloat>().data[0];
// We always want [min, max] to contain 0.
if (minmax.min > 0 || minmax.max < 0) {
LOG(ERROR) << "For " << LogName(*fakequant_op) << " the MinMax range "
<< "[" << minmax.min << ", " << minmax.max
<< "] does not contain 0. "
<< "Proceeding by tweaking it to contain 0, which will result "
"in poor accuracy.";
}
minmax.min = std::min(minmax.min, 0.);
minmax.max = std::max(minmax.max, 0.);
// We won't use the input arrays that provided these min and max
// values, anymore. Delete them unless they are used by something
// else.
for (int i = 1; i <= 2; i++) {
if (CountOpsWithInput(*model, fakequant_op->inputs[i]) == 1) {
model->EraseArray(fakequant_op->inputs[i]);
}
}
fakequant_op->inputs.resize(1);
changed = true;
}
// At this point, this FakeQuantOperator should have a MinMax
// attached to it, and should only have 1 input (it should not have
// 2nd and 3rd input arrays giving min and max anymore).
CHECK(fakequant_op->minmax);
CHECK_EQ(1, fakequant_op->inputs.size());
const MinMax& minmax = *fakequant_op->minmax;
// Record the MinMax info on the input and output arrays
changed |= ApplyMinMaxToArray(this, model, minmax, fakequant_op->inputs[0]);
changed |= ApplyMinMaxToArray(this, model, minmax, fakequant_op->outputs[0]);
return changed;
}
} // namespace toco
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