1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
|
/* 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 <memory>
#include <string>
#include <unordered_map>
#include <vector>
#include "absl/strings/str_cat.h"
#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 {
bool ProcessConvOperator(Model* model, ConvOperator* op) {
if (op->outputs.size() == 2) {
// We already have an im2col array
return false;
}
const auto& weights_array = model->GetArray(op->inputs[1]);
if (!weights_array.has_shape()) {
// We need to yield until weights dims have been resolved, because
// from the weights dims we determine whether an im2col array is
// needed.
return false;
}
const auto& weights_shape = weights_array.shape();
const int kheight = weights_shape.dims(1);
const int kwidth = weights_shape.dims(2);
if (kwidth == 1 && kheight == 1 && op->stride_width == 1 &&
op->stride_height == 1 && op->dilation_width_factor == 1 &&
op->dilation_height_factor == 1) {
// 1x1 unstrided undilated conv does not need an im2col array.
return false;
}
// Create the im2col array.
CHECK_EQ(op->outputs.size(), 1);
const string& im2col_array_name =
AvailableArrayName(*model, op->inputs[0] + "_im2col");
model->GetOrCreateArray(im2col_array_name);
op->outputs.push_back(im2col_array_name);
return true;
}
bool ProcessTransposeConvOperator(Model* model, TransposeConvOperator* op) {
if (op->outputs.size() == 2) {
// We already have an im2col array
return false;
}
// Always create an im2col array for transpose_conv.
CHECK_EQ(op->outputs.size(), 1);
const string& im2col_array_name = AvailableArrayName(
*model, op->inputs[TransposeConvOperator::DATA_INPUT] + "_im2col");
model->GetOrCreateArray(im2col_array_name);
op->outputs.push_back(im2col_array_name);
return true;
}
::tensorflow::Status CreateIm2colArrays::Run(Model* model, std::size_t op_index,
bool* modified) {
*modified = false;
auto it = model->operators.begin() + op_index;
auto* op = it->get();
switch (op->type) {
case OperatorType::kConv:
*modified = ProcessConvOperator(model, static_cast<ConvOperator*>(op));
return ::tensorflow::Status::OK();
case OperatorType::kTransposeConv:
*modified = ProcessTransposeConvOperator(
model, static_cast<TransposeConvOperator*>(op));
return ::tensorflow::Status::OK();
default:
return ::tensorflow::Status::OK();
}
}
} // namespace toco
|
