-
Notifications
You must be signed in to change notification settings - Fork 684
/
resnet-verify.cpp
135 lines (115 loc) · 4.33 KB
/
resnet-verify.cpp
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
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
/**
* Copyright (c) Glow Contributors. See CONTRIBUTORS file.
*
* 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 "glow/Base/Image.h"
#include "glow/ExecutionEngine/ExecutionEngine.h"
#include "glow/Graph/Hook.h"
#include "glow/Importer/Caffe2ModelLoader.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/Format.h"
using namespace glow;
const char inputName[] = "gpu_0/data";
class Tester {
PlaceholderBindings bindings, inferBindings;
ExecutionEngine EEI;
std::unique_ptr<Module> mod;
Function *F;
TypeRef inputType;
Placeholder *input;
Placeholder *output;
public:
explicit Tester(llvm::StringRef backendName)
: EEI(backendName), mod(new Module), F(mod->createFunction("resnet50")),
inputType(mod->uniqueType(ElemKind::FloatTy, {1, 3, 224, 224})) {
// Load and compile ResNet-50.
Caffe2ModelLoader loader("resnet50/predict_net.pb", "resnet50/init_net.pb",
{inputName}, {inputType}, *F);
input = llvm::cast<Placeholder>(
EXIT_ON_ERR(loader.getNodeValueByName(inputName)));
output = EXIT_ON_ERR(loader.getSingleOutput());
}
void bindInput(Tensor *batch) {
// Allocate memory for input and bind it to the placeholders.
bindings.allocate(mod->getPlaceholders());
updateInputPlaceholders(bindings, {input}, {batch});
}
TypeRef getInputType() const { return inputType; }
Function *getFunction() const { return F; }
std::list<Tensor *> hookAndRun(llvm::StringRef name) {
EEI.setBackendName(EEI.getBackendName());
inferBindings.clear();
auto modI = &EEI.getModule();
auto *FI = modI->createFunction("resnet50");
Caffe2ModelLoader loader(
"resnet50/predict_net.pb", "resnet50/init_net.pb", {inputName},
{mod->uniqueType(ElemKind::FloatTy, {1, 3, 224, 224})}, *FI);
auto hook = hookNode(FI, name);
inferBindings.allocate(modI->getPlaceholders());
for (const auto &PH : bindings.pairs()) {
auto iPH = inferBindings.getPlaceholderByNameSlow(PH.first->getName());
inferBindings.get(iPH)->assign(&PH.second);
}
std::list<Tensor *> outs;
for (const auto &P : hook.outputs) {
outs.emplace_back(inferBindings.get(P));
}
auto fName = hook.function->getName();
EEI.compile(CompilationMode::Infer);
EEI.run(inferBindings, fName);
return outs;
}
};
/// Compare layer-by-layer execution of ResNet on two backends.
int main() {
Tester interp{"Interpreter"};
Tester cpu{"CPU"};
// Read an example PNG and add it to an input batch.
auto image = readPngPpmImageAndPreprocess(
"tests/images/imagenet/cat_285.png", ImageNormalizationMode::k0to1,
ImageChannelOrder::BGR, ImageLayout::NCHW, imagenetNormMean,
imagenetNormStd);
Tensor batch(interp.getInputType());
batch.getHandle<float>().insertSlice(image, 0);
interp.bindInput(&batch);
cpu.bindInput(&batch);
for (auto const &node : interp.getFunction()->getNodes()) {
if (llvm::isa<SaveNode>(&node)) {
continue;
}
llvm::errs() << "Verifying layer: " << node.getName() << "\n";
auto interpOuts = interp.hookAndRun(node.getName());
auto cpuOuts = cpu.hookAndRun(node.getName());
if (interpOuts.size() == cpuOuts.size()) {
auto interpOutIt = interpOuts.begin(), interpOutEnd = interpOuts.end();
auto cpuOutIt = cpuOuts.begin(), cpuOutEnd = cpuOuts.end();
while (interpOutIt != interpOutEnd && cpuOutIt != cpuOutEnd) {
auto *interpOut = *interpOutIt;
auto *cpuOut = *cpuOutIt;
if (!interpOut->isEqual(*cpuOut)) {
llvm::errs() << "Results differ\n";
dumpImpl(interpOut);
dumpImpl(cpuOut);
}
++interpOutIt;
++cpuOutIt;
}
} else {
llvm::errs()
<< "Backends produced different number of results using hook at "
<< node.getName() << "\n";
}
}
return 0;
}