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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2019/01/14 10:56:51 UTC
[GitHub] hengdos opened a new issue #13870: [C++] Linking static library
(error or bug?) to load python trained model
hengdos opened a new issue #13870: [C++] Linking static library (error or bug?) to load python trained model
URL: https://github.com/apache/incubator-mxnet/issues/13870
Hi. I'm trying to train a simple model using python api and load the trained model for predicting using c++ api.
Following the [tutorials](http://mxnet.incubator.apache.org/versions/master/install/c_plus_plus.html), I've already built mxnet from source with `USE_CPP_PACKAGE=1` and `USE_BLAS = openblas`. Then I got `libmxnet.a libmxnet.so` files.
While I build executable program with linking to dynamic library (libmxnet.so), my codes work well. However, if I tried to build executable program with linking to static library (libmxnet.a), then I receive an error like `"transformer: lrcplus/main.cc:101: int main(): Assertion 'pred_hnd' failed."`
Please help out. Thanks in advance!!!
### Environment info
Compiler: gcc version 4.8.5 20150623 (Red Hat 4.8.5-4) (GCC) | Cmake version 3.9.4
Package used (Python/R/Scala/Julia): C++
MXNet commit hash (`git rev-parse HEAD`): c2110ada6d43f10710d181c0deb0673fe6d829b2
### Steps to reproduce
I'm running my own project. The tree is like this.
```
lrcplus
├── CMakeLists-static.txt
├── CMakeLists-dynamic.txt
├── build
├── main.cc
├── include
│ ├── mxnet (Contains hedder files from `/mxnet/include/mxnet/`)
└── lib
│ ├── libmxnet.so (Copy from `/mxnet/lib/`)
│ ├── libmxnet.a (Copy from `/mxnet/lib/`)
│ ├── libdmlc.a (Copy from `/mxnet/3rdparty/dmlc-core/`)
│ └── libnnvm.a (Copy from `/mxnet/3rdparty/3rdparty/tvm/nnvm/lib/`)
└── python
│ ├── train.py (Implement a simple network and save with export)
│ ├── net-0000.params (Generated by train.py)
│ ├── net-symbol.json (Generated by train.py)
│ └── test.csv (Generated by train.py)
```
The `train.py` file is like this:
```
from __future__ import print_function
import mxnet as mx
from mxnet import nd, autograd, gluon
data_ctx = mx.cpu()
model_ctx = mx.cpu()
num_inputs = 2
num_outputs = 1
num_examples = 10000
def real_fn(X):
return 2 * X[:, 0] - 3.4 * X[:, 1] + 4.2
X = nd.random_normal(shape=(num_examples, num_inputs))
noise = 0.01 * nd.random_normal(shape=(num_examples,))
y = real_fn(X) + noise
batch_size = 4
train_data = gluon.data.DataLoader(gluon.data.ArrayDataset(X, y), batch_size=batch_size, shuffle=True)
net = gluon.nn.Dense(1, in_units=2)
net.initialize(mx.init.Normal(sigma=1.), ctx=model_ctx)
net.hybridize()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.0001})
sq_loss = gluon.loss.L2Loss()
sq_loss.hybridize()
epochs = 5
loss_sequence = []
num_batches = num_examples / batch_size
for e in range(epochs):
cumulative_loss = 0
for i, (data, label) in enumerate(train_data):
data = data.as_in_context(model_ctx)
label = label.as_in_context(model_ctx)
with autograd.record():
output = net(data)
loss = sq_loss(output, label)
loss.backward()
trainer.step(batch_size)
cumulative_loss += nd.mean(loss).asscalar()
print("Epoch %s, loss: %s" % (e, cumulative_loss / num_examples))
loss_sequence.append(cumulative_loss)
net.export('net')
f = open('test.csv','w',encoding='utf-8')
a = X[0].asnumpy()
s = ','.join([str(x) for x in a])
f.write(s)
f.close()
```
The `main.cc` file is like this:
```
#include "mxnet/c_predict_api.h"
#include <assert.h>
#include <iostream>
#include <sstream>
#include <fstream>
#include <string>
#include <vector>
using namespace std;
class BufferFile {
public:
string file_path_;
int length_;
char* buffer_;
explicit BufferFile(string file_path)
:file_path_(file_path) {
ifstream ifs(file_path.c_str(), ios::in | ios::binary);
if (!ifs) {
cerr << "Can't open the file. Please check " << file_path << ". \n";
length_ = 0;
buffer_ = NULL;
return;
}
ifs.seekg(0, ios::end);
length_ = ifs.tellg();
ifs.seekg(0, ios::beg);
cout << file_path.c_str() << " ... " << length_ << " bytes\n";
buffer_ = new char[sizeof(char) * length_];
ifs.read(buffer_, length_);
ifs.close();
}
int GetLength() {
return length_;
}
char* GetBuffer() {
return buffer_;
}
~BufferFile() {
if (buffer_) {
delete[] buffer_;
buffer_ = NULL;
}
}
};
int main(){
ifstream fin("test.csv");
string line;
vector<mx_float> data;
while (getline(fin, line)) {
istringstream sin(line);
string field;
while (getline(sin, field, ',')) {
data.push_back(atof(field.c_str()));
}
}
fin.close();
string json_file = "net-symbol.json";
string param_file = "net-0000.params";
BufferFile json_data(json_file);
BufferFile param_data(param_file);
mx_uint num_input_nodes = 1;
const char* input_key[1] = { "data" };
const char** input_keys = input_key;
int input_shape[1] = { 2 };
const mx_uint input_shape_indptr[2] = { 0,2 };
const mx_uint input_shape_data[2] = { 1, static_cast<mx_uint>(input_shape[0])};
mx_uint input_data_size = input_shape[0];
PredictorHandle pred_hnd = 0;
int dev_type = 1;
int dev_id = 1;
MXPredCreate(static_cast<const char*>(json_data.GetBuffer()),
static_cast<const char*>(param_data.GetBuffer()),
static_cast<int>(param_data.GetLength()),
dev_type,
dev_id,
num_input_nodes,
input_keys,
input_shape_indptr,
input_shape_data,
&pred_hnd);
assert(pred_hnd);
MXPredSetInput(pred_hnd, "data", data.data(), input_data_size);
MXPredForward(pred_hnd);
mx_uint output_index = 0;
mx_uint* shape = nullptr;
mx_uint shape_len;
// Get Output Result
MXPredGetOutputShape(pred_hnd, output_index, &shape, &shape_len);
size_t size = 1;
for (mx_uint i = 0; i < shape_len; ++i) { size *= shape[i]; }
vector<float> output(size);
MXPredGetOutput(pred_hnd, output_index, &(output[0]), static_cast<mx_uint>(size));
for (auto i = output.begin(); i != output.end(); ++i) {
std::cout << *i << ' ';
}
}
```
The `CMakeLists-dynamic.txt` file is like this:
```
cmake_minimum_required(VERSION 3.9)
set (CMAKE_CXX_STANDARD 11)
add_executable(transformer
main.cc
)
target_link_libraries(transformer
${PROJECT_SOURCE_DIR}/lib/libmxnet.so
)
target_include_directories(transformer PUBLIC
${PROJECT_SOURCE_DIR}/include
)
```
The `CMakeLists-static.txt` file is like this:
```
cmake_minimum_required(VERSION 3.9)
set (CMAKE_CXX_STANDARD 11)
add_executable(transformer
main.cc
)
target_link_libraries(transformer
rt
${PROJECT_SOURCE_DIR}/lib/libmxnet.a
${PROJECT_SOURCE_DIR}/lib/libdmlc.a
${PROJECT_SOURCE_DIR}/lib/libnnvm.a
)
target_include_directories(transformer PUBLIC
${PROJECT_SOURCE_DIR}/include
)
```
so, to try my situation easily.
```
# Try model using python script
cd lrcplus/python
python train.py
# Try to build executable program with linking to dynamic library
cd ../build
cp ../CMakeLists-dynamic.txt ../CMakeLists.txt
cmake ../
make
mv ./transformer ../python/
cd ../python
./transformer
#Then you may see message like this
#net-symbol.json ... 1022 bytes
#net-0000.params ... 164 bytes
#4.0142
# Try to build executable program with linking to static library
cd ../build
cp ../CMakeLists-static.txt ../CMakeLists.txt
cmake ../
make
mv ./transformer ../python/
cd ../python
./transformer
#Then you may see message like this
#net-symbol.json ... 1022 bytes
#net-0000.params ... 164 bytes
#transformer: /data1/qspace/christding/workspace/mxnet_c++/Basic/lrcplus/main.cc:101: int main(): Assertion `pred_hnd' failed.
```
I really appreciate your cooperation.
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