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print error message for mxnet::cpp::Operator::Invoke when failed (#14318
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* raise exceptions for mxnet::cpp::Operator::Invoke when failed

* fix input shape

* fix cpplint

* fix cpp-package example shape
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@wkcn @nswamy
wkcn authored and nswamy committed Apr 5, 2019
1 parent 9a6cd58 commit 2447f80
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Showing 5 changed files with 155 additions and 50 deletions.
75 changes: 53 additions & 22 deletions cpp-package/example/alexnet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -196,28 +196,50 @@ Symbol AlexnetSymbol(int num_classes) {
return softmax;
}

NDArray ResizeInput(NDArray data, const Shape new_shape) {
NDArray pic = data.Reshape(Shape(0, 1, 28, 28));
NDArray pic_1channel;
Operator("_contrib_BilinearResize2D")
.SetParam("height", new_shape[2])
.SetParam("width", new_shape[3])
(pic).Invoke(pic_1channel);
NDArray output;
Operator("tile")
.SetParam("reps", Shape(1, 3, 1, 1))
(pic_1channel).Invoke(output);
return output;
}

int main(int argc, char const *argv[]) {
/*basic config*/
int batch_size = 256;
int max_epo = argc > 1 ? strtol(argv[1], NULL, 10) : 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;

/*context and net symbol*/
auto ctx = Context::gpu();
#if MXNET_USE_CPU
ctx = Context::cpu();
/*context*/
auto ctx = Context::cpu();
int num_gpu;
MXGetGPUCount(&num_gpu);
int batch_size = 32;
#if !MXNET_USE_CPU
if (num_gpu > 0) {
ctx = Context::gpu();
batch_size = 256;
}
#endif

/*net symbol*/
auto Net = AlexnetSymbol(10);

/*args_map and aux_map is used for parameters' saving*/
std::map<std::string, NDArray> args_map;
std::map<std::string, NDArray> aux_map;

/*we should tell mxnet the shape of data and label*/
args_map["data"] = NDArray(Shape(batch_size, 3, 256, 256), ctx);
args_map["label"] = NDArray(Shape(batch_size), ctx);
const Shape data_shape = Shape(batch_size, 3, 256, 256),
label_shape = Shape(batch_size);
args_map["data"] = NDArray(data_shape, ctx);
args_map["label"] = NDArray(label_shape, ctx);

/*with data and label, executor can be generated automatically*/
auto *exec = Net.SimpleBind(ctx, args_map);
Expand Down Expand Up @@ -261,17 +283,18 @@ int main(int argc, char const *argv[]) {
->SetParam("wd", weight_decay);

Accuracy acu_train, acu_val;
LogLoss logloss_val;
for (int iter = 0; iter < max_epo; ++iter) {
LG << "Train Epoch: " << iter;
LogLoss logloss_train, logloss_val;
for (int epoch = 0; epoch < max_epo; ++epoch) {
LG << "Train Epoch: " << epoch;
/*reset the metric every epoch*/
acu_train.Reset();
/*reset the data iter every epoch*/
train_iter.Reset();
int iter = 0;
while (train_iter.Next()) {
auto batch = train_iter.GetDataBatch();
/*use copyto to feed new data and label to the executor*/
batch.data.CopyTo(&args_map["data"]);
ResizeInput(batch.data, data_shape).CopyTo(&args_map["data"]);
batch.label.CopyTo(&args_map["label"]);
exec->Forward(true);
exec->Backward();
Expand All @@ -282,39 +305,47 @@ int main(int argc, char const *argv[]) {

NDArray::WaitAll();
acu_train.Update(batch.label, exec->outputs[0]);
logloss_train.Reset();
logloss_train.Update(batch.label, exec->outputs[0]);
++iter;
LG << "EPOCH: " << epoch << " ITER: " << iter
<< " Train Accuracy: " << acu_train.Get()
<< " Train Loss: " << logloss_train.Get();
}
LG << "ITER: " << iter << " Train Accuracy: " << acu_train.Get();
LG << "EPOCH: " << epoch << " Train Accuracy: " << acu_train.Get();

LG << "Val Epoch: " << iter;
LG << "Val Epoch: " << epoch;
acu_val.Reset();
val_iter.Reset();
logloss_val.Reset();
iter = 0;
while (val_iter.Next()) {
auto batch = val_iter.GetDataBatch();
LG << val_iter.GetDataBatch().index.size();
batch.data.CopyTo(&args_map["data"]);
ResizeInput(batch.data, data_shape).CopyTo(&args_map["data"]);
batch.label.CopyTo(&args_map["label"]);
exec->Forward(false);
NDArray::WaitAll();
acu_val.Update(batch.label, exec->outputs[0]);
logloss_val.Update(batch.label, exec->outputs[0]);
LG << "EPOCH: " << epoch << " ITER: " << iter << " Val Accuracy: " << acu_val.Get();
++iter;
}
LG << "ITER: " << iter << " Val Accuracy: " << acu_val.Get();
LG << "ITER: " << iter << " Val LogLoss: " << logloss_val.Get();
LG << "EPOCH: " << epoch << " Val Accuracy: " << acu_val.Get();
LG << "EPOCH: " << epoch << " Val LogLoss: " << logloss_val.Get();

/*save the parameters*/
std::stringstream ss;
ss << iter;
std::string iter_str;
ss >> iter_str;
std::string save_path_param = "alex_param_" + iter_str;
ss << epoch;
std::string epoch_str;
ss >> epoch_str;
std::string save_path_param = "alex_param_" + epoch_str;
auto save_args = args_map;
/*we do not want to save the data and label*/
save_args.erase(save_args.find("data"));
save_args.erase(save_args.find("label"));
/*the alexnet does not get any aux array, so we do not need to save
* aux_map*/
LG << "ITER: " << iter << " Saving to..." << save_path_param;
LG << "EPOCH: " << epoch << " Saving to..." << save_path_param;
NDArray::Save(save_path_param, save_args);
}
/*don't foget to release the executor*/
Expand Down
35 changes: 28 additions & 7 deletions cpp-package/example/inception_bn.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -142,23 +142,44 @@ Symbol InceptionSymbol(int num_classes) {
return SoftmaxOutput("softmax", fc1, data_label);
}

NDArray ResizeInput(NDArray data, const Shape new_shape) {
NDArray pic = data.Reshape(Shape(0, 1, 28, 28));
NDArray pic_1channel;
Operator("_contrib_BilinearResize2D")
.SetParam("height", new_shape[2])
.SetParam("width", new_shape[3])
(pic).Invoke(pic_1channel);
NDArray output;
Operator("tile")
.SetParam("reps", Shape(1, 3, 1, 1))
(pic_1channel).Invoke(output);
return output;
}

int main(int argc, char const *argv[]) {
int batch_size = 40;
int max_epoch = argc > 1 ? strtol(argv[1], NULL, 10) : 100;
float learning_rate = 1e-2;
float weight_decay = 1e-4;

auto ctx = Context::gpu();
#if MXNET_USE_CPU
ctx = Context::cpu();
/*context*/
auto ctx = Context::cpu();
int num_gpu;
MXGetGPUCount(&num_gpu);
#if !MXNET_USE_CPU
if (num_gpu > 0) {
ctx = Context::gpu();
}
#endif

auto inception_bn_net = InceptionSymbol(10);
std::map<std::string, NDArray> args_map;
std::map<std::string, NDArray> aux_map;

args_map["data"] = NDArray(Shape(batch_size, 3, 224, 224), ctx);
args_map["data_label"] = NDArray(Shape(batch_size), ctx);
const Shape data_shape = Shape(batch_size, 3, 224, 224),
label_shape = Shape(batch_size);
args_map["data"] = NDArray(data_shape, ctx);
args_map["data_label"] = NDArray(label_shape, ctx);
inception_bn_net.InferArgsMap(ctx, &args_map, args_map);

std::vector<std::string> data_files = { "./data/mnist_data/train-images-idx3-ubyte",
Expand Down Expand Up @@ -201,7 +222,7 @@ int main(int argc, char const *argv[]) {
train_acc.Reset();
while (train_iter.Next()) {
auto data_batch = train_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();

Expand All @@ -221,7 +242,7 @@ int main(int argc, char const *argv[]) {
val_acc.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
exec->Forward(false);
Expand Down
32 changes: 25 additions & 7 deletions cpp-package/example/lenet_with_mxdataiter.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -66,6 +66,16 @@ Symbol LenetSymbol() {
return lenet;
}

NDArray ResizeInput(NDArray data, const Shape new_shape) {
NDArray pic = data.Reshape(Shape(0, 1, 28, 28));
NDArray output;
Operator("_contrib_BilinearResize2D")
.SetParam("height", new_shape[2])
.SetParam("width", new_shape[3])
(pic).Invoke(output);
return output;
}

int main(int argc, char const *argv[]) {
/*setup basic configs*/
int W = 28;
Expand All @@ -74,15 +84,23 @@ int main(int argc, char const *argv[]) {
int max_epoch = argc > 1 ? strtol(argv[1], NULL, 10) : 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
auto dev_ctx = Context::gpu();
#if MXNET_USE_CPU
dev_ctx = Context::cpu();

auto dev_ctx = Context::cpu();
int num_gpu;
MXGetGPUCount(&num_gpu);
#if !MXNET_USE_CPU
if (num_gpu > 0) {
dev_ctx = Context::gpu();
}
#endif

auto lenet = LenetSymbol();
std::map<std::string, NDArray> args_map;

args_map["data"] = NDArray(Shape(batch_size, 1, W, H), dev_ctx);
args_map["data_label"] = NDArray(Shape(batch_size), dev_ctx);
const Shape data_shape = Shape(batch_size, 1, H, W),
label_shape = Shape(batch_size);
args_map["data"] = NDArray(data_shape, dev_ctx);
args_map["data_label"] = NDArray(label_shape, dev_ctx);
lenet.InferArgsMap(dev_ctx, &args_map, args_map);

args_map["fc1_w"] = NDArray(Shape(500, 4 * 4 * 50), dev_ctx);
Expand Down Expand Up @@ -131,7 +149,7 @@ int main(int argc, char const *argv[]) {
samples += batch_size;
auto data_batch = train_iter.GetDataBatch();

data_batch.data.CopyTo(&args_map["data"]);
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();

Expand Down Expand Up @@ -163,7 +181,7 @@ int main(int argc, char const *argv[]) {
val_iter.Reset();
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();

Expand Down
55 changes: 44 additions & 11 deletions cpp-package/example/resnet.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -153,8 +153,21 @@ Symbol ResNetSymbol(int num_class, int num_level = 3, int num_block = 9,
return SoftmaxOutput("softmax", fc, data_label);
}

NDArray ResizeInput(NDArray data, const Shape new_shape) {
NDArray pic = data.Reshape(Shape(0, 1, 28, 28));
NDArray pic_1channel;
Operator("_contrib_BilinearResize2D")
.SetParam("height", new_shape[2])
.SetParam("width", new_shape[3])
(pic).Invoke(pic_1channel);
NDArray output;
Operator("tile")
.SetParam("reps", Shape(1, 3, 1, 1))
(pic_1channel).Invoke(output);
return output;
}

int main(int argc, char const *argv[]) {
int batch_size = 50;
int max_epoch = argc > 1 ? strtol(argv[1], NULL, 10) : 100;
float learning_rate = 1e-4;
float weight_decay = 1e-4;
Expand All @@ -163,13 +176,22 @@ int main(int argc, char const *argv[]) {
std::map<std::string, NDArray> args_map;
std::map<std::string, NDArray> aux_map;

auto ctx = Context::gpu();
#if MXNET_USE_CPU
ctx = Context::cpu();;
/*context*/
auto ctx = Context::cpu();
int num_gpu;
MXGetGPUCount(&num_gpu);
int batch_size = 8;
#if !MXNET_USE_CPU
if (num_gpu > 0) {
ctx = Context::gpu();
batch_size = 50;
}
#endif

args_map["data"] = NDArray(Shape(batch_size, 3, 256, 256), ctx);
args_map["data_label"] = NDArray(Shape(batch_size), ctx);
const Shape data_shape = Shape(batch_size, 3, 224, 224),
label_shape = Shape(batch_size);
args_map["data"] = NDArray(data_shape, ctx);
args_map["data_label"] = NDArray(label_shape, ctx);
resnet.InferArgsMap(ctx, &args_map, args_map);

std::vector<std::string> data_files = { "./data/mnist_data/train-images-idx3-ubyte",
Expand Down Expand Up @@ -206,13 +228,15 @@ int main(int argc, char const *argv[]) {

// Create metrics
Accuracy train_acc, val_acc;
for (int iter = 0; iter < max_epoch; ++iter) {
LG << "Epoch: " << iter;
LogLoss logloss_train, logloss_val;
for (int epoch = 0; epoch < max_epoch; ++epoch) {
LG << "Epoch: " << epoch;
train_iter.Reset();
train_acc.Reset();
int iter = 0;
while (train_iter.Next()) {
auto data_batch = train_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();

Expand All @@ -225,20 +249,29 @@ int main(int argc, char const *argv[]) {
}
NDArray::WaitAll();
train_acc.Update(data_batch.label, exec->outputs[0]);
logloss_train.Reset();
logloss_train.Update(data_batch.label, exec->outputs[0]);
++iter;
LG << "EPOCH: " << epoch << " ITER: " << iter
<< " Train Accuracy: " << train_acc.Get()
<< " Train Loss: " << logloss_train.Get();
}
LG << "EPOCH: " << epoch << " Train Accuracy: " << train_acc.Get();

val_iter.Reset();
val_acc.Reset();
iter = 0;
while (val_iter.Next()) {
auto data_batch = val_iter.GetDataBatch();
data_batch.data.CopyTo(&args_map["data"]);
ResizeInput(data_batch.data, data_shape).CopyTo(&args_map["data"]);
data_batch.label.CopyTo(&args_map["data_label"]);
NDArray::WaitAll();
exec->Forward(false);
NDArray::WaitAll();
val_acc.Update(data_batch.label, exec->outputs[0]);
LG << "EPOCH: " << epoch << " ITER: " << iter << " Val Accuracy: " << val_acc.Get();
++iter;
}
LG << "Train Accuracy: " << train_acc.Get();
LG << "Validation Accuracy: " << val_acc.Get();
}
delete exec;
Expand Down
8 changes: 5 additions & 3 deletions cpp-package/include/mxnet-cpp/operator.hpp
View file
Original file line number Diff line number Diff line change
Expand Up @@ -134,9 +134,11 @@ inline void Operator::Invoke(std::vector<NDArray> &outputs) {
outputs_receiver = output_handles.data();
}

MXImperativeInvoke(handle_, num_inputs, input_ndarrays_.data(),
&num_outputs, &outputs_receiver,
param_keys.size(), param_keys.data(), param_values.data());
if (MXImperativeInvoke(handle_, num_inputs, input_ndarrays_.data(),
&num_outputs, &outputs_receiver,
param_keys.size(), param_keys.data(),
param_values.data()))
LOG(FATAL) << MXGetLastError();

if (outputs.size() > 0)
return;
Expand Down

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