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Commit ee040034 authored by Carlos Alfredo Yeverino Rodriguez's avatar Carlos Alfredo Yeverino Rodriguez
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Extended layer support checker to analyze also elements of constructed layers

parent e7606f12
Pipeline #101444 passed with stages
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src/main/java/de/monticore/lang/monticar/cnnarch/caffe2generator/CNNArch2Caffe2.java
View file @ ee040034
......@@ -26,6 +26,7 @@ import de.monticore.lang.monticar.cnnarch._cocos.CNNArchCocos;
import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureSymbol;
import de.monticore.lang.monticar.cnnarch._symboltable.ArchitectureElementSymbol;
import de.monticore.lang.monticar.cnnarch._symboltable.CompositeElementSymbol;
import de.monticore.lang.monticar.cnnarch._symboltable.IOSymbol;
import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchCompilationUnitSymbol;
import de.monticore.lang.monticar.cnnarch._symboltable.CNNArchLanguage;
import de.monticore.lang.monticar.generator.FileContent;
......@@ -41,19 +42,37 @@ import java.nio.file.Path;
import java.util.HashMap;
import java.util.Map;
import java.util.Optional;
import java.util.List;
public class CNNArch2Caffe2 implements CNNArchGenerator{
private String generationTargetPath;
private void supportCheck(ArchitectureSymbol architecture){
private boolean isSupportedLayer(ArchitectureElementSymbol element, LayerSupportChecker layerChecker){
List<ArchitectureElementSymbol> constructLayerElemList;
if (!(element instanceof IOSymbol) && (element.getResolvedThis().get() instanceof CompositeElementSymbol))
{
constructLayerElemList = ((CompositeElementSymbol)element.getResolvedThis().get()).getElements();
for (ArchitectureElementSymbol constructedLayerElement : constructLayerElemList) {
if (!isSupportedLayer(constructedLayerElement, layerChecker)) return false;
}
}
if (!layerChecker.isSupported(element.toString())) {
Log.error("Unsupported layer " + "'" + element.getName() + "'" + " for the backend CAFFE2.");
return false;
}
else {
return true;
}
}
private boolean supportCheck(ArchitectureSymbol architecture){
LayerSupportChecker layerChecker = new LayerSupportChecker();
for (ArchitectureElementSymbol element : ((CompositeElementSymbol)architecture.getBody()).getElements()){
if (!layerChecker.isSupported(element.toString())) {
Log.error("Unsupported layer " + "'" + element.getName() + "'" + " for the backend CAFFE2. Code generation aborted.");
System.exit(1);
}
if(!isSupportedLayer(element, layerChecker)) return false;
}
return true;
}
public CNNArch2Caffe2() {
......@@ -90,7 +109,10 @@ public class CNNArch2Caffe2 implements CNNArchGenerator{
}
CNNArchCocos.checkAll(compilationUnit.get());
supportCheck(compilationUnit.get().getArchitecture());
if (!supportCheck(compilationUnit.get().getArchitecture())){
Log.error("Code generation aborted.");
System.exit(1);
}
try{
generateFiles(compilationUnit.get().getArchitecture());
......
src/test/java/de/monticore/lang/monticar/cnnarch/caffe2generator/GenerationTest.java
View file @ ee040034
......@@ -46,9 +46,9 @@ public class GenerationTest extends AbstractSymtabTest{
}
@Test
public void testCifar10Classifier() throws IOException, TemplateException {
public void testLeNetGeneration() throws IOException, TemplateException {
Log.getFindings().clear();
String[] args = {"-m", "src/test/resources/valid_tests", "-r", "CifarClassifierNetwork", "-o", "./target/generated-sources-cnnarch/"};
String[] args = {"-m", "src/test/resources/architectures", "-r", "LeNet", "-o", "./target/generated-sources-cnnarch/"};
CNNArch2Caffe2Cli.main(args);
assertTrue(Log.getFindings().isEmpty());
......@@ -56,9 +56,18 @@ public class GenerationTest extends AbstractSymtabTest{
Paths.get("./target/generated-sources-cnnarch"),
Paths.get("./src/test/resources/target_code"),
Arrays.asList(
"CNNCreator_CifarClassifierNetwork.py",
"CNNPredictor_CifarClassifierNetwork.h",
"execute_CifarClassifierNetwork"));
"CNNCreator_LeNet.py",
"CNNPredictor_LeNet.h",
"execute_LeNet"));
}
@Test
public void testCifar10Classifier() throws IOException, TemplateException {
Log.getFindings().clear();
String[] args = {"-m", "src/test/resources/valid_tests", "-r", "CifarClassifierNetwork", "-o", "./target/generated-sources-cnnarch/"};
exit.expectSystemExit();
CNNArch2Caffe2Cli.main(args);
assertTrue(Log.getFindings().size() == 2);
}
@Test
......@@ -67,6 +76,8 @@ public class GenerationTest extends AbstractSymtabTest{
String[] args = {"-m", "src/test/resources/architectures", "-r", "Alexnet", "-o", "./target/generated-sources-cnnarch/"};
exit.expectSystemExit();
CNNArch2Caffe2Cli.main(args);
assertTrue(Log.getFindings().size() == 2);
}
@Test
......@@ -92,14 +103,16 @@ public class GenerationTest extends AbstractSymtabTest{
String[] args = {"-m", "src/test/resources/architectures", "-r", "ThreeInputCNN_M14"};
exit.expectSystemExit();
CNNArch2Caffe2Cli.main(args);
assertTrue(Log.getFindings().size() == 2);
}
@Test
public void testResNeXtGeneration() throws IOException, TemplateException {
Log.getFindings().clear();;
String[] args = {"-m", "src/test/resources/architectures", "-r", "ResNeXt50"};
exit.expectSystemExit();
CNNArch2Caffe2Cli.main(args);
assertTrue(Log.getFindings().isEmpty());
assertTrue(Log.getFindings().size() == 2);
}
@Test
......
src/test/resources/target_code/CNNCreator_LeNet.py 0 → 100644
View file @ ee040034
from caffe2.python import workspace, core, model_helper, brew, optimizer
from caffe2.python.predictor import mobile_exporter
from caffe2.proto import caffe2_pb2
import numpy as np
import math
import logging
import os
import sys
import lmdb
class CNNCreator_LeNet:
module = None
_current_dir_ = os.path.join('./')
_data_dir_ = os.path.join(_current_dir_, 'data', 'LeNet')
_model_dir_ = os.path.join(_current_dir_, 'model', 'LeNet')
_init_net_ = os.path.join(_model_dir_, 'init_net.pb')
_predict_net_ = os.path.join(_model_dir_, 'predict_net.pb')
def get_total_num_iter(self, num_epoch, batch_size, dataset_size):
#Force floating point calculation
batch_size_float = float(batch_size)
dataset_size_float = float(dataset_size)
iterations_float = math.ceil(num_epoch*(dataset_size_float/batch_size_float))
iterations_int = int(iterations_float)
return iterations_int
def add_input(self, model, batch_size, db, db_type, device_opts):
with core.DeviceScope(device_opts):
if not os.path.isdir(db):
logging.error("Data loading failure. Directory '" + os.path.abspath(db) + "' does not exist.")
sys.exit(1)
elif not (os.path.isfile(os.path.join(db, 'data.mdb')) and os.path.isfile(os.path.join(db, 'lock.mdb'))):
logging.error("Data loading failure. Directory '" + os.path.abspath(db) + "' does not contain lmdb files.")
sys.exit(1)
# load the data
data_uint8, label = brew.db_input(
model,
blobs_out=["data_uint8", "label"],
batch_size=batch_size,
db=db,
db_type=db_type,
)
# cast the data to float
data = model.Cast(data_uint8, "data", to=core.DataType.FLOAT)
# scale data from [0,255] down to [0,1]
data = model.Scale(data, data, scale=float(1./256))
# don't need the gradient for the backward pass
data = model.StopGradient(data, data)
dataset_size = int (lmdb.open(db).stat()['entries'])
return data, label, dataset_size
def create_model(self, model, data, device_opts):
with core.DeviceScope(device_opts):
image = data
# image, output shape: {[1,28,28]}
conv1_ = brew.conv(model, image, 'conv1_', dim_in=1, dim_out=20, kernel=5, stride=1)
# conv1_, output shape: {[20,24,24]}
pool1_ = brew.max_pool(model, conv1_, 'pool1_', kernel=2, stride=2)
# pool1_, output shape: {[20,12,12]}
conv2_ = brew.conv(model, pool1_, 'conv2_', dim_in=20, dim_out=50, kernel=5, stride=1)
# conv2_, output shape: {[50,8,8]}
pool2_ = brew.max_pool(model, conv2_, 'pool2_', kernel=2, stride=2)
# pool2_, output shape: {[50,4,4]}
fc2_ = brew.fc(model, pool2_, 'fc2_', dim_in=50 * 4 * 4, dim_out=500)
# fc2_, output shape: {[500,1,1]}
relu2_ = brew.relu(model, fc2_, fc2_)
fc3_ = brew.fc(model, relu2_, 'fc3_', dim_in=500, dim_out=10)
# fc3_, output shape: {[10,1,1]}
predictions = brew.softmax(model, fc3_, 'predictions')
return predictions
# this adds the loss and optimizer
def add_training_operators(self, model, output, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum) :
with core.DeviceScope(device_opts):
xent = model.LabelCrossEntropy([output, label], 'xent')
loss = model.AveragedLoss(xent, "loss")
model.AddGradientOperators([loss])
if opt_type == 'adam':
if policy == 'step':
opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, beta1=beta1, beta2=beta2, epsilon=epsilon)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_adam(model, base_learning_rate=base_learning_rate, policy=policy, beta1=beta1, beta2=beta2, epsilon=epsilon)
print("adam optimizer selected")
elif opt_type == 'sgd':
if policy == 'step':
opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, gamma=gamma, momentum=momentum)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_sgd(model, base_learning_rate=base_learning_rate, policy=policy, gamma=gamma, momentum=momentum)
print("sgd optimizer selected")
elif opt_type == 'rmsprop':
if policy == 'step':
opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, momentum=momentum, epsilon=epsilon)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_rms_prop(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, momentum=momentum, epsilon=epsilon)
print("rmsprop optimizer selected")
elif opt_type == 'adagrad':
if policy == 'step':
opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, stepsize=stepsize, decay=gamma, epsilon=epsilon)
elif policy == 'fixed' or policy == 'inv':
opt = optimizer.build_adagrad(model, base_learning_rate=base_learning_rate, policy=policy, decay=gamma, epsilon=epsilon)
print("adagrad optimizer selected")
def add_accuracy(self, model, output, label, device_opts, eval_metric):
with core.DeviceScope(device_opts):
if eval_metric == 'accuracy':
accuracy = brew.accuracy(model, [output, label], "accuracy")
elif eval_metric == 'top_k_accuracy':
accuracy = brew.accuracy(model, [output, label], "accuracy", top_k=3)
return accuracy
def train(self, num_epoch=1000, batch_size=64, context='gpu', eval_metric='accuracy', opt_type='adam', base_learning_rate=0.001, weight_decay=0.001, policy='fixed', stepsize=1, epsilon=1E-8, beta1=0.9, beta2=0.999, gamma=0.999, momentum=0.9) :
if context == 'cpu':
device_opts = core.DeviceOption(caffe2_pb2.CPU, 0)
print("CPU mode selected")
elif context == 'gpu':
device_opts = core.DeviceOption(caffe2_pb2.CUDA, 0)
print("GPU mode selected")
workspace.ResetWorkspace(self._model_dir_)
arg_scope = {"order": "NCHW"}
# == Training model ==
train_model= model_helper.ModelHelper(name="train_net", arg_scope=arg_scope)
data, label, train_dataset_size = self.add_input(train_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'train_lmdb'), db_type='lmdb', device_opts=device_opts)
predictions = self.create_model(train_model, data, device_opts=device_opts)
self.add_training_operators(train_model, predictions, label, device_opts, opt_type, base_learning_rate, policy, stepsize, epsilon, beta1, beta2, gamma, momentum)
self.add_accuracy(train_model, predictions, label, device_opts, eval_metric)
with core.DeviceScope(device_opts):
brew.add_weight_decay(train_model, weight_decay)
# Initialize and create the training network
workspace.RunNetOnce(train_model.param_init_net)
workspace.CreateNet(train_model.net, overwrite=True)
# Main Training Loop
iterations = self.get_total_num_iter(num_epoch, batch_size, train_dataset_size)
print("** Starting Training for " + str(num_epoch) + " epochs = " + str(iterations) + " iterations **")
for i in range(iterations):
workspace.RunNet(train_model.net)
if i % 50 == 0:
print 'Iter ' + str(i) + ': ' + 'Loss ' + str(workspace.FetchBlob("loss")) + ' - ' + 'Accuracy ' + str(workspace.FetchBlob('accuracy'))
print("Training done")
print("== Running Test model ==")
# == Testing model. ==
test_model= model_helper.ModelHelper(name="test_net", arg_scope=arg_scope, init_params=False)
data, label, test_dataset_size = self.add_input(test_model, batch_size=batch_size, db=os.path.join(self._data_dir_, 'test_lmdb'), db_type='lmdb', device_opts=device_opts)
predictions = self.create_model(test_model, data, device_opts=device_opts)
self.add_accuracy(test_model, predictions, label, device_opts, eval_metric)
workspace.RunNetOnce(test_model.param_init_net)
workspace.CreateNet(test_model.net, overwrite=True)
# Main Testing Loop
test_accuracy = np.zeros(test_dataset_size/batch_size)
for i in range(test_dataset_size/batch_size):
# Run a forward pass of the net on the current batch
workspace.RunNet(test_model.net)
# Collect the batch accuracy from the workspace
test_accuracy[i] = workspace.FetchBlob('accuracy')
print('Test_accuracy: {:.4f}'.format(test_accuracy.mean()))
# == Deployment model. ==
# We simply need the main AddModel part.
deploy_model = model_helper.ModelHelper(name="deploy_net", arg_scope=arg_scope, init_params=False)
self.create_model(deploy_model, "data", device_opts)
print("Saving deploy model")
self.save_net(self._init_net_, self._predict_net_, deploy_model)
def save_net(self, init_net_path, predict_net_path, model):
init_net, predict_net = mobile_exporter.Export(
workspace,
model.net,
model.params
)
try:
os.makedirs(self._model_dir_)
except OSError:
if not os.path.isdir(self._model_dir_):
raise
print("Save the model to init_net.pb and predict_net.pb")
with open(predict_net_path, 'wb') as f:
f.write(model.net._net.SerializeToString())
with open(init_net_path, 'wb') as f:
f.write(init_net.SerializeToString())
print("Save the model to init_net.pbtxt and predict_net.pbtxt")
with open(init_net_path.replace('.pb','.pbtxt'), 'w') as f:
f.write(str(init_net))
with open(predict_net_path.replace('.pb','.pbtxt'), 'w') as f:
f.write(str(predict_net))
print("== Saved init_net and predict_net ==")
def load_net(self, init_net_path, predict_net_path, device_opts):
if not os.path.isfile(init_net_path):
logging.error("Network loading failure. File '" + os.path.abspath(init_net_path) + "' does not exist.")
sys.exit(1)
elif not os.path.isfile(predict_net_path):
logging.error("Network loading failure. File '" + os.path.abspath(predict_net_path) + "' does not exist.")
sys.exit(1)
init_def = caffe2_pb2.NetDef()
with open(init_net_path, 'rb') as f:
init_def.ParseFromString(f.read())
init_def.device_option.CopyFrom(device_opts)
workspace.RunNetOnce(init_def.SerializeToString())
net_def = caffe2_pb2.NetDef()
with open(predict_net_path, 'rb') as f:
net_def.ParseFromString(f.read())
net_def.device_option.CopyFrom(device_opts)
workspace.CreateNet(net_def.SerializeToString(), overwrite=True)
print("== Loaded init_net and predict_net ==")
src/test/resources/target_code/CNNPredictor_LeNet.h 0 → 100644
View file @ ee040034
#ifndef CNNPREDICTOR_LENET
#define CNNPREDICTOR_LENET
#include "caffe2/core/common.h"
#include "caffe2/utils/proto_utils.h"
#include "caffe2/core/workspace.h"
#include "caffe2/core/tensor.h"
#include "caffe2/core/init.h"
// Define USE_GPU for GPU computation. Default is CPU computation.
//#define USE_GPU
#ifdef USE_GPU
#include "caffe2/core/context_gpu.h"
#endif
#include <string>
#include <iostream>
#include <map>
CAFFE2_DEFINE_string(init_net, "./model/LeNet/init_net.pb", "The given path to the init protobuffer.");
CAFFE2_DEFINE_string(predict_net, "./model/LeNet/predict_net.pb", "The given path to the predict protobuffer.");
using namespace caffe2;
class CNNPredictor_LeNet{
private:
TensorCPU input;
Workspace workSpace;
NetDef initNet, predictNet;
public:
const std::vector<TIndex> input_shapes = {{1,1,28,28}};
explicit CNNPredictor_LeNet(){
init(input_shapes);
}
~CNNPredictor_LeNet(){};
void init(const std::vector<TIndex> &input_shapes){
int n = 0;
char **a[1];
caffe2::GlobalInit(&n, a);
if (!std::ifstream(FLAGS_init_net).good()) {
std::cerr << "\nNetwork loading failure, init_net file '" << FLAGS_init_net << "' does not exist." << std::endl;
exit(1);
}
if (!std::ifstream(FLAGS_predict_net).good()) {
std::cerr << "\nNetwork loading failure, predict_net file '" << FLAGS_predict_net << "' does not exist." << std::endl;
exit(1);
}
std::cout << "\nLoading network..." << std::endl;
// Read protobuf
CAFFE_ENFORCE(ReadProtoFromFile(FLAGS_init_net, &initNet));
CAFFE_ENFORCE(ReadProtoFromFile(FLAGS_predict_net, &predictNet));
// Set device type
#ifdef USE_GPU
predictNet.mutable_device_option()->set_device_type(CUDA);
initNet.mutable_device_option()->set_device_type(CUDA);
std::cout << "== GPU mode selected " << " ==" << std::endl;
#else
predictNet.mutable_device_option()->set_device_type(CPU);
initNet.mutable_device_option()->set_device_type(CPU);
for(int i = 0; i < predictNet.op_size(); ++i){
predictNet.mutable_op(i)->mutable_device_option()->set_device_type(CPU);
}
for(int i = 0; i < initNet.op_size(); ++i){
initNet.mutable_op(i)->mutable_device_option()->set_device_type(CPU);
}
std::cout << "== CPU mode selected " << " ==" << std::endl;
#endif
// Load network
CAFFE_ENFORCE(workSpace.RunNetOnce(initNet));
CAFFE_ENFORCE(workSpace.CreateNet(predictNet));
std::cout << "== Network loaded " << " ==" << std::endl;
input.Resize(input_shapes);
}
void predict(const std::vector<float> &image, std::vector<float> &predictions){
//Note: ShareExternalPointer requires a float pointer.
input.ShareExternalPointer((float *) image.data());
// Get input blob
#ifdef USE_GPU
auto dataBlob = workSpace.GetBlob("data")->GetMutable<TensorCUDA>();
#else
auto dataBlob = workSpace.GetBlob("data")->GetMutable<TensorCPU>();
#endif
// Copy from input data
dataBlob->CopyFrom(input);
// Forward
workSpace.RunNet(predictNet.name());
// Get output blob
#ifdef USE_GPU
auto predictionsBlob = TensorCPU(workSpace.GetBlob("predictions")->Get<TensorCUDA>());
#else
auto predictionsBlob = workSpace.GetBlob("predictions")->Get<TensorCPU>();
#endif
predictions.assign(predictionsBlob.data<float>(),predictionsBlob.data<float>() + predictionsBlob.size());
google::protobuf::ShutdownProtobufLibrary();
}
};
#endif // CNNPREDICTOR_LENET
src/test/resources/target_code/execute_LeNet 0 → 100644
View file @ ee040034
vector<float> CNN_predictions(10);
_cnn_.predict(CNNTranslator::translate(image),
CNN_predictions);
predictions = CNNTranslator::translateToCol(CNN_predictions, std::vector<size_t> {10});
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