def train_cifar_resnet_for_eval(test_device, output_dir):
output_dir = os.path.abspath(output_dir)
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
base_path = prepare_test_data.prepare_CIFAR10_data()
# change dir to locate data.zip correctly
os.chdir(base_path)
if test_device == 'cpu':
print('train cifar_resnet only on GPU device. Use pre-trained models.')
else:
print('training cifar_resnet on GPU device...')
reader_train = TrainResNet_CIFAR10.create_reader(
os.path.join(base_path, 'train_map.txt'),
os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
reader_test = TrainResNet_CIFAR10.create_reader(
os.path.join(base_path, 'test_map.txt'),
os.path.join(base_path, 'CIFAR-10_mean.xml'), False)
TrainResNet_CIFAR10.train_and_evaluate(reader_train,
reader_test,
'resnet20',
epoch_size=512,
max_epochs=1,
profiler_dir=None,
model_dir=output_dir)
return base_path
def train_cifar_resnet_for_eval(test_device, output_dir):
output_dir = os.path.abspath(output_dir)
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
base_path = prepare_test_data.prepare_CIFAR10_data()
# change dir to locate data.zip correctly
os.chdir(base_path)
if test_device == 'cpu':
print('train cifar_resnet only on GPU device. Use pre-trained models.')
else:
print('training cifar_resnet on GPU device...')
reader_train = TrainResNet_CIFAR10.create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
reader_test = TrainResNet_CIFAR10.create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False)
TrainResNet_CIFAR10.train_and_evaluate(reader_train, reader_test, 'resnet20', epoch_size=512, max_epochs=1, profiler_dir=None, model_dir=output_dir)
return base_path
def train_cifar_resnet_for_eval(test_device, output_dir):
output_dir = os.path.abspath(output_dir)
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
base_path = prepare_test_data.prepare_CIFAR10_data()
# change dir to locate data.zip correctly
os.chdir(base_path)
# unzip test images for eval
with zipfile.ZipFile(
os.path.join(base_path, 'cifar-10-batches-py',
'data.zip')) as myzip:
for fn in range(6):
myzip.extract('data/train/%05d.png' % (fn), output_dir)
if test_device == 'cpu':
print('train cifar_resnet only on GPU device. Use pre-trained models.')
else:
print('training cifar_resnet on GPU device...')
reader_train = TrainResNet_CIFAR10.create_reader(
os.path.join(base_path, 'train_map.txt'),
os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
reader_test = TrainResNet_CIFAR10.create_reader(
os.path.join(base_path, 'test_map.txt'),
os.path.join(base_path, 'CIFAR-10_mean.xml'), False)
TrainResNet_CIFAR10.train_and_evaluate(reader_train,
reader_test,
'resnet20',
epoch_size=512,
max_epochs=1,
profiler_dir=None,
model_dir=output_dir)
return base_path
def train_cifar_resnet_for_eval(test_device, output_dir):
output_dir = os.path.abspath(output_dir)
if not os.path.isdir(output_dir):
os.mkdir(output_dir)
base_path = prepare_test_data.prepare_CIFAR10_data()
# change dir to locate data.zip correctly
os.chdir(base_path)
# unzip test images for eval
with zipfile.ZipFile(os.path.join(base_path, 'cifar-10-batches-py', 'data.zip')) as myzip:
for fn in range(6):
myzip.extract('data/train/%05d.png'%(fn), output_dir)
if test_device == 'cpu':
print('train cifar_resnet only on GPU device. Use pre-trained models.')
else:
print('training cifar_resnet on GPU device...')
reader_train = TrainResNet_CIFAR10.create_reader(os.path.join(base_path, 'train_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), True)
reader_test = TrainResNet_CIFAR10.create_reader(os.path.join(base_path, 'test_map.txt'), os.path.join(base_path, 'CIFAR-10_mean.xml'), False)
TrainResNet_CIFAR10.train_and_evaluate(reader_train, reader_test, 'resnet20', epoch_size=512, max_epochs=1, profiler_dir=None, model_dir=output_dir)
return base_path