def main(args):
"""Main function"""
# User model files
model_file = args.model_file
weights_file = args.weights_file
# amct.set_cpu_mode() or set_gpu_mode() decides whether using CPU/GPU
# to do weights calibration, but activation calibration is controled
# by caffe APIs: caffe.set_mode_cpu() or set_mode_gpu().
# Need to set amct mode before the whole calibration process, default
# using CPU mode to do weights calibration.
# amct.set_gpu_mode() does not set which GPU card to use. Users can set
# GPU card in two ways:
# 1) use pycaffe API set_device(gpu_id)
# 2) use environment variable CUDA_VISIBLE_DEVICES
if args.gpu_id is not None and not args.cpu_mode:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
amct.set_gpu_mode()
else:
caffe.set_mode_cpu()
# Run pre model test
ori_top1 = do_benchmark_test(model_file, weights_file, args.iterations)
# Quantize configurations
config_json_file = os.path.join(TMP, 'config.json')
skip_layers = []
batch_num = 2
amct.create_quant_config(config_json_file, model_file, weights_file,
skip_layers, batch_num)
# Phase0: Init amct task
scale_offset_record_file = os.path.join(TMP, 'record.txt')
graph = amct.init(config_json_file, model_file, weights_file,
scale_offset_record_file)
# Phase1: do conv+bn+scale fusion, weights calibration and fake quant,
# insert quant and dequant layer
modified_model_file = os.path.join(TMP, 'modified_model.prototxt')
modified_weights_file = os.path.join(TMP, 'modified_model.caffemodel')
amct.quantize_model(graph, modified_model_file, modified_weights_file)
# Phase2: run caffe model to do activation calibration
do_benchmark_test(modified_model_file, modified_weights_file, batch_num)
# Phase3: save final model, one for caffe do fake quant test,
# one deploy model for GE
result_path = os.path.join(RESULT, 'mnist')
amct.save_model(graph, 'Both', result_path)
# Phase4: do final fake quant model test
fake_quant_model = os.path.join(RESULT, 'mnist_fake_quant_model.prototxt')
fake_quant_weights = os.path.join(
RESULT, 'mnist_fake_quant_weights.caffemodel')
quant_top1 = do_benchmark_test(
fake_quant_model, fake_quant_weights, args.iterations)
print('[AMCT][INFO] mnist top1 before quantize is {}, after quantize ' \
'is {}'.format(ori_top1, quant_top1))
print('[AMCT][INFO]Run mnist sample with quantize success!')
def main():
parser = argparse.ArgumentParser(description='Example of convert yolov3 caffe model to int8')
parser.add_argument('--prototxt', type=str, help='path to yolov3_pp.prototxt', default='model/yolov3_pp.prototxt')
parser.add_argument('--caffemodel', type=str, help='path to yolov3.caffemodel', default='model/yolov3.caffemodel')
parser.add_argument('--tmp_dir', type=str, help='path to save temp files', default='./tmp')
parser.add_argument('--output_dir', type=str, help='path to save output model file',
default='model/')
parser.add_argument('--output_model_name', type=str, help='prefix of output model files',
default='yolov3_int8')
parser.add_argument('--calib_video', action='append', required=True,
help='videos used in calibration, it can be specified multiple times.')
parser.add_argument('--calib_frame', type=int,
help='number of frames used in each video file',
default=200)
args = parser.parse_args()
tmp_dir = args.tmp_dir
mkdir(tmp_dir)
caffe.set_mode_cpu()
config_json_file = os.path.join(tmp_dir, 'config.json')
skip_layers = []
batch_num = 1
amct.create_quant_config(config_json_file,
args.prototxt,
args.caffemodel,
skip_layers,
batch_num)
scale_offset_record_file = os.path.join(tmp_dir, 'scale_offset_record.txt')
graph = amct.init(config_json_file,
args.prototxt,
args.caffemodel,
scale_offset_record_file)
print("done init")
modified_model_file = os.path.join(tmp_dir, 'modified_model.prototxt')
modified_weights_file = os.path.join(tmp_dir, 'modified_model.caffemodel')
amct.quantize_model(graph, modified_model_file, modified_weights_file)
print("done quantize")
net = caffe.Net(modified_model_file, modified_weights_file, caffe.TEST)
calibration(net, args.calib_video, args.calib_frame)
result_path = os.path.join(args.output_dir, args.output_model_name)
amct.save_model(graph, 'Both', result_path)
print("done save")
def main(args):
"""main function"""
# set_cpu_mode or set_gpu_mode decides whether using
# CPU/GPU to do weights calibration, but activation calibration is
# controled by caffe APIs: caffe.set_mode_cpu() or set_mode_gpu().
# Need to set amct mode before the whole calibration process,
# default using CPU mode to do weights calibration.
# amct.set_gpu_mode() does not set which GPU card to use. Users can
# set GPU card in two ways:
# 1) use pycaffe API set_device(gpu_id)
# 2) use environment variable CUDA_VISIBLE_DEVICES
if args.gpu_id is not None and not args.cpu_mode:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
amct.set_gpu_mode()
else:
caffe.set_mode_cpu()
# Run pre model test
if not args.pre_test:
do_benchmark_test(args, args.model_file, args.weights_file,
args.iterations)
print('[AMCT][INFO]Run ResNet-50 without quantize success!')
return
# Quantize configurations
config_json_file = os.path.join(TMP, 'config.json')
skip_layers = []
batch_num = 2
activation_offset = True
# do weights calibration with non uniform quantize configure
amct.create_quant_config(config_json_file, args.model_file,
args.weights_file, skip_layers, batch_num,
activation_offset, args.cfg_define)
scale_offset_record_file = os.path.join(TMP, 'scale_offset_record.txt')
result_path = os.path.join(RESULT, 'ResNet50')
evaluator = AutoNuqEvaluator(args.iterations)
amct.auto_nuq(args.model_file, args.weights_file, evaluator,
config_json_file, scale_offset_record_file, result_path)
def main(args):
"""main function"""
if args.gpu_id is not None:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
amct.set_gpu_mode()
else:
caffe.set_mode_cpu()
# User model files
model_file = os.path.realpath(args.model_file)
weights_file = os.path.realpath(args.weights_file)
# Run pre model test
if not args.pre_test:
do_benchmark_test(args, model_file, weights_file, args.iterations)
print('[AMCT][INFO]Run Mobilenet_v2 without quantize success!')
return
# step 1: create the quant config file
config_json_file = './config.json'
skip_layers = []
batch_num = CALIBRATION_BATCH_NUM
activation_offset = True
amct.create_quant_config(config_json_file, model_file, weights_file,
skip_layers, batch_num, activation_offset)
scale_offset_record_file = os.path.join(TMP, 'scale_offset_record.txt')
result_path = os.path.join(RESULT, 'MobileNetV2')
evaluator = AutoCalibrationEvaluator(target_loss=0.2,
batch_num=batch_num,
args=args)
# step 2: start the accuracy_based_auto_calibration process
amct.accuracy_based_auto_calibration(args.model_file, args.weights_file,
evaluator, config_json_file,
scale_offset_record_file, result_path)
def main():
"""Main function"""
args = parse_args()
faster_rcnn_args_check(args)
if args.cpu_mode:
args.gpu_id = None
# User model files
model_file = args.model_file
weights_file = args.weights_file
if args.pre_test:
caffe_test(model_file,
weights_file,
args.iterations,
gpu_id=args.gpu_id,
calibration=False,
data_dir='./datasets',
is_quantize=False)
print('[AMCT][INFO]Run faster_rcnn without quantize success!')
m_ap = do_voc2007_benchmark_test(model_file, weights_file, args.gpu_id)
print(
'[AMCT][INFO]Run faster_rcnn without quantize success, and mAP is {}'
.format(m_ap))
return
# amct.set_cpu_mode() or set_gpu_mode() decides whether using CPU/GPU
# to do weights calibration, but activation calibration is controled
# by caffe APIs: caffe.set_mode_cpu() or set_mode_gpu().
# Need to set amct mode before the whole calibration process, default
# using CPU mode to do weights calibration.
# amct.set_gpu_mode() does not set which GPU card to use. Users can set
# GPU card in two ways:
# 1) use pycaffe API set_device(gpu_id)
# 2) use environment variable CUDA_VISIBLE_DEVICES
if args.gpu_id is not None:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
amct.set_gpu_mode()
# Quantize configurations
config_json_file = './config.json'
skip_layers = []
batch_num = 1
amct.create_quant_config(config_json_file, model_file, weights_file, \
skip_layers, batch_num)
# Phase0: Init amct task
scale_offset_record_file = './tmp/scale_offset_record/record.txt'
graph = amct.init(config_json_file, model_file, weights_file,
scale_offset_record_file)
# Phase1: Do conv+bn+scale fusion, weights calibration and fake quant,
# insert quant and dequant layer.
modified_model_file = os.path.realpath('./tmp/modified_model.prototxt')
modified_weights_file = os.path.realpath('./tmp/modified_model.caffemodel')
amct.quantize_model(graph, modified_model_file, modified_weights_file)
# Phase2: run caffe model to do activation calibration
caffe_test(modified_model_file, modified_weights_file, batch_num, \
gpu_id=args.gpu_id, calibration=True, data_dir='./datasets',
is_quantize=False)
# Phase3: Save final model, one for caffe do fake quant test,
# one deploy model for GE
amct.save_model(graph, 'Both', './results/faster_rcnn')
# Phase4: do final fake quant model test
fake_quant_model = './results/faster_rcnn_fake_quant_model.prototxt'
fake_quant_weights = './results/faster_rcnn_fake_quant_weights.caffemodel'
caffe_test(fake_quant_model, fake_quant_weights, \
args.iterations, gpu_id=args.gpu_id, calibration=False, data_dir='./datasets',
is_quantize=True)
print('[AMCT][INFO]Run faster_rcnn with quantize success!')
m_ap = do_voc2007_benchmark_test(fake_quant_model, fake_quant_weights,
args.gpu_id)
print('[AMCT][INFO]Run faster_rcnn with quantize success, and mAP is {}!'.
format(m_ap))
def main(args):
"""Main function"""
args_check(args)
mkdir(TMP)
mkdir(RESULT)
# set_cpu_mode or set_gpu_mode decides whether using
# CPU/GPU to do weights calibration, but activation calibration is
# controled by caffe APIs: caffe.set_mode_cpu() or set_mode_gpu().
# Need to set amct mode before the whole calibration process,
# default using CPU mode to do weights calibration.
# amct.set_gpu_mode() does not set which GPU card to use. Users can
# set GPU card in two ways:
# 1) use pycaffe API set_device(gpu_id)
# 2) use environment variable CUDA_VISIBLE_DEVICES
if args.gpu_id is not None and not args.cpu_mode:
caffe.set_mode_gpu()
caffe.set_device(args.gpu_id)
amct.set_gpu_mode()
else:
caffe.set_mode_cpu()
# Run pre model test
if args.pre_test:
if not args.benchmark:
run_caffe_model(args.model_file, args.weights_file,
args.iterations)
else:
do_benchmark_test(args, args.model_file, args.weights_file,
args.iterations)
print('[AMCT][INFO]Run ResNet-50 without quantize success!')
return
# Quantize configurations
config_json_file = os.path.join(TMP, 'config.json')
skip_layers = []
batch_num = 2
if args.cfg_define is not None:
# do weights calibration with non uniform quantize configure
amct.create_quant_config(config_json_file,
args.model_file,
args.weights_file,
skip_layers,
batch_num,
config_defination=args.cfg_define)
else:
amct.create_quant_config(config_json_file, args.model_file,
args.weights_file, skip_layers, batch_num)
# Phase0: Init amct task
scale_offset_record_file = os.path.join(TMP, 'scale_offset_record.txt')
graph = amct.init(config_json_file, args.model_file, args.weights_file,
scale_offset_record_file)
# Phase1: do conv+bn+scale fusion, weights calibration and fake
# quant, insert quant and dequant layer
modified_model_file = os.path.join(TMP, 'modified_model.prototxt')
modified_weights_file = os.path.join(TMP, 'modified_model.caffemodel')
amct.quantize_model(graph, modified_model_file, modified_weights_file)
# Phase2: run caffe model to do activation calibration
if not args.benchmark:
run_caffe_model(modified_model_file, modified_weights_file, batch_num)
else:
do_benchmark_test(args, modified_model_file, modified_weights_file,
batch_num)
# Phase3: save final model, one for caffe do fake quant test, one
# deploy model for GE
result_path = os.path.join(RESULT, 'ResNet50')
amct.save_model(graph, 'Both', result_path)
# Phase4: do final fake quant model test
fake_quant_model = os.path.join(RESULT,
'ResNet50_fake_quant_model.prototxt')
fake_quant_weights = os.path.join(
RESULT, 'ResNet50_fake_quant_weights.caffemodel')
if not args.benchmark:
run_caffe_model(fake_quant_model, fake_quant_weights, args.iterations)
else:
do_benchmark_test(args, fake_quant_model, fake_quant_weights,
args.iterations)
print('[AMCT][INFO]Run ResNet-50 with quantize success!')