PulseFocusPlatform/static/slim/quantization/export_model.py

122 lines
4.0 KiB
Python

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
# add python path of PadleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3)))
if parent_path not in sys.path:
sys.path.append(parent_path)
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
from paddle import fluid
try:
from ppdet.core.workspace import load_config, merge_config, create
from ppdet.utils.cli import ArgsParser
import ppdet.utils.checkpoint as checkpoint
from ppdet.utils.export_utils import save_infer_model, dump_infer_config
from ppdet.utils.check import check_config, check_version, enable_static_mode
except ImportError as e:
if sys.argv[0].find('static') >= 0:
logger.error("Importing ppdet failed when running static model "
"with error: {}\n"
"please try:\n"
"\t1. run static model under PaddleDetection/static "
"directory\n"
"\t2. run 'pip uninstall ppdet' to uninstall ppdet "
"dynamic version firstly.".format(e))
sys.exit(-1)
else:
raise e
from paddleslim.quant import quant_aware, convert
def main():
cfg = load_config(FLAGS.config)
merge_config(FLAGS.opt)
check_config(cfg)
check_version()
main_arch = cfg.architecture
# Use CPU for exporting inference model instead of GPU
place = fluid.CPUPlace()
exe = fluid.Executor(place)
model = create(main_arch)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
inputs_def = cfg['TestReader']['inputs_def']
inputs_def['use_dataloader'] = False
feed_vars, _ = model.build_inputs(**inputs_def)
test_fetches = model.test(feed_vars)
infer_prog = infer_prog.clone(True)
not_quant_pattern = []
if FLAGS.not_quant_pattern:
not_quant_pattern = FLAGS.not_quant_pattern
config = {
'weight_quantize_type': 'channel_wise_abs_max',
'activation_quantize_type': 'moving_average_abs_max',
'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
'not_quant_pattern': not_quant_pattern
}
infer_prog = quant_aware(infer_prog, place, config, for_test=True)
exe.run(startup_prog)
checkpoint.load_params(exe, infer_prog, cfg.weights)
infer_prog, int8_program = convert(
infer_prog, place, config, save_int8=True)
FLAGS.output_dir = os.path.join(FLAGS.output_dir, 'float')
save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog)
FLAGS.output_dir = os.path.join(FLAGS.output_dir, 'int')
save_infer_model(FLAGS, exe, feed_vars, test_fetches, int8_program)
if __name__ == '__main__':
enable_static_mode()
parser = ArgsParser()
parser.add_argument(
"--output_dir",
type=str,
default="output",
help="Directory for storing the output model files.")
parser.add_argument(
"--not_quant_pattern",
nargs='+',
type=str,
help="Layers which name_scope contains string in not_quant_pattern will not be quantized"
)
FLAGS = parser.parse_args()
main()