PaddleClas/deploy/slim/quant_post_static.py

77 lines
2.6 KiB
Python

# Copyright (c) 2021 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, division, print_function
import os
import sys
import numpy as np
import paddle
import paddleslim
from paddle.jit import to_static
from paddleslim.analysis import dygraph_flops as flops
__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
from paddleslim.dygraph.quant import QAT
from ppcls.data import build_dataloader
from ppcls.utils import config as conf
from ppcls.utils.logger import init_logger
def main():
args = conf.parse_args()
config = conf.get_config(args.config, overrides=args.override, show=False)
assert os.path.exists(
os.path.join(config["Global"]["save_inference_dir"],
'inference.pdmodel')) and os.path.exists(
os.path.join(config["Global"]["save_inference_dir"],
'inference.pdiparams'))
config["DataLoader"]["Eval"]["sampler"]["batch_size"] = 1
config["DataLoader"]["Eval"]["loader"]["num_workers"] = 0
init_logger()
device = paddle.set_device("cpu")
train_dataloader = build_dataloader(config["DataLoader"], "Eval", device,
False)
def sample_generator(loader):
def __reader__():
for indx, data in enumerate(loader):
images = np.array(data[0])
yield images
return __reader__
paddle.enable_static()
place = paddle.CPUPlace()
exe = paddle.static.Executor(place)
paddleslim.quant.quant_post_static(
executor=exe,
model_dir=config["Global"]["save_inference_dir"],
model_filename='inference.pdmodel',
params_filename='inference.pdiparams',
quantize_model_path=os.path.join(
config["Global"]["save_inference_dir"], "quant_post_static_model"),
sample_generator=sample_generator(train_dataloader),
batch_size=config["DataLoader"]["Eval"]["sampler"]["batch_size"],
batch_nums=10)
if __name__ == "__main__":
main()