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export_model.py
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export_model.py
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# Copyright (c) 2020 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.
import argparse
from ppcls.modeling import architectures
import paddle.fluid as fluid
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("-m", "--model", type=str)
parser.add_argument("-p", "--pretrained_model", type=str)
parser.add_argument("-o", "--output_path", type=str)
parser.add_argument("--class_dim", type=int, default=1000)
parser.add_argument("--img_size", type=int, default=224)
return parser.parse_args()
def create_input(img_size=224):
image = fluid.data(
name='image', shape=[None, 3, img_size, img_size], dtype='float32')
return image
def create_model(args, model, input, class_dim=1000):
if args.model == "GoogLeNet":
out, _, _ = model.net(input=input, class_dim=class_dim)
else:
out = model.net(input=input, class_dim=class_dim)
out = fluid.layers.softmax(out)
return out
def main():
args = parse_args()
model = architectures.__dict__[args.model]()
place = fluid.CPUPlace()
exe = fluid.Executor(place)
startup_prog = fluid.Program()
infer_prog = fluid.Program()
with fluid.program_guard(infer_prog, startup_prog):
with fluid.unique_name.guard():
image = create_input(args.img_size)
out = create_model(args, model, image, class_dim=args.class_dim)
infer_prog = infer_prog.clone(for_test=True)
fluid.load(
program=infer_prog, model_path=args.pretrained_model, executor=exe)
fluid.io.save_inference_model(
dirname=args.output_path,
feeded_var_names=[image.name],
main_program=infer_prog,
target_vars=out,
executor=exe,
model_filename='model',
params_filename='params')
if __name__ == "__main__":
main()