export_models.py

# Copyright (c) 2021 PPViT 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, softw
# distributed under the License is distributed on an "AS IS" BASIS
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
# See the License for the specific language governing permissions
# limitations under the License.
"""
T2T-ViT export to inference model in Paddle
An implementation for BEiT model to save static model,
which can be used in paddleinference
"""
import os
import paddle
from config import get_config
from t2t_vit import build_t2t_vit as build_model


def main():
    """load .pdparams model file, save to static model:
        .pdiparams, .pdiparams.info, .pdmodel
    """
    model_name = "t2t_vit_7"
    model_folder = './'
    out_folder = './t2t_vit_7_static'
    os.makedirs(out_folder, exist_ok=True)

    model_path = f'{model_folder}/{model_name}.pdparams'
    out_path = f'{out_folder}/inference'
    # STEP1: load config file and craete model
    config = get_config(f'./configs/{model_name}.yaml')
    model = build_model(config)
    # STEP2: load model weights 
    model_state = paddle.load(model_path)
    if 'model' in model_state:
        if 'model_ema' in model_state:
            model_state = model_state['model_ema']
        else:
            model_state = model_state['model']
    model.set_state_dict(model_state)    
    model.eval()

    # STEP3: craete export build strategy
    build_strategy = paddle.static.BuildStrategy()
    # some optimized settings
    build_strategy.enable_inplace = True
    build_strategy.memory_optimize = True
    build_strategy.reduce_strategy = paddle.static.BuildStrategy.ReduceStrategy.Reduce
    build_strategy.fuse_broadcast_ops = True
    build_strategy.fuse_elewise_add_act_ops = True

    # STEP4: export model to static 
    img_size = [config.DATA.IMAGE_CHANNELS, config.DATA.IMAGE_SIZE, config.DATA.IMAGE_SIZE]
    model = paddle.jit.to_static(
        model,
        input_spec=[paddle.static.InputSpec(shape=[None] + img_size, dtype='float32'), name='x'],
        build_strategy=build_strategy)

    # STEP5: save static model to file
    paddle.jit.save(model, out_path)
    print(f'export model {model_name} done')


if __name__ == "__main__":
    main()