main.py

#encoding=utf8
# 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 trainer import Trainer
import argparse


# options
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", default=64, type=int)
parser.add_argument("--num_workers", default=4, type=int)
parser.add_argument("--epochs", default=200, type=int)
parser.add_argument("--split", default=0, type=int)
parser.add_argument("--validation", default=False, action='store_true')
parser.add_argument("--pretrain_model", default=None)
parser.add_argument("--noise_dim", default=100, type=int)
parser.add_argument("--projected_embed_dim", default=128, type=int)
parser.add_argument("--ngf", default=64, type=int)
parser.add_argument("--ndf", default=64, type=int)
args = parser.parse_args()
# initialize the trainer
Train = Trainer(batch_size=args.batch_size,
                num_workers=args.num_workers,
                epochs=args.epochs,
                split=args.split,
                noise_dim=args.noise_dim,
                projected_embed_dim=args.projected_embed_dim,
                ngf=args.ngf,
                ndf=args.ndf)
if args.validation:
    # test the model
    Train.sample(args.pretrain_model)
else:
    # train the model
    Train.train()