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75 lines
2.8 KiB
75 lines
2.8 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Cycle GAN train."""
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import mindspore.nn as nn
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from mindspore.common import set_seed
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from src.models import get_generator, get_discriminator, Generator, TrainOneStepG, TrainOneStepD, \
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DiscriminatorLoss, GeneratorLoss
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from src.utils import get_lr, get_args, Reporter, ImagePool, load_ckpt
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from src.dataset import create_dataset
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set_seed(1)
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def train():
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"""Train function."""
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args = get_args("train")
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if args.need_profiler:
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from mindspore.profiler.profiling import Profiler
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profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)
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ds = create_dataset(args)
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G_A = get_generator(args)
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G_B = get_generator(args)
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D_A = get_discriminator(args)
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D_B = get_discriminator(args)
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load_ckpt(args, G_A, G_B, D_A, D_B)
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imgae_pool_A = ImagePool(args.pool_size)
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imgae_pool_B = ImagePool(args.pool_size)
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generator = Generator(G_A, G_B, args.lambda_idt > 0)
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loss_D = DiscriminatorLoss(args, D_A, D_B)
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loss_G = GeneratorLoss(args, generator, D_A, D_B)
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optimizer_G = nn.Adam(generator.trainable_params(), get_lr(args), beta1=args.beta1)
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optimizer_D = nn.Adam(loss_D.trainable_params(), get_lr(args), beta1=args.beta1)
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net_G = TrainOneStepG(loss_G, generator, optimizer_G)
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net_D = TrainOneStepD(loss_D, optimizer_D)
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data_loader = ds.create_dict_iterator()
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reporter = Reporter(args)
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reporter.info('==========start training===============')
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for _ in range(args.max_epoch):
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reporter.epoch_start()
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for data in data_loader:
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img_A = data["image_A"]
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img_B = data["image_B"]
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res_G = net_G(img_A, img_B)
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fake_A = res_G[0]
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fake_B = res_G[1]
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res_D = net_D(img_A, img_B, imgae_pool_A.query(fake_A), imgae_pool_B.query(fake_B))
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reporter.step_end(res_G, res_D)
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reporter.visualizer(img_A, img_B, fake_A, fake_B)
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reporter.epoch_end(net_G)
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if args.need_profiler:
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profiler.analyse()
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break
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reporter.info('==========end training===============')
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if __name__ == "__main__":
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train()
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