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- # Copyright 2021 Huawei Technologies Co., Ltd
- #
- # 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.
- # ============================================================================
-
- """General-purpose training script for image-to-image translation.
- You need to specify the dataset ('--dataroot'), experiment name ('--name'), and model ('--model').
- Example:
- Train a resnet model:
- python train.py --dataroot ./data/horse2zebra --model ResNet
- """
- import os
- import numpy as np
- import mindspore as ms
- import mindspore.nn as nn
- from mindspore.communication.management import init, get_rank, get_group_size
- from src.utils.args import get_args
- from src.utils.reporter import Reporter
- from src.utils.tools import get_lr, ImagePool, load_ckpt, save_image
- from src.dataset.cyclegan_dataset import create_dataset
- from src.models.losses import DiscriminatorLoss, GeneratorLoss
- from src.models.cycle_gan import get_generator, get_discriminator, Generator, TrainOneStepG, TrainOneStepD
-
- ms.set_seed(1)
-
- def do_eval(G_A, G_B, args, epoch):
- args = get_args("predict")
- ms.set_context(mode=ms.GRAPH_MODE,
- device_target=args.platform,
- save_graphs=args.save_graphs)
- args.rank = 0
- args.device_num = 1
- args.model = 'DepthResNet'
- if args.platform == "GPU":
- ms.set_context(enable_graph_kernel=True)
- imgs_out = os.path.join(args.outputs_dir, "eval")
- imgs_out = os.path.join(imgs_out, "epoch_" + str(epoch))
- if not os.path.exists(imgs_out):
- os.makedirs(imgs_out)
- if not os.path.exists(os.path.join(imgs_out, "fake_A")):
- os.makedirs(os.path.join(imgs_out, "fake_A"))
- if not os.path.exists(os.path.join(imgs_out, "fake_B")):
- os.makedirs(os.path.join(imgs_out, "fake_B"))
- args.data_dir = 'testA'
- ds = create_dataset(args)
- reporter = Reporter(args)
- reporter.start_predict("A to B")
- for data in ds.create_dict_iterator(output_numpy=True):
- img_A = ms.Tensor(data["image"])
- path_A = data["image_name"][0]
- if isinstance(path_A, np.bytes_):
- path_A = path_A.decode("UTF-8")
- path_B = path_A[0:-4] + "_fake_B.jpg"
- fake_B = G_A(img_A)
- save_image(fake_B, os.path.join(imgs_out, "fake_B", path_B))
- save_image(img_A, os.path.join(imgs_out, "fake_B", path_A))
- reporter.info('save fake_B at %s', os.path.join(imgs_out, "fake_B",
- path_A))
- reporter.end_predict()
-
- def train():
- """Train function."""
- args = get_args("train")
- if args.device_num > 1:
- ms.set_context(mode=ms.GRAPH_MODE, device_target=args.platform, save_graphs=args.save_graphs)
- init()
- ms.reset_auto_parallel_context()
- ms.set_auto_parallel_context(parallel_mode=ms.ParallelMode.DATA_PARALLEL, gradients_mean=True)
- args.rank = get_rank()
- args.group_size = get_group_size()
- else:
- ms.set_context(mode=ms.GRAPH_MODE, device_target=args.platform,
- save_graphs=args.save_graphs, device_id=args.device_id)
- args.rank = 0
- args.device_num = 1
-
- if args.platform == "GPU":
- ms.set_context(enable_graph_kernel=True)
- if args.need_profiler:
- from mindspore.profiler.profiling import Profiler
- profiler = Profiler(output_path=args.outputs_dir, is_detail=True, is_show_op_path=True)
- ds = create_dataset(args)
- G_A = get_generator(args)
- G_B = get_generator(args)
- D_A = get_discriminator(args)
- D_B = get_discriminator(args)
- if args.load_ckpt:
- load_ckpt(args, G_A, G_B, D_A, D_B)
- imgae_pool_A = ImagePool(args.pool_size)
- imgae_pool_B = ImagePool(args.pool_size)
- generator = Generator(G_A, G_B, args.lambda_idt > 0)
-
- loss_D = DiscriminatorLoss(args, D_A, D_B)
- loss_G = GeneratorLoss(args, generator, D_A, D_B)
- optimizer_G = nn.Adam(generator.trainable_params(), get_lr(args), beta1=args.beta1)
- optimizer_D = nn.Adam(loss_D.trainable_params(), get_lr(args), beta1=args.beta1)
-
- net_G = TrainOneStepG(loss_G, generator, optimizer_G)
- net_D = TrainOneStepD(loss_D, optimizer_D)
-
- data_loader = ds.create_dict_iterator()
- if args.rank == 0:
- reporter = Reporter(args)
- reporter.info('==========start training===============')
- for epoch in range(args.max_epoch):
- if args.rank == 0:
- reporter.epoch_start()
- for data in data_loader:
- img_A = data["image_A"]
- img_B = data["image_B"]
- res_G = net_G(img_A, img_B)
- fake_A = res_G[0]
- fake_B = res_G[1]
- res_D = net_D(img_A, img_B, imgae_pool_A.query(fake_A), imgae_pool_B.query(fake_B))
- if args.rank == 0:
- reporter.step_end(res_G, res_D)
- reporter.visualizer(img_A, img_B, fake_A, fake_B)
- if args.rank == 0:
- reporter.epoch_end(net_G)
- if args.need_profiler:
- profiler.analyse()
- break
- do_eval(G_A, G_B, args, epoch)
-
- if args.rank == 0:
- reporter.info('==========end training===============')
-
-
- if __name__ == "__main__":
- train()
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