forked from mindspore-Ecosystem/mindspore
141 lines
5.2 KiB
Python
141 lines
5.2 KiB
Python
# Copyright 2021 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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"""train"""
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import os
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import time
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import argparse
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import numpy as np
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from mindspore import nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.context import ParallelMode
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from mindspore.common import dtype as mstype
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from mindspore.communication.management import init, get_group_size
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import mindspore.ops as ops
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from src.dataset import create_dataset
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from src.ckpt_util import save_ckpt
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from src.model import Generator, Discriminator
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from src.cell import GenWithLossCell, DisWithLossCell, TrainOneStepCell
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def preLauch():
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"""parse the console argument"""
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parser = argparse.ArgumentParser(description='MindSpore cgan training')
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parser.add_argument("--distribute", type=bool, default=False,
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help="Run distribute, default is false.")
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parser.add_argument('--device_id', type=int, default=0,
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help='device id of Ascend (Default: 0)')
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parser.add_argument('--ckpt_dir', type=str,
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default='ckpt', help='checkpoint dir of CGAN')
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parser.add_argument('--dataset', type=str, default='data/MNIST_Data/train',
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help='dataset dir (default data/MNISt_Data/train)')
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args = parser.parse_args()
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# if not exists 'imgs4', 'gif' or 'ckpt_dir', make it
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if not os.path.exists(args.ckpt_dir):
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os.mkdir(args.ckpt_dir)
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# deal with the distribute analyze problem
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if args.distribute:
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device_id = args.device_id
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context.set_context(save_graphs=False,
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device_id=device_id,
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device_target="Ascend",
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mode=context.GRAPH_MODE)
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init()
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args.device_num = get_group_size()
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context.set_auto_parallel_context(gradients_mean=True,
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device_num=args.device_num,
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parallel_mode=ParallelMode.DATA_PARALLEL)
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else:
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device_id = args.device_id
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args.device_num = 1
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context.set_context(save_graphs=False,
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mode=context.GRAPH_MODE,
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device_target="Ascend")
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context.set_context(device_id=device_id)
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return args
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def main():
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# before training, we should set some arguments
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args = preLauch()
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# training argument
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batch_size = 128
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input_dim = 100
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epoch_start = 0
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epoch_end = 51
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lr = 0.001
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dataset = create_dataset(args.dataset,
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flatten_size=28 * 28,
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batch_size=batch_size,
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num_parallel_workers=args.device_num)
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# create G Cell & D Cell
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netG = Generator(input_dim)
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netD = Discriminator(batch_size)
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# create WithLossCell
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netG_with_loss = GenWithLossCell(netG, netD)
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netD_with_loss = DisWithLossCell(netG, netD)
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# create optimizer cell
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optimizerG = nn.Adam(netG.trainable_params(), lr)
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optimizerD = nn.Adam(netD.trainable_params(), lr)
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net_train = TrainOneStepCell(netG_with_loss,
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netD_with_loss,
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optimizerG,
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optimizerD)
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netG.set_train()
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netD.set_train()
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# latent_code_eval = Tensor(np.random.randn(
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# 200, input_dim), dtype=mstype.float32)
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# label_eval = np.zeros((200, 10))
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# for i in range(200):
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# j = i // 20
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# label_eval[i][j] = 1
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# label_eval = Tensor(label_eval, dtype=mstype.float32)
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data_size = dataset.get_dataset_size()
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print("data-size", data_size)
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print("=========== start training ===========")
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for epoch in range(epoch_start, epoch_end):
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step = 0
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start = time.time()
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for data in dataset:
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img = data[0]
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label = data[1]
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img = ops.Reshape()(img, (batch_size, 1, 28, 28))
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latent_code = Tensor(np.random.randn(
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batch_size, input_dim), dtype=mstype.float32)
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dout, gout = net_train(img, latent_code, label)
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step += 1
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if step % data_size == 0:
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end = time.time()
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pref = (end-start)*1000 / data_size
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print("epoch {}, {:.3f} ms per step, d_loss is {:.4f}, g_loss is {:.4f}".format(epoch,
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pref, dout.asnumpy(),
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gout.asnumpy()))
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save_ckpt(args, netG, netD, epoch)
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print("===========training success================")
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if __name__ == '__main__':
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main()
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