forked from mindspore-Ecosystem/mindspore
132 lines
5.6 KiB
Python
132 lines
5.6 KiB
Python
# 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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"""Train mobilenetV2 on ImageNet"""
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import os
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import argparse
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import random
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import numpy as np
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from mindspore import context
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from mindspore import Tensor
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from mindspore import nn
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from mindspore.train.model import Model, ParallelMode
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.communication.management import init
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from mindspore.train.quant import quant
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import mindspore.dataset.engine as de
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from src.dataset import create_dataset
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from src.lr_generator import get_lr
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from src.utils import Monitor, CrossEntropyWithLabelSmooth
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from src.config import config_ascend, config_ascend_quant
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from src.mobilenetV2 import mobilenetV2
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random.seed(1)
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np.random.seed(1)
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de.config.set_seed(1)
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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parser.add_argument('--pre_trained', type=str, default=None, help='Pertained checkpoint path')
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parser.add_argument('--device_target', type=str, default=None, help='Run device target')
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parser.add_argument('--quantization_aware', type=bool, default=False, help='Use quantization aware training')
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args_opt = parser.parse_args()
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if args_opt.device_target == "Ascend":
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device_id = int(os.getenv('DEVICE_ID'))
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rank_id = int(os.getenv('RANK_ID'))
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rank_size = int(os.getenv('RANK_SIZE'))
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run_distribute = rank_size > 1
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE,
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device_target="Ascend",
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device_id=device_id, save_graphs=False)
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else:
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raise ValueError("Unsupported device target.")
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if __name__ == '__main__':
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# train on ascend
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config = config_ascend_quant if args_opt.quantization_aware else config_ascend
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print("training args: {}".format(args_opt))
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print("training configure: {}".format(config))
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print("parallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size))
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epoch_size = config.epoch_size
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# distribute init
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if run_distribute:
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context.set_auto_parallel_context(device_num=rank_size,
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parallel_mode=ParallelMode.DATA_PARALLEL,
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parameter_broadcast=True,
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mirror_mean=True)
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init()
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# define network
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network = mobilenetV2(num_classes=config.num_classes)
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# define loss
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if config.label_smooth > 0:
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loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes)
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else:
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loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean')
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# define dataset
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dataset = create_dataset(dataset_path=args_opt.dataset_path,
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do_train=True,
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config=config,
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device_target=args_opt.device_target,
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repeat_num=1,
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batch_size=config.batch_size)
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step_size = dataset.get_dataset_size()
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# load pre trained ckpt
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if args_opt.pre_trained:
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param_dict = load_checkpoint(args_opt.pre_trained)
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load_param_into_net(network, param_dict)
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# convert fusion network to quantization aware network
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if config.quantization_aware:
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network = quant.convert_quant_network(network,
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bn_fold=True,
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per_channel=[True, False],
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symmetric=[True, False])
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# get learning rate
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lr = Tensor(get_lr(global_step=config.start_epoch * step_size,
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lr_init=0,
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lr_end=0,
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lr_max=config.lr,
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warmup_epochs=config.warmup_epochs,
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total_epochs=epoch_size + config.start_epoch,
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steps_per_epoch=step_size))
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# define optimization
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opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr, config.momentum,
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config.weight_decay)
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# define model
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model = Model(network, loss_fn=loss, optimizer=opt)
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print("============== Starting Training ==============")
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callback = None
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if rank_id == 0:
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callback = [Monitor(lr_init=lr.asnumpy())]
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if config.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix="mobilenetV2",
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directory=config.save_checkpoint_path,
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config=config_ck)
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callback += [ckpt_cb]
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model.train(epoch_size, dataset, callbacks=callback)
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print("============== End Training ==============")
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