forked from mindspore-Ecosystem/mindspore
133 lines
5.9 KiB
Python
133 lines
5.9 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_imagenet."""
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import argparse
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import os
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import random
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import numpy as np
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from mindspore import Tensor
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from mindspore import context
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from mindspore.communication.management import init
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from mindspore.parallel._auto_parallel_context import auto_parallel_context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.model import ParallelMode
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from src.model_thor import Model
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from src.resnet_thor import resnet50
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from src.thor import THOR
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from src.config import config
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from src.crossentropy import CrossEntropy
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from src.dataset_imagenet import create_dataset
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random.seed(1)
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np.random.seed(1)
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parser = argparse.ArgumentParser(description='Image classification')
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parser.add_argument('--run_distribute', type=bool, default=False, help='Run distribute')
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parser.add_argument('--device_num', type=int, default=1, help='Device num.')
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parser.add_argument('--do_train', type=bool, default=True, help='Do train or not.')
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parser.add_argument('--do_eval', type=bool, default=False, help='Do eval or not.')
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parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
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args_opt = parser.parse_args()
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device_id = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False, device_id=device_id)
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def get_model_lr(global_step, lr_init, decay, total_epochs, steps_per_epoch):
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"""get_model_lr"""
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lr_each_step = []
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total_steps = steps_per_epoch * total_epochs
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for i in range(total_steps):
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epoch = (i + 1) / steps_per_epoch
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base = (1.0 - float(epoch) / total_epochs) ** decay
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lr_local = lr_init * base
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if epoch >= 39:
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lr_local = lr_local * 0.5
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if epoch >= 40:
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lr_local = lr_local * 0.5
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lr_each_step.append(lr_local)
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current_step = global_step
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lr_each_step = np.array(lr_each_step).astype(np.float32)
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learning_rate = lr_each_step[current_step:]
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return learning_rate
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def get_model_damping(global_step, damping_init, decay_rate, total_epochs, steps_per_epoch):
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"""get_model_damping"""
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damping_each_step = []
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total_steps = steps_per_epoch * total_epochs
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for step in range(total_steps):
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epoch = (step + 1) / steps_per_epoch
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damping_here = damping_init * (decay_rate ** (epoch / 10))
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damping_each_step.append(damping_here)
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current_step = global_step
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damping_each_step = np.array(damping_each_step).astype(np.float32)
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damping_now = damping_each_step[current_step:]
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return damping_now
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if __name__ == '__main__':
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if not args_opt.do_eval and args_opt.run_distribute:
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context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
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mirror_mean=True, parameter_broadcast=True)
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auto_parallel_context().set_all_reduce_fusion_split_indices([107], "hccl_world_groupsum1")
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auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum2")
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auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum3")
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auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum4")
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auto_parallel_context().set_all_reduce_fusion_split_indices([27], "hccl_world_groupsum5")
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init()
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epoch_size = config.epoch_size
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damping = get_model_damping(0, 0.03, 0.87, 50, 5004)
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net = resnet50(class_num=config.class_num, damping=damping, loss_scale=config.loss_scale,
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frequency=config.frequency)
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if not config.label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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if args_opt.do_train:
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dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True,
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batch_size=config.batch_size)
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step_size = dataset.get_dataset_size()
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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lr = Tensor(get_model_lr(0, 0.045, 6, 70, 5004))
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opt = THOR(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
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filter(lambda x: 'matrix_A' in x.name, net.get_parameters()),
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filter(lambda x: 'matrix_G' in x.name, net.get_parameters()),
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filter(lambda x: 'A_inv_max' in x.name, net.get_parameters()),
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filter(lambda x: 'G_inv_max' in x.name, net.get_parameters()),
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config.weight_decay, config.loss_scale)
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model = Model(net, loss_fn=loss, optimizer=opt, amp_level='O2', loss_scale_manager=loss_scale,
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keep_batchnorm_fp32=False, metrics={'acc'}, frequency=config.frequency)
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time_cb = TimeMonitor(data_size=step_size)
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loss_cb = LossMonitor()
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cb = [time_cb, loss_cb]
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if config.save_checkpoint:
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_steps,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix="resnet", directory=config.save_checkpoint_path, config=config_ck)
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cb += [ckpt_cb]
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model.train(epoch_size, dataset, callbacks=cb)
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