# Copyright 2020 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. # ============================================================================ """Train mobilenetV2 on ImageNet""" import os import argparse import random import numpy as np from mindspore import context from mindspore import Tensor from mindspore import nn from mindspore.train.model import Model, ParallelMode from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.callback import ModelCheckpoint, CheckpointConfig from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.communication.management import init, get_group_size, get_rank from mindspore.train.quant import quant import mindspore.dataset.engine as de from src.dataset import create_dataset from src.lr_generator import get_lr from src.utils import Monitor, CrossEntropyWithLabelSmooth from src.config import config_ascend_quant, config_ascend, config_gpu_quant, config_gpu from src.mobilenetV2 import mobilenetV2 random.seed(1) np.random.seed(1) de.config.set_seed(1) parser = argparse.ArgumentParser(description='Image classification') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') parser.add_argument('--pre_trained', type=str, default=None, help='Pertained checkpoint path') parser.add_argument('--device_target', type=str, default=None, help='Run device target') parser.add_argument('--quantization_aware', type=bool, default=False, help='Use quantization aware training') args_opt = parser.parse_args() if args_opt.device_target == "Ascend": device_id = int(os.getenv('DEVICE_ID')) rank_id = int(os.getenv('RANK_ID')) rank_size = int(os.getenv('RANK_SIZE')) run_distribute = rank_size > 1 device_id = int(os.getenv('DEVICE_ID')) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id, save_graphs=False) elif args_opt.device_target == "GPU": init("nccl") context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True) context.set_context(mode=context.GRAPH_MODE, device_target="GPU", save_graphs=False) else: raise ValueError("Unsupported device target.") def train_on_ascend(): config = config_ascend_quant if args_opt.quantization_aware else config_ascend print("training args: {}".format(args_opt)) print("training configure: {}".format(config)) print("parallel args: rank_id {}, device_id {}, rank_size {}".format(rank_id, device_id, rank_size)) epoch_size = config.epoch_size # distribute init if run_distribute: context.set_auto_parallel_context(device_num=rank_size, parallel_mode=ParallelMode.DATA_PARALLEL, parameter_broadcast=True, mirror_mean=True) init() # define network network = mobilenetV2(num_classes=config.num_classes) # define loss if config.label_smooth > 0: loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes) else: loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') # define dataset dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, config=config, device_target=args_opt.device_target, repeat_num=1, batch_size=config.batch_size) step_size = dataset.get_dataset_size() # load pre trained ckpt if args_opt.pre_trained: param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(network, param_dict) # convert fusion network to quantization aware network if config.quantization_aware: network = quant.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, False]) # get learning rate lr = Tensor(get_lr(global_step=config.start_epoch * step_size, lr_init=0, lr_end=0, lr_max=config.lr, warmup_epochs=config.warmup_epochs, total_epochs=epoch_size + config.start_epoch, steps_per_epoch=step_size)) # define optimization opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr, config.momentum, config.weight_decay) # define model model = Model(network, loss_fn=loss, optimizer=opt) print("============== Starting Training ==============") callback = None if rank_id == 0: callback = [Monitor(lr_init=lr.asnumpy())] if config.save_checkpoint: config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size, keep_checkpoint_max=config.keep_checkpoint_max) ckpt_cb = ModelCheckpoint(prefix="mobilenetV2", directory=config.save_checkpoint_path, config=config_ck) callback += [ckpt_cb] model.train(epoch_size, dataset, callbacks=callback) print("============== End Training ==============") def train_on_gpu(): config = config_gpu_quant if args_opt.quantization_aware else config_gpu print("training args: {}".format(args_opt)) print("training configure: {}".format(config)) # define network network = mobilenetV2(num_classes=config.num_classes) # define loss if config.label_smooth > 0: loss = CrossEntropyWithLabelSmooth(smooth_factor=config.label_smooth, num_classes=config.num_classes) else: loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction='mean') # define dataset epoch_size = config.epoch_size dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, config=config, device_target=args_opt.device_target, repeat_num=1, batch_size=config.batch_size) step_size = dataset.get_dataset_size() # resume if args_opt.pre_trained: param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(network, param_dict) # convert fusion network to quantization aware network if config.quantization_aware: network = quant.convert_quant_network(network, bn_fold=True, per_channel=[True, False], symmetric=[True, True]) # get learning rate loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) lr = Tensor(get_lr(global_step=config.start_epoch * step_size, lr_init=0, lr_end=0, lr_max=config.lr, warmup_epochs=config.warmup_epochs, total_epochs=epoch_size + config.start_epoch, steps_per_epoch=step_size)) # define optimization opt = nn.Momentum(filter(lambda x: x.requires_grad, network.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) # define model model = Model(network, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale) print("============== Starting Training ==============") callback = [Monitor(lr_init=lr.asnumpy())] ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/" if config.save_checkpoint: config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size, keep_checkpoint_max=config.keep_checkpoint_max) ckpt_cb = ModelCheckpoint(prefix="mobilenetV2", directory=ckpt_save_dir, config=config_ck) callback += [ckpt_cb] model.train(epoch_size, dataset, callbacks=callback) print("============== End Training ==============") if __name__ == '__main__': if args_opt.device_target == "Ascend": train_on_ascend() elif args_opt.device_target == "GPU": train_on_gpu()