# 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 mobilenet_v1.""" import os import argparse import ast from mindspore import context from mindspore import Tensor from mindspore.nn.optim.momentum import Momentum from mindspore.train.model import Model from mindspore.context import ParallelMode from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits from mindspore.train.loss_scale_manager import FixedLossScaleManager from mindspore.train.serialization import load_checkpoint, load_param_into_net from mindspore.communication.management import init, get_rank, get_group_size from mindspore.common import set_seed import mindspore.nn as nn import mindspore.common.initializer as weight_init from src.lr_generator import get_lr from src.CrossEntropySmooth import CrossEntropySmooth from src.mobilenet_v1 import mobilenet_v1 as mobilenet parser = argparse.ArgumentParser(description='Image classification') parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012') parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute') parser.add_argument('--device_num', type=int, default=1, help='Device num.') parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path') parser.add_argument('--device_target', type=str, default='Ascend', help='Device target') parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path') parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train') args_opt = parser.parse_args() set_seed(1) if args_opt.dataset == 'cifar10': from src.config import config1 as config from src.dataset import create_dataset1 as create_dataset else: from src.config import config2 as config from src.dataset import create_dataset2 as create_dataset if __name__ == '__main__': target = args_opt.device_target ckpt_save_dir = config.save_checkpoint_path # init context context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False) if args_opt.parameter_server: context.set_ps_context(enable_ps=True) if args_opt.run_distribute: if target == "Ascend": device_id = int(os.getenv('DEVICE_ID')) context.set_context(device_id=device_id, enable_auto_mixed_precision=True) context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True) init() context.set_auto_parallel_context(all_reduce_fusion_config=[75]) # GPU target else: init() context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True) ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/" # create dataset dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1, batch_size=config.batch_size, target=target) step_size = dataset.get_dataset_size() # define net net = mobilenet(class_num=config.class_num) if args_opt.parameter_server: net.set_param_ps() # init weight if args_opt.pre_trained: param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(net, param_dict) else: for _, cell in net.cells_and_names(): if isinstance(cell, nn.Conv2d): cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(), cell.weight.shape, cell.weight.dtype)) if isinstance(cell, nn.Dense): cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(), cell.weight.shape, cell.weight.dtype)) # init lr lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max, warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size, lr_decay_mode=config.lr_decay_mode) lr = Tensor(lr) # define opt decayed_params = [] no_decayed_params = [] for param in net.trainable_params(): if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name: decayed_params.append(param) else: no_decayed_params.append(param) group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay}, {'params': no_decayed_params}, {'order_params': net.trainable_params()}] opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale) # define loss, model if target == "Ascend": if args_opt.dataset == "imagenet2012": if not config.use_label_smooth: config.label_smooth_factor = 0.0 loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=config.label_smooth_factor, num_classes=config.class_num) else: loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean') loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2", keep_batchnorm_fp32=False) else: # GPU target if args_opt.dataset == "imagenet2012": if not config.use_label_smooth: config.label_smooth_factor = 0.0 loss = CrossEntropySmooth(sparse=True, reduction="mean", smooth_factor=config.label_smooth_factor, num_classes=config.class_num) else: loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean") opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, config.loss_scale) loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False) # Mixed precision model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O2", keep_batchnorm_fp32=False) # define callbacks time_cb = TimeMonitor(data_size=step_size) loss_cb = LossMonitor() cb = [time_cb, loss_cb] 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="mobilenetv1", directory=ckpt_save_dir, config=config_ck) cb += [ckpt_cb] # train model model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb, sink_size=dataset.get_dataset_size(), dataset_sink_mode=(not args_opt.parameter_server))