mindspore/model_zoo/official/cv/efficientnet/train.py

# 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 imagenet."""
import argparse
import math
import os
import random
import time

import numpy as np
import mindspore
from mindspore import Tensor, context
from mindspore.communication.management import get_group_size, get_rank, init
from mindspore.nn import SGD, RMSProp
from mindspore.train.callback import (CheckpointConfig, LossMonitor,
                                      ModelCheckpoint, TimeMonitor)
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.train.model import Model, ParallelMode
from mindspore.train.serialization import load_checkpoint, load_param_into_net
from src.config import efficientnet_b0_config_gpu as cfg
from src.dataset import create_dataset
from src.efficientnet import efficientnet_b0
from src.loss import LabelSmoothingCrossEntropy

mindspore.common.set_seed(cfg.random_seed)
random.seed(cfg.random_seed)
np.random.seed(cfg.random_seed)


def get_lr(base_lr, total_epochs, steps_per_epoch, decay_steps=1,
           decay_rate=0.9, warmup_steps=0., warmup_lr_init=0., global_epoch=0):
    lr_each_step = []
    total_steps = steps_per_epoch * total_epochs
    global_steps = steps_per_epoch * global_epoch
    self_warmup_delta = ((base_lr - warmup_lr_init) /
                         warmup_steps) if warmup_steps > 0 else 0
    self_decay_rate = decay_rate if decay_rate < 1 else 1 / decay_rate
    for i in range(total_steps):
        steps = math.floor(i / steps_per_epoch)
        cond = 1 if (steps < warmup_steps) else 0
        warmup_lr = warmup_lr_init + steps * self_warmup_delta
        decay_nums = math.floor(steps / decay_steps)
        decay_rate = math.pow(self_decay_rate, decay_nums)
        decay_lr = base_lr * decay_rate
        lr = cond * warmup_lr + (1 - cond) * decay_lr
        lr_each_step.append(lr)
    lr_each_step = lr_each_step[global_steps:]
    lr_each_step = np.array(lr_each_step).astype(np.float32)
    return lr_each_step


def get_outdir(path, *paths, inc=False):
    outdir = os.path.join(path, *paths)
    if not os.path.exists(outdir):
        os.makedirs(outdir)

    elif inc:
        count = 1
        outdir_inc = outdir + '-' + str(count)
        while os.path.exists(outdir_inc):
            count = count + 1
            outdir_inc = outdir + '-' + str(count)
            assert count < 100
        outdir = outdir_inc
        os.makedirs(outdir)
    return outdir


parser = argparse.ArgumentParser(
    description='Training configuration', add_help=False)
parser.add_argument('--data_path', type=str, default='/home/dataset/imagenet_jpeg/', metavar='DIR',
                    help='path to dataset')
parser.add_argument('--distributed', action='store_true', default=False)
parser.add_argument('--GPU', action='store_true', default=False,
                    help='Use GPU for training (default: False)')
parser.add_argument('--cur_time', type=str,
                    default='19701010-000000', help='current time')
parser.add_argument('--resume', default='', type=str, metavar='PATH',
                    help='Resume full model and optimizer state from checkpoint (default: none)')


def main():
    args, _ = parser.parse_known_args()
    devid, rank_id, rank_size = 0, 0, 1

    context.set_context(mode=context.GRAPH_MODE)

    if args.distributed:
        if args.GPU:
            init("nccl")
            context.set_context(device_target='GPU')
        else:
            init()
            devid = int(os.getenv('DEVICE_ID'))
            context.set_context(
                device_target='Ascend', device_id=devid, reserve_class_name_in_scope=False)
        context.reset_auto_parallel_context()
        rank_id = get_rank()
        rank_size = get_group_size()
        context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
                                          gradients_mean=True, device_num=rank_size)
    else:
        if args.GPU:
            context.set_context(device_target='GPU')

    is_master = not args.distributed or (rank_id == 0)

    net = efficientnet_b0(num_classes=cfg.num_classes,
                          drop_rate=cfg.drop,
                          drop_connect_rate=cfg.drop_connect,
                          global_pool=cfg.gp,
                          bn_tf=cfg.bn_tf,
                          )

    cur_time = args.cur_time
    output_base = './output'

    exp_name = '-'.join([
        cur_time,
        cfg.model,
        str(224)
    ])
    time.sleep(rank_id)
    output_dir = get_outdir(output_base, exp_name)

    train_data_url = os.path.join(args.data_path, 'train')
    train_dataset = create_dataset(
        cfg.batch_size, train_data_url, workers=cfg.workers, distributed=args.distributed)
    batches_per_epoch = train_dataset.get_dataset_size()

    loss_cb = LossMonitor(per_print_times=batches_per_epoch)
    loss = LabelSmoothingCrossEntropy(smooth_factor=cfg.smoothing)
    time_cb = TimeMonitor(data_size=batches_per_epoch)
    loss_scale_manager = FixedLossScaleManager(
        cfg.loss_scale, drop_overflow_update=False)

    callbacks = [time_cb, loss_cb]

    if cfg.save_checkpoint:
        config_ck = CheckpointConfig(
            save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=cfg.keep_checkpoint_max)
        ckpoint_cb = ModelCheckpoint(
            prefix=cfg.model, directory=output_dir, config=config_ck)
        callbacks += [ckpoint_cb]

    lr = Tensor(get_lr(base_lr=cfg.lr, total_epochs=cfg.epochs, steps_per_epoch=batches_per_epoch,
                       decay_steps=cfg.decay_epochs, decay_rate=cfg.decay_rate,
                       warmup_steps=cfg.warmup_epochs, warmup_lr_init=cfg.warmup_lr_init,
                       global_epoch=cfg.resume_start_epoch))
    if cfg.opt == 'sgd':
        optimizer = SGD(net.trainable_params(), learning_rate=lr, momentum=cfg.momentum,
                        weight_decay=cfg.weight_decay,
                        loss_scale=cfg.loss_scale
                        )
    elif cfg.opt == 'rmsprop':
        optimizer = RMSProp(net.trainable_params(), learning_rate=lr, decay=0.9, weight_decay=cfg.weight_decay,
                            momentum=cfg.momentum, epsilon=cfg.opt_eps, loss_scale=cfg.loss_scale
                            )

    loss.add_flags_recursive(fp32=True, fp16=False)

    if args.resume:
        ckpt = load_checkpoint(args.resume)
        load_param_into_net(net, ckpt)

    model = Model(net, loss, optimizer,
                  loss_scale_manager=loss_scale_manager,
                  amp_level=cfg.amp_level
                  )

    callbacks = callbacks if is_master else []

    if args.resume:
        real_epoch = cfg.epochs - cfg.resume_start_epoch
        model.train(real_epoch, train_dataset,
                    callbacks=callbacks, dataset_sink_mode=True)
    else:
        model.train(cfg.epochs, train_dataset,
                    callbacks=callbacks, dataset_sink_mode=True)


if __name__ == '__main__':
    main()