mindspore/model_zoo/official/cv/mobilenetv3/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 time
import argparse
import random
import numpy as np

from mindspore import context
from mindspore import Tensor
from mindspore import nn
from mindspore.nn.optim.momentum import Momentum
from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
from mindspore.nn.loss.loss import _Loss
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common import dtype as mstype
from mindspore.train.model import Model, ParallelMode
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, Callback
from mindspore.train.loss_scale_manager import FixedLossScaleManager
from mindspore.train.serialization import load_checkpoint, load_param_into_net
import mindspore.dataset.engine as de
from mindspore.communication.management import init, get_group_size, get_rank

from src.dataset import create_dataset
from src.lr_generator import get_lr
from src.config import config_gpu
from src.mobilenetV3 import mobilenet_v3_large

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='Pretrained checkpoint path')
parser.add_argument('--device_target', type=str, default="GPU", help='run device_target')
args_opt = parser.parse_args()

if args_opt.device_target == "GPU":
    context.set_context(mode=context.GRAPH_MODE,
                        device_target="GPU",
                        save_graphs=False)
    init("nccl")
    context.set_auto_parallel_context(device_num=get_group_size(),
                                      parallel_mode=ParallelMode.DATA_PARALLEL,
                                      mirror_mean=True)
else:
    raise ValueError("Unsupported device_target.")


class CrossEntropyWithLabelSmooth(_Loss):
    """
    CrossEntropyWith LabelSmooth.

    Args:
        smooth_factor (float): smooth factor, default=0.
        num_classes (int): num classes

    Returns:
        None.

    Examples:
        >>> CrossEntropyWithLabelSmooth(smooth_factor=0., num_classes=1000)
    """

    def __init__(self, smooth_factor=0., num_classes=1000):
        super(CrossEntropyWithLabelSmooth, self).__init__()
        self.onehot = P.OneHot()
        self.on_value = Tensor(1.0 - smooth_factor, mstype.float32)
        self.off_value = Tensor(1.0 * smooth_factor /
                                (num_classes - 1), mstype.float32)
        self.ce = nn.SoftmaxCrossEntropyWithLogits()
        self.mean = P.ReduceMean(False)
        self.cast = P.Cast()

    def construct(self, logit, label):
        one_hot_label = self.onehot(self.cast(label, mstype.int32), F.shape(logit)[1],
                                    self.on_value, self.off_value)
        out_loss = self.ce(logit, one_hot_label)
        out_loss = self.mean(out_loss, 0)
        return out_loss


class Monitor(Callback):
    """
    Monitor loss and time.

    Args:
        lr_init (numpy array): train lr

    Returns:
        None

    Examples:
        >>> Monitor(100,lr_init=Tensor([0.05]*100).asnumpy())
    """

    def __init__(self, lr_init=None):
        super(Monitor, self).__init__()
        self.lr_init = lr_init
        self.lr_init_len = len(lr_init)

    def epoch_begin(self, run_context):
        self.losses = []
        self.epoch_time = time.time()

    def epoch_end(self, run_context):
        cb_params = run_context.original_args()

        epoch_mseconds = (time.time() - self.epoch_time) * 1000
        per_step_mseconds = epoch_mseconds / cb_params.batch_num
        print("epoch time: {:5.3f}, per step time: {:5.3f}, avg loss: {:5.3f}".format(epoch_mseconds,
                                                                                      per_step_mseconds,
                                                                                      np.mean(self.losses)))

    def step_begin(self, run_context):
        self.step_time = time.time()

    def step_end(self, run_context):
        cb_params = run_context.original_args()
        step_mseconds = (time.time() - self.step_time) * 1000
        step_loss = cb_params.net_outputs

        if isinstance(step_loss, (tuple, list)) and isinstance(step_loss[0], Tensor):
            step_loss = step_loss[0]
        if isinstance(step_loss, Tensor):
            step_loss = np.mean(step_loss.asnumpy())

        self.losses.append(step_loss)
        cur_step_in_epoch = (cb_params.cur_step_num - 1) % cb_params.batch_num

        print("epoch: [{:3d}/{:3d}], step:[{:5d}/{:5d}], loss:[{:5.3f}/{:5.3f}], time:[{:5.3f}], lr:[{:5.3f}]".format(
            cb_params.cur_epoch_num -
            1, cb_params.epoch_num, cur_step_in_epoch, cb_params.batch_num, step_loss,
            np.mean(self.losses), step_mseconds, self.lr_init[cb_params.cur_step_num - 1]))


if __name__ == '__main__':
    if args_opt.device_target == "GPU":
        # train on gpu
        print("train args: ", args_opt)
        print("cfg: ", config_gpu)

        # define net
        net = mobilenet_v3_large(num_classes=config_gpu.num_classes)
        # define loss
        if config_gpu.label_smooth > 0:
            loss = CrossEntropyWithLabelSmooth(
                smooth_factor=config_gpu.label_smooth, num_classes=config_gpu.num_classes)
        else:
            loss = SoftmaxCrossEntropyWithLogits(
                is_grad=False, sparse=True, reduction='mean')
        # define dataset
        epoch_size = config_gpu.epoch_size
        dataset = create_dataset(dataset_path=args_opt.dataset_path,
                                 do_train=True,
                                 config=config_gpu,
                                 device_target=args_opt.device_target,
                                 repeat_num=1,
                                 batch_size=config_gpu.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(net, param_dict)
        # define optimizer
        loss_scale = FixedLossScaleManager(
            config_gpu.loss_scale, drop_overflow_update=False)
        lr = Tensor(get_lr(global_step=0,
                           lr_init=0,
                           lr_end=0,
                           lr_max=config_gpu.lr,
                           warmup_epochs=config_gpu.warmup_epochs,
                           total_epochs=epoch_size,
                           steps_per_epoch=step_size))
        opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config_gpu.momentum,
                       config_gpu.weight_decay, config_gpu.loss_scale)
        # define model
        model = Model(net, loss_fn=loss, optimizer=opt,
                      loss_scale_manager=loss_scale)

        cb = [Monitor(lr_init=lr.asnumpy())]
        ckpt_save_dir = config_gpu.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
        if config_gpu.save_checkpoint:
            config_ck = CheckpointConfig(save_checkpoint_steps=config_gpu.save_checkpoint_epochs * step_size,
                                         keep_checkpoint_max=config_gpu.keep_checkpoint_max)
            ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", directory=ckpt_save_dir, config=config_ck)
            cb += [ckpt_cb]
        # begine train
        model.train(epoch_size, dataset, callbacks=cb)