mindspore/model_zoo/official/cv/resnet50_quant/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 Resnet50 on ImageNet"""

import os
import argparse

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.train.loss_scale_manager import FixedLossScaleManager
from mindspore.train.serialization import load_checkpoint
from mindspore.compression.quant import QuantizationAwareTraining
from mindspore.compression.quant.quant_utils import load_nonquant_param_into_quant_net
from mindspore.communication.management import init
import mindspore.nn as nn
import mindspore.common.initializer as weight_init
from mindspore.common import set_seed

#from models.resnet_quant import resnet50_quant #auto construct quantative network of resnet50
from models.resnet_quant_manual import resnet50_quant #manually construct quantative network of resnet50
from src.dataset import create_dataset
from src.lr_generator import get_lr
from src.config import config_quant
from src.crossentropy import CrossEntropy

set_seed(1)

parser = argparse.ArgumentParser(description='Image classification')
parser.add_argument('--run_distribute', type=bool, 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='Pertained checkpoint path')
args_opt = parser.parse_args()
config = config_quant

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
    context.set_context(mode=context.GRAPH_MODE,
                        device_target="Ascend",
                        save_graphs=False,
                        device_id=device_id,
                        enable_auto_mixed_precision=True)
else:
    raise ValueError("Unsupported device target.")

if __name__ == '__main__':
    # train on 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,
                                          gradients_mean=True)
        init()
        context.set_auto_parallel_context(device_num=args_opt.device_num,
                                          parallel_mode=ParallelMode.DATA_PARALLEL,
                                          gradients_mean=True, all_reduce_fusion_config=[107, 160])

    # define network
    net = resnet50_quant(class_num=config.class_num)
    net.set_train(True)

    # weight init and load checkpoint file
    if args_opt.pre_trained:
        param_dict = load_checkpoint(args_opt.pre_trained)
        load_nonquant_param_into_quant_net(net, param_dict, ['step'])
        epoch_size = config.epoch_size - config.pretrained_epoch_size
    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))
    if not config.use_label_smooth:
        config.label_smooth_factor = 0.0
    loss = CrossEntropy(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
    loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)

    # define dataset
    dataset = create_dataset(dataset_path=args_opt.dataset_path,
                             do_train=True,
                             repeat_num=1,
                             batch_size=config.batch_size,
                             target=args_opt.device_target)
    step_size = dataset.get_dataset_size()

    # convert fusion network to quantization aware network
    quantizer = QuantizationAwareTraining(bn_fold=True,
                                          per_channel=[True, False],
                                          symmetric=[True, False],
                                          one_conv_fold=False)
    net = quantizer.quantize(net)

    # get learning rate
    lr = get_lr(lr_init=config.lr_init,
                lr_end=0.0,
                lr_max=config.lr_max,
                warmup_epochs=config.warmup_epochs,
                total_epochs=config.epoch_size,
                steps_per_epoch=step_size,
                lr_decay_mode='cosine')
    if args_opt.pre_trained:
        lr = lr[config.pretrained_epoch_size * step_size:]
    lr = Tensor(lr)

    # define optimization
    opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum,
                   config.weight_decay, config.loss_scale)

    # define model
    model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'})

    print("============== Starting Training ==============")
    time_callback = TimeMonitor(data_size=step_size)
    loss_callback = LossMonitor()
    callbacks = [time_callback, loss_callback]
    if rank_id == 0:
        if config.save_checkpoint:
            config_ckpt = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
                                           keep_checkpoint_max=config.keep_checkpoint_max)
            ckpt_callback = ModelCheckpoint(prefix="ResNet50",
                                            directory=config.save_checkpoint_path,
                                            config=config_ckpt)
            callbacks += [ckpt_callback]
    model.train(epoch_size, dataset, callbacks=callbacks)
    print("============== End Training ==============")