85 lines
3.7 KiB
Python
85 lines
3.7 KiB
Python
# 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 lenet example ########################
|
|
train lenet and get network model files(.ckpt) :
|
|
python train.py --data_path /YourDataPath
|
|
"""
|
|
|
|
import os
|
|
import argparse
|
|
import mindspore.nn as nn
|
|
from mindspore import context
|
|
from mindspore.train.serialization import load_checkpoint
|
|
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
|
|
from mindspore.train import Model
|
|
from mindspore.nn.metrics import Accuracy
|
|
from mindspore.compression.quant import QuantizationAwareTraining
|
|
from mindspore.compression.quant.quant_utils import load_nonquant_param_into_quant_net
|
|
from mindspore.common import set_seed
|
|
from src.dataset import create_dataset
|
|
from src.config import mnist_cfg as cfg
|
|
from src.lenet_fusion import LeNet5 as LeNet5Fusion
|
|
from src.loss_monitor import LossMonitor
|
|
|
|
set_seed(1)
|
|
|
|
parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
|
|
parser.add_argument('--device_target', type=str, default="Ascend",
|
|
choices=['Ascend', 'GPU'],
|
|
help='device where the code will be implemented (default: Ascend)')
|
|
parser.add_argument('--data_path', type=str, default="./MNIST_Data",
|
|
help='path where the dataset is saved')
|
|
parser.add_argument('--ckpt_path', type=str, default="",
|
|
help='if mode is test, must provide path where the trained ckpt file')
|
|
args = parser.parse_args()
|
|
|
|
if __name__ == "__main__":
|
|
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
|
|
ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, 1)
|
|
step_size = ds_train.get_dataset_size()
|
|
|
|
# define fusion network
|
|
network = LeNet5Fusion(cfg.num_classes)
|
|
|
|
# load quantization aware network checkpoint
|
|
param_dict = load_checkpoint(args.ckpt_path)
|
|
load_nonquant_param_into_quant_net(network, param_dict)
|
|
|
|
# convert fusion network to quantization aware network
|
|
quantizer = QuantizationAwareTraining(quant_delay=900,
|
|
bn_fold=False,
|
|
per_channel=[True, False],
|
|
symmetric=[True, False])
|
|
network = quantizer.quantize(network)
|
|
|
|
# define network loss
|
|
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
|
|
# define network optimization
|
|
net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
|
|
|
|
# call back and monitor
|
|
config_ckpt = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
|
|
keep_checkpoint_max=cfg.keep_checkpoint_max)
|
|
ckpt_callback = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ckpt)
|
|
|
|
# define model
|
|
model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
|
|
|
|
print("============== Starting Training ==============")
|
|
model.train(cfg['epoch_size'], ds_train, callbacks=[ckpt_callback, LossMonitor()],
|
|
dataset_sink_mode=True)
|
|
print("============== End Training ==============")
|