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example.py
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# -*-coding: utf-8 -*-
"""
@Author : panjq
@E-mail : [email protected]
@Date : 2021-07-28 22:09:32
"""
import os
import sys
sys.path.append(os.getcwd())
import argparse
import basetrainer
from torchvision import transforms
from torchvision.datasets import ImageFolder
from basetrainer.engine import trainer
from basetrainer.engine.launch import launch
from basetrainer.criterion.criterion import get_criterion
from basetrainer.metric import accuracy_recorder
from basetrainer.callbacks import log_history, model_checkpoint, losses_recorder, multi_losses_recorder
from basetrainer.scheduler import build_scheduler
from basetrainer.optimizer.build_optimizer import get_optimizer
from basetrainer.utils import log, setup_config, torch_tools
from basetrainer.models import build_models
from pybaseutils import file_utils
print("basetrainer version:{}".format(basetrainer.__version__))
class ClassificationTrainer(trainer.EngineTrainer):
""" Training Pipeline """
def __init__(self, cfg):
super(ClassificationTrainer, self).__init__(cfg)
torch_tools.set_env_random_seed()
cfg.model_root = os.path.join(cfg.work_dir, "model")
cfg.log_root = os.path.join(cfg.work_dir, "log")
if self.is_main_process:
file_utils.create_dir(cfg.work_dir)
file_utils.create_dir(cfg.model_root)
file_utils.create_dir(cfg.log_root)
file_utils.copy_file_to_dir(cfg.config_file, cfg.work_dir)
setup_config.save_config(cfg, os.path.join(cfg.work_dir, "setup_config.yaml"))
self.logger = log.set_logger(level="debug",
logfile=os.path.join(cfg.log_root, "train.log"),
is_main_process=self.is_main_process)
# build project
self.build(cfg)
self.logger.info("=" * 60)
self.logger.info("work_dir :{}".format(cfg.work_dir))
self.logger.info("config_file :{}".format(cfg.config_file))
self.logger.info("gpu_id :{}".format(cfg.gpu_id))
self.logger.info("main device :{}".format(self.device))
self.logger.info("num_samples(train):{}".format(self.num_samples))
self.logger.info("num_classes :{}".format(cfg.num_classes))
self.logger.info("mean_num :{}".format(self.num_samples / cfg.num_classes))
self.logger.info("=" * 60)
def build_optimizer(self, cfg, **kwargs):
"""build_optimizer"""
self.logger.info("build_optimizer")
self.logger.info("optim_type:{},init_lr:{},weight_decay:{}".format(cfg.optim_type, cfg.lr, cfg.weight_decay))
optimizer = get_optimizer(self.model,
optim_type=cfg.optim_type,
lr=cfg.lr,
momentum=cfg.momentum,
weight_decay=cfg.weight_decay)
return optimizer
def build_criterion(self, cfg, **kwargs):
"""build_criterion"""
self.logger.info("build_criterion,loss_type:{},num_classes:{}".format(cfg.loss_type, cfg.num_classes))
criterion = get_criterion(cfg.loss_type, cfg.num_classes, device=self.device)
return criterion
def build_train_loader(self, cfg, **kwargs):
"""build_train_loader"""
self.logger.info("build_train_loader,input_size:{}".format(cfg.input_size))
transform = transforms.Compose([
transforms.Resize([int(128 * cfg.input_size[1] / 112), int(128 * cfg.input_size[0] / 112)]),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop([cfg.input_size[1], cfg.input_size[0]]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
dataset = ImageFolder(root=cfg.train_data, transform=transform)
cfg.num_classes = len(dataset.classes)
cfg.classes = dataset.classes
loader = self.build_dataloader(dataset, cfg.batch_size, cfg.num_workers, phase="train",
shuffle=True, pin_memory=False, drop_last=True, distributed=cfg.distributed)
return loader
def build_test_loader(self, cfg, **kwargs):
"""build_test_loader"""
self.logger.info("build_test_loader,input_size:{}".format(cfg.input_size))
transform = transforms.Compose([
transforms.Resize([int(128 * cfg.input_size[1] / 112), int(128 * cfg.input_size[0] / 112)]),
transforms.CenterCrop([cfg.input_size[1], cfg.input_size[0]]),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
])
dataset = ImageFolder(root=cfg.train_data, transform=transform)
loader = self.build_dataloader(dataset, cfg.batch_size, cfg.num_workers, phase="test",
shuffle=False, pin_memory=False, drop_last=False, distributed=False)
return loader
def build_model(self, cfg, **kwargs):
"""build_model"""
self.logger.info("build_model,net_type:{}".format(cfg.net_type))
model = build_models.get_models(net_type=cfg.net_type, input_size=cfg.input_size,
num_classes=cfg.num_classes, pretrained=True)
if cfg.finetune:
self.logger.info("finetune:{}".format(cfg.finetune))
state_dict = torch_tools.load_state_dict(cfg.finetune)
model.load_state_dict(state_dict)
if cfg.use_prune:
from basetrainer.pruning import nni_pruning
sparsity = 0.2
self.logger.info("use_prune:{},sparsity:{}".format(cfg.use_prune, sparsity))
model = nni_pruning.model_pruning(model,
input_size=[1, 3, cfg.input_size[1], cfg.input_size[0]],
sparsity=sparsity,
reuse=False,
output_prune=os.path.join(cfg.work_dir, "prune"))
model = self.build_model_parallel(model, cfg.gpu_id, distributed=cfg.distributed)
return model
def build_callbacks(self, cfg, **kwargs):
"""定义回调函数"""
self.logger.info("build_callbacks")
# 准确率记录回调函数
acc_record = accuracy_recorder.AccuracyRecorder(target_names=cfg.classes,
indicator="Accuracy")
# loss记录回调函数
loss_record = losses_recorder.LossesRecorder(indicator="loss")
# Tensorboard Log等历史记录回调函数
history = log_history.LogHistory(log_dir=cfg.log_root,
log_freq=cfg.log_freq,
logger=self.logger,
indicators=["loss", "Accuracy"],
is_main_process=self.is_main_process)
# 模型保存回调函数
checkpointer = model_checkpoint.ModelCheckpoint(model=self.model,
optimizer=self.optimizer,
moder_dir=cfg.model_root,
epochs=cfg.num_epochs,
start_save=-1,
indicator="Accuracy",
logger=self.logger)
# 学习率调整策略回调函数
lr_scheduler = build_scheduler.get_scheduler(cfg.scheduler,
optimizer=self.optimizer,
lr_init=cfg.lr,
num_epochs=cfg.num_epochs,
num_steps=self.num_steps,
milestones=cfg.milestones,
num_warn_up=cfg.num_warn_up)
callbacks = [acc_record,
loss_record,
lr_scheduler,
history,
checkpointer]
return callbacks
def run(self, logs: dict = {}):
self.logger.info("start train")
super().run(logs)
def main(cfg):
t = ClassificationTrainer(cfg)
return t.run()
def get_parser():
parser = argparse.ArgumentParser(description="Training Pipeline")
# parser.add_argument("-c", "--config_file", help="configs file", default="configs/config.yaml", type=str)
parser.add_argument("-c", "--config_file", help="configs file", default=None, type=str)
parser.add_argument("--train_data", help="train data", default="./data/dataset/train", type=str)
parser.add_argument("--test_data", help="test data", default="./data/dataset/val", type=str)
parser.add_argument("--work_dir", help="work_dir", default="output", type=str)
parser.add_argument("--input_size", help="input size", nargs="+", default=[224, 224], type=int)
parser.add_argument("--batch_size", help="batch_size", default=8, type=int)
parser.add_argument("--gpu_id", help="specify your GPU ids", nargs="+", default=[0], type=int)
parser.add_argument("--num_workers", help="num_workers", default=2, type=int)
parser.add_argument("--num_epochs", help="total epoch number", default=50, type=int)
parser.add_argument("--scheduler", help=" learning scheduler: multi-step,cosine", default="multi-step", type=str)
parser.add_argument("--milestones", help="epoch stages to decay learning rate", nargs="+",
default=[10, 20, 40], type=int)
parser.add_argument("--num_warn_up", help="num_warn_up", default=3, type=int)
parser.add_argument("--net_type", help="net_type", default="mobilenet_v2", type=str)
parser.add_argument("--finetune", help="finetune model file", default=None, type=str)
parser.add_argument("--loss_type", help="loss_type", default="CELoss", type=str)
parser.add_argument("--optim_type", help="optim_type", default="SGD", type=str)
parser.add_argument("--lr", help="learning rate", default=0.1, type=float)
parser.add_argument("--weight_decay", help="weight_decay", default=0.0005, type=float)
parser.add_argument("--momentum", help="momentum", default=0.9, type=float)
parser.add_argument("--log_freq", help="log_freq", default=10, type=int)
parser.add_argument('--use_prune', action='store_true', help='use prune', default=False)
parser.add_argument('--progress', action='store_true', help='display progress bar', default=True)
parser.add_argument('--distributed', action='store_true', help='use distributed training', default=False)
return parser
if __name__ == "__main__":
parser = get_parser()
cfg = setup_config.parser_config(parser.parse_args(), cfg_updata=True)
launch(main,
num_gpus_per_machine=len(cfg.gpu_id),
dist_url="tcp://127.0.0.1:28661",
num_machines=1,
machine_rank=0,
distributed=cfg.distributed,
args=(cfg,))