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train_pt.py
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train_pt.py
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"""
Script for training model on PyTorch.
"""
import os
import time
import logging
import argparse
import random
import numpy as np
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.utils.data
# from common.logger_utils import initialize_logging
from cvutil.logger import initialize_logging
from cvutil.train import TrainProcessController
# from common.train_process_controller import TrainProcessController
from pytorch.utils import prepare_pt_context, prepare_model, validate
from pytorch.utils import report_accuracy, get_composite_metric, get_metric_name
from pytorch.dataset_utils import get_dataset_metainfo
from pytorch.dataset_utils import get_train_data_source, get_val_data_source
def add_train_cls_parser_arguments(parser):
"""
Create python script parameters (for training/classification specific subpart).
Parameters
----------
parser : ArgumentParser
ArgumentParser instance.
"""
parser.add_argument(
"--model",
type=str,
required=True,
help="type of model to use. see model_provider for options")
parser.add_argument(
"--use-pretrained",
action="store_true",
help="enable using pretrained model from github repo")
parser.add_argument(
"--resume",
type=str,
default="",
help="resume from previously saved parameters if not None")
parser.add_argument(
"--resume-state",
type=str,
default="",
help="resume from previously saved optimizer state if not None")
parser.add_argument(
"--num-gpus",
type=int,
default=0,
help="number of gpus to use")
parser.add_argument(
"-j",
"--num-data-workers",
dest="num_workers",
default=4,
type=int,
help="number of preprocessing workers")
parser.add_argument(
"--batch-size",
type=int,
default=512,
help="training batch size per device (CPU/GPU)")
parser.add_argument(
"--batch-size-scale",
type=int,
default=1,
help="manual batch-size increasing factor")
parser.add_argument(
"--num-epochs",
type=int,
default=120,
help="number of training epochs")
parser.add_argument(
"--start-epoch",
type=int,
default=1,
help="starting epoch for resuming, default is 1 for new training")
parser.add_argument(
"--attempt",
type=int,
default=1,
help="current attempt number for training")
parser.add_argument(
"--optimizer-name",
type=str,
default="nag",
help="optimizer name")
parser.add_argument(
"--lr",
type=float,
default=0.1,
help="learning rate")
parser.add_argument(
"--lr-mode",
type=str,
default="cosine",
help="learning rate scheduler mode. options are step, poly and cosine")
parser.add_argument(
"--lr-decay",
type=float,
default=0.1,
help="decay rate of learning rate")
parser.add_argument(
"--lr-decay-period",
type=int,
default=0,
help="interval for periodic learning rate decays. default is 0 to disable")
parser.add_argument(
"--lr-decay-epoch",
type=str,
default="40,60",
help="epoches at which learning rate decays")
parser.add_argument(
"--target-lr",
type=float,
default=1e-8,
help="ending learning rate")
parser.add_argument(
"--poly-power",
type=float,
default=2,
help="power value for poly LR scheduler")
parser.add_argument(
"--warmup-epochs",
type=int,
default=0,
help="number of warmup epochs")
parser.add_argument(
"--warmup-lr",
type=float,
default=1e-8,
help="starting warmup learning rate")
parser.add_argument(
"--warmup-mode",
type=str,
default="linear",
help="learning rate scheduler warmup mode. options are linear, poly and constant")
parser.add_argument(
"--momentum",
type=float,
default=0.9,
help="momentum value for optimizer")
parser.add_argument(
"--wd",
type=float,
default=0.0001,
help="weight decay rate")
parser.add_argument(
"--gamma-wd-mult",
type=float,
default=1.0,
help="weight decay multiplier for batchnorm gamma")
parser.add_argument(
"--beta-wd-mult",
type=float,
default=1.0,
help="weight decay multiplier for batchnorm beta")
parser.add_argument(
"--bias-wd-mult",
type=float,
default=1.0,
help="weight decay multiplier for bias")
parser.add_argument(
"--grad-clip",
type=float,
default=None,
help="max_norm for gradient clipping")
parser.add_argument(
"--label-smoothing",
action="store_true",
help="use label smoothing")
parser.add_argument(
"--mixup",
action="store_true",
help="use mixup strategy")
parser.add_argument(
"--mixup-epoch-tail",
type=int,
default=15,
help="number of epochs without mixup at the end of training")
parser.add_argument(
"--log-interval",
type=int,
default=50,
help="number of batches to wait before logging")
parser.add_argument(
"--save-interval",
type=int,
default=4,
help="saving parameters epoch interval, best model will always be saved")
parser.add_argument(
"--save-dir",
type=str,
default="",
help="directory of saved models and log-files")
parser.add_argument(
"--logging-file-name",
type=str,
default="train.log",
help="filename of training log")
parser.add_argument(
"--seed",
type=int,
default=-1,
help="Random seed to be fixed")
parser.add_argument(
"--log-packages",
type=str,
default="torch, torchvision",
help="list of python packages for logging")
parser.add_argument(
"--log-pip-packages",
type=str,
default="",
help="list of pip packages for logging")
parser.add_argument(
"--tune-layers",
type=str,
default="",
help="regexp for selecting layers for fine tuning")
def parse_args():
"""
Parse python script parameters (common part).
Returns
-------
ArgumentParser
Resulted args.
"""
parser = argparse.ArgumentParser(
description="Train a model for image classification (PyTorch)",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"--dataset",
type=str,
default="ImageNet1K",
help="dataset name. options are ImageNet1K, CUB200_2011, CIFAR10, CIFAR100, SVHN")
parser.add_argument(
"--work-dir",
type=str,
default=os.path.join("..", "imgclsmob_data"),
help="path to working directory only for dataset root path preset")
args, _ = parser.parse_known_args()
dataset_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
dataset_metainfo.add_dataset_parser_arguments(
parser=parser,
work_dir_path=args.work_dir)
add_train_cls_parser_arguments(parser)
args = parser.parse_args()
return args
def init_rand(seed):
"""
Initialize all random generators by seed.
Parameters
----------
seed : int
Seed value.
Returns
-------
int
Generated seed value.
"""
if seed <= 0:
seed = np.random.randint(10000)
else:
cudnn.deterministic = True
logging.warning(
"You have chosen to seed training. This will turn on the CUDNN deterministic setting, which can slow down "
"your training considerably! You may see unexpected behavior when restarting from checkpoints.")
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
return seed
def prepare_trainer(net,
optimizer_name,
wd,
momentum,
lr_mode,
lr,
lr_decay_period,
lr_decay_epoch,
lr_decay,
num_epochs,
state_file_path):
"""
Prepare trainer.
Parameters
----------
net : Module
Model.
optimizer_name : str
Name of optimizer.
wd : float
Weight decay rate.
momentum : float
Momentum value.
lr_mode : str
Learning rate scheduler mode.
lr : float
Learning rate.
lr_decay_period : int
Interval for periodic learning rate decays.
lr_decay_epoch : str
Epoches at which learning rate decays.
lr_decay : float
Decay rate of learning rate.
num_epochs : int
Number of training epochs.
state_file_path : str
Path for file with trainer state.
Returns
-------
Optimizer
Optimizer.
LRScheduler
Learning rate scheduler.
int
Start epoch.
"""
optimizer_name = optimizer_name.lower()
if (optimizer_name == "sgd") or (optimizer_name == "nag"):
optimizer = torch.optim.SGD(
params=net.parameters(),
lr=lr,
momentum=momentum,
weight_decay=wd,
nesterov=(optimizer_name == "nag"))
else:
raise ValueError("Usupported optimizer: {}".format(optimizer_name))
if state_file_path:
checkpoint = torch.load(state_file_path)
if isinstance(checkpoint, dict):
optimizer.load_state_dict(checkpoint["optimizer"])
start_epoch = checkpoint["epoch"]
else:
start_epoch = None
else:
start_epoch = None
cudnn.benchmark = True
lr_mode = lr_mode.lower()
if lr_decay_period > 0:
lr_decay_epoch = list(range(lr_decay_period, num_epochs, lr_decay_period))
else:
lr_decay_epoch = [int(i) for i in lr_decay_epoch.split(",")]
if (lr_mode == "step") and (lr_decay_period != 0):
lr_scheduler = torch.optim.lr_scheduler.StepLR(
optimizer=optimizer,
step_size=lr_decay_period,
gamma=lr_decay,
last_epoch=-1)
elif (lr_mode == "multistep") or ((lr_mode == "step") and (lr_decay_period == 0)):
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer=optimizer,
milestones=lr_decay_epoch,
gamma=lr_decay,
last_epoch=-1)
elif lr_mode == "cosine":
for group in optimizer.param_groups:
group.setdefault("initial_lr", group["lr"])
lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer=optimizer,
T_max=num_epochs,
last_epoch=(num_epochs - 1))
else:
raise ValueError("Usupported lr_scheduler: {}".format(lr_mode))
return optimizer, lr_scheduler, start_epoch
def save_params(file_stem,
state):
"""
Save current model/trainer parameters.
Parameters
----------
file_stem : str
File stem (with path).
state : dict
Whole state of model & trainer.
trainer : Trainer
Trainer.
"""
torch.save(
obj=state["state_dict"],
f=(file_stem + ".pth"))
torch.save(
obj=state,
f=(file_stem + ".states"))
def train_epoch(epoch,
net,
train_metric,
train_data,
use_cuda,
L,
optimizer,
# lr_scheduler,
batch_size,
log_interval):
"""
Train model on particular epoch.
Parameters
----------
epoch : int
Epoch number.
net : Module
Model.
train_metric : EvalMetric
Metric object instance.
train_data : DataLoader
Data loader.
use_cuda : bool
Whether to use CUDA.
L : Loss
Loss function.
optimizer : Optimizer
Optimizer.
batch_size : int
Training batch size.
log_interval : int
Batch count period for logging.
Returns
-------
float
Loss value.
"""
tic = time.time()
net.train()
train_metric.reset()
train_loss = 0.0
btic = time.time()
for i, (data, target) in enumerate(train_data):
if use_cuda:
data = data.cuda(non_blocking=True)
target = target.cuda(non_blocking=True)
output = net(data)
loss = L(output, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
train_loss += loss.item()
train_metric.update(
labels=target,
preds=output)
if log_interval and not (i + 1) % log_interval:
speed = batch_size * log_interval / (time.time() - btic)
btic = time.time()
train_accuracy_msg = report_accuracy(metric=train_metric)
logging.info("Epoch[{}] Batch [{}]\tSpeed: {:.2f} samples/sec\t{}\tlr={:.5f}".format(
epoch + 1, i, speed, train_accuracy_msg, optimizer.param_groups[0]["lr"]))
throughput = int(batch_size * (i + 1) / (time.time() - tic))
logging.info("[Epoch {}] speed: {:.2f} samples/sec\ttime cost: {:.2f} sec".format(
epoch + 1, throughput, time.time() - tic))
train_loss /= (i + 1)
train_accuracy_msg = report_accuracy(metric=train_metric)
logging.info("[Epoch {}] training: {}\tloss={:.4f}".format(
epoch + 1, train_accuracy_msg, train_loss))
return train_loss
def train_net(batch_size,
num_epochs,
start_epoch1,
train_data,
val_data,
net,
optimizer,
lr_scheduler,
lp_saver,
log_interval,
num_classes,
val_metric,
train_metric,
use_cuda):
"""
Main procedure for training model.
Parameters
----------
batch_size : int
Training batch size.
num_epochs : int
Number of training epochs.
start_epoch1 : int
Number of starting epoch (1-based).
train_data : DataLoader
Data loader (training subset).
val_data : DataLoader
Data loader (validation subset).
net : Module
Model.
optimizer : Optimizer
Optimizer.
lr_scheduler : LRScheduler
Learning rate scheduler.
lp_saver : TrainProcessController
Model/trainer state saver.
log_interval : int
Batch count period for logging.
num_classes : int
Number of model classes.
val_metric : EvalMetric
Metric object instance (validation subset).
train_metric : EvalMetric
Metric object instance (training subset).
use_cuda : bool
Whether to use CUDA.
"""
assert (num_classes > 0)
L = nn.CrossEntropyLoss()
if use_cuda:
L = L.cuda()
assert isinstance(start_epoch1, int)
assert (start_epoch1 >= 1)
if start_epoch1 > 1:
logging.info("Start training from [Epoch {}]".format(start_epoch1))
validate(
metric=val_metric,
net=net,
val_data=val_data,
use_cuda=use_cuda)
val_accuracy_msg = report_accuracy(metric=val_metric)
logging.info("[Epoch {}] validation: {}".format(start_epoch1 - 1, val_accuracy_msg))
gtic = time.time()
for epoch in range(start_epoch1 - 1, num_epochs):
lr_scheduler.step()
train_loss = train_epoch(
epoch=epoch,
net=net,
train_metric=train_metric,
train_data=train_data,
use_cuda=use_cuda,
L=L,
optimizer=optimizer,
# lr_scheduler,
batch_size=batch_size,
log_interval=log_interval)
validate(
metric=val_metric,
net=net,
val_data=val_data,
use_cuda=use_cuda)
val_accuracy_msg = report_accuracy(metric=val_metric)
logging.info("[Epoch {}] validation: {}".format(epoch + 1, val_accuracy_msg))
if lp_saver is not None:
state = {
"epoch": epoch + 1,
"state_dict": net.state_dict(),
"optimizer": optimizer.state_dict(),
}
lp_saver_kwargs = {"state": state}
val_acc_values = val_metric.get()[1]
train_acc_values = train_metric.get()[1]
val_acc_values = val_acc_values if isinstance(val_acc_values, list) else [val_acc_values]
train_acc_values = train_acc_values if isinstance(train_acc_values, list) else [train_acc_values]
lp_saver.update_epoch_and_callback(
epoch1=(epoch + 1),
params=(val_acc_values + train_acc_values + [train_loss, optimizer.param_groups[0]["lr"]]),
**lp_saver_kwargs)
logging.info("Total time cost: {:.2f} sec".format(time.time() - gtic))
if lp_saver is not None:
opt_metric_name = get_metric_name(val_metric, lp_saver.key_metric_idx)
logging.info("Best {}: {:.4f} at {} epoch".format(
opt_metric_name, lp_saver.best_eval_metric_value, lp_saver.best_eval_metric_epoch))
def main():
"""
Main body of script.
"""
args = parse_args()
args.seed = init_rand(seed=args.seed)
_, _ = initialize_logging(
logging_dir_path=args.save_dir,
logging_file_name=args.logging_file_name,
main_script_path=__file__,
script_args=args)
use_cuda, batch_size = prepare_pt_context(
num_gpus=args.num_gpus,
batch_size=args.batch_size)
net = prepare_model(
model_name=args.model,
use_pretrained=args.use_pretrained,
pretrained_model_file_path=args.resume.strip(),
use_cuda=use_cuda)
real_net = net.module if hasattr(net, "module") else net
assert (hasattr(real_net, "num_classes"))
num_classes = real_net.num_classes
ds_metainfo = get_dataset_metainfo(dataset_name=args.dataset)
ds_metainfo.update(args=args)
train_data = get_train_data_source(
ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
val_data = get_val_data_source(
ds_metainfo=ds_metainfo,
batch_size=batch_size,
num_workers=args.num_workers)
optimizer, lr_scheduler, start_epoch = prepare_trainer(
net=net,
optimizer_name=args.optimizer_name,
wd=args.wd,
momentum=args.momentum,
lr_mode=args.lr_mode,
lr=args.lr,
lr_decay_period=args.lr_decay_period,
lr_decay_epoch=args.lr_decay_epoch,
lr_decay=args.lr_decay,
num_epochs=args.num_epochs,
state_file_path=args.resume_state)
if args.save_dir and args.save_interval:
param_names = ds_metainfo.val_metric_capts + ds_metainfo.train_metric_capts + ["Train.Loss", "LR"]
lp_saver = TrainProcessController(
checkpoint_file_name_prefix="{}_{}".format(ds_metainfo.short_label, args.model),
last_checkpoint_file_name_suffix="last",
best_checkpoint_file_name_suffix=None,
last_checkpoint_dir_path=args.save_dir,
best_checkpoint_dir_path=None,
last_checkpoint_file_count=2,
best_checkpoint_file_count=2,
checkpoint_file_save_callback=save_params,
checkpoint_file_exts=(".pth", ".states"),
save_interval=args.save_interval,
num_epochs=args.num_epochs,
param_names=param_names,
key_metric_idx=ds_metainfo.saver_acc_ind,
# bigger=[True],
# mask=None,
score_log_file_path=os.path.join(args.save_dir, "score.log"),
score_log_attempt_value=args.attempt,
best_map_log_file_path=os.path.join(args.save_dir, "best_map.log"))
else:
lp_saver = None
train_net(
batch_size=batch_size,
num_epochs=args.num_epochs,
start_epoch1=args.start_epoch,
train_data=train_data,
val_data=val_data,
net=net,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
lp_saver=lp_saver,
log_interval=args.log_interval,
num_classes=num_classes,
val_metric=get_composite_metric(ds_metainfo.val_metric_names, ds_metainfo.val_metric_extra_kwargs),
train_metric=get_composite_metric(ds_metainfo.train_metric_names, ds_metainfo.train_metric_extra_kwargs),
use_cuda=use_cuda)
if __name__ == "__main__":
main()