File size: 7,651 Bytes
9e14838 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 | # -*- coding: utf-8 -*-
from __future__ import absolute_import
import os
import sys
import time
if os.getcwd() not in sys.path:
sys.path.append(os.getcwd())
import argparse
import random
from datetime import datetime
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from configs.get_config import load_config
from datasets import *
from lib.core_function import test, train, validate
from lib.optimizers.sam import SAM
from lib.scheduler.linear_decay import LinearDecayLR
from logs.logger import LOG_DIR, Logger
from losses import *
from models import *
from package_utils.misc import NativeScalerWithGradNormCount as NativeScaler
from tensorboardX import SummaryWriter
from torch.utils.data import DataLoader
def args_parser(args=None):
parser = argparse.ArgumentParser("Training process...")
parser.add_argument("--cfg", help="Config file", required=True)
parser.add_argument(
"--alloc_mem", "-a", help="Pre allocating GPU memory", action="store_true"
)
return parser.parse_args(args)
if __name__ == "__main__":
if len(sys.argv[1:]):
args = sys.argv[1:]
else:
args = None
args = args_parser(args)
cfg = load_config(args.cfg)
logger = Logger(task=f"training_{cfg.TASK}")
# Seed
seed = cfg.SEED
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
torch.cuda.manual_seed(seed)
# Allocate memory
if args.alloc_mem:
mem_all_tensors = torch.rand(60, 10000, 10000)
mem_all_tensors.to("cuda:0")
# Configuing GPU devices
devices = torch.device("cpu")
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
if "gpus" in cfg.TRAIN.gpus and cfg.TRAIN.gpus is not None:
# Only support a single gpu for training now
devices = torch.device("cuda:1")
model = build_model(cfg.MODEL, MODELS).cuda()
# Loading Dataloader
start_loading = time.time()
val_dataset = build_dataset(
cfg.DATASET, DATASETS, default_args=dict(split="val", config=cfg.DATASET)
)
val_dataloader = DataLoader(
val_dataset,
batch_size=cfg.TRAIN.batch_size * len(cfg.TRAIN.gpus),
shuffle=False,
pin_memory=cfg.DATASET.PIN_MEMORY,
num_workers=cfg.DATASET.NUM_WORKERS,
worker_init_fn=val_dataset.train_worker_init_fn,
collate_fn=val_dataset.train_collate_fn,
)
logger.info(
"Loading val dataloader successfully! -- {}".format(time.time() - start_loading)
)
start_loading = time.time()
train_dataset = build_dataset(
cfg.DATASET, DATASETS, default_args=dict(split="train", config=cfg.DATASET)
)
train_dataloader = DataLoader(
train_dataset,
batch_size=cfg.TRAIN.batch_size * len(cfg.TRAIN.gpus),
shuffle=True,
pin_memory=cfg.DATASET.PIN_MEMORY,
num_workers=cfg.DATASET.NUM_WORKERS,
worker_init_fn=train_dataset.train_worker_init_fn,
collate_fn=train_dataset.train_collate_fn,
)
logger.info(
"Loading Train dataloader successfully! -- {}".format(
time.time() - start_loading
)
)
# Defining Loss function and Optimizer
critetion = build_losses(
cfg.TRAIN.loss, LOSSES, default_args=dict(cfg=cfg.TRAIN.loss)
).cuda()
if cfg.TRAIN.use_amp:
eff_lr = (
cfg.TRAIN.lr
* cfg.TRAIN.accumulation_steps
* cfg.TRAIN.batch_size
* len(cfg.TRAIN.gpus)
/ 64
) # 16*4=64 as default, might change
else:
eff_lr = cfg.TRAIN.lr
if cfg.TRAIN.optimizer == "Adam":
optimizer = optim.Adam(model.parameters(), lr=eff_lr, weight_decay=1e-4)
elif cfg.TRAIN.optimizer == "AdamW":
optimizer = optim.AdamW(
model.parameters(), lr=eff_lr, betas=(0.9, 0.999), weight_decay=1e-4
)
elif cfg.TRAIN.optimizer == "SAM":
# optimizer = SAM(model.parameters(), optim.Adam, lr=cfg.TRAIN.lr, weight_decay=1e-4)
optimizer = SAM(
model.parameters(),
optim.Adam,
lr=eff_lr,
betas=(0.9, 0.995),
weight_decay=1e-4,
)
else:
optimizer = optim.SGD(
model.parameters(), lr=eff_lr, weight_decay=1e-5, momentum=0.9
)
# Defining scaler
scaler = NativeScaler() if cfg.TRAIN.use_amp else None
# Loading model
model, optimizer, start_epoch, scaler = preset_model(
cfg, model, optimizer=optimizer, scaler=scaler
)
if len(cfg.TRAIN.gpus) > 0:
model = nn.DataParallel(model, device_ids=cfg.TRAIN.gpus).cuda()
else:
model = model.cuda()
# Learning rate Scheduler
if cfg.TRAIN.lr_scheduler == "MultiStepLR":
lr_scheduler = optim.lr_scheduler.MultiStepLR(
optimizer, **cfg.TRAIN.lr_scheduler
)
else:
lr_scheduler = LinearDecayLR(
optimizer,
cfg.TRAIN.epochs,
cfg.TRAIN.epochs // cfg.TRAIN.start_decay,
last_epoch=cfg.TRAIN.begin_epoch,
booster=cfg.TRAIN.booster,
)
# Enabling tensorboard
writer = SummaryWriter(
".tensorboard/{}_{}".format(datetime.today().strftime("%Y-%m-%d"), cfg.TASK)
)
trainIters = 0
valIters = 0
min_val_loss = 1e10
max_val_acc = 0
max_test_auc = 0
metrics_base = (
cfg.METRICS_BASE
) # Combine heatmap + cls prediction to calculate accuracy
# Starting training process
logger.info("Starting training process...")
for epoch in range(start_epoch, cfg.TRAIN.epochs):
# Unfreezin backbone to update weights
if cfg.TRAIN.freeze_backbone and epoch == cfg.TRAIN.warm_up:
unfreeze_backbone(model)
np.random.seed(seed + epoch)
if epoch > 0 and cfg.DATA_RELOAD:
logger.info(f"Reloading data for epoch {epoch}...")
train_dataset._reload_data(epoch=epoch)
train_dataloader = DataLoader(
train_dataset,
batch_size=cfg.TRAIN.batch_size * len(cfg.TRAIN.gpus),
shuffle=True,
pin_memory=cfg.DATASET.PIN_MEMORY,
num_workers=cfg.DATASET.NUM_WORKERS,
worker_init_fn=train_dataset.train_worker_init_fn,
collate_fn=train_dataset.train_collate_fn,
)
loss_avg, acc_avg, trainIters = train(
cfg,
model,
critetion,
optimizer,
epoch,
train_dataloader,
logger,
writer,
devices,
trainIters,
metrics_base=metrics_base,
scaler=scaler,
)
if epoch % cfg.TRAIN.every_val_epochs == 0:
loss_val, acc_val, valIters = validate(
cfg,
model,
critetion,
epoch,
val_dataloader,
logger,
writer,
devices,
valIters,
metrics_base=metrics_base,
)
if acc_val.avg > max_val_acc:
# Saving checkpoint
ckp_path = os.path.join(
LOG_DIR, "{}_{}_model_best.pth".format(cfg.MODEL.type, cfg.TASK)
)
save_model(path=ckp_path, epoch=epoch, model=model, optimizer=optimizer)
min_val_loss = loss_val.avg
max_val_acc = acc_val.avg
logger.info(f"Saved best model at epoch --- {epoch}")
lr_scheduler.step()
|