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main_torch.py
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main_torch.py
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import argparse
import shutil
import time
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torch.utils.data.distributed
import torchvision.transforms as transforms
import math
from torchvision.datasets import CIFAR10
from Models.ResNetTorch import ResNet20, ResNet32, ResNet44, ResNet56, ResNet110
import wandb
try:
import torch.cuda.amp as amp
except ImportError:
raise ImportError("Your version of PyTorch is too old.")
best_prec1 = 0
def parse():
parser = argparse.ArgumentParser(description="PyTorch CIFAR10 Training")
parser.add_argument(
"-data",
"--data",
default="ML/",
type=str,
metavar="DIR",
help="path to dataset",
)
parser.add_argument(
"-j",
"--workers",
default=4,
type=int,
metavar="N",
help="number of data loading workers (default: 4)",
)
parser.add_argument(
"--epochs",
default=180,
type=int,
metavar="N",
help="number of total epochs to run",
)
parser.add_argument(
"--start-epoch",
default=0,
type=int,
metavar="N",
help="manual epoch number (useful on restarts)",
)
parser.add_argument(
"-b",
"--batch-size",
default=128,
type=int,
metavar="N",
help="mini-batch size per process (default: 128)",
)
parser.add_argument(
"--weight-decay",
"--wd",
default=1e-4,
type=float,
metavar="W",
help="weight decay (default: 1e-4)",
)
parser.add_argument(
"--print-freq",
"-p",
default=100,
type=int,
metavar="N",
help="print frequency (default: 10)",
)
parser.add_argument("--local_rank", default=0, type=int)
# My additional args
parser.add_argument("--model", type=str, default="ResNet20")
parser.add_argument("--CIFAR10", type=bool, default=False)
parser.add_argument("--Mixed-Precision", type=bool, default=True)
parser.add_argument("--num-classes", type=int, default=10)
parser.add_argument("--step-lr", type=bool, default=True)
parser.add_argument("--base-lr", type=float, default=0.1)
args = parser.parse_args()
return args
def main():
global best_prec1, args
args = parse()
cudnn.benchmark = True
if torch.cuda.is_available():
if args.model == "ResNet20":
model = ResNet20()
elif args.model == "ResNet32":
model = ResNet32()
elif args.model == "ResNet44":
model = ResNet44()
elif args.model == "ResNet56":
model = ResNet56()
elif args.model == "ResNet110":
model = ResNet110()
model = model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
if args.cos_anneal:
assert args.step_lr == False
optimizer = create_optimizer(model, args.weight_decay, args.base_lr)
if args.step_lr:
assert args.cos_anneal == False
optimizer = create_optimizer(model, args.weight_decay, args.base_lr)
scheduler = torch.optim.lr_scheduler.MultiStepLR(
optimizer, milestones=[90, 130], gamma=0.1
)
if args.CIFAR10:
assert args.num_classes == 10, "Must have 10 output classes for CIFAR10"
# Use CIFAR-10 data augmentations
transform_train = transforms.Compose(
[
transforms.RandomCrop(
(32, 32),
padding=4,
fill=0,
padding_mode="constant",
),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616]
),
]
)
transform_test = transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize(
mean=[0.4914, 0.4822, 0.4465], std=[0.2470, 0.2435, 0.2616]
),
]
)
train_dataset = CIFAR10(
root="./CIFAR", train=True, download=True, transform=transform_train
)
train_dataset, validation_dataset = torch.utils.data.random_split(
train_dataset, [45000, 5000]
)
test_dataset = CIFAR10(
root="./CIFAR", train=False, download=True, transform=transform_test
)
train_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
)
test_loader = torch.utils.data.DataLoader(
test_dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
)
validation_loader = torch.utils.data.DataLoader(
validation_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
pin_memory=True,
)
# Setup WandB logging here
wandb_run = wandb.init(project="Flax Torch")
wandb.config.max_epochs = args.epochs
wandb.config.batch_size = args.batch_size
wandb.config.weight_decay = args.weight_decay
wandb.config.ModelName = args.model
wandb.config.Dataset = "CIFAR10"
wandb.config.Package = "PyTorch"
scaler = None
if args.Mixed_Precision:
scaler = amp.GradScaler()
for epoch in range(0, args.epochs):
if args.cos_anneal:
lr = adjust_learning_rate(optimizer, epoch, args)
scheduler = None
train_loss = train(
train_loader,
model,
criterion,
optimizer,
epoch,
scaler=scaler,
scheduler=scheduler,
)
if args.step_lr:
scheduler.step()
lr = (scheduler.get_last_lr())[0]
_, _, val_loss = validate(validation_loader, model, criterion)
if epoch % 10 == 0:
prec1, prec5, test_loss = validate(test_loader, model, criterion)
wandb.log(
{
"acc@1": prec1,
"Learning Rate": lr,
"Training Loss": train_loss,
"Validation Loss": val_loss,
}
)
else:
wandb.log(
{
"Learning Rate": lr,
"Training Loss": train_loss,
"Validation Loss": val_loss,
}
)
# remember best prec@1 and save checkpoint
if args.local_rank == 0:
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint(
{
"epoch": epoch + 1,
"state_dict": model.state_dict(),
"best_prec1": best_prec1,
"optimizer": optimizer.state_dict(),
},
is_best,
)
def train(train_loader, model, criterion, optimizer, epoch, scaler, scheduler=None):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to train mode
model.train()
for i, (images, target) in enumerate(train_loader):
optimizer.zero_grad()
if torch.cuda.is_available():
images = images.cuda()
target = target.cuda()
if scaler is not None:
with amp.autocast():
output = model(images)
loss = criterion(output, target)
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# Measure accuracy
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
reduced_loss = loss.item()
# to_python_float incurs a host<->device sync
losses.update((reduced_loss), images.size(0))
top1.update((prec1), images.size(0))
top5.update((prec5), images.size(0))
if i % args.print_freq == 0 and args.local_rank == 0:
print(
f"Epoch: [{epoch}][{i}/{len(train_loader)}]\t Loss {losses.test:.10f} ({losses.avg:.4f})\t Prec@1 {top1.test.item():.3f} ({top1.avg.item():.3f})Prec@5 {top5.test.item():.3f} ({top5.avg.item():.3f})"
)
return losses.avg
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.test = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, test, n=1):
self.test = test
self.sum += test * n
self.count += n
self.avg = self.sum / self.count
def validate(loader, model, criterion, scaler=None):
batch_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
# switch to evaluate mode
model.eval()
end = time.time()
for i, (images, target) in enumerate(loader):
# compute output
if torch.cuda.is_available():
images = images.cuda()
target = target.cuda()
with torch.no_grad():
output = model(images)
loss = criterion(output, target)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
reduced_loss = loss.item()
losses.update((reduced_loss), images.size(0))
top1.update((prec1), images.size(0))
top5.update((prec5), images.size(0))
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
print(f"* Prec@1 {top1.avg.item():.3f} Prec@5 {top5.avg.item():.3f}")
return top1.avg.item(), top5.avg.item(), losses.avg
def save_checkpoint(state, is_best, filename="checkpoint.pth.tar"):
torch.save(state, filename)
if is_best:
shutil.copyfile(filename, "model_best.pth.tar")
def accuracy(output, target, topk=(1,)):
"""Computes the precision@k for the specified testues of k"""
maxk = max(topk)
batch_size = target.size(0)
_, pred = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
res.append(correct_k.mul_(100.0 / batch_size))
return res
def adjust_learning_rate(optimizer, epoch, args):
lr = args.base_lr
if hasattr(args, "warmup") and epoch < args.warmup:
lr = lr / (args.warmup - epoch)
else:
lr *= 0.5 * (
1.0
+ math.cos(math.pi * (epoch - args.warmup) / (args.epochs - args.warmup))
)
for param_group in optimizer.param_groups:
param_group["lr"] = lr
# for tracking
return lr
def create_optimizer(model, weight_decay, lr):
params = []
for key, value in model.named_parameters():
if "fc.bias" in key or "bias" in key or "bn" in key:
print(f"No weight decay for paramater: {key}")
apply_weight_decay = 0
params += [
{"params": [value], "lr": lr, "weight_decay": apply_weight_decay}
]
else:
apply_weight_decay = weight_decay
params += [
{"params": [value], "lr": lr, "weight_decay": apply_weight_decay}
]
return torch.optim.SGD(params, lr, momentum=0.9, nesterov=True)
if __name__ == "__main__":
main()