def evaluate():
model = nn.EfficientNet()
model.load_state_dict(torch.load(os.path.join('weights', 'best.pt'), map_location='cpu')['state_dict'])
model = model.to(device)
model.eval()
v_criterion = torch.nn.CrossEntropyLoss().to(device)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
v_dataset = datasets.ImageFolder(os.path.join(data_dir, 'val'),
transforms.Compose([transforms.Resize(416),
transforms.CenterCrop(384),
transforms.ToTensor(), normalize]))
v_loader = data.DataLoader(v_dataset, batch_size=64, shuffle=False,
num_workers=4, pin_memory=True)
top1 = util.AverageMeter()
top5 = util.AverageMeter()
with torch.no_grad():
for images, target in tqdm.tqdm(v_loader, ('%10s' * 2) % ('acc@1', 'acc@5')):
loss, acc1, acc5, output = batch_fn(images, target, model, v_criterion, False)
torch.cuda.synchronize()
top1.update(acc1.item(), images.size(0))
top5.update(acc5.item(), images.size(0))
acc1, acc5 = top1.avg, top5.avg
print('%10.3g' * 2 % (acc1, acc5))
torch.cuda.empty_cache()
def benchmark():
shape = (1, 3, version[2], version[2])
util.torch2onnx(
nn.EfficientNet(num_class, version[0], version[1],
version[3]).fuse().eval(), shape)
util.onnx2caffe()
util.print_benchmark(shape)
def test(model=None):
if model is None:
model = nn.EfficientNet()
model.load_state_dict(torch.load('weights/best.pt', 'cpu')['state_dict'])
model = model.cuda()
model.eval()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
dataset = datasets.ImageFolder(os.path.join(data_dir, 'val'),
transforms.Compose([transforms.Resize(416),
transforms.CenterCrop(384),
transforms.ToTensor(), normalize]))
loader = data.DataLoader(dataset, 48, num_workers=os.cpu_count(), pin_memory=True)
top1 = util.AverageMeter()
top5 = util.AverageMeter()
with torch.no_grad():
for images, target in tqdm.tqdm(loader, ('%10s' * 2) % ('acc@1', 'acc@5')):
acc1, acc5 = batch(images, target, model)
torch.cuda.synchronize()
top1.update(acc1.item(), images.size(0))
top5.update(acc5.item(), images.size(0))
acc1, acc5 = top1.avg, top5.avg
print('%10.3g' * 2 % (acc1, acc5))
if model is None:
torch.cuda.empty_cache()
else:
return acc1, acc5
def main():
epochs = 450
device = torch.device('cuda')
data_dir = '../Dataset/IMAGENET'
num_gpu = torch.cuda.device_count()
v_batch_size = 16 * num_gpu
t_batch_size = 256 * num_gpu
model = nn.EfficientNet(num_class, version[0], version[1],
version[3]).to(device)
optimizer = nn.RMSprop(util.add_weight_decay(model),
0.012 * num_gpu,
0.9,
1e-3,
momentum=0.9)
model = torch.nn.DataParallel(model)
_ = model(torch.zeros(1, 3, version[2], version[2]).to(device))
ema = nn.EMA(model)
t_criterion = nn.CrossEntropyLoss().to(device)
v_criterion = torch.nn.CrossEntropyLoss().to(device)
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
t_dataset = datasets.ImageFolder(
os.path.join(data_dir, 'train'),
transforms.Compose([
util.RandomResize(version[2]),
transforms.ColorJitter(0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(), normalize
]))
v_dataset = datasets.ImageFolder(
os.path.join(data_dir, 'val'),
transforms.Compose([
transforms.Resize(version[2] + 32),
transforms.CenterCrop(version[2]),
transforms.ToTensor(), normalize
]))
t_loader = data.DataLoader(t_dataset,
batch_size=t_batch_size,
shuffle=True,
num_workers=os.cpu_count(),
pin_memory=True)
v_loader = data.DataLoader(v_dataset,
batch_size=v_batch_size,
shuffle=False,
num_workers=os.cpu_count(),
pin_memory=True)
scheduler = nn.StepLR(optimizer)
amp_scale = torch.cuda.amp.GradScaler(enabled=torch.cuda.is_available())
with open(f'weights/{scheduler.__str__()}.csv', 'w') as summary:
writer = csv.DictWriter(
summary,
fieldnames=['epoch', 't_loss', 'v_loss', 'acc@1', 'acc@5'])
writer.writeheader()
best_acc1 = 0
for epoch in range(0, epochs):
print(('\n' + '%10s' * 2) % ('epoch', 'loss'))
t_bar = tqdm.tqdm(t_loader, total=len(t_loader))
model.train()
t_loss = util.AverageMeter()
v_loss = util.AverageMeter()
for images, target in t_bar:
loss, _, _, _ = batch_fn(images, target, model, device,
t_criterion)
optimizer.zero_grad()
amp_scale.scale(loss).backward()
amp_scale.step(optimizer)
amp_scale.update()
ema.update(model)
torch.cuda.synchronize()
t_loss.update(loss.item(), images.size(0))
t_bar.set_description(('%10s' + '%10.4g') %
('%g/%g' % (epoch + 1, epochs), loss))
top1 = util.AverageMeter()
top5 = util.AverageMeter()
ema_model = ema.model.eval()
with torch.no_grad():
for images, target in tqdm.tqdm(v_loader, ('%10s' * 2) %
('acc@1', 'acc@5')):
loss, acc1, acc5, output = batch_fn(
images, target, ema_model, device, v_criterion, False)
torch.cuda.synchronize()
v_loss.update(loss.item(), output.size(0))
top1.update(acc1.item(), images.size(0))
top5.update(acc5.item(), images.size(0))
acc1, acc5 = top1.avg, top5.avg
print('%10.3g' * 2 % (acc1, acc5))
scheduler.step(epoch + 1)
writer.writerow({
'epoch': epoch + 1,
't_loss': str(f'{t_loss.avg:.4f}'),
'v_loss': str(f'{v_loss.avg:.4f}'),
'acc@1': str(f'{acc1:.3f}'),
'acc@5': str(f'{acc5:.3f}')
})
util.save_checkpoint({'state_dict': ema.model.state_dict()},
acc1 > best_acc1)
best_acc1 = max(acc1, best_acc1)
torch.cuda.empty_cache()
def print_parameters():
model = nn.EfficientNet(num_class, version[0], version[1],
version[3]).fuse().eval()
_ = model(torch.zeros(1, 3, version[2], version[2]))
params = sum(p.numel() for p in model.parameters())
print('{:<20} {:<8}'.format('Number of parameters ', int(params)))
def benchmark():
shape = (1, 3, 384, 384)
util.torch2onnx(nn.EfficientNet().export().eval(), shape)
util.onnx2caffe()
util.print_benchmark(shape)
def print_parameters():
model = nn.EfficientNet().eval()
_ = model(torch.zeros(1, 3, 224, 224))
params = sum(p.numel() for p in model.parameters())
print(f'Number of parameters: {int(params)}')
def train(args):
epochs = 350
batch_size = 288
util.set_seeds(args.rank)
model = nn.EfficientNet().cuda()
lr = batch_size * torch.cuda.device_count() * 0.256 / 4096
optimizer = nn.RMSprop(util.add_weight_decay(model), lr, 0.9, 1e-3, momentum=0.9)
ema = nn.EMA(model)
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.local_rank])
else:
model = torch.nn.DataParallel(model)
criterion = nn.CrossEntropyLoss().cuda()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
dataset = datasets.ImageFolder(os.path.join(data_dir, 'train'),
transforms.Compose([util.RandomResize(),
transforms.ColorJitter(0.4, 0.4, 0.4),
transforms.RandomHorizontalFlip(),
util.RandomAugment(),
transforms.ToTensor(), normalize]))
if args.distributed:
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
else:
sampler = None
loader = data.DataLoader(dataset, batch_size, sampler=sampler, num_workers=8, pin_memory=True)
scheduler = nn.StepLR(optimizer)
amp_scale = torch.cuda.amp.GradScaler()
with open(f'weights/{scheduler.__str__()}.csv', 'w') as f:
if args.local_rank == 0:
writer = csv.DictWriter(f, fieldnames=['epoch', 'acc@1', 'acc@5'])
writer.writeheader()
best_acc1 = 0
for epoch in range(0, epochs):
if args.distributed:
sampler.set_epoch(epoch)
if args.local_rank == 0:
print(('\n' + '%10s' * 2) % ('epoch', 'loss'))
bar = tqdm.tqdm(loader, total=len(loader))
else:
bar = loader
model.train()
for images, target in bar:
loss = batch(images, target, model, criterion)
optimizer.zero_grad()
amp_scale.scale(loss).backward()
amp_scale.step(optimizer)
amp_scale.update()
ema.update(model)
torch.cuda.synchronize()
if args.local_rank == 0:
bar.set_description(('%10s' + '%10.4g') % ('%g/%g' % (epoch + 1, epochs), loss))
scheduler.step(epoch + 1)
if args.local_rank == 0:
acc1, acc5 = test(ema.model.eval())
writer.writerow({'acc@1': str(f'{acc1:.3f}'),
'acc@5': str(f'{acc5:.3f}'),
'epoch': str(epoch + 1).zfill(3)})
util.save_checkpoint({'state_dict': ema.model.state_dict()}, acc1 > best_acc1)
best_acc1 = max(acc1, best_acc1)
if args.distributed:
torch.distributed.destroy_process_group()
torch.cuda.empty_cache()
