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cifar_eval.py
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cifar_eval.py
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import os
import time
import argparse
import torch
import torch.nn as nn
import torch.distributed as dist
from nets.eval_model import NetworkCIFAR
from utils.utils import count_parameters, count_flops, DisablePrint
from utils.dataset import CIFAR_split
from utils.preprocessing import cifar_search_transform
from utils.summary import create_summary, create_logger
torch.backends.cudnn.benchmark = True
# Training settings
parser = argparse.ArgumentParser(description='darts')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--dist', action='store_true')
parser.add_argument('--root_dir', type=str, default='./')
parser.add_argument('--data_dir', type=str, default='./data')
parser.add_argument('--log_name', type=str, default='test')
parser.add_argument('--lr', type=float, default=0.025)
parser.add_argument('--wd', type=float, default=3e-4)
parser.add_argument('--init_ch', type=int, default=36)
parser.add_argument('--num_cells', type=int, default=20)
parser.add_argument('--auxiliary', type=float, default=0.4)
parser.add_argument('--cutout', type=int, default=16)
parser.add_argument('--drop_path_prob', type=float, default=0.2)
parser.add_argument('--batch_size', type=int, default=96)
parser.add_argument('--max_epochs', type=int, default=600)
parser.add_argument('--log_interval', type=int, default=10)
parser.add_argument('--gpus', type=str, default='0')
parser.add_argument('--num_workers', type=int, default=3)
cfg = parser.parse_args()
os.chdir(cfg.root_dir)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.gpus
cfg.log_dir = os.path.join(cfg.root_dir, 'logs', cfg.log_name + '_eval')
cfg.ckpt_dir = os.path.join(cfg.root_dir, 'ckpt', cfg.log_name)
os.makedirs(cfg.log_dir, exist_ok=True)
os.makedirs(cfg.ckpt_dir, exist_ok=True)
def main():
logger = create_logger(cfg.local_rank, save_dir=cfg.log_dir)
summary_writer = create_summary(cfg.local_rank, log_dir=cfg.log_dir)
print = logger.info
print(cfg)
num_gpus = torch.cuda.device_count()
if cfg.dist:
device = torch.device('cuda:%d' % cfg.local_rank) if cfg.dist else torch.device('cuda')
torch.cuda.set_device(cfg.local_rank)
dist.init_process_group(backend='nccl', init_method='env://',
world_size=num_gpus, rank=cfg.local_rank)
else:
device = torch.device('cuda')
print('==> Preparing data..')
cifar = 100 if 'cifar100' in cfg.log_name else 10
train_dataset = CIFAR_split(cifar=cifar, root=cfg.data_dir, split='train', ratio=1.0,
transform=cifar_search_transform(is_training=True, cutout=cfg.cutout))
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset,
num_replicas=num_gpus,
rank=cfg.local_rank)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=cfg.batch_size // num_gpus if cfg.dist
else cfg.batch_size,
shuffle=not cfg.dist,
num_workers=cfg.num_workers,
sampler=train_sampler if cfg.dist else None)
test_dataset = CIFAR_split(cifar=cifar, root=cfg.data_dir, split='test',
transform=cifar_search_transform(is_training=False))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=cfg.batch_size,
shuffle=False,
num_workers=cfg.num_workers)
print('==> Building model..')
genotype = torch.load(os.path.join(cfg.ckpt_dir, 'genotype.pickle'))['genotype']
model = NetworkCIFAR(genotype, cfg.init_ch, cfg.num_cells, cfg.auxiliary, num_classes=cifar)
if not cfg.dist:
model = nn.DataParallel(model).to(device)
else:
# model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = model.to(device)
model = nn.parallel.DistributedDataParallel(model,
device_ids=[cfg.local_rank, ],
output_device=cfg.local_rank)
optimizer = torch.optim.SGD(model.parameters(), cfg.lr, momentum=0.9, weight_decay=cfg.wd)
criterion = nn.CrossEntropyLoss().to(device)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, cfg.max_epochs)
# Training
def train(epoch):
model.train()
start_time = time.time()
for batch_idx, (inputs, targets) in enumerate(train_loader):
inputs, targets = inputs.to(device), targets.to(device, non_blocking=True)
outputs, outputs_aux = model(inputs)
loss = criterion(outputs, targets)
loss_aux = criterion(outputs_aux, targets)
loss += cfg.auxiliary * loss_aux
optimizer.zero_grad()
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 5.0)
optimizer.step()
if batch_idx % cfg.log_interval == 0:
step = len(train_loader) * epoch + batch_idx
duration = time.time() - start_time
print('[%d/%d - %d/%d] cls_loss= %.5f (%d samples/sec)' %
(epoch, cfg.max_epochs, batch_idx, len(train_loader),
loss.item(), cfg.batch_size * cfg.log_interval / duration))
start_time = time.time()
summary_writer.add_scalar('cls_loss', loss.item(), step)
summary_writer.add_scalar('learning rate', optimizer.param_groups[0]['lr'], step)
def test(epoch):
model.eval()
correct = 0
with torch.no_grad():
for batch_idx, (inputs, targets) in enumerate(test_loader):
inputs, targets = inputs.to(device), targets.to(device, non_blocking=True)
outputs, _ = model(inputs)
_, predicted = torch.max(outputs.data, 1)
correct += predicted.eq(targets.data).cpu().sum().item()
acc = 100. * correct / len(test_loader.dataset)
print(' Precision@1 ==> %.2f%% \n' % acc)
summary_writer.add_scalar('Precision@1', acc, global_step=epoch)
return
for epoch in range(cfg.max_epochs):
print('\nEpoch: %d lr: %.5f drop_path_prob: %.3f' %
(epoch, scheduler.get_lr()[0], cfg.drop_path_prob * epoch / cfg.max_epochs))
model._modules['module'].drop_path_prob = cfg.drop_path_prob * epoch / cfg.max_epochs
train_sampler.set_epoch(epoch)
train(epoch)
test(epoch)
scheduler.step(epoch) # move to here after pytorch1.1.0
print(model.module.genotype())
if cfg.local_rank == 0:
torch.save(model.state_dict(), os.path.join(cfg.ckpt_dir, 'checkpoint.t7'))
summary_writer.close()
count_parameters(model)
count_flops(model, input_size=32)
if __name__ == '__main__':
if cfg.local_rank == 0:
main()
else:
with DisablePrint():
main()