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Update test weights and shapes

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This commit is contained in:
D-X-Y
2020-03-20 23:38:47 -07:00
parent d8784b3070
commit b702ddf5a2
5 changed files with 115 additions and 64 deletions
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14
lib/procedures/funcs_nasbench.py
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@@ -17,13 +17,13 @@ __all__ = ['evaluate_for_seed', 'pure_evaluate', 'get_nas_bench_loaders']
def pure_evaluate(xloader, network, criterion=torch.nn.CrossEntropyLoss()):
data_time, batch_time, batch = AverageMeter(), AverageMeter(), None
losses, top1, top5 = AverageMeter(), AverageMeter(), AverageMeter()
latencies = []
latencies, device = [], torch.cuda.current_device()
network.eval()
with torch.no_grad():
end = time.time()
for i, (inputs, targets) in enumerate(xloader):
targets = targets.cuda(non_blocking=True)
inputs = inputs.cuda(non_blocking=True)
targets = targets.cuda(device=device, non_blocking=True)
inputs = inputs.cuda(device=device, non_blocking=True)
data_time.update(time.time() - end)
# forward
features, logits = network(inputs)
@@ -48,12 +48,12 @@ def procedure(xloader, network, criterion, scheduler, optimizer, mode: str):
if mode == 'train' : network.train()
elif mode == 'valid': network.eval()
else: raise ValueError("The mode is not right : {:}".format(mode))
device = torch.cuda.current_device()
data_time, batch_time, end = AverageMeter(), AverageMeter(), time.time()
for i, (inputs, targets) in enumerate(xloader):
if mode == 'train': scheduler.update(None, 1.0 * i / len(xloader))
targets = targets.cuda(non_blocking=True)
targets = targets.cuda(device=device, non_blocking=True)
if mode == 'train': optimizer.zero_grad()
# forward
features, logits = network(inputs)
@@ -84,7 +84,9 @@ def evaluate_for_seed(arch_config, opt_config, train_loader, valid_loaders, seed
logger.log('FLOP = {:} MB, Param = {:} MB'.format(flop, param))
# train and valid
optimizer, scheduler, criterion = get_optim_scheduler(net.parameters(), opt_config)
network, criterion = torch.nn.DataParallel(net).cuda(), criterion.cuda()
default_device = torch.cuda.current_device()
network = torch.nn.DataParallel(net, device_ids=[default_device]).cuda(device=default_device)
criterion = criterion.cuda(device=default_device)
# start training
start_time, epoch_time, total_epoch = time.time(), AverageMeter(), opt_config.epochs + opt_config.warmup
train_losses, train_acc1es, train_acc5es, valid_losses, valid_acc1es, valid_acc5es = {}, {}, {}, {}, {}, {}