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Fix small bugs

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Xuanyi Dong 2019-04-08 11:04:08 +08:00
parent 3b1d8f1e4b
commit 36bb07ef1a
4 changed files with 13 additions and 6 deletions
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4
.gitignore vendored
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@ -103,3 +103,7 @@ main_main.py
scripts-nas/.nfs00* scripts-nas/.nfs00*
*/.nfs00* */.nfs00*
*.DS_Store *.DS_Store
# logs and snapshots
output
logs

6
exps-cnn/train_utils_imagenet.py
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@ -108,11 +108,11 @@ def main_procedure_imagenet(config, data_path, args, genotype, init_channels, la
for epoch in range(start_epoch, config.epochs): for epoch in range(start_epoch, config.epochs):
scheduler.step() scheduler.step()
need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size), log)
basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs) basemodel.update_drop_path(config.drop_path_prob * epoch / config.epochs)
need_time = convert_secs2time(epoch_time.val * (config.epochs-epoch), True)
print_log("\n==>>{:s} [Epoch={:03d}/{:03d}] {:s} LR={:6.4f} ~ {:6.4f}, Batch={:d}, Drop-Path-Prob={:}".format(time_string(), epoch, config.epochs, need_time, min(scheduler.get_lr()), max(scheduler.get_lr()), config.batch_size, basemodel.get_drop_path()), log)
train_acc1, train_acc5, train_los = _train(train_queue, model, criterion_smooth, optimizer, 'train', epoch, config, args.print_freq, log) train_acc1, train_acc5, train_los = _train(train_queue, model, criterion_smooth, optimizer, 'train', epoch, config, args.print_freq, log)
with torch.no_grad(): with torch.no_grad():

6
lib/datasets/get_dataset_with_transform.py
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@ -60,14 +60,14 @@ def get_datasets(name, root, cutout):
else: raise TypeError("Unknow dataset : {:}".format(name)) else: raise TypeError("Unknow dataset : {:}".format(name))
if name == 'cifar10': if name == 'cifar10':
train_data = dset.CIFAR10(root, train=True , transform=train_transform, download=True) train_data = dset.CIFAR10 (root, train=True , transform=train_transform, download=True)
test_data = dset.CIFAR10(root, train=False, transform=test_transform , download=True) test_data = dset.CIFAR10 (root, train=False, transform=test_transform , download=True)
elif name == 'cifar100': elif name == 'cifar100':
train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True) train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True) test_data = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
elif name == 'imagenet-1k' or name == 'imagenet-100': elif name == 'imagenet-1k' or name == 'imagenet-100':
train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform) train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
test_data = dset.ImageFolder(osp.join(root, 'val'), train_transform) test_data = dset.ImageFolder(osp.join(root, 'val'), test_transform)
else: raise TypeError("Unknow dataset : {:}".format(name)) else: raise TypeError("Unknow dataset : {:}".format(name))
class_num = Dataset2Class[name] class_num = Dataset2Class[name]

3
lib/nas/ImageNet.py
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@ -80,6 +80,9 @@ class NetworkImageNet(nn.Module):
def update_drop_path(self, drop_path_prob): def update_drop_path(self, drop_path_prob):
self.drop_path_prob = drop_path_prob self.drop_path_prob = drop_path_prob
def get_drop_path(self):
return self.drop_path_prob
def auxiliary_param(self): def auxiliary_param(self):
if self.auxiliary_head is None: return [] if self.auxiliary_head is None: return []
else: return list( self.auxiliary_head.parameters() ) else: return list( self.auxiliary_head.parameters() )