def main():
torch.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
np.random.seed(1234)
setup_seed(1234)
print(args)
config = [
('conv2d', [64, 3, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 1, 0]),
('flatten', []),
('linear', [args.n_way, 64 * 5 * 5])
]
#device = torch.device('cuda')
maml = Meta(args, config)
# ------------------------------------------
dataset_train = MiniImageNet(phase='train')
dataset_val = MiniImageNet(phase='val')
data_loader = FewShotDataloader
dloader_train = data_loader(
dataset=dataset_train,
nKnovel=args.n_way,
nKbase=0,
nExemplars=args.k_spt, # num training examples per novel category
nTestNovel=args.n_way * args.k_spt, # num test examples for all the novel categories
nTestBase=0, # num test examples for all the base categories
batch_size=args.task_num,
num_workers=4,
epoch_size=args.task_num * 1000, # num of batches per epoch
)
dloader_val = data_loader(
dataset=dataset_val,
nKnovel=args.n_way,
nKbase=0,
nExemplars=args.k_qry, # num training examples per novel category
nTestNovel=args.k_qry * args.n_way, # num test examples for all the novel categories
nTestBase=0, # num test examples for all the base categories
batch_size=1,
num_workers=0,
epoch_size=1000, # num of batches per epoch
)
# ---------------------------------------------------
max_val_acc = 0
if not os.path.exists(os.path.join(args.save_path)):
os.makedirs(os.path.join(args.save_path))
for epoch in range(1, args.epoch+1):
# fetch meta_batchsz num of episode each time
train_accuracies = []
for i, batch in enumerate(tqdm(dloader_train(epoch)), 1):
data_support, labels_support, data_query, labels_query, _, _ = [x.cuda() for x in batch]
data_support = data_support.float()
data_query = data_query.float()
labels_support = labels_support.long()
labels_query = labels_query.long()
#x_spt, y_spt, x_qry, y_qry = x_spt.to(device), y_spt.to(device), x_qry.to(device), y_qry.to(device)
accs, loss_cls = maml.forward(data_support, labels_support, data_query, labels_query)
train_accuracies.append(accs)
if i % 100 == 0:
avg_accs = np.array(train_accuracies).mean(axis=0).astype(np.float16)* 100
print( 'Train Epoch: {} \tLoss_cls: {:.4f}'.format(i, loss_cls), '\ttraining acc:', avg_accs)
# Evaluate on the validation split
val_accuracies = []
for i, batch in enumerate(tqdm(dloader_val(epoch)), 1):
data_support, labels_support, data_query, labels_query, _, _ = [x.cuda() for x in batch]
data_support = data_support.float().squeeze(0)
data_query = data_query.float().squeeze(0)
labels_support = labels_support.long().squeeze(0)
labels_query = labels_query.long().squeeze(0)
accs = maml.finetunning(data_support, labels_support, data_query, labels_query)
val_accuracies.append(accs)
# [b, update_step+1]
val_acc_avg = np.array(val_accuracies).mean(axis=0).astype(np.float16)[-1]*100
maml.load_selfvars(data_support)
if val_acc_avg > max_val_acc:
max_val_acc = val_acc_avg
state = {'epoch': epoch + 1, 'model': maml.net.state_dict(),
'optimizer': maml.meta_optim.state_dict()}
torch.save(state
, os.path.join(args.save_path, 'best_model.pth.tar'.format(epoch)))
print( 'Validation Epoch: {}\t\t\tAccuracy: {:.2f} % (Best)' \
.format(epoch, val_acc_avg))
else:
print( 'Validation Epoch: {}\t\t\tAccuracy: {:.2f} %' \
.format(epoch, val_acc_avg))
if epoch % 2 == 0:
state = {'epoch': epoch + 1, 'model': maml.net.state_dict(),
'optimizer': maml.meta_optim.state_dict()}
torch.save(state
, os.path.join(args.save_path, 'epoch_{}.pth.tar'.format(epoch)))
print("=> loading checkpoint '{}'".format(filename))
checkpoint = torch.load(filename)
start_epoch = checkpoint['epoch']
embedding_net.load_state_dict(checkpoint['embedding'])
seg_head.load_state_dict(checkpoint['head'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
filename, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(filename))
return embedding_net, seg_head, optimizer, start_epoch
if __name__ == '__main__':
setup_seed(1234)
parser = argparse.ArgumentParser()
parser.add_argument('--cls-head',
type=str,
default='R2D2',
help='R2D2 or SVM')
parser.add_argument('--num-epoch',
type=int,
default=50,
help='number of training epochs')
parser.add_argument('--save-epoch',
type=int,
default=2,
help='frequency of model saving')
parser.add_argument('--train-shot',
type=int,
def main():
torch.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
np.random.seed(1234)
setup_seed(1234)
print(args)
config = [
('conv2d', [64, 3, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 1, 0]),
('flatten', []),
('linear', [args.n_way, 64 * 5 * 5])
]
#device = torch.device('cuda')
maml = Meta(args, config)
maml.net.load_state_dict(torch.load(args.load_path)['model'])
# ------------------------------------------
dataset_test = tieredImageNet(phase='test')
data_loader = FewShotDataloader
dloader_test = data_loader(
dataset=dataset_test,
nKnovel=args.n_way,
nKbase=0,
nExemplars=args.k_spt, # num training examples per novel category
nTestNovel=args.k_qry * args.n_way, # num test examples for all the novel categories
nTestBase=0, # num test examples for all the base categories
batch_size=1,
num_workers=1,
epoch_size=args.epoch, # num of batches per epoch
)
# ---------------------------------------------------
test_accuracies = []
for i, batch in enumerate(tqdm(dloader_test()), 1):
data_support, labels_support, data_query, labels_query, _, _ = [x.cuda() for x in batch]
data_support = data_support.float().squeeze(0)
data_query = data_query.float().squeeze(0)
labels_support = labels_support.long().squeeze(0)
labels_query = labels_query.long().squeeze(0)
accs = maml.finetunning(data_support, labels_support, data_query, labels_query)
test_accuracies.append(accs)
# [b, update_step+1]
accuracies = np.array(test_accuracies)
accuracies = accuracies[:,-1]*100
avg = np.array(accuracies).mean().astype(np.float16)
std = np.std(np.array(accuracies))
ci95 = 1.96 * std / np.sqrt(i + 1)
if i % 50 == 0:
print('Episode [{}/{}]:\t\t\tAccuracy: {:.2f} ± {:.2f} %)' \
.format(i, args.epoch, avg, ci95))
def main():
torch.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
np.random.seed(1234)
setup_seed(1234)
print(args)
config = [('conv2d', [64, 3, 3, 3, 1, 0]), ('relu', [True]), ('bn', [64]),
('max_pool2d', [2, 2, 0]), ('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]), ('bn', [64]), ('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]), ('relu', [True]), ('bn', [64]),
('max_pool2d', [2, 2, 0]), ('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]), ('bn', [64]), ('max_pool2d', [2, 1, 0]),
('flatten', []), ('linear', [args.n_way, 64 * 5 * 5])]
#device = torch.device('cuda')
maml = Meta(args, config)
max_val_acc = 0
if not os.path.exists(os.path.join(args.save_path)):
os.makedirs(os.path.join(args.save_path))
for epoch in range(1, args.epoch + 1):
# fetch meta_batchsz num of episode each time
train_accuracies = []
train_dataset = Office31Dataset(num_classes=args.n_way,
num_support=args.k_spt,
num_query=args.k_qry,
num_epoch=32,
phase='train')
dloader_train = DataLoader(train_dataset,
shuffle=True,
num_workers=32,
batch_size=args.task_num)
for i, batch in enumerate(tqdm(dloader_train)):
data_support, _, labels_support, data_query, _, labels_query = [
x.cuda() for x in batch
]
data_support = data_support.float()
data_query = data_query.float()
labels_support = labels_support.long()
labels_query = labels_query.long()
#x_spt, y_spt, x_qry, y_qry = x_spt.to(device), y_spt.to(device), x_qry.to(device), y_qry.to(device)
accs, loss_cls = maml.forward(data_support, labels_support,
data_query, labels_query)
train_accuracies.append(accs)
if i % 100 == 0:
avg_accs = np.array(train_accuracies).mean(axis=0).astype(
np.float16) * 100
print('Train Epoch: {} \tLoss_cls: {:.4f}'.format(i, loss_cls),
'\ttraining acc:', avg_accs)
# Evaluate on the validation split
val_accuracies = []
val_dataset = Office31Dataset(num_classes=args.n_way,
num_support=args.k_spt,
num_query=args.k_qry,
num_epoch=500,
phase='val')
dloader_val = DataLoader(val_dataset,
shuffle=False,
num_workers=32,
batch_size=1)
for i, batch in enumerate(tqdm(dloader_val)):
data_support, _, labels_support, data_query, _, labels_query = [
x.cuda() for x in batch
]
data_support = data_support.float().squeeze(0)
data_query = data_query.float().squeeze(0)
labels_support = labels_support.long().squeeze(0)
labels_query = labels_query.long().squeeze(0)
accs = maml.finetunning(data_support, labels_support, data_query,
labels_query)
val_accuracies.append(accs)
# [b, update_step+1]
val_acc_avg = np.array(val_accuracies).mean(axis=0).astype(
np.float16)[-1] * 100
maml.load_selfvars(data_support)
if val_acc_avg > max_val_acc:
max_val_acc = val_acc_avg
state = {
'epoch': epoch + 1,
'model': maml.net.state_dict(),
'optimizer': maml.meta_optim.state_dict()
}
torch.save(
state,
os.path.join(args.save_path,
'best_model.pth.tar'.format(epoch)))
print( 'Validation Epoch: {}\t\t\tAccuracy: {:.2f} % (Best)' \
.format(epoch, val_acc_avg))
else:
print( 'Validation Epoch: {}\t\t\tAccuracy: {:.2f} %' \
.format(epoch, val_acc_avg))
if epoch % 2 == 0:
state = {
'epoch': epoch + 1,
'model': maml.net.state_dict(),
'optimizer': maml.meta_optim.state_dict()
}
torch.save(
state,
os.path.join(args.save_path, 'epoch_{}.pth.tar'.format(epoch)))
def main():
torch.manual_seed(1234)
torch.cuda.manual_seed_all(1234)
np.random.seed(1234)
setup_seed(1234)
print(args)
config = [
('conv2d', [64, 3, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 2, 0]),
('conv2d', [64, 64, 3, 3, 1, 0]),
('relu', [True]),
('bn', [64]),
('max_pool2d', [2, 1, 0]),
('flatten', []),
('linear', [args.n_way, 64 * 5 * 5])
]
#device = torch.device('cuda')
maml = Meta(args, config)
maml.net.load_state_dict(torch.load(args.load_path)['model'])
# ---------------------------------------------------
test_dataset = Office31Dataset(num_classes=args.n_way, num_support=args.k_spt,
num_query=args.k_qry, num_epoch=args.epoch, phase='test')
dloader_test = DataLoader(test_dataset, shuffle=True, num_workers=32, batch_size= 1)
test_accuracies = []
for i, batch in enumerate(tqdm(dloader_test)):
data_support, _, labels_support, data_query, _, labels_query = [x.cuda() for x in batch]
data_support = data_support.float().squeeze(0)
data_query = data_query.float().squeeze(0)
labels_support = labels_support.long().squeeze(0)
labels_query = labels_query.long().squeeze(0)
accs = maml.finetunning(data_support, labels_support, data_query, labels_query)
test_accuracies.append(accs)
# [b, update_step+1]
accuracies = np.array(test_accuracies)
accuracies = 100*accuracies[:,-1]
avg = np.array(accuracies).mean().astype(np.float16)
std = np.std(np.array(accuracies))
ci95 = 1.96 * std / np.sqrt(i + 1)
if i % 50 == 0:
print('Episode [{}/{}]:\t\t\tAccuracy: {:.2f} ± {:.2f} %)' \
.format(i, args.epoch, avg, ci95))
