def main():
# Step 1: init dataloader
print("init data loader")
args_data = {}
args_data['x_dim'] = x_dim
args_data['ratio'] = ratio
args_data['seed'] = seed
if dataset == 'mini':
loader_train = dataset_mini(n_examples, n_episodes, 'train', args_data)
loader_val = dataset_mini(n_examples, n_episodes, 'val', args_data)
elif dataset == 'tiered':
loader_train = dataset_tiered(n_examples, n_episodes, 'train',
args_data)
loader_val = dataset_tiered(n_examples, n_episodes, 'val', args_data)
if not pkl:
loader_train.load_data()
loader_val.load_data()
else:
loader_train.load_data_pkl()
loader_val.load_data_pkl()
# Step 2: init neural networks
print("init neural networks")
# construct the model
model = models.LabelPropagation(args)
model.cuda(0)
# optimizer
model_optim = torch.optim.Adam(model.parameters(), lr=lr)
model_scheduler = StepLR(model_optim, step_size=step_size, gamma=gamma)
# load the saved model
if iters > 0:
model.load_state_dict(
torch.load('checkpoints/%s/models/%s_%d_model.t7' %
(args['exp_name'], alg, iters)))
print('Loading Parameters from %s: %d' % (args['exp_name'], iters))
# Step 3: Train and validation
print("Training...")
best_acc = 0.0
best_loss = np.inf
wait = 0
for ep in range(iters, n_epochs):
loss_tr = []
ce_list = []
acc_tr = []
loss_val = []
acc_val = []
for epi in tqdm(range(n_episodes), desc='train_epoc:{}'.format(ep)):
model_scheduler.step(ep * n_episodes + epi)
# set train mode
model.train()
# sample data for next batch
support, s_labels, query, q_labels, unlabel = loader_train.next_data(
n_way, n_shot, n_query)
support = np.reshape(support,
(support.shape[0] * support.shape[1], ) +
support.shape[2:])
support = torch.from_numpy(np.transpose(support, (0, 3, 1, 2)))
query = np.reshape(query, (query.shape[0] * query.shape[1], ) +
query.shape[2:])
query = torch.from_numpy(np.transpose(query, (0, 3, 1, 2)))
s_labels = torch.from_numpy(np.reshape(s_labels, (-1, )))
q_labels = torch.from_numpy(np.reshape(q_labels, (-1, )))
s_labels = s_labels.type(torch.LongTensor)
q_labels = q_labels.type(torch.LongTensor)
s_onehot = torch.zeros(n_way * n_shot,
n_way).scatter_(1, s_labels.view(-1, 1), 1)
q_onehot = torch.zeros(n_way * n_query,
n_way).scatter_(1, q_labels.view(-1, 1), 1)
inputs = [
support.cuda(0),
s_onehot.cuda(0),
query.cuda(0),
q_onehot.cuda(0)
]
loss, acc = model(inputs)
loss_tr.append(loss.item())
acc_tr.append(acc.item())
model.zero_grad()
loss.backward()
#torch.nn.utils.clip_grad_norm(model.parameters(), 4.0)
model_optim.step()
for epi in tqdm(range(n_episodes), desc='val epoc:{}'.format(ep)):
# set eval mode
model.eval()
# sample data for next batch
support, s_labels, query, q_labels, unlabel = loader_val.next_data(
n_test_way, n_test_shot, n_test_query)
support = np.reshape(support,
(support.shape[0] * support.shape[1], ) +
support.shape[2:])
support = torch.from_numpy(np.transpose(support, (0, 3, 1, 2)))
query = np.reshape(query, (query.shape[0] * query.shape[1], ) +
query.shape[2:])
query = torch.from_numpy(np.transpose(query, (0, 3, 1, 2)))
s_labels = torch.from_numpy(np.reshape(s_labels, (-1, )))
q_labels = torch.from_numpy(np.reshape(q_labels, (-1, )))
s_labels = s_labels.type(torch.LongTensor)
q_labels = q_labels.type(torch.LongTensor)
s_onehot = torch.zeros(n_test_way * n_test_shot,
n_test_way).scatter_(
1, s_labels.view(-1, 1), 1)
q_onehot = torch.zeros(n_test_way * n_test_query,
n_test_way).scatter_(
1, q_labels.view(-1, 1), 1)
with torch.no_grad():
inputs = [
support.cuda(0),
s_onehot.cuda(0),
query.cuda(0),
q_onehot.cuda(0)
]
loss, acc = model(inputs)
loss_val.append(loss.item())
acc_val.append(acc.item())
print(
'epoch:{}, loss_tr:{:.5f}, acc_tr:{:.5f}, loss_val:{:.5f}, acc_val:{:.5f}'
.format(ep, np.mean(loss_tr), np.mean(acc_tr), np.mean(loss_val),
np.mean(acc_val)))
# Model Save and Stop Criterion
cond1 = (np.mean(acc_val) > best_acc)
cond2 = (np.mean(loss_val) < best_loss)
if cond1 or cond2:
best_acc = np.mean(acc_val)
best_loss = np.mean(loss_val)
print('best val loss:{:.5f}, acc:{:.5f}'.format(
best_loss, best_acc))
# save model
torch.save(
model.state_dict(), 'checkpoints/%s/models/%s_%d_model.t7' %
(args['exp_name'], alg, (ep + 1) * n_episodes))
f = open('checkpoints/' + args['exp_name'] + '/log.txt', 'a')
print('{} {:.5f} {:.5f}'.format((ep + 1) * n_episodes, best_loss,
best_acc),
file=f)
f.close()
wait = 0
else:
wait += 1
if ep % 100 == 0:
torch.save(
model.state_dict(),
'checkpoints/%s/models/%s_%d_model.t7' %
(args['exp_name'], alg, (ep + 1) * n_episodes))
f = open('checkpoints/' + args['exp_name'] + '/log.txt', 'a')
print('{} {:.5f} {:.5f}'.format((ep + 1) * n_episodes,
np.mean(loss_val),
np.mean(acc_val)),
file=f)
f.close()
if wait > patience and ep > n_epochs:
break
def main():
# init dataloader
print("init data loader")
args_data = {}
args_data['x_dim'] = '84,84,3'
args_data['ratio'] = 1.0
args_data['seed'] = seed
print('seed:',seed)
if dataset=='mini':
loader_test = dataset_mini(n_examples, n_episodes, 'test', args_data)
elif dataset=='tiered':
loader_test = dataset_tiered(n_examples, n_episodes, 'test', args_data)
if not pkl:
loader_test.load_data()
else:
loader_test.load_data_pkl()
# Step 2: init neural networks
print("init neural networks")
# construct the model
model = models.LabelPropagation(args)
model.cuda(0)
# load the saved model
if iters>0:
model.load_state_dict(torch.load('checkpoints/%s/models/%s_%d_model.t7' % (args['exp_name'], alg, iters)))
else:
a = torch.load('checkpoints/%s/models/%s_model_best_%s.t7' %(args['exp_name'], alg, part) )
#a[Relation]
model.load_state_dict(torch.load('checkpoints/%s/models/%s_model_best_%s.t7' %(args['exp_name'], alg, part) ))
print('Loading Parameters from %s' %(args['exp_name']))
# Step 3: build graph
print("Testing...")
all_acc = []
all_std = []
all_ci95 = []
ce_list = []
for rep in range(repeat):
list_acc = []
for epi in tqdm(range(n_test_episodes), desc='test:{}'.format(rep)):
model.eval()
# sample data for next batch
support, s_labels, query, q_labels, unlabel = loader_test.next_data(n_test_way, n_test_shot, n_test_query, train=False)
support = np.reshape(support, (support.shape[0]*support.shape[1],)+support.shape[2:])
support = torch.from_numpy(np.transpose(support, (0,3,1,2)))
query = np.reshape(query, (query.shape[0]*query.shape[1],)+query.shape[2:])
query = torch.from_numpy(np.transpose(query, (0,3,1,2)))
s_labels = torch.from_numpy(np.reshape(s_labels,(-1,)))
q_labels = torch.from_numpy(np.reshape(q_labels,(-1,)))
s_labels = s_labels.type(torch.LongTensor)
q_labels = q_labels.type(torch.LongTensor)
s_onehot = torch.zeros(n_test_way*n_test_shot, n_test_way).scatter_(1, s_labels.view(-1,1), 1)
q_onehot = torch.zeros(n_test_way*n_test_query, n_test_way).scatter_(1, q_labels.view(-1,1), 1)
with torch.no_grad():
inputs = [support.cuda(0), s_onehot.cuda(0), query.cuda(0), q_onehot.cuda(0)]
loss, acc = model(inputs)
list_acc.append(acc.item())
mean_acc = np.mean(list_acc)
std_acc = np.std(list_acc)
ci95 = 1.96*std_acc/np.sqrt(n_test_episodes)
m,ci = mean_confidence_interval(list_acc)
print('label, acc:{:.4f},std:{:.4f},ci95:{:.4f},ci:{:.4f}'.format(mean_acc, std_acc, ci95, ci))
all_acc.append(mean_acc)
all_std.append(std_acc)
all_ci95.append(ci95)
ind = np.argmax(all_acc)
print('Max acc:{:.5f}, std:{:.5f}, ci95: {:.5f}'.format(all_acc[ind], all_std[ind], all_ci95[ind]))
print('Avg over {} runs: mean:{:.5f}, std:{:.5f}, ci95: {:.5f}'.format(repeat,np.mean(all_acc),np.mean(all_std),np.mean(all_ci95)))