trlog['max_acc'] = 0.0
trlog['max_acc_epoch'] = 0
trlog['cur_train_epoch'] = 0
print("start new training")
start_epoch = trlog['cur_train_epoch'] + 1
print(f"start training from epoch: {start_epoch}")
timer = Timer()
global_count = 0
writer = SummaryWriter(logdir=args.save_path)
for epoch in range(start_epoch, args.max_epoch + 1):
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
label = torch.arange(args.way).repeat(args.query)
if torch.cuda.is_available():
label = label.type(torch.cuda.LongTensor)
else:
label = label.type(torch.LongTensor)
for i, batch in enumerate(train_loader, 1):
global_count = global_count + 1
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
p = args.shot * args.way
torch.backends.cudnn.benchmark = True
model = model.cuda()
test_set = Dataset('test', args)
sampler = CategoriesSampler(test_set.label, 10000, args.way,
args.shot + args.query)
loader = DataLoader(test_set,
batch_sampler=sampler,
num_workers=8,
pin_memory=True)
test_acc_record = np.zeros((10000, ))
model.load_state_dict(torch.load(args.model_path)['params'])
model.eval()
ave_acc = Averager()
label = torch.arange(args.way).repeat(args.query)
if torch.cuda.is_available():
label = label.type(torch.cuda.LongTensor)
else:
label = label.type(torch.LongTensor)
for i, batch in enumerate(loader, 1):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
k = args.way * args.shot
data_shot, data_query = data[:k], data[k:]
logits, _ = model(data_shot, data_query)
acc = count_acc(logits, label)
trlog['args'] = vars(args)
trlog['train_loss'] = []
trlog['val_loss'] = []
trlog['train_acc'] = []
trlog['val_acc'] = []
trlog['max_acc'] = 0.0
trlog['max_acc_epoch'] = 0
timer = Timer()
global_count = 0
writer = SummaryWriter(logdir=args.save_path)
for epoch in range(1, args.max_epoch + 1):
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
label = torch.arange(args.way).repeat(args.query)
if torch.cuda.is_available():
label = label.type(torch.cuda.LongTensor)
else:
label = label.type(torch.LongTensor)
for i, batch in enumerate(train_loader, 1):
print("\n\nbatch: ", len(batch))
global_count = global_count + 1
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
label = torch.arange(args.way, dtype=torch.int8).repeat(args.query)
#label.shape[0] = n_way * n_q uery
att_label = torch.zeros(label.shape[0], args.way + 1, args.way + 1)
for i in range(att_label.shape[0]):
att_label[i,:] = att_label_basis[label[i].item()] #每一项是一个矩阵
label = label.type(torch.LongTensor)
if torch.cuda.is_available():
label = label.cuda()
att_label = att_label.cuda()
for epoch in range(1, args.max_epoch + 1):
lr_scheduler.step()
model.train()
tl = Averager()
ta = Averager()
for i, batch in enumerate(train_loader, 1): #idx start from 1
global_count = global_count + 1
#is batch[1] label ?
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
p = args.shot * args.way
data_shot, data_query = data[:p], data[p:]
logits, att = model(data_shot, data_query)
loss = F.cross_entropy(logits, label)
acc = count_acc(logits, label)
writer.add_scalar('data/loss', float(loss), global_count)
else:
raise ValueError('Non-supported Dataset.')
model = ProtoNet(args)
if torch.cuda.is_available():
torch.backends.cudnn.benchmark = True
model = model.cuda()
test_set = Dataset('test', args)
sampler = CategoriesSampler(test_set.label, 10000, args.way, args.shot + args.query)
loader = DataLoader(test_set, batch_sampler=sampler, num_workers=8, pin_memory=True)
test_acc_record = np.zeros((10000,))
model.load_state_dict(torch.load(args.model_path)['params'])
model.eval()
ave_acc = Averager()
label = torch.arange(args.way).repeat(args.query)
if torch.cuda.is_available():
label = label.type(torch.cuda.LongTensor)
else:
label = label.type(torch.LongTensor)
with torch.no_grad():
for i, batch in enumerate(loader, 1):
if torch.cuda.is_available():
data, _ = [_.cuda() for _ in batch]
else:
data = batch[0]
k = args.way * args.shot
data_shot, data_query = data[:k], data[k:]