def main(args):
path = os.path.join(os.getcwd(), 'soft_label', 'soft_label_resnet50.txt')
if not os.path.isfile(path):
print('soft label file is not exist')
train_loader = getTrainLoader(args, path)
_, val_loader, num_query, num_classes, train_size = make_data_loader(args)
#train_loader, val_loader, num_query, num_classes, train_size = make_data_loader(args)
model = build_model(args, num_classes)
optimizer = make_optimizer(args, model)
scheduler = WarmupMultiStepLR(optimizer, [30, 55], 0.1, 0.01, 5, "linear")
loss_func = make_loss(args)
model.to(device)
for epoch in range(args.Epochs):
model.train()
running_loss = 0.0
running_klloss = 0.0
running_softloss = 0.0
running_corrects = 0.0
for index, data in enumerate(tqdm(train_loader)):
img, target, soft_target = data
img = img.cuda()
target = target.cuda()
soft_target = soft_target.cuda()
score, _ = model(img)
preds = torch.max(score.data, 1)[1]
loss, klloss, softloss = loss_func(score, target, soft_target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
running_klloss += klloss.item()
running_softloss += softloss.item()
running_corrects += float(torch.sum(preds == target.data))
scheduler.step()
epoch_loss = running_loss / train_size
epoch_klloss = running_klloss / train_size
epoch_softloss = running_softloss / train_size
epoch_acc = running_corrects / train_size
print(
"Epoch {} Loss : {:.4f} KLLoss:{:.8f} SoftLoss:{:.4f} Acc:{:.4f}"
.format(epoch, epoch_loss, epoch_klloss, epoch_softloss,
epoch_acc))
if (epoch + 1) % args.n_save == 0:
evaluator = Evaluator(model, val_loader, num_query)
cmc, mAP = evaluator.run()
print('---------------------------')
print("CMC Curve:")
for r in [1, 5, 10]:
print("Rank-{} : {:.1%}".format(r, cmc[r - 1]))
print("mAP : {:.1%}".format(mAP))
print('---------------------------')
save_model(args, model, optimizer, epoch)
def main():
filename = 'main.yeet'
file = open(filename, 'r')
lexer = Lexer(file)
parse = Parse(lexer.tokens)
lexer.tokenizer()
# print("Tokens: ")
# print(lexer.tokens, "\n")
parse.build_AST()
# print("AST:")
# print (parse.AST, "\n")
evaluator = Evaluator(parse.AST)
print("the f*****g output:")
evaluator.run(parse.AST)
def main(args):
sys.stdout = Logger(
os.path.join(args.log_path, args.log_description,
'log' + time.strftime(".%m_%d_%H:%M:%S") + '.txt'))
train_loader, val_loader, num_query, num_classes, train_size = make_data_loader(
args)
model = build_model(args, num_classes)
print(model)
optimizer = make_optimizer(args, model)
scheduler = WarmupMultiStepLR(optimizer, [30, 55], 0.1, 0.01, 5, "linear")
loss_func = make_loss(args)
model.to(device)
for epoch in range(args.Epochs):
model.train()
running_loss = 0.0
running_corrects = 0.0
for index, data in enumerate(tqdm(train_loader)):
img, target = data
img = img.cuda()
target = target.cuda()
score, _ = model(img)
preds = torch.max(score.data, 1)[1]
loss = loss_func(score, target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
running_loss += loss.item()
running_corrects += float(torch.sum(preds == target.data))
scheduler.step()
epoch_loss = running_loss / train_size
epoch_acc = running_corrects / train_size
print("Epoch {} Loss : {:.6f} Acc:{:.4f}".format(
epoch, epoch_loss, epoch_acc))
if (epoch + 1) % args.n_save == 0:
evaluator = Evaluator(model, val_loader, num_query)
cmc, mAP = evaluator.run()
print('---------------------------')
print("CMC Curve:")
for r in [1, 5, 10]:
print("Rank-{} : {:.1%}".format(r, cmc[r - 1]))
print("mAP : {:.1%}".format(mAP))
print('---------------------------')
save_model(args, model, optimizer, epoch)
def main(args):
train_loader, val_loader, num_query, num_classes, train_size = make_data_loader(
args)
#load the parameters
net = Net(reid=True)
state_dict = torch.load(
'./ckpt.t7', map_location=lambda storage, loc: storage)['net_dict']
net.load_state_dict(state_dict)
evaluator = Evaluator(net, val_loader, num_query)
cmc, mAP = evaluator.run()
print('---------------------------')
print("CMC Curve:")
for r in [1, 5, 10]:
print("Rank-{} : {:.1%}".format(r, cmc[r - 1]))
print("mAP : {:.1%}".format(mAP))
print('---------------------------')
