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('---------------------------')