def test(): if args.cuda: print('use cuda') cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") # load net input_size = [args.input_size, args.input_size] num_classes = 20 testset = VOCDetection(VOC_ROOT, img_size=None, image_sets=[('2007', 'test')], transform=None) # build model if args.version == 'yolo': from models.yolo import myYOLO net = myYOLO(device, input_size=input_size, num_classes=num_classes, trainable=False) print('Let us test yolo on the VOC0712 dataset ......') else: print('Unknown Version !!!') exit() net.load_state_dict(torch.load(args.trained_model, map_location=device)) net.eval() print('Finished loading model!') net = net.to(device) # evaluation test_net(net, device, testset, BaseTransform(net.input_size), thresh=args.visual_threshold)
def test(): # get device if args.cuda: cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") # load net num_classes = 80 if args.dataset == 'COCO_val': cfg = config.coco_af input_size = cfg['min_dim'] testset = COCODataset(data_dir=args.dataset_root, json_file='instances_val2017.json', name='val2017', img_size=cfg['min_dim'][0], debug=args.debug) elif args.dataset == 'COCO_test-dev': cfg = config.coco_af input_size = cfg['min_dim'] testset = COCODataset(data_dir=args.dataset_root, json_file='image_info_test-dev2017.json', name='test2017', img_size=cfg['min_dim'][0], debug=args.debug) elif args.dataset == 'VOC': cfg = config.voc_af input_size = cfg['min_dim'] testset = VOCDetection(VOC_ROOT, [('2007', 'test')], None, VOCAnnotationTransform()) # build model if args.version == 'yolo': from models.yolo import myYOLO net = myYOLO(device, input_size=input_size, num_classes=num_classes, trainable=False) print('Let us test YOLO on the %s dataset ......' % (args.dataset)) else: print('Unknown Version !!!') exit() net.load_state_dict(torch.load(args.trained_model, map_location=device)) net.to(device).eval() print('Finished loading model!') # evaluation test_net(net, device, testset, BaseTransform(net.input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)), thresh=args.visual_threshold)
def test(): if args.cuda: print('use cuda') cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") # load net cfg = config.voc_af input_size = cfg['min_dim'] num_classes = len(VOC_CLASSES) testset = VOCDetection(args.voc_root, [('2007', 'test')], None, VOCAnnotationTransform()) # build model if args.version == 'yolo': from models.yolo import myYOLO net = myYOLO(device, input_size=input_size, num_classes=num_classes, trainable=False) print('Let us test yolo on the VOC0712 dataset ......') else: print('Unknown Version !!!') exit() net.load_state_dict(torch.load(args.trained_model, map_location=device)) net.eval() print('Finished loading model!') net = net.to(device) # evaluation test_net(net, device, testset, BaseTransform(net.input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)), thresh=args.visual_threshold)
if __name__ == '__main__': if args.cuda: print('use cuda') torch.backends.cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") num_classes = args.num_classes input_size = [args.input_size, args.input_size] if args.version == 'yolo': from models.yolo import myYOLO model = myYOLO(device, input_size=input_size, num_classes=num_classes, trainable=False) print('Let us evaluate YOLO on the COCO dataset ......') else: print('Unknown Version !!!') exit() # load model model.load_state_dict(torch.load(args.trained_model, map_location=device)) model.eval().to(device) print('Finished loading model!') test(model, device, input_size)
def train(): args = parse_args() path_to_save = os.path.join(args.save_folder, args.version) os.makedirs(path_to_save, exist_ok=True) hr = False if args.high_resolution: print('use hi-res backbone') hr = True cfg = voc_af if args.cuda: print('use cuda') cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") # use multi-scale trick if args.multi_scale: print('use multi-scale trick.') input_size = [608, 608] dataset = VOCDetection(root=args.dataset_root, transform=SSDAugmentation([608, 608], mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229))) else: input_size = cfg['min_dim'] dataset = VOCDetection(root=args.dataset_root, transform=SSDAugmentation(cfg['min_dim'], mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229))) # build model if args.version == 'yolo': from models.yolo import myYOLO yolo_net = myYOLO(device, input_size=input_size, num_classes=args.num_classes, trainable=True, hr=hr) print('Let us train yolo on the VOC0712 dataset ......') else: print('We only support YOLO !!!') exit() # finetune the model trained on COCO if args.resume is not None: print('finetune COCO trained ') yolo_net.load_state_dict(torch.load(args.resume, map_location=device), strict=False) # use tfboard if args.tfboard: print('use tensorboard') from torch.utils.tensorboard import SummaryWriter c_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) log_path = os.path.join('log/voc/', args.version, c_time) os.makedirs(log_path, exist_ok=True) writer = SummaryWriter(log_path) print( "----------------------------------------Object Detection--------------------------------------------" ) model = yolo_net model.to(device) base_lr = args.lr tmp_lr = base_lr optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) # loss counters print("----------------------------------------------------------") print("Let's train OD network !") print('Training on:', dataset.name) print('The dataset size:', len(dataset)) print("----------------------------------------------------------") epoch_size = len(dataset) // args.batch_size max_epoch = cfg['max_epoch'] data_loader = data.DataLoader(dataset, args.batch_size, num_workers=args.num_workers, shuffle=True, collate_fn=detection_collate, pin_memory=True) # create batch iterator t0 = time.time() # start training for epoch in range(max_epoch): # use cos lr if args.cos and epoch > 20 and epoch <= max_epoch - 20: # use cos lr tmp_lr = 0.00001 + 0.5 * (base_lr - 0.00001) * ( 1 + math.cos(math.pi * (epoch - 20) * 1. / (max_epoch - 20))) set_lr(optimizer, tmp_lr) elif args.cos and epoch > max_epoch - 20: tmp_lr = 0.00001 set_lr(optimizer, tmp_lr) # use step lr else: if epoch in cfg['lr_epoch']: tmp_lr = tmp_lr * 0.1 set_lr(optimizer, tmp_lr) for iter_i, (images, targets) in enumerate(data_loader): # WarmUp strategy for learning rate if not args.no_warm_up: if epoch < args.wp_epoch: tmp_lr = base_lr * pow((iter_i + epoch * epoch_size) * 1. / (args.wp_epoch * epoch_size), 4) # tmp_lr = 1e-6 + (base_lr-1e-6) * (iter_i+epoch*epoch_size) / (epoch_size * (args.wp_epoch)) set_lr(optimizer, tmp_lr) elif epoch == args.wp_epoch and iter_i == 0: tmp_lr = base_lr set_lr(optimizer, tmp_lr) targets = [label.tolist() for label in targets] # make train label targets = tools.gt_creator(input_size=input_size, stride=yolo_net.stride, label_lists=targets) # to device images = images.to(device) targets = torch.tensor(targets).float().to(device) # forward and loss conf_loss, cls_loss, txtytwth_loss, total_loss = model( images, target=targets) # backprop and update total_loss.backward() optimizer.step() optimizer.zero_grad() if iter_i % 10 == 0: if args.tfboard: # viz loss writer.add_scalar('object loss', conf_loss.item(), iter_i + epoch * epoch_size) writer.add_scalar('class loss', cls_loss.item(), iter_i + epoch * epoch_size) writer.add_scalar('local loss', txtytwth_loss.item(), iter_i + epoch * epoch_size) t1 = time.time() print( '[Epoch %d/%d][Iter %d/%d][lr %.6f]' '[Loss: obj %.2f || cls %.2f || bbox %.2f || total %.2f || size %d || time: %.2f]' % (epoch + 1, max_epoch, iter_i, epoch_size, tmp_lr, conf_loss.item(), cls_loss.item(), txtytwth_loss.item(), total_loss.item(), input_size[0], t1 - t0), flush=True) t0 = time.time() # multi-scale trick if iter_i % 10 == 0 and iter_i > 0 and args.multi_scale: size = random.randint(10, 19) * 32 input_size = [size, size] # change input dim # But this operation will report bugs when we use more workers in data loader, so I have to use 0 workers. # I don't know how to make it suit more workers, and I'm trying to solve this question. data_loader.dataset.reset_transform( SSDAugmentation(input_size, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229))) if (epoch + 1) % 10 == 0: print('Saving state, epoch:', epoch + 1) torch.save( model.state_dict(), os.path.join(path_to_save, args.version + '_' + repr(epoch + 1) + '.pth'))
if __name__ == '__main__': global cfg cfg = coco_af if args.cuda: print('use cuda') torch.backends.cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") num_classes = args.num_classes if args.version == 'yolo': from models.yolo import myYOLO model = myYOLO(device, input_size=cfg['min_dim'], num_classes=num_classes, trainable=False) print('Let us evaluate YOLO on the COCO dataset ......') else: print('Unknown Version !!!') exit() # load model model.load_state_dict(torch.load(args.trained_model, map_location=device)) model.eval().to(device) print('Finished loading model!') test(model, device)
def train(): args = parse_args() path_to_save = os.path.join(args.save_folder, args.dataset, args.version) # path_to_save = os.path.join('weights', 'voc', 'yolov') os.makedirs(path_to_save, exist_ok=True) # use hi-res backbone if args.high_resolution: print('use hi-res backbone') hr = True else: hr = False # cuda if args.cuda: print('use cuda') cudnn.benchmark = True device = torch.device("cuda", 2) else: device = torch.device("cpu") # multi-scale if args.multi_scale: print('use the multi-scale trick ...') train_size = [640, 640] val_size = [416, 416] else: train_size = [416, 416] val_size = [416, 416] cfg = train_cfg # dataset and evaluator print("Setting Arguments.. : ", args) print("----------------------------------------------------------") print('Loading the dataset...') if args.dataset == 'voc': VOC_ROOT = '/home/liyi219/python/new-YOLOv1_PyTorch-master/VOCdevkit' data_dir = VOC_ROOT num_classes = 20 dataset = VOCDetection(root=data_dir, img_size=train_size[0], transform=SSDAugmentation(train_size)) evaluator = VOCAPIEvaluator(data_root=data_dir, img_size=val_size, device=device, transform=BaseTransform(val_size), labelmap=VOC_CLASSES) # elif args.dataset == 'coco': # data_dir = coco_root # num_classes = 80 # dataset = COCODataset( # data_dir=data_dir, # img_size=train_size[0], # transform=SSDAugmentation(train_size), # debug=args.debug # ) # # evaluator = COCOAPIEvaluator( # data_dir=data_dir, # img_size=val_size, # device=device, # transform=BaseTransform(val_size) # ) else: print('unknow dataset !! Only support voc and coco !!') exit(0) print('Training model on:', dataset.name) print('The dataset size:', len(dataset)) print("----------------------------------------------------------") # dataloader dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=detection_collate, num_workers=args.num_workers, pin_memory=True) """ dataloader = torch.utils.data.DataLoader( dataset, batch_size=32, shuffle=True, collate_fn=detection_collate, num_workers=8, pin_memory=True ) """ # build model if args.version == 'yolo': from models.yolo import myYOLO yolo_net = myYOLO(device, input_size=train_size, num_classes=num_classes, trainable=True) print('Let us train yolo on the %s dataset ......' % (args.dataset)) else: print('We only support YOLO !!!') exit() model = yolo_net model.to(device).train() # use tfboard if args.tfboard: print('use tensorboard') from torch.utils.tensorboard import SummaryWriter c_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) log_path = os.path.join('log/coco/', args.version, c_time) # log_path = os.path.join('log/coco/', 'yolo', c_time) os.makedirs(log_path, exist_ok=True) writer = SummaryWriter(log_path) # keep training if args.resume is not None: print('keep training model: %s' % (args.resume)) model.load_state_dict(torch.load(args.resume, map_location=device)) # model.load_state_dict(torch.load('keep training', map_location=device)) # optimizer setup base_lr = args.lr tmp_lr = base_lr optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) """ optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.9, weight_decay=5e-4 ) """ max_epoch = cfg['max_epoch'] epoch_size = len(dataset) // args.batch_size # epoch_size = len(dataset) // 32 # start training loop t0 = time.time() for epoch in range(args.start_epoch, max_epoch): # use cos lr if args.cos and epoch > 20 and epoch <= max_epoch - 20: # use cos lr tmp_lr = 0.00001 + 0.5 * (base_lr - 0.00001) * ( 1 + math.cos(math.pi * (epoch - 20) * 1. / (max_epoch - 20))) set_lr(optimizer, tmp_lr) elif args.cos and epoch > max_epoch - 20: tmp_lr = 0.00001 set_lr(optimizer, tmp_lr) # use step lr else: if epoch in cfg['lr_epoch']: tmp_lr = tmp_lr * 0.1 set_lr(optimizer, tmp_lr) for iter_i, (images, targets) in enumerate(dataloader): # WarmUp strategy for learning rate if not args.no_warm_up: if epoch < args.wp_epoch: tmp_lr = base_lr * pow((iter_i + epoch * epoch_size) * 1. / (args.wp_epoch * epoch_size), 4) # tmp_lr = base_lr * pow((iter_i + epoch * epoch_size) * 1. / (2 * epoch_size), 4) # tmp_lr = 1e-6 + (base_lr-1e-6) * (iter_i+epoch*epoch_size) / (epoch_size * (args.wp_epoch)) set_lr(optimizer, tmp_lr) elif epoch == args.wp_epoch and iter_i == 0: tmp_lr = base_lr set_lr(optimizer, tmp_lr) # to device images = images.to(device) # multi-scale trick if iter_i % 10 == 0 and iter_i > 0 and args.multi_scale: # randomly choose a new size size = random.randint(10, 19) * 32 train_size = [size, size] model.set_grid(train_size) if args.multi_scale: # interpolate images = torch.nn.functional.interpolate(images, size=train_size, mode='bilinear', align_corners=False) # make train label # 原本的targets里面只有bbox坐标和类别参数,用gt_creator函数加上置信度和权重参数 targets = [label.tolist() for label in targets] targets = tools.gt_creator(input_size=train_size, stride=yolo_net.stride, label_lists=targets) targets = torch.tensor(targets).float().to(device) # forward and loss conf_loss, cls_loss, txtytwth_loss, total_loss = model( images, target=targets) # backprop total_loss.backward() optimizer.step() optimizer.zero_grad() # display if iter_i % 10 == 0: if args.tfboard: # viz loss writer.add_scalar('object loss', conf_loss.item(), iter_i + epoch * epoch_size) writer.add_scalar('class loss', cls_loss.item(), iter_i + epoch * epoch_size) writer.add_scalar('local loss', txtytwth_loss.item(), iter_i + epoch * epoch_size) t1 = time.time() print( '[Epoch %d/%d][Iter %d/%d][lr %.6f]' '[Loss: obj %.2f || cls %.2f || bbox %.2f || total %.2f || size %d || time: %.2f]' % (epoch + 1, max_epoch, iter_i, epoch_size, tmp_lr, conf_loss.item(), cls_loss.item(), txtytwth_loss.item(), total_loss.item(), train_size[0], t1 - t0), flush=True) t0 = time.time() # evaluation if (epoch + 1) % args.eval_epoch == 0: model.trainable = False model.set_grid(val_size) model.eval() # evaluate evaluator.evaluate(model) # convert to training mode. model.trainable = True model.set_grid(train_size) model.train() # save model if (epoch + 1) % 10 == 0: print('Saving state, epoch:', epoch + 1) torch.save( model.state_dict(), os.path.join(path_to_save, args.version + '_' + repr(epoch + 1) + '.pth'))
def train(): args = parse_args() data_dir = coco_root path_to_save = os.path.join(args.save_folder, args.version) os.makedirs(path_to_save, exist_ok=True) hr = False if args.high_resolution: print('use hi-res backbone') hr = True cfg = coco_af if args.cuda: print('use cuda') cudnn.benchmark = True device = torch.device("cuda") else: device = torch.device("cpu") if args.mosaic: print("use Mosaic Augmentation ...") # multi scale if args.multi_scale: print('Let us use the multi-scale trick.') input_size = [640, 640] else: input_size = [416, 416] print("Setting Arguments.. : ", args) print("----------------------------------------------------------") print('Loading the MSCOCO dataset...') # dataset dataset = COCODataset(data_dir=data_dir, img_size=input_size[0], transform=SSDAugmentation(input_size), debug=args.debug, mosaic=args.mosaic) # build model if args.version == 'yolo': from models.yolo import myYOLO yolo_net = myYOLO(device, input_size=input_size, num_classes=args.num_classes, trainable=True, hr=hr) print('Let us train yolo on the COCO dataset ......') else: print('We only support YOLO !!!') exit() print("----------------------------------------------------------") print('The dataset size:', len(dataset)) print("----------------------------------------------------------") # use tfboard if args.tfboard: print('use tensorboard') from torch.utils.tensorboard import SummaryWriter c_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) log_path = os.path.join('log/coco/', args.version, c_time) os.makedirs(log_path, exist_ok=True) writer = SummaryWriter(log_path) model = yolo_net model.to(device).train() dataloader = torch.utils.data.DataLoader(dataset, batch_size=args.batch_size, shuffle=True, collate_fn=detection_collate, num_workers=args.num_workers) evaluator = COCOAPIEvaluator(data_dir=data_dir, img_size=cfg['min_dim'], device=device, transform=BaseTransform(cfg['min_dim'])) # optimizer setup base_lr = args.lr tmp_lr = base_lr optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay) max_epoch = cfg['max_epoch'] epoch_size = len(dataset) // args.batch_size # start training loop t0 = time.time() for epoch in range(max_epoch): # use cos lr if args.cos and epoch > 20 and epoch <= max_epoch - 20: # use cos lr tmp_lr = 0.00001 + 0.5 * (base_lr - 0.00001) * ( 1 + math.cos(math.pi * (epoch - 20) * 1. / (max_epoch - 20))) set_lr(optimizer, tmp_lr) elif args.cos and epoch > max_epoch - 20: tmp_lr = 0.00001 set_lr(optimizer, tmp_lr) # use step lr else: if epoch in cfg['lr_epoch']: tmp_lr = tmp_lr * 0.1 set_lr(optimizer, tmp_lr) for iter_i, (images, targets) in enumerate(dataloader): # WarmUp strategy for learning rate if not args.no_warm_up: if epoch < args.wp_epoch: tmp_lr = base_lr * pow((iter_i + epoch * epoch_size) * 1. / (args.wp_epoch * epoch_size), 4) # tmp_lr = 1e-6 + (base_lr-1e-6) * (iter_i+epoch*epoch_size) / (epoch_size * (args.wp_epoch)) set_lr(optimizer, tmp_lr) elif epoch == args.wp_epoch and iter_i == 0: tmp_lr = base_lr set_lr(optimizer, tmp_lr) # to device images = images.to(device) # multi-scale trick if iter_i % 10 == 0 and iter_i > 0 and args.multi_scale: # randomly choose a new size size = random.randint(10, 19) * 32 input_size = [size, size] model.set_grid(input_size) if args.multi_scale: # interpolate images = torch.nn.functional.interpolate(images, size=input_size, mode='bilinear', align_corners=False) # make labels targets = [label.tolist() for label in targets] targets = tools.gt_creator(input_size=input_size, stride=yolo_net.stride, label_lists=targets) targets = torch.tensor(targets).float().to(device) # forward and loss conf_loss, cls_loss, txtytwth_loss, total_loss = model( images, target=targets) # backprop total_loss.backward() optimizer.step() optimizer.zero_grad() if args.tfboard: # viz loss writer.add_scalar('object loss', conf_loss.item(), iter_i + epoch * epoch_size) writer.add_scalar('class loss', cls_loss.item(), iter_i + epoch * epoch_size) writer.add_scalar('local loss', txtytwth_loss.item(), iter_i + epoch * epoch_size) if iter_i % 10 == 0: t1 = time.time() print( '[Epoch %d/%d][Iter %d/%d][lr %.6f]' '[Loss: obj %.2f || cls %.2f || bbox %.2f || total %.2f || size %d || time: %.2f]' % (epoch + 1, max_epoch, iter_i, epoch_size, tmp_lr, conf_loss.item(), cls_loss.item(), txtytwth_loss.item(), total_loss.item(), input_size[0], t1 - t0), flush=True) t0 = time.time() if (epoch + 1) % 10 == 0: print('Saving state, epoch:', epoch + 1) torch.save( model.state_dict(), os.path.join(path_to_save, args.version + '_' + repr(epoch + 1) + '.pth')) # COCO evaluation if (epoch + 1) % args.eval_epoch == 0: model.trainable = False model.set_grid(cfg['min_dim']) # evaluate ap50_95, ap50 = evaluator.evaluate(model) print('ap50 : ', ap50) print('ap50_95 : ', ap50_95) # convert to training mode. model.trainable = True model.train() if args.tfboard: writer.add_scalar('val/COCOAP50', ap50, epoch + 1) writer.add_scalar('val/COCOAP50_95', ap50_95, epoch + 1)