def Val_Dataset(self, root_dir, coco_dir, img_dir, set_dir): self.system_dict["dataset"]["val"]["status"] = True self.system_dict["dataset"]["val"]["root_dir"] = root_dir self.system_dict["dataset"]["val"]["coco_dir"] = coco_dir self.system_dict["dataset"]["val"]["img_dir"] = img_dir self.system_dict["dataset"]["val"]["set_dir"] = set_dir self.system_dict["local"]["dataset_val"] = CocoDataset( self.system_dict["dataset"]["val"]["root_dir"] + "/" + self.system_dict["dataset"]["val"]["coco_dir"], img_dir=self.system_dict["dataset"]["val"]["img_dir"], set_dir=self.system_dict["dataset"]["val"]["set_dir"], transform=transforms.Compose([Normalizer(), Resizer()])) self.system_dict["local"]["sampler_val"] = AspectRatioBasedSampler( self.system_dict["local"]["dataset_val"], batch_size=self.system_dict["params"]["batch_size"], drop_last=False) self.system_dict["local"]["dataloader_val"] = DataLoader( self.system_dict["local"]["dataset_val"], num_workers=self.system_dict["params"]["num_workers"], collate_fn=collater, batch_sampler=self.system_dict["local"]["sampler_val"]) print('Num validation images: {}'.format( len(self.system_dict["local"]["dataset_val"])))
def init_dataloader(the_dataset, batch_size=1, num_worker=1): ''' 创建数据加载对象 参数: image_csv_file: 需要测试图片信息,可以包含标注信息,也可以不包括,例如: ./dataset/chongyin/15050400/2_d68e121b909ee9c2.jpg,,,,,, ./dataset/biguashikongtiao/176249224_0070163647_1.jpg,18,98,377,233,biguashikongtiao class_csv_file: label信息,例如: baozhuang,0 biguashikongtiao,1 kongtiaoshan,2 liguishikongtiao,3 yaokongqi,4 batch_size : 设置batch_size大小 num_worker: 加载训练数据的线程数量 ''' # 初始化Sampler对象 the_sampler = AspectRatioBasedSampler(the_dataset, batch_size=batch_size, drop_last=False) # 创建dataloader对象 the_dataloader = DataLoader(the_dataset, num_workers=num_worker, collate_fn=collater, batch_sampler=the_sampler) return the_dataloader
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--coco_path', help='Path to COCO directory') parser = parser.parse_args(args) os.makedirs('after_augmentation_image_sample', exist_ok=True) set_name = 'test' dataset_sample = CocoDataset( parser.coco_path, set_name=set_name, # transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) transform=transforms.Compose( [Normalizer(), AugmenterWithImgaug(), Resizer()])) sampler = AspectRatioBasedSampler(dataset_sample, batch_size=1, drop_last=False) dataloader_sample = DataLoader(dataset_sample, num_workers=1, collate_fn=collater, batch_sampler=sampler) unnormalize = UnNormalizer() for idx, data in enumerate(dataloader_sample): img = np.array(255 * unnormalize(data['img'][0, :, :, :])).copy() img[img < 0] = 0 img[img > 255] = 255 img = np.transpose(img, (1, 2, 0)) img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB) for annot in data['annot']: annot = annot[0].data.numpy() x1 = int(annot[0]) y1 = int(annot[1]) x2 = int(annot[2]) y2 = int(annot[3]) cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2) cv2.imwrite( 'D:\\StreetView\\RenHong\\Pytorch_RetinaNet\\after_augmentation_image_sample\\' + 'sample_from_({})_'.format(set_name) + str(idx) + '.jpg', img) print("finish")
def hidden_distance_retina(model, path, class_names, dist_type, save_dir, attack_type='Randn', eps=2, batch_size=8): dataset = CSVDataset(train_file=path, class_list=class_names, transform=transforms.Compose([Resizer()])) sampler = AspectRatioBasedSampler(dataset, batch_size=batch_size, drop_last=False) dataloader = DataLoader(dataset, num_workers=1, collate_fn=collater, batch_sampler=sampler) Tensor = torch.cuda.FloatTensor if torch.cuda.is_available( ) else torch.FloatTensor print("Getting activations for distance type:", dist_type, "with attack_type: ", attack_type, "and epsilon: ", eps) model.eval() final_activations = {} iters = 0 for batch_i, data in enumerate(tqdm.tqdm(dataloader)): imgs = data['img'] imgs = Variable(imgs.type(Tensor), requires_grad=False) acts = model(imgs, send_activations=True) if attack_type == 'Randn': noi_imgs = generate_noisy_image(imgs, eps) elif attack_type == 'FGSM': model.train() model.training = True noi_imgs = generate_fgsm_image(model, imgs, data['annot'], eps, model_type="retina") model.training = False model.eval() noi_acts = model(noi_imgs, send_activations=True) final_activations = compute_loss(acts, dist_type, noi_acts, final_activations) iters += 1 all_acts = { layer: (act / iters) for layer, act in final_activations.items() } output_to_txt(all_acts, save_dir)
def Train_Dataset(self, root_dir, coco_dir, img_dir, set_dir, batch_size=8, image_size=512, use_gpu=True, num_workers=3): self.system_dict["dataset"]["train"]["root_dir"] = root_dir self.system_dict["dataset"]["train"]["coco_dir"] = coco_dir self.system_dict["dataset"]["train"]["img_dir"] = img_dir self.system_dict["dataset"]["train"]["set_dir"] = set_dir self.system_dict["params"]["batch_size"] = batch_size self.system_dict["params"]["image_size"] = image_size self.system_dict["params"]["use_gpu"] = use_gpu self.system_dict["params"]["num_workers"] = num_workers self.system_dict["local"]["dataset_train"] = CocoDataset( self.system_dict["dataset"]["train"]["root_dir"] + "/" + self.system_dict["dataset"]["train"]["coco_dir"], img_dir=self.system_dict["dataset"]["train"]["img_dir"], set_dir=self.system_dict["dataset"]["train"]["set_dir"], transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) self.system_dict["local"]["sampler"] = AspectRatioBasedSampler( self.system_dict["local"]["dataset_train"], batch_size=self.system_dict["params"]["batch_size"], drop_last=False) self.system_dict["local"]["dataloader_train"] = DataLoader( self.system_dict["local"]["dataset_train"], num_workers=self.system_dict["params"]["num_workers"], collate_fn=collater, batch_sampler=self.system_dict["local"]["sampler"]) print('Num training images: {}'.format( len(self.system_dict["local"]["dataset_train"])))
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) epoch_loss_mem = -999999 in_a_row = 0 for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) map_val = float(mAP.get(0)[0]) if map_val < epoch_loss_mem: in_a_row += 1 if in_a_row >= PATIENCE: print('Early Stop, Epoch', epoch_num) break else: print('Validation Performance Decreased for', in_a_row, 'Run(s)') else: epoch_loss_mem = map_val in_a_row = 0 scheduler.step(np.mean(epoch_loss)) torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)) retinanet.eval() torch.save(retinanet, 'model_final.pt')
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)') parser.add_argument('--img_path', help='Path to file to save val images') parser.add_argument('--model', help='Path to model (.pt) file.') parser = parser.parse_args(args) if parser.dataset == 'coco': dataset_val = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose([Normalizer(), Resizer()])) elif parser.dataset == 'csv': dataset_val = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) else: raise ValueError('Dataset type not understood (must be csv or coco), exiting.') sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=1, collate_fn=collater, batch_sampler=sampler_val) retinanet = torch.load(parser.model) use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.eval() unnormalize = UnNormalizer() def draw_caption(image, box, caption): b = np.array(box).astype(int) cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2) cv2.putText(image, caption, (b[0], b[1] - 10), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1) for idx, data in enumerate(dataloader_val): with torch.no_grad(): st = time.time() if torch.cuda.is_available(): scores, classification, transformed_anchors = retinanet(data['img'].cuda().float()) else: scores, classification, transformed_anchors = retinanet(data['img'].float()) print('Elapsed time: {}'.format(time.time()-st)) idxs = np.where(scores.cpu()>0.5) img = np.array(255 * unnormalize(data['img'][0, :, :, :])).copy() img[img<0] = 0 img[img>255] = 255 img = np.transpose(img, (1, 2, 0)) img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB) for j in range(idxs[0].shape[0]): bbox = transformed_anchors[idxs[0][j], :] x1 = int(bbox[0]) y1 = int(bbox[1]) x2 = int(bbox[2]) y2 = int(bbox[3]) label_name = dataset_val.labels[int(classification[idxs[0][j]])] draw_caption(img, (x1, y1, x2, y2), label_name) cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2) print(label_name) cv2.write(img_path + str(idx) + ".jpeg", img) cv2.waitKey(0)
def main(args=None): parser = argparse.ArgumentParser( description= 'Simple script for visualizing results from a RetinaNet network using the csv dataset.' ) parser.add_argument( '--csv', help='Path to file containing annotations (optional, see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument('--model_path', help='Path to model (.pt) file.') parser.add_argument('--configfile', help='Path to the config file.') parser.add_argument('--out_path', help='Path to the folder where to save the images.') parser = parser.parse_args(args) configs = configparser.ConfigParser() configs.read(parser.configfile) try: maxside = int(configs['TRAINING']['maxside']) minside = int(configs['TRAINING']['minside']) except Exception as e: print(e) print( 'CONFIG FILE IS INVALID. PLEASE REFER TO THE EXAMPLE CONFIG FILE AT config.txt' ) sys.exit() if parser.csv is None: dataset_eval = None print('No validation annotations provided.') else: dataset_eval = CSVDataset(train_file=parser.csv, class_list=parser.csv_classes, transform=transforms.Compose([ Normalizer(), Resizer(min_side=minside, max_side=maxside) ])) dataloader_val = None if dataset_eval is not None: sampler_val = AspectRatioBasedSampler(dataset_eval, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_eval, num_workers=1, collate_fn=collater, batch_sampler=sampler_val) retinanet = load_model(parser.model_path, parser.configfile) unnormalize = UnNormalizer() if not os.path.exists(parser.out_path): os.makedirs(parser.out_path, exist_ok=True) for idx, data in enumerate(dataloader_val): with torch.no_grad(): st = time.time() if torch.cuda.is_available(): scores, classification, transformed_anchors = retinanet( data['img'].cuda().float()) else: scores, classification, transformed_anchors = retinanet( data['img'].float()) print('Elapsed time: {}'.format(time.time() - st)) idxs = np.where(scores.cpu() > 0.5) img = np.array(255 * unnormalize(data['img'][0, :, :, :])).copy() img[img < 0] = 0 img[img > 255] = 255 img = np.transpose(img, (1, 2, 0)) img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2BGR) for j in range(idxs[0].shape[0]): bbox = transformed_anchors[idxs[0][j], :] x1 = int(bbox[0]) y1 = int(bbox[1]) x2 = int(bbox[2]) y2 = int(bbox[3]) label_name = dataset_eval.labels[int( classification[idxs[0][j]])] draw_caption(img, (x1, y1, x2, y2), label_name) cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2) cv2.imwrite(os.path.join(parser.out_path, f'image_{idx}.png'), img)
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)') parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--config', help='Config file path that contains scale and ratio values', type=str) parser.add_argument('--epochs', help='Number of epochs', type=int, default=50) parser.add_argument('--init-lr', help='Initial learning rate for training process', type=float, default=1e-3) parser.add_argument('--batch-size', help='Number of input images per step', type=int, default=1) parser.add_argument('--num-workers', help='Number of worker used in dataloader', type=int, default=1) # For resuming training from saved checkpoint parser.add_argument('--resume', help='Whether to resume training from checkpoint', action='store_true') parser.add_argument('--saved-ckpt', help='Resume training from this checkpoint', type=str) parser.add_argument('--multi-gpus', help='Allow to use multi gpus for training task', action='store_true') parser.add_argument('--snapshots', help='Location to save training snapshots', type=str, default="snapshots") parser.add_argument('--log-dir', help='Location to save training logs', type=str, default="logs") parser.add_argument('--expr-augs', help='Allow to use use experiment augmentation methods', action='store_true') parser.add_argument('--aug-methods', help='(Experiment) Augmentation methods to use, separate by comma symbol', type=str, default="rotate,hflip,brightness,contrast") parser.add_argument('--aug-prob', help='Probability of applying (experiment) augmentation in range [0.,1.]', type=float, default=0.5) parser = parser.parse_args(args) train_transforms = [Normalizer(), Resizer(), Augmenter()] # Define transform methods if parser.expr_augs: aug_map = get_aug_map(p=parser.aug_prob) aug_methods = parser.aug_methods.split(",") for aug in aug_methods: if aug in aug_map.keys(): train_transforms.append(aug_map[aug]) else: print(f"{aug} is not available.") # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose(train_transforms)) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose([Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError('Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose(train_transforms)) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) else: raise ValueError('Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=parser.num_workers, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=parser.batch_size, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=parser.num_workers, collate_fn=collater, batch_sampler=sampler_val) config = dict({"scales": None, "ratios": None}) if parser.config: config = load_config(parser.config, config) if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"]) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"]) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"]) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"]) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True, ratios=config["ratios"], scales=config["scales"]) else: raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152') optimizer = optim.Adam(retinanet.parameters(), lr=parser.init_lr) if parser.resume: if not parser.saved_ckpt: print("No saved checkpoint provided for resuming training. Exiting now...") return if not os.path.exists(parser.saved_ckpt): print("Invalid saved checkpoint path. Exiting now...") return # Restore last state retinanet, optimizer, start_epoch = load_ckpt(parser.saved_ckpt, retinanet, optimizer) if parser.epochs <= start_epoch: print("Number of epochs must be higher than number of trained epochs of saved checkpoint.") return use_gpu = True if use_gpu: print("Using GPU for training process") if torch.cuda.is_available(): if parser.multi_gpus: print("Using multi-gpus for training process") retinanet = torch.nn.DataParallel(retinanet.cuda(), device_ids=[0,1]) else: retinanet = torch.nn.DataParallel(retinanet.cuda()) else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=1, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) # Tensorboard writer writer = SummaryWriter(parser.log_dir) # Save snapshots dir if not os.path.exists(parser.snapshots): os.makedirs(parser.snapshots) best_mAP = 0 start_epoch = 0 if not parser.resume else start_epoch for epoch_num in range(start_epoch, parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] epoch_csf_loss = [] epoch_reg_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): with torch.cuda.device(0): classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss epoch_csf_loss.append(float(classification_loss)) epoch_reg_loss.append(float(regression_loss)) if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( '\rEpoch: {}/{} | Iteration: {}/{} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format( (epoch_num + 1), parser.epochs, (iter_num + 1), len(dataloader_train), float(classification_loss), float(regression_loss), np.mean(loss_hist)), end='') del classification_loss del regression_loss except Exception as e: print(e) continue # writer.add_scalar("Loss/train", loss, epoch_num) _epoch_loss = np.mean(epoch_loss) _epoch_csf_loss = np.mean(epoch_reg_loss) _epoch_reg_loss = np.mean(epoch_reg_loss) if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) scheduler.step(_epoch_loss) elif parser.dataset == 'csv' and parser.csv_val is not None: print('\nEvaluating dataset') APs = csv_eval.evaluate(dataset_val, retinanet) mAP = round(mean(APs[ap][0] for ap in APs.keys()), 5) print("mAP: %f" %mAP) writer.add_scalar("validate/mAP", mAP, epoch_num) # Handle lr_scheduler wuth mAP value scheduler.step(mAP) lr = get_lr(optimizer) writer.add_scalar("train/classification-loss", _epoch_csf_loss, epoch_num) writer.add_scalar("train/regression-loss", _epoch_reg_loss, epoch_num) writer.add_scalar("train/loss", _epoch_loss, epoch_num) writer.add_scalar("train/learning-rate", lr, epoch_num) # Save model file, optimizer and epoch number checkpoint = { 'epoch': epoch_num, 'state_dict': retinanet.state_dict(), 'optimizer': optimizer.state_dict(), } # torch.save(retinanet.module, os.path.join(parser.snapshots, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))) # Check whether this epoch's model achieves highest mAP value is_best = False if best_mAP < mAP: best_mAP = mAP is_best = True save_ckpt(checkpoint, is_best, parser.snapshots, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num + 1)) print('\n') retinanet.eval() torch.save(retinanet, 'model_final.pt') writer.flush()
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.', default='show') parser.add_argument('--coco_path', help='Path to COCO directory', default='/mnt/marathon') parser.add_argument('--image_size', help='image size', type=int, nargs=2, default=IMAGE_SIZE) parser.add_argument('--limit', help='limit', type=int, nargs=2, default=(0, 0)) parser.add_argument('--batch_size', help='batch size', type=int, default=BATCH_SIZE) parser.add_argument('--num_works', help='num works', type=int, default=NUM_WORKERS) parser.add_argument('--num_classes', help='num classes', type=int, default=3) parser.add_argument('--merge_val', help='merge_val', type=int, default=MERGE_VAL) parser.add_argument('--do_aug', help='do_aug', type=int, default=DO_AUG) parser.add_argument('--lr_choice', default=LR_CHOICE, choices=['lr_scheduler', 'lr_map', 'lr_fn'], type=str) parser.add_argument('--lr', help='lr', type=float, default=LR) parser.add_argument("--lr_map", dest="lr_map", action=StoreDictKeyPair, default=LR_MAP) parser.add_argument("--lr_fn", dest="lr_fn", action=StoreDictKeyPair, default=LR_FN) parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=DEPTH) parser.add_argument('--epochs', help='Number of epochs', type=int, default=EPOCHS) parser = parser.parse_args(args) print('dataset:', parser.dataset) print('depth:', parser.depth) print('epochs:', parser.epochs) print('image_size:', parser.image_size) print('batch_size:', parser.batch_size) print('num_works:', parser.num_works) print('merge_val:', parser.merge_val) print('do_aug:', parser.do_aug) print('lr_choice:', parser.lr_choice) print('lr:', parser.lr) print('lr_map:', parser.lr_map) print('lr_fn:', parser.lr_fn) print('num_classes:', parser.num_classes) print('limit:', parser.limit) # Create the data loaders # dataset_train, _ = torch.utils.data.random_split(dataset_train, [NUM_COCO_DATASET_TRAIN, len(dataset_train) - NUM_COCO_DATASET_TRAIN]) # dataset_val, _ = torch.utils.data.random_split(dataset_val, [NUM_COCO_DATASET_VAL, len(dataset_val) - NUM_COCO_DATASET_VAL]) transform_train = None transform_vail = None collate_fn = None if parser.do_aug: transform_train = get_augumentation('train', parser.image_size[0], parser.image_size[1]) transform_vail = get_augumentation('test', parser.image_size[0], parser.image_size[1]) collate_fn = detection_collate else: transform_train = transforms.Compose([ # Normalizer(), # Augmenter(), Resizer(*parser.image_size)]) transform_vail = transforms.Compose([ # Normalizer(), Resizer(*parser.image_size)]) collate_fn = collater if parser.dataset == 'h5': dataset_train = H5CoCoDataset('{}/train_small.hdf5'.format(parser.coco_path), 'train_small') dataset_val = H5CoCoDataset('{}/test.hdf5'.format(parser.coco_path), 'test') else: dataset_train = CocoDataset(parser.coco_path, set_name='train_small', do_aug=parser.do_aug, transform=transform_train, limit_len=parser.limit[0]) dataset_val = CocoDataset(parser.coco_path, set_name='test', do_aug=parser.do_aug, transform=transform_vail, limit_len=parser.limit[1]) # 混合val if parser.merge_val: dataset_train += dataset_val print('training images: {}'.format(len(dataset_train))) print('val images: {}'.format(len(dataset_val))) steps_pre_epoch = len(dataset_train) // parser.batch_size print('steps_pre_epoch:', steps_pre_epoch) sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, batch_size=1, num_workers=parser.num_works, shuffle=False, collate_fn=collate_fn, batch_sampler=sampler) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=parser.num_classes, pretrained=PRETRAINED) elif parser.depth == 34: retinanet = model.resnet34(num_classes=parser.num_classes, pretrained=PRETRAINED) elif parser.depth == 50: retinanet = model.resnet50(num_classes=parser.num_classes, pretrained=PRETRAINED) elif parser.depth == 101250: retinanet = model.resnet101with50weight(num_classes=parser.num_classes, pretrained=PRETRAINED) elif parser.depth == 101: retinanet = model.resnet101(num_classes=parser.num_classes, pretrained=PRETRAINED) elif parser.depth == 152: retinanet = model.resnet152(num_classes=parser.num_classes, pretrained=PRETRAINED) else: raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152') retinanet = retinanet.cuda() retinanet = torch.nn.DataParallel(retinanet).cuda() retinanet.training = True if parser.lr_choice == 'lr_map': lr_now = lr_change_map(1, 0, parser.lr_map) elif parser.lr_choice == 'lr_fn': lr_now = float(parser.lr_fn['LR_START']) elif parser.lr_choice == 'lr_scheduler': lr_now = parser.lr # optimizer = optim.Adam(retinanet.parameters(), lr=lr_now) optimizer = optim.AdamW(retinanet.parameters(), lr=lr_now) # optimizer = optim.SGD(retinanet.parameters(), lr=lr_now, momentum=0.9, weight_decay=5e-4) # optimizer = optim.SGD(retinanet.parameters(), lr=lr_now) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=PATIENCE, factor=FACTOR, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() iteration_loss_path = 'iteration_loss.csv' if os.path.isfile(iteration_loss_path): os.remove(iteration_loss_path) epoch_loss_path = 'epoch_loss.csv' if os.path.isfile(epoch_loss_path): os.remove(epoch_loss_path) eval_train_path = 'eval_train_result.csv' if os.path.isfile(eval_train_path): os.remove(eval_train_path) eval_val_path = 'eval_val_result.csv' if os.path.isfile(eval_val_path): os.remove(eval_val_path) USE_KAGGLE = True if os.environ.get('KAGGLE_KERNEL_RUN_TYPE', False) else False if USE_KAGGLE: iteration_loss_path = '/kaggle/working/' + iteration_loss_path epoch_loss_path = '/kaggle/working/' + epoch_loss_path eval_val_path = '/kaggle/working/' + eval_val_path eval_train_path = '/kaggle/working/' + eval_train_path with open(epoch_loss_path, 'a+') as epoch_loss_file, \ open(iteration_loss_path, 'a+') as iteration_loss_file, \ open(eval_train_path, 'a+') as eval_train_file, \ open(eval_val_path, 'a+') as eval_val_file: epoch_loss_file.write('epoch_num,mean_epoch_loss\n') iteration_loss_file.write('epoch_num,iteration,classification_loss,regression_loss,iteration_loss\n') eval_train_file.write('epoch_num,map50\n') eval_val_file.write('epoch_num,map50\n') for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): optimizer.zero_grad() classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) iteration_loss = np.mean(loss_hist) print('\rEpoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format( epoch_num+1, iter_num+1, float(classification_loss), float(regression_loss), iteration_loss), end=' ' * 50) iteration_loss_file.write('{},{},{:1.5f},{:1.5f},{:1.5f}\n'.format(epoch_num+1, epoch_num * steps_pre_epoch + (iter_num+1), float(classification_loss), float(regression_loss), iteration_loss)) iteration_loss_file.flush() del classification_loss del regression_loss mean_epoch_loss = np.mean(epoch_loss) epoch_loss_file.write('{},{:1.5f}\n'.format(epoch_num+1, mean_epoch_loss)) epoch_loss_file.flush() if parser.lr_choice == 'lr_map': lr_now = lr_change_map(epoch_num+1, lr_now, parser.lr_map) adjust_learning_rate(optimizer, lr_now) elif parser.lr_choice == 'lr_fn': lr_now = lrfn(epoch_num+1, parser.lr_fn) adjust_learning_rate(optimizer, lr_now) elif parser.lr_choice == 'lr_scheduler': scheduler.step(mean_epoch_loss) # if parser.dataset != 'show': # print('Evaluating dataset_train') # coco_eval.evaluate_coco(dataset_train, retinanet, parser.dataset, parser.do_aug, eval_train_file, epoch_num) print('Evaluating dataset_val') coco_eval.evaluate_coco(dataset_val, retinanet, parser.dataset, parser.do_aug, eval_val_file, epoch_num) return parser
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)') parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--dcn_layers', type =str, help = 'comma seperated str where laters to be used, 0..3',default = None) parser.add_argument('--use_depth', action='store_true', help='if specified, use depth for deformconv') parser = parser.parse_args(args) use_dcn = [False, False, False, False] if parser.dcn_layers is not None: _t = parser.dcn_layers.split(',') for __t in _t: use_dcn[int(__t)] = True # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose([Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError('Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) else: raise ValueError('Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=128, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True, use_dcn = use_dcn, use_depth = parser.use_depth) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True writer = SummaryWriter() if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) #mAP = csv_eval.evaluate(dataset_val, retinanet) global_step = 0 for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() global_step += 1 if torch.cuda.is_available(): if parser.use_depth and 'depth' in data: classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']],depth = data['depth'].cuda()) else: classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']]) else: if parser.use_depth and 'depth' in data: classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']],depth=data['depth']) else: classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() writer.add_scalar('CLS Loss',classification_loss,global_step) writer.add_scalar('REG Loss',regression_loss,global_step) loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format( epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)) retinanet.eval() torch.save(retinanet, 'model_final.pt') writer.close()
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', default='csv', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument('--csv_train', default='dataset/pascal_train.csv', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', default='dataset/classes.csv', help='Path to file containing class list (see readme)') parser.add_argument('--csv_val', default='dataset/pascal_val.csv', help='Path to file containing validation annotations (optional, see readme)') parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--weights_folder', help='path to save weight', type=str, required=True) parser = parser.parse_args(args) if not os.path.exists(parser.weights_folder): os.makedirs(parser.weights_folder) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose([Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError('Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) else: raise ValueError('Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=5, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=4, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=8, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=4, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) # import ipdb; ipdb.set_trace() for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] total_loss = 0 total_regression_loss = 0 total_classification_loss = 0 with tqdm(dataloader_train, unit="batch") as tepoch: for data in tepoch: # for iter_num, data in tepoch:#enumerate(dataloader_train): tepoch.set_description(f"Epoch {epoch_num}") try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss total_loss = total_loss + loss total_regression_loss = total_regression_loss + regression_loss total_classification_loss = total_classification_loss + classification_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) # print( # 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format( # epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) tepoch.set_postfix(cls_loss="{:1.5f}".format(classification_loss), reg_loss="{:1.5f}".format(regression_loss)) time.sleep(0.1) del classification_loss del regression_loss except Exception as e: print(e) continue tb.add_scalar('Training loss', total_loss, epoch_num) tb.add_scalar('Training regression loss', total_regression_loss, epoch_num) tb.add_scalar('Training accuracy loss', total_classification_loss, epoch_num) if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) torch.save(retinanet.module, '{}/{}_retinanet_{}.pt'.format(parser.weights_folder,parser.dataset, epoch_num)) retinanet.eval() torch.save(retinanet, '{}/model_final.pt'.format(parser.weights_folder))
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.', default='csv') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)', default='data/train_retinanet.csv') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)', default='data/class_retinanet.csv') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)', default='data/val_retinanet.csv') parser.add_argument('--model_path', default='coco_resnet_50_map_0_335_state_dict.pt', help='Path to file containing pretrained retinanet') parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs_detection', help='Number of epochs for detection', type=int, default=50) parser.add_argument('--epochs_classification', help='Number of epochs for classification', type=int, default=50) parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=1, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if parser.model_path is not None: print('loading ', parser.model_path) if 'coco' in parser.model_path: retinanet.load_state_dict(torch.load(parser.model_path), strict=False) else: retinanet = torch.load(parser.model_path) print('Pretrained model loaded!') if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) #Here training the detection retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=4, verbose=True) loss_hist = collections.deque(maxlen=500) loss_style_classif = nn.CrossEntropyLoss() retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) mAP_list = [] mAPbest = 0 for epoch_num in range(parser.epochs_detection): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): [classification_loss, regression_loss], style = retinanet( [data['img'].cuda().float(), data['annot']]) else: [classification_loss, regression_loss ], style = retinanet([data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() if torch.cuda.is_available(): style_loss = loss_style_classif( style, torch.tensor(data['style']).cuda()) else: style_loss = loss_style_classif( style, torch.tensor(data['style'])) loss = classification_loss + regression_loss + style_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.4f} | Regression loss: {:1.4f} | Style loss: {:1.4f} | Running loss: {:1.4f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), float(style_loss), np.mean(loss_hist))) del classification_loss del regression_loss del style_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAPclasses, mAP, accu = csv_eval.evaluate(dataset_val, retinanet) mAP_list.append(mAP) print('mAP_list', mAP_list) if mAP > mAPbest: print('Saving best checkpoint') torch.save(retinanet, 'model_best.pt') mAPbest = mAP scheduler.step(np.mean(epoch_loss)) torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)) retinanet.eval() torch.save(retinanet, 'model_final.pt') # Here we aggregate all the data to don't have to appy the Retinanet during training. retinanet.load_state_dict(torch.load('model_best.pt').state_dict()) List_feature = [] List_target = [] retinanet.training = False retinanet.eval() retinanet.module.style_inference = True retinanet.module.freeze_bn() epoch_loss = [] with torch.no_grad(): for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): _, _, feature_vec = retinanet(data['img'].cuda().float()) else: _, _, feature_vec = retinanet(data['img'].float()) List_feature.append(torch.squeeze(feature_vec).cpu()) List_target.append(data['style'][0]) except Exception as e: print(e) continue print('END of preparation of the data for classification of style') # Here begins Style training. Need to set to style_train. They are using the same loader, as it was expected to train both at the same time. batch_size_classification = 64 dataloader_train_style = torch.utils.data.DataLoader( StyleDataset(List_feature, List_target), batch_size=batch_size_classification) retinanet.load_state_dict(torch.load('model_best.pt').state_dict()) # Here training the detection retinanet.module.style_inference = False retinanet.module.style_train(True) retinanet.training = True retinanet.train() optimizer = optim.Adam( retinanet.module.styleClassificationModel.parameters(), lr=5e-3, weight_decay=1e-3) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='max', patience=4, verbose=True) loss_hist = collections.deque(maxlen=500) loss_style_classif = nn.CrossEntropyLoss() retinanet.train() retinanet.module.freeze_bn() criterion = nn.CrossEntropyLoss() accu_list = [] accubest = 0 for epoch_num in range(parser.epochs_classification): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] total = 0 correct = 0 for iter_num, data in enumerate(dataloader_train_style): try: optimizer.zero_grad() inputs, targets = data if torch.cuda.is_available(): inputs, targets = inputs.cuda(), targets.cuda() outputs = retinanet.module.styleClassificationModel( inputs, 0, 0, 0, True) loss = criterion(outputs, targets) loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) total += targets.size(0) _, predicted = torch.max(outputs.data, 1) correct += predicted.eq(targets.data).cpu().sum() print( '| Epoch [%3d/%3d] Iter[%3d/%3d]\t\tLoss: %.4f Acc@1: %.3f%%' % (epoch_num, parser.epochs_classification, iter_num + 1, (len(dataloader_train_style) // batch_size_classification) + 1, loss.item(), 100. * correct / total)) except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAPclasses, mAP, accu = csv_eval.evaluate(dataset_val, retinanet) accu_list.append(accu) print('mAP_list', mAP_list, 'accu_list', accu_list) if accu > accubest: print('Saving best checkpoint') torch.save(retinanet.module, 'model_best_classif.pt') accubest = accu scheduler.step(accu) torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)) retinanet.eval() torch.save(retinanet.module, 'model_final.pt')
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument("--load_model_path", type=str, default=None, help="Path to model (.pt) file.") parser.add_argument('--dataset_type', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument('--backbone', help='Backbone choice: [ResNet, ResNeXt]', type=str, default='ResNet') parser.add_argument( '--depth', help='ResNet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument("--batch_size", type=int, default=2, help="size of the batches") parser.add_argument("--lr", type=float, default=1e-5, help="adam: learning rate") parser = parser.parse_args(args) results_dir = "results" save_images_dir = os.path.join(results_dir, "images") save_models_dir = os.path.join(results_dir, "saved_models") os.makedirs(results_dir, exist_ok=True) os.makedirs(save_images_dir, exist_ok=True) os.makedirs(save_models_dir, exist_ok=True) # Get today datetime today = datetime.date.today() today = "%d%02d%02d" % (today.year, today.month, today.day) # Get current timme now = time.strftime("%H%M%S") # Backbone name backbone_name = parser.backbone + str(parser.depth) # DataSet name dataset_path = '' # Create the data loaders if parser.dataset_type == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') # dataset_train = CocoDataset(parser.coco_path, set_name='train2017', # transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) # dataset_val = CocoDataset(parser.coco_path, set_name='val2017', # transform=transforms.Compose([Normalizer(), Resizer()])) dataset_train = CocoDataset( parser.coco_path, set_name='train', # transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) transform=transforms.Compose( [Normalizer(), AugmenterWithImgaug(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val', transform=transforms.Compose( [Normalizer(), Resizer()])) dataset_path = parser.coco_path elif parser.dataset_type == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) dataset_path = parser.csv_train else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Retrain the model if parser.load_model_path is not None: # Load pretrained models print("\nLoading model from: [%s]" % parser.load_model_path) retinanet = torch.load(parser.load_model_path) print("\nStart retrain...") # Create the model else: print("\nStart train...") if parser.backbone == 'ResNet': if parser.depth == 18: retinanet = model.resnet18( num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34( num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50( num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101( num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152( num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152' ) elif parser.backbone == 'ResNeXt': if parser.depth == 50: retinanet = model.resnext50_32x4d( num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnext101_32x8d( num_classes=dataset_train.num_classes(), pretrained=True) pass else: raise ValueError( "Unsupported model depth, must be one of 50, 101") else: raise ValueError("Choice a backbone, [ResNet, ResNeXt]") # Get dataset name dataset_name = os.path.split(dataset_path)[-1] # Checkpoint name save_ckpt_name = r"%s_%s-%s-RetinaNet-backbone(%s)-ep(%d)-bs(%d)-lr(%s)" \ % (today, now, dataset_name, backbone_name, parser.epochs, parser.batch_size, parser.lr) os.makedirs(os.path.join(save_images_dir, "%s" % save_ckpt_name), exist_ok=True) os.makedirs(os.path.join(save_models_dir, "%s" % save_ckpt_name), exist_ok=True) tb_log_path = os.path.join("tf_log", save_ckpt_name) tb_writer = SummaryWriter(os.path.join(results_dir, tb_log_path)) use_gpu = True if use_gpu: retinanet = retinanet.cuda() retinanet = torch.nn.DataParallel(retinanet).cuda() retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=parser.lr) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) val_loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) epoch_prev_time = time.time() for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] total_classification_loss = 0.0 total_regression_loss = 0.0 total_running_loss = 0.0 total_val_classification_loss = 0.0 total_val_regression_loss = 0.0 total_val_running_loss = 0.0 batch_prev_time = time.time() for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) # sum the loss for tensorboard at this batch total_regression_loss += regression_loss total_classification_loss += classification_loss total_running_loss += loss.item() # log = 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}'.format( # epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)) # Determine approximate time left data_done = iter_num data_left = len(dataloader_train) - data_done batch_time_left = datetime.timedelta( seconds=data_left * (time.time() - batch_prev_time)) batch_time_left = chop_microseconds(batch_time_left) batches_done = epoch_num * len(dataloader_train) + iter_num batches_left = parser.epochs * len( dataloader_train) - batches_done total_time_left = datetime.timedelta( seconds=batches_left * (time.time() - epoch_prev_time)) total_time_left = chop_microseconds(total_time_left) batch_prev_time = time.time() epoch_prev_time = time.time() # Print training step log prefix_log = '[Epoch: {}/{}] | [Batch: {}/{}]'.format( epoch_num + 1, parser.epochs, iter_num + 1, len(dataloader_train)) suffix_log = '[Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}] ETA: {} / {}'.format( float(classification_loss), float(regression_loss), np.mean(loss_hist), batch_time_left, total_time_left) printProgressBar(iteration=iter_num + 1, total=len(dataloader_train), prefix=prefix_log, suffix=suffix_log) del classification_loss del regression_loss except Exception as e: print(e) continue # Validation with torch.no_grad(): val_batch_prev_time = time.time() for iter_num, data in enumerate(dataloader_val): try: val_classification_loss, val_regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) val_classification_loss = val_classification_loss.mean() val_regression_loss = val_regression_loss.mean() val_loss = val_classification_loss + val_regression_loss if bool(val_loss == 0): continue val_loss_hist.append(float(val_loss)) # sum the loss for tensorboard at this batch total_val_regression_loss += val_regression_loss total_val_classification_loss += val_classification_loss total_val_running_loss += val_loss.item() # Determine approximate time left data_done = iter_num data_left = len(dataloader_val) - data_done val_batch_time_left = datetime.timedelta( seconds=data_left * (time.time() - val_batch_prev_time)) val_batch_time_left = chop_microseconds( val_batch_time_left) batches_done = epoch_num * len(dataloader_val) + ( epoch_num + 1) * len(dataloader_train) + iter_num batches_left = parser.epochs * (len( dataloader_train) + len(dataloader_val)) - batches_done total_time_left = datetime.timedelta( seconds=batches_left * (time.time() - epoch_prev_time)) total_time_left = chop_microseconds(total_time_left) val_batch_prev_time = time.time() epoch_prev_time = time.time() # Print training step log prefix_log = 'Validation: [Epoch: {}/{}] | [Batch: {}/{}]'.format( epoch_num + 1, parser.epochs, iter_num + 1, len(dataloader_val)) suffix_log = '[Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}] ETA: {} / {}'.format( float(val_classification_loss), float(val_regression_loss), np.mean(val_loss_hist), val_batch_time_left, total_time_left) printProgressBar(iteration=iter_num + 1, total=len(dataloader_val), prefix=prefix_log, suffix=suffix_log) del val_classification_loss del val_regression_loss except Exception as e: print(e) continue # Evaluate AP if parser.dataset_type == 'coco': print('Evaluating dataset') # coco_eval.evaluate_coco(dataset_val, retinanet) coco_eval.evaluate_coco_and_save_image( dataset_val, retinanet, os.path.join(save_images_dir, save_ckpt_name), epoch_num + 1) elif parser.dataset_type == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) # calculate loss average average_classification_loss = total_classification_loss / len( dataloader_train) average_regression_loss = total_regression_loss / len(dataloader_train) average_running_loss = total_running_loss / len(dataloader_train) # TensorBoard tb_writer.add_scalar(tag='Classification Loss', scalar_value=average_classification_loss, global_step=epoch_num + 1) tb_writer.add_scalar(tag='Regression Loss', scalar_value=average_regression_loss, global_step=epoch_num + 1) tb_writer.add_scalar(tag='Total Loss', scalar_value=average_running_loss, global_step=epoch_num + 1) # Save model print("\nSave model to [%s] at %d epoch\n" % (save_ckpt_name, epoch_num + 1)) checkpoint_path = os.path.join( save_models_dir, "%s/RetinaNet_backbone(%s)_%d.pt" % (save_ckpt_name, backbone_name, epoch_num + 1)) torch.save(retinanet.module, checkpoint_path) # torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset_type, epoch_num + 1)) retinanet.eval() torch.save(retinanet, 'model_final.pt')
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.', default='coco') parser.add_argument( '--coco_path', help='Path to COCO directory', default= '/media/zhuzhu/ec114170-f406-444f-bee7-a3dc0a86cfa2/dataset/coco') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--use-gpu', help='training on cpu or gpu', action='store_false', default=True) parser.add_argument('--device-ids', help='GPU device ids', default=[0]) args = parser.parse_args() # ------------------------------ Create the data loaders ----------------------------- if args.dataset == 'coco': if args.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(args.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(args.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) sampler_train = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler_train) sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if args.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif args.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif args.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=False) elif args.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif args.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') if args.use_gpu: retinanet = nn.DataParallel(retinanet, device_ids=args.device_ids).cuda() # retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) print('Num training images: {}'.format(len(dataset_train))) for epoch_num in range(args.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) # 梯度的最大范数为0.1 optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss except Exception as e: print(e) continue if args.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(args.dataset, epoch_num)) retinanet.eval() torch.save(retinanet, 'model_final.pt')
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--finetune', help='if load trained retina model', type=bool, default=False) parser.add_argument('--gpu', help='', type=bool, default=False) parser.add_argument('--batch_size', help='', type=int, default=2) parser.add_argument('--c', help='continue with formal model', type=bool, default=False) parser.add_argument('--model', help='model path') parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') #sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False) sampler = AspectRatioBasedSampler(dataset_train, parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=16, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=8, collate_fn=collater, batch_sampler=sampler_val) epochpassed = 0 # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') if parser.c: retinanet = torch.load(parser.model) #import pdb #pdb.set_trace() epochpassed = int(parser.model.split('.')[1].split('_')[-1]) use_gpu = parser.gpu #torch.cuda.set_device(5) #import pdb #pdb.set_trace() if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if use_gpu and torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) #original:1e-5 #optimizer =optim.SGD(retinanet.parameters(), lr=0.01,weight_decay=0.0001, momentum=0.9) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) writer = SummaryWriter() for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] epoch_classification_loss = [] epoch_regression_loss = [] for iter_num, data in enumerate(dataloader_train): try: #import pdb #pdb.set_trace() optimizer.zero_grad() if use_gpu and torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot'].cuda()]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) epoch_classification_loss.append(float(classification_loss)) epoch_regression_loss.append(float(regression_loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Epoch loss: {:1.5f}\r' .format(epoch_num + epochpassed, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)), end='') del classification_loss del regression_loss except Exception as e: print(e) continue print( 'Epoch: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Epoch loss: {:1.5f}' .format(epoch_num + epochpassed, np.mean(epoch_classification_loss), np.mean(epoch_regression_loss), np.mean(epoch_loss))) writer.add_scalar('lossrecord/regressionloss', np.mean(epoch_regression_loss), epoch_num + epochpassed) writer.add_scalar('lossrecord/classificationloss', np.mean(epoch_regression_loss), epoch_num + epochpassed) writer.add_scalar('lossrecord/epochloss', np.mean(epoch_loss), epoch_num + epochpassed) if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) if epoch_num % 10 == 0: torch.save( retinanet.module, './models/{}_retinanet{}_highResolution4fold_{}.pt'.format( parser.dataset, parser.depth, epoch_num + epochpassed)) #retinanet.eval() torch.save( retinanet.module, './models/{}_retinanet{}_highResolution4fold_{}.pt'.format( parser.dataset, parser.depth, parser.epochs + epochpassed)) writer.close()
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.', default='csv') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--batch_size', help='Batch size', type=int, default=2) parser.add_argument('--num_workers', help='Number of workers', type=int, default=4) parser.add_argument('--models_out', help='The directory to save models', type=str) parser = parser.parse_args(args) if not os.path.exists(parser.models_out): os.makedirs(parser.models_out) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=parser.num_workers, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=parser.num_workers, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: retinanet = retinanet.cuda() retinanet = torch.nn.DataParallel(retinanet).cuda() retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) writer = SummaryWriter(log_dir="tensor_log/" + parser.models_out) global_steps = 0 for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) running_loss = np.mean(loss_hist) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), running_loss)) global_steps += 1 writer.add_scalar("Loss/Classification", float(classification_loss), global_steps) writer.add_scalar("Loss/Regression", float(regression_loss), global_steps) writer.add_scalar("Loss/Running", running_loss, global_steps) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) #for k, v in mAP.items(): # writer.add_scalar("Accuracy/map_{}".format(k), v, epoch_num) scheduler.step(np.mean(epoch_loss)) torch.save( retinanet.module, os.path.join( parser.models_out, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))) retinanet.eval() torch.save(retinanet, os.path.join(parser.models_out, 'model_final.pt'))
def main(args=None): parser = argparse.ArgumentParser( description="Simple training script for training a RetinaNet network.") parser.add_argument("--dataset", help="Dataset type, must be one of csv or coco.") parser.add_argument("--coco_path", help="Path to COCO directory") parser.add_argument( "--csv_train", help="Path to file containing training annotations (see readme)") parser.add_argument("--csv_classes", help="Path to file containing class list (see readme)") parser.add_argument( "--csv_val", help= "Path to file containing validation annotations (optional, see readme)", ) parser.add_argument( "--depth", help="Resnet depth, must be one of 18, 34, 50, 101, 152", type=int, default=50, ) parser.add_argument("--batch_size", help="Batch size", type=int, default=2) parser.add_argument("--epochs", help="Number of epochs", type=int, default=100) parser.add_argument("--workers", help="Number of workers of dataleader", type=int, default=4) parser = parser.parse_args(args) writer = SummaryWriter("logs") # Create the data loaders if parser.dataset == "coco": if parser.coco_path is None: raise ValueError("Must provide --coco_path when training on COCO,") dataset_train = CocoDataset( parser.coco_path, set_name="train2017", transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()]), ) dataset_val = CocoDataset( parser.coco_path, set_name="val2017", transform=transforms.Compose([Normalizer(), Resizer()]), ) elif parser.dataset == "csv": if parser.csv_train is None: raise ValueError("Must provide --csv_train when training on COCO,") if parser.csv_classes is None: raise ValueError( "Must provide --csv_classes when training on COCO,") dataset_train = CSVDataset( train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()]), ) if parser.csv_val is None: dataset_val = None print("No validation annotations provided.") else: dataset_val = CSVDataset( train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()]), ) else: raise ValueError( "Dataset type not understood (must be csv or coco), exiting.") sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False) dataloader_train = DataLoader( dataset_train, num_workers=parser.workers, collate_fn=collater, batch_sampler=sampler, ) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=parser.workers, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( "Unsupported model depth, must be one of 18, 34, 50, 101, 152") use_gpu = True if use_gpu: retinanet = retinanet.cuda() retinanet = torch.nn.DataParallel(retinanet).cuda() retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=10, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print("Num training images: {}".format(len(dataset_train))) global_step = 0 for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): global_step = iter_num + epoch_num * len(dataloader_train) try: optimizer.zero_grad() classification_loss, regression_loss = retinanet( [data["img"].cuda().float(), data["annot"]]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) if iter_num % 10 == 0: print( "Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}" .format( epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist), )) writer.add_scalars( "training", { "loss": loss, "loss_cls": classification_loss, "loss_reg": regression_loss, }, global_step, ) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == "coco": print("Evaluating dataset") coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == "csv" and parser.csv_val is not None: print("Evaluating dataset") mAP = csv_eval.evaluate(dataset_val, retinanet) valid_mAP = [x[0] for x in mAP.values() if x[1] > 0] mmAP = sum(valid_mAP) / len(mAP) writer.add_scalars("validation", {"mmAP": mmAP}, global_step) scheduler.step(np.mean(epoch_loss)) torch.save( retinanet.module, "checkpoints/{}_retinanet_{}.pt".format(parser.dataset, epoch_num), ) retinanet.eval() torch.save(retinanet, "checkpoints/odel_final.pt")
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument('--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)') parser.add_argument('--iou',default='05') parser.add_argument('--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose([Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError('Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) val_dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) else: raise ValueError('Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=8, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=8, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=5e-5) lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) multistep_scheduler = optim.lr_scheduler.MultiStepLR(optimizer, milestones=[5,8,11,20], gamma=0.2) loss_hist = collections.deque(maxlen=500) val_loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] val_epoch_loss=[] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet([data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet([data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Train: Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f} | Epoch loss: {:1.5f} '.format( epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist),epoch_loss[-1])) del classification_loss del regression_loss except Exception as e: print(e) continue for iter_num, data in enumerate(dataloader_val): try: #optimizer.zero_grad() #retinanet.eval() with torch.no_grad(): if torch.cuda.is_available(): classification_loss, regression_loss = retinanet((data['img'].cuda().float(), data['annot'])) else: classification_loss, regression_loss = retinanet((data['img'].float(), data['annot'])) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue #loss.backward() #torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) #optimizer.step() val_loss_hist.append(float(loss)) val_epoch_loss.append(float(loss)) print( 'Val: Epoch: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f} | Epoch loss: {:1.5f} '.format( epoch_num, float(classification_loss), float(regression_loss), np.mean(val_loss_hist),val_epoch_loss[-1])) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') #mAP_train = csv_eval.evaluate(val_dataset_train,retinanet,iou_threshold=float(parser.iou)/10) mAP_val = csv_eval.evaluate(dataset_val, retinanet,iou_threshold=float(parser.iou)/10) #writer.add_scalar('train_mAP_Questions',mAP_train[0][0],epoch_num) writer.add_scalar('val_mAP_Questions', mAP_val[0][0], epoch_num) writer.add_scalar('val_loss',np.mean(val_epoch_loss),epoch_num) writer.add_scalar('train_loss',np.mean(epoch_loss),epoch_num) lr_scheduler.step(np.mean(epoch_loss)) #one_scheduler.step() multistep_scheduler.step() torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.iou, epoch_num)) retinanet.eval() torch.save(retinanet, 'model_final.pt')
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument( '--dataset', help='Dataset type, must be one of csv or coco.') #数据集类型 parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) #选择与训练模型 parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') #决定图片数据集的顺序和batch_size,返回的是图片的分组 sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: retinanet = retinanet.cuda() #多GPU运行 retinanet = torch.nn.DataParallel(retinanet).cuda() retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) #collections:模块实现了特定目标的容器,以提供Python标准内建容器 dict、list、set、tuple 的替代选择 #collections.deque:返回双向队列对象,最长长度为500 loss_hist = collections.deque(maxlen=500) # model.train() :启用 BatchNormalization 和 Dropout # model.eval() :不启用 BatchNormalization 和 Dropout retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue #反向传播 loss.backward() #梯度裁剪,梯度小于/大于阈值时,更新的梯度为阈值(此处为小于0.1) torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) #更新所有的参数,一旦梯度被如backward()之类的函数计算好后,我们就可以调用这个函数 optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) #optimizer.step()通常用在每个mini-batch之中,而scheduler.step()通常用在epoch里面 #有用了optimizer.step(),模型才会更新,而scheduler.step()是对lr进行调整。 scheduler.step(np.mean(epoch_loss)) torch.save(retinanet.module, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num)) retinanet.eval() torch.save(retinanet, 'model_final.pt')
def main(args=None): ''' test.py 会计算原始图片中的box的位置,而visualize.py返回的是resize和padding后的图片boudning box 位置 另外test.py支持对识别结果的保存 ''' parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument('--csv_val', help='Path to file containing validation annotations (optional, see readme)') parser.add_argument('--model', help='Path to model (.pt) file.') parser.add_argument('--resultsavepath', help='path to save detection images') parser.add_argument('--thresh_score', help="thresh score", type=float, default=.5) parser = parser.parse_args(args) # 创建结果的保存路径 if parser.resultsavepath: os.makedirs(parser.resultsavepath, exist_ok=True) if parser.dataset == 'coco': dataset_val = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose([Normalizer(), Resizer()])) elif parser.dataset == 'csv': #dataset_val = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) # 提示错误 dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()])) else: raise ValueError('Dataset type not understood (must be csv or coco), exiting.') sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=1, collate_fn=collater, batch_sampler=sampler_val) retinanet = torch.load(parser.model) use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() # 设置为多GPU的并行模式 else: retinanet = torch.nn.DataParallel(retinanet) retinanet.eval()# 设置为评估模式 def draw_caption(image, box, caption): b = np.array(box).astype(int) # b[1]-20防止label超过上边界 cv2.putText(image, caption, (b[0], b[1]-10 if b[1]-20>0 else 30), cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 0), 2) cv2.putText(image, caption, (b[0], b[1]-10 if b[1]-20>0 else 30), cv2.FONT_HERSHEY_PLAIN, 1, (255, 255, 255), 1) if parser.resultsavepath: result_csv = "{}_result.csv".format(os.path.splitext(os.path.basename(parser.csv_val))[0]) result_csv = os.path.join(parser.resultsavepath, result_csv) print(result_csv) result_csv_fd = open(result_csv, 'w') for idx, data in enumerate(dataloader_val): #print("data shape:", data.shape) print(data['image_path']) with torch.no_grad(): st = time.time() if torch.cuda.is_available(): the_result = retinanet(data['img'].cuda().float()) else: the_result = retinanet(data['img'].float()) print('Elapsed time: {}'.format(time.time()-st)) for image_index, (scores, classification, transformed_anchors) in enumerate(the_result): idxs = np.where(scores.cpu()>parser.thresh_score) image_path = data['image_path'][image_index] scale = data['scale'][image_index] img = cv2.imread(image_path) if idxs[0].shape[0]==0: result_csv_fd.write("{},,,,,\n".format(image_path)) else: for j in range(idxs[0].shape[0]): bbox = transformed_anchors[idxs[0][j], :] x1 = int(bbox[0]/scale) y1 = int(bbox[1]/scale) x2 = int(bbox[2]/scale) y2 = int(bbox[3]/scale) label_name = dataset_val.labels[int(classification[idxs[0][j]])] txt_draw = "%s %.2f" %(label_name, scores[j]) draw_caption(img, (x1, y1, x2, y2), txt_draw) cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2) if parser.resultsavepath: result_csv_fd.write("{},{},{},{},{},{}\n".format(image_path,x1,y1,x2,y2,label_name)) print(label_name) if parser.resultsavepath: new_dir = os.path.join(parser.resultsavepath, os.path.dirname(image_path)) if not os.path.exists(new_dir): os.makedirs(new_dir) new_path = os.path.join(parser.resultsavepath, image_path) cv2.imwrite(new_path, img) #new_path = os.path.join(parser.resultsavepath, os.path.basename(image_path)) #cv2.imwrite(new_path, img) #print("create result image:{} ".format(new_path)) else: cv2.imshow('img', img) cv2.waitKey(0) if parser.resultsavepath: result_csv_fd.close()
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument('--configfile', help='Path to the config file', default='config.txt', type=str) parser.add_argument( '--model', help= 'Path to the pretrained model file state dict where training must start from, ' 'if you want to use a pretrained retinanet.', default=None, type=str) parser = parser.parse_args(args) configs = configparser.ConfigParser() configs.read(parser.configfile) try: batchsize = int(configs['TRAINING']['batchsize']) depth = int(configs['TRAINING']['depth']) maxepochs = int(configs['TRAINING']['maxepochs']) maxside = int(configs['TRAINING']['maxside']) minside = int(configs['TRAINING']['minside']) savepath = configs['TRAINING']['savepath'] lr_start = float(configs['TRAINING']['lr_start']) lr_reduce_on_plateau_factor = float( configs['TRAINING']['lr_reduce_on_plateau_factor']) lr_reduce_on_plateau_patience = int( configs['TRAINING']['lr_reduce_on_plateau_patience']) earlystopping_patience = int( configs['TRAINING']['earlystopping_patience']) try: ratios = json.loads(configs['MODEL']['ratios']) scales = json.loads(configs['MODEL']['scales']) except Exception as e: print(e) print('USING DEFAULT RATIOS AND SCALES') ratios = None scales = None except Exception as e: print(e) print( 'CONFIG FILE IS INVALID. PLEASE REFER TO THE EXAMPLE CONFIG FILE AT config.txt' ) sys.exit() model_save_dir = datetime.now().strftime( "%d_%b_%Y_%H_%M") if savepath == 'datetime' else savepath if not os.path.exists(model_save_dir): os.makedirs(model_save_dir, exist_ok=True) # Copy the config file into the model save directory shutil.copy(parser.configfile, os.path.join(model_save_dir, 'config.txt')) # Create the data loaders if parser.csv_train is None: raise ValueError('Must provide --csv_train,') if parser.csv_classes is None: raise ValueError('Must provide --csv_classes,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([ Normalizer(), Augmenter(), Resizer(min_side=minside, max_side=maxside) ])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([ Normalizer(), Resizer(min_side=minside, max_side=maxside) ])) sampler = AspectRatioBasedSampler(dataset_train, batch_size=batchsize, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) dataloader_val = None if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True, ratios=ratios, scales=scales, no_nms=False) elif depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True, ratios=ratios, scales=scales, no_nms=False) elif depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True, ratios=ratios, scales=scales, no_nms=False) elif depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True, ratios=ratios, scales=scales, no_nms=False) elif depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True, ratios=ratios, scales=scales, no_nms=False) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=lr_start) scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, patience=lr_reduce_on_plateau_patience, verbose=True, factor=lr_reduce_on_plateau_factor, cooldown=1, min_lr=1e-10) loss_hist = collections.deque(maxlen=500) if (parser.model): print( f'TRYING TO LOAD PRETRAINED MODEL AVAILABLE AT: {parser.model}. MAKE SURE THE MODEL CONFIGS MATCH!!!!!' ) if torch.cuda.is_available(): retinanet.load_state_dict(torch.load(parser.model)) else: retinanet.load_state_dict( torch.load(parser.model, map_location=torch.device('cpu'))) print(f'LOADED PRETRAINED MODEL : {parser.model}') retinanet.train() retinanet.module.freeze_bn() earlystopping = EarlyStopping(patience=earlystopping_patience, verbose=True, delta=1e-10, path=os.path.join(model_save_dir, 'best_model.pt')) print('Num training images: {}'.format(len(dataset_train))) loss_dict = OrderedDict() val_loss_dict = OrderedDict() for epoch_num in range(maxepochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] epoch_val_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist)), end='\r', flush=True) del classification_loss del regression_loss except Exception as e: print(e) continue if (len(epoch_loss)): loss_dict[epoch_num] = np.mean(epoch_loss) print('') if dataloader_val is not None: print('Evaluating dataset') for iter_num, data in enumerate(dataloader_val): try: with torch.no_grad(): if torch.cuda.is_available(): val_classification_loss, val_regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) else: val_classification_loss, val_regression_loss = retinanet( [data['img'].float(), data['annot']]) val_classification_loss = val_classification_loss.mean( ) val_regression_loss = val_classification_loss.mean() val_loss = val_classification_loss + val_regression_loss print('Validation Loss: {:1.5f}'.format(val_loss), end='\r', flush=True) epoch_val_loss.append(float(val_loss)) except Exception as e: print(e) continue print('') if (len(epoch_val_loss)): val_loss_dict[epoch_num] = np.mean(epoch_val_loss) retinanet.eval() mAP = csv_eval.evaluate(dataset_val, retinanet) print('-----------------') print(mAP) print('-----------------') scheduler.step(np.mean(epoch_loss)) model_save_path = os.path.join(model_save_dir, f'retinanet_{epoch_num}.pt') save_model(retinanet, model_save_path) print(f'Saved model of epoch {epoch_num} to {model_save_path}') earlystopping(val_loss_dict[epoch_num], retinanet) if earlystopping.early_stop: print("Early stopping") break retinanet.eval() save_model(retinanet, os.path.join(model_save_dir, 'model_final.pt')) with open(os.path.join(model_save_dir, 'loss_history.txt'), 'w') as f: for epoch_num, loss in loss_dict.items(): f.write(f'{epoch_num}:{loss} \n') with open(os.path.join(model_save_dir, 'val_loss_history.txt'), 'w') as f: for epoch_num, loss in val_loss_dict.items(): f.write(f'{epoch_num}:{loss} \n') # Write configs to model save directory configs = configparser.ConfigParser() configs.read(os.path.join(model_save_dir, 'config.txt')) configs['TRAINING']['num_classes'] = str(dataset_train.num_classes()) for iter_num, data in enumerate(dataloader_train): configs['MODEL']['input_shape'] = str( list(data['img'].float().numpy().shape[1:])) break # Write class mapping to the model configs. with open(parser.csv_classes, 'r') as f: labels = load_classes_from_csv_reader(csv.reader(f, delimiter=',')) configs['LABELMAP'] = {str(i): str(j) for i, j in labels.items()} with open(os.path.join(model_save_dir, 'config.txt'), 'w') as configfile: configs.write(configfile)
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument('--model_save_path', help='Path to save model', type=str) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=8, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) # add draw tensorboard code writer = SummaryWriter(log_dir='./logs/416*416/', flush_secs=60) # if Cuda: # graph_inputs = torch.from_numpy(np.random.rand(1, 3, input_shape[0], input_shape[1])).type( # torch.FloatTensor).cuda() # else: # graph_inputs = torch.from_numpy(np.random.rand(1, 3, input_shape[0], input_shape[1])).type(torch.FloatTensor) # writer.add_graph(model, (graph_inputs,)) # add gap save model count variable n = 0 for epoch_num in range(parser.epochs): n += 1 retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] ### begin calculate train loss for iter_num, data in enumerate(dataloader_train): # try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss # except Exception as e: # print(e) # continue ### begin calculate valid loss for iter_num, data in enumerate(dataloader_val): # try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss_hist.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Valid-Classification loss: {:1.5f} | Valid-Regression loss: {:1.5f} | Running Valid loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) print('Epoch: {} | mAP: {:.3f}'.format(epoch_num, float(mAP))) scheduler.step(np.mean(epoch_loss)) if n % 10 == 0: torch.save( retinanet.module, parser.model_save_path + '/' + '{}_retinanet_{}_{:.3f}.pt'.format( parser.dataset, epoch_num, mAP)) retinanet.eval() torch.save(retinanet, parser.model_save_path + '/' + 'model_final.pt')
def main(args=None): parser = argparse.ArgumentParser( description="Simple training script for training a RetinaNet network." ) parser.add_argument("--dataset", help="Dataset type, must be one of csv or coco.") parser.add_argument("--model", default=None, help="Path to trained model") parser.add_argument("--coco_path", help="Path to COCO directory") parser.add_argument( "--csv_train", help="Path to file containing training annotations (see readme)" ) parser.add_argument( "--csv_classes", help="Path to file containing class list (see readme)" ) parser.add_argument( "--csv_val", help="Path to file containing validation annotations (optional, see readme)", ) parser.add_argument( "--depth", help="Resnet depth, must be one of 18, 34, 50, 101, 152", type=int, default=50, ) parser.add_argument("--epochs", help="Number of epochs", type=int, default=100) parser.add_argument( "--result_dir", default="results", help="Path to store training results", type=str, ) parser.add_argument( "--batch_num", default=8, help="Number of samples in a batch", type=int ) parser = parser.parse_args(args) print(parser) # parameters BATCH_SIZE = parser.batch_num IMAGE_MIN_SIDE = 1440 IMAGE_MAX_SIDE = 2560 # Create the data loaders if parser.dataset == "coco": if parser.coco_path is None: raise ValueError("Must provide --coco_path when training on COCO,") # TODO: parameterize arguments for Resizer, and other transform functions # resizer: min_side=608, max_side=1024 dataset_train = CocoDataset( parser.coco_path, # set_name="train2017", set_name="train_images_full", transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer(passthrough=True),] ), ) dataset_val = CocoDataset( parser.coco_path, # set_name="val2017", set_name="val_images_full", transform=transforms.Compose([Normalizer(), Resizer(passthrough=True),]), ) elif parser.dataset == "csv": if parser.csv_train is None: raise ValueError("Must provide --csv_train when training on COCO,") if parser.csv_classes is None: raise ValueError("Must provide --csv_classes when training on COCO,") dataset_train = CSVDataset( train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Augmenter(), Resizer()]), ) if parser.csv_val is None: dataset_val = None print("No validation annotations provided.") else: dataset_val = CSVDataset( train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose([Normalizer(), Resizer()]), ) else: raise ValueError("Dataset type not understood (must be csv or coco), exiting.") sampler = AspectRatioBasedSampler( dataset_train, batch_size=BATCH_SIZE, drop_last=False ) dataloader_train = DataLoader( dataset_train, num_workers=16, collate_fn=collater, batch_sampler=sampler ) if dataset_val is not None: sampler_val = AspectRatioBasedSampler( dataset_val, batch_size=BATCH_SIZE, drop_last=False ) dataloader_val = DataLoader( dataset_val, num_workers=16, collate_fn=collater, batch_sampler=sampler_val ) # Create the model if parser.depth == 18: retinanet = model.resnet18( num_classes=dataset_train.num_classes(), pretrained=True ) elif parser.depth == 34: retinanet = model.resnet34( num_classes=dataset_train.num_classes(), pretrained=True ) elif parser.depth == 50: retinanet = model.resnet50( num_classes=dataset_train.num_classes(), pretrained=True ) elif parser.depth == 101: retinanet = model.resnet101( num_classes=dataset_train.num_classes(), pretrained=True ) elif parser.depth == 152: retinanet = model.resnet152( num_classes=dataset_train.num_classes(), pretrained=True ) else: raise ValueError("Unsupported model depth, must be one of 18, 34, 50, 101, 152") if parser.model: retinanet = torch.load(parser.model) use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-4) scheduler = optim.lr_scheduler.ReduceLROnPlateau( optimizer, patience=3, verbose=True ) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print("Num training images: {}".format(len(dataset_train))) for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] p_bar = tqdm(dataloader_train) for iter_num, data in enumerate(p_bar): try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data["img"].cuda().float(), data["annot"]] ) else: classification_loss, regression_loss = retinanet( [data["img"].float(), data["annot"]] ) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) mean_loss = np.mean(loss_hist) p_bar.set_description( f"Epoch: {epoch_num} | Iteration: {iter_num} | " f"Class loss: {float(classification_loss.item()):.5f} | " f"Regr loss: {float(regression_loss.item()):.5f} | " f"Running loss: {mean_loss:.5f}" ) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == "coco": print("Evaluating dataset") coco_eval.evaluate_coco( dataset_val, retinanet, result_dir=parser.result_dir ) elif parser.dataset == "csv" and parser.csv_val is not None: print("Evaluating dataset") mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) # TODO: Fix string formating mix (adopt homogeneous format) torch.save( retinanet.module, f"{parser.result_dir}/" + "{}_retinanet_{}.pt".format(parser.dataset, epoch_num), ) retinanet.eval() torch.save(retinanet, "model_final.pt")
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--model', help='Path to model (.pt) file.') parser.add_argument('--finetune', help='if load trained retina model', type=bool, default=False) parser.add_argument('--gpu', help='', type=bool, default=False) parser.add_argument('--batch_size', help='', type=int, default=2) parser = parser.parse_args(args) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') #sampler = AspectRatioBasedSampler(dataset_train, batch_size=2, drop_last=False) sampler = AspectRatioBasedSampler(dataset_train, parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model ''' if parser.depth == 18: retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 34: retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 50: retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 101: retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=True) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=True) else: raise ValueError('Unsupported model depth, must be one of 18, 34, 50, 101, 152') ''' use_gpu = parser.gpu #import pdb #pdb.set_trace() #读coco预训练模型 retinanet = model.resnet50(num_classes=80, pretrained=True) retinanet.load_state_dict(torch.load(parser.model)) for param in retinanet.parameters(): param.requires_grad = False retinanet.regressionModel = model.RegressionModel(256) retinanet.classificationModel = model.ClassificationModel( 256, num_classes=dataset_train.num_classes()) prior = 0.01 retinanet.classificationModel.output.weight.data.fill_(0) retinanet.classificationModel.output.bias.data.fill_(-math.log( (1.0 - prior) / prior)) retinanet.regressionModel.output.weight.data.fill_(0) retinanet.regressionModel.output.bias.data.fill_(0) # for m in retinanet.classificationModel.modules(): # if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) # elif isinstance(m, nn.BatchNorm2d): # m.weight.data.fill_(1) # m.bias.data.zero_() # for m in retinanet.regressionModel.modules(): # if isinstance(m, nn.Conv2d): # n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels # m.weight.data.normal_(0, math.sqrt(2. / n)) # elif isinstance(m, nn.BatchNorm2d): # m.weight.data.fill_(1) # m.bias.data.zero_() if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if use_gpu and torch.cuda.is_available(): #retinanet.load_state_dict(torch.load(parser.model)) retinanet = torch.nn.DataParallel(retinanet).cuda() else: #retinanet.load_state_dict(torch.load(parser.model)) retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam( [{ 'params': retinanet.module.regressionModel.parameters() }, { 'params': retinanet.module.classificationModel.parameters() }], 1e-6) #optimizer = optim.Adam(retinanet.parameters(), lr=1e-6) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) for epoch_num in range(parser.epochs): retinanet.train() retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: #import pdb #pdb.set_trace() optimizer.zero_grad() if use_gpu and torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot'].cuda()]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) if epoch_num % 5 == 0: torch.save( retinanet.module, '{}_freezinetune_{}.pt'.format(parser.dataset, epoch_num)) retinanet.eval()
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument('--exp_name', help='Path to folder for saving the model and log', type=str) parser.add_argument('--output_folder', help='Path to folder for saving all the experiments', type=str) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) # 100 parser.add_argument('--batch_size', help='Batch size', type=int, default=2) parser.add_argument('--lr', help='Number of epochs', type=float, default=1e-5) parser.add_argument('--caption', help='Any thing in particular about the experiment', type=str) parser.add_argument('--server', help='seerver name', type=str, default='ultron') parser.add_argument('--detector', help='detection algo', type=str, default='RetinaNet') parser.add_argument('--arch', help='model architecture', type=str) parser.add_argument('--pretrain', default=False, action='store_true') parser.add_argument('--freeze_batchnorm', default=False, action='store_true') parser = parser.parse_args(args) output_folder_path = os.path.join(parser.output_folder, parser.exp_name) if not os.path.exists(output_folder_path): os.makedirs(output_folder_path) PARAMS = { 'dataset': parser.dataset, 'exp_name': parser.exp_name, 'depth': parser.depth, 'epochs': parser.epochs, 'batch_size': parser.batch_size, 'lr': parser.lr, 'caption': parser.caption, 'server': parser.server, 'arch': parser.arch, 'pretrain': parser.pretrain, 'freeze_batchorm': parser.freeze_batchnorm } exp = neptune.create_experiment( name=parser.exp_name, params=PARAMS, tags=[parser.arch, parser.detector, parser.dataset, parser.server]) # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') sampler = AspectRatioBasedSampler(dataset_train, batch_size=parser.batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # Create the model if parser.depth == 18 and parser.arch == 'Resnet': retinanet = model.resnet18(num_classes=dataset_train.num_classes(), pretrained=parser.pretrain) elif parser.depth == 10 and parser.arch == 'Resnet': retinanet = model.resnet10(num_classes=dataset_train.num_classes(), pretrained=parser.pretrain) elif parser.depth == 18 and parser.arch == 'BiRealNet18': checkpoint_path = None if parser.pretrain: checkpoint_path = '/media/Rozhok/Bi-Real-net/pytorch_implementation/BiReal18_34/models/imagenet_baseline/checkpoint.pth.tar' retinanet = birealnet18(checkpoint_path, num_classes=dataset_train.num_classes()) elif parser.depth == 34 and parser.arch == 'Resnet': retinanet = model.resnet34(num_classes=dataset_train.num_classes(), pretrained=parser.pretrain) elif parser.depth == 50 and parser.arch == 'Resnet': retinanet = model.resnet50(num_classes=dataset_train.num_classes(), pretrained=parser.pretrain) elif parser.depth == 101 and parser.arch == 'Resnet': retinanet = model.resnet101(num_classes=dataset_train.num_classes(), pretrained=parser.pretrain) elif parser.depth == 152: retinanet = model.resnet152(num_classes=dataset_train.num_classes(), pretrained=parser.pretrain) elif parser.arch == 'ofa': print("Model is ResNet50D.") bn_momentum = 0.1 bn_eps = 1e-5 retinanet = ResNet50D( n_classes=dataset_train.num_classes(), bn_param=(bn_momentum, bn_eps), dropout_rate=0, width_mult=1.0, depth_param=3, expand_ratio=0.35, ) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') print(retinanet) use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): retinanet = torch.nn.DataParallel(retinanet).cuda() else: retinanet = torch.nn.DataParallel(retinanet) retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=parser.lr) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() if parser.freeze_batchnorm: retinanet.module.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) for epoch_num in range(parser.epochs): exp.log_metric('Current lr', float(optimizer.param_groups[0]['lr'])) exp.log_metric('Current epoch', int(epoch_num)) retinanet.train() if parser.freeze_batchnorm: retinanet.module.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if torch.cuda.is_available(): classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot']]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) exp.log_metric('Training: Classification loss', float(classification_loss)) exp.log_metric('Training: Regression loss', float(regression_loss)) exp.log_metric('Training: Totalloss', float(loss)) del classification_loss del regression_loss except Exception as e: print(e) continue if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet, output_folder_path, exp=exp) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) torch.save( retinanet.module, os.path.join( output_folder_path, '{}_retinanet_{}.pt'.format(parser.dataset, epoch_num))) retinanet.eval() torch.save(retinanet, os.path.join(output_folder_path, 'model_final.pt'))
def main(args=None): parser = argparse.ArgumentParser( description='Simple training script for training a RetinaNet network.') parser.add_argument('--dataset', help='Dataset type, must be one of csv or coco.') parser.add_argument('--coco_path', help='Path to COCO directory') parser.add_argument( '--csv_train', help='Path to file containing training annotations (see readme)') parser.add_argument('--csv_classes', help='Path to file containing class list (see readme)') parser.add_argument( '--csv_val', help= 'Path to file containing validation annotations (optional, see readme)' ) parser.add_argument( '--depth', help='Resnet depth, must be one of 18, 34, 50, 101, 152', type=int, default=50) parser.add_argument('--epochs', help='Number of epochs', type=int, default=100) parser.add_argument('--local_rank', help='Local rank', type=int, default=0) parser.add_argument('--distributed', action='store_true') parser.add_argument('--pretrained', action='store_true') parser = parser.parse_args(args) torch.cuda.set_device(parser.local_rank) DISTRIBUTED = parser.distributed and config.DISTRIBUTED if DISTRIBUTED: distributed.init_process_group(backend="nccl") device = torch.device(f'cuda:{parser.local_rank}') # Create the data loaders if parser.dataset == 'coco': if parser.coco_path is None: raise ValueError('Must provide --coco_path when training on COCO,') dataset_train = CocoDataset(parser.coco_path, set_name='train2017', transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) dataset_val = CocoDataset(parser.coco_path, set_name='val2017', transform=transforms.Compose( [Normalizer(), Resizer()])) elif parser.dataset == 'csv': if parser.csv_train is None: raise ValueError('Must provide --csv_train when training on COCO,') if parser.csv_classes is None: raise ValueError( 'Must provide --csv_classes when training on COCO,') dataset_train = CSVDataset(train_file=parser.csv_train, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Augmenter(), Resizer()])) if parser.csv_val is None: dataset_val = None print('No validation annotations provided.') else: dataset_val = CSVDataset(train_file=parser.csv_val, class_list=parser.csv_classes, transform=transforms.Compose( [Normalizer(), Resizer()])) else: raise ValueError( 'Dataset type not understood (must be csv or coco), exiting.') if DISTRIBUTED: sampler = DistributedSampler(dataset_train) dataloader_train = DataLoader(dataset_train, num_workers=4, batch_size=batch_size, collate_fn=collater, sampler=sampler, pin_memory=True, drop_last=True) if dataset_val is not None: sampler_val = DistributedSampler(dataset_val) dataloader_val = DataLoader(dataset_val, batch_size=1, num_workers=4, collate_fn=collater, sampler=sampler_val, pin_memory=True, drop_last=True) else: sampler = AspectRatioBasedSampler(dataset_train, batch_size=batch_size, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=4, collate_fn=collater, batch_sampler=sampler, pin_memory=True) if dataset_val is not None: sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=1, drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=4, collate_fn=collater, batch_sampler=sampler_val, pin_memory=True) # Create the model if parser.depth == 18: retinanet = model.retinanet18(num_classes=dataset_train.num_classes(), pretrained=parser.pretrained) elif parser.depth == 34: retinanet = model.retinanet34(num_classes=dataset_train.num_classes(), pretrained=parser.pretrained) elif parser.depth == 50: retinanet = model.retinanet50(num_classes=dataset_train.num_classes(), pretrained=parser.pretrained) elif parser.depth == 101: retinanet = model.retinanet101(num_classes=dataset_train.num_classes(), pretrained=parser.pretrained) elif parser.depth == 152: retinanet = model.retinanet152(num_classes=dataset_train.num_classes(), pretrained=parser.pretrained) else: raise ValueError( 'Unsupported model depth, must be one of 18, 34, 50, 101, 152') if use_cuda: retinanet = retinanet.cuda() if RESTORE: retinanet.load_state_dict(torch.load(RESTORE)) if DISTRIBUTED: retinanet = torch.nn.parallel.DistributedDataParallel( retinanet, device_ids=[parser.local_rank]) print("Let's use", parser.local_rank, "GPU!") retinanet.training = True optimizer = optim.Adam(retinanet.parameters(), lr=1e-5) scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=3, verbose=True) loss_hist = collections.deque(maxlen=500) retinanet.train() if DISTRIBUTED: retinanet.module.freeze_bn() else: retinanet.freeze_bn() print('Num training images: {}'.format(len(dataset_train))) for epoch_num in range(parser.epochs): save_to_disk = parser.local_rank == 0 retinanet.train() if DISTRIBUTED: retinanet.module.freeze_bn() else: retinanet.freeze_bn() epoch_loss = [] for iter_num, data in enumerate(dataloader_train): try: optimizer.zero_grad() if use_cuda: classification_loss, regression_loss = retinanet( [data['img'].cuda().float(), data['annot'].cuda()]) else: classification_loss, regression_loss = retinanet( [data['img'].float(), data['annot']]) classification_loss = classification_loss.mean() regression_loss = regression_loss.mean() loss = classification_loss + regression_loss if bool(loss == 0): continue loss.backward() torch.nn.utils.clip_grad_norm_(retinanet.parameters(), 0.1) optimizer.step() loss_hist.append(float(loss)) epoch_loss.append(float(loss)) if save_to_disk: print( 'Epoch: {} | Iteration: {} | Classification loss: {:1.5f} | Regression loss: {:1.5f} | Running loss: {:1.5f}' .format(epoch_num, iter_num, float(classification_loss), float(regression_loss), np.mean(loss_hist))) del classification_loss del regression_loss except Exception as e: print(e) continue if save_to_disk: if parser.dataset == 'coco': print('Evaluating dataset') coco_eval.evaluate_coco(dataset_val, retinanet) elif parser.dataset == 'csv' and parser.csv_val is not None: print('Evaluating dataset') mAP = csv_eval.evaluate(dataset_val, retinanet) scheduler.step(np.mean(epoch_loss)) if DISTRIBUTED: torch.save( retinanet.module.state_dict(), '{}/{}_retinanet_{}.pt'.format(checkpoints_dir, parser.dataset, epoch_num)) else: torch.save( retinanet.state_dict(), '{}/{}_retinanet_{}.pt'.format(checkpoints_dir, parser.dataset, epoch_num))
dataset_train = CSVDataset(train_file=f'{dir_path}/train.csv', class_list=f'{dir_path}/class.csv', transform=tv.transforms.Compose( [Normalizer(), Augmenter(), Resizer()]), header=True) dataset_test = CSVDataset(train_file=f'{dir_path}/test.csv', class_list=f'{dir_path}/class.csv', transform=tv.transforms.Compose( [Normalizer(), Resizer()]), header=True) sampler = AspectRatioBasedSampler(dataset_train, batch_size=10, drop_last=False) # dataloader_train = pt.utils.data.DataLoader(dataset_train, num_workers=3, # collate_fn=collater, batch_sampler=sampler) dataloader_train = pt.utils.data.DataLoader(dataset_train, collate_fn=collater, batch_sampler=sampler) #create model retinanet = model.resnet34(num_classes=num_classes, pretrained=True) use_gpu = True if use_gpu: if pt.cuda.is_available(): retinanet = retinanet.cuda()
from torch.utils.data.sampler import SubsetRandomSampler import retinanet.model as retinanet_model warnings.filterwarnings("ignore") assert torch.__version__.split('.')[0] == '1' print('CUDA available: {}'.format(torch.cuda.is_available())) # In[3]: dataset_val = CSVDataset(train_file="./myvaliddataset.csv", class_list="./class_detail.csv", transform=transforms.Compose([Normalizer(), Resizer()])) sampler_val = AspectRatioBasedSampler(dataset_val, batch_size=4,drop_last=False) dataloader_val = DataLoader(dataset_val, num_workers=3, collate_fn=collater, batch_sampler=sampler_val) # In[9]: retinanet = torch.load("./model_final_without_finetune2.pt").cuda() # In[10]: mAP = csv_eval.evaluate(dataset_val, retinanet)
def main(args=None): parser = argparse.ArgumentParser(description='Simple training script for training a RetinaNet network.') parser.add_argument('--model', help='Path to model (.pt) file.') parser.add_argument('--data_dir', help='Path to imgs (.jpg) file.') parser.add_argument('--num_totest', help='How many imgs to test.') parser = parser.parse_args(args) #imglist= sorted(glob.glob(os.path.join(parser.data_dir, '*.jpg'))) #random.shuffle(imglist) #imglist=imglist[:int(parser.num_totest)] dataset_train = CSVDataset(train_file='./foreign_object_dataset/train_standard_annotation.csv', class_list='./foreign_object_dataset/class4_classlist.csv', transform=transforms.Compose([Normalizer(), Resizer()])) sampler = AspectRatioBasedSampler(dataset_train, 1, drop_last=False) dataloader_train = DataLoader(dataset_train, num_workers=3, collate_fn=collater, batch_sampler=sampler) # retinanet = torch.load(parser.model) # use_gpu = True # if use_gpu: # if torch.cuda.is_available(): # retinanet = retinanet.cuda() # if torch.cuda.is_available(): # retinanet = torch.nn.DataParallel(retinanet).cuda() # else: # retinanet = torch.nn.DataParallel(retinanet) #retinanet = model.resnet50(num_classes=80, pretrained=True) #retinanet.load_state_dict(torch.load(parser.model)) retinanet = torch.load(parser.model) use_gpu = True if use_gpu: if torch.cuda.is_available(): retinanet = retinanet.cuda() if torch.cuda.is_available(): #retinanet.load_state_dict(torch.load(parser.model)) retinanet = torch.nn.DataParallel(retinanet).cuda() else: #retinanet.load_state_dict(torch.load(parser.model)) retinanet = torch.nn.DataParallel(retinanet) retinanet.eval() unnormalize = UnNormalizer() trans=transforms.ToTensor() FODlabels=['nail','clipper_B','clipper_Y','Lstick','butt','foreign'] for iter_num, data in enumerate(dataloader_train): with torch.no_grad(): st = time.time() if torch.cuda.is_available(): scores, classification, transformed_anchors = retinanet(data['img'].cuda().float()) #scores, classification, transformed_anchors = retinanet(img_tensor.float()) print('Elapsed time: {}'.format(time.time()-st)) idxs = np.where(scores.cpu()>0.5) #img[img<0] = 0 #img[img>255] = 255 #img = np.transpose(img, (1, 2, 0)) #img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB) #img=tensor_to_np(data['img']).astype(np.uint8) #img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #img=cv2.imread(data['filename'][0]) #img=cv2.resize(img,(1056,608)) img = np.array(255 * unnormalize(data['img'][0, :, :, :])).copy() img[img<0] = 0 img[img>255] = 255 img = np.transpose(img, (1, 2, 0)) img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_BGR2RGB) for j in range(idxs[0].shape[0]): bbox = transformed_anchors[idxs[0][j], :] x1 = int(bbox[0]) y1 = int(bbox[1]) x2 = int(bbox[2]) y2 = int(bbox[3]) label_name = FODlabels[int(classification[idxs[0][j]])] #label_name='FOD' draw_caption(img, (x1, y1, x2, y2), label_name) cv2.rectangle(img, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2) print(label_name) #cv2.imwrite('./visualizeResult/visualize'+str(iter_num)+'.jpg', img) #import pdb #pdb.set_trace() cv2.imwrite('./visualizeResult/visualize'+data['filename'][0].split('/')[-1], img) if iter_num>int(parser.num_totest): break