prefix = args_opt.mindrecord_prefix config.train_images = args_opt.imgs_path config.train_txts = args_opt.annos_path mindrecord_dir = config.mindrecord_dir mindrecord_file = os.path.join(mindrecord_dir, prefix + "0") print("CHECKING MINDRECORD FILES ...") if rank == 0 and not os.path.exists(mindrecord_file): if not os.path.isdir(mindrecord_dir): os.makedirs(mindrecord_dir) if os.path.isdir(config.coco_root): if not os.path.exists(config.coco_root): print("Please make sure config:coco_root is valid.") raise ValueError(config.coco_root) print("Create Mindrecord. It may take some time.") data_to_mindrecord_byte_image(True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("coco_root not exits.") while not os.path.exists(mindrecord_file + ".db"): time.sleep(5) print("CHECKING MINDRECORD FILES DONE!") loss_scale = float(config.loss_scale) # When create MindDataset, using the fitst mindrecord file, such as FasterRcnn.mindrecord0. dataset = create_deeptext_dataset(mindrecord_file, repeat_num=1, batch_size=config.batch_size,
if __name__ == '__main__': prefix = "FasterRcnn_eval.mindrecord" mindrecord_dir = config.mindrecord_dir mindrecord_file = os.path.join(mindrecord_dir, prefix) print("CHECKING MINDRECORD FILES ...") if not os.path.exists(mindrecord_file): if not os.path.isdir(mindrecord_dir): os.makedirs(mindrecord_dir) if args_opt.dataset == "coco": if os.path.isdir(config.coco_root): print("Create Mindrecord. It may take some time.") data_to_mindrecord_byte_image("coco", False, prefix, file_num=1) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("coco_root not exits.") else: if os.path.isdir(config.IMAGE_DIR) and os.path.exists( config.ANNO_PATH): print("Create Mindrecord. It may take some time.") data_to_mindrecord_byte_image("other", False, prefix, file_num=1) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("IMAGE_DIR or ANNO_PATH not exits.")
def main(): parser = argparse.ArgumentParser(description="YOLOv3 train") parser.add_argument("--only_create_dataset", type=bool, default=False, help="If set it true, only create " "Mindrecord, default is false.") parser.add_argument("--distribute", type=bool, default=False, help="Run distribute, default is false.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") parser.add_argument("--lr", type=float, default=0.001, help="Learning rate, default is 0.001.") parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink") parser.add_argument("--epoch_size", type=int, default=10, help="Epoch size, default is 10") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained checkpoint file path") parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size") parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.") parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.") parser.add_argument("--mindrecord_dir", type=str, default="./Mindrecord", help="Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by" "image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir " "rather than image_dir and anno_path. Default is ./Mindrecord_train") parser.add_argument('--data_url', type=str, default=None, help='Dataset path') parser.add_argument('--train_url', type=str, default=None, help='Train output path') parser.add_argument("--anno_path", type=str, default="", help="Annotation path.") args_opt = parser.parse_args() device_id = int(os.getenv('DEVICE_ID')) device_num = int(os.getenv('RANK_SIZE')) rankid = int(os.getenv('RANK_ID')) local_data_url = '/cache/data' local_train_url = '/cache/ckpt' local_anno_url = '/cache/anno' local_mindrecord_url = '/cache/mindrecord' mox.file.copy_parallel(args_opt.mindrecord_dir,local_mindrecord_url) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id) if args_opt.distribute: context.reset_auto_parallel_context() context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True, device_num=device_num) init() rank = rankid local_train_url = os.path.join(local_train_url,str(device_id)) else: rank = 0 device_num = 1 print("Start create dataset!") # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is yolo.mindrecord0, 1, ... file_num. if not os.path.isdir(local_mindrecord_url): os.makedirs(local_mindrecord_url) prefix = "train.mindrecord" mindrecord_file = os.path.join(local_mindrecord_url, prefix + "0") if not os.path.exists(mindrecord_file): mox.file.copy_parallel(args_opt.data_url,local_data_url) if args_opt.anno_path: anno_file=os.path.join(local_anno_url,os.path.split(args_opt.anno_path)[1]) mox.file.copy_parallel(args_opt.anno_path,anno_file) if os.path.isdir(local_data_url) or os.path.exists(anno_file): print("Create Mindrecord.") data_to_mindrecord_byte_image(local_data_url, anno_file, local_mindrecord_url, prefix=prefix, file_num=8) print("Create Mindrecord Done, at {}".format(args_opt.mindrecord_dir)) mox.file.copy_parallel(local_mindrecord_url,args_opt.mindrecord_dir) else: print("image_dir or anno_path not exits.") if not args_opt.only_create_dataset: loss_scale = float(args_opt.loss_scale) # When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0. dataset = create_yolo_dataset(mindrecord_file, repeat_num=args_opt.epoch_size, batch_size=args_opt.batch_size, device_num=device_num, rank=rank) dataset_size = dataset.get_dataset_size() print("Create dataset done!") net = yolov3_resnet18(ConfigYOLOV3ResNet18()) net = YoloWithLossCell(net, ConfigYOLOV3ResNet18()) init_net_param(net, "XavierUniform") # checkpoint ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs) ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=local_train_url, config=ckpt_config) if args_opt.pre_trained: if args_opt.pre_trained_epoch_size <= 0: raise KeyError("pre_trained_epoch_size must be greater than 0.") param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(net, param_dict) total_epoch_size = 60 if args_opt.distribute: total_epoch_size = 160 lr = Tensor(get_lr(learning_rate=args_opt.lr, start_step=args_opt.pre_trained_epoch_size * dataset_size, global_step=total_epoch_size * dataset_size, decay_step=1000, decay_rate=0.95, steps=True)) opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale) net = TrainingWrapper(net, opt, loss_scale) callback = [TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb] model = Model(net) dataset_sink_mode = False if args_opt.mode == "sink": print("In sink mode, one epoch return a loss.") dataset_sink_mode = True print("Start train YOLOv3, the first epoch will be slower because of the graph compilation.") model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode) if device_id ==1: mox.file.copy_parallel(local_train_url,args_opt.train_url)
def main(): parser = argparse.ArgumentParser(description="SSD training") parser.add_argument( "--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create Mindrecord, default is False.") parser.add_argument("--distribute", type=ast.literal_eval, default=False, help="Run distribute, default is False.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") parser.add_argument("--lr", type=float, default=0.05, help="Learning rate, default is 0.05.") parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink.") parser.add_argument("--dataset", type=str, default="coco", help="Dataset, defalut is coco.") parser.add_argument("--epoch_size", type=int, default=500, help="Epoch size, default is 500.") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained Checkpoint file path.") parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size.") parser.add_argument("--save_checkpoint_epochs", type=int, default=10, help="Save checkpoint epochs, default is 10.") parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.") parser.add_argument("--filter_weight", type=ast.literal_eval, default=False, help="Filter weight parameters, default is False.") args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) if args_opt.distribute: device_num = args_opt.device_num context.reset_auto_parallel_context() context.set_auto_parallel_context( parallel_mode=ParallelMode.DATA_PARALLEL, mirror_mean=True, device_num=device_num) init() rank = args_opt.device_id % device_num else: rank = 0 device_num = 1 print("Start create dataset!") # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is ssd.mindrecord0, 1, ... file_num. prefix = "ssd.mindrecord" mindrecord_dir = config.mindrecord_dir mindrecord_file = os.path.join(mindrecord_dir, prefix + "0") if not os.path.exists(mindrecord_file): if not os.path.isdir(mindrecord_dir): os.makedirs(mindrecord_dir) if args_opt.dataset == "coco": if os.path.isdir(config.coco_root): print("Create Mindrecord.") data_to_mindrecord_byte_image("coco", True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("coco_root not exits.") elif args_opt.dataset == "voc": if os.path.isdir(config.voc_dir): print("Create Mindrecord.") voc_data_to_mindrecord(mindrecord_dir, True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("voc_dir not exits.") else: if os.path.isdir(config.image_dir) and os.path.exists( config.anno_path): print("Create Mindrecord.") data_to_mindrecord_byte_image("other", True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: print("image_dir or anno_path not exits.") if not args_opt.only_create_dataset: loss_scale = float(args_opt.loss_scale) # When create MindDataset, using the fitst mindrecord file, such as ssd.mindrecord0. dataset = create_ssd_dataset(mindrecord_file, repeat_num=1, batch_size=args_opt.batch_size, device_num=device_num, rank=rank) dataset_size = dataset.get_dataset_size() print("Create dataset done!") backbone = ssd_mobilenet_v2() ssd = SSD300(backbone=backbone, config=config) net = SSDWithLossCell(ssd, config) init_net_param(net) # checkpoint ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs) ckpoint_cb = ModelCheckpoint(prefix="ssd", directory=None, config=ckpt_config) if args_opt.pre_trained: if args_opt.pre_trained_epoch_size <= 0: raise KeyError( "pre_trained_epoch_size must be greater than 0.") param_dict = load_checkpoint(args_opt.pre_trained) if args_opt.filter_weight: filter_checkpoint_parameter(param_dict) load_param_into_net(net, param_dict) lr = Tensor( get_lr(global_step=config.global_step, lr_init=config.lr_init, lr_end=config.lr_end_rate * args_opt.lr, lr_max=args_opt.lr, warmup_epochs=config.warmup_epochs, total_epochs=args_opt.epoch_size, steps_per_epoch=dataset_size)) opt = nn.Momentum( filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay, loss_scale) net = TrainingWrapper(net, opt, loss_scale) callback = [ TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb ] model = Model(net) dataset_sink_mode = False if args_opt.mode == "sink": print("In sink mode, one epoch return a loss.") dataset_sink_mode = True print( "Start train SSD, the first epoch will be slower because of the graph compilation." ) model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
device_num = 1 print("Start create dataset!") # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is MaskRcnn.mindrecord0, 1, ... file_num. prefix = "MaskRcnn.mindrecord" mindrecord_dir = config.mindrecord_dir mindrecord_file = os.path.join(mindrecord_dir, prefix + "0") if rank == 0 and not os.path.exists(mindrecord_file): if not os.path.isdir(mindrecord_dir): os.makedirs(mindrecord_dir) if args_opt.dataset == "coco": if os.path.isdir(config.coco_root): print("Create Mindrecord.") data_to_mindrecord_byte_image("coco", True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: raise Exception("coco_root not exits.") else: if os.path.isdir(config.IMAGE_DIR) and os.path.exists( config.ANNO_PATH): print("Create Mindrecord.") data_to_mindrecord_byte_image("other", True, prefix) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) else: raise Exception("IMAGE_DIR or ANNO_PATH not exits.") while not os.path.exists(mindrecord_file + ".db"): time.sleep(5) if not args_opt.only_create_dataset:
print(' %s [%d/%d]' % (str(percent) + '%', eval_iter, total), end='\r') precisions, recalls = metrics(pred_data) print("\n========================================\n") for i in range(config.num_classes - 1): j = i + 1 F1 = (2 * precisions[j] * recalls[j]) / (precisions[j] + recalls[j] + 1e-6) print("class {} precision is {:.2f}%, recall is {:.2f}%," "F1 is {:.2f}%".format(j, precisions[j] * 100, recalls[j] * 100, F1 * 100)) if config.use_ambigous_sample: break if __name__ == '__main__': prefix = args_opt.mindrecord_prefix config.test_images = args_opt.imgs_path config.test_txts = args_opt.annos_path mindrecord_dir = config.mindrecord_dir mindrecord_file = os.path.join(mindrecord_dir, prefix) print("CHECKING MINDRECORD FILES ...") if not os.path.exists(mindrecord_file): if not os.path.isdir(mindrecord_dir): os.makedirs(mindrecord_dir) print("Create Mindrecord. It may take some time.") data_to_mindrecord_byte_image(False, prefix, file_num=1) print("Create Mindrecord Done, at {}".format(mindrecord_dir)) print("CHECKING MINDRECORD FILES DONE!") print("Start Eval!") Deeptext_eval_test(mindrecord_file, args_opt.checkpoint_path)
def main(): parser = argparse.ArgumentParser(description="YOLOv3 train") parser.add_argument( "--only_create_dataset", type=ast.literal_eval, default=False, help="If set it true, only create Mindrecord, default is False.") parser.add_argument("--distribute", type=ast.literal_eval, default=False, help="Run distribute, default is False.") parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.") parser.add_argument("--device_num", type=int, default=1, help="Use device nums, default is 1.") parser.add_argument("--lr", type=float, default=0.001, help="Learning rate, default is 0.001.") parser.add_argument("--mode", type=str, default="sink", help="Run sink mode or not, default is sink") parser.add_argument("--epoch_size", type=int, default=50, help="Epoch size, default is 50") parser.add_argument("--batch_size", type=int, default=32, help="Batch size, default is 32.") parser.add_argument("--pre_trained", type=str, default=None, help="Pretrained checkpoint file path") parser.add_argument("--pre_trained_epoch_size", type=int, default=0, help="Pretrained epoch size") parser.add_argument("--save_checkpoint_epochs", type=int, default=5, help="Save checkpoint epochs, default is 5.") parser.add_argument("--loss_scale", type=int, default=1024, help="Loss scale, default is 1024.") parser.add_argument( "--mindrecord_dir", type=str, default="./Mindrecord_train", help= "Mindrecord directory. If the mindrecord_dir is empty, it wil generate mindrecord file by " "image_dir and anno_path. Note if mindrecord_dir isn't empty, it will use mindrecord_dir " "rather than image_dir and anno_path. Default is ./Mindrecord_train") parser.add_argument("--image_dir", type=str, default="", help="Dataset directory, " "the absolute image path is joined by the image_dir " "and the relative path in anno_path") parser.add_argument("--anno_path", type=str, default="", help="Annotation path.") args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) if args_opt.distribute: device_num = args_opt.device_num context.reset_auto_parallel_context() context.set_auto_parallel_context( parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True, device_num=device_num) init() rank = args_opt.device_id % device_num else: rank = 0 device_num = 1 print("Start create dataset!") # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is yolo.mindrecord0, 1, ... file_num. if not os.path.isdir(args_opt.mindrecord_dir): os.makedirs(args_opt.mindrecord_dir) prefix = "yolo.mindrecord" mindrecord_file = os.path.join(args_opt.mindrecord_dir, prefix + "0") if not os.path.exists(mindrecord_file): if os.path.isdir(args_opt.image_dir) and os.path.exists( args_opt.anno_path): print("Create Mindrecord.") data_to_mindrecord_byte_image(args_opt.image_dir, args_opt.anno_path, args_opt.mindrecord_dir, prefix, 8) print("Create Mindrecord Done, at {}".format( args_opt.mindrecord_dir)) else: raise ValueError('image_dir {} or anno_path {} does not exist'.format(\ args_opt.image_dir, args_opt.anno_path)) if not args_opt.only_create_dataset: loss_scale = float(args_opt.loss_scale) # When create MindDataset, using the fitst mindrecord file, such as yolo.mindrecord0. dataset = create_yolo_dataset(mindrecord_file, batch_size=args_opt.batch_size, device_num=device_num, rank=rank) dataset_size = dataset.get_dataset_size() print("Create dataset done!") net = yolov3_resnet18(ConfigYOLOV3ResNet18()) net = YoloWithLossCell(net, ConfigYOLOV3ResNet18()) init_net_param(net, "XavierUniform") # checkpoint ckpt_config = CheckpointConfig(save_checkpoint_steps=dataset_size * args_opt.save_checkpoint_epochs) ckpoint_cb = ModelCheckpoint(prefix="yolov3", directory=None, config=ckpt_config) if args_opt.pre_trained: if args_opt.pre_trained_epoch_size <= 0: raise KeyError( "pre_trained_epoch_size must be greater than 0.") param_dict = load_checkpoint(args_opt.pre_trained) load_param_into_net(net, param_dict) total_epoch_size = 60 if args_opt.distribute: total_epoch_size = 160 lr = Tensor( get_lr(learning_rate=args_opt.lr, start_step=args_opt.pre_trained_epoch_size * dataset_size, global_step=total_epoch_size * dataset_size, decay_step=1000, decay_rate=0.95, steps=True)) opt = nn.Adam(filter(lambda x: x.requires_grad, net.get_parameters()), lr, loss_scale=loss_scale) net = TrainingWrapper(net, opt, loss_scale) callback = [ TimeMonitor(data_size=dataset_size), LossMonitor(), ckpoint_cb ] model = Model(net) dataset_sink_mode = False if args_opt.mode == "sink": print("In sink mode, one epoch return a loss.") dataset_sink_mode = True print( "Start train YOLOv3, the first epoch will be slower because of the graph compilation." ) model.train(args_opt.epoch_size, dataset, callbacks=callback, dataset_sink_mode=dataset_sink_mode)
required=True, help="Checkpoint path.") args_opt = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args_opt.device_id) # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is yolo.mindrecord0, 1, ... file_num. if not os.path.isdir(args_opt.mindrecord_dir): os.makedirs(args_opt.mindrecord_dir) yolo_prefix = "yolo.mindrecord" mindrecord_file = os.path.join(args_opt.mindrecord_dir, yolo_prefix + "0") if not os.path.exists(mindrecord_file): if os.path.isdir(args_opt.image_dir) and os.path.exists( args_opt.anno_path): print("Create Mindrecord") data_to_mindrecord_byte_image(args_opt.image_dir, args_opt.anno_path, args_opt.mindrecord_dir, prefix=yolo_prefix, file_num=8) print("Create Mindrecord Done, at {}".format( args_opt.mindrecord_dir)) else: print("image_dir or anno_path not exits") print("Start Eval!") yolo_eval(mindrecord_file, args_opt.ckpt_path)
mox.file.copy_parallel(args_opt.mindrecord_dir,local_mindrecord_url) if args_opt.checkpoint_path: checkpoint_file=os.path.join(local_ckpt_url,os.path.split(args_opt.checkpoint_path)[1]) mox.file.copy_parallel(args_opt.checkpoint_path,checkpoint_file) context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=device_id) # It will generate mindrecord file in args_opt.mindrecord_dir, # and the file name is yolo.mindrecord0, 1, ... file_num. if not os.path.isdir(local_mindrecord_url): os.makedirs(local_mindrecord_url) yolo_prefix = "val.mindrecord" mindrecord_file = os.path.join(local_mindrecord_url, yolo_prefix + "0") if not os.path.exists(mindrecord_file): mox.file.copy_parallel(args_opt.data_url,local_data_url) mox.file.copy_parallel(args_opt.anno_path,local_anno_url) if os.path.isdir(local_data_url) and os.path.exists(local_anno_url): print("Create Mindrecord") data_to_mindrecord_byte_image(local_data_url, local_anno_url, local_mindrecord_url, prefix=yolo_prefix, file_num=8) print("Create Mindrecord Done, at {}".format(local_mindrecord_url)) else: print("image_dir or anno_path not exits") print("Start Eval!") yolo_eval(mindrecord_file, checkpoint_file)