(mind) xiaoyao@xiaoyao:~/mindyolo-master$ python demo/predict.py --config /home/xiaoyao/mindyolo-master/datasets/mydata.yaml --weight=/home/xiaoyao/mindyolo-master/runs/2024.11.01-19.10.46/weights/EMA_mydata-130_366.ckpt --image_path /home/xiaoyao/mindyolo-master/datasets/images/train/00011.jpg --device_target=GPU --iou_thres=0.5 --conf_thres=0.1
2024-11-02 03:17:06,799 [WARNING] Parse Model, args: nearest, keep str type
2024-11-02 03:17:06,810 [WARNING] Parse Model, args: nearest, keep str type
2024-11-02 03:17:06,868 [INFO] number of network params, total: 3.025151M, trainable: 3.014732M
[WARNING] ME(26038:124966295865152,MainProcess):2024-11-02-03:17:09.395.666 [mindspore/train/serialization.py:1469] For 'load_param_into_net', remove parameter prefix name: ema., continue to load.
2024-11-02 03:17:09,400 [INFO] Load checkpoint from [/home/xiaoyao/mindyolo-master/runs/2024.11.01-19.10.46/weights/EMA_mydata-130_366.ckpt] success.
2024-11-02 03:17:12,248 [INFO] Predict result is: {'category_id': [1, 0, 19, 14, 6, 5, 3, 2, 8, 4, 12, 11, 7, 13, 11, 10, 8, 2, 0, 15, 12, 11, 2, 1, 15, 6, 0, 18, 17, 9, 8, 4, 11, 7, 12, 6, 1, 0, 19, 14, 5, 3, 2, 6, 1, 12, 11, 8, 7, 0, 17, 9, 4, 2, 10, 19, 14, 8, 7, 6, 3, 2, 16, 13, 9, 16, 15, 5, 16, 12, 9, 13, 1, 0, 1, 19, 9, 16, 13, 15, 13, 8, 6, 18, 15, 9, 7, 5, 4, 10, 14, 13, 12, 11, 10, 9, 3, 5, 1, 19, 16, 15, 12, 10, 9, 3, 1, 17, 16, 14, 7, 6, 5, 11, 9, 8, 1, 0, 8, 7, 6, 15, 14, 13, 12, 1, 19, 16, 8, 7, 6, 5, 12, 11, 9, 16, 15, 14, 13, 5, 1, 19, 7, 6, 5, 13, 12, 9, 8, 14, 11, 9, 8, 7, 2, 0, 11, 16, 1, 6, 5, 4, 4, 17, 5, 5, 10, 9, 8, 3, 4, 19, 8, 8, 6, 4, 15, 11, 13, 17, 12, 7, 18, 15, 13, 11, 8, 7, 6, 0, 15, 7, 3, 2, 15, 2, 11, 9, 12, 9, 14, 11, 6, 1, 14, 13, 3, 9, 8, 7, 6, 4, 6, 12, 11, 10, 2, 19, 2, 0, 6, 4, 1, 16, 14, 11, 18, 16, 12, 16, 15, 13, 5, 4, 6, 8, 6, 1, 15, 10, 9, 5, 2, 11, 10, 13, 12, 9, 8, 7, 0, 15, 5, 19, 6, 3, 2, 1, 16, 10, 9, 8, 7, 0, 11, 16, 14, 12, 4, 6, 1, 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2024-11-02 03:17:12,248 [INFO] Speed: 2176.3/649.5/2825.8 ms inference/NMS/total per 640x640 image at batch-size 1;
2024-11-02 03:17:12,249 [INFO] Detect a image success.
2024-11-02 03:17:12,266 [INFO] Infer completed.
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已解决
问题描述
MindYOLO使用自定义coco2017数据集训练后，最后推理结果为空，可能是模型训练不好
ubuntu系统
CUDA11.6
MindSpore 2.2.14
MindYOLO 0.3.0
训练指令
执行训练的输出
好像是有些模型没有加载
(mind) xiaoyao@xiaoyao:~/mindyolo-master$ python train.py --config /home/xiaoyao/mindyolo-master/datasets/mydata.yaml --epochs=100 --iou_thres=0.6 --conf_thres=0.25 2024-11-02 03:33:08,085 [INFO] parse_args: 2024-11-02 03:33:08,085 [INFO] task detect 2024-11-02 03:33:08,085 [INFO] device_target GPU 2024-11-02 03:33:08,085 [INFO] save_dir ./runs/2024.11.02-03.33.08 2024-11-02 03:33:08,085 [INFO] log_level INFO 2024-11-02 03:33:08,085 [INFO] is_parallel False 2024-11-02 03:33:08,085 [INFO] ms_mode 0 2024-11-02 03:33:08,085 [INFO] ms_amp_level O0 2024-11-02 03:33:08,085 [INFO] keep_loss_fp32 True 2024-11-02 03:33:08,085 [INFO] anchor_base False 2024-11-02 03:33:08,085 [INFO] ms_loss_scaler static 2024-11-02 03:33:08,085 [INFO] ms_loss_scaler_value 1024.0 2024-11-02 03:33:08,085 [INFO] ms_jit True 2024-11-02 03:33:08,085 [INFO] ms_enable_graph_kernel False 2024-11-02 03:33:08,085 [INFO] ms_datasink False 2024-11-02 03:33:08,085 [INFO] overflow_still_update False 2024-11-02 03:33:08,085 [INFO] clip_grad False 2024-11-02 03:33:08,085 [INFO] clip_grad_value 10.0 2024-11-02 03:33:08,085 [INFO] ema True 2024-11-02 03:33:08,085 [INFO] weight /home/xiaoyao/mindyolo-master/yolov8-n.ckpt 2024-11-02 03:33:08,085 [INFO] ema_weight 2024-11-02 03:33:08,085 [INFO] freeze [] 2024-11-02 03:33:08,085 [INFO] epochs 100 2024-11-02 03:33:08,085 [INFO] per_batch_size 16 2024-11-02 03:33:08,085 [INFO] img_size 640 2024-11-02 03:33:08,085 [INFO] nbs 64 2024-11-02 03:33:08,085 [INFO] accumulate 1 2024-11-02 03:33:08,085 [INFO] auto_accumulate False 2024-11-02 03:33:08,085 [INFO] log_interval 10 2024-11-02 03:33:08,085 [INFO] single_cls False 2024-11-02 03:33:08,085 [INFO] sync_bn False 2024-11-02 03:33:08,085 [INFO] keep_checkpoint_max 100 2024-11-02 03:33:08,085 [INFO] run_eval True 2024-11-02 03:33:08,085 [INFO] conf_thres 0.25 2024-11-02 03:33:08,085 [INFO] iou_thres 0.6 2024-11-02 03:33:08,085 [INFO] conf_free True 2024-11-02 03:33:08,085 [INFO] rect False 2024-11-02 03:33:08,085 [INFO] nms_time_limit 20.0 2024-11-02 03:33:08,085 [INFO] recompute False 2024-11-02 03:33:08,085 [INFO] recompute_layers 0 2024-11-02 03:33:08,085 [INFO] seed 2 2024-11-02 03:33:08,085 [INFO] summary True 2024-11-02 03:33:08,085 [INFO] profiler False 2024-11-02 03:33:08,085 [INFO] profiler_step_num 1 2024-11-02 03:33:08,085 [INFO] opencv_threads_num 0 2024-11-02 03:33:08,085 [INFO] strict_load False 2024-11-02 03:33:08,085 [INFO] enable_modelarts False 2024-11-02 03:33:08,085 [INFO] data_url 2024-11-02 03:33:08,085 [INFO] ckpt_url 2024-11-02 03:33:08,085 [INFO] multi_data_url 2024-11-02 03:33:08,085 [INFO] pretrain_url 2024-11-02 03:33:08,085 [INFO] train_url 2024-11-02 03:33:08,085 [INFO] data_dir /cache/data/ 2024-11-02 03:33:08,085 [INFO] ckpt_dir /cache/pretrain_ckpt/ 2024-11-02 03:33:08,085 [INFO] data.dataset_name datasets 2024-11-02 03:33:08,085 [INFO] data.train_set /home/xiaoyao/mindyolo-master/datasets/images/train 2024-11-02 03:33:08,085 [INFO] data.val_set /home/xiaoyao/mindyolo-master/datasets/images/val 2024-11-02 03:33:08,085 [INFO] data.test_set /home/xiaoyao/mindyolo-master/datasets/images/test 2024-11-02 03:33:08,085 [INFO] data.nc 20 2024-11-02 03:33:08,085 [INFO] data.names ['airplane', 'airport', 'baseballfield', 'basketballcourt', 'bridge', 'chimney', 'dam', 'Expressway-Service-area', 'Expressway-toll-station', 'golffield', 'groundtrackfield', 'harbor', 'overpass', 'ship', 'stadium', 'storagetank', 'tenniscourt', 'trainstation', 'vehicle', 'windmill'] 2024-11-02 03:33:08,085 [INFO] train_transforms.stage_epochs [90, 10] 2024-11-02 03:33:08,085 [INFO] train_transforms.trans_list [[{'func_name': 'mosaic', 'prob': 1.0}, {'func_name': 'resample_segments'}, {'func_name': 'random_perspective', 'prob': 1.0, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0}, {'func_name': 'albumentations'}, {'func_name': 'hsv_augment', 'prob': 1.0, 'hgain': 0.015, 'sgain': 0.7, 'vgain': 0.4}, {'func_name': 'fliplr', 'prob': 0.5}, {'func_name': 'label_norm', 'xyxy2xywh_': True}, {'func_name': 'label_pad', 'padding_size': 160, 'padding_value': -1}, {'func_name': 'image_norm', 'scale': 255.0}, {'func_name': 'image_transpose', 'bgr2rgb': True, 'hwc2chw': True}], [{'func_name': 'letterbox', 'scaleup': True}, {'func_name': 'resample_segments'}, {'func_name': 'random_perspective', 'prob': 1.0, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0}, {'func_name': 'albumentations'}, {'func_name': 'hsv_augment', 'prob': 1.0, 'hgain': 0.015, 'sgain': 0.7, 'vgain': 0.4}, {'func_name': 'fliplr', 'prob': 0.5}, {'func_name': 'label_norm', 'xyxy2xywh_': True}, {'func_name': 'label_pad', 'padding_size': 160, 'padding_value': -1}, {'func_name': 'image_norm', 'scale': 255.0}, {'func_name': 'image_transpose', 'bgr2rgb': True, 'hwc2chw': True}]] 2024-11-02 03:33:08,085 [INFO] data.test_transforms [{'func_name': 'letterbox', 'scaleup': False, 'only_image': True}, {'func_name': 'image_norm', 'scale': 255.0}, {'func_name': 'image_transpose', 'bgr2rgb': True, 'hwc2chw': True}] 2024-11-02 03:33:08,085 [INFO] data.num_parallel_workers 2 2024-11-02 03:33:08,085 [INFO] optimizer.optimizer momentum 2024-11-02 03:33:08,085 [INFO] optimizer.lr_init 0.001 2024-11-02 03:33:08,085 [INFO] optimizer.momentum 0.937 2024-11-02 03:33:08,085 [INFO] optimizer.nesterov True 2024-11-02 03:33:08,085 [INFO] optimizer.loss_scale 1.0 2024-11-02 03:33:08,085 [INFO] optimizer.warmup_epochs 3 2024-11-02 03:33:08,085 [INFO] optimizer.warmup_momentum 0.8 2024-11-02 03:33:08,085 [INFO] optimizer.warmup_bias_lr 0.1 2024-11-02 03:33:08,085 [INFO] optimizer.min_warmup_step 1000 2024-11-02 03:33:08,085 [INFO] optimizer.group_param yolov8 2024-11-02 03:33:08,085 [INFO] optimizer.gp_weight_decay 0.0005 2024-11-02 03:33:08,085 [INFO] optimizer.start_factor 1.0 2024-11-02 03:33:08,085 [INFO] optimizer.end_factor 0.01 2024-11-02 03:33:08,085 [INFO] optimizer.epochs 100 2024-11-02 03:33:08,085 [INFO] optimizer.nbs 64 2024-11-02 03:33:08,085 [INFO] optimizer.accumulate 1 2024-11-02 03:33:08,085 [INFO] optimizer.total_batch_size 16 2024-11-02 03:33:08,085 [INFO] loss.name YOLOv8Loss 2024-11-02 03:33:08,085 [INFO] loss.box 7.5 2024-11-02 03:33:08,085 [INFO] loss.cls 0.5 2024-11-02 03:33:08,085 [INFO] loss.dfl 1.5 2024-11-02 03:33:08,085 [INFO] loss.reg_max 16 2024-11-02 03:33:08,085 [INFO] network.model_name yolov8 2024-11-02 03:33:08,085 [INFO] network.nc 20 2024-11-02 03:33:08,085 [INFO] network.reg_max 16 2024-11-02 03:33:08,085 [INFO] network.stride [8, 16, 32] 2024-11-02 03:33:08,085 [INFO] network.backbone [[-1, 1, 'ConvNormAct', [64, 3, 2]], [-1, 1, 'ConvNormAct', [128, 3, 2]], [-1, 3, 'C2f', [128, True]], [-1, 1, 'ConvNormAct', [256, 3, 2]], [-1, 6, 'C2f', [256, True]], [-1, 1, 'ConvNormAct', [512, 3, 2]], [-1, 6, 'C2f', [512, True]], [-1, 1, 'ConvNormAct', [1024, 3, 2]], [-1, 3, 'C2f', [1024, True]], [-1, 1, 'SPPF', [1024, 5]]] 2024-11-02 03:33:08,085 [INFO] network.head [[-1, 1, 'Upsample', ['None', 2, 'nearest']], [[-1, 6], 1, 'Concat', [1]], [-1, 3, 'C2f', [512]], [-1, 1, 'Upsample', ['None', 2, 'nearest']], [[-1, 4], 1, 'Concat', [1]], [-1, 3, 'C2f', [256]], [-1, 1, 'ConvNormAct', [256, 3, 2]], [[-1, 12], 1, 'Concat', [1]], [-1, 3, 'C2f', [512]], [-1, 1, 'ConvNormAct', [512, 3, 2]], [[-1, 9], 1, 'Concat', [1]], [-1, 3, 'C2f', [1024]], [[15, 18, 21], 1, 'YOLOv8Head', ['nc', 'reg_max', 'stride']]] 2024-11-02 03:33:08,085 [INFO] network.depth_multiple 0.33 2024-11-02 03:33:08,085 [INFO] network.width_multiple 0.25 2024-11-02 03:33:08,085 [INFO] network.max_channels 1024 2024-11-02 03:33:08,085 [INFO] config /home/xiaoyao/mindyolo-master/datasets/mydata.yaml 2024-11-02 03:33:08,085 [INFO] rank 0 2024-11-02 03:33:08,085 [INFO] rank_size 1 2024-11-02 03:33:08,085 [INFO] total_batch_size 16 2024-11-02 03:33:08,085 [INFO] callback [] 2024-11-02 03:33:08,085 [INFO] 2024-11-02 03:33:08,085 [INFO] Please check the above information for the configurations 2024-11-02 03:33:08,142 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:33:08,150 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:33:08,210 [INFO] number of network params, total: 3.025151M, trainable: 3.014732M 2024-11-02 03:33:11,895 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:33:11,904 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:33:11,965 [INFO] number of network params, total: 3.025151M, trainable: 3.014732M 2024-11-02 03:33:12,049 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.0.conv.weight` with shape `(80, 64, 3, 3)`, which is inconsistent with cell shape `(64, 64, 3, 3)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.0.bn.moving_mean` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.0.bn.moving_variance` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.0.bn.gamma` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.0.bn.beta` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.1.conv.weight` with shape `(80, 80, 3, 3)`, which is inconsistent with cell shape `(64, 64, 3, 3)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.1.bn.moving_mean` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.1.bn.moving_variance` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.1.bn.gamma` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.1.bn.beta` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,050 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.2.weight` with shape `(80, 80, 1, 1)`, which is inconsistent with cell shape `(20, 64, 1, 1)` 2024-11-02 03:33:12,051 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.0.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(20,)` 2024-11-02 03:33:12,051 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.0.conv.weight` with shape `(80, 128, 3, 3)`, which is inconsistent with cell shape `(64, 128, 3, 3)` 2024-11-02 03:33:12,051 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.0.bn.moving_mean` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,051 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.0.bn.moving_variance` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,051 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.0.bn.gamma` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.0.bn.beta` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.1.conv.weight` with shape `(80, 80, 3, 3)`, which is inconsistent with cell shape `(64, 64, 3, 3)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.1.bn.moving_mean` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.1.bn.moving_variance` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.1.bn.gamma` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.1.bn.beta` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.2.weight` with shape `(80, 80, 1, 1)`, which is inconsistent with cell shape `(20, 64, 1, 1)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.1.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(20,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.0.conv.weight` with shape `(80, 256, 3, 3)`, which is inconsistent with cell shape `(64, 256, 3, 3)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.0.bn.moving_mean` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.0.bn.moving_variance` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.0.bn.gamma` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.0.bn.beta` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.1.conv.weight` with shape `(80, 80, 3, 3)`, which is inconsistent with cell shape `(64, 64, 3, 3)` 2024-11-02 03:33:12,052 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.1.bn.moving_mean` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,053 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.1.bn.moving_variance` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,053 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.1.bn.gamma` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,053 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.1.bn.beta` with shape `(80,)`, which is inconsistent with cell shape `(64,)` 2024-11-02 03:33:12,053 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.2.weight` with shape `(80, 80, 1, 1)`, which is inconsistent with cell shape `(20, 64, 1, 1)` 2024-11-02 03:33:12,053 [WARNING] Dropping checkpoint parameter `model.model.22.cv3.2.2.bias` with shape `(80,)`, which is inconsistent with cell shape `(20,)` [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.754.04 [mindspore/train/serialization.py:1378] For 'load_param_into_net', 36 parameters in the 'net' are not loaded, because they are not in the 'parameter_dict', please check whether the network structure is consistent when training and loading checkpoint. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.755.86 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.0.conv.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.06 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.0.bn.moving_mean is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.20 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.0.bn.moving_variance is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.34 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.0.bn.gamma is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.47 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.0.bn.beta is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.60 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.1.conv.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.72 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.1.bn.moving_mean is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.84 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.1.bn.moving_variance is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.756.96 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.1.bn.gamma is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.08 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.1.bn.beta is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.20 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.2.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.32 [mindspore/train/serialization.py:1383] model.model.22.cv3.0.2.bias is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.43 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.0.conv.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.55 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.0.bn.moving_mean is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.67 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.0.bn.moving_variance is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.78 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.0.bn.gamma is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.757.90 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.0.bn.beta is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.02 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.1.conv.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.13 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.1.bn.moving_mean is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.25 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.1.bn.moving_variance is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.36 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.1.bn.gamma is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.47 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.1.bn.beta is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.59 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.2.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.70 [mindspore/train/serialization.py:1383] model.model.22.cv3.1.2.bias is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.81 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.0.conv.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.758.92 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.0.bn.moving_mean is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.04 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.0.bn.moving_variance is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.16 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.0.bn.gamma is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.27 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.0.bn.beta is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.39 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.1.conv.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.51 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.1.bn.moving_mean is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.62 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.1.bn.moving_variance is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.73 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.1.bn.gamma is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.85 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.1.bn.beta is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.759.96 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.2.weight is not loaded. [WARNING] ME(27832:128372702312256,MainProcess):2024-11-02-03:33:12.760.08 [mindspore/train/serialization.py:1383] model.model.22.cv3.2.2.bias is not loaded. 2024-11-02 03:33:12,076 [INFO] Pretrain model load from "/home/xiaoyao/mindyolo-master/yolov8-n.ckpt" success. 2024-11-02 03:33:13,512 [INFO] ema_weight not exist, default pretrain weight is currently used. 2024-11-02 03:33:13,624 [INFO] Dataset Cache file hash/version check success. 2024-11-02 03:33:13,624 [INFO] Load dataset cache from [/home/xiaoyao/mindyolo-master/datasets/labels/train.cache.npy] success. Scanning '/home/xiaoyao/mindyolo-master/datasets/labels/train.cache.npy' images and labels... 5862 found, 0 missing, 0 empty, 0 corrupted: 100%|███████████████████████████████████████████████| 5862/5862 [00:00<?, ?it/s] 2024-11-02 03:33:13,648 [INFO] Dataloader num parallel workers: [2] 2024-11-02 03:33:13,720 [INFO] Dataset Cache file hash/version check success. 2024-11-02 03:33:13,720 [INFO] Load dataset cache from [/home/xiaoyao/mindyolo-master/datasets/labels/train.cache.npy] success. Scanning '/home/xiaoyao/mindyolo-master/datasets/labels/train.cache.npy' images and labels... 5862 found, 0 missing, 0 empty, 0 corrupted: 100%|███████████████████████████████████████████████| 5862/5862 [00:00<?, ?it/s] 2024-11-02 03:33:13,741 [INFO] Dataloader num parallel workers: [2] 2024-11-02 03:33:17,830 [INFO] Dataset Cache file hash/version check success. 2024-11-02 03:33:17,830 [INFO] Load dataset cache from [/home/xiaoyao/mindyolo-master/datasets/labels/val.cache.npy] success. Scanning '/home/xiaoyao/mindyolo-master/datasets/labels/val.cache.npy' images and labels... 5863 found, 0 missing, 0 empty, 0 corrupted: 100%|█████████████████████████████████████████████████| 5863/5863 [00:00<?, ?it/s] 2024-11-02 03:33:17,849 [INFO] Dataloader num parallel workers: [1] 2024-11-02 03:33:17,919 [INFO] Registry(name=callback, total=4) 2024-11-02 03:33:17,919 [INFO] (0): YoloxSwitchTrain in mindyolo/utils/callback.py 2024-11-02 03:33:17,919 [INFO] (1): EvalWhileTrain in mindyolo/utils/callback.py 2024-11-02 03:33:17,919 [INFO] (2): SummaryCallback in mindyolo/utils/callback.py 2024-11-02 03:33:17,919 [INFO] (3): ProfilerCallback in mindyolo/utils/callback.py 2024-11-02 03:33:17,919 [INFO] 2024-11-02 03:33:18,298 [INFO] got 2 active callback as follows: 2024-11-02 03:33:18,298 [INFO] SummaryCallback() 2024-11-02 03:33:18,298 [INFO] EvalWhileTrain(stage_intervals=[1], stage_epochs=[9223372036854775807], stage_cum_epochs=[9223372036854775807], eval_last_epoch=True, isolated_epochs=[], keep_checkpoint_max=10, manager_best=<mindyolo.utils.checkpoint_manager.CheckpointManager object at 0x74c0d963a7c0>, ckpt_filelist_best=[]) 2024-11-02 03:33:18,298 [WARNING] The first epoch will be compiled for the graph, which may take a long time; You can come back later :). /home/xiaoyao/anaconda3/envs/mind/lib/python3.9/site-packages/albumentations/__init__.py:24: UserWarning: A new version of Albumentations is available: 1.4.21 (you have 1.4.20). Upgrade using: pip install -U albumentations. To disable automatic update checks, set the environment variable NO_ALBUMENTATIONS_UPDATE to 1. check_for_updates() albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) [INFO] albumentations load success /home/xiaoyao/anaconda3/envs/mind/lib/python3.9/site-packages/albumentations/check_version.py:51: UserWarning: Error fetching version info The read operation timed out data = fetch_version_info() albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) [INFO] albumentations load success /home/xiaoyao/anaconda3/envs/mind/lib/python3.9/site-packages/albumentations/check_version.py:51: UserWarning: Error fetching version info The read operation timed out data = fetch_version_info() albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) [INFO] albumentations load success /home/xiaoyao/anaconda3/envs/mind/lib/python3.9/site-packages/albumentations/check_version.py:51: UserWarning: Error fetching version info The read operation timed out data = fetch_version_info() albumentations: Blur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01, num_output_channels=3, method='weighted_average'), CLAHE(p=0.01, clip_limit=(1.0, 4.0), tile_grid_size=(8, 8)) [INFO] albumentations load success 2024-11-02 03:33:46,966 [INFO] Epoch 1/100, Step 10/366, imgsize (640, 640), loss: 8.1337, lbox: 4.2059, lcls: 1.2588, dfl: 2.6690, cur_lr: 0.09909836202859879 2024-11-02 03:33:46,974 [INFO] Epoch 1/100, Step 10/366, step time: 2867.62 ms 2024-11-02 03:33:53,985 [INFO] Epoch 1/100, Step 20/366, imgsize (640, 640), loss: 8.6314, lbox: 4.4014, lcls: 1.2647, dfl: 2.9652, cur_lr: 0.09819672256708145 2024-11-02 03:33:53,991 [INFO] Epoch 1/100, Step 20/366, step time: 701.68 ms 2024-11-02 03:33:59,656 [INFO] Epoch 1/100, Step 30/366, imgsize (640, 640), loss: 7.6046, lbox: 3.6538, lcls: 1.2664, dfl: 2.6845, cur_lr: 0.09729508310556412 2024-11-02 03:33:59,657 [INFO] Epoch 1/100, Step 30/366, step time: 566.52 ms训练了130代训练后推理是空的
用mydata推理
(mind) xiaoyao@xiaoyao:~/mindyolo-master$ python demo/predict.py --config /home/xiaoyao/mindyolo-master/datasets/mydata.yaml --weight=/home/xiaoyao/mindyolo-master/runs/2024.11.01-19.10.46/weights/mydata-130_366.ckpt --image_path /home/xiaoyao/mindyolo-master/datasets/images/train/00011.jpg --device_target=GPU --iou_thres=0.5 --conf_thres=0.01 2024-11-02 03:13:18,617 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:13:18,628 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:13:18,689 [INFO] number of network params, total: 3.025151M, trainable: 3.014732M 2024-11-02 03:13:21,335 [INFO] Load checkpoint from [/home/xiaoyao/mindyolo-master/runs/2024.11.01-19.10.46/weights/mydata-130_366.ckpt] success. 2024-11-02 03:13:23,292 [INFO] Predict result is: {'category_id': [], 'bbox': [], 'score': []} 2024-11-02 03:13:23,292 [INFO] Speed: 1942.1/1.9/1944.0 ms inference/NMS/total per 640x640 image at batch-size 1; 2024-11-02 03:13:23,292 [INFO] Detect a image success. 2024-11-02 03:13:23,298 [INFO] Infer completed.用EMA_mydata的推理，就是直接乱了
输出日志
(mind) xiaoyao@xiaoyao:~/mindyolo-master$ python demo/predict.py --config /home/xiaoyao/mindyolo-master/datasets/mydata.yaml --weight=/home/xiaoyao/mindyolo-master/runs/2024.11.01-19.10.46/weights/EMA_mydata-130_366.ckpt --image_path /home/xiaoyao/mindyolo-master/datasets/images/train/00011.jpg --device_target=GPU --iou_thres=0.5 --conf_thres=0.1 2024-11-02 03:17:06,799 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:17:06,810 [WARNING] Parse Model, args: nearest, keep str type 2024-11-02 03:17:06,868 [INFO] number of network params, total: 3.025151M, trainable: 3.014732M [WARNING] ME(26038:124966295865152,MainProcess):2024-11-02-03:17:09.395.666 [mindspore/train/serialization.py:1469] For 'load_param_into_net', remove parameter prefix name: ema., continue to load. 2024-11-02 03:17:09,400 [INFO] Load checkpoint from [/home/xiaoyao/mindyolo-master/runs/2024.11.01-19.10.46/weights/EMA_mydata-130_366.ckpt] success. 2024-11-02 03:17:12,248 [INFO] Predict result is: {'category_id': [1, 0, 19, 14, 6, 5, 3, 2, 8, 4, 12, 11, 7, 13, 11, 10, 8, 2, 0, 15, 12, 11, 2, 1, 15, 6, 0, 18, 17, 9, 8, 4, 11, 7, 12, 6, 1, 0, 19, 14, 5, 3, 2, 6, 1, 12, 11, 8, 7, 0, 17, 9, 4, 2, 10, 19, 14, 8, 7, 6, 3, 2, 16, 13, 9, 16, 15, 5, 16, 12, 9, 13, 1, 0, 1, 19, 9, 16, 13, 15, 13, 8, 6, 18, 15, 9, 7, 5, 4, 10, 14, 13, 12, 11, 10, 9, 3, 5, 1, 19, 16, 15, 12, 10, 9, 3, 1, 17, 16, 14, 7, 6, 5, 11, 9, 8, 1, 0, 8, 7, 6, 15, 14, 13, 12, 1, 19, 16, 8, 7, 6, 5, 12, 11, 9, 16, 15, 14, 13, 5, 1, 19, 7, 6, 5, 13, 12, 9, 8, 14, 11, 9, 8, 7, 2, 0, 11, 16, 1, 6, 5, 4, 4, 17, 5, 5, 10, 9, 8, 3, 4, 19, 8, 8, 6, 4, 15, 11, 13, 17, 12, 7, 18, 15, 13, 11, 8, 7, 6, 0, 15, 7, 3, 2, 15, 2, 11, 9, 12, 9, 14, 11, 6, 1, 14, 13, 3, 9, 8, 7, 6, 4, 6, 12, 11, 10, 2, 19, 2, 0, 6, 4, 1, 16, 14, 11, 18, 16, 12, 16, 15, 13, 5, 4, 6, 8, 6, 1, 15, 10, 9, 5, 2, 11, 10, 13, 12, 9, 8, 7, 0, 15, 5, 19, 6, 3, 2, 1, 16, 10, 9, 8, 7, 0, 11, 16, 14, 12, 4, 6, 1, 11, 1, 0, 11, 2, 5, 3, 2, 16, 0, 11, 7, 19, 6, 1, 17, 4, 8, 15, 0, 18, 9, 5, 16, 4, 8, 7, 6, 4], 'bbox': [[420.0, 300.0, 380.0, 500.0], [420.0, 300.0, 380.0, 500.0], [380.0, 300.0, 420.0, 500.0], [380.0, 300.0, 420.0, 500.0], [380.0, 300.0, 420.0, 500.0], [380.0, 300.0, 420.0, 500.0], [380.0, 300.0, 420.0, 500.0], [380.0, 300.0, 420.0, 500.0], [420.0, 300.0, 380.0, 500.0], [420.0, 300.0, 380.0, 500.0], [340.0, 300.0, 460.0, 500.0], [340.0, 300.0, 460.0, 500.0], [340.0, 300.0, 460.0, 500.0], [15.0, 0.0, 80.0, 45.0], [15.0, 0.0, 80.0, 45.0], [15.0, 0.0, 80.0, 45.0], [220.0, 300.0, 580.0, 500.0], [15.0, 0.0, 80.0, 45.0], [15.0, 0.0, 80.0, 45.0], [0.0, 0.0, 85.0, 65.0], [0.0, 0.0, 85.0, 65.0], [0.0, 0.0, 85.0, 65.0], [0.0, 0.0, 105.0, 75.0], [0.0, 0.0, 105.0, 75.0], [15.0, 0.0, 80.0, 45.0], [0.0, 0.0, 85.0, 65.0], [0.0, 0.0, 85.0, 65.0], [0.0, 0.0, 115.0, 75.0], [0.0, 0.0, 115.0, 75.0], [15.0, 0.0, 80.0, 45.0], [15.0, 0.0, 80.0, 45.0], [15.0, 0.0, 80.0, 45.0], [100.0, 300.0, 600.0, 500.0], [100.0, 300.0, 600.0, 500.0], [140.0, 300.0, 600.0, 500.0], [140.0, 300.0, 600.0, 500.0], [140.0, 300.0, 600.0, 500.0], [140.0, 300.0, 600.0, 500.0], [100.0, 300.0, 600.0, 500.0], [100.0, 300.0, 600.0, 500.0], [20.0, 300.0, 600.0, 500.0], [20.0, 300.0, 600.0, 500.0], [20.0, 300.0, 600.0, 500.0], [0.0, 300.0, 540.0, 500.0], [0.0, 300.0, 540.0, 500.0], [0.0, 300.0, 500.0, 500.0], [0.0, 300.0, 500.0, 500.0], [0.0, 300.0, 500.0, 500.0], [0.0, 300.0, 500.0, 500.0], [0.0, 300.0, 380.0, 500.0], [0.0, 300.0, 340.0, 500.0], [0.0, 300.0, 420.0, 500.0], [0.0, 300.0, 420.0, 500.0], [0.0, 300.0, 420.0, 500.0], [0.0, 300.0, 380.0, 500.0], [0.0, 300.0, 500.0, 500.0], [0.0, 300.0, 500.0, 500.0], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [460.0, 260.0, 340.0, 540.0], [460.0, 260.0, 340.0, 540.0], [460.0, 260.0, 340.0, 540.0], [0.0, 300.0, 460.0, 500.0], [340.0, 260.0, 460.0, 540.0], [0.0, 300.0, 340.0, 500.0], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [0.0, 300.0, 340.0, 500.0], [620.0, 579.78, 180.0, 220.22], [620.0, 579.78, 180.0, 220.22], [0.0, 300.0, 340.0, 500.0], [620.0, 579.78, 180.0, 220.22], [140.0, 260.0, 600.0, 540.0], [180.0, 260.0, 600.0, 540.0], [180.0, 260.0, 600.0, 540.0], [140.0, 260.0, 600.0, 540.0], [0.0, 260.0, 580.0, 540.0], [0.0, 140.0, 380.0, 660.0], [0.0, 140.0, 380.0, 660.0], [0.0, 260.0, 460.0, 540.0], [0.0, 260.0, 460.0, 540.0], [220.0, 100.0, 580.0, 700.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [500.0, 220.0, 300.0, 580.0], [446.48, 555.846, 316.722, 244.154], [446.48, 555.846, 316.722, 244.154], [446.48, 555.846, 316.722, 244.154], [485.511, 556.488, 314.488, 243.512], [485.511, 556.488, 314.488, 243.512], [485.511, 556.488, 314.488, 243.512], [0.0, 260.0, 380.0, 540.0], [485.511, 556.488, 314.488, 243.512], [485.511, 556.488, 314.488, 243.512], [446.48, 555.846, 316.722, 244.154], [446.48, 555.846, 316.722, 244.154], [446.48, 555.846, 316.722, 244.154], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [0.0, 140.0, 260.0, 660.0], [366.481, 555.833, 316.744, 244.167], [366.481, 555.833, 316.744, 244.167], [366.481, 555.833, 316.744, 244.167], [366.481, 555.833, 316.744, 244.167], [366.481, 555.833, 316.744, 244.167], [366.481, 555.833, 316.744, 244.167], [220.0, 100.0, 580.0, 700.0], [220.0, 100.0, 580.0, 700.0], [485.511, 556.488, 314.488, 243.512], [485.511, 556.488, 314.488, 243.512], [485.511, 556.488, 314.488, 243.512], [246.481, 555.833, 316.744, 244.167], [246.481, 555.833, 316.744, 244.167], [246.481, 555.833, 316.744, 244.167], [286.481, 555.833, 316.744, 244.167], [286.481, 555.833, 316.744, 244.167], [246.481, 555.833, 316.744, 244.167], [246.481, 555.833, 316.744, 244.167], [166.472, 555.834, 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