【悉读经典】ConvNext 在PaddleClas、Seg、Det中的应用

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正克隆到 'PaddleClas'...
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/home/aistudio/PaddleClas
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Collecting scikit-learn==0.23.2
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Installing collected packages: faiss-cpu, opencv-python, scikit-learn
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Successfully installed faiss-cpu-1.7.1.post2 opencv-python-4.4.0.46 scikit-learn-0.23.2

A new filed (epoch) detected!
[2022/03/31 18:04:25] root INFO: 
===========================================================
==        PaddleClas is powered by PaddlePaddle !        ==
===========================================================
==                                                       ==
==   For more info please go to the following website.   ==
==                                                       ==
==       https://github.com/PaddlePaddle/PaddleClas      ==
===========================================================

[2022/03/31 18:04:25] root INFO: Arch : 
[2022/03/31 18:04:25] root INFO:     class_num : 12
[2022/03/31 18:04:25] root INFO:     name : ConvNeXt_Tiny
[2022/03/31 18:04:25] root INFO: DataLoader : 
[2022/03/31 18:04:25] root INFO:     Eval : 
[2022/03/31 18:04:25] root INFO:         dataset : 
[2022/03/31 18:04:25] root INFO:             cls_label_path : /home/aistudio/data/data10954/val_list.txt
[2022/03/31 18:04:25] root INFO:             image_root : /home/aistudio/data/data10954/
[2022/03/31 18:04:25] root INFO:             name : ImageNetDataset
[2022/03/31 18:04:25] root INFO:             transform_ops : 
[2022/03/31 18:04:25] root INFO:                 DecodeImage : 
[2022/03/31 18:04:25] root INFO:                     channel_first : False
[2022/03/31 18:04:25] root INFO:                     to_rgb : True
[2022/03/31 18:04:25] root INFO:                 ResizeImage : 
[2022/03/31 18:04:25] root INFO:                     resize_short : 256
[2022/03/31 18:04:25] root INFO:                 CropImage : 
[2022/03/31 18:04:25] root INFO:                     size : 224
[2022/03/31 18:04:25] root INFO:                 NormalizeImage : 
[2022/03/31 18:04:25] root INFO:                     mean : [0.485, 0.456, 0.406]
[2022/03/31 18:04:25] root INFO:                     order : 
[2022/03/31 18:04:25] root INFO:                     scale : 1.0/255.0
[2022/03/31 18:04:25] root INFO:                     std : [0.229, 0.224, 0.225]
[2022/03/31 18:04:25] root INFO:         loader : 
[2022/03/31 18:04:25] root INFO:             num_workers : 4
[2022/03/31 18:04:25] root INFO:             use_shared_memory : True
[2022/03/31 18:04:25] root INFO:         sampler : 
[2022/03/31 18:04:25] root INFO:             batch_size : 128
[2022/03/31 18:04:25] root INFO:             drop_last : False
[2022/03/31 18:04:25] root INFO:             name : DistributedBatchSampler
[2022/03/31 18:04:25] root INFO:             shuffle : False
[2022/03/31 18:04:25] root INFO:     Train : 
[2022/03/31 18:04:25] root INFO:         dataset : 
[2022/03/31 18:04:25] root INFO:             cls_label_path : /home/aistudio/data/data10954/train_list.txt
[2022/03/31 18:04:25] root INFO:             image_root : /home/aistudio/data/data10954/
[2022/03/31 18:04:25] root INFO:             name : ImageNetDataset
[2022/03/31 18:04:25] root INFO:             transform_ops : 
[2022/03/31 18:04:25] root INFO:                 DecodeImage : 
[2022/03/31 18:04:25] root INFO:                     channel_first : False
[2022/03/31 18:04:25] root INFO:                     to_rgb : True
[2022/03/31 18:04:25] root INFO:                 RandomResizedCrop : 
[2022/03/31 18:04:25] root INFO:                     interpolation : bicubic
[2022/03/31 18:04:25] root INFO:                     ratio : [1.0, 1.0]
[2022/03/31 18:04:25] root INFO:                     scale : [0.75, 1.0]
[2022/03/31 18:04:25] root INFO:                     size : 224
[2022/03/31 18:04:25] root INFO:                 NormalizeImage : 
[2022/03/31 18:04:25] root INFO:                     mean : [0.485, 0.456, 0.406]
[2022/03/31 18:04:25] root INFO:                     order : 
[2022/03/31 18:04:25] root INFO:                     scale : 1.0/255.0
[2022/03/31 18:04:25] root INFO:                     std : [0.229, 0.224, 0.225]
[2022/03/31 18:04:25] root INFO:         loader : 
[2022/03/31 18:04:25] root INFO:             num_workers : 4
[2022/03/31 18:04:25] root INFO:             use_shared_memory : True
[2022/03/31 18:04:25] root INFO:         sampler : 
[2022/03/31 18:04:25] root INFO:             batch_size : 64
[2022/03/31 18:04:25] root INFO:             drop_last : False
[2022/03/31 18:04:25] root INFO:             name : DistributedBatchSampler
[2022/03/31 18:04:25] root INFO:             shuffle : True
[2022/03/31 18:04:25] root INFO: Global : 
[2022/03/31 18:04:25] root INFO:     checkpoints : None
[2022/03/31 18:04:25] root INFO:     device : gpu
[2022/03/31 18:04:25] root INFO:     epochs : 5
[2022/03/31 18:04:25] root INFO:     eval_during_train : True
[2022/03/31 18:04:25] root INFO:     eval_interval : 1
[2022/03/31 18:04:25] root INFO:     image_shape : [3, 300, 300]
[2022/03/31 18:04:25] root INFO:     output_dir : /home/aistudio/data/output_class/
[2022/03/31 18:04:25] root INFO:     pretrained_model : https://passl.bj.bcebos.com/models/convnext_tiny_1k_224.pdparams
[2022/03/31 18:04:25] root INFO:     print_batch_step : 10
[2022/03/31 18:04:25] root INFO:     save_inference_dir : /home/aistudio/data/inference
[2022/03/31 18:04:25] root INFO:     save_interval : 1
[2022/03/31 18:04:25] root INFO:     use_visualdl : False
[2022/03/31 18:04:25] root INFO: Infer : 
[2022/03/31 18:04:25] root INFO:     PostProcess : 
[2022/03/31 18:04:25] root INFO:         class_id_map_file : /home/aistudio/work/class/label_list.txt
[2022/03/31 18:04:25] root INFO:         name : Topk
[2022/03/31 18:04:25] root INFO:         topk : 5
[2022/03/31 18:04:25] root INFO:     batch_size : 100
[2022/03/31 18:04:25] root INFO:     infer_imgs : docs/images/inference_deployment/whl_demo.jpg
[2022/03/31 18:04:25] root INFO:     transforms : 
[2022/03/31 18:04:25] root INFO:         DecodeImage : 
[2022/03/31 18:04:25] root INFO:             channel_first : False
[2022/03/31 18:04:25] root INFO:             to_rgb : True
[2022/03/31 18:04:25] root INFO:         ResizeImage : 
[2022/03/31 18:04:25] root INFO:             resize_short : 256
[2022/03/31 18:04:25] root INFO:         CropImage : 
[2022/03/31 18:04:25] root INFO:             size : 224
[2022/03/31 18:04:25] root INFO:         NormalizeImage : 
[2022/03/31 18:04:25] root INFO:             mean : [0.485, 0.456, 0.406]
[2022/03/31 18:04:25] root INFO:             order : 
[2022/03/31 18:04:25] root INFO:             scale : 1.0/255.0
[2022/03/31 18:04:25] root INFO:             std : [0.229, 0.224, 0.225]
[2022/03/31 18:04:25] root INFO:         ToCHWImage : None
[2022/03/31 18:04:25] root INFO: Loss : 
[2022/03/31 18:04:25] root INFO:     Eval : 
[2022/03/31 18:04:25] root INFO:         CELoss : 
[2022/03/31 18:04:25] root INFO:             weight : 1.0
[2022/03/31 18:04:25] root INFO:     Train : 
[2022/03/31 18:04:25] root INFO:         CELoss : 
[2022/03/31 18:04:25] root INFO:             epsilon : 0.1
[2022/03/31 18:04:25] root INFO:             weight : 1.0
[2022/03/31 18:04:25] root INFO: Metric : 
[2022/03/31 18:04:25] root INFO:     Eval : 
[2022/03/31 18:04:25] root INFO:         TopkAcc : 
[2022/03/31 18:04:25] root INFO:             topk : [1, 5]
[2022/03/31 18:04:25] root INFO:     Train : 
[2022/03/31 18:04:25] root INFO:         TopkAcc : 
[2022/03/31 18:04:25] root INFO:             topk : [1, 5]
[2022/03/31 18:04:25] root INFO: Optimizer : 
[2022/03/31 18:04:25] root INFO:     beta1 : 0.9
[2022/03/31 18:04:25] root INFO:     beta2 : 0.999
[2022/03/31 18:04:25] root INFO:     lr : 
[2022/03/31 18:04:25] root INFO:         epoch : 5
[2022/03/31 18:04:25] root INFO:         epochs : 300
[2022/03/31 18:04:25] root INFO:         eta_min : 1e-05
[2022/03/31 18:04:25] root INFO:         learning_rate : 0.000125
[2022/03/31 18:04:25] root INFO:         name : Cosine
[2022/03/31 18:04:25] root INFO:         step_each_epoch : 32
[2022/03/31 18:04:25] root INFO:         warmup_epoch : 1
[2022/03/31 18:04:25] root INFO:         warmup_start_lr : 1e-06
[2022/03/31 18:04:25] root INFO:     name : AdamW
[2022/03/31 18:04:25] root INFO:     weight_decay : 0.05
[2022/03/31 18:04:25] root INFO: profiler_options : None
[2022/03/31 18:04:25] root INFO: train with paddle 2.2.2 and device CUDAPlace(0)
[2022/03/31 18:04:25] root WARNING: 'TopkAcc' metric can not be used when setting 'batch_transform_ops' in config. The 'TopkAcc' metric has been removed.
W0331 18:04:25.969805   303 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0331 18:04:25.974506   303 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[2022/03/31 18:04:30] root INFO: unique_endpoints {''}
[2022/03/31 18:04:30] root INFO: Downloading convnext_tiny_1k_224.pdparams from https://passl.bj.bcebos.com/models/convnext_tiny_1k_224.pdparams
100%|████████████████████████████████| 111694/111694 [00:03<00:00, 37222.88it/s]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1441: UserWarning: Skip loading for head.fc_cls.weight. head.fc_cls.weight receives a shape [768, 1000], but the expected shape is [768, 12].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/layers.py:1441: UserWarning: Skip loading for head.fc_cls.bias. head.fc_cls.bias receives a shape [1000], but the expected shape is [12].
  warnings.warn(("Skip loading for {}. ".format(key) + str(err)))
model loading : [182]/[182]
[2022/03/31 18:04:33] root WARNING: The training strategy in config files provided by PaddleClas is based on 4 gpus. But the number of gpus is 1 in current training. Please modify the stategy (learning rate, batch size and so on) if use config files in PaddleClas to train.
[2022/03/31 18:04:35] root INFO: [Train][Epoch 1/5][Iter: 0/32]lr: 0.00000, CELoss: 2.50516, loss: 2.50516, batch_cost: 2.09711s, reader_cost: 1.59906, ips: 30.51826 images/sec, eta: 0:05:35
[2022/03/31 18:04:38] root INFO: [Train][Epoch 1/5][Iter: 10/32]lr: 0.00004, CELoss: 2.50141, loss: 2.50141, batch_cost: 0.30005s, reader_cost: 0.00157, ips: 213.29944 images/sec, eta: 0:00:45
[2022/03/31 18:04:41] root INFO: [Train][Epoch 1/5][Iter: 20/32]lr: 0.00008, CELoss: 2.45706, loss: 2.45706, batch_cost: 0.29993s, reader_cost: 0.00162, ips: 213.38383 images/sec, eta: 0:00:41
[2022/03/31 18:04:45] root INFO: [Train][Epoch 1/5][Iter: 30/32]lr: 0.00012, CELoss: 2.37709, loss: 2.37709, batch_cost: 0.30253s, reader_cost: 0.00129, ips: 211.55021 images/sec, eta: 0:00:39
[2022/03/31 18:04:45] root INFO: [Train][Epoch 1/5][Avg]CELoss: 2.37555, loss: 2.37555
[2022/03/31 18:04:46] root INFO: [Eval][Epoch 1][Iter: 0/2]CELoss: 3.13076, loss: 3.13076, top1: 0.00000, top5: 0.00000, batch_cost: 1.38679s, reader_cost: 1.21769, ips: 92.29973 images/sec
[2022/03/31 18:04:46] root INFO: [Eval][Epoch 1][Avg]CELoss: 3.12133, loss: 3.12133, top1: 0.00000, top5: 0.00000
[2022/03/31 18:04:46] root INFO: [Eval][Epoch 1][best metric: 0.0]
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/framework/io.py:415: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
  if isinstance(obj, collections.Iterable) and not isinstance(obj, (
[2022/03/31 18:04:47] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/epoch_1
[2022/03/31 18:04:48] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/latest
[2022/03/31 18:04:50] root INFO: [Train][Epoch 2/5][Iter: 0/32]lr: 0.00012, CELoss: 1.99909, loss: 1.99909, batch_cost: 0.35991s, reader_cost: 0.06413, ips: 177.82215 images/sec, eta: 0:00:46
[2022/03/31 18:04:53] root INFO: [Train][Epoch 2/5][Iter: 10/32]lr: 0.00012, CELoss: 1.79004, loss: 1.79004, batch_cost: 0.30007s, reader_cost: 0.00032, ips: 213.28602 images/sec, eta: 0:00:35
[2022/03/31 18:04:56] root INFO: [Train][Epoch 2/5][Iter: 20/32]lr: 0.00012, CELoss: 1.59704, loss: 1.59704, batch_cost: 0.31241246s, reader_cost: 0.00283, ips: 204.82522 images/sec, eta: 0:00:33
[2022/03/31 18:04:59] root INFO: [Train][Epoch 2/5][Iter: 30/32]lr: 0.00011, CELoss: 1.41669, loss: 1.41669, batch_cost: 0.30708s, reader_cost: 0.00258, ips: 208.41727 images/sec, eta: 0:00:30
[2022/03/31 18:05:00] root INFO: [Train][Epoch 2/5][Avg]CELoss: 1.41307, loss: 1.41307
[2022/03/31 18:05:01] root INFO: [Eval][Epoch 2][Iter: 0/2]CELoss: 3.16468, loss: 3.16468, top1: 0.00000, top5: 0.06250, batch_cost: 1.35001s, reader_cost: 1.18088, ips: 94.81415 images/sec
[2022/03/31 18:05:01] root INFO: [Eval][Epoch 2][Avg]CELoss: 3.16081, loss: 3.16081, top1: 0.00000, top5: 0.10063
[2022/03/31 18:05:01] root INFO: [Eval][Epoch 2][best metric: 0.0]
[2022/03/31 18:05:02] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/epoch_2
[2022/03/31 18:05:03] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/latest
[2022/03/31 18:05:05] root INFO: [Train][Epoch 3/5][Iter: 0/32]lr: 0.00011, CELoss: 0.83580, loss: 0.83580, batch_cost: 0.35745s, reader_cost: 0.05875, ips: 179.04732 images/sec, eta: 0:00:34
[2022/03/31 18:05:08] root INFO: [Train][Epoch 3/5][Iter: 10/32]lr: 0.00010, CELoss: 0.79101, loss: 0.79101, batch_cost: 0.30012s, reader_cost: 0.00047, ips: 213.25137 images/sec, eta: 0:00:25
[2022/03/31 18:05:11] root INFO: [Train][Epoch 3/5][Iter: 20/32]lr: 0.00008, CELoss: 0.75128, loss: 0.75128, batch_cost: 0.29991s, reader_cost: 0.00046, ips: 213.39916 images/sec, eta: 0:00:22
[2022/03/31 18:05:14] root INFO: [Train][Epoch 3/5][Iter: 30/32]lr: 0.00007, CELoss: 0.73154, loss: 0.73154, batch_cost: 0.29861s, reader_cost: 0.00047, ips: 214.32552 images/sec, eta: 0:00:19
[2022/03/31 18:05:14] root INFO: [Train][Epoch 3/5][Avg]CELoss: 0.73081, loss: 0.73081
[2022/03/31 18:05:15] root INFO: [Eval][Epoch 3][Iter: 0/2]CELoss: 2.32611, loss: 2.32611, top1: 0.03906, top5: 0.89844, batch_cost: 1.40323s, reader_cost: 1.22578, ips: 91.21806 images/sec
[2022/03/31 18:05:16] root INFO: [Eval][Epoch 3][Avg]CELoss: 2.34307, loss: 2.34307, top1: 0.03774, top5: 0.89308
[2022/03/31 18:05:17] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/best_model
[2022/03/31 18:05:17] root INFO: [Eval][Epoch 3][best metric: 0.03773584886916778]
[2022/03/31 18:05:18] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/epoch_3
[2022/03/31 18:05:19] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/latest
[2022/03/31 18:05:20] root INFO: [Train][Epoch 4/5][Iter: 0/32]lr: 0.00007, CELoss: 0.66662, loss: 0.66662, batch_cost: 0.33352s, reader_cost: 0.04039, ips: 191.89510 images/sec, eta: 0:00:21
[2022/03/31 18:05:23] root INFO: [Train][Epoch 4/5][Iter: 10/32]lr: 0.00005, CELoss: 0.64193, loss: 0.64193, batch_cost: 0.33196s, reader_cost: 0.01953, ips: 192.79643 images/sec, eta: 0:00:17
[2022/03/31 18:05:26] root INFO: [Train][Epoch 4/5][Iter: 20/32]lr: 0.00004, CELoss: 0.63435, loss: 0.63435, batch_cost: 0.31193s, reader_cost: 0.00752, ips: 205.17229 images/sec, eta: 0:00:13
[2022/03/31 18:05:29] root INFO: [Train][Epoch 4/5][Iter: 30/32]lr: 0.00003, CELoss: 0.62602, loss: 0.62602, batch_cost: 0.30605s, reader_cost: 0.00473, ips: 209.11636 images/sec, eta: 0:00:10
[2022/03/31 18:05:29] root INFO: [Train][Epoch 4/5][Avg]CELoss: 0.62564, loss: 0.62564
[2022/03/31 18:05:31] root INFO: [Eval][Epoch 4][Iter: 0/2]CELoss: 1.86182, loss: 1.86182, top1: 0.25781, top5: 0.96875, batch_cost: 1.35239s, reader_cost: 1.18338, ips: 94.64708 images/sec
[2022/03/31 18:05:31] root INFO: [Eval][Epoch 4][Avg]CELoss: 1.88848, loss: 1.88848, top1: 0.25157, top5: 0.96855
[2022/03/31 18:05:32] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/best_model
[2022/03/31 18:05:32] root INFO: [Eval][Epoch 4][best metric: 0.25157232573197325]
[2022/03/31 18:05:33] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/epoch_4
[2022/03/31 18:05:34] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/latest
[2022/03/31 18:05:35] root INFO: [Train][Epoch 5/5][Iter: 0/32]lr: 0.00003, CELoss: 0.62709, loss: 0.62709, batch_cost: 0.34112s, reader_cost: 0.04507, ips: 187.61492 images/sec, eta: 0:00:10
[2022/03/31 18:05:38] root INFO: [Train][Epoch 5/5][Iter: 10/32]lr: 0.00002, CELoss: 0.59784, loss: 0.59784, batch_cost: 0.31666s, reader_cost: 0.00276, ips: 202.10665 images/sec, eta: 0:00:06
[2022/03/31 18:05:41] root INFO: [Train][Epoch 5/5][Iter: 20/32]lr: 0.00001, CELoss: 0.58959, loss: 0.58959, batch_cost: 0.31241246s, reader_cost: 0.00219, ips: 204.82474 images/sec, eta: 0:00:03
[2022/03/31 18:05:44] root INFO: [Train][Epoch 5/5][Iter: 30/32]lr: 0.00001, CELoss: 0.58536, loss: 0.58536, batch_cost: 0.30651s, reader_cost: 0.00157, ips: 208.80046 images/sec, eta: 0:00:00
[2022/03/31 18:05:45] root INFO: [Train][Epoch 5/5][Avg]CELoss: 0.58511, loss: 0.58511
[2022/03/31 18:05:46] root INFO: [Eval][Epoch 5][Iter: 0/2]CELoss: 1.68354, loss: 1.68354, top1: 0.29688, top5: 0.96094, batch_cost: 1.36943s, reader_cost: 1.19968, ips: 93.46979 images/sec
[2022/03/31 18:05:46] root INFO: [Eval][Epoch 5][Avg]CELoss: 1.70561, loss: 1.70561, top1: 0.28931, top5: 0.96855
[2022/03/31 18:05:47] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/best_model
[2022/03/31 18:05:47] root INFO: [Eval][Epoch 5][best metric: 0.289308174601141]
[2022/03/31 18:05:48] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/epoch_5
[2022/03/31 18:05:49] root INFO: Already save model in /home/aistudio/data/output_class/ConvNeXt_Tiny/latest

[2022/03/31 18:05:53] root INFO: 
===========================================================
==        PaddleClas is powered by PaddlePaddle !        ==
===========================================================
==                                                       ==
==   For more info please go to the following website.   ==
==                                                       ==
==       https://github.com/PaddlePaddle/PaddleClas      ==
===========================================================

[2022/03/31 18:05:53] root INFO: Arch : 
[2022/03/31 18:05:53] root INFO:     class_num : 12
[2022/03/31 18:05:53] root INFO:     name : ConvNeXt_Tiny
[2022/03/31 18:05:53] root INFO: DataLoader : 
[2022/03/31 18:05:53] root INFO:     Eval : 
[2022/03/31 18:05:53] root INFO:         dataset : 
[2022/03/31 18:05:53] root INFO:             cls_label_path : /home/aistudio/data/data10954/val_list.txt
[2022/03/31 18:05:53] root INFO:             image_root : /home/aistudio/data/data10954/
[2022/03/31 18:05:53] root INFO:             name : ImageNetDataset
[2022/03/31 18:05:53] root INFO:             transform_ops : 
[2022/03/31 18:05:53] root INFO:                 DecodeImage : 
[2022/03/31 18:05:53] root INFO:                     channel_first : False
[2022/03/31 18:05:53] root INFO:                     to_rgb : True
[2022/03/31 18:05:53] root INFO:                 ResizeImage : 
[2022/03/31 18:05:53] root INFO:                     resize_short : 256
[2022/03/31 18:05:53] root INFO:                 CropImage : 
[2022/03/31 18:05:53] root INFO:                     size : 224
[2022/03/31 18:05:53] root INFO:                 NormalizeImage : 
[2022/03/31 18:05:53] root INFO:                     mean : [0.485, 0.456, 0.406]
[2022/03/31 18:05:53] root INFO:                     order : 
[2022/03/31 18:05:53] root INFO:                     scale : 1.0/255.0
[2022/03/31 18:05:53] root INFO:                     std : [0.229, 0.224, 0.225]
[2022/03/31 18:05:53] root INFO:         loader : 
[2022/03/31 18:05:53] root INFO:             num_workers : 4
[2022/03/31 18:05:53] root INFO:             use_shared_memory : True
[2022/03/31 18:05:53] root INFO:         sampler : 
[2022/03/31 18:05:53] root INFO:             batch_size : 128
[2022/03/31 18:05:53] root INFO:             drop_last : False
[2022/03/31 18:05:53] root INFO:             name : DistributedBatchSampler
[2022/03/31 18:05:53] root INFO:             shuffle : False
[2022/03/31 18:05:53] root INFO:     Train : 
[2022/03/31 18:05:53] root INFO:         dataset : 
[2022/03/31 18:05:53] root INFO:             cls_label_path : /home/aistudio/data/data10954/train_list.txt
[2022/03/31 18:05:53] root INFO:             image_root : /home/aistudio/data/data10954/
[2022/03/31 18:05:53] root INFO:             name : ImageNetDataset
[2022/03/31 18:05:53] root INFO:             transform_ops : 
[2022/03/31 18:05:53] root INFO:                 DecodeImage : 
[2022/03/31 18:05:53] root INFO:                     channel_first : False
[2022/03/31 18:05:53] root INFO:                     to_rgb : True
[2022/03/31 18:05:53] root INFO:                 RandomResizedCrop : 
[2022/03/31 18:05:53] root INFO:                     interpolation : bicubic
[2022/03/31 18:05:53] root INFO:                     ratio : [1.0, 1.0]
[2022/03/31 18:05:53] root INFO:                     scale : [0.75, 1.0]
[2022/03/31 18:05:53] root INFO:                     size : 224
[2022/03/31 18:05:53] root INFO:                 NormalizeImage : 
[2022/03/31 18:05:53] root INFO:                     mean : [0.485, 0.456, 0.406]
[2022/03/31 18:05:53] root INFO:                     order : 
[2022/03/31 18:05:53] root INFO:                     scale : 1.0/255.0
[2022/03/31 18:05:53] root INFO:                     std : [0.229, 0.224, 0.225]
[2022/03/31 18:05:53] root INFO:         loader : 
[2022/03/31 18:05:53] root INFO:             num_workers : 4
[2022/03/31 18:05:53] root INFO:             use_shared_memory : True
[2022/03/31 18:05:53] root INFO:         sampler : 
[2022/03/31 18:05:53] root INFO:             batch_size : 128
[2022/03/31 18:05:53] root INFO:             drop_last : False
[2022/03/31 18:05:53] root INFO:             name : DistributedBatchSampler
[2022/03/31 18:05:53] root INFO:             shuffle : True
[2022/03/31 18:05:53] root INFO: Global : 
[2022/03/31 18:05:53] root INFO:     checkpoints : None
[2022/03/31 18:05:53] root INFO:     device : gpu
[2022/03/31 18:05:53] root INFO:     epochs : 300
[2022/03/31 18:05:53] root INFO:     eval_during_train : True
[2022/03/31 18:05:53] root INFO:     eval_interval : 1
[2022/03/31 18:05:53] root INFO:     image_shape : [3, 300, 300]
[2022/03/31 18:05:53] root INFO:     output_dir : /home/aistudio/data/output_class/
[2022/03/31 18:05:53] root INFO:     pretrained_model : /home/aistudio/best_model/class/model.pdparams
[2022/03/31 18:05:53] root INFO:     print_batch_step : 10
[2022/03/31 18:05:53] root INFO:     save_inference_dir : /home/aistudio/data/inference
[2022/03/31 18:05:53] root INFO:     save_interval : 1
[2022/03/31 18:05:53] root INFO:     use_visualdl : False
[2022/03/31 18:05:53] root INFO: Infer : 
[2022/03/31 18:05:53] root INFO:     PostProcess : 
[2022/03/31 18:05:53] root INFO:         class_id_map_file : /home/aistudio/work/class/label_list.txt
[2022/03/31 18:05:53] root INFO:         name : Topk
[2022/03/31 18:05:53] root INFO:         topk : 5
[2022/03/31 18:05:53] root INFO:     batch_size : 100
[2022/03/31 18:05:53] root INFO:     infer_imgs : /home/aistudio/data/data10954/cat_12_test/
[2022/03/31 18:05:53] root INFO:     transforms : 
[2022/03/31 18:05:53] root INFO:         DecodeImage : 
[2022/03/31 18:05:53] root INFO:             channel_first : False
[2022/03/31 18:05:53] root INFO:             to_rgb : True
[2022/03/31 18:05:53] root INFO:         ResizeImage : 
[2022/03/31 18:05:53] root INFO:             resize_short : 256
[2022/03/31 18:05:53] root INFO:         CropImage : 
[2022/03/31 18:05:53] root INFO:             size : 224
[2022/03/31 18:05:53] root INFO:         NormalizeImage : 
[2022/03/31 18:05:53] root INFO:             mean : [0.485, 0.456, 0.406]
[2022/03/31 18:05:53] root INFO:             order : 
[2022/03/31 18:05:53] root INFO:             scale : 1.0/255.0
[2022/03/31 18:05:53] root INFO:             std : [0.229, 0.224, 0.225]
[2022/03/31 18:05:53] root INFO:         ToCHWImage : None
[2022/03/31 18:05:53] root INFO: Loss : 
[2022/03/31 18:05:53] root INFO:     Eval : 
[2022/03/31 18:05:53] root INFO:         CELoss : 
[2022/03/31 18:05:53] root INFO:             weight : 1.0
[2022/03/31 18:05:53] root INFO:     Train : 
[2022/03/31 18:05:53] root INFO:         CELoss : 
[2022/03/31 18:05:53] root INFO:             epsilon : 0.1
[2022/03/31 18:05:53] root INFO:             weight : 1.0
[2022/03/31 18:05:53] root INFO: Metric : 
[2022/03/31 18:05:53] root INFO:     Eval : 
[2022/03/31 18:05:53] root INFO:         TopkAcc : 
[2022/03/31 18:05:53] root INFO:             topk : [1, 5]
[2022/03/31 18:05:53] root INFO:     Train : 
[2022/03/31 18:05:53] root INFO:         TopkAcc : 
[2022/03/31 18:05:53] root INFO:             topk : [1, 5]
[2022/03/31 18:05:53] root INFO: Optimizer : 
[2022/03/31 18:05:53] root INFO:     beta1 : 0.9
[2022/03/31 18:05:53] root INFO:     beta2 : 0.999
[2022/03/31 18:05:53] root INFO:     lr : 
[2022/03/31 18:05:53] root INFO:         epochs : 300
[2022/03/31 18:05:53] root INFO:         eta_min : 1e-05
[2022/03/31 18:05:53] root INFO:         learning_rate : 0.000125
[2022/03/31 18:05:53] root INFO:         name : Cosine
[2022/03/31 18:05:53] root INFO:         step_each_epoch : 1000
[2022/03/31 18:05:53] root INFO:         warmup_epoch : 20
[2022/03/31 18:05:53] root INFO:         warmup_start_lr : 1e-06
[2022/03/31 18:05:53] root INFO:     name : AdamW
[2022/03/31 18:05:53] root INFO:     weight_decay : 0.05
[2022/03/31 18:05:53] root INFO: train with paddle 2.2.2 and device CUDAPlace(0)
W0331 18:05:53.266223   642 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0331 18:05:53.270807   642 device_context.cc:465] device: 0, cuDNN Version: 7.6.
model loading : [182]/[182]
infer process : 100%|████████████████████████| 240/240 [00:01<00:00, 128.35it/s]
result save to  /home/aistudio/data/infer_class/result.csv

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: pip in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (22.0.4)
/home/aistudio
正克隆到 'PaddleSeg'...
remote: Enumerating objects: 16439, done.
remote: Counting objects: 100% (1402/1402), done.
remote: Compressing objects: 100% (811/811), done.
remote: Total 16439 (delta 710), reused 1166 (delta 573), pack-reused 15037
接收对象中: 100% (16439/16439), 341.09 MiB | 14.09 MiB/s, 完成.
处理 delta 中: 100% (10574/10574), 完成.
检查连接... 完成。
/home/aistudio/PaddleSeg
分支 release/2.3 设置为跟踪来自 origin 的远程分支 release/2.3。
切换到一个新分支 'release/2.3'
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: pre-commit in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (1.21.0)
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Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from requests->visualdl>=2.0.0->-r requirements.txt (line 5)) (1.25.11)
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Requirement already satisfied: setuptools in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->visualdl>=2.0.0->-r requirements.txt (line 5)) (56.2.0)

total 66652, split: train 65318 - 0.98 rate, val 1334 - 0.02 rate, test 0 - 0.00 rate

/home/aistudio/PaddleSeg/paddleseg/models/losses/rmi_loss.py:78: DeprecationWarning: invalid escape sequence \i
  """
2022-03-31 18:07:33 [INFO]	
------------Environment Information-------------
platform: Linux-4.4.0-150-generic-x86_64-with-debian-stretch-sid
Python: 3.7.4 (default, Aug 13 2019, 20:35:49) [GCC 7.3.0]
Paddle compiled with cuda: True
NVCC: Cuda compilation tools, release 10.1, V10.1.243
cudnn: 7.6
GPUs used: 1
CUDA_VISIBLE_DEVICES: None
GPU: ['GPU 0: Tesla V100-SXM2-16GB']
GCC: gcc (Ubuntu 7.5.0-3ubuntu1~16.04) 7.5.0
PaddlePaddle: 2.2.2
OpenCV: 4.4.0
------------------------------------------------
2022-03-31 18:07:33 [INFO]	
---------------Config Information---------------
batch_size: 64
iters: 300
loss:
  coef:
  - 1
  - 0.4
  types:
  - ignore_index: 255
    type: CrossEntropyLoss
  - ignore_index: 255
    type: CrossEntropyLoss
lr_scheduler:
  learning_rate: 0.00125
  power: 0.9
  type: PolynomialDecay
model:
  backbone:
    class_num: 6
    pretrained: https://passl.bj.bcebos.com/models/convnext_tiny_1k_224.pdparams
    type: ConvNeXt_Tiny
  backbone_indices:
  - 0
  type: OCRNet
optimizer:
  type: sgd
train_dataset:
  dataset_root: /home/aistudio/data/data77571/
  mode: train
  num_classes: 7
  train_path: /home/aistudio/data/data77571//train.txt
  transforms:
  - max_scale_factor: 2.0
    min_scale_factor: 0.5
    scale_step_size: 0.25
    type: ResizeStepScaling
  - crop_size:
    - 256
    - 256
    type: RandomPaddingCrop
  - type: RandomHorizontalFlip
  - type: Normalize
  type: Dataset
val_dataset:
  dataset_root: /home/aistudio/data/data77571/
  mode: val
  num_classes: 7
  transforms:
  - type: Normalize
  type: Dataset
  val_path: /home/aistudio/data/data77571//val.txt
------------------------------------------------
W0331 18:07:34.499039   829 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0331 18:07:34.499097   829 device_context.cc:465] device: 0, cuDNN Version: 7.6.
2022-03-31 18:07:39 [INFO]	use amp to train
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float16, but right dtype is paddle.float32, the right dtype will convert to paddle.float16
  format(lhs_dtype, rhs_dtype, lhs_dtype))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.float16, the right dtype will convert to paddle.float32
  format(lhs_dtype, rhs_dtype, lhs_dtype))
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/nn/layer/norm.py:653: UserWarning: When training, we now always track global mean and variance.
  "When training, we now always track global mean and variance.")
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/paddle/fluid/dygraph/math_op_patch.py:253: UserWarning: The dtype of left and right variables are not the same, left dtype is paddle.float32, but right dtype is paddle.int64, the right dtype will convert to paddle.float32
  format(lhs_dtype, rhs_dtype, lhs_dtype))
2022-03-31 18:07:51 [INFO]	[TRAIN] epoch: 1, iter: 20/300, loss: 2.4015, lr: 0.001179, batch_cost: 0.6064, reader_cost: 0.25345, ips: 105.5410 samples/sec | ETA 00:02:49
2022-03-31 18:08:02 [INFO]	[TRAIN] epoch: 1, iter: 40/300, loss: 2.0847, lr: 0.001103, batch_cost: 0.5819, reader_cost: 0.23513, ips: 109.9897 samples/sec | ETA 00:02:31
2022-03-31 18:08:14 [INFO]	[TRAIN] epoch: 1, iter: 60/300, loss: 1.9073, lr: 0.001026, batch_cost: 0.5722, reader_cost: 0.22481, ips: 111.8435 samples/sec | ETA 00:02:17
2022-03-31 18:08:25 [INFO]	[TRAIN] epoch: 1, iter: 80/300, loss: 1.9318, lr: 0.000949, batch_cost: 0.5649, reader_cost: 0.22012, ips: 113.2866 samples/sec | ETA 00:02:04
2022-03-31 18:08:37 [INFO]	[TRAIN] epoch: 1, iter: 100/300, loss: 1.8939, lr: 0.000872, batch_cost: 0.5726, reader_cost: 0.22607, ips: 111.7723 samples/sec | ETA 00:01:54
2022-03-31 18:08:37 [INFO]	Start evaluating (total_samples: 1334, total_iters: 1334)...
1334/1334 [==============================] - 33s 24ms/step - batch_cost: 0.0243 - reader cost: 1.2418e-04
2022-03-31 18:09:09 [INFO]	[EVAL] #Images: 1334 mIoU: 0.1310 Acc: 0.4490 Kappa: 0.2189 
2022-03-31 18:09:09 [INFO]	[EVAL] Class IoU: 
[0.3424 0.3964 0.1779 0.     0.     0.     0.    ]
2022-03-31 18:09:09 [INFO]	[EVAL] Class Acc: 
[0.4389 0.5091 0.3204 0.     0.     0.     0.    ]
2022-03-31 18:09:10 [INFO]	[EVAL] The model with the best validation mIoU (0.1310) was saved at iter 100.
2022-03-31 18:09:20 [INFO]	[TRAIN] epoch: 1, iter: 120/300, loss: 1.8603, lr: 0.000793, batch_cost: 0.5013, reader_cost: 0.15525, ips: 127.6626 samples/sec | ETA 00:01:30
2022-03-31 18:09:32 [INFO]	[TRAIN] epoch: 1, iter: 140/300, loss: 1.7972, lr: 0.000714, batch_cost: 0.5840, reader_cost: 0.23824, ips: 109.5959 samples/sec | ETA 00:01:33
2022-03-31 18:09:43 [INFO]	[TRAIN] epoch: 1, iter: 160/300, loss: 1.8544, lr: 0.000634, batch_cost: 0.5719, reader_cost: 0.22721, ips: 111.9147 samples/sec | ETA 00:01:20
2022-03-31 18:09:55 [INFO]	[TRAIN] epoch: 1, iter: 180/300, loss: 1.8221, lr: 0.000552, batch_cost: 0.5771, reader_cost: 0.23047, ips: 110.9003 samples/sec | ETA 00:01:09
2022-03-31 18:10:06 [INFO]	[TRAIN] epoch: 1, iter: 200/300, loss: 1.8082, lr: 0.000469, batch_cost: 0.5988, reader_cost: 0.24987, ips: 106.8825 samples/sec | ETA 00:00:59
2022-03-31 18:10:06 [INFO]	Start evaluating (total_samples: 1334, total_iters: 1334)...
1334/1334 [==============================] - 32s 24ms/step - batch_cost: 0.0241 - reader cost: 9.0381e-05
2022-03-31 18:10:39 [INFO]	[EVAL] #Images: 1334 mIoU: 0.1478 Acc: 0.4874 Kappa: 0.2597 
2022-03-31 18:10:39 [INFO]	[EVAL] Class IoU: 
[0.3592 0.422  0.0859 0.1673 0.     0.     0.    ]
2022-03-31 18:10:39 [INFO]	[EVAL] Class Acc: 
[0.4692 0.4845 0.5087 0.6331 0.     0.     0.    ]
2022-03-31 18:10:39 [INFO]	[EVAL] The model with the best validation mIoU (0.1478) was saved at iter 200.
2022-03-31 18:10:50 [INFO]	[TRAIN] epoch: 1, iter: 220/300, loss: 1.7500, lr: 0.000385, batch_cost: 0.5090, reader_cost: 0.16244, ips: 125.7461 samples/sec | ETA 00:00:40
2022-03-31 18:11:02 [INFO]	[TRAIN] epoch: 1, iter: 240/300, loss: 1.7677, lr: 0.000298, batch_cost: 0.5957, reader_cost: 0.24562, ips: 107.4420 samples/sec | ETA 00:00:35
2022-03-31 18:11:13 [INFO]	[TRAIN] epoch: 1, iter: 260/300, loss: 1.7278, lr: 0.000208, batch_cost: 0.5890, reader_cost: 0.24190, ips: 108.6544 samples/sec | ETA 00:00:23
2022-03-31 18:11:25 [INFO]	[TRAIN] epoch: 1, iter: 280/300, loss: 1.7522, lr: 0.000114, batch_cost: 0.6062, reader_cost: 0.26048, ips: 105.5836 samples/sec | ETA 00:00:12
2022-03-31 18:11:38 [INFO]	[TRAIN] epoch: 1, iter: 300/300, loss: 1.7757, lr: 0.000007, batch_cost: 0.6104, reader_cost: 0.26477, ips: 104.8474 samples/sec | ETA 00:00:00
2022-03-31 18:11:38 [INFO]	Start evaluating (total_samples: 1334, total_iters: 1334)...
1334/1334 [==============================] - 33s 25ms/step - batch_cost: 0.0246 - reader cost: 9.6194e-05
2022-03-31 18:12:11 [INFO]	[EVAL] #Images: 1334 mIoU: 0.1765 Acc: 0.5187 Kappa: 0.3205 
2022-03-31 18:12:11 [INFO]	[EVAL] Class IoU: 
[0.361  0.4712 0.2135 0.1899 0.     0.     0.    ]
2022-03-31 18:12:11 [INFO]	[EVAL] Class Acc: 
[0.4795 0.5663 0.4366 0.5706 0.     0.     0.    ]
2022-03-31 18:12:11 [INFO]	[EVAL] The model with the best validation mIoU (0.1765) was saved at iter 300.
's flops has been counted
Cannot find suitable count function for . Treat it as zero FLOPs.
's flops has been counted
Cannot find suitable count function for . Treat it as zero FLOPs.
Cannot find suitable count function for . Treat it as zero FLOPs.
Cannot find suitable count function for . Treat it as zero FLOPs.
Cannot find suitable count function for . Treat it as zero FLOPs.
Cannot find suitable count function for . Treat it as zero FLOPs.
's flops has been counted
's flops has been counted
Cannot find suitable count function for . Treat it as zero FLOPs.
Total Flops: 11207499520     Total Params: 29462030

mkdir: 无法创建目录"/home/aistudio/best_model": 文件已存在

2022-03-31 18:12:15 [INFO]	
---------------Config Information---------------
batch_size: 4
iters: 15000
loss:
  coef:
  - 1
  - 0.4
  types:
  - type: CrossEntropyLoss
  - type: CrossEntropyLoss
lr_scheduler:
  learning_rate: 0.00125
  power: 0.9
  type: PolynomialDecay
model:
  backbone:
    class_num: 6
    pretrained: https://passl.bj.bcebos.com/models/convnext_tiny_1k_224.pdparams
    type: ConvNeXt_Tiny
  backbone_indices:
  - 0
  type: OCRNet
optimizer:
  type: sgd
train_dataset:
  dataset_root: /home/aistudio/data/data77571/
  mode: train
  num_classes: 7
  train_path: /home/aistudio/data/data77571//train.txt
  transforms:
  - max_scale_factor: 2.0
    min_scale_factor: 0.5
    scale_step_size: 0.25
    type: ResizeStepScaling
  - crop_size:
    - 256
    - 256
    type: RandomPaddingCrop
  - type: RandomHorizontalFlip
  - type: Normalize
  type: Dataset
val_dataset:
  dataset_root: /home/aistudio/data/data77571/
  mode: val
  num_classes: 7
  transforms:
  - type: Normalize
  type: Dataset
  val_path: /home/aistudio/data/data77571//val.txt
------------------------------------------------
W0331 18:12:16.364639  1223 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0331 18:12:16.364699  1223 device_context.cc:465] device: 0, cuDNN Version: 7.6.
2022-03-31 18:12:20 [INFO]	Number of predict images = 4608
2022-03-31 18:12:20 [INFO]	Loading pretrained model from /home/aistudio/best_model/seg/model.pdparams
2022-03-31 18:12:20 [INFO]	There are 240/240 variables loaded into OCRNet.
2022-03-31 18:12:20 [INFO]	Start to predict...
4608/4608 [==============================] - 94s 20ms/step

Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: pip in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (22.0.4)
/home/aistudio
正克隆到 'PaddleDetection'...
remote: Enumerating objects: 22586, done.
remote: Counting objects: 100% (3056/3056), done.
remote: Compressing objects: 100% (1496/1496), done.
remote: Total 22586 (delta 2170), reused 2221 (delta 1553), pack-reused 19530
接收对象中: 100% (22586/22586), 261.38 MiB | 21.95 MiB/s, 完成.
处理 delta 中: 100% (16719/16719), 完成.
检查连接... 完成。
/home/aistudio/PaddleDetection
fatal: 一个分支名 'release/2.3' 已经存在。
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: tqdm in /opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages (from -r requirements.txt (line 1)) (4.27.0)
Collecting typeguard
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Building wheels for collected packages: pycocotools, lap, cython_bbox, py-cpuinfo
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Successfully built pycocotools lap cython_bbox py-cpuinfo
Installing collected packages: py-cpuinfo, lap, iniconfig, cython_bbox, xmltodict, typeguard, tomli, terminaltables, shapely, py, flake8-import-order, pytest, pycocotools, pytest-benchmark, motmetrics
Successfully installed cython_bbox-0.1.3 flake8-import-order-0.18.1 iniconfig-1.1.1 lap-0.4.0 motmetrics-1.2.0 py-1.11.0 py-cpuinfo-8.0.0 pycocotools-2.0.4 pytest-7.1.1 pytest-benchmark-3.4.1 shapely-1.8.1.post1 terminaltables-3.1.10 tomli-2.0.1 typeguard-2.13.3 xmltodict-0.12.0

--2022-03-31 18:15:22--  https://bj.bcebos.com/v1/ai-studio-online/0c57afdf4ab44f00991b96550a84739340f6ec8232dd40b4873d94a690e17eb0?responseContentDisposition=attachment%3B%20filename%3Dsteel_bug_detect.zip
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Warning: import ppdet from source directory without installing, run 'python setup.py install' to install ppdet firstly
W0331 18:16:29.811268  1844 device_context.cc:447] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 10.1, Runtime API Version: 10.1
W0331 18:16:29.836920  1844 device_context.cc:465] device: 0, cuDNN Version: 7.6.
[03/31 18:16:32] ppdet.utils.download INFO: Downloading convnext_tiny_1k_224.pdparams from https://passl.bj.bcebos.com/models/convnext_tiny_1k_224.pdparams
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[03/31 18:16:39] ppdet.utils.checkpoint INFO: The shape [1000] in pretrained weight head.fc_cls.bias is unmatched with the shape [1] in model backbone.head.fc_cls.bias. And the weight head.fc_cls.bias will not be loaded
[03/31 18:16:39] ppdet.utils.checkpoint INFO: The shape [768, 1000] in pretrained weight head.fc_cls.weight is unmatched with the shape [768, 1] in model backbone.head.fc_cls.weight. And the weight head.fc_cls.weight will not be loaded
[03/31 18:16:39] ppdet.utils.checkpoint INFO: Finish loading model weights: /home/aistudio/.cache/paddle/weights/convnext_tiny_1k_224.pdparams
[03/31 18:16:39] ppdet.engine INFO: Epoch: [0] [  0/126] learning_rate: 0.000010 loss_rpn_cls: 0.868715 loss_rpn_reg: 1.379543 loss_bbox_cls_stage0: 2.675596 loss_bbox_reg_stage0: 0.007741 loss_bbox_cls_stage1: 1.309931 loss_bbox_reg_stage1: 0.010101 loss_bbox_cls_stage2: 2.300117 loss_bbox_reg_stage2: 0.009881 loss: 8.561625 eta: 0:01:12 batch_cost: 0.1926 data_cost: 0.0003 ips: 5.1914 images/s
[03/31 18:16:42] ppdet.engine INFO: Epoch: [0] [ 20/126] learning_rate: 0.000210 loss_rpn_cls: 0.177607 loss_rpn_reg: 0.209631 loss_bbox_cls_stage0: 0.226774 loss_bbox_reg_stage0: 0.020316 loss_bbox_cls_stage1: 0.114375 loss_bbox_reg_stage1: 0.019034 loss_bbox_cls_stage2: 0.126810 loss_bbox_reg_stage2: 0.013394 loss: 1.188014 eta: 0:00:49 batch_cost: 0.1349 data_cost: 0.0003 ips: 7.4117 images/s
[03/31 18:16:44] ppdet.engine INFO: Epoch: [0] [ 40/126] learning_rate: 0.000410 loss_rpn_cls: 0.187713 loss_rpn_reg: 0.106287 loss_bbox_cls_stage0: 0.183981 loss_bbox_reg_stage0: 0.041466 loss_bbox_cls_stage1: 0.062141 loss_bbox_reg_stage1: 0.023217 loss_bbox_cls_stage2: 0.060378 loss_bbox_reg_stage2: 0.007851 loss: 0.779464 eta: 0:00:46 batch_cost: 0.1347 data_cost: 0.0003 ips: 7.4215 images/s
[03/31 18:16:47] ppdet.engine INFO: Epoch: [0] [ 60/126] learning_rate: 0.000609 loss_rpn_cls: 0.151224 loss_rpn_reg: 0.093652 loss_bbox_cls_stage0: 0.095361 loss_bbox_reg_stage0: 0.028566 loss_bbox_cls_stage1: 0.033791 loss_bbox_reg_stage1: 0.014273 loss_bbox_cls_stage2: 0.037734 loss_bbox_reg_stage2: 0.010816 loss: 0.551668 eta: 0:00:42 batch_cost: 0.1266 data_cost: 0.0003 ips: 7.8990 images/s
[03/31 18:16:50] ppdet.engine INFO: Epoch: [0] [ 80/126] learning_rate: 0.000809 loss_rpn_cls: 0.121782 loss_rpn_reg: 0.047220 loss_bbox_cls_stage0: 0.093039 loss_bbox_reg_stage0: 0.053310 loss_bbox_cls_stage1: 0.048381 loss_bbox_reg_stage1: 0.032904 loss_bbox_cls_stage2: 0.033058 loss_bbox_reg_stage2: 0.014520 loss: 0.498449 eta: 0:00:39 batch_cost: 0.1241245 data_cost: 0.0003 ips: 8.0348 images/s
[03/31 18:16:52] ppdet.engine INFO: Epoch: [0] [100/126] learning_rate: 0.001009 loss_rpn_cls: 0.130423 loss_rpn_reg: 0.055140 loss_bbox_cls_stage0: 0.070923 loss_bbox_reg_stage0: 0.041984 loss_bbox_cls_stage1: 0.035746 loss_bbox_reg_stage1: 0.026651 loss_bbox_cls_stage2: 0.025823 loss_bbox_reg_stage2: 0.014539 loss: 0.459595 eta: 0:00:36 batch_cost: 0.1316 data_cost: 0.0003 ips: 7.5960 images/s
[03/31 18:16:55] ppdet.engine INFO: Epoch: [0] [120/126] learning_rate: 0.001209 loss_rpn_cls: 0.152737 loss_rpn_reg: 0.050989 loss_bbox_cls_stage0: 0.073883 loss_bbox_reg_stage0: 0.046244 loss_bbox_cls_stage1: 0.035809 loss_bbox_reg_stage1: 0.027861 loss_bbox_cls_stage2: 0.022346 loss_bbox_reg_stage2: 0.010038 loss: 0.573754 eta: 0:00:33 batch_cost: 0.1301 data_cost: 0.0003 ips: 7.6887 images/s
[03/31 18:16:57] ppdet.utils.checkpoint INFO: Save checkpoint: /home/aistudio/data/output_det/convnext
[03/31 18:16:58] ppdet.engine INFO: Eval iter: 0
[03/31 18:16:58] ppdet.metrics.metrics INFO: Accumulating evaluatation results...
[03/31 18:16:58] ppdet.metrics.metrics INFO: mAP(0.50, 11point) = 2.77%
[03/31 18:16:58] ppdet.engine INFO: Total sample number: 14, averge FPS: 14.486713457165575
[03/31 18:16:58] ppdet.engine INFO: Best test bbox ap is 0.028.