训练文本识别的时候 gpu占用不满

[xinyujituan]

GPU占用少的可怜，请问怎么提速？下面是我的yml代码

Global: use_gpu: true epoch_num: 50000 log_smooth_window: 20 print_batch_step: 10 save_model_dir: ./output/rec_chinese_lite_v2.0 save_epoch_step: 50

evaluation is run every 5000 iterations after the 4000th iteration

eval_batch_step: [0, 2000] cal_metric_during_train: True pretrained_model: pretrain_models/ch_ppocr_mobile_v1.1_det_train/best_accuracy.pdparams checkpoints: save_inference_dir: use_visualdl: False infer_img: doc/imgs_words/ch/word_1.jpg

for data or label process

character_dict_path: train_data/labels.txt max_text_length: 25 infer_mode: False use_space_char: True save_res_path: ./output/rec/predicts_chinese_lite_v2.0.txt

Optimizer: name: Adam beta1: 0.9 beta2: 0.999 lr: name: Cosine learning_rate: 0.001 warmup_epoch: 5 regularizer: name: 'L2' factor: 0.00001

Architecture: model_type: rec algorithm: CRNN Transform: Backbone: name: MobileNetV3 scale: 0.5 model_name: small small_stride: [1, 2, 2, 2] Neck: name: SequenceEncoder encoder_type: rnn hidden_size: 48 Head: name: CTCHead fc_decay: 0.00001

Loss: name: CTCLoss

PostProcess: name: CTCLabelDecode

Metric: name: RecMetric main_indicator: acc

Train: dataset: name: SimpleDataSet data_dir: ./train_data/ label_file_list: ["./train_data/train.txt"] transforms:

• DecodeImage: # load image img_mode: BGR channel_first: False
• RecAug:
• CTCLabelEncode: # Class handling label
• RecResizeImg: image_shape: [3, 32, 320]
• KeepKeys: keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order loader: shuffle: True batch_size_per_card: 256 drop_last: True num_workers: 8

Eval: dataset: name: SimpleDataSet data_dir: ./train_data label_file_list: ["./train_data/test.txt"] transforms:

• DecodeImage: # load image img_mode: BGR channel_first: False
• CTCLabelEncode: # Class handling label
• RecResizeImg: image_shape: [3, 32, 320]
• KeepKeys: keep_keys: ['image', 'label', 'length'] # dataloader will return list in this order loader: shuffle: False drop_last: False batch_size_per_card: 256 num_workers: 8

[xinyujituan]

样本只有5000难道是样本太少了吗

[song4875343]

我估计是你的显卡核心数比较少，你这个一次 的批次比较大，瓶颈在存取上不在计算上，我的3080ti，刚开始批次640时，gpu，一直在30%，批次改到256就上来了，批次改到128，基本gpu就满了，你可以试试