即test_batch_size_per_card=1 A:测试的时候,对图像等比例缩放,最长边960,不同图像等比例缩放后长宽不一致,无法组成batch,所以设置为test_batch_size为1。 Q3.4.5:为什么使用c++ inference和python inference结果不一致? A:可能是导出的inference model版本与预测库版本需要保持一致,比如在Windows下,Paddle官网提供...
# batch_size_per_card: 1024 batch_size_per_card: 256 drop_last: True num_workers: 4Eval: dataset: name: SimpleDataSet data_dir: /home/aistudio/data/data128403/TAL_OCR_ENG手写英文数据集/data_composition label_file_list: ["/home/aistudio/data/data128403/TAL_OCR_ENG手写英文数据集/test.tx...
(5)Out of memory error on GPU 0. Cannot allocate xxxxMB memory on GPU 0, xxxxGB memory has been allocated and available memory is only 0.000000B. 将训练的配置yml文件中的batch_size_per_card参数不断改小(除以2),直到不再报这个错即可。
shuffle: true batch_size_per_card: 128 drop_last: true num_workers: 4 Eval: dataset: name: SimpleDataSet data_dir: /home/aistudio/data label_file_list: - /home/aistudio/data/dev.txt transforms: - DecodeImage: img_mode: BGR channel_first: false - MultiLabelEncode: - RecResizeImg: ima...
[2021/09/15 17:35:06] root INFO: batch_size_per_card : 16[2021/09/15 17:35:06] root INFO: drop_last : False[2021/09/15 17:35:06] root INFO: num_workers : 8[2021/09/15 17:35:06] root INFO: shuffle : True[2021/09/15 17:35:06] root INFO: use_shared_memory : False...
Global.eval_batch_step="[0,300]" \ Global.load_static_weights=true \ Global.checkpoints='./outputall/db_mv3/best_accuracy' \ Global.pretrained_model='PaddleOCR/pretrain_models/MobileNetV3_large_x0_5_pretrained' \ Train.loader.batch_size_per_card=32 \ ...
'eval_batch_step': 500, 'train_batch_size_per_card': 256, 'test_batch_size_per_card': 256, 'image_shape': [3, 32, 320], 'max_text_length': 8, 'character_type': 'ch', 'character_dict_path': '../word_dict.txt', 'loss_type': 'attention', 'tps': True, 'reader_yml':...
[2021/11/22 10:22:22] root INFO: batch_size_per_card : 256 [2021/11/22 10:22:22] root INFO: drop_last : False [2021/11/22 10:22:22] root INFO: num_workers : 8 [2021/11/22 10:22:22] root INFO: shuffle : False [2021/11/22 10:22:22] root INFO: Global : [2021/11...
1、Windows上这边用paddlepaddle-gpu==2.2.2没问题,可以跑(上面已经说了) 2、ValueError: all input arrays must have the same shape 这个问题也解决了,Eval.loader.batch_size_per_card设置成1就行(上面也说过了) 3、Windows下显卡跑不满Train.loader.num_workers只能设置成0(不能使用子线程),速度会慢很多,...
)# 启动训练model.fit(train_dataset, epochs=1, batch_size=64, log_freq=400)# 启动评估model.evaluate(test_dataset, log_freq=100, batch_size=64) 单机单卡启动,默认使用第0号卡 # 单机单卡启动,默认使用第0号卡!python hapitrain.py Begin to download ...