用Predict.py进行推理时，无法使用GPU进行推理，一直默认使用CPU，请问如何解决？

[Qiuyanghang]

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刚接触paddleseg，配置好环境后想直接测试提供的预训练模型效果如何，但其始终用CPU进行推理，不清楚是什么原因，请问如何调用GPU推理。 尝试了以下操作都不管：1.用命令行参数设置GPU设备 2.pycharm终端运行前用 set CUDA_VISIBLE_DEVICES=0 设置GPU python版本：3.9 paddlepaddle-gpu版本： 2.6.1.post116 paddleseg版本：2.8.0 cuda版本：11.6 window系统，一张3060显卡。 预测脚本运行时的信息： ------------Environment Information-------------
platform: Windows-10-10.0.19045-SP0
Python: 3.9.19 (main, Mar 21 2024, 17:21:27) [MSC v.1916 64 bit (AMD64)] Paddle compiled with cuda: True
NVCC: Build cuda_11.6.r11.6/compiler.30794723_0
cudnn: 8.6
GPUs used: 1 CUDA_VISIBLE_DEVICES: None GPU: ['GPU 0: NVIDIA GeForce'] PaddleSeg: 2.8.0 PaddlePaddle: 2.6.1 OpenCV: 4.5.5

2024-05-29 19:05:57 [INFO] ---------------Config Information--------------- batch_size: 4 iters: 160000 train_dataset: dataset_root: data/cityscapes mode: train transforms:

• max_scale_factor: 2.0 min_scale_factor: 0.5 scale_step_size: 0.25 type: ResizeStepScaling
• crop_size:
  • 1024
  • 512 type: RandomPaddingCrop
• type: RandomHorizontalFlip
• brightness_range: 0.5 contrast_range: 0.5 saturation_range: 0.5 type: RandomDistort
• type: Normalize type: Cityscapes val_dataset: dataset_root: data/cityscapes mode: val transforms:
• type: Normalize type: Cityscapes optimizer: momentum: 0.9 type: SGD weight_decay: 0.0005 lr_scheduler: end_lr: 0 learning_rate: 0.005 power: 0.9 type: PolynomialDecay warmup_iters: 1000 warmup_start_lr: 1.0e-05 loss: coef:
• 1
• 1
• 1 types:
• min_kept: 130000 type: OhemCrossEntropyLoss
• min_kept: 130000 type: OhemCrossEntropyLoss
• min_kept: 130000 type: OhemCrossEntropyLoss model: backbone: pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz type: STDC2 type: PPLiteSeg test_config: aug_eval: true scales: 1.0

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2024-05-29 19:05:57 [INFO] Set device: cpu 2024-05-29 19:05:57 [WARNING] Add the num_classes in train_dataset class to model config. We suggest you manually set num_classes in model config. 2024-05-29 19:05:57 [WARNING] Add the in_channels in train_dataset class to model config. We suggest you manually set in_channels in model config. 2024-05-29 19:05:57 [INFO] Use the following config to build model model: backbone: in_channels: 3 pretrained: https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz type: STDC2 num_classes: 19 type: PPLiteSeg 2024-05-29 19:05:58 [INFO] Loading pretrained model from https://bj.bcebos.com/paddleseg/dygraph/PP_STDCNet2.tar.gz 2024-05-29 19:05:58 [INFO] There are 265/265 variables loaded into STDCNet. 2024-05-29 19:05:58 [INFO] The number of images: 33 2024-05-29 19:05:58 [INFO] Loading pretrained model from my_model/model_pp.pdparams 2024-05-29 19:05:59 [WARNING] ppseg_head.arm_list.0._scale is not in pretrained model 2024-05-29 19:05:59 [WARNING] ppseg_head.arm_list.1._scale is not in pretrained model 2024-05-29 19:05:59 [WARNING] ppseg_head.arm_list.2._scale is not in pretrained model 2024-05-29 19:05:59 [INFO] There are 367/370 variables loaded into PPLiteSeg. 2024-05-29 19:05:59 [INFO] Start to predict... 7/33 [=====>........................] - ETA: 58s Traceback (most recent call last):