paddle.set_device('gpu') 1. 如果你有多块GPU,你可以选择指定使用的GPU编号: paddle.set_device('gpu:0') # 使用第一块GPU 1. 在你的代码中,使用PaddlePaddle提供的API进行模型训练或推理等操作。 这样,你的代码就会在GPU上运行了。
paddle.set_device("gpu:0") if use_gpu else paddle.set_device("cpu") import warnings warnings.filterwarnings('ignore') paddle.seed(1024) epochs=3 BATCH_SIZE=32 model_path='./mnist_pdparams' paddle.vision.set_image_backend('cv2') train_dataset=paddle.io.DataLoader(paddle.vision.datasets.MNI...
# 设置GPU为推理设备paddle.device.set_device("gpu:0")# 实例化DRNpredictor = DRNPredictor(output)LESRpredictor = LESRCNNPredictor(output)RealSRpredictor = RealSRPredictor(output)# 生成文件列表list_images = glob.glob(input_dir + "/*.**g")#遍历文件列表,模型推理for filename in list_images: ...
def train(model, opt, train_loader, valid_loader): use_gpu = True paddle.device.set_device('gpu:0') if use_gpu else paddle.device.set_device('cpu') print('start training ... ') model.train() for epoch in range(EPOCH_NUM): for batch_id, data in enumerate(train_loader()): img...
input_dir = r"./gan_low_solution_sample" # 设置GPU为推理设备 paddle.device.set_device("gpu:0") # 实例化 DRNpredictor = DRNPredictor(output) LESRpredictor = LESRCNNPredictor(output) RealSRpredictor = RealSRPredictor(output) # 生成文件列表 list_images = glob.glob(input_dir + "/*.**g"...
paddle.device.set_device('gpu:0') print(paddle.device.get_device()) In [ ] #创建ckpts文件夹 ckpts = "./trained_models/" os.makedirs(ckpts, exist_ok=True) 4.1 定义评价函数 该部分,我们定义了psnr和ssim两种评价指标的计算方式 In [ ] def matlab_style_gauss2D(shape=(3, 3), sigma=0.5...
# 参数转换代码测试 # 设置GPU环境 paddle.set_device("gpu") base64str = image_to_base64(Image.open('/home/aistudio/image/01.jpeg')) data_args = {'image': base64str.decode("utf-8")} res = output(None, data_args) image = base64_to_image(res['image']) image.show() In [ ]...
在使用百度飞桨的paddleocr过程中,安装配置gpu运行环境,代码部署后运行报错,无法检测到gpu。(实际我的环境是有八张显卡gpu的)。具体错误信息如下: Compiled with WITH_GPU, but no GPU found in runtime. You are using GPU version Paddle, but your CUDA device is not set properly. CPU device will be use...
paddle.set_device("gpu") self._test_sharing(mp.get_context("spawn"), "gpu") def test_pass_tensor(self): with _test_eager_guard(): self.func_test_pass_tensor() self.func_test_pass_tensor() if __name__ == "__main__": unittest.main() 14 changes: 8 additions & 6 deletions 14...