| import os |
| import pdb |
| import torch |
| import torch.nn as nn |
|
|
|
|
| class GpuDataParallel(object): |
| def __init__(self): |
| self.gpu_list = [] |
| self.output_device = None |
|
|
| def set_device(self, device): |
| device = str(device) |
| if device != 'None': |
| self.gpu_list = [i for i in range(len(device.split(',')))] |
| os.environ["CUDA_VISIBLE_DEVICES"] = device |
| output_device = self.gpu_list[0] |
| self.occupy_gpu(self.gpu_list) |
| self.output_device = output_device if len(self.gpu_list) > 0 else "cpu" |
|
|
| def model_to_device(self, model): |
| |
| model = model.to(self.output_device) |
| if len(self.gpu_list) > 1: |
| model = nn.DataParallel( |
| model, |
| device_ids=self.gpu_list, |
| output_device=self.output_device) |
| return model |
|
|
| def data_to_device(self, data): |
| if isinstance(data, torch.FloatTensor): |
| return data.to(self.output_device) |
| elif isinstance(data, torch.DoubleTensor): |
| return data.float().to(self.output_device) |
| elif isinstance(data, torch.ByteTensor): |
| return data.long().to(self.output_device) |
| elif isinstance(data, torch.LongTensor): |
| return data.to(self.output_device) |
| elif isinstance(data, list) or isinstance(data, tuple): |
| return [self.data_to_device(d) for d in data] |
| else: |
| raise ValueError(data.shape, "Unknown Dtype: {}".format(data.dtype)) |
|
|
| def criterion_to_device(self, loss): |
| return loss.to(self.output_device) |
|
|
| def occupy_gpu(self, gpus=None): |
| """ |
| make program appear on nvidia-smi. |
| """ |
| if len(gpus) == 0: |
| torch.zeros(1).cuda() |
| else: |
| gpus = [gpus] if isinstance(gpus, int) else list(gpus) |
| for g in gpus: |
| torch.zeros(1).cuda(g) |
|
|
