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| import torch | |
| import numpy as np | |
| def soft_update_from_to(source, target, tau): | |
| for target_param, param in zip(target.parameters(), source.parameters()): | |
| target_param.data.copy_( | |
| target_param.data * (1.0 - tau) + param.data * tau | |
| ) | |
| def copy_model_params_from_to(source, target): | |
| for target_param, param in zip(target.parameters(), source.parameters()): | |
| target_param.data.copy_(param.data) | |
| def fanin_init(tensor): | |
| size = tensor.size() | |
| if len(size) == 2: | |
| fan_in = size[0] | |
| elif len(size) > 2: | |
| fan_in = np.prod(size[1:]) | |
| else: | |
| raise Exception("Shape must be have dimension at least 2.") | |
| bound = 1. / np.sqrt(fan_in) | |
| return tensor.data.uniform_(-bound, bound) | |
| def fanin_init_weights_like(tensor): | |
| size = tensor.size() | |
| if len(size) == 2: | |
| fan_in = size[0] | |
| elif len(size) > 2: | |
| fan_in = np.prod(size[1:]) | |
| else: | |
| raise Exception("Shape must be have dimension at least 2.") | |
| bound = 1. / np.sqrt(fan_in) | |
| new_tensor = FloatTensor(tensor.size()) | |
| new_tensor.uniform_(-bound, bound) | |
| return new_tensor | |
| """ | |
| GPU wrappers | |
| """ | |
| _use_gpu = False | |
| device = None | |
| _gpu_id = 0 | |
| def set_gpu_mode(mode, gpu_id=0): | |
| global _use_gpu | |
| global device | |
| global _gpu_id | |
| _gpu_id = gpu_id | |
| _use_gpu = mode | |
| device = torch.device("cuda:" + str(gpu_id) if _use_gpu else "cpu") | |
| def gpu_enabled(): | |
| return _use_gpu | |
| def set_device(gpu_id): | |
| torch.cuda.set_device(gpu_id) | |
| # noinspection PyPep8Naming | |
| def FloatTensor(*args, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.FloatTensor(*args, **kwargs, device=torch_device) | |
| def from_numpy(*args, **kwargs): | |
| return torch.from_numpy(*args, **kwargs).float().to(device) | |
| def get_numpy(tensor): | |
| return tensor.to('cpu').detach().numpy() | |
| def zeros(*sizes, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.zeros(*sizes, **kwargs, device=torch_device) | |
| def ones(*sizes, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.ones(*sizes, **kwargs, device=torch_device) | |
| def ones_like(*args, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.ones_like(*args, **kwargs, device=torch_device) | |
| def randn(*args, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.randn(*args, **kwargs, device=torch_device) | |
| def zeros_like(*args, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.zeros_like(*args, **kwargs, device=torch_device) | |
| def tensor(*args, torch_device=None, **kwargs): | |
| if torch_device is None: | |
| torch_device = device | |
| return torch.tensor(*args, **kwargs, device=torch_device) | |
| def normal(*args, **kwargs): | |
| return torch.normal(*args, **kwargs).to(device) | |