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)
"""
CPU wrappers
"""
def state_dict_cpu(network):
pol_state_dict = network.state_dict()
for k, v in pol_state_dict.items():
# Returns a copy of v in CPU memory.
pol_state_dict[k] = v.cpu()
return pol_state_dict
