util.py

import os
import torch
from torch.autograd import Variable

USE_CUDA = False #torch.cuda.is_available()
FLOAT = torch.cuda.FloatTensor if USE_CUDA else torch.FloatTensor

def prRed(prt): print("\033[91m {}\033[00m" .format(prt))
def prGreen(prt): print("\033[92m {}\033[00m" .format(prt))
def prYellow(prt): print("\033[93m {}\033[00m" .format(prt))
def prLightPurple(prt): print("\033[94m {}\033[00m" .format(prt))
def prPurple(prt): print("\033[95m {}\033[00m" .format(prt))
def prCyan(prt): print("\033[96m {}\033[00m" .format(prt))
def prLightGray(prt): print("\033[97m {}\033[00m" .format(prt))
def prBlack(prt): print("\033[98m {}\033[00m" .format(prt))

def to_numpy(var):
    return var.cpu().data.numpy() if USE_CUDA else var.data.numpy()

def to_tensor(ndarray, volatile=False, requires_grad=False, dtype=FLOAT):
    return Variable(
        torch.from_numpy(ndarray), volatile=volatile, requires_grad=requires_grad
    ).type(dtype)

def soft_update(target, source, 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 hard_update(target, source):
    for target_param, param in zip(target.parameters(), source.parameters()):
            target_param.data.copy_(param.data)