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import torch
import torch.nn as nn
from torch.nn.functional import mse_loss
class GANLoss(nn.Module):
def __init__(self, use_lsgan=True, target_real_label=1.0, target_fake_label=0.0,
tensor=torch.FloatTensor):
super(GANLoss, self).__init__()
self.real_label = target_real_label
self.fake_label = target_fake_label
self.real_label_var = None
self.fake_label_var = None
self.Tensor = tensor
if use_lsgan:
self.loss = nn.MSELoss()
else:
self.loss = nn.BCELoss()
def get_target_tensor(self, input, target_is_real):
target_tensor = None
if target_is_real:
create_label = ((self.real_label_var is None) or(self.real_label_var.numel() != input.numel()))
# pdb.set_trace()
if create_label:
real_tensor = self.Tensor(input.size()).fill_(self.real_label)
# self.real_label_var = Variable(real_tensor, requires_grad=False)
# self.real_label_var = torch.Tensor(real_tensor)
self.real_label_var = real_tensor
target_tensor = self.real_label_var
else:
# pdb.set_trace()
create_label = ((self.fake_label_var is None) or (self.fake_label_var.numel() != input.numel()))
if create_label:
fake_tensor = self.Tensor(input.size()).fill_(self.fake_label)
# self.fake_label_var = Variable(fake_tensor, requires_grad=False)
# self.fake_label_var = torch.Tensor(fake_tensor)
self.fake_label_var = fake_tensor
target_tensor = self.fake_label_var
return target_tensor
def __call__(self, input, target_is_real):
target_tensor = self.get_target_tensor(input, target_is_real)
# pdb.set_trace()
return self.loss(input, target_tensor)
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