def backward_D(self):
"""Calculate the GAN loss for the discriminators"""
base_function._unfreeze(self.net_D)
#print(self.input_P2.shape, self.img_gen.shape)
self.loss_dis_img_gen = self.backward_D_basic(self.net_D,
self.input_P2,
self.img_gen)
def backward_D(self):
"""Calculate the GAN loss for the discriminators"""
base_function._unfreeze(self.net_D)
i = np.random.randint(len(self.img_gen))
fake = self.img_gen[i]
real = self.P_frame_step[:,i,...]
self.loss_dis_img_gen = self.backward_D_basic(self.net_D, real, fake)
base_function._unfreeze(self.net_D_V)
i = np.random.randint(len(self.img_gen)-self.opt.frames_D_V+1)
# fake = [self.img_gen[i]]
# real = [self.P_frame_step[:,i,...]]
fake = []
real = []
for frame in range(self.opt.frames_D_V-1):
fake.append(self.img_gen[i+frame]-self.img_gen[i+frame+1])
real.append(self.P_frame_step[:,i+frame,...]
-self.P_frame_step[:,i+frame+1,...])
fake = torch.cat(fake, dim=1)
real = torch.cat(real, dim=1)
self.loss_dis_img_gen_v = self.backward_D_basic(self.net_D_V, real, fake)
def backward_D(self):
"""Calculate the GAN loss for the discriminators"""
# Spatial GAN Loss
base_function._unfreeze(self.net_D)
i = np.random.randint(len(self.img_gen))
fake = self.img_gen[i]
real = self.P_step[:,i,...]
self.loss_dis_img_gen = self.backward_D_basic(self.net_D, real, fake)
# Temporal GAN Loss
base_function._unfreeze(self.net_D_V)
i = np.random.randint(len(self.img_gen)-self.opt.frames_D_V+1)
fake = []
real = []
for frame in range(self.opt.frames_D_V):
fake.append(self.img_gen[i+frame].unsqueeze(2))
real.append(self.P_step[:,i+frame,...].unsqueeze(2))
fake = torch.cat(fake, dim=2)
real = torch.cat(real, dim=2)
self.loss_dis_img_gen_v = self.backward_D_basic(self.net_D_V, real, fake)
def backward_D(self):
base_function._unfreeze(self.net_D)
self.loss_dis_img_gen = self.backward_D_basic(self.net_D,
self.input_P2,
self.img_gen)
def backward_D(self):
"""Calculate the GAN loss for the discriminators"""
base_function._unfreeze(self.net_D)
self.loss_dis_img_gen = self.backward_D_basic(self.net_D,
self.input_fullP2,
self.img_gen) #注意有无背景！
