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!