diff options
Diffstat (limited to 'models/cycle_gan_model.py')
| -rw-r--r-- | models/cycle_gan_model.py | 16 |
1 files changed, 4 insertions, 12 deletions
diff --git a/models/cycle_gan_model.py b/models/cycle_gan_model.py index b7b840d..85432bb 100644 --- a/models/cycle_gan_model.py +++ b/models/cycle_gan_model.py @@ -1,6 +1,4 @@ -import numpy as np import torch -import os from collections import OrderedDict from torch.autograd import Variable import itertools @@ -8,7 +6,6 @@ import util.util as util from util.image_pool import ImagePool from .base_model import BaseModel from . import networks -import sys class CycleGANModel(BaseModel): @@ -17,10 +14,6 @@ class CycleGANModel(BaseModel): def initialize(self, opt): BaseModel.initialize(self, opt) - - nb = opt.batchSize - size = opt.fineSize - # load/define networks # The naming conversion is different from those used in the paper # Code (paper): G_A (G), G_B (F), D_A (D_Y), D_B (D_X) @@ -47,7 +40,6 @@ class CycleGANModel(BaseModel): self.load_network(self.netD_B, 'D_B', which_epoch) if self.isTrain: - self.old_lr = opt.lr self.fake_A_pool = ImagePool(opt.pool_size) self.fake_B_pool = ImagePool(opt.pool_size) # define loss functions @@ -129,7 +121,7 @@ class CycleGANModel(BaseModel): self.loss_D_B = loss_D_B.data[0] def backward_G(self): - lambda_idt = self.opt.identity + lambda_idt = self.opt.lambda_identity lambda_A = self.opt.lambda_A lambda_B = self.opt.lambda_B # Identity loss @@ -200,8 +192,8 @@ class CycleGANModel(BaseModel): def get_current_errors(self): ret_errors = OrderedDict([('D_A', self.loss_D_A), ('G_A', self.loss_G_A), ('Cyc_A', self.loss_cycle_A), - ('D_B', self.loss_D_B), ('G_B', self.loss_G_B), ('Cyc_B', self.loss_cycle_B)]) - if self.opt.identity > 0.0: + ('D_B', self.loss_D_B), ('G_B', self.loss_G_B), ('Cyc_B', self.loss_cycle_B)]) + if self.opt.lambda_identity > 0.0: ret_errors['idt_A'] = self.loss_idt_A ret_errors['idt_B'] = self.loss_idt_B return ret_errors @@ -215,7 +207,7 @@ class CycleGANModel(BaseModel): rec_B = util.tensor2im(self.rec_B) ret_visuals = OrderedDict([('real_A', real_A), ('fake_B', fake_B), ('rec_A', rec_A), ('real_B', real_B), ('fake_A', fake_A), ('rec_B', rec_B)]) - if self.opt.isTrain and self.opt.identity > 0.0: + if self.opt.isTrain and self.opt.lambda_identity > 0.0: ret_visuals['idt_A'] = util.tensor2im(self.idt_A) ret_visuals['idt_B'] = util.tensor2im(self.idt_B) return ret_visuals |