Cherry pick gan (#2737)

Cherry pick gan

PaddleCV/PaddleGAN/README.md

@@ -18,7 +18,9 @@
 
 本图像生成模型库包含CGAN\[[3](#参考文献)\], DCGAN\[[4](#参考文献)\], Pix2Pix\[[5](#参考文献)\], CycleGAN\[[6](#参考文献)\], StarGAN\[[7](#参考文献)\], AttGAN\[[8](#参考文献)\], STGAN\[[9](#参考文献)\]。
 
-注意：AttGAN和STGAN的网络结构中，判别器去掉了instance norm。
+注意： 
+1. AttGAN和STGAN的网络结构中，判别器去掉了instance norm。  
+2. StarGAN，AttGAN和STGAN由于梯度惩罚所需的操作目前只支持GPU，需使用GPU训练。
 
 图像生成模型库库的目录结构如下：
 ```
@@ -235,7 +237,7 @@ STGAN的网络结构[9]
 
 **Q:** 为什么STGAN和ATTGAN中变男性得到的预测结果是变女性呢？  
 **A:** 这是由于预测时标签的设置，目标标签是基于原本的标签进行改变，比如原本图片是男生，预测代码对标签进行转变的时候会自动变成相对立的标签，即女
-性，所以得到的结果是女生。如果想要原本是男生，转变之后还是男生，可以参考模型库中预测代码的StarGAN的标签设置。
+性，所以得到的结果是女生。如果想要原本是男生，转变之后还是男生，保持要转变的标签不变即可。
 
 
 ## 参考论文

PaddleCV/PaddleGAN/infer.py

@@ -27,7 +27,7 @@ import imageio
 import glob
 from util.config import add_arguments, print_arguments
 from data_reader import celeba_reader_creator
-from util.utility import check_attribute_conflict
+from util.utility import check_attribute_conflict, check_gpu
 import copy
 
 parser = argparse.ArgumentParser(description=__doc__)
@@ -122,6 +122,8 @@ def infer(args):
     if not os.path.exists(args.output):
         os.makedirs(args.output)
 
+    attr_names = args.selected_attrs.split(',')
+
     if args.model_net == 'AttGAN' or args.model_net == 'STGAN':
         test_reader = celeba_reader_creator(
             image_dir=args.dataset_dir,
@@ -133,7 +135,6 @@ def infer(args):
             args, shuffle=False, return_name=True)
         for data in zip(reader_test()):
             real_img, label_org, name = data[0]
-            attr_names = args.selected_attrs.split(',')
             print("read {}".format(name))
             label_trg = copy.deepcopy(label_org)
             tensor_img = fluid.LoDTensor()
@@ -189,10 +190,11 @@ def infer(args):
             real_img_temp = np.squeeze(real_img).transpose([0, 2, 3, 1])
             images = [real_img_temp]
             for i in range(args.c_dim):
-                label_trg = np.zeros(
-                    [len(label_org), args.c_dim]).astype("float32")
+                label_trg_tmp = copy.deepcopy(label_org)
                 for j in range(len(label_org)):
-                    label_trg[j][i] = 1
+                    label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
+                    label_trg = check_attribute_conflict(
+                        label_trg_tmp, attr_names[i], attr_names)
                 tensor_label_trg = fluid.LoDTensor()
                 tensor_label_trg.set(label_trg, place)
                 out = exe.run(
@@ -233,4 +235,5 @@ def infer(args):
 if __name__ == "__main__":
     args = parser.parse_args()
     print_arguments(args)
+    check_gpu(args.use_gpu)
     infer(args)

PaddleCV/PaddleGAN/train.py

@@ -76,6 +76,7 @@ def train(cfg):
 if __name__ == "__main__":
     cfg = config.parse_args()
     config.print_arguments(cfg)
+    utility.check_gpu(cfg.use_gpu)
     #assert cfg.load_size >= cfg.crop_size, "Load Size CANNOT less than Crop Size!"
     if cfg.profile:
         if cfg.use_gpu:

PaddleCV/PaddleGAN/trainer/StarGAN.py

@@ -308,12 +308,8 @@ class StarGAN(object):
                 loss_name=dis_trainer.d_loss.name,
                 build_strategy=build_strategy)
 
-        #losses = [[], []]
         t_time = 0
 
-        test_program = gen_trainer.infer_program
-        utility.save_test_image(0, self.cfg, exe, place, test_program,
-                                gen_trainer, self.test_reader)
         for epoch_id in range(self.cfg.epoch):
             batch_id = 0
             for i in range(self.batch_num):

PaddleCV/PaddleGAN/util/utility.py

@@ -104,6 +104,7 @@ def save_test_image(epoch,
     elif cfg.model_net == "StarGAN":
         for data in zip(A_test_reader()):
             real_img, label_org, name = data[0]
+            attr_names = cfg.selected_attrs.split(',')
             tensor_img = fluid.LoDTensor()
             tensor_label_org = fluid.LoDTensor()
             tensor_img.set(real_img, place)
@@ -111,8 +112,10 @@ def save_test_image(epoch,
             real_img_temp = np.squeeze(real_img).transpose([1, 2, 0])
             images = [real_img_temp]
             for i in range(cfg.c_dim):
-                label_trg = np.zeros([1, cfg.c_dim]).astype("float32")
-                label_trg[0][i] = 1
+                label_trg_tmp = copy.deepcopy(label_org)
+                label_trg_tmp[0][i] = 1.0 - label_trg_tmp[j][i]
+                label_trg = check_attribute_conflict(
+                    label_trg_tmp, attr_names[i], attr_names)
                 tensor_label_trg = fluid.LoDTensor()
                 tensor_label_trg.set(label_trg, place)
                 fake_temp, rec_temp = exe.run(
@@ -261,3 +264,22 @@ def check_attribute_conflict(label_batch, attr, attrs):
                 if a != attr:
                     _set(label, 0, a)
     return label_batch
+
+
+def check_gpu(use_gpu):
+     """
+     Log error and exit when set use_gpu=true in paddlepaddle
+     cpu version.
+     """
+     err = "Config use_gpu cannot be set as true while you are " \
+           "using paddlepaddle cpu version ! \nPlease try: \n" \
+           "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
+           "\t2. Set use_gpu as false in config file to run " \
+           "model on CPU"
+
+     try:
+         if use_gpu and not fluid.is_compiled_with_cuda():
+             logger.error(err)
+             sys.exit(1)
+     except Exception as e:
+         pass