fix_gan_gpu (#2864)

* fix dir and gpu

PaddleCV/PaddleGAN/download.py

@@ -159,9 +159,9 @@ if __name__ == '__main__':
 
     if args.dataset == 'mnist':
         print('Download dataset: {}'.format(args.dataset))
-        download_mnist('./data/')
+        download_mnist('data')
     elif args.dataset in cycle_pix_dataset:
         print('Download dataset: {}'.format(args.dataset))
-        download_cycle_pix('./data/', args.dataset)
+        download_cycle_pix(os.path.join('data', args.dataset))
     else:
         print('Please download by yourself, thanks')

PaddleCV/PaddleGAN/infer.py

@@ -138,7 +138,7 @@ def infer(args):
     for var in fluid.default_main_program().global_block().all_parameters():
         print(var.name)
     print(args.init_model + '/' + model_name)
-    fluid.io.load_persistables(exe, args.init_model + "/" + model_name)
+    fluid.io.load_persistables(exe, os.path.join(args.init_model, model_name))
     print('load params done')
     if not os.path.exists(args.output):
         os.makedirs(args.output)
@@ -270,7 +270,8 @@ def infer(args):
         fake_image = np.reshape(fake_temp, (args.batch_size, -1))
 
         fig = utility.plot(fake_image)
-        plt.savefig(args.output + '/fake_cgan.png', bbox_inches='tight')
+        plt.savefig(
+            os.path.join(args.output, 'fake_cgan.png'), bbox_inches='tight')
         plt.close(fig)
 
     elif args.model_net == 'DCGAN':
@@ -284,7 +285,8 @@ def infer(args):
         fake_image = np.reshape(fake_temp, (args.batch_size, -1))
 
         fig = utility.plot(fake_image)
-        plt.savefig(args.output + '/fake_dcgan.png', bbox_inches='tight')
+        plt.savefig(
+            os.path.join(args.output, '/fake_dcgan.png'), bbox_inches='tight')
         plt.close(fig)
     else:
         raise NotImplementedError("model_net {} is not support".format(

PaddleCV/PaddleGAN/trainer/CGAN.py

@@ -181,7 +181,7 @@ class CGAN(object):
                 t_time += batch_time
 
                 if batch_id % self.cfg.print_freq == 0:
-                    image_path = self.cfg.output + '/images'
+                    image_path = os.path.join(self.cfg.output, 'images')
                     if not os.path.exists(image_path):
                         os.makedirs(image_path)
                     generate_const_image = exe.run(
@@ -201,10 +201,9 @@ class CGAN(object):
                                batch_time))
                     plt.title('Epoch ID={}, Batch ID={}'.format(epoch_id,
                                                                 batch_id))
+                    img_name = '{:04d}_{:04d}.png'.format(epoch_id, batch_id)
                     plt.savefig(
-                        '{}/{:04d}_{:04d}.png'.format(image_path, epoch_id,
-                                                      batch_id),
-                        bbox_inches='tight')
+                        os.path.join(image_path, img_name), bbox_inches='tight')
                     plt.close(fig)
 
             if self.cfg.save_checkpoints:

PaddleCV/PaddleGAN/trainer/DCGAN.py

@@ -166,7 +166,7 @@ class DCGAN(object):
                 t_time += batch_time
 
                 if batch_id % self.cfg.print_freq == 0:
-                    image_path = self.cfg.output + '/images'
+                    image_path = os.path.join(self.cfg.output, 'images')
                     if not os.path.exists(image_path):
                         os.makedirs(image_path)
                     generate_const_image = exe.run(
@@ -185,10 +185,9 @@ class DCGAN(object):
                                batch_time))
                     plt.title('Epoch ID={}, Batch ID={}'.format(epoch_id,
                                                                 batch_id))
+                    img_name = '{:04d}_{:04d}.png'.format(epoch_id, batch_id)
                     plt.savefig(
-                        '{}/{:04d}_{:04d}.png'.format(image_path, epoch_id,
-                                                      batch_id),
-                        bbox_inches='tight')
+                        os.path.join(image_path, img_name), bbox_inches='tight')
                     plt.close(fig)
 
             if self.cfg.save_checkpoints:

PaddleCV/PaddleGAN/util/utility.py

@@ -49,7 +49,7 @@ def plot(gen_data):
 
 
 def checkpoints(epoch, cfg, exe, trainer, name):
-    output_path = cfg.output + '/checkpoints/' + str(epoch)
+    output_path = os.path.join(cfg.output, 'checkpoints', str(epoch))
     if not os.path.exists(output_path):
         os.makedirs(output_path)
     fluid.io.save_persistables(
@@ -75,7 +75,7 @@ def save_test_image(epoch,
                     g_trainer,
                     A_test_reader,
                     B_test_reader=None):
-    out_path = cfg.output + '/test'
+    out_path = os.path.join(cfg.output, 'test')
     if not os.path.exists(out_path):
         os.makedirs(out_path)
     if cfg.model_net == "Pix2pix":
@@ -242,6 +242,8 @@ class ImagePool(object):
 
 
 def check_attribute_conflict(label_batch, attr, attrs):
+    ''' Based on https://github.com/LynnHo/AttGAN-Tensorflow'''
+
     def _set(label, value, attr):
         if attr in attrs:
             label[attrs.index(attr)] = value
@@ -262,10 +264,6 @@ def check_attribute_conflict(label_batch, attr, attrs):
             for a in ['Straight_Hair', 'Wavy_Hair']:
                 if a != attr:
                     _set(label, 0, a)
-        elif attr in ['Mustache', 'No_Beard'] and attrs[attr_id] != 0:
-            for a in ['Mustache', 'No_Beard']:
-                if a != attr:
-                    _set(label, 0, a)
     return label_batch
 
 
@@ -290,7 +288,7 @@ def check_gpu(use_gpu):
 
     try:
         if use_gpu and not fluid.is_compiled_with_cuda():
-            logger.error(err)
+            print(err)
             sys.exit(1)
     except Exception as e:
         pass