solve conflict

configs/cyclegan/cyclegan_lsgan_id0_resnet_in_1x1_250k_summer2winter.py

@@ -151,6 +151,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/cyclegan/cyclegan_lsgan_id0_resnet_in_1x1_270k_horse2zebra.py

@@ -150,6 +150,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/cyclegan/cyclegan_lsgan_id0_resnet_in_1x1_80k_facades.py

@@ -153,6 +153,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/cyclegan/cyclegan_lsgan_resnet_in_1x1_250k_summer2winter.py

@@ -166,6 +166,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/cyclegan/cyclegan_lsgan_resnet_in_1x1_270k_horse2zebra.py

@@ -164,6 +164,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/cyclegan/cyclegan_lsgan_resnet_in_1x1_80k_facades.py

@@ -168,6 +168,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/pix2pix/pix2pix_vanilla_unet_bn_1x1_220k_aerial2maps.py

@@ -122,6 +122,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
     best_metric=['fid', 'is'])

configs/pix2pix/pix2pix_vanilla_unet_bn_1x1_220k_maps2aerial.py

@@ -121,6 +121,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/pix2pix/pix2pix_vanilla_unet_bn_1x1_80k_facades.py

@@ -122,6 +122,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

configs/pix2pix/pix2pix_vanilla_unet_bn_wo_jitter_flip_1x4_190k_edges2shoes.py

@@ -121,6 +121,9 @@
     interval=10000,
     metrics=[
         dict(type='FID', num_images=num_images, bgr2rgb=True),
-        dict(type='IS', num_images=num_images, inception_args=dict(type='pytorch'))
+        dict(
+            type='IS',
+            num_images=num_images,
+            inception_args=dict(type='pytorch'))
     ],
-    best_metric=['fid', 'is'])
+    best_metric=['fid', 'is'])

mmgen/core/evaluation/eval_hooks.py

@@ -326,9 +326,9 @@ def _save_best_ckpt(self, runner, new_score, metric_name):
 class TranslationEvalHook(GenerativeEvalHook):
     """Evaluation Hook for Translation Models.
 
-    This evaluation hook can be used to evaluate translation models. Note 
-    that only ``FID`` and ``IS`` metric are supported for the distributed 
-    training now. In the future, we will support more metrics for the 
+    This evaluation hook can be used to evaluate translation models. Note
+    that only ``FID`` and ``IS`` metric are supported for the distributed
+    training now. In the future, we will support more metrics for the
     evaluation during the training procedure.
 
     In our config system, you only need to add `evaluation` with the detailed
@@ -364,7 +364,10 @@ class TranslationEvalHook(GenerativeEvalHook):
             interval=10000,
             metrics=[
                 dict(type='FID', num_images=106, bgr2rgb=True),
-                dict(type='IS', num_images=106, inception_args=dict(type='pytorch'))
+                dict(
+                    type='IS',
+                    num_images=106,
+                    inception_args=dict(type='pytorch'))
             ],
             best_metric=['fid', 'is'])
 
@@ -392,7 +395,7 @@ class TranslationEvalHook(GenerativeEvalHook):
     Args:
         target_domain (str): Target domain of output image.
     """
-    
+
     def __init__(self, *args, target_domain, **kwargs):
         super().__init__(*args, **kwargs)
         self.target_domain = target_domain
@@ -431,7 +434,6 @@ def after_train_iter(self, runner):
 
         mmcv.print_log(f'Sample {max_num_images} fake images for evaluation',
                        'mmgen')
-        batch_size = self.dataloader.batch_size
 
         rank, ws = get_dist_info()
 
@@ -452,8 +454,7 @@ def after_train_iter(self, runner):
                 fakes = output_dict['target']
             # key Error
             else:
-                raise KeyError(
-                    'Cannot found key for images in data_dict. ')
+                raise KeyError('Cannot found key for images in data_dict. ')
             # sampling fake images and directly send them to metrics
             # pbar update number for one proc
             num_update = 0
@@ -467,7 +468,7 @@ def after_train_iter(self, runner):
 
             if rank == 0:
                 if num_update > 0:
-                    pbar.update(num_update* ws)
+                    pbar.update(num_update * ws)
 
         runner.log_buffer.clear()
         # a dirty walkround to change the line at the end of pbar
@@ -488,4 +489,4 @@ def after_train_iter(self, runner):
 
         # clear all current states for next evaluation
         for metric in self.metrics:
-            metric.clear()
+            metric.clear()

mmgen/datasets/paired_image_dataset.py

@@ -27,7 +27,7 @@ class PairedImageDataset(Dataset):
         pipeline (List[dict | callable]): A sequence of data transformations.
         test_mode (bool): Store `True` when building test dataset.
             Default: `False`.
-        testdir (str): Subfolder of dataroot which contain test images. 
+        testdir (str): Subfolder of dataroot which contain test images.
             Default: 'test'.
     """