[Fix] wgangp config error

configs/wgan-gp/wgangp_GN_celeba-cropped_128_b64x1_160kiter.py

@@ -1,6 +1,6 @@
 _base_ = [
     '../_base_/datasets/unconditional_imgs_128x128.py',
-    '../_base_/models/wgangp_base.py'
+    '../_base_/models/wgangp_base.py', '../_base_/default_runtime.py'
 ]
 
 data = dict(
@@ -20,6 +20,11 @@
 lr_config = None
 total_iters = 160000
 
+runner = dict(
+    type='DynamicIterBasedRunner',
+    is_dynamic_ddp=False,  # Note that this flag should be False.
+    pass_training_status=True)
+
 metrics = dict(
     ms_ssim10k=dict(type='MS_SSIM', num_images=10000),
     swd16k=dict(type='SWD', num_images=16384, image_shape=(3, 128, 128)))

mmgen/datasets/pipelines/crop.py

@@ -49,8 +49,8 @@ def _crop(self, data):
                 y_offset = max(0, (data_h - crop_h)) // 2
 
             crop_bbox = [x_offset, y_offset, crop_w, crop_h]
-            item_ = item[y_offset:y_offset + crop_h,
-                         x_offset:x_offset + crop_w, ...]
+            item_ = item[y_offset:y_offset + crop_h, x_offset:x_offset +
+                         crop_w, ...]
             crop_bbox_list.append(crop_bbox)
             data_list_.append(item_)
 
@@ -111,8 +111,8 @@ def __init__(self, keys, crop_size, crop_pos=None):
 
     def _crop(self, data, x_offset, y_offset, crop_w, crop_h):
         crop_bbox = [x_offset, y_offset, crop_w, crop_h]
-        data_ = data[y_offset:y_offset + crop_h, x_offset:x_offset + crop_w,
-                     ...]
+        data_ = data[y_offset:y_offset + crop_h, x_offset:x_offset +
+                     crop_w, ...]
         return data_, crop_bbox
 
     def __call__(self, results):

mmgen/datasets/unconditional_image_dataset.py

@@ -23,7 +23,7 @@ class UnconditionalImageDataset(Dataset):
             mode. Otherwise, in train mode. Default to False.
     """
 
-    _VALID_IMG_SUFFIX = ('.jpg', '.png', '.jpeg', '.JPEG')
+    _VALID_IMG_SUFFIX = ('.jpg', '.png', '.jpeg', '.JPEG', '.webp')
 
     def __init__(self, imgs_root, pipeline, test_mode=False):
         super().__init__()

mmgen/models/gans/base_gan.py

@@ -28,9 +28,9 @@ def with_ema_gen(self):
     @property
     def with_gen_auxiliary_loss(self):
         """bool: whether the GAN adopts auxiliary loss in the generator."""
-        return hasattr(self,
-                       'gen_auxiliary_losses') and (self.gen_auxiliary_losses
-                                                    is not None)
+        return hasattr(
+            self,
+            'gen_auxiliary_losses') and (self.gen_auxiliary_losses is not None)
 
     @property
     def with_disc_auxiliary_loss(self):

mmgen/models/gans/singan.py

@@ -383,8 +383,8 @@ def train_step(self,
 
         # end of each scale
         # calculate noise weight for next scale
-        if (curr_iter % self.train_cfg['iters_per_scale']
-                == 0) and (self.curr_stage < len(self.reals) - 1):
+        if (curr_iter % self.train_cfg['iters_per_scale'] == 0) and (
+                self.curr_stage < len(self.reals) - 1):
 
             with torch.no_grad():
                 g_recon = self.generator(

mmgen/ops/conv2d_gradfix.py

@@ -121,12 +121,7 @@ def no_weight_gradients():
     weight_gradients_disabled = old
 
 
-def conv2d(input,
-           weight,
-           bias=None,
-           stride=1,
-           padding=0,
-           dilation=1,
+def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1,
            groups=1):
     if _should_use_custom_op(input):
         return _conv2d_gradfix(

tests/test_datasets/test_pipelines/test_crop.py

@@ -131,12 +131,12 @@ def test_fixed_crop(self):
         assert crop_w == results['img_b_crop_bbox'][2]
         assert crop_h == results['img_b_crop_bbox'][3]
         assert np.array_equal(
-            self.results['img_a'][y_offset:y_offset + crop_h,
-                                  x_offset:x_offset + crop_w, :],
+            self.results['img_a'][y_offset:y_offset +
+                                  crop_h, x_offset:x_offset + crop_w, :],
             results['img_a'])
         assert np.array_equal(
-            self.results['img_b'][y_offset:y_offset + crop_h,
-                                  x_offset:x_offset + crop_w, :],
+            self.results['img_b'][y_offset:y_offset +
+                                  crop_h, x_offset:x_offset + crop_w, :],
             results['img_b'])
 
         # test given pos crop for lager size than the original shape