Fix the typo uncoditional -> unconditional

demo/conditional_demo.py

@@ -21,7 +21,7 @@ def parse_args():
         '--save-path',
         type=str,
         default='./work_dirs/demos/conditional_samples.png',
-        help='path to save uncoditional samples')
+        help='path to save unconditional samples')
     parser.add_argument(
         '--device', type=str, default='cuda:0', help='CUDA device id')
 

demo/ddpm_demo.py

@@ -24,7 +24,7 @@ def parse_args():
         '--save-path',
         type=str,
         default='./work_dirs/demos/ddpm_samples.png',
-        help='path to save uncoditional samples')
+        help='path to save unconditional samples')
     parser.add_argument(
         '--device', type=str, default='cuda:0', help='CUDA device id')
 

demo/unconditional_demo.py

@@ -9,7 +9,7 @@
 # yapf: disable
 sys.path.append(os.path.abspath(os.path.join(__file__, '../..')))  # isort:skip  # noqa
 
-from mmgen.apis import init_model, sample_uncoditional_model  # isort:skip  # noqa
+from mmgen.apis import init_model, sample_unconditional_model  # isort:skip  # noqa
 # yapf: enable
 
 
@@ -21,7 +21,7 @@ def parse_args():
         '--save-path',
         type=str,
         default='./work_dirs/demos/unconditional_samples.png',
-        help='path to save uncoditional samples')
+        help='path to save unconditional samples')
     parser.add_argument(
         '--device', type=str, default='cuda:0', help='CUDA device id')
 
@@ -65,9 +65,9 @@ def main():
     if args.sample_cfg is None:
         args.sample_cfg = dict()
 
-    results = sample_uncoditional_model(model, args.num_samples,
-                                        args.num_batches, args.sample_model,
-                                        **args.sample_cfg)
+    results = sample_unconditional_model(model, args.num_samples,
+                                         args.num_batches, args.sample_model,
+                                         **args.sample_cfg)
     results = (results[:, [2, 1, 0]] + 1.) / 2.
 
     # save images

docs/en/get_started.md

@@ -150,7 +150,7 @@ PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
 To verify whether MMGeneration and the required environment are installed correctly, we can run sample Python code to initialize an unconditional model and use it to generate random samples:
 
 ```python
-from mmgen.apis import init_model, sample_uncoditional_model
+from mmgen.apis import init_model, sample_unconditional_model
 
 config_file = 'configs/styleganv2/stylegan2_c2_lsun-church_256_b4x8_800k.py'
 # you can download this checkpoint in advance and use a local file path.
@@ -159,7 +159,7 @@ device = 'cuda:0'
 # init a generatvie
 model = init_model(config_file, checkpoint_file, device=device)
 # sample images
-fake_imgs = sample_uncoditional_model(model, 4)
+fake_imgs = sample_unconditional_model(model, 4)
 ```
 
 The above code is supposed to run successfully upon you finish the installation.

docs/en/quick_run.md

@@ -16,7 +16,7 @@ MMGeneration provides high-level APIs for sampling images with unconditional GAN
 
 ```python
 import mmcv
-from mmgen.apis import init_model, sample_uncoditional_model
+from mmgen.apis import init_model, sample_unconditional_model
 
 # Specify the path to model config and checkpoint file
 config_file = 'configs/styleganv2/stylegan2_c2_ffhq_1024_b4x8.py'

mmgen/apis/__init__.py

@@ -1,11 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .inference import (init_model, sample_conditional_model,
                         sample_ddpm_model, sample_img2img_model,
-                        sample_uncoditional_model)
+                        sample_unconditional_model)
 from .train import set_random_seed, train_model
 
 __all__ = [
     'set_random_seed', 'train_model', 'init_model', 'sample_img2img_model',
-    'sample_uncoditional_model', 'sample_conditional_model',
+    'sample_unconditional_model', 'sample_conditional_model',
     'sample_ddpm_model'
 ]

mmgen/apis/inference.py

@@ -46,11 +46,11 @@ def init_model(config, checkpoint=None, device='cuda:0', cfg_options=None):
 
 
 @torch.no_grad()
-def sample_uncoditional_model(model,
-                              num_samples=16,
-                              num_batches=4,
-                              sample_model='ema',
-                              **kwargs):
+def sample_unconditional_model(model,
+                               num_samples=16,
+                               num_batches=4,
+                               sample_model='ema',
+                               **kwargs):
     """Sampling from unconditional models.
 
     Args:

mmgen/datasets/grow_scale_image_dataset.py

@@ -10,7 +10,7 @@
 
 @DATASETS.register_module()
 class GrowScaleImgDataset(Dataset):
-    """Grow Scale Uncoditional Image Dataset.
+    """Grow Scale Unconditional Image Dataset.
 
     This dataset is similar with ``UnconditionalImageDataset``, but offer
     more dynamic functionalities for the supporting complex algorithms, like

mmgen/datasets/unconditional_image_dataset.py

@@ -10,7 +10,7 @@
 
 @DATASETS.register_module()
 class UnconditionalImageDataset(Dataset):
-    """Uncoditional Image Dataset.
+    """Unconditional Image Dataset.
 
     This dataset contains raw images for training unconditional GANs. Given
     a root dir, we will recursively find all images in this root. The

tests/test_apis/test_inference.py

@@ -5,7 +5,7 @@
 import torch
 
 from mmgen.apis import (init_model, sample_ddpm_model, sample_img2img_model,
-                        sample_uncoditional_model)
+                        sample_unconditional_model)
 
 
 class TestSampleUnconditionalModel:
@@ -20,22 +20,22 @@ def setup_class(cls):
         cls.model = init_model(config, checkpoint=None, device='cpu')
 
     def test_sample_unconditional_model_cpu(self):
-        res = sample_uncoditional_model(
+        res = sample_unconditional_model(
             self.model, 5, num_batches=2, sample_model='orig')
         assert res.shape == (5, 3, 64, 64)
 
-        res = sample_uncoditional_model(
+        res = sample_unconditional_model(
             self.model, 4, num_batches=2, sample_model='orig')
         assert res.shape == (4, 3, 64, 64)
 
     @pytest.mark.skipif(not torch.cuda.is_available(), reason='requires cuda')
     def test_sample_unconditional_model_cuda(self):
         model = self.model.cuda()
-        res = sample_uncoditional_model(
+        res = sample_unconditional_model(
             model, 5, num_batches=2, sample_model='orig')
         assert res.shape == (5, 3, 64, 64)
 
-        res = sample_uncoditional_model(
+        res = sample_unconditional_model(
             model, 4, num_batches=2, sample_model='orig')
         assert res.shape == (4, 3, 64, 64)