[Enhancement] Revise SWD metric and DCGAN's configs

configs/dcgan/dcgan_1xb128-300kiters_celeba-cropped-64.py

@@ -45,6 +45,8 @@
         sample_model='orig',
         image_shape=(3, 64, 64))
 ]
+# save best checkpoints
+default_hooks = dict(checkpoint=dict(save_best='swd/avg', rule='less'))
 
 val_evaluator = dict(metrics=metrics)
 test_evaluator = dict(metrics=metrics)

configs/dcgan/dcgan_1xb128-5epoches_lsun-bedroom-64x64.py

@@ -44,6 +44,8 @@
         sample_model='orig',
         image_shape=(3, 64, 64))
 ]
+# save best checkpoints
+default_hooks = dict(checkpoint=dict(save_best='swd/avg', rule='less'))
 
 val_evaluator = dict(metrics=metrics)
 test_evaluator = dict(metrics=metrics)

configs/dcgan/dcgan_Glr4e-4_Dlr1e-4_1xb128-5kiters_mnist-64x64.py

@@ -5,47 +5,44 @@
 ]
 
 # output single channel
-model = dict(generator=dict(out_channels=1), discriminator=dict(in_channels=1))
+model = dict(
+    data_preprocessor=dict(mean=[127.5], std=[127.5]),
+    generator=dict(out_channels=1),
+    discriminator=dict(in_channels=1))
 
 # define dataset
 # modify train_pipeline to load gray scale images
 train_pipeline = [
-    dict(
-        type='LoadImageFromFile',
-        key='img',
-        io_backend='disk',
-        color_type='grayscale'),
+    dict(type='LoadImageFromFile', key='img', color_type='grayscale'),
     dict(type='Resize', scale=(64, 64)),
-    dict(type='PackEditInputs', meta_keys=[])
+    dict(type='PackEditInputs')
 ]
 
 # set ``batch_size``` and ``data_root```
 batch_size = 128
 data_root = 'data/mnist_64/train'
 train_dataloader = dict(
-    batch_size=batch_size, dataset=dict(data_root=data_root))
+    batch_size=batch_size,
+    dataset=dict(data_root=data_root, pipeline=train_pipeline))
 
-val_dataloader = dict(batch_size=batch_size, dataset=dict(data_root=data_root))
+val_dataloader = dict(
+    batch_size=batch_size,
+    dataset=dict(data_root=data_root, pipeline=train_pipeline))
 
 test_dataloader = dict(
-    batch_size=batch_size, dataset=dict(data_root=data_root))
-
-default_hooks = dict(
-    checkpoint=dict(
-        interval=500,
-        save_best=['swd/avg', 'ms-ssim/avg'],
-        rule=['less', 'greater']))
+    batch_size=batch_size,
+    dataset=dict(data_root=data_root, pipeline=train_pipeline))
 
 # VIS_HOOK
 custom_hooks = [
     dict(
         type='GenVisualizationHook',
-        interval=10000,
+        interval=500,
         fixed_input=True,
         vis_kwargs_list=dict(type='GAN', name='fake_img'))
 ]
 
-train_cfg = dict(max_iters=5000)
+train_cfg = dict(max_iters=5000, val_interval=500)
 
 # METRICS
 metrics = [
@@ -55,10 +52,13 @@
     dict(
         type='SWD',
         prefix='swd',
-        fake_nums=16384,
+        fake_nums=-1,
         sample_model='orig',
-        image_shape=(3, 64, 64))
+        image_shape=(1, 64, 64))
 ]
+# save best checkpoints
+default_hooks = dict(
+    checkpoint=dict(interval=500, save_best='swd/avg', rule='less'))
 
 val_evaluator = dict(metrics=metrics)
 test_evaluator = dict(metrics=metrics)

mmedit/evaluation/metrics/base_gen_metric.py

@@ -155,10 +155,14 @@ def get_metric_sampler(self, model: nn.Module, dataloader: DataLoader,
             DataLoader: Default sampler for normal metrics.
         """
         batch_size = dataloader.batch_size
-
+        dataset_length = len(dataloader.dataset)
         rank, num_gpus = get_dist_info()
-        item_subset = [(i * num_gpus + rank) % self.real_nums
-                       for i in range((self.real_nums - 1) // num_gpus + 1)]
+        assert self.real_nums <= dataset_length, (
+            f'\'real_nums\'({self.real_nums}) can not larger than length of '
+            f'dataset ({dataset_length}).')
+        nums = dataset_length if self.real_nums == -1 else self.real_nums
+        item_subset = [(i * num_gpus + rank) % nums
+                       for i in range((nums - 1) // num_gpus + 1)]
 
         metric_dataloader = DataLoader(
             dataloader.dataset,

mmedit/evaluation/metrics/swd.py

@@ -255,7 +255,8 @@ def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
             data_batch (dict): A batch of data from the dataloader.
             data_samples (Sequence[dict]): A batch of outputs from the model.
         """
-        if self._num_processed >= self.fake_nums_per_device:
+        if self.fake_nums != -1 and (self._num_processed >=
+                                     self.fake_nums_per_device):
             return
 
         real_imgs, fake_imgs = [], []
@@ -279,6 +280,8 @@ def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
 
         # real images
         assert real_imgs.shape[1:] == self.image_shape
+        if real_imgs.shape[1] == 1:
+            real_imgs = real_imgs.repeat(1, 3, 1, 1)
         real_pyramid = laplacian_pyramid(real_imgs, self.n_pyramids - 1,
                                          self.gaussian_k)
         # lod: layer_of_descriptors
@@ -291,6 +294,8 @@ def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None:
 
         # fake images
         assert fake_imgs.shape[1:] == self.image_shape
+        if fake_imgs.shape[1] == 1:
+            fake_imgs = fake_imgs.repeat(1, 3, 1, 1)
         fake_pyramid = laplacian_pyramid(fake_imgs, self.n_pyramids - 1,
                                          self.gaussian_k)
         # lod: layer_of_descriptors

tests/test_evaluation/test_metrics/test_base_gen_metric.py

@@ -1,6 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from unittest.mock import MagicMock, patch
 
+import pytest
 import torch
 from mmengine.model import MMDistributedDataParallel
 
@@ -43,6 +44,7 @@ def test_GenMetric():
     # test get_metric_sampler
     model = MagicMock()
     dataset = MagicMock()
+    dataset.__len__.return_value = 10
     dataloader = MagicMock()
     dataloader.batch_size = 4
     dataloader.dataset = dataset
@@ -57,6 +59,11 @@ def test_GenMetric():
     metric.prepare(model, dataloader)
     assert metric.data_preprocessor == preprocessor
 
+    # test raise error with dataset is length than real_nums
+    dataset.__len__.return_value = 5
+    with pytest.raises(AssertionError):
+        metric.get_metric_sampler(model, dataloader, [metric])
+
 
 def test_GenerativeMetric():
     metric = ToyGenerativeMetric(11, need_cond_input=True)

tests/test_evaluation/test_metrics/test_swd.py

@@ -13,8 +13,7 @@
 class TestSWD(TestCase):
 
     def test_init(self):
-        swd = SlicedWassersteinDistance(
-            fake_nums=10, image_shape=(3, 32, 32))  # noqa
+        swd = SlicedWassersteinDistance(fake_nums=10, image_shape=(3, 32, 32))
         self.assertEqual(len(swd.real_results), 2)
 
     def test_prosess(self):
@@ -35,6 +34,13 @@ def test_prosess(self):
         ]
 
         swd.process(real_samples, fake_samples)
+        # 100 samples are passed in 1 batch, _num_processed should be 100
+        self.assertEqual(swd._num_processed, 100)
+        # _num_processed(100) > fake_nums(4), _num_processed should be
+        # unchanged
+        swd.process(real_samples, fake_samples)
+        self.assertEqual(swd._num_processed, 100)
+
         output = swd.evaluate()
         result = [16.495922580361366, 24.15413036942482, 20.325026474893093]
         output = [item / 100 for item in output.values()]
@@ -48,3 +54,34 @@ def test_prosess(self):
             sample_model='orig',
             image_shape=(3, 32, 32))
         swd.prepare(model, None)
+
+        # test gray scale input
+        swd.image_shape = (1, 32, 32)
+        real_samples = [
+            dict(inputs=torch.rand(1, 32, 32) * 255.) for _ in range(100)
+        ]
+        fake_samples = [
+            EditDataSample(
+                fake_img=PixelData(data=torch.rand(1, 32, 32) * 2 - 1),
+                gt_img=PixelData(data=torch.rand(1, 32, 32) * 2 -
+                                 1)).to_dict() for _ in range(100)
+        ]
+        swd.process(real_samples, fake_samples)
+
+        # test fake_nums is -1
+        swd = SlicedWassersteinDistance(
+            fake_nums=-1,
+            fake_key='fake',
+            real_key='img',
+            sample_model='orig',
+            image_shape=(3, 32, 32))
+        fake_samples = [
+            EditDataSample(
+                fake_img=PixelData(data=torch.rand(3, 32, 32) * 2 - 1),
+                gt_img=PixelData(data=torch.rand(3, 32, 32) * 2 -
+                                 1)).to_dict() for _ in range(10)
+        ]
+        for _ in range(3):
+            swd.process(None, fake_samples)
+        # fake_nums is -1, all samples (10 * 3 = 30) is processed
+        self.assertEqual(swd._num_processed, 30)

tests/test_models/test_editors/test_wgan_gp/test_wgan_gp.py

@@ -1,7 +1,9 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
 from copy import deepcopy
 from unittest import TestCase
 
+import pytest
 import torch
 from mmengine import MessageHub
 from mmengine.optim import OptimWrapper, OptimWrapperDict
@@ -54,6 +56,9 @@ def test_init(self):
         gan = WGANGP(generator=gen, data_preprocessor=GenDataPreprocessor())
         self.assertEqual(gan.discriminator, None)
 
+    @pytest.mark.skipif(
+        'win' in platform.system().lower() and 'cu' in torch.__version__,
+        reason='skip on windows-cuda due to limited RAM.')
     def test_train_step(self):
         # prepare model
         accu_iter = 1

tests/test_models/test_losses/test_feature_loss.py

@@ -1,10 +1,15 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
+
 import pytest
 import torch
 
 from mmedit.models.losses import LightCNNFeatureLoss
 
 
+@pytest.mark.skipif(
+    'win' in platform.system().lower() and 'cu' in torch.__version__,
+    reason='skip on windows-cuda due to limited RAM.')
 def test_light_cnn_feature_loss():
 
     pretrained = 'https://download.openmmlab.com/mmediting/' + \

tests/test_models/test_losses/test_loss_comps/test_gen_auxiliary_loss_comps.py

@@ -1,4 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
+
 import pytest
 import torch
 from mmengine.utils.dl_utils import TORCH_VERSION
@@ -8,6 +10,9 @@
 from mmedit.models.losses import GeneratorPathRegularizerComps
 
 
+@pytest.mark.skipif(
+    'win' in platform.system().lower() and 'cu' in torch.__version__,
+    reason='skip on windows-cuda due to limited RAM.')
 class TestPathRegularizer:
 
     @classmethod

tests/test_models/test_losses/test_perceptual_loss.py

@@ -1,4 +1,5 @@
 # Copyright (c) OpenMMLab. All rights reserved.
+import platform
 from unittest.mock import patch
 
 import pytest
@@ -8,6 +9,9 @@
                            TransferalPerceptualLoss)
 
 
+@pytest.mark.skipif(
+    'win' in platform.system().lower() and 'cu' in torch.__version__,
+    reason='skip on windows-cuda due to limited RAM.')
 @patch.object(PerceptualVGG, 'init_weights')
 def test_perceptual_loss(init_weights):
     if torch.cuda.is_available():