Merge branch 'master' into groups-conv2d

docs/en/tutorials/inception_eval.md

@@ -0,0 +1,38 @@
+# Tutorial 5: How to compute FID and KID for measruing the difference between the distribution of real data and restored-data
+
+<!-- TOC -->
+
+- [Tutorial 5: How to compute FID and KID for measruing the difference between the distribution of real data and restored-data](#tutorial-5-how-to-compute-fid-and-kid-for-measruing-the-difference-between-the-distribution-of-real-data-and-restored-data)
+  - [Why FID and KID for image restoration tasks?](#why-fid-and-kid-for-image-restoration-tasks)
+- [Set Config File](#set-config-file)
+
+<!-- TOC -->
+
+## Why FID and KID for image restoration tasks?
+
+Commonly used metrics for image/video resoration are PSNR, SSIM or LPIPS which directly compare high-quality original data and restored data.
+While these are good metrics to order the restoration performance of different models, they have the requirement that undegraded images should be given.
+
+Recently, some unpaired restoration methods have been proposed to restore real-world images in the condition where only degraded images are accessible.
+In this condition, it is difficult to compare the various proposed models with common metrics such as PSNR, SSIM since there is no corresponding ground-truth data.
+
+An alternative way to evaluate these models in real-world settings is to compare the distributions of real data and restored-data, rather than directly comparing corresponding images.
+
+To this end, MMEditing provides functions to compute  *Fréchet inception distance* (FID) and *Kernel Inception Distance* (KID), which are commonly used in image generation tasks to check fidelity of generated images, metrics that measure the difference between the two distributions.
+Currently, computing FID and KID is only available for restoration tasks.
+
+## Set Config File
+
+FID and KID can be meausred after images from two distributions are extractes as feature vectors with the InceptionV3 model.
+
+To compute the distance between extracted feature vectors, we can add `FID` and `KID` metric in `test_cfg` as follow:
+
+```python3
+test_cfg = dict(
+    metrics=[
+        'PSNR', 'SSIM', 'FID',
+        dict(type='KID', num_repeats=100, sample_size=1000)
+    ],
+    inception_style='StyleGAN',  # or pytorch
+    crop_border=0)
+```

mmedit/core/__init__.py

@@ -1,14 +1,16 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-from .evaluation import (DistEvalIterHook, EvalIterHook, L1Evaluation, mae,
-                         mse, psnr, reorder_image, sad, ssim)
+from .evaluation import (FID, KID, DistEvalIterHook, EvalIterHook, InceptionV3,
+                         L1Evaluation, mae, mse, psnr, reorder_image, sad,
+                         ssim)
 from .hooks import MMEditVisualizationHook, VisualizationHook
 from .misc import tensor2img
 from .optimizer import build_optimizers
+from .registry import build_metric
 from .scheduler import LinearLrUpdaterHook, ReduceLrUpdaterHook
 
 __all__ = [
     'build_optimizers', 'tensor2img', 'EvalIterHook', 'DistEvalIterHook',
     'mse', 'psnr', 'reorder_image', 'sad', 'ssim', 'LinearLrUpdaterHook',
-    'VisualizationHook', 'MMEditVisualizationHook', 'L1Evaluation',
-    'ReduceLrUpdaterHook', 'mae'
+    'VisualizationHook', 'MMEditVisualizationHook', 'L1Evaluation', 'FID',
+    'KID', 'InceptionV3', 'build_metric', 'ReduceLrUpdaterHook', 'mae'
 ]

mmedit/core/evaluation/__init__.py

@@ -1,10 +1,11 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from .eval_hooks import DistEvalIterHook, EvalIterHook
+from .inceptions import FID, KID, InceptionV3
 from .metrics import (L1Evaluation, connectivity, gradient_error, mae, mse,
                       niqe, psnr, reorder_image, sad, ssim)
 
 __all__ = [
     'mse', 'sad', 'psnr', 'reorder_image', 'ssim', 'EvalIterHook',
     'DistEvalIterHook', 'L1Evaluation', 'gradient_error', 'connectivity',
-    'niqe', 'mae'
+    'niqe', 'mae', 'FID', 'KID', 'InceptionV3'
 ]

mmedit/core/evaluation/eval_hooks.py

@@ -57,6 +57,9 @@ def evaluate(self, runner, results):
         eval_res = self.dataloader.dataset.evaluate(
             results, logger=runner.logger, **self.eval_kwargs)
         for name, val in eval_res.items():
+            if isinstance(val, dict):
+                runner.log_buffer.output.update(val)
+                continue
             runner.log_buffer.output[name] = val
         runner.log_buffer.ready = True
         # call `after_val_epoch` after evaluation.