Merge branch 'master' into groups-conv2d

.circleci/test.yml

@@ -140,7 +140,7 @@ workflows:
           name: minimum_version_cpu
           torch: 1.6.0
           torchvision: 0.7.0
-          python: 3.6.9  # The lowest python 3.6.x version available on CircleCI images
+          python: 3.7.4
           requires:
             - lint
       - build_cpu:

.github/workflows/merge_stage_test.yml

@@ -21,7 +21,7 @@ jobs:
     runs-on: ubuntu-18.04
     strategy:
       matrix:
-        python-version: [3.6, 3.8, 3.9]
+        python-version: [3.8, 3.9]
         torch: [1.8.1]
         include:
           - torch: 1.8.1

.pre-commit-config.yaml

@@ -5,7 +5,7 @@ repos:
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.11.5
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf

README_zh-CN.md

@@ -84,7 +84,7 @@ MMEditing 同时维护 0.x 和 1.x 版本，详情见[分支维护计划](README
 
 最新的 **0.16.0** 版本已经在 31/10/2022 发布：
 
-- `VisualizationHook` 将被启用，建议用户使用 `MMEditVisualizationHook`。
+- `VisualizationHook` 将被弃用，建议用户使用 `MMEditVisualizationHook`。
 - 修复 FLAVR 的注册问题。
 - 修正 RDB 模型中的通道数。
 

setup.py

@@ -223,7 +223,6 @@ def add_mim_extension():
             'License :: OSI Approved :: Apache Software License',
             'Operating System :: OS Independent',
             'Programming Language :: Python :: 3',
-            'Programming Language :: Python :: 3.6',
             'Programming Language :: Python :: 3.7',
             'Programming Language :: Python :: 3.8',
             'Programming Language :: Python :: 3.9',

tests/test_metrics/test_inceptions.py

@@ -2,10 +2,14 @@
 import numpy as np
 import pytest
 import torch
+from packaging import version
 
 from mmedit.core.evaluation.inceptions import FID, KID, InceptionV3
 
 
+@pytest.mark.skipif(
+    version.parse(torch.__version__) <= version.parse('1.5.1'),
+    reason='torch 1.5.1 do not support interpolation with uint8')
 def test_inception():
     img1 = np.random.randint(0, 256, (224, 224, 3))
     img2 = np.random.randint(0, 256, (224, 224, 3))

tests/test_models/test_restorers/test_basic_restorer.py

@@ -6,6 +6,7 @@
 import pytest
 import torch
 from mmcv.runner import obj_from_dict
+from packaging import version
 
 from mmedit.core.evaluation import InceptionV3
 from mmedit.models import build_model
@@ -139,60 +140,61 @@ def test_basic_restorer():
         # evaluation with metrics must have gt images
         restorer(lq=inputs, test_mode=True)
 
-    with tempfile.TemporaryDirectory() as tmpdir:
-        outputs = restorer(
-            **data_batch,
-            test_mode=True,
-            save_image=True,
-            save_path=tmpdir,
-            iteration=None)
-        assert isinstance(outputs, dict)
-        assert isinstance(outputs['eval_result'], dict)
-        assert isinstance(outputs['eval_result']['PSNR'], float)
-        assert isinstance(outputs['eval_result']['SSIM'], float)
-
-        # for feature-based metrics
-        assert isinstance(outputs['eval_result']['FID'], dict)
-        assert isinstance(outputs['eval_result']['KID'], dict)
-        assert '_inception_feat' in restorer.allowed_metrics
-        assert isinstance(restorer.allowed_metrics['_inception_feat'],
-                          InceptionV3)
-
-        incept_result = outputs['eval_result']['_inception_feat']
-        assert isinstance(incept_result, tuple) and len(incept_result) == 2
-        for feat in incept_result:
-            assert isinstance(feat, np.ndarray)
-            assert feat.shape == (1, 2048)
-
-        outputs = restorer(
-            **data_batch,
-            test_mode=True,
-            save_image=True,
-            save_path=tmpdir,
-            iteration=100)
-        assert isinstance(outputs, dict)
-        assert isinstance(outputs['eval_result'], dict)
-        assert isinstance(outputs['eval_result']['PSNR'], float)
-        assert isinstance(outputs['eval_result']['SSIM'], float)
-
-        # for feature-based metrics
-        assert isinstance(outputs['eval_result']['FID'], dict)
-        assert isinstance(outputs['eval_result']['KID'], dict)
-        assert '_inception_feat' in restorer.allowed_metrics
-        assert isinstance(restorer.allowed_metrics['_inception_feat'],
-                          InceptionV3)
-
-        incept_result = outputs['eval_result']['_inception_feat']
-        assert isinstance(incept_result, tuple) and len(incept_result) == 2
-        for feat in incept_result:
-            assert isinstance(feat, np.ndarray)
-            assert feat.shape == (1, 2048)
-
-        with pytest.raises(ValueError):
-            # iteration should be number or None
-            restorer(
+    if version.parse(torch.__version__) > version.parse('1.5.1'):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            outputs = restorer(
+                **data_batch,
+                test_mode=True,
+                save_image=True,
+                save_path=tmpdir,
+                iteration=None)
+            assert isinstance(outputs, dict)
+            assert isinstance(outputs['eval_result'], dict)
+            assert isinstance(outputs['eval_result']['PSNR'], float)
+            assert isinstance(outputs['eval_result']['SSIM'], float)
+
+            # for feature-based metrics
+            assert isinstance(outputs['eval_result']['FID'], dict)
+            assert isinstance(outputs['eval_result']['KID'], dict)
+            assert '_inception_feat' in restorer.allowed_metrics
+            assert isinstance(restorer.allowed_metrics['_inception_feat'],
+                              InceptionV3)
+
+            incept_result = outputs['eval_result']['_inception_feat']
+            assert isinstance(incept_result, tuple) and len(incept_result) == 2
+            for feat in incept_result:
+                assert isinstance(feat, np.ndarray)
+                assert feat.shape == (1, 2048)
+
+            outputs = restorer(
                 **data_batch,
                 test_mode=True,
                 save_image=True,
                 save_path=tmpdir,
-                iteration='100')
+                iteration=100)
+            assert isinstance(outputs, dict)
+            assert isinstance(outputs['eval_result'], dict)
+            assert isinstance(outputs['eval_result']['PSNR'], float)
+            assert isinstance(outputs['eval_result']['SSIM'], float)
+
+            # for feature-based metrics
+            assert isinstance(outputs['eval_result']['FID'], dict)
+            assert isinstance(outputs['eval_result']['KID'], dict)
+            assert '_inception_feat' in restorer.allowed_metrics
+            assert isinstance(restorer.allowed_metrics['_inception_feat'],
+                              InceptionV3)
+
+            incept_result = outputs['eval_result']['_inception_feat']
+            assert isinstance(incept_result, tuple) and len(incept_result) == 2
+            for feat in incept_result:
+                assert isinstance(feat, np.ndarray)
+                assert feat.shape == (1, 2048)
+
+            with pytest.raises(ValueError):
+                # iteration should be number or None
+                restorer(
+                    **data_batch,
+                    test_mode=True,
+                    save_image=True,
+                    save_path=tmpdir,
+                    iteration='100')