rename to mmagic in tools

open-mmlab · Apr 19, 2023 · 2acc31c · 2acc31c
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diff --git a/tools/analysis_tools/get_flops.py b/tools/analysis_tools/get_flops.py
@@ -5,7 +5,7 @@
 from mmengine import Config
 from mmengine.registry import init_default_scope
 
-from mmedit.registry import MODELS
+from mmagic.registry import MODELS
 
 try:
     from mmengine.analysis import get_model_complexity_info
@@ -83,7 +83,7 @@ def main():
 
     cfg = Config.fromfile(args.config)
 
-    init_default_scope(cfg.get('default_scope', 'mmedit'))
+    init_default_scope(cfg.get('default_scope', 'mmagic'))
 
     model = MODELS.build(cfg.model)
     inputs = torch.randn(1, *input_shape)

diff --git a/tools/dataset_converters/celeba-hq/README.md b/tools/dataset_converters/celeba-hq/README.md
@@ -14,8 +14,8 @@
 Follow the instructions [here](https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training) to prepare the dataset.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/celeba-hq/README_zh-CN.md b/tools/dataset_converters/celeba-hq/README_zh-CN.md
@@ -14,8 +14,8 @@
 请按照[此处](https://github.com/tkarras/progressive_growing_of_gans#preparing-datasets-for-training)准备数据集。
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/classic5/README.md b/tools/dataset_converters/classic5/README.md
@@ -19,8 +19,8 @@ The test datasets can be download from [here](https://github.com/cszn/DnCNN/tree
 The folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/classic5/README_zh-CN.md b/tools/dataset_converters/classic5/README_zh-CN.md
@@ -19,8 +19,8 @@
 文件目录结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/comp1k/README.md b/tools/dataset_converters/comp1k/README.md
@@ -28,7 +28,7 @@ It is needed to merge `fg` with COCO data (training) or VOC data (test) before t
 Use the following script to perform image composition and generate annotation files for training or testing:
 
 ```shell
-# The script is run under the root folder of MMEditing
+# The script is run under the root folder of MMagic
 python tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --composite
 ```
 
@@ -54,8 +54,8 @@ python tools/data/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composi
 The result folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -108,8 +108,8 @@ python tools/dataset_converters/matting/comp1k/extend_fg.py data/adobe_compositi
 The final folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/comp1k/README_zh-CN.md b/tools/dataset_converters/comp1k/README_zh-CN.md
@@ -25,7 +25,7 @@ Adobe Composition-1k 数据集由前景图像及其相应的 alpha 图像组成
 Adobe composition-1k 数据集仅包含 `alpha` 和 `fg`（以及测试集中的 `trimap`）。在训练或评估之前，需要将 `fg` 与 COCO 数据（训练）或 VOC 数据（测试）合并。使用以下脚本执行图像合成并生成用于训练或测试的注释文件：
 
 ```shell
-# 在 MMEditing 的根文件夹下运行脚本
+# 在 MMagic 的根文件夹下运行脚本
 python tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data/adobe_composition-1k data/coco data/VOCdevkit --composite
 ```
 
@@ -50,8 +50,8 @@ python tools/dataset_converters/matting/comp1k/preprocess_comp1k_dataset.py data
 最终的文件夹结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -102,8 +102,8 @@ python tools/dataset_converters/matting/comp1k/extend_fg.py data/adobe_compositi
 最终的文件夹结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/comp1k/evaluate_comp1k.py b/tools/dataset_converters/comp1k/evaluate_comp1k.py
@@ -8,8 +8,8 @@
 import mmengine
 import numpy as np
 
-from mmedit.evaluation import gauss_gradient
-from mmedit.utils import modify_args
+from mmagic.evaluation import gauss_gradient
+from mmagic.utils import modify_args
 
 
 def sad(alpha, trimap, pred_alpha):

diff --git a/tools/dataset_converters/comp1k/preprocess_comp1k_dataset.py b/tools/dataset_converters/comp1k/preprocess_comp1k_dataset.py
@@ -9,7 +9,7 @@
 import numpy as np
 from PIL import Image
 
-from mmedit.utils import modify_args
+from mmagic.utils import modify_args
 
 
 def fix_png_files(directory):

diff --git a/tools/dataset_converters/denoising/README.md b/tools/dataset_converters/denoising/README.md
@@ -16,8 +16,8 @@ The test datasets (Set12, BSD68, CBSD68, Kodak, McMaster, Urban100) can be downl
 The folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/denoising/README_zh-CN.md b/tools/dataset_converters/denoising/README_zh-CN.md
@@ -16,8 +16,8 @@
 文件目录结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/deraining/README.md b/tools/dataset_converters/deraining/README.md
@@ -16,8 +16,8 @@ The test datasets (Rain100H, Rain100L, Test100, Test1200, Test2800) can be downl
 The folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/deraining/README_zh-CN.md b/tools/dataset_converters/deraining/README_zh-CN.md
@@ -16,8 +16,8 @@
 文件目录结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/df2k_ost/README.md b/tools/dataset_converters/df2k_ost/README.md
@@ -19,8 +19,8 @@
 Please first put all the images into the `GT` folder (naming does not need to be in order):
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -43,8 +43,8 @@ python tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py --data-r
 The generated data is stored under `df2k_ost` and the data structure is as follows, where `_sub` indicates the sub-images.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/df2k_ost/README_zh-CN.md b/tools/dataset_converters/df2k_ost/README_zh-CN.md
@@ -19,8 +19,8 @@
 请先将所有图片放入 `GT` 文件夹（命名不需要按顺序）：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -43,8 +43,8 @@ python tools/dataset_converters/df2k_ost/preprocess_df2k_ost_dataset.py --data-r
 生成的数据存放在 `df2k_ost` 下，数据结构如下，其中 `_sub` 表示子图像。
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/div2k/README.md b/tools/dataset_converters/div2k/README.md
@@ -18,8 +18,8 @@
 Note that we merge the original val dataset (image names from 0801 to 0900) to the original train dataset (image names from 0001 to 0800). The folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -64,8 +64,8 @@ python tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./
 The generated data is stored under `DIV2K` and the data structure is as follows, where `_sub` indicates the sub-images.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/div2k/README_zh-CN.md b/tools/dataset_converters/div2k/README_zh-CN.md
@@ -18,8 +18,8 @@
 请注意，我们将原始的验证集（文件名 0801 到 0900）合并进了原始的训练集（文件名 0001 到 0800）。文件目录结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -64,8 +64,8 @@ python tools/dataset_converters/div2k/preprocess_div2k_dataset.py --data-root ./
 生成的数据保存在 `DIV2K` 目录下，其文件结构如下所示，其中 `_sub` 表示子图:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/dpdd/README.md b/tools/dataset_converters/dpdd/README.md
@@ -16,8 +16,8 @@ The test datasets can be download from [here](https://drive.google.com/file/d/1d
 The folder structure should look like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/dpdd/README_zh-CN.md b/tools/dataset_converters/dpdd/README_zh-CN.md
@@ -16,8 +16,8 @@
 文件目录结构应如下所示：
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data

diff --git a/tools/dataset_converters/glean/README.md b/tools/dataset_converters/glean/README.md
@@ -28,8 +28,8 @@ python tools/dataset_converters/glean/preprocess_cat_train_dataset.py --lmdb-pat
 The generated data is stored under `cat_train` and the folder structure is as follows.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -58,8 +58,8 @@ python tools/dataset_converters/glean/preprocess_cat_test_dataset.py --data-path
 The generated data is stored under `cat_test` and the folder structure is as follows.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -99,8 +99,8 @@ python tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py --data
 The generated data is stored under `ffhq` and the folder structure is as follows.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data
@@ -140,8 +140,8 @@ python tools/dataset_converters/glean/preprocess_ffhq_celebahq_dataset.py --data
 The generated data is stored under `CelebA-HQ` and the folder structure is as follows.
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configsdata
 ├── data
@@ -160,8 +160,8 @@ We merge FFHQ(`ffhq/images`) and CelebA-HQ(`CelebA-HQ/GT`) to generate FFHQ_Cele
 The folder structure should looks like:
 
 ```text
-mmediting
-├── mmedit
+mmagic
+├── mmagic
 ├── tools
 ├── configs
 ├── data