From 69ed8343881fa2dfdc7af6742bd021933f569912 Mon Sep 17 00:00:00 2001 From: Jeffreytsai1004 Date: Sat, 15 Feb 2025 14:17:33 +0800 Subject: [PATCH] Update --- Reference/Segment_Anything.md | 183 ------------------------ Reference/activate.bat | 2 - Reference/detectron2.md | 68 --------- Reference/install.bat | 32 ----- Reference/micromamba.exe | 3 - Reference/requirements.txt | 42 ------ Reference/run.bat | 11 -- micromamba/condabin/_mamba_activate.bat | 52 ------- micromamba/condabin/activate.bat | 4 - micromamba/condabin/mamba_hook.bat | 18 --- micromamba/condabin/micromamba.bat | 30 ---- 11 files changed, 445 deletions(-) delete mode 100644 Reference/Segment_Anything.md delete mode 100644 Reference/activate.bat delete mode 100644 Reference/detectron2.md delete mode 100644 Reference/install.bat delete mode 100644 Reference/micromamba.exe delete mode 100644 Reference/requirements.txt delete mode 100644 Reference/run.bat delete mode 100644 micromamba/condabin/_mamba_activate.bat delete mode 100644 micromamba/condabin/activate.bat delete mode 100644 micromamba/condabin/mamba_hook.bat delete mode 100644 micromamba/condabin/micromamba.bat diff --git a/Reference/Segment_Anything.md b/Reference/Segment_Anything.md deleted file mode 100644 index ddeacad..0000000 --- a/Reference/Segment_Anything.md +++ /dev/null @@ -1,183 +0,0 @@ -## Latest updates -- SAM 2: Segment Anything in Images and Videos - -Please check out our new release on [**Segment Anything Model 2 (SAM 2)**](https://github.com/facebookresearch/segment-anything-2). - -* SAM 2 code: https://github.com/facebookresearch/segment-anything-2 -* SAM 2 demo: https://sam2.metademolab.com/ -* SAM 2 paper: https://arxiv.org/abs/2408.00714 - - ![SAM 2 architecture](https://github.com/facebookresearch/segment-anything-2/blob/main/assets/model_diagram.png?raw=true) - -**Segment Anything Model 2 (SAM 2)** is a foundation model towards solving promptable visual segmentation in images and videos. We extend SAM to video by considering images as a video with a single frame. The model design is a simple transformer architecture with streaming memory for real-time video processing. We build a model-in-the-loop data engine, which improves model and data via user interaction, to collect [**our SA-V dataset**](https://ai.meta.com/datasets/segment-anything-video), the largest video segmentation dataset to date. SAM 2 trained on our data provides strong performance across a wide range of tasks and visual domains. - -# Segment Anything - -**[Meta AI Research, FAIR](https://ai.facebook.com/research/)** - -[Alexander Kirillov](https://alexander-kirillov.github.io/), [Eric Mintun](https://ericmintun.github.io/), [Nikhila Ravi](https://nikhilaravi.com/), [Hanzi Mao](https://hanzimao.me/), Chloe Rolland, Laura Gustafson, [Tete Xiao](https://tetexiao.com), [Spencer Whitehead](https://www.spencerwhitehead.com/), Alex Berg, Wan-Yen Lo, [Piotr Dollar](https://pdollar.github.io/), [Ross Girshick](https://www.rossgirshick.info/) - -[[`Paper`](https://ai.facebook.com/research/publications/segment-anything/)] [[`Project`](https://segment-anything.com/)] [[`Demo`](https://segment-anything.com/demo)] [[`Dataset`](https://segment-anything.com/dataset/index.html)] [[`Blog`](https://ai.facebook.com/blog/segment-anything-foundation-model-image-segmentation/)] [[`BibTeX`](#citing-segment-anything)] - -![SAM design](assets/model_diagram.png?raw=true) - -The **Segment Anything Model (SAM)** produces high quality object masks from input prompts such as points or boxes, and it can be used to generate masks for all objects in an image. It has been trained on a [dataset](https://segment-anything.com/dataset/index.html) of 11 million images and 1.1 billion masks, and has strong zero-shot performance on a variety of segmentation tasks. - -

- - -

- -## Installation - -The code requires `python>=3.8`, as well as `pytorch>=1.7` and `torchvision>=0.8`. Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended. - -Install Segment Anything: - -``` -pip install git+https://github.com/facebookresearch/segment-anything.git -``` - -or clone the repository locally and install with - -``` -git clone git@github.com:facebookresearch/segment-anything.git -cd segment-anything; pip install -e . -``` - -The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. `jupyter` is also required to run the example notebooks. - -``` -pip install opencv-python pycocotools matplotlib onnxruntime onnx -``` - -## Getting Started - -First download a [model checkpoint](#model-checkpoints). Then the model can be used in just a few lines to get masks from a given prompt: - -``` -from segment_anything import SamPredictor, sam_model_registry -sam = sam_model_registry[""](checkpoint="") -predictor = SamPredictor(sam) -predictor.set_image() -masks, _, _ = predictor.predict() -``` - -or generate masks for an entire image: - -``` -from segment_anything import SamAutomaticMaskGenerator, sam_model_registry -sam = sam_model_registry[""](checkpoint="") -mask_generator = SamAutomaticMaskGenerator(sam) -masks = mask_generator.generate() -``` - -Additionally, masks can be generated for images from the command line: - -``` -python scripts/amg.py --checkpoint --model-type --input --output -``` - -See the examples notebooks on [using SAM with prompts](/notebooks/predictor_example.ipynb) and [automatically generating masks](/notebooks/automatic_mask_generator_example.ipynb) for more details. - -

- - -

- -## ONNX Export - -SAM's lightweight mask decoder can be exported to ONNX format so that it can be run in any environment that supports ONNX runtime, such as in-browser as showcased in the [demo](https://segment-anything.com/demo). Export the model with - -``` -python scripts/export_onnx_model.py --checkpoint --model-type --output -``` - -See the [example notebook](https://github.com/facebookresearch/segment-anything/blob/main/notebooks/onnx_model_example.ipynb) for details on how to combine image preprocessing via SAM's backbone with mask prediction using the ONNX model. It is recommended to use the latest stable version of PyTorch for ONNX export. - -### Web demo - -The `demo/` folder has a simple one page React app which shows how to run mask prediction with the exported ONNX model in a web browser with multithreading. Please see [`demo/README.md`](https://github.com/facebookresearch/segment-anything/blob/main/demo/README.md) for more details. - -## Model Checkpoints - -Three model versions of the model are available with different backbone sizes. These models can be instantiated by running - -``` -from segment_anything import sam_model_registry -sam = sam_model_registry[""](checkpoint="") -``` - -Click the links below to download the checkpoint for the corresponding model type. - -- **`default` or `vit_h`: [ViT-H SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth)** -- `vit_l`: [ViT-L SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_l_0b3195.pth) -- `vit_b`: [ViT-B SAM model.](https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth) - -## Dataset - -See [here](https://ai.facebook.com/datasets/segment-anything/) for an overview of the datastet. The dataset can be downloaded [here](https://ai.facebook.com/datasets/segment-anything-downloads/). By downloading the datasets you agree that you have read and accepted the terms of the SA-1B Dataset Research License. - -We save masks per image as a json file. It can be loaded as a dictionary in python in the below format. - -```python -{ - "image" : image_info, - "annotations" : [annotation], -} - -image_info { - "image_id" : int, # Image id - "width" : int, # Image width - "height" : int, # Image height - "file_name" : str, # Image filename -} - -annotation { - "id" : int, # Annotation id - "segmentation" : dict, # Mask saved in COCO RLE format. - "bbox" : [x, y, w, h], # The box around the mask, in XYWH format - "area" : int, # The area in pixels of the mask - "predicted_iou" : float, # The model's own prediction of the mask's quality - "stability_score" : float, # A measure of the mask's quality - "crop_box" : [x, y, w, h], # The crop of the image used to generate the mask, in XYWH format - "point_coords" : [[x, y]], # The point coordinates input to the model to generate the mask -} -``` - -Image ids can be found in sa_images_ids.txt which can be downloaded using the above [link](https://ai.facebook.com/datasets/segment-anything-downloads/) as well. - -To decode a mask in COCO RLE format into binary: - -``` -from pycocotools import mask as mask_utils -mask = mask_utils.decode(annotation["segmentation"]) -``` - -See [here](https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/mask.py) for more instructions to manipulate masks stored in RLE format. - -## License - -The model is licensed under the [Apache 2.0 license](LICENSE). - -## Contributing - -See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md). - -## Contributors - -The Segment Anything project was made possible with the help of many contributors (alphabetical): - -Aaron Adcock, Vaibhav Aggarwal, Morteza Behrooz, Cheng-Yang Fu, Ashley Gabriel, Ahuva Goldstand, Allen Goodman, Sumanth Gurram, Jiabo Hu, Somya Jain, Devansh Kukreja, Robert Kuo, Joshua Lane, Yanghao Li, Lilian Luong, Jitendra Malik, Mallika Malhotra, William Ngan, Omkar Parkhi, Nikhil Raina, Dirk Rowe, Neil Sejoor, Vanessa Stark, Bala Varadarajan, Bram Wasti, Zachary Winstrom - -## Citing Segment Anything - -If you use SAM or SA-1B in your research, please use the following BibTeX entry. - -``` -@article{kirillov2023segany, - title={Segment Anything}, - author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross}, - journal={arXiv:2304.02643}, - year={2023} -} -``` diff --git a/Reference/activate.bat b/Reference/activate.bat deleted file mode 100644 index 1d7160f..0000000 --- a/Reference/activate.bat +++ /dev/null @@ -1,2 +0,0 @@ -@CALL "%~dp0micromamba.exe" shell init --shell cmd.exe --prefix "%~dp0\" -start cmd /k "%~dp0condabin\micromamba.bat" activate StableDiffusion_001 diff --git a/Reference/detectron2.md b/Reference/detectron2.md deleted file mode 100644 index 84d47c6..0000000 --- a/Reference/detectron2.md +++ /dev/null @@ -1,68 +0,0 @@ - - - - Support Ukraine - Help Provide Humanitarian Aid to Ukraine. - - -Detectron2 is Facebook AI Research's next generation library -that provides state-of-the-art detection and segmentation algorithms. -It is the successor of -[Detectron](https://github.com/facebookresearch/Detectron/) -and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/). -It supports a number of computer vision research projects and production applications in Facebook. - -
- -
-
- -## Learn More about Detectron2 - -Explain Like I’m 5: Detectron2 | Using Machine Learning with Detectron2 -:-------------------------:|:-------------------------: -[![Explain Like I’m 5: Detectron2](https://img.youtube.com/vi/1oq1Ye7dFqc/0.jpg)](https://www.youtube.com/watch?v=1oq1Ye7dFqc) | [![Using Machine Learning with Detectron2](https://img.youtube.com/vi/eUSgtfK4ivk/0.jpg)](https://www.youtube.com/watch?v=eUSgtfK4ivk) - -## What's New -* Includes new capabilities such as panoptic segmentation, Densepose, Cascade R-CNN, rotated bounding boxes, PointRend, - DeepLab, ViTDet, MViTv2 etc. -* Used as a library to support building [research projects](projects/) on top of it. -* Models can be exported to TorchScript format or Caffe2 format for deployment. -* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html). - -See our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/) -to see more demos and learn about detectron2. - -## Installation - -See [installation instructions](https://detectron2.readthedocs.io/tutorials/install.html). - -## Getting Started - -See [Getting Started with Detectron2](https://detectron2.readthedocs.io/tutorials/getting_started.html), -and the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5) -to learn about basic usage. - -Learn more at our [documentation](https://detectron2.readthedocs.org). -And see [projects/](projects/) for some projects that are built on top of detectron2. - -## Model Zoo and Baselines - -We provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md). - -## License - -Detectron2 is released under the [Apache 2.0 license](LICENSE). - -## Citing Detectron2 - -If you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry. - -```BibTeX -@misc{wu2019detectron2, - author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and - Wan-Yen Lo and Ross Girshick}, - title = {Detectron2}, - howpublished = {\url{https://github.com/facebookresearch/detectron2}}, - year = {2019} -} -``` diff --git a/Reference/install.bat b/Reference/install.bat deleted file mode 100644 index b59b304..0000000 --- a/Reference/install.bat +++ /dev/null @@ -1,32 +0,0 @@ -CALL "%~dp0micromamba.exe" create -n StableDiffusion_001 python==3.10.14 git==2.41.0 git-lfs==3.2.0 -c pytorch -c conda-forge -r "%~dp0\" -y -@CALL "%~dp0micromamba.exe" shell init --shell cmd.exe --prefix "%~dp0\" -@CALL condabin\micromamba.bat activate StableDiffusion_001 -@CALL set GDOWN_CACHE=cache\gdown -@CALL set TORCH_HOME=cache\torch -@CALL set HF_HOME=cache\huggingface -@CALL set PYTHONDONTWRITEBYTECODE=1 -@CALL set CUDA_HOME=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1 -@CALL pip install --force-reinstall torch==2.2.0+cu121 torchvision==0.17.0+cu121 torchaudio==2.2.0+cu121 --index-url https://download.pytorch.org/whl/cu121 --no-cache-dir -@CALL git clone https://github.com/facebookresearch/xformers.git -@CALL cd xformers -@CALL git submodule update --init --recursive -@CALL pip install . -@CALL cd .. -@CALL git clone https://github.com/NVlabs/nvdiffrast .\nvdiffrast -@CALL cd nvdiffrast -@CALL pip install . -@CALL cd .. -@CALL git clone -b v1.9.4 https://github.com/AUTOMATIC1111/stable-diffusion-webui/ .\stable-diffusion-webui\ -@CALL mkdir .\stable-diffusion-webui\cache\gdown\ -@CALL mkdir .\stable-diffusion-webui\cache\torch\ -@CALL mkdir .\stable-diffusion-webui\cache\huggingface\ -@CALL mkdir .\stable-diffusion-webui\repositories\ -@CALL git clone https://github.com/CompVis/stable-diffusion.git .\stable-diffusion-webui\repositories\stable-diffusion -@CALL git clone https://github.com/CompVis/taming-transformers.git .\stable-diffusion-webui\repositories\taming-transformers -@CALL git clone https://github.com/crowsonkb/k-diffusion.git .\stable-diffusion-webui\repositories\k-diffusion -@CALL git clone https://github.com/sczhou/CodeFormer.git .\stable-diffusion-webui\repositories\CodeFormer -@CALL git clone http://github.com/salesforce/BLIP.git .\stable-diffusion-webui\repositories\BLIP -@CALL cd .\stable-diffusion-webui -@CALL pip install -r .\stable-diffusion-webui\requirements.txt -@CALL python -B launch.py --xformers --opt-sdp-attention --autolaunch --theme dark --listen --port 7860 --enable-insecure-extension-access --skip-torch-cuda-test -@CALL PAUSE diff --git a/Reference/micromamba.exe b/Reference/micromamba.exe deleted file mode 100644 index bd9e56a..0000000 --- a/Reference/micromamba.exe +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:77f9efb2c50a480086b93320829bb12cfd78ebaa9a5285a917b143e9b848378c -size 8381952 diff --git a/Reference/requirements.txt b/Reference/requirements.txt deleted file mode 100644 index b338ba6..0000000 --- a/Reference/requirements.txt +++ /dev/null @@ -1,42 +0,0 @@ -pip==23.3.1 -xformers==0.0.21 -torch==2.0.1+cu118 -torchvision -torchaudio -torchtext -PySoundFile -torchdiffeq==0.2.3 -torchsde==0.2.5 -GitPython==3.1.32 -Pillow==9.5.0 -accelerate==0.21.0 -basicsr==1.4.2 -blendmodes==2022 -clean-fid==0.1.35 -einops==0.4.1 -fastapi==0.94.0 -gfpgan==1.3.8 -gradio==3.41.2 -httpcore==0.15 -inflection==0.5.1 -jsonmerge==1.8.0 -kornia==0.6.7 -lark==1.1.2 -numpy==1.23.5 -omegaconf==2.2.3 -open-clip-torch==2.20.0 -piexif==1.1.3 -psutil==5.9.5 -pytorch_lightning==1.9.4 -realesrgan==0.3.0 -resize-right==0.0.2 -safetensors==0.3.1 -scikit-image==0.21.0 -timm==0.9.2 -tomesd==0.1.3 -transformers==4.30.2 -httpx==0.24.1 -groundingdino -segment_anything -supervision -pyfunctional \ No newline at end of file diff --git a/Reference/run.bat b/Reference/run.bat deleted file mode 100644 index 244ce61..0000000 --- a/Reference/run.bat +++ /dev/null @@ -1,11 +0,0 @@ -@CALL "%~dp0micromamba.exe" shell init --shell cmd.exe --prefix "%~dp0\" -@CALL condabin\micromamba.bat activate StableDiffusion_001 -@CALL set GDOWN_CACHE=cache\gdown -@CALL set TORCH_HOME=cache\torch -@CALL set HF_HOME=cache\huggingface -@CALL set PYTHONDONTWRITEBYTECODE=1 -@CALL set CUDA_HOME=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1 -@CALL set COMMANDLINE_ARGS=--listen --port 7860 --autolaunch --theme dark --xformers --opt-sdp-attention --api --skip-install --skip-torch-cuda-test --skip-version-check --enable-insecure-extension-access -@CALL cd stable-diffusion-webui -@CALL python -B webui.py %COMMANDLINE_ARGS% -@CALL PAUSE diff --git a/micromamba/condabin/_mamba_activate.bat b/micromamba/condabin/_mamba_activate.bat deleted file mode 100644 index 9890946..0000000 --- a/micromamba/condabin/_mamba_activate.bat +++ /dev/null @@ -1,52 +0,0 @@ -@REM Copyright (C) 2012 Anaconda, Inc -@REM SPDX-License-Identifier: BSD-3-Clause -@REM Helper routine for activation, deactivation, and reactivation. - -@IF "%CONDA_PS1_BACKUP%"=="" GOTO FIXUP43 - @REM Handle transition from shell activated with conda 4.3 to a subsequent activation - @REM after conda updated to 4.4. See issue #6173. - @SET "PROMPT=%CONDA_PS1_BACKUP%" - @SET CONDA_PS1_BACKUP= -:FIXUP43 - -@SETLOCAL EnableDelayedExpansion - -@REM This is the standard user case. This script is run in root\condabin. -@REM FOR %%A IN ("%~dp0.") DO @SET _sysp=%%~dpA -@REM IF NOT EXIST "!_sysp!\Scripts\micromamba.exe" @SET "_sysp=!_sysp!..\" - -@FOR %%A in ("%TMP%") do @SET TMP=%%~sA -@IF "%MAMBA_ROOT_PREFIX%" NEQ "" ( - @SET "_sysp=%MAMBA_ROOT_PREFIX%" - @SET "PATH=!_sysp!;!_sysp!\Library\mingw-w64\bin;!_sysp!\Library\usr\bin;!_sysp!\Library\bin;!_sysp!\Scripts;!_sysp!\bin;%PATH%" -) -@REM It seems that it is not possible to have "CONDA_EXE=Something With Spaces" -@REM and %* to contain: activate "Something With Spaces does not exist". -@REM MSDOS associates the outer "'s and is unable to run very much at all. -@REM @SET CONDA_EXES="%CONDA_EXE%" %_CE_M% %_CE_CONDA% -@REM @FOR /F %%i IN ('%CONDA_EXES% shell.cmd.exe %*') DO @SET _TEMP_SCRIPT_PATH=%%i not return error -@REM This method will not work if %TMP% contains any spaces. -@FOR /L %%I IN (1,1,100) DO @( - SET UNIQUE_DIR=%TMP%\conda-!RANDOM! - MKDIR !UNIQUE_DIR! > NUL 2>&1 - IF NOT ERRORLEVEL 1 ( - SET UNIQUE=!UNIQUE_DIR!\conda.tmp - TYPE NUL 1> !UNIQUE! - GOTO tmp_file_created - ) -) -@ECHO Failed to create temp directory "%TMP%\conda-\" & exit /b 1 -:tmp_file_created - -@"%MAMBA_EXE%" shell --shell cmd.exe %* 1>%UNIQUE% -@IF %ErrorLevel% NEQ 0 @EXIT /B %ErrorLevel% -@FOR /F %%i IN (%UNIQUE%) DO @SET _TEMP_SCRIPT_PATH=%%i -@RMDIR /S /Q %UNIQUE_DIR% -@FOR /F "delims=" %%A in (""!_TEMP_SCRIPT_PATH!"") DO @ENDLOCAL & @SET _TEMP_SCRIPT_PATH=%%~A -@IF "%_TEMP_SCRIPT_PATH%" == "" @EXIT /B 1 -@IF NOT "%CONDA_PROMPT_MODIFIER%" == "" @CALL SET "PROMPT=%%PROMPT:%CONDA_PROMPT_MODIFIER%=%_empty_not_set_%%%" -@CALL "%_TEMP_SCRIPT_PATH%" -@IF NOT "%CONDA_TEST_SAVE_TEMPS%x"=="x" @ECHO CONDA_TEST_SAVE_TEMPS :: retaining activate_batch %_TEMP_SCRIPT_PATH% 1>&2 -@IF "%CONDA_TEST_SAVE_TEMPS%x"=="x" @DEL /F /Q "%_TEMP_SCRIPT_PATH%" -@SET _TEMP_SCRIPT_PATH= -@SET "PROMPT=%CONDA_PROMPT_MODIFIER%%PROMPT%" diff --git a/micromamba/condabin/activate.bat b/micromamba/condabin/activate.bat deleted file mode 100644 index 0ac0e4d..0000000 --- a/micromamba/condabin/activate.bat +++ /dev/null @@ -1,4 +0,0 @@ -@REM Copyright (C) 2021 QuantStack -@REM SPDX-License-Identifier: BSD-3-Clause -@CALL "%~dp0..\condabin\mamba_hook.bat" -micromamba activate %* diff --git a/micromamba/condabin/mamba_hook.bat b/micromamba/condabin/mamba_hook.bat deleted file mode 100644 index 710ba95..0000000 --- a/micromamba/condabin/mamba_hook.bat +++ /dev/null @@ -1,18 +0,0 @@ -@REM Copyright (C) 2021 QuantStack -@REM SPDX-License-Identifier: BSD-3-Clause -@REM This file is derived from conda_hook.bat - -@IF DEFINED CONDA_SHLVL GOTO :EOF - -@FOR %%F in ("%~dp0") do @SET "__mambabin_dir=%%~dpF" -@SET "__mambabin_dir=%__mambabin_dir:~0,-1%" -@SET "PATH=%__mambabin_dir%;%PATH%" -@SET "MAMBA_BAT=%__mambabin_dir%\micromamba.bat" -@FOR %%F in ("%__mambabin_dir%") do @SET "__mamba_root=%%~dpF" -@SET "MAMBA_EXE=E:\Zoroot\Dev\GaussianHaircut\micromamba.exe" -@SET __mambabin_dir= -@SET __mamba_root= - -@DOSKEY micromamba="%MAMBA_BAT%" $* - -@SET CONDA_SHLVL=0 diff --git a/micromamba/condabin/micromamba.bat b/micromamba/condabin/micromamba.bat deleted file mode 100644 index 8f26d22..0000000 --- a/micromamba/condabin/micromamba.bat +++ /dev/null @@ -1,30 +0,0 @@ -@REM Copyright (C) 2012 Anaconda, Inc -@REM SPDX-License-Identifier: BSD-3-Clause - -@REM echo _CE_CONDA is %_CE_CONDA% -@REM echo _CE_M is %_CE_M% -@REM echo CONDA_EXE is %CONDA_EXE% - -@REM @SET "MAMBA_EXE=%~dp0..\Scripts\micromamba.exe" -@SET "MAMBA_EXE=E:\Zoroot\Dev\GaussianHaircut\micromamba.exe" -@SET "MAMBA_ROOT_PREFIX=E:\Zoroot\Dev\GaussianHaircut\micromamba" -@IF [%1]==[activate] "%~dp0_mamba_activate" %* -@IF [%1]==[deactivate] "%~dp0_mamba_activate" %* - -@SETLOCAL EnableDelayedExpansion -@REM This is the standard user case. This script is run in root\condabin. -@REM FOR %%A IN ("%~dp0.") DO @SET _sysp=%%~dpA -@REM IF NOT EXIST "!_sysp!\Scripts\micromamba.exe" @SET "_sysp=!_sysp!..\" -@SET PATH=!MAMBA_ROOT_PREFIX!;!MAMBA_ROOT_PREFIX!\Library\mingw-w64\bin;!MAMBA_ROOT_PREFIX!\Library\usr\bin;!MAMBA_ROOT_PREFIX!\Library\bin;!MAMBA_ROOT_PREFIX!\Scripts;!MAMBA_ROOT_PREFIX!\bin;%PATH% -@CALL %MAMBA_EXE% %* -@ENDLOCAL - -@IF %errorlevel% NEQ 0 EXIT /B %errorlevel% - -@IF [%1]==[install] "%~dp0_mamba_activate" reactivate -@IF [%1]==[update] "%~dp0_mamba_activate" reactivate -@IF [%1]==[upgrade] "%~dp0_mamba_activate" reactivate -@IF [%1]==[remove] "%~dp0_mamba_activate" reactivate -@IF [%1]==[uninstall] "%~dp0_mamba_activate" reactivate - -@EXIT /B %errorlevel%