Segformer works using Gcloud

Makefile

@@ -1,7 +1,7 @@
 # Makefile for COSC525-Project4
 .ONESHELL:
+PYTHON_VERSION=3.8
 SHELL=/bin/bash
-PYTHON_VERSION=3.9
 
 # You can use either venv (venv) or conda env
 # by specifying the correct argument (env=<conda, venv>)
@@ -15,11 +15,12 @@ ifeq ($(env),venv)
 	DEACTIVATE_COMMAND="deactivate"
 else
 	# Use Conda
-	BASE=~/anaconda3/envs/cosc525_finalproject
+	BASE=/opt/conda/envs/cosc525_finalproject
+	NAME=cosc525_finalproject
 	BIN=$(BASE)/bin
-	CREATE_COMMAND="conda create --prefix $(BASE) python=$(PYTHON_VERSION) -y"
-	DELETE_COMMAND="conda env remove -p $(BASE)"
-	ACTIVATE_COMMAND="conda activate -p $(BASE)"
+	CREATE_COMMAND="conda create -n $(NAME) python=$(PYTHON_VERSION) -y"
+	DELETE_COMMAND="conda env remove -n $(NAME)"
+	ACTIVATE_COMMAND="conda activate -n $(NAME)"
 	DEACTIVATE_COMMAND="conda deactivate"
 endif
 
@@ -70,11 +71,18 @@ setup_dev:
 	$(BIN)/pip install setuptools
 	$(BIN)/python setup.py install --dev
 requirements:
-	#conda install -c fastchan pytorch torchvision
+	#cd mmcv && CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' MMCV_WITH_OPS=1 $(BIN)/pip install -e .
 	pip install -r requirements.txt
-	cd SegFormer && pip install -e . --user
-	#conda install --file requirements.txt -y
-
+	conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=11.0 -c pytorch -y
+	pip install "mmcv-full>=1.1.4,<=1.3.0" -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html
+	cd SegFormer && pip install -e .
+clone_segformer:
+	git clone https://github.com/NVlabs/SegFormer.git
+	rm -rf SegFormer/.git
+clone_mmcv:
+	git clone https://github.com/open-mmlab/mmcv.git --branch v1.3.0
+	rm -rf mmcv/.git
+clean:
 	$(BIN)/python setup.py clean
 create_env:
 	@echo "Creating virtual environment.."

SegFormer/.gitignore

@@ -0,0 +1,121 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+data
+.vscode
+.idea
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+work_dirs/
+work_dirs
+pretrained
+pretrained/
+# Pytorch
+*.pth
+trash/
+trash

SegFormer/LICENSE

@@ -0,0 +1,64 @@
+NVIDIA Source Code License for SegFormer
+
+1. Definitions
+
+“Licensor” means any person or entity that distributes its Work.
+
+“Software” means the original work of authorship made available under this License.
+
+“Work” means the Software and any additions to or derivative works of the Software that are made available under
+this License.
+
+The terms “reproduce,” “reproduction,” “derivative works,” and “distribution” have the meaning as provided under
+U.S. copyright law; provided, however, that for the purposes of this License, derivative works shall not include
+works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work.
+
+Works, including the Software, are “made available” under this License by including in or with the Work either
+(a) a copyright notice referencing the applicability of this License to the Work, or (b) a copy of this License.
+
+2. License Grant
+
+2.1 Copyright Grant. Subject to the terms and conditions of this License, each Licensor grants to you a perpetual,
+worldwide, non-exclusive, royalty-free, copyright license to reproduce, prepare derivative works of, publicly
+display, publicly perform, sublicense and distribute its Work and any resulting derivative works in any form.
+
+3. Limitations
+
+3.1 Redistribution. You may reproduce or distribute the Work only if (a) you do so under this License, (b) you
+include a complete copy of this License with your distribution, and (c) you retain without modification any
+copyright, patent, trademark, or attribution notices that are present in the Work.
+
+3.2 Derivative Works. You may specify that additional or different terms apply to the use, reproduction, and
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+limitation in Section 3.3 applies to your derivative works, and (b) you identify the specific derivative works
+that are subject to Your Terms. Notwithstanding Your Terms, this License (including the redistribution
+requirements in Section 3.1) will continue to apply to the Work itself.
+
+3.3 Use Limitation. The Work and any derivative works thereof only may be used or intended for use
+non-commercially. Notwithstanding the foregoing, NVIDIA and its affiliates may use the Work and any derivative
+works commercially. As used herein, “non-commercially” means for research or evaluation purposes only.
+
+3.4 Patent Claims. If you bring or threaten to bring a patent claim against any Licensor (including any claim,
+cross-claim or counterclaim in a lawsuit) to enforce any patents that you allege are infringed by any Work, then
+your rights under this License from such Licensor (including the grant in Section 2.1) will terminate immediately.
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+
+4. Disclaimer of Warranty.
+
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+BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER THIS LICENSE.
+
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+
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SegFormer/README.md

@@ -0,0 +1,110 @@
+[![NVIDIA Source Code License](https://img.shields.io/badge/license-NSCL-blue.svg)](https://github.com/NVlabs/SegFormer/blob/master/LICENSE)
+![Python 3.8](https://img.shields.io/badge/python-3.8-green.svg)
+
+# SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers
+
+<!-- ![image](resources/image.png) -->
+<div align="center">
+  <img src="./resources/image.png" height="400">
+</div>
+<p align="center">
+  Figure 1: Performance of SegFormer-B0 to SegFormer-B5.
+</p>
+
+### [Project page](https://github.com/NVlabs/SegFormer) | [Paper](https://arxiv.org/abs/2105.15203) | [Demo (Youtube)](https://www.youtube.com/watch?v=J0MoRQzZe8U) | [Demo (Bilibili)](https://www.bilibili.com/video/BV1MV41147Ko/)
+
+SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers.<br>
+[Enze Xie](https://xieenze.github.io/), [Wenhai Wang](https://whai362.github.io/), [Zhiding Yu](https://chrisding.github.io/), [Anima Anandkumar](http://tensorlab.cms.caltech.edu/users/anima/), [Jose M. Alvarez](https://rsu.data61.csiro.au/people/jalvarez/), and [Ping Luo](http://luoping.me/).<br>
+NeurIPS 2021.
+
+This repository contains the official Pytorch implementation of training & evaluation code and the pretrained models for [SegFormer](https://arxiv.org/abs/2105.15203).
+
+SegFormer is a simple, efficient and powerful semantic segmentation method, as shown in Figure 1.
+
+We use [MMSegmentation v0.13.0](https://github.com/open-mmlab/mmsegmentation/tree/v0.13.0) as the codebase.
+
+🔥🔥 SegFormer is on [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segformer). 🔥🔥 
+
+
+## Installation
+
+For install and data preparation, please refer to the guidelines in [MMSegmentation v0.13.0](https://github.com/open-mmlab/mmsegmentation/tree/v0.13.0).
+
+Other requirements:
+```pip install timm==0.3.2```
+
+An example (works for me): ```CUDA 10.1``` and  ```pytorch 1.7.1``` 
+
+```
+pip install torchvision==0.8.2
+pip install timm==0.3.2
+pip install mmcv-full==1.2.7
+pip install opencv-python==4.5.1.48
+cd SegFormer && pip install -e . --user
+```
+
+## Evaluation
+
+Download [trained weights](https://drive.google.com/drive/folders/1GAku0G0iR9DsBxCbfENWMJ27c5lYUeQA?usp=sharing).
+
+Example: evaluate ```SegFormer-B1``` on ```ADE20K```:
+
+```
+# Single-gpu testing
+python tools/test.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file
+
+# Multi-gpu testing
+./tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM>
+
+# Multi-gpu, multi-scale testing
+tools/dist_test.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py /path/to/checkpoint_file <GPU_NUM> --aug-test
+```
+
+## Training
+
+Download [weights](https://drive.google.com/drive/folders/1b7bwrInTW4VLEm27YawHOAMSMikga2Ia?usp=sharing) pretrained on ImageNet-1K, and put them in a folder ```pretrained/```.
+
+Example: train ```SegFormer-B1``` on ```ADE20K```:
+
+```
+# Single-gpu training
+python tools/train.py local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py 
+
+# Multi-gpu training
+./tools/dist_train.sh local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py <GPU_NUM>
+```
+
+## Visualize
+
+Here is a demo script to test a single image. More details refer to [MMSegmentation's Doc](https://mmsegmentation.readthedocs.io/en/latest/get_started.html).
+
+```shell
+python demo/image_demo.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} [--device ${DEVICE_NAME}] [--palette-thr ${PALETTE}]
+```
+
+Example: visualize ```SegFormer-B1``` on ```CityScapes```: 
+
+```shell
+python demo/image_demo.py demo/demo.png local_configs/segformer/B1/segformer.b1.512x512.ade.160k.py \
+/path/to/checkpoint_file --device cuda:0 --palette cityscapes
+```
+
+
+
+
+
+## License
+Please check the LICENSE file. SegFormer may be used non-commercially, meaning for research or 
+evaluation purposes only. For business inquiries, please contact 
+[[email protected]](mailto:[email protected]).
+
+
+## Citation
+```
+@article{xie2021segformer,
+  title={SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers},
+  author={Xie, Enze and Wang, Wenhai and Yu, Zhiding and Anandkumar, Anima and Alvarez, Jose M and Luo, Ping},
+  journal={arXiv preprint arXiv:2105.15203},
+  year={2021}
+}
+```