Clean up the mess - (Eg. of a bad commit message)

Author: sleebapaul

.gitignore

@@ -1,6 +1,174 @@
-backup
-output
-code/*.pyc
-code/miscc/*.pyc
+# Created by https://www.gitignore.io/api/macos,python,visualstudiocode
+# Edit at https://www.gitignore.io/?templates=macos,python,visualstudiocode
+
+### macOS ###
+# General
 .DS_Store
-.idea/
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+### Python ###
+# 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/
+pip-wheel-metadata/
+share/python-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/
+.nox/
+.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
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don’t work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# 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/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+### VisualStudioCode ###
+.vscode/*
+!.vscode/settings.json
+!.vscode/tasks.json
+!.vscode/launch.json
+!.vscode/extensions.json
+
+### VisualStudioCode Patch ###
+# Ignore all local history of files
+.history
+
+# End of https://www.gitignore.io/api/macos,python,visualstudiocode
+
+# Folders
+output/*
+DAMSMencoders/coco/*
+models/*
+data/*

DAMSMencoders/.gitignore

@@ -45,7 +45,7 @@ def func_attention(query, context, gamma1):
     # Get attention
     # (batch x sourceL x ndf)(batch x ndf x queryL)
     # -->batch x sourceL x queryL
-    attn = torch.bmm(contextT, query) # Eq. (7) in AttnGAN paper
+    attn = torch.bmm(contextT, query)  # Eq. (7) in AttnGAN paper
     # --> batch*sourceL x queryL
     attn = attn.view(batch_size*sourceL, queryL)
     attn = nn.Softmax()(attn)  # Eq. (8)

project_folder/GlobalAttention.py renamed to GlobalAttention.py

@@ -1,5 +1,7 @@
 **First things first**  
-This repo is a convenient clone of AttnGAN by MSFT Research. You can find the original repo at [here](https://github.com/taoxugit/AttnGAN.git).
+This repo is a convenient clone of AttnGAN by MSFT Research. You can find the original repo at [here](https://github.com/taoxugit/AttnGAN.git). 
+
+AttnGAN original repo contains the training and testing code. But this repo is solely intended to use for generating images from text using MSCOCO pretrained weights. 
 
 # AttnGAN
 
@@ -9,11 +11,11 @@ with Attentional Generative Adversarial Networks](http://openaccess.thecvf.com/c
 <img src="framework.png" width="900px" height="350px"/>
 
 
-### Python version 
+**Python version**
 
 `python 2.7`
 
-### Dependencies
+**Dependencies**
 
 - `pytorch`
 - `python-dateutil`
@@ -23,52 +25,27 @@ with Attentional Generative Adversarial Networks](http://openaccess.thecvf.com/c
 - `nltk`
 - `scikit-image`
 
-
-**Data**
-
-1. Download our preprocessed metadata for [birds](https://drive.google.com/open?id=1O_LtUP9sch09QH3s_EBAgLEctBQ5JBSJ) or [coco](https://drive.google.com/open?id=1rSnbIGNDGZeHlsUlLdahj0RJ9oo6lgH9) and save them to `data/`
-2. Download the [birds](http://www.vision.caltech.edu/visipedia/CUB-200-2011.html) image data. Extract them to `data/birds/`
-3. Download [coco](http://cocodataset.org/#download) dataset and extract the images to `data/coco/`
-
-
-**Training**
-- Pre-train DAMSM models:
-  - For bird dataset: `python pretrain_DAMSM.py --cfg cfg/DAMSM/bird.yml --gpu 0`
-  - For coco dataset: `python pretrain_DAMSM.py --cfg cfg/DAMSM/coco.yml --gpu 1`
- 
-- Train AttnGAN models:
-  - For bird dataset: `python main.py --cfg cfg/bird_attn2.yml --gpu 2`
-  - For coco dataset: `python main.py --cfg cfg/coco_attn2.yml --gpu 3`
-
+**YML files**
 - `*.yml` files are example configuration files for training/evaluation our models.
 
 
 **Pretrained Model**
-- [DAMSM for bird](https://drive.google.com/open?id=1GNUKjVeyWYBJ8hEU-yrfYQpDOkxEyP3V). Download and save it to `DAMSMencoders/`
-- [DAMSM for coco](https://drive.google.com/open?id=1zIrXCE9F6yfbEJIbNP5-YrEe2pZcPSGJ). Download and save it to `DAMSMencoders/`
-- [AttnGAN for bird](https://drive.google.com/open?id=1lqNG75suOuR_8gjoEPYNp8VyT_ufPPig). Download and save it to `models/`
+- [DAMSM for coco](https://drive.google.com/open?id=1zIrXCE9F6yfbEJIbNP5-YrEe2pZcPSGJ). Download and save it to `DAMSMencoders/coco/`
 - [AttnGAN for coco](https://drive.google.com/open?id=1i9Xkg9nU74RAvkcqKE-rJYhjvzKAMnCi). Download and save it to `models/`
 
-- [AttnDCGAN for bird](https://drive.google.com/open?id=19TG0JUoXurxsmZLaJ82Yo6O0UJ6aDBpg). Download and save it to `models/`
-  - This is an variant of AttnGAN which applies the propsoed attention mechanisms to DCGAN framework. 
-
-**Sampling**
-- Run `python main.py --cfg cfg/eval_bird.yml --gpu 1` to generate examples from captions in files listed in "./data/birds/example_filenames.txt". Results are saved to `DAMSMencoders/`. 
-- Change the `eval_*.yml` files to generate images from other pre-trained models. 
-- Input your own sentence in "./data/birds/example_captions.txt" if you wannt to generate images from customized sentences. 
-
-**Validation**
-- To generate images for all captions in the validation dataset, change B_VALIDATION to True in the eval_*.yml. and then run `python main.py --cfg cfg/eval_bird.yml --gpu 1`
-- We compute inception score for models trained on birds using [StackGAN-inception-model](https://github.com/hanzhanggit/StackGAN-inception-model).
-- We compute inception score for models trained on coco using [improved-gan/inception_score](https://github.com/openai/improved-gan/tree/master/inception_score).
+**Setup script**
 
+Run the following script for setting up the dependencies.
 
-**Examples generated by AttnGAN [[Blog]](https://blogs.microsoft.com/ai/drawing-ai/)**
-
- bird example              |  coco example
-:-------------------------:|:-------------------------:
-![](https://github.com/taoxugit/AttnGAN/blob/master/example_bird.png)  |  ![](https://github.com/taoxugit/AttnGAN/blob/master/example_coco.png)
+```
+./setup.sh
+```
 
+**Sampling**
+- Run the command from project folder.
+```
+python2 gen_art.py --gpu 0 --input_text "Mary had a little lamb" --data_dir data/coco --model_path models/coco_AttnGAN2.pth --textencoder_path DAMSMencoders/coco/text_encoder100.pth --output_dir output` 
+```
+- Output will be stored into `output_dir` folder. 
+- Change the `eval_*.yml` files to generate images from other pre-trained models. Default config file is `cfg/eval_coco.yml`. 
 
-### Creating an API
-[Evaluation code](eval) embedded into a callable containerized API is included in the `eval\` folder.