[GSoC] StackGAN Model

tensorflow_examples/models/stack_gan/README.md

@@ -0,0 +1,57 @@
+# StackGAN
+### Text to Photo-Realistic Image Synthesis
+---
+#### Dependencies
+```
+tensorflow==2.0.0b1
+numpy==1.16.4
+absl_py==0.7.0
+matplotlib==2.2.3
+pandas==0.23.4
+Pillow==6.1.0
+```
+#### Downloads
+- To download all the dependencies, simply execute 
+```
+pip install -r requirements.txt
+```
+- To download the CUB 200 dataset, simply execute the `data_download.py` file
+```
+python data_download.py
+```
+- Download the Char-RNN-CNN embeddings from this link: [**download link**](https://drive.google.com/file/d/0B3y_msrWZaXLT1BZdVdycDY5TEE) and unzip it in place. 
+```
+unzip birds.zip
+```
+#### Training
+- The `model.py` file contains the bare minimum code to run the stage 1 and stage 2 architecture. It automatically stores the weights after the specified/default number of epochs have completed. Note that the weights will be stored at the same directory level as `model.py`.
+```
+python model.py
+```
+#### Architecture
+- Stage 1
+	- Text Encoder Network
+		- Text description to a 1024 dimensional text embedding
+		- Learning Deep Representations of Fine-Grained Visual Descriptions [Arxiv Link](https://arxiv.org/abs/1605.05395)
+	- Conditioning Augmentation Network
+		- Adds randomness to the network
+		- Produces more image-text pairs
+	- Generator Network
+	- Discriminator Network
+	- Embedding Compressor Network
+	- Outputs a 64x64 image
+#
+- Stage 2
+	- Text Encoder Network
+	- Conditioning Augmentation Network
+	- Generator Network
+	- Discriminator Network
+	- Embedding Compressor Network
+	- Outputs a 256x256 image
+---
+#### Reference Papers
+1. **StackGAN: Text to photo-realistic image synthesis** [[Arxiv Link](https://arxiv.org/pdf/1612.03242.pdf)]
+2. **Improved Techniques for Training GANs** [[Arxiv Link](https://arxiv.org/pdf/1606.03498.pdf)]
+3. **Generative Adversarial Text to Image Synthesis** [[Arxiv Link](https://arxiv.org/pdf/1605.05396.pdf)]
+4. **Learning Deep Representations of Fine-Grained Visual Descriptions** [[Arxiv Link](https://arxiv.org/abs/1605.05395)]
+---

tensorflow_examples/models/stack_gan/data_download.py

@@ -0,0 +1,50 @@
+# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Download CUB data and the Char-CNN-RNN embeddings.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import argparse
+from absl import app
+import tensorflow as tf
+assert tf.__version__.startswith('2')
+
+ap = argparse.ArgumentParser()
+ap.add_argument('-dp', '--download_path', required=False, help='Download Path')
+args = vars(ap.parse_args())
+
+data_url = "http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz"
+
+def download_data(download_path):
+	path_to_zip = tf.keras.utils.get_file(
+		'CUB_200_2011.tgz', cache_subdir=download_path,
+		origin = data_url, extract=True)
+
+	path_to_folder = os.path.join(os.path.dirname(path_to_zip), 'data/birds/')
+
+	return path_to_folder
+
+if __name__ == '__main__':
+	if args['download_path'] is not None:
+		path = download_data(args["download_path"])
+	else:
+		cur = os.getcwd()
+		path = download_data(cur)
+
+	# embeddings_url = "https://drive.google.com/file/d/0B3y_msrWZaXLT1BZdVdycDY5TEE"