Merge pull request #8 from hoangbros03/update

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hoangbros03 · Dec 8, 2023 · b66181b · b66181b
2 parents c3856db + 77547c1
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diff --git a/Midterm_Report.pdf b/Midterm_Report.pdf
diff --git a/README.md b/README.md
@@ -1,18 +1,34 @@
 # Introduction
 
-This repository contains our GAN implementations, reports, and experiment results for the mid-term project of INT3412E 20 in VNU-UET
+This repository contains our GAN (Generative Adversarial Network) implementations, reports, and experiment results for the mid-term project of INT3412E 20 in VNU-UET.
+
+# Description
+
+We provided two version of GAN: The original GAN with Multi Layer Perception layers and the enhanced GAN using Convolutional Neural Network layers. People can train, test, and generate the images within few commands and don't need a deep understanding of GAN.
+
+MNIST and CelebA datasets were used and tested. Other datasets also can be used, but require a few changes in the dataloader class. We welcome any improvement to this repo.
 
 # Authors
 
+This repository was made by two VNU-UET students:
+
 [Nguyen Binh Nguyen](https://github.com/nguyenrtm)
 
 [Tran Ba Hoang](github.com/hoangbros03)
 
+
 # Usage
 
-Before using, please clone this repo, install the requirements, and install this repo as a pip package
+## Installation
+
+Before using, please clone this repo, install the requirements, and install this repo as a pip package.
 
 ```
+// (Optional) Create a new conda environment
+conda create -n gan_env
+conda activate gan_env
+
+// Clone and install the necessary packages
 git clone https://github.com/hoangbros03/GAN-implement.git
 cd GAN-implement
 pip install -r requirements.txt
@@ -29,17 +45,18 @@ cd src/pure_gan
 python train.py -e 200 -b 64 --lr 0.0002 --latent_dim 100 --output_model_dir models
 ```
 
-Feel free to change the config specified :D
+If confused of configuration, please type `python train.py -h` to get the guide. Feel free to change the config specified :D
 
 ## Train the enhanced GAN
 
-First, please make sure that celebA data is downloaded if you want to use the celebA dataset. It should has the following folder structure:
+First, please make sure that celebA data is downloaded if you want to use the celebA dataset (this dataset is widely available on the Internet). It should has the following folder structure:
 
 ```
 cnn_gan
 ├──data
     ├──celebA
         ├──image1.png
+        ├──image2.png
         ...
         ├──imagen.png
 ```

diff --git a/evaluate/evaluate.py b/evaluate/evaluate.py
@@ -21,8 +21,9 @@ def convert_dataloader_to_sample(dataloader):
                 gt = batch_data[0]
             else:
                 gt = torch.vstack((gt, batch_data[0]))
-
-    return gt
+        else:
+            return gt
+    return None
 
 
 def unnormalize(tensor):
@@ -59,7 +60,7 @@ def kernel_inception_distance(prediction, gt):
     Returns:
         A number indicate the value from the specified metric
     """
-    kid = KernelInceptionDistance(subset_size=1)
+    kid = KernelInceptionDistance(subset_size=64)
     kid.update(prediction, real=False)
     kid.update(gt, real=True)
     return kid.compute()

diff --git a/mnist/MNIST/raw/t10k-images-idx3-ubyte b/mnist/MNIST/raw/t10k-images-idx3-ubyte
diff --git a/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz b/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz
diff --git a/mnist/MNIST/raw/t10k-labels-idx1-ubyte b/mnist/MNIST/raw/t10k-labels-idx1-ubyte
diff --git a/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz b/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz
diff --git a/mnist/MNIST/raw/train-images-idx3-ubyte b/mnist/MNIST/raw/train-images-idx3-ubyte
diff --git a/mnist/MNIST/raw/train-images-idx3-ubyte.gz b/mnist/MNIST/raw/train-images-idx3-ubyte.gz
diff --git a/mnist/MNIST/raw/train-labels-idx1-ubyte b/mnist/MNIST/raw/train-labels-idx1-ubyte
diff --git a/mnist/MNIST/raw/train-labels-idx1-ubyte.gz b/mnist/MNIST/raw/train-labels-idx1-ubyte.gz
diff --git a/notebooks/celeb_a_experiment.ipynb b/notebooks/celeb_a_experiment.ipynb
diff --git a/notebooks/mnist_experiment.ipynb b/notebooks/mnist_experiment.ipynb
diff --git a/src/pure_gan/mnist/MNIST/raw/t10k-images-idx3-ubyte b/src/pure_gan/mnist/MNIST/raw/t10k-images-idx3-ubyte
diff --git a/src/pure_gan/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz b/src/pure_gan/mnist/MNIST/raw/t10k-images-idx3-ubyte.gz
diff --git a/src/pure_gan/mnist/MNIST/raw/t10k-labels-idx1-ubyte b/src/pure_gan/mnist/MNIST/raw/t10k-labels-idx1-ubyte
diff --git a/src/pure_gan/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz b/src/pure_gan/mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz
diff --git a/src/pure_gan/mnist/MNIST/raw/train-images-idx3-ubyte b/src/pure_gan/mnist/MNIST/raw/train-images-idx3-ubyte
diff --git a/src/pure_gan/mnist/MNIST/raw/train-images-idx3-ubyte.gz b/src/pure_gan/mnist/MNIST/raw/train-images-idx3-ubyte.gz
diff --git a/src/pure_gan/mnist/MNIST/raw/train-labels-idx1-ubyte b/src/pure_gan/mnist/MNIST/raw/train-labels-idx1-ubyte
diff --git a/src/pure_gan/mnist/MNIST/raw/train-labels-idx1-ubyte.gz b/src/pure_gan/mnist/MNIST/raw/train-labels-idx1-ubyte.gz