DCGAN

Title

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Abstract

In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations.

Train

$ python main.py --mode train \
                 --scope [scope name] \
                 --name_data [data name] \
                 --dir_data [data directory] \
                 --dir_log [log directory] \
                 --dir_checkpoint [checkpoint directory]
                 --gpu_ids [gpu id; '-1': no gpu, '0, 1, ..., N-1': gpus]

---

$ python main.py --mode train \
                 --scope dcgan \
                 --name_data celeba \
                 --dir_data ./datasets \
                 --dir_log ./log \
                 --dir_checkpoint ./checkpoint
                 --gpu_ids 0

• Set [scope name] uniquely.
• Hyperparameters were written to arg.txt under the [log directory].
• To understand hierarchy of directories based on their arguments, see directories structure below.

Test

$ python main.py --mode test \
                 --scope [scope name] \
                 --name_data [data name] \
                 --dir_data [data directory] \
                 --dir_log [log directory] \
                 --dir_checkpoint [checkpoint directory] \
                 --dir_result [result directory]
                 --gpu_ids [gpu id; '-1': no gpu, '0, 1, ..., N-1': gpus]

---

$ python main.py --mode test \
                 --scope dcgan \
                 --name_data celeba \
                 --dir_data ./datasets \
                 --dir_log ./log \
                 --dir_checkpoint ./checkpoints \
                 --dir_result ./results
                 --gpu_ids 0

• To test using trained network, set [scope name] defined in the train phase.
• Generated images are saved in the images subfolder along with [result directory] folder.
• index.html is also generated to display the generated images.

Tensorboard

$ tensorboard --logdir [log directory]/[scope name]/[data name] \
              --port [(optional) 4 digit port number]

---

$ tensorboard --logdir ./log/dcgan/celeba \
              --port 6006

After the above comment executes, go http://localhost:6006

• You can change [(optional) 4 digit port number].
• Default 4 digit port number is 6006.

Results

• The results were generated by a network trained with celeba dataset during 10 epochs.
• After the Test phase runs, execute display_result.py to display the figure.

Directories structure

pytorch-DCGAN
+---[dir_checkpoint]
|   \---[scope]
|       \---[name_data]
|           +---model_epoch00000.pth
|           |   ...
|           \---model_epoch12345.pth
+---[dir_data]
|   \---[name_data]
|       +---000000.png
|       |   ...
|       \---12345.png
+---[dir_log]
|   \---[scope]
|       \---[name_data]
|           +---arg.txt
|           \---events.out.tfevents
\---[dir_result]
    \---[scope]
        \---[name_data]
            +---images
            |   +---00000-output.png
            |   |   ...
            |   +---12345-output.png
            \---index.html

---

pytorch-DCGAN
+---checkpoints
|   \---dcgan
|       \---celeba
|           +---model_epoch00001.pth
|           |   ...
|           \---model_epoch0010.pth
+---datasets
|   \---celeba
|       +---000001.jpg
|       |   ...
|       \---202599.jpg
+---log
|   \---dcgan
|       \---celeba
|           +---arg.txt
|           \---events.out.tfevents
\---results
    \---dcgan
        \---celeba
            +---images
            |   +---0000-output.png
            |   |   ...
            |   +---0127-output.png
            \---index.html

• Above directory is created by setting arguments when main.py is executed.