lee-daeho / pytorch-pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

pix2pix

Title

Image-to-Image Translation with Conditional Adversarial Networks

Abstract

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

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] \
                 --direction [A2B or B2A]
                 --gpu_ids [gpu id; '-1': no gpu, '0, 1, ..., N-1': gpus]

---

$ python main.py --mode train \
                 --scope pix2pix \
                 --name_data facades \
                 --dir_data ./datasets \
                 --dir_log ./log \
                 --dir_checkpoint ./checkpoint \
                 --direction B2A
                 --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] \
                 --direction [A2B or B2A] \
                 --dir_result [result directory]
                 --gpu_ids [gpu id; '-1': no gpu, '0, 1, ..., N-1': gpus]

---

$ python main.py --mode test \
                 --scope pix2pix \
                 --name_data facades \
                 --dir_data ./datasets \
                 --dir_log ./log \
                 --dir_checkpoint ./checkpoints \
                 --direction B2A \
                 --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/pix2pix/facades \
              --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

1st row: input-segmentation (domain B)
2nd row: label-photo (domain A)
3rd row: output-pix2pix

• The results were generated by a network trained with facades dataset during 300 epochs.
• After the Test phase runs, execute display_result.py to display the figure.

Directories structure

pytorch-pix2pix
+---[dir_checkpoint]
|   \---[scope]
|       \---[name_data]
|           +---model_epoch00000.pth
|           |   ...
|           \---model_epoch12345.pth
+---[dir_data]
|   \---[name_data]
|       +---test
|       |   +---00000.png
|       |   |   ...
|       |   \---12345.png
|       +---train
|       |   +---00000.png
|       |   |   ...
|       |   \---12345.png
|       \---val
|           +---00000.png
|           |   ...
|           \---12345.png
+---[dir_log]
|   \---[scope]
|       \---[name_data]
|           +---arg.txt
|           \---events.out.tfevents
\---[dir_result]
    \---[scope]
        \---[name_data]
            +---images
            |   +---00000-input.png
            |   +---00000-label.png
            |   +---00000-output.png
            |   |   ...
            |   +---12345-input.png
            |   +---12345-label.png
            |   +---12345-output.png
            \---index.html

---

pytorch-pix2pix
+---checkpoints
|   \---pix2pix
|       \---facades
|           +---model_epoch0000.pth
|           |   ...
|           \---model_epoch0200.pth
+---datasets
|   \---facades
|       +---test
|       |   +---1.jpg
|       |   |   ...
|       |   \---106.jpg
|       +---train
|       |   +---1.jpg
|       |   |   ...
|       |   \---400.jpg
|       \---val
|           +---1.jpg
|           |   ...
|           \---100.jpg
+---log
|   \---pix2pix
|       \---facades
|           +---arg.txt
|           \---events.out.tfevents
\---results
    \---pix2pix
        \---facades
            +---images
            |   +---00000-input.png
            |   +---00000-label.png
            |   +---00000-output.png
            |   |   ...
            |   +---00106-input.png
            |   +---00106-label.png
            |   +---00106-output.png
            \---index.html

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