a new feature of ChainerUI v0.3 and ImageReport extension

1. Jun. 9th, 2018 @Preferred Networks
ChainerUI
v0.3 and ImageReport

2. ChainerUI
- mission
- Provide useful user interface for Chainer users!

3. Basic information
- github: https://github.com/chainer/chainerui
- doc: https://docs.chainer.org/en/stable/
- MIT license
- latest: v0.2.0 v0.3.0 (today!)
- announce:
- JP: https://research.preferred.jp/2017/12/chainerui-release/
- EN: https://preferredresearch.jp/2017/12/20/chainerui-release/

4. Getting Started
Official document:
http://chainerui.readthedocs.io/en/latest/getstart.html
(JP) Tutorial by KIKAGAKU:
https://qiita.com/yoshizaki_kkgk/items/2e4021c614529ab
9ba7b
Thank you!

5. Quick start
$ pip install chainerui
$ chainerui db create
$ chainerui db upgrade
$ chainerui project create -d /path/to/results
$ chainerui server
→ Demo!
place to “log” file
LogReport extension

6. Quick upgrade (to v0.3)
$ # stop chainerui server
$ pip install -U chainerui
$ chainerui db upgrade
$ chainerui server

7. What’s new, v0.3
- sampled log
- more useful result table
- performance tuning etc...
- https://www.slideshare.net/pfi/chainer-ui-v02-v03
- https://github.com/chainer/chainerui/releases/tag/v0.3.0
→ Let’s see!

8. Image API
- ImageReport extension (v0.3~)
- Experimental feature
- API is not stable
- only basic fucntion
- no link to image page
- /projects/{:project_id}/results/{:result_id}/images

9. examples/dcgan
Example: DCGAN
def out_generated_image(gen, dis, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
np.random.seed(seed)
n_images = rows * cols
xp = gen.xp
z = Variable(xp.asarray(gen.make_hidden(n_images)))
with chainer.using_config('train', False):
x = gen(z)
x = chainer.backends.cuda.to_cpu(x.data)
np.random.seed()
x = np.asarray(np.clip(x * 255, 0.0, 255.0), dtype=np.uint8)
_, _, H, W = x.shape
x = x.reshape((rows, cols, 3, H, W))
x = x.transpose(0, 3, 1, 4, 2)
x = x.reshape((rows * H, cols * W, 3))
preview_dir = '{}/preview'.format(dst)
preview_path = preview_dir +
'/image{:0>8}.png'.format(trainer.updater.iteration)
if not os.path.exists(preview_dir):
os.makedirs(preview_dir)
Image.fromarray(x).save(preview_path)
return make_image
def main():
# ...snip
trainer = training.Trainer(
updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
out_generated_image(
gen, dis,
10, 10, args.seed, args.out),
trigger=snapshot_interval)
train_dcgan.py
visualize.py

10. Example: DCGAN
def out_generated_image(gen, dis, rows, cols, seed):
@chainer.training.make_extension()
def make_image(trainer):
np.random.seed(seed)
n_images = rows * cols
xp = gen.xp
z = Variable(xp.asarray(gen.make_hidden(n_images)))
with chainer.using_config('train', False):
x = gen(z)
x = chainer.backends.cuda.to_cpu(x.data)
np.random.seed()
x = np.asarray(np.clip(x * 255, 0.0, 255.0), dtype=np.uint8)
_, _, H, W = x.shape
x = x.reshape((rows, cols, 3, H, W))
x = x.transpose(0, 3, 1, 4, 2)
x = x.reshape((rows * H, cols * W, 3))
preview_dir = '{}/preview'.format(dst)
preview_path = preview_dir +
'/image{:0>8}.png'.format(trainer.updater.iteration)
if not os.path.exists(preview_dir):
os.makedirs(preview_dir)
Image.fromarray(x).save(preview_path)
return make_image
def main():
# ...snip
trainer = training.Trainer(
updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
out_generated_image(
gen, dis,
10, 10, args.seed, args.out),
trigger=snapshot_interval)
chainerui.summary.image(
x, row=rows)
visualizer = out_generated_image(gen, dis, 10, 10, args.seed)
trainer.extend(chainerui.extensions.ImageReport(
trigger=(1, 'epoch'), image_generator=visualizer))

11. Web view
Example: DCGAN

12. Example: VAE (MNIST)
# Run the training
trainer.run()
# Visualize the results
def save_images(x, filename):
# ...snip
model.to_cpu()
train_ind = [1, 3, 5, 10, 2, 0, 13, 15, 17]
x = chainer.Variable(np.asarray(train[train_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = model(x)
save_images(x.data, os.path.join(args.out, 'train'))
save_images(x1.data, os.path.join(args.out, 'train_reconstructed'))
test_ind = [3, 2, 1, 18, 4, 8, 11, 17, 61]
x = chainer.Variable(np.asarray(test[test_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = model(x)
save_images(x.data, os.path.join(args.out, 'test'))
save_images(x1.data, os.path.join(args.out, 'test_reconstructed'))
# draw images from randomly sampled z
z = chainer.Variable(
np.random.normal(0, 1, (9, args.dimz)).astype(np.float32))
x = model.decode(z)
save_images(x.data, os.path.join(args.out, 'sampled'))
examples/vae
train_vae.py

13. Example: VAE (MNIST)
# Run the training
trainer.run()
# Visualize the results
def save_images(x, filename):
# ...snip
model.to_cpu()
train_ind = [1, 3, 5, 10, 2, 0, 13, 15, 17]
x = chainer.Variable(np.asarray(train[train_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = model(x)
save_images(x.data, os.path.join(args.out, 'train'))
save_images(x1.data, os.path.join(args.out, 'train_reconstructed'))
test_ind = [3, 2, 1, 18, 4, 8, 11, 17, 61]
x = chainer.Variable(np.asarray(test[test_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = model(x)
save_images(x.data, os.path.join(args.out, 'test'))
save_images(x1.data, os.path.join(args.out, 'test_reconstructed'))
# draw images from randomly sampled z
z = chainer.Variable(
np.random.normal(0, 1, (9, args.dimz)).astype(np.float32))
x = model.decode(z)
save_images(x.data, os.path.join(args.out, 'sampled'))
def visualize(trainer):
train_ind = [1, 3, 5, 10, 2, 0, 13, 15, 17]
x = chainer.Variable(np.asarray(train[train_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = model(x)
def convert(x):
return x.reshape(9, 28, 28).transpose(0, 2, 1)
summary.image(convert(x), name='train', row=3)
summary.image(convert(x1), name='train_reconstructed', row=3)
test_ind = [3, 2, 1, 18, 4, 8, 11, 17, 61]
x = chainer.Variable(np.asarray(test[test_ind]))
with chainer.using_config('train', False), chainer.no_backprop_mode():
x1 = model(x)
summary.image(convert(x), name='test', row=3)
summary.image(convert(x1), name='test_reconstructed', row=3)
# draw images from randomly sampled z
z = chainer.Variable(
np.random.normal(0, 1, (9, args.dimz)).astype(np.float32))
x = model.decode(z)
summary.image(convert(x), name='sampled', row=3)
trainer.extend(ImageReport(image_generator=visualize))
# Run the training
trainer.run()

14. Example: VAE (MNIST)

15. examples/pix2pix
Example: pix2pix
def out_image(updater, enc, dec, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
# ...snip
def save_image(x, name, mode=None):
# ...snip
x = np.asarray(np.clip(gen_all * 128 + 128, 0.0, 255.0), dtype=np.uint8)
save_image(x, "gen")
x = np.ones((n_images, 3, w_in, w_in)).astype(np.uint8)*255
x[:,0,:,:] = 0
for i in range(12):
x[:,0,:,:] += np.uint8(15*i*in_all[:,i,:,:])
save_image(x, "in", mode='HSV')
x = np.asarray(np.clip(gt_all * 128+128, 0.0, 255.0), dtype=np.uint8)
save_image(x, "gt")
def main():
# ...snip
trainer = training.Trainer(
updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
out_image(
updater, enc, dec,
5, 5, args.seed, args.out),
trigger=snapshot_interval)
train_facade.py
facade_visualizer.py

16. Example: pix2pix
def out_image(updater, enc, dec, rows, cols, seed, dst):
@chainer.training.make_extension()
def make_image(trainer):
# ...snip
def save_image(x, name, mode=None):
# ...snip
x = np.asarray(np.clip(gen_all * 128 + 128, 0.0, 255.0), dtype=np.uint8)
save_image(x, "gen")
x = np.ones((n_images, 3, w_in, w_in)).astype(np.uint8)*255
x[:,0,:,:] = 0
for i in range(12):
x[:,0,:,:] += np.uint8(15*i*in_all[:,i,:,:])
save_image(x, "in", mode='HSV')
x = np.asarray(np.clip(gt_all * 128+128, 0.0, 255.0), dtype=np.uint8)
save_image(x, "gt")
def main():
# ...snip
trainer = training.Trainer(
updater, (args.epoch, 'epoch'), out=args.out)
trainer.extend(
out_image(
updater, enc, dec,
5, 5, args.seed, args.out),
trigger=snapshot_interval)
visualizer = out_image(updater, enc, dec, args.visualize_batchsize,
args.rows, args.seed)
trainer.extend(chainerui.extensions.ImageReport(
trigger=snapshot_interval, image_generator=visualizer))
summary.image(gen_all, name='gen', row=rows)
summary.image(x, name='in', row=rows, mode='HSV')
summary.image(gt_all, name='gt', row=rows)

17. Example: pix2pix
web view

18. Demo
https://github.com/disktnk/chainerui-demo-20180609

19. ImageReport TODO
- Issue#101
- DB cache / lazy load / document etc...
- API usability
- Show images with label
- Support grayscale
- Support hidden layer?

20. Roadmap
- v0.3.1 / v0.4
- output chart (PR#112)
- print(‘msg’) → logger
- improve UX
- easy to distinguish each log chart
- deal with too many columns or keys