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import fastai
from fastai.vision import *
from fastai.callbacks import *
from fastai.vision.gan import *
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path = untar_data(URLs.PETS)
path_hr = path/'images'
path_lr = path/'crappy'
Prepare the input data by crappifying images.
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from crappify import *
Uncomment the first time you run this notebook.
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#il = ImageList.from_folder(path_hr)
#parallel(crappifier(path_lr, path_hr), il.items)
For gradual resizing we can change the commented line here.
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bs,size=32, 128
# bs,size = 24,160
#bs,size = 8,256
arch = models.resnet34
Now let's pretrain the generator.
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arch = models.resnet34
src = ImageImageList.from_folder(path_lr).split_by_rand_pct(0.1, seed=42)
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def get_data(bs,size):
data = (src.label_from_func(lambda x: path_hr/x.name)
.transform(get_transforms(max_zoom=2.), size=size, tfm_y=True)
.databunch(bs=bs).normalize(imagenet_stats, do_y=True))
data.c = 3
return data
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data_gen = get_data(bs,size)
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data_gen.show_batch(4)
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wd = 1e-3
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y_range = (-3.,3.)
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loss_gen = MSELossFlat()
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def create_gen_learner():
return unet_learner(data_gen, arch, wd=wd, blur=True, norm_type=NormType.Weight,
self_attention=True, y_range=y_range, loss_func=loss_gen)
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learn_gen = create_gen_learner()
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learn_gen.fit_one_cycle(2, pct_start=0.8)
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learn_gen.unfreeze()
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learn_gen.fit_one_cycle(3, slice(1e-6,1e-3))
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learn_gen.show_results(rows=4)
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learn_gen.save('gen-pre2')
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learn_gen.load('gen-pre2');
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name_gen = 'image_gen'
path_gen = path/name_gen
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# shutil.rmtree(path_gen)
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path_gen.mkdir(exist_ok=True)
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def save_preds(dl):
i=0
names = dl.dataset.items
for b in dl:
preds = learn_gen.pred_batch(batch=b, reconstruct=True)
for o in preds:
o.save(path_gen/names[i].name)
i += 1
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save_preds(data_gen.fix_dl)
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PIL.Image.open(path_gen.ls()[0])
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learn_gen=None
gc.collect()
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Pretrain the critic on crappy vs not crappy.
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def get_crit_data(classes, bs, size):
src = ImageList.from_folder(path, include=classes).split_by_rand_pct(0.1, seed=42)
ll = src.label_from_folder(classes=classes)
data = (ll.transform(get_transforms(max_zoom=2.), size=size)
.databunch(bs=bs).normalize(imagenet_stats))
data.c = 3
return data
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data_crit = get_crit_data([name_gen, 'images'], bs=bs, size=size)
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data_crit.show_batch(rows=3, ds_type=DatasetType.Train, imgsize=3)
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loss_critic = AdaptiveLoss(nn.BCEWithLogitsLoss())
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def create_critic_learner(data, metrics):
return Learner(data, gan_critic(), metrics=metrics, loss_func=loss_critic, wd=wd)
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learn_critic = create_critic_learner(data_crit, accuracy_thresh_expand)
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learn_critic.fit_one_cycle(6, 1e-3)
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learn_critic.save('critic-pre2')
Now we'll combine those pretrained model in a GAN.
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learn_crit=None
learn_gen=None
gc.collect()
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data_crit = get_crit_data(['crappy', 'images'], bs=bs, size=size)
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learn_crit = create_critic_learner(data_crit, metrics=None).load('critic-pre2')
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learn_gen = create_gen_learner().load('gen-pre2')
To define a GAN Learner, we just have to specify the learner objects foor the generator and the critic. The switcher is a callback that decides when to switch from discriminator to generator and vice versa. Here we do as many iterations of the discriminator as needed to get its loss back < 0.5 then one iteration of the generator.
The loss of the critic is given by learn_crit.loss_func. We take the average of this loss function on the batch of real predictions (target 1) and the batch of fake predicitions (target 0).
The loss of the generator is weighted sum (weights in weights_gen) of learn_crit.loss_func on the batch of fake (passed throught the critic to become predictions) with a target of 1, and the learn_gen.loss_func applied to the output (batch of fake) and the target (corresponding batch of superres images).
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switcher = partial(AdaptiveGANSwitcher, critic_thresh=0.65)
learn = GANLearner.from_learners(learn_gen, learn_crit, weights_gen=(1.,50.), show_img=False, switcher=switcher,
opt_func=partial(optim.Adam, betas=(0.,0.99)), wd=wd)
learn.callback_fns.append(partial(GANDiscriminativeLR, mult_lr=5.))
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lr = 1e-4
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learn.fit(40,lr)
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learn.save('gan-1c')
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learn.data=get_data(16,192)
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learn.fit(10,lr/2)
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learn.show_results(rows=16)
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learn.save('gan-1c')
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