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Fix PGAN notebook #319

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merged 3 commits into from
Apr 4, 2024
Merged

Fix PGAN notebook #319

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42f7d3d
Update generation.py
satra Apr 3, 2024
7a954e3
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 3, 2024
71c3f63
Merge branch 'master' into fix/pgan
satra Apr 3, 2024
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20 changes: 10 additions & 10 deletions nobrainer/processing/generation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,7 +5,7 @@

from .base import BaseEstimator
from .. import losses
from ..dataset import get_dataset
from ..dataset import Dataset


class ProgressiveGeneration(BaseEstimator):
Expand Down Expand Up @@ -147,15 +147,17 @@ def _compile():
if batch_size % self.strategy.num_replicas_in_sync:
raise ValueError("batch size must be a multiple of the number of GPUs")

dataset = get_dataset(
dataset = Dataset.from_tfrecords(
file_pattern=info.get("file_pattern"),
batch_size=batch_size,
num_parallel_calls=num_parallel_calls,
volume_shape=(resolution, resolution, resolution),
n_classes=1,
scalar_label=True,
normalizer=info.get("normalizer") or normalizer,
scalar_labels=True,
)
n_epochs = info.get("epochs") or epochs
dataset.batch(batch_size).normalize(
info.get("normalizer") or normalizer
).repeat(n_epochs)

with self.strategy.scope():
# grow the networks by one (2^x) resolution
Expand All @@ -164,9 +166,7 @@ def _compile():
self.model_.discriminator.add_resolution()
_compile()

steps_per_epoch = (info.get("epochs") or epochs) // info.get(
"batch_size"
)
steps_per_epoch = n_epochs // info.get("batch_size")

# save_best_only is set to False as it is an adversarial loss
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
Expand All @@ -182,7 +182,7 @@ def _compile():

print("Transition phase")
self.model_.fit(
dataset,
dataset.dataset,
phase="transition",
resolution=resolution,
steps_per_epoch=steps_per_epoch, # necessary for repeat dataset
Expand All @@ -191,7 +191,7 @@ def _compile():

print("Resolution phase")
self.model_.fit(
dataset,
dataset.dataset,
phase="resolution",
resolution=resolution,
steps_per_epoch=steps_per_epoch,
Expand Down
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