class LearningPipeline(ff.BaseModel):
samples = ff.PickleFeature(ff.IteratorNode)
shuffled = ff.PickleFeature(ShuffledSamples,
nsamples=ff.Var('nsamples'),
dtype=ff.Var('dtype'),
needs=samples)
class InfiniteLearningPipeline(cls):
dataset = ff.Feature(
InfiniteSampler,
nsamples=ff.Var('nsamples'),
dtype=ff.Var('dtype'),
feature_filter=ff.Var('feature_filter'),
parallel=ff.Var('parallel'))
pipeline = ff.ClobberPickleFeature(
PreprocessingPipeline,
needs=cls.features,
store=True)
@classmethod
def load_network(cls):
if not cls.exists():
raise RuntimeError('No network has been trained or saved')
instance = cls()
for p in instance.pipeline:
try:
return p.network
except AttributeError:
pass
raise RuntimeError('There is no network in the pipeline')
class Gan(ff.BaseModel):
scaled = ff.PickleFeature(
InstanceScaling)
wgan = ff.PickleFeature(
PyTorchGan,
trainer=ff.Var('trainer'),
needs=scaled)
class EmbeddingPipeline(BasePipeline, Settings):
scaled = ff.PickleFeature(zounds.InstanceScaling,
needs=BasePipeline.shuffled)
embedding = ff.PickleFeature(
zounds.PyTorchNetwork,
trainer=ff.Var('trainer'),
post_training_func=(lambda x: x[:, anchor_slice]),
needs=dict(data=scaled))
unitnorm = ff.PickleFeature(zounds.UnitNorm, needs=embedding)
simhash = ff.PickleFeature(zounds.SimHash,
bits=ff.Var('bits'),
packbits=True,
needs=unitnorm)
pipeline = ff.PickleFeature(zounds.PreprocessingPipeline,
needs=(scaled, embedding, unitnorm, simhash),
store=True)
class CategoricalAutoEncoderPipeline(ff.BaseModel):
samples = ff.PickleFeature(ff.IteratorNode)
shuffled = ff.PickleFeature(zounds.ShuffledSamples,
nsamples=int(1e5),
dtype=np.float32,
needs=samples)
autoencoder = ff.PickleFeature(zounds.PyTorchAutoEncoder,
trainer=ff.Var('trainer'),
needs=shuffled)
pipeline = ff.PickleFeature(zounds.PreprocessingPipeline,
needs=(autoencoder, ),
store=True)
class Gan(ff.BaseModel):
samples = ff.PickleFeature(ff.IteratorNode)
shuffled = ff.PickleFeature(zounds.ShuffledSamples,
nsamples=int(1e5),
dtype=np.float32,
needs=samples)
scaled = ff.PickleFeature(zounds.InstanceScaling, needs=shuffled)
wgan = ff.PickleFeature(zounds.PyTorchGan,
trainer=ff.Var('trainer'),
needs=scaled)
pipeline = ff.PickleFeature(zounds.PreprocessingPipeline,
needs=(
scaled,
wgan,
),
store=True)