def _demodata_toy_harn():
# This will train a toy model with toy data using netharn
import netharn as nh
hyper = nh.HyperParams(
**{
'workdir': ub.ensure_app_cache_dir('torch_liberator/tests/deploy'),
'name': 'demo_liberator_static',
'xpu': nh.XPU.coerce('cpu'),
'datasets': {
'train': nh.data.ToyData2d(size=3, rng=0)
},
'loaders': {
'batch_size': 64
},
'model': (nh.models.ToyNet2d, {}),
'optimizer': (nh.optimizers.SGD, {
'lr': 0.0001
}),
'criterion': (nh.criterions.FocalLoss, {}),
'initializer': (nh.initializers.KaimingNormal, {}),
'monitor': (nh.Monitor, {
'max_epoch': 1
}),
})
harn = nh.FitHarn(hyper)
harn.preferences['use_tensorboard'] = False
harn.preferences['log_gradients'] = False
harn.preferences['timeout'] = 1
return harn
def _demodata_trained_dpath():
# This will train a toy model with toy data using netharn
import netharn as nh
hyper = nh.HyperParams(
**{
'workdir': ub.ensure_app_cache_dir('netharn/tests/deploy'),
'nice': 'deploy_demo_static',
'xpu': nh.XPU.cast('cpu'),
'datasets': {
'train': nh.data.ToyData2d(size=3, rng=0)
},
'loaders': {
'batch_size': 64
},
'model': (nh.models.ToyNet2d, {}),
'optimizer': (nh.optimizers.SGD, {
'lr': 0.0001
}),
'criterion': (nh.criterions.FocalLoss, {}),
'initializer': (nh.initializers.KaimingNormal, {}),
'monitor': (nh.Monitor, {
'max_epoch': 1
}),
})
harn = nh.FitHarn(hyper)
harn.run() # TODO: make this run faster if we don't need to rerun
if len(list(glob.glob(join(harn.train_dpath, '*.py')))) > 1:
# If multiple models are deployed some hash changed. Need to reset
harn.initialize(reset='delete')
harn.run() # don't relearn if we already finished this one
return harn.train_dpath
def _demodata_toy_sesssion(workdir, name='demo_session', lr=1e-4):
"""
workdir = ub.ensure_app_cache_dir('netharn/tests/sessions')
workdir
"""
# This will train a toy model with toy data using netharn
import netharn as nh
hyper = nh.HyperParams(
**{
'workdir': ub.ensure_app_cache_dir('netharn/tests/sessions'),
'name': name,
'xpu': nh.XPU.coerce('cpu'),
'datasets': {
'train': nh.data.ToyData2d(size=3, rng=0),
'vali': nh.data.ToyData2d(size=3, rng=0)
},
'loaders': {
'batch_size': 64
},
'model': (nh.models.ToyNet2d, {}),
'optimizer': (nh.optimizers.SGD, {
'lr': lr
}),
'criterion': (nh.criterions.FocalLoss, {}),
'initializer': (nh.initializers.KaimingNormal, {}),
'monitor': (nh.Monitor, {
'max_epoch': 1
}),
})
harn = nh.FitHarn(hyper)
harn.preferences['use_tensorboard'] = False
harn.preferences['timeout'] = 1
harn.run() # TODO: make this run faster if we don't need to rerun