"netD_lr": lambda: np.random.uniform(1e-2, 1e-5),
})
tune_iter = 5 if args.smoke_test else 300
analysis = tune.run(
PytorchTrainable,
name="pbt_dcgan_mnist",
scheduler=scheduler,
reuse_actors=True,
verbose=1,
checkpoint_at_end=True,
stop={
"training_iteration": tune_iter,
},
num_samples=8,
export_formats=[ExportFormat.MODEL],
config={
"netG_lr": tune.sample_from(
lambda spec: random.choice([0.0001, 0.0002, 0.0005])),
"netD_lr": tune.sample_from(
lambda spec: random.choice([0.0001, 0.0002, 0.0005])),
"mnist_model_ref": mnist_model_ref
})
# __tune_end__
# demo of the trained Generators
if not args.smoke_test:
logdirs = analysis.dataframe()["logdir"].tolist()
model_paths = [os.path.join(d, "exported_models") for d in logdirs]
demo_gan(analysis, model_paths)
)
tune_iter = 5 if args.smoke_test else 300
analysis = tune.run(
dcgan_train,
name="pbt_dcgan_mnist",
scheduler=scheduler,
verbose=1,
stop={
"training_iteration": tune_iter,
},
metric="is_score",
mode="max",
num_samples=8,
config={
"netG_lr": tune.choice([0.0001, 0.0002, 0.0005]),
"netD_lr": tune.choice([0.0001, 0.0002, 0.0005]),
"mnist_model_ref": mnist_model_ref,
},
)
# __tune_end__
# demo of the trained Generators
if not args.smoke_test:
all_trials = analysis.trials
checkpoint_paths = [
os.path.join(analysis.get_best_checkpoint(t), "checkpoint")
for t in all_trials
]
demo_gan(analysis, checkpoint_paths)