def main(params):
arg_parser = argparse.ArgumentParser()
arg_parser = populate_experiment_params(arg_parser)
arg_parser = populate_common_params(arg_parser)
opts = arg_parser.parse_args(params)
# fix seed
pl.seed_everything(opts.random_seed)
signal_game = get_model(opts)
experiment_name = 'signal-game'
model_name = '%s/%s' % (experiment_name, opts.mode)
other_info = [
"lr-{}".format(opts.lr),
]
other_info.append("entrcoeff-{}".format(opts.entropy_coeff))
if opts.mode == "gs":
if opts.straight_through:
other_info.append("straight_through")
other_info.append("decay-{}".format(opts.temperature_decay))
other_info.append("updatefreq-{}".format(opts.temperature_update_freq))
elif opts.mode == 'sfe':
other_info.append("baseline-{}".format(opts.baseline_type))
elif opts.mode == 'vimco':
other_info.append("k-{}".format(opts.vimco_k))
elif opts.mode == "marg":
other_info.append("norm-{}".format(opts.normalizer))
elif opts.mode == 'sumsample':
other_info.append("k-{}".format(opts.topk))
other_info.append("baseline-{}".format(opts.baseline_type))
model_name = '%s/%s' % (model_name, '_'.join(other_info))
tb_logger = pl_loggers.TensorBoardLogger('logs/', name=model_name)
tb_logger.log_hyperparams(opts, metrics=None)
trainer = pl.Trainer(
progress_bar_refresh_rate=20,
logger=tb_logger,
callbacks=[CheckpointEveryNSteps(opts.batches_per_epoch)],
max_steps=opts.batches_per_epoch * opts.n_epochs,
limit_val_batches=1024 / opts.batch_size,
limit_test_batches=10000 // opts.batch_size,
val_check_interval=opts.batches_per_epoch,
weights_save_path='checkpoints/',
weights_summary='full',
gpus=1 if torch.cuda.is_available() else 0,
resume_from_checkpoint=opts.load_from_checkpoint,
deterministic=True)
trainer.fit(signal_game)
def main(params):
arg_parser = argparse.ArgumentParser()
arg_parser = populate_experiment_params(arg_parser)
arg_parser = populate_common_params(arg_parser)
opts = arg_parser.parse_args(params)
# fix seed
pl.seed_everything(opts.random_seed)
pathlib.Path('data/bit_vector-vae/fmnist_data/').mkdir(parents=True,
exist_ok=True)
bit_vector_vae = get_model(opts)
experiment_name = 'bit-vector'
model_name = '%s/%s' % (experiment_name, opts.mode)
other_info = [
"lr-{}".format(opts.lr),
"latent_size-{}".format(opts.latent_size),
]
if opts.mode == "sparsemap":
if opts.budget > 0:
other_info.append(f"b{opts.budget}")
if opts.noinit:
other_info.append("noinit")
elif opts.mode == "gs":
if opts.straight_through:
other_info.append("straight_through")
other_info.append("decay-{}".format(opts.temperature_decay))
other_info.append("updatefreq-{}".format(opts.temperature_update_freq))
elif opts.mode == 'sfe':
other_info.append("baseline-{}".format(opts.baseline_type))
elif opts.mode == 'vimco':
other_info.append("k-{}".format(opts.vimco_k))
elif opts.mode == "topksparse":
other_info.append("k-{}".format(opts.topksparse))
model_name = '%s/%s' % (model_name, '_'.join(other_info))
tb_logger = pl_loggers.TensorBoardLogger('logs/', name=model_name)
tb_logger.log_hyperparams(opts, metrics=None)
trainer = pl.Trainer(progress_bar_refresh_rate=20,
logger=tb_logger,
max_epochs=opts.n_epochs,
weights_save_path='checkpoints/',
weights_summary='full',
gpus=1 if torch.cuda.is_available() else 0,
resume_from_checkpoint=opts.load_from_checkpoint,
deterministic=True)
trainer.fit(bit_vector_vae)
def main(params):
arg_parser = argparse.ArgumentParser()
arg_parser = populate_experiment_params(arg_parser)
arg_parser = populate_common_params(arg_parser)
opts = arg_parser.parse_args(params)
# fix seed
pl.seed_everything(opts.random_seed)
model = get_model(opts)
experiment_name = 'ssvae'
if not opts.labeled_only:
model_name = '%s/%s' % (experiment_name, opts.mode)
else:
model_name = '%s/warm_start/%s' % (experiment_name, opts.normalizer)
other_info = [
"lr-{}".format(opts.lr),
]
if opts.mode == "gs":
if opts.straight_through:
other_info.append("straight_through")
other_info.append("decay-{}".format(opts.temperature_decay))
other_info.append("updatefreq-{}".format(opts.temperature_update_freq))
elif opts.mode == 'sfe':
other_info.append("baseline-{}".format(opts.baseline_type))
elif opts.mode == 'vimco':
other_info.append("k-{}".format(opts.vimco_k))
elif opts.mode == "marg":
other_info.append("norm-{}".format(opts.normalizer))
elif opts.mode == 'sumsample':
other_info.append("k-{}".format(opts.topk))
other_info.append("baseline-{}".format(opts.baseline_type))
model_name = '%s/%s' % (model_name, '_'.join(other_info))
tb_logger = pl_loggers.TensorBoardLogger('logs/', name=model_name)
tb_logger.log_hyperparams(opts, metrics=None)
trainer = pl.Trainer(progress_bar_refresh_rate=20,
logger=tb_logger,
max_epochs=opts.n_epochs,
weights_save_path='checkpoints/',
weights_summary='full',
gpus=1 if torch.cuda.is_available() else 0,
resume_from_checkpoint=opts.load_from_checkpoint,
deterministic=True)
trainer.fit(model)