)
else:
hparams.include_classes = hparams.include_classes.split('_')
#####################################################################################
# Instantiate model
torch.backends.cudnn.deterministic = True
print("==> creating model FUSION '{}' '{}'".format(hparams.arch,
hparams.rnn_model))
model = FusionModel(hparams)
#####################################################################################
print('Logging to: % s' % hparams.logging_dir)
logger = TensorBoardLogger(hparams.logging_dir,
name='%s/%s_%s_%s' %
(hparams.datadir.split('/')[-1], hparams.arch,
hparams.trainable_base, hparams.rnn_model))
logger.log_hyperparams(hparams) # Log the hyperparameters
# Set default device
# torch.cuda.set_device(hparams.gpu)
checkpoint_callback = ModelCheckpoint(filepath=os.path.join(
logger.log_dir, 'checkpoints'),
save_top_k=3,
verbose=True,
monitor='val_acc',
mode='max',
prefix='')
kwargs = {
'gpus': [hparams.gpu],
'logger': logger,
'check_val_every_n_epoch': 1,
def test_tensorboard_log_hyperparams(tmpdir):
logger = TensorBoardLogger(tmpdir)
hparams = {"float": 0.3, "int": 1, "string": "abc", "bool": True}
logger.log_hyperparams(hparams)
run_ID = generate_run_ID(options)
print('Run:', run_ID)
options.run_ID = run_ID
run_directory = "./experiments/"+run_ID+'/'
models = {'RNN': VanillaRNN, 'LSTM': LSTM, 'IRNN': IRNN, 'FixedIRNN': FixedIRNN, 'SIREN':SirenModel}
model = models[options.RNN_type](options)
# we set version to 0 to keep adding to the same experiment log
logger = TensorBoardLogger('./logs/', name=run_ID, version=0)
# hparams == options
logger.log_hyperparams(options)
checkpoint_callback = ModelCheckpoint(
filepath= run_directory+'{epoch}-{val_loss:.2f}',
verbose=True,
monitor='val_loss',
mode='min'
)
# Trainer config
gpus = 1
num_nodes=1
nb_sanity_val_steps=1
track_grad_norm=2
log_gpu_memory=True
