def tick(spec, unit):
'''
Method to tick lab unit (experiment, trial, session) in meta spec to advance their indices
Reset lower lab indices to -1 so that they tick to 0
spec_util.tick(spec, 'session')
session = Session(spec)
'''
meta_spec = spec['meta']
if unit == 'experiment':
meta_spec['experiment_ts'] = util.get_ts()
meta_spec['experiment'] += 1
meta_spec['trial'] = -1
meta_spec['session'] = -1
elif unit == 'trial':
if meta_spec['experiment'] == -1:
meta_spec['experiment'] += 1
meta_spec['trial'] += 1
meta_spec['session'] = -1
elif unit == 'session':
if meta_spec['experiment'] == -1:
meta_spec['experiment'] += 1
if meta_spec['trial'] == -1:
meta_spec['trial'] += 1
meta_spec['session'] += 1
else:
raise ValueError(f'Unrecognized lab unit to tick: {unit}')
# set prepath since it is determined at this point
meta_spec['prepath'] = prepath = util.get_prepath(spec, unit)
for folder in ('graph', 'info', 'log', 'model'):
folder_prepath = util.insert_folder(prepath, folder)
os.makedirs(os.path.dirname(util.smart_path(folder_prepath)),
exist_ok=True)
meta_spec[f'{folder}_prepath'] = folder_prepath
return spec
def save_image(figure, filepath):
if os.environ['PY_ENV'] == 'test':
return
filepath = util.smart_path(filepath)
try:
pio.write_image(figure, filepath)
except Exception as e:
orca_warn_once(e)
def load(net, model_path):
'''Save model weights from a path into a net module'''
device = None if torch.cuda.is_available() else 'cpu'
net.load_state_dict(
torch.load(util.smart_path(model_path), map_location=device))
def save(net, model_path):
'''Save model weights to path'''
torch.save(net.state_dict(), util.smart_path(model_path))