def __init__(self,
experiment_manifest_name,
subject_ids,
checkpoint_dir='',
restore_epoch=None,
SN_kwargs=(),
DG_kwargs=(),
RP_kwargs=(),
ES_kwargs=(),
VERBOSE=True,
**kwargs):
# load the experiment_manifest
with open(os.path.join(text_dir, experiment_manifest_name)) as file:
self.experiment_manifest = yaml.full_load(file)
# checks
token_type = self.experiment_manifest[subject_ids[-1]]['token_type']
assert token_type in TOKEN_TYPES, 'Unrecognized token_type!! -- jgm'
# attribute
self._token_type = token_type # NB: changes will not propagate
self._RP_kwargs = dict(RP_kwargs)
# create ECoG subjects
self.ecog_subjects = [
ECoGSubject(self.experiment_manifest[subject_id],
subject_id,
pretrain_all_blocks=(subject_id != subject_ids[-1]),
**dict(ES_kwargs),
_DG_kwargs=dict(DG_kwargs)
#####
# target_specs=target_specs
#####
) for subject_id in subject_ids
]
# create the SequenceNetwork according to the experiment_manifest
self.net = sequence_networks.SequenceNetwork(
self.experiment_manifest[subject_ids[-1]],
EOS_token=EOS_token,
pad_token=pad_token,
OOV_token=OOV_token,
training_GPUs=[0],
TARGETS_ARE_SEQUENCES='sequence' in token_type,
VERBOSE=VERBOSE,
**dict(SN_kwargs))
# invoke some setters
# NB: these attributes adjust self.ecog_subjects and self.net, so they
# must be invoked *after* those are created. Hence no auto_attribute!
self.VERBOSE = VERBOSE
self.checkpoint_dir = checkpoint_dir
self.restore_epoch = restore_epoch
# update the data_manifests for our case
for subject in self.ecog_subjects:
for data_key, data_manifest in subject.data_manifests.items():
try:
data_manifest.penalty_scale = self.experiment_manifest[
subject.subnet_id][data_key + '_penalty_scale']
except KeyError:
pass
self.set_feature_lists(**kwargs)
def __init__(
self,
experiment_manifest_name,
subject_ids,
checkpoint_dir='',
restore_epoch=None,
SN_kwargs=(),
DG_kwargs=(),
RP_kwargs=(),
ES_kwargs=(),
VERBOSE=True,
**kwargs
):
# load the experiment_manifest
with open(os.path.join(text_dir, experiment_manifest_name)) as file:
self.experiment_manifest = yaml.full_load(file)
# checks
token_type = self.experiment_manifest[subject_ids[-1]]['token_type']
assert token_type in TOKEN_TYPES, 'Unrecognized token_type!! -- jgm'
# attribute
self._token_type = token_type # NB: changes will not propagate
self._RP_kwargs = dict(RP_kwargs)
# create ECoG subjects
self.ecog_subjects = [
ECoGSubject(
self.experiment_manifest[subject_id],
subject_id,
pretrain_all_blocks=(subject_id != subject_ids[-1]),
**dict(ES_kwargs),
_DG_kwargs=dict(DG_kwargs)
#####
# target_specs=target_specs
#####
) for subject_id in subject_ids]
# invoke some setters
# NB: these attributes adjust self.ecog_subjects, so they must be
# invoked *after* those are created (hence no auto_attribute). But
# the changes to the ecog_subjects below in turn depend on the
# self.checkpoint_dir, so they have to be set after these lines.
self.VERBOSE = VERBOSE
self.checkpoint_dir = checkpoint_dir
self.restore_epoch = restore_epoch
# update the data_manifests for our case
for subject in self.ecog_subjects:
for data_key, data_manifest in subject.data_manifests.items():
if data_key == 'decoder_targets' and 'sequence' in token_type:
data_manifest.APPEND_EOS = True
try:
data_manifest.penalty_scale = self.experiment_manifest[
subject.subnet_id][data_key + '_penalty_scale']
except KeyError:
pass
self.set_feature_lists(**kwargs)
# create the SequenceNetwork according to the experiment_manifest
if int(tf.__version__.split('.')[0]) == 2:
self.net = NeuralNetwork(
self.experiment_manifest[subject_ids[-1]],
Seq2Seq,
self.ecog_subjects[-1], # temporary hack
EOS_token=EOS_token,
pad_token=pad_token,
OOV_token=OOV_token,
**dict(SN_kwargs)
)
else:
self.net = sequence_networks.SequenceNetwork(
self.experiment_manifest[subject_ids[-1]],
EOS_token=EOS_token,
pad_token=pad_token,
OOV_token=OOV_token,
training_GPUs=[0],
TARGETS_ARE_SEQUENCES='sequence' in token_type,
VERBOSE=VERBOSE,
**dict(SN_kwargs)
)
# re-run to set the net's checkpoint_path
self.checkpoint_dir = checkpoint_dir