def load(cls, path: str, reset_paths=True, verbose=True):
model = super().load(path=path, reset_paths=reset_paths, verbose=verbose)
if model._load_model:
try_import_autogluon_vision()
from autogluon.vision import ImagePredictor
model.model = ImagePredictor.load(os.path.join(path, cls.nn_model_name))
model._load_model = None
return model
def _fit(self,
X: pd.DataFrame,
y: pd.Series,
X_val: Optional[pd.DataFrame] = None,
y_val: Optional[pd.Series] = None,
time_limit: Optional[int] = None,
sample_weight=None,
verbosity=2,
**kwargs):
try_import_mxnet()
try_import_autogluon_vision()
from autogluon.vision import ImagePredictor
params = self._get_model_params()
X = self.preprocess(X, fit=True)
if X_val is not None:
X_val = self.preprocess(X_val)
if sample_weight is not None: # TODO: support
logger.log(
15,
"\tsample_weight not yet supported for ImagePredictorModel, this model will ignore them in training."
)
X = X.reset_index(drop=True)
y = y.reset_index(drop=True)
if X_val is not None:
X_val = X_val.reset_index(drop=True)
y_val = y_val.reset_index(drop=True)
X[self._label_column_name] = y
if X_val is not None:
X_val[self._label_column_name] = y_val
verbosity_image = max(0, verbosity - 1)
root_logger = logging.getLogger()
root_log_level = root_logger.level
# TODO: ImagePredictor doesn't use problem_type in any way at present.
# It also doesn't error or warn if problem_type is not one it expects.
self.model = ImagePredictor(
problem_type=self.problem_type,
path=self.path,
# eval_metric=self.eval_metric, # TODO: multiclass/binary vision problem works only with accuracy, regression with rmse
verbosity=verbosity_image)
logger.log(15, f'\tHyperparameters: {params}')
# FIXME: ImagePredictor crashes if given float time_limit
if time_limit is not None:
time_limit = int(time_limit)
self.model.fit(train_data=X,
tuning_data=X_val,
time_limit=time_limit,
hyperparameters=params,
random_state=0)
# self.model.set_verbosity(verbosity) # TODO: How to set verbosity of fit predictor?
root_logger.setLevel(root_log_level) # Reset log level
def _fit(self,
X: pd.DataFrame,
y: pd.Series,
X_val: Optional[pd.DataFrame] = None,
y_val: Optional[pd.Series] = None,
time_limit: Optional[int] = None,
sample_weight=None,
verbosity=2,
**kwargs):
# try_import_mxnet()
try_import_autogluon_vision()
from autogluon.vision import ImagePredictor
params = self._get_model_params()
X = self.preprocess(X, fit=True)
if X_val is not None:
X_val = self.preprocess(X_val)
if sample_weight is not None: # TODO: support
logger.log(
15,
"\tsample_weight not yet supported for ImagePredictorModel, this model will ignore them in training."
)
X = X.reset_index(drop=True)
y = y.reset_index(drop=True)
if X_val is not None:
X_val = X_val.reset_index(drop=True)
y_val = y_val.reset_index(drop=True)
X[self._label_column_name] = y
if X_val is not None:
X_val[self._label_column_name] = y_val
null_indices = X['image'] == ''
# TODO: Consider some kind of weighting of the two options so there isn't a harsh cutoff at 50
# FIXME: What if all rows in a class are null? Will probably crash.
if null_indices.sum() > 50:
self._dummy_pred_proba = self._compute_dummy_pred_proba(
y[null_indices]) # FIXME: Do this one for better results
else:
# Not enough null to get a confident estimate of null label average, instead use all data average
self._dummy_pred_proba = self._compute_dummy_pred_proba(y)
if null_indices.sum() > 0:
X = X[~null_indices]
if X_val is not None:
null_indices_val = X_val['image'] == ''
if null_indices_val.sum() > 0:
X_val = X_val[~null_indices_val]
verbosity_image = max(0, verbosity - 1)
# TODO: ImagePredictor doesn't use problem_type in any way at present.
# It also doesn't error or warn if problem_type is not one it expects.
self.model = ImagePredictor(
problem_type=self.problem_type,
path=self.path,
# eval_metric=self.eval_metric, # TODO: multiclass/binary vision problem works only with accuracy, regression with rmse
verbosity=verbosity_image)
logger.log(15, f'\tHyperparameters: {params}')
# FIXME: ImagePredictor crashes if given float time_limit
if time_limit is not None:
time_limit = int(time_limit)
self.model.fit(train_data=X,
tuning_data=X_val,
time_limit=time_limit,
hyperparameters=params,
random_state=0)
def _get_default_resources(self):
num_cpus = get_cpu_count()
try_import_autogluon_vision()
from autogluon.vision import ImagePredictor
num_gpus = ImagePredictor._get_num_gpus_available()
return num_cpus, num_gpus
def _get_default_searchspace(self):
try_import_autogluon_vision()
from autogluon.vision.configs import presets_configs
searchspace = presets_configs.preset_image_predictor[
'good_quality_fast_inference']['hyperparameters']
return searchspace
