def evaluate_tflite(
self, tflite_filepath: str,
data: object_detector_dataloader.DataLoader) -> Dict[str, float]:
"""Evaluate the TFLite model."""
ds = data.gen_dataset(self.model_spec, batch_size=1, is_training=False)
return self.model_spec.evaluate_tflite(tflite_filepath, ds, len(data),
data.annotations_json_file)
def _get_dataset_and_steps(
self,
data: object_detector_dataloader.DataLoader,
batch_size: int,
is_training: bool,
) -> Tuple[Optional[tf.data.Dataset], int, Optional[str]]:
"""Gets dataset, steps and annotations json file."""
if not data:
return None, 0, None
# TODO(b/171449557): Put this into DataLoader.
dataset = data.gen_dataset(
self.model_spec, batch_size, is_training=is_training)
steps = len(data) // batch_size
return dataset, steps, data.annotations_json_file
def evaluate(self,
data: object_detector_dataloader.DataLoader,
batch_size: Optional[int] = None) -> Dict[str, float]:
"""Evaluates the model."""
batch_size = batch_size if batch_size else self.model_spec.batch_size
# Not to drop the smaller batch to evaluate the whole dataset.
self.model_spec.config.drop_remainder = False
ds = data.gen_dataset(self.model_spec, batch_size, is_training=False)
steps = (len(data) + batch_size - 1) // batch_size
# TODO(b/171449557): Upstream this to the parent class.
if steps <= 0:
raise ValueError('The size of the validation_data (%d) couldn\'t be '
'smaller than batch_size (%d). To solve this problem, '
'set the batch_size smaller or increase the size of the '
'validation_data.' % (len(data), batch_size))
eval_metrics = self.model_spec.evaluate(self.model, ds, steps,
data.annotations_json_file)
# Set back drop_remainder=True since it must be True during training.
# Otherwise it will fail.
self.model_spec.config.drop_remainder = True
return eval_metrics
