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Add option to load model weights from checkpoint before starting to t… #368

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7 changes: 6 additions & 1 deletion tensorflow_examples/lite/model_maker/core/task/model_spec/object_detector_spec.py
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Original file line number Diff line number Diff line change
Expand Up @@ -246,7 +246,8 @@ def train(self,
validation_steps: int,
epochs: Optional[int] = None,
batch_size: Optional[int] = None,
val_json_file: Optional[str] = None) -> tf.keras.Model:
val_json_file: Optional[str] = None,
load_checkpoint_path: Optional[str] = None) -> tf.keras.Model:
"""Run EfficientDet training."""
config = self.config
if not epochs:
Expand All @@ -263,6 +264,10 @@ def train(self,
batch_size=batch_size))
train.setup_model(model, config)
train.init_experimental(config)

if load_checkpoint_path is not None:
model.load_weights(load_checkpoint_path)

model.fit(
train_dataset,
epochs=epochs,
Expand Down
12 changes: 8 additions & 4 deletions tensorflow_examples/lite/model_maker/core/task/object_detector.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,7 +94,8 @@ def train(self,
validation_data: Optional[
object_detector_dataloader.DataLoader] = None,
epochs: Optional[int] = None,
batch_size: Optional[int] = None) -> tf.keras.Model:
batch_size: Optional[int] = None,
load_checkpoint_path: Optional[str] = None) -> tf.keras.Model:
"""Feeds the training data for training."""
if not self.model_spec.config.drop_remainder:
raise ValueError('Must set `drop_remainder=True` during training. '
Expand Down Expand Up @@ -122,7 +123,7 @@ def train(self,
validation_data, batch_size, is_training=False)
return self.model_spec.train(self.model, train_ds, steps_per_epoch,
validation_ds, validation_steps, epochs,
batch_size, val_json_file)
batch_size, val_json_file, load_checkpoint_path)

def evaluate(self,
data: object_detector_dataloader.DataLoader,
Expand Down Expand Up @@ -225,7 +226,8 @@ def create(cls,
epochs: Optional[object_detector_dataloader.DataLoader] = None,
batch_size: Optional[int] = None,
train_whole_model: bool = False,
do_train: bool = True) -> T:
do_train: bool = True,
load_checkpoint_path: Optional[str] = None) -> T:
"""Loads data and train the model for object detection.

Args:
Expand All @@ -238,6 +240,8 @@ def create(cls,
model. Otherwise, only train the layers that are not match
`model_spec.config.var_freeze_expr`.
do_train: Whether to run training.
load_checkpoint_path: Optional, Path to checkpoint to load model weights from,
before training is started.

Returns:
An instance based on ObjectDetector.
Expand All @@ -257,7 +261,7 @@ def create(cls,

if do_train:
tf.compat.v1.logging.info('Retraining the models...')
object_detector.train(train_data, validation_data, epochs, batch_size)
object_detector.train(train_data, validation_data, epochs, batch_size, load_checkpoint_path)
else:
object_detector.create_model()

Expand Down