def test_prediction_size(self):
mask_prediction_head = (
keras_mask_head.WeightSharedConvolutionalMaskHead(
num_classes=20,
num_predictions_per_location=1,
conv_hyperparams=self._build_conv_hyperparams(),
mask_height=7,
mask_width=7))
image_feature = tf.random_uniform([64, 17, 19, 1024],
minval=-10.0,
maxval=10.0,
dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
self.assertAllEqual([64, 323, 20, 7, 7],
mask_predictions.get_shape().as_list())
def test_class_agnostic_prediction_size(self):
mask_prediction_head = (
keras_mask_head.WeightSharedConvolutionalMaskHead(
num_classes=20,
num_predictions_per_location=1,
conv_hyperparams=self._build_conv_hyperparams(),
mask_height=7,
mask_width=7,
masks_are_class_agnostic=True))
def graph_fn():
image_feature = tf.random_uniform(
[64, 17, 19, 1024], minval=-10.0, maxval=10.0, dtype=tf.float32)
mask_predictions = mask_prediction_head(image_feature)
return mask_predictions
mask_predictions = self.execute(graph_fn, [])
self.assertAllEqual([64, 323, 1, 7, 7], mask_predictions.shape)
def graph_fn(image_features):
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=box_code_size,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
class_prediction_head = keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=num_classes_without_background + 1,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
other_heads = {
other_head_name:
keras_mask_head.WeightSharedConvolutionalMaskHead(
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location,
mask_height=mask_height,
mask_width=mask_width)
}
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
box_prediction_head=box_prediction_head,
class_prediction_head=class_prediction_head,
other_heads=other_heads,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
depth=32,
num_layers_before_predictor=2)
box_predictions = conv_box_predictor([image_features])
for key, value in box_predictions.items():
box_predictions[key] = tf.concat(value, axis=1)
assert len(box_predictions) == 3
return (box_predictions[box_predictor.BOX_ENCODINGS],
box_predictions[
box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
box_predictions[other_head_name])
def test_other_heads_predictions(self):
box_code_size = 4
num_classes_without_background = 3
other_head_name = 'Mask'
mask_height = 5
mask_width = 5
num_predictions_per_location = 5
box_prediction_head = keras_box_head.WeightSharedConvolutionalBoxHead(
box_code_size=box_code_size,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
class_prediction_head = keras_class_head.WeightSharedConvolutionalClassHead(
num_class_slots=num_classes_without_background + 1,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location)
other_heads = {
other_head_name:
keras_mask_head.WeightSharedConvolutionalMaskHead(
num_classes=num_classes_without_background,
conv_hyperparams=self._build_conv_hyperparams(),
num_predictions_per_location=num_predictions_per_location,
mask_height=mask_height,
mask_width=mask_width)
}
conv_box_predictor = box_predictor.WeightSharedConvolutionalBoxPredictor(
is_training=False,
num_classes=num_classes_without_background,
box_prediction_head=box_prediction_head,
class_prediction_head=class_prediction_head,
other_heads=other_heads,
conv_hyperparams=self._build_conv_hyperparams(),
freeze_batchnorm=False,
inplace_batchnorm_update=False,
depth=32,
num_layers_before_predictor=2)
def graph_fn(image_features):
box_predictions = conv_box_predictor([image_features])
for key, value in box_predictions.items():
box_predictions[key] = tf.concat(value, axis=1)
assert len(box_predictions) == 3
return (box_predictions[box_predictor.BOX_ENCODINGS],
box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND],
box_predictions[other_head_name])
batch_size = 4
feature_ht = 8
feature_wt = 8
image_features = np.random.rand(batch_size, feature_ht, feature_wt,
64).astype(np.float32)
(box_encodings, class_predictions, other_head_predictions) = self.execute(
graph_fn, [image_features])
num_anchors = feature_ht * feature_wt * num_predictions_per_location
self.assertAllEqual(box_encodings.shape,
[batch_size, num_anchors, box_code_size])
self.assertAllEqual(
class_predictions.shape,
[batch_size, num_anchors, num_classes_without_background + 1])
self.assertAllEqual(other_head_predictions.shape, [
batch_size, num_anchors, num_classes_without_background, mask_height,
mask_width
])
