def test_use_depthwise_convolution(self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = ( box_predictor_builder.build_convolutional_keras_box_predictor( is_training=False, num_classes=0, conv_hyperparams=self._build_conv_hyperparams(), freeze_batchnorm=False, inplace_batchnorm_update=False, num_predictions_per_location_list=[5], min_depth=0, max_depth=32, num_layers_before_predictor=1, use_dropout=True, dropout_keep_prob=0.8, kernel_size=1, box_code_size=4, use_depthwise=True)) box_predictions = conv_box_predictor([image_features]) box_encodings = tf.concat(box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) init_op = tf.global_variables_initializer() resolution = 32 expected_num_anchors = resolution * resolution * 5 with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, objectness_predictions_shape) = sess.run( [tf.shape(box_encodings), tf.shape(objectness_predictions)], feed_dict={ image_features: np.random.rand(4, resolution, resolution, 64) }) actual_variable_set = set( [var.op.name for var in tf.trainable_variables()]) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1]) expected_variable_set = set([ 'BoxPredictor/PreHeadConvolutions_0/Conv2d_0_1x1_32/bias', 'BoxPredictor/PreHeadConvolutions_0/Conv2d_0_1x1_32/kernel', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor_depthwise/' 'bias', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor_depthwise/' 'depthwise_kernel', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/bias', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/kernel', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor_depthwise/bias', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor_depthwise/' 'depthwise_kernel', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/bias', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/kernel' ]) self.assertEqual(expected_variable_set, actual_variable_set)
def test_get_boxes_for_one_aspect_ratio_per_location(self): conv_box_predictor = ( box_predictor_builder.build_convolutional_keras_box_predictor( is_training=False, num_classes=0, conv_hyperparams=self._build_conv_hyperparams(), freeze_batchnorm=False, inplace_batchnorm_update=False, num_predictions_per_location_list=[1], min_depth=0, max_depth=32, num_layers_before_predictor=1, use_dropout=True, dropout_keep_prob=0.8, kernel_size=1, box_code_size=4 )) def graph_fn(image_features): box_predictions = conv_box_predictor([image_features]) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions = tf.concat(box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (box_encodings, objectness_predictions) image_features = np.random.rand(4, 8, 8, 64).astype(np.float32) (box_encodings, objectness_predictions) = self.execute(graph_fn, [image_features]) self.assertAllEqual(box_encodings.shape, [4, 64, 1, 4]) self.assertAllEqual(objectness_predictions.shape, [4, 64, 1])
def test_get_predictions_with_feature_maps_of_dynamic_shape( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = ( box_predictor_builder.build_convolutional_keras_box_predictor( is_training=False, num_classes=0, conv_hyperparams=self._build_conv_hyperparams(), freeze_batchnorm=False, inplace_batchnorm_update=False, num_predictions_per_location_list=[5], min_depth=0, max_depth=32, num_layers_before_predictor=1, use_dropout=True, dropout_keep_prob=0.8, kernel_size=1, box_code_size=4 )) box_predictions = conv_box_predictor([image_features]) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) init_op = tf.global_variables_initializer() resolution = 32 expected_num_anchors = resolution*resolution*5 with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, objectness_predictions_shape) = sess.run( [tf.shape(box_encodings), tf.shape(objectness_predictions)], feed_dict={image_features: np.random.rand(4, resolution, resolution, 64)}) actual_variable_set = set( [var.op.name for var in tf.trainable_variables()]) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1]) expected_variable_set = set([ 'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/bias', 'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/kernel', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/bias', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/kernel', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/bias', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/kernel']) self.assertEqual(expected_variable_set, actual_variable_set) self.assertEqual(conv_box_predictor._sorted_head_names, ['box_encodings', 'class_predictions_with_background'])
def test_get_predictions_with_feature_maps_of_dynamic_shape( self): tf.keras.backend.clear_session() conv_box_predictor = ( box_predictor_builder.build_convolutional_keras_box_predictor( is_training=False, num_classes=0, conv_hyperparams=self._build_conv_hyperparams(), freeze_batchnorm=False, inplace_batchnorm_update=False, num_predictions_per_location_list=[5], min_depth=0, max_depth=32, num_layers_before_predictor=1, use_dropout=True, dropout_keep_prob=0.8, kernel_size=1, box_code_size=4 )) variables = [] def graph_fn(image_features): box_predictions = conv_box_predictor([image_features]) variables.extend(list(conv_box_predictor.variables)) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return box_encodings, objectness_predictions resolution = 32 expected_num_anchors = resolution*resolution*5 box_encodings, objectness_predictions = self.execute( graph_fn, [np.random.rand(4, resolution, resolution, 64)]) actual_variable_set = set([var.name.split(':')[0] for var in variables]) self.assertAllEqual(box_encodings.shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions.shape, [4, expected_num_anchors, 1]) expected_variable_set = set([ 'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/bias', 'BoxPredictor/SharedConvolutions_0/Conv2d_0_1x1_32/kernel', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/bias', 'BoxPredictor/ConvolutionalBoxHead_0/BoxEncodingPredictor/kernel', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/bias', 'BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/kernel']) self.assertEqual(expected_variable_set, actual_variable_set) self.assertEqual(conv_box_predictor._sorted_head_names, ['box_encodings', 'class_predictions_with_background'])
def graph_fn(image_features): conv_box_predictor = ( box_predictor_builder.build_convolutional_keras_box_predictor( is_training=False, num_classes=0, conv_hyperparams=self._build_conv_hyperparams(), freeze_batchnorm=False, inplace_batchnorm_update=False, num_predictions_per_location_list=[1], min_depth=0, max_depth=32, num_layers_before_predictor=1, use_dropout=True, dropout_keep_prob=0.8, kernel_size=1, box_code_size=4)) box_predictions = conv_box_predictor([image_features]) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions = tf.concat(box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (box_encodings, objectness_predictions)
def graph_fn(image_features): conv_box_predictor = ( box_predictor_builder.build_convolutional_keras_box_predictor( is_training=False, num_classes=0, conv_hyperparams=self._build_conv_hyperparams(), freeze_batchnorm=False, inplace_batchnorm_update=False, num_predictions_per_location_list=[1], min_depth=0, max_depth=32, num_layers_before_predictor=1, use_dropout=True, dropout_keep_prob=0.8, kernel_size=1, box_code_size=4 )) box_predictions = conv_box_predictor([image_features]) box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) objectness_predictions = tf.concat(box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (box_encodings, objectness_predictions)