Python object_detection.core.box_predictor.ConvolutionalBoxPredictor() Examples
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Example #1
Source File: box_predictor_test.py From moveo_ros with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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)}) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1])
Example #2
Source File: box_predictor_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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)}) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1])
Example #3
Source File: box_predictor_test.py From object_detection_kitti with Apache License 2.0 | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=1, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 64, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 64, 1])
Example #4
Source File: box_predictor_test.py From Elphas with Apache License 2.0 | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[1], scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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])
Example #5
Source File: box_predictor_test.py From Elphas with Apache License 2.0 | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = np.random.rand(4, 8, 8, 64).astype(np.float32) def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[5], scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] return (box_encodings, class_predictions_with_background) (box_encodings, class_predictions_with_background) = self.execute(graph_fn, [image_features]) self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background.shape, [4, 320, num_classes_without_background+1])
Example #6
Source File: box_predictor_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 320, 1])
Example #7
Source File: box_predictor_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=1, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 64, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 64, 1])
Example #8
Source File: box_predictor_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, class_predictions_with_background_shape ) = sess.run([ tf.shape(box_encodings), tf.shape(class_predictions_with_background)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background_shape, [4, 320, num_classes_without_background+1])
Example #9
Source File: box_predictor_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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)}) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1])
Example #10
Source File: box_predictor_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 320, 1])
Example #11
Source File: box_predictor_test.py From object_detection_with_tensorflow with MIT License | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, class_predictions_with_background_shape ) = sess.run([ tf.shape(box_encodings), tf.shape(class_predictions_with_background)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background_shape, [4, 320, num_classes_without_background+1])
Example #12
Source File: box_predictor_test.py From Elphas with Apache License 2.0 | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[5], scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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, 320, 1, 4]) self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
Example #13
Source File: box_predictor_test.py From hands-detection with MIT License | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, class_predictions_with_background_shape ) = sess.run([ tf.shape(box_encodings), tf.shape(class_predictions_with_background)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background_shape, [4, 320, num_classes_without_background+1])
Example #14
Source File: box_predictor_test.py From hands-detection with MIT License | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=1, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 64, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 64, 1])
Example #15
Source File: box_predictor_test.py From hands-detection with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 320, 1])
Example #16
Source File: box_predictor_test.py From hands-detection with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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)}) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1])
Example #17
Source File: box_predictor_test.py From moveo_ros with MIT License | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, class_predictions_with_background_shape ) = sess.run([ tf.shape(box_encodings), tf.shape(class_predictions_with_background)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background_shape, [4, 320, num_classes_without_background+1])
Example #18
Source File: box_predictor_test.py From moveo_ros with MIT License | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=1, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 64, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 64, 1])
Example #19
Source File: box_predictor_test.py From moveo_ros with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 320, 1])
Example #20
Source File: box_predictor_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = np.random.rand(4, 8, 8, 64).astype(np.float32) def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[5], scope='BoxPredictor') box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) class_predictions_with_background = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (box_encodings, class_predictions_with_background) (box_encodings, class_predictions_with_background) = self.execute(graph_fn, [image_features]) self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background.shape, [4, 320, num_classes_without_background+1])
Example #21
Source File: box_predictor_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[1], scope='BoxPredictor') 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])
Example #22
Source File: box_predictor_test.py From ros_tensorflow with Apache License 2.0 | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[5], scope='BoxPredictor') 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, 320, 1, 4]) self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
Example #23
Source File: box_predictor_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = np.random.rand(4, 8, 8, 64).astype(np.float32) def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[5], scope='BoxPredictor') box_encodings = tf.concat( box_predictions[box_predictor.BOX_ENCODINGS], axis=1) class_predictions_with_background = tf.concat( box_predictions[box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND], axis=1) return (box_encodings, class_predictions_with_background) (box_encodings, class_predictions_with_background) = self.execute(graph_fn, [image_features]) self.assertAllEqual(box_encodings.shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background.shape, [4, 320, num_classes_without_background+1])
Example #24
Source File: box_predictor_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
def test_get_boxes_for_one_aspect_ratio_per_location(self): def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[1], scope='BoxPredictor') 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])
Example #25
Source File: box_predictor_test.py From Gun-Detector with Apache License 2.0 | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): def graph_fn(image_features): conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams_fn=self._build_arg_scope_with_conv_hyperparams(), 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.predict( [image_features], num_predictions_per_location=[5], scope='BoxPredictor') 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, 320, 1, 4]) self.assertAllEqual(objectness_predictions.shape, [4, 320, 1])
Example #26
Source File: box_predictor_test.py From tensorflow with BSD 2-Clause "Simplified" License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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)}) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1])
Example #27
Source File: box_predictor_test.py From tensorflow with BSD 2-Clause "Simplified" License | 5 votes |
def test_get_multi_class_predictions_for_five_aspect_ratios_per_location( self): num_classes_without_background = 6 image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=num_classes_without_background, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] class_predictions_with_background = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() with self.test_session() as sess: sess.run(init_op) (box_encodings_shape, class_predictions_with_background_shape ) = sess.run([ tf.shape(box_encodings), tf.shape(class_predictions_with_background)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(class_predictions_with_background_shape, [4, 320, num_classes_without_background+1])
Example #28
Source File: box_predictor_test.py From DOTA_models with Apache License 2.0 | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 320, 1])
Example #29
Source File: box_predictor_test.py From tensorflow with BSD 2-Clause "Simplified" License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location(self): image_features = tf.random_uniform([4, 8, 8, 64], dtype=tf.float32) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] init_op = tf.global_variables_initializer() 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)]) self.assertAllEqual(box_encodings_shape, [4, 320, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, 320, 1])
Example #30
Source File: box_predictor_test.py From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License | 5 votes |
def test_get_boxes_for_five_aspect_ratios_per_location_fully_convolutional( self): image_features = tf.placeholder(dtype=tf.float32, shape=[4, None, None, 64]) conv_box_predictor = box_predictor.ConvolutionalBoxPredictor( is_training=False, num_classes=0, conv_hyperparams=self._build_arg_scope_with_conv_hyperparams(), 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.predict( image_features, num_predictions_per_location=5, scope='BoxPredictor') box_encodings = box_predictions[box_predictor.BOX_ENCODINGS] objectness_predictions = box_predictions[ box_predictor.CLASS_PREDICTIONS_WITH_BACKGROUND] 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)}) self.assertAllEqual(box_encodings_shape, [4, expected_num_anchors, 1, 4]) self.assertAllEqual(objectness_predictions_shape, [4, expected_num_anchors, 1])