Python box_predictor_builder.build_convolutional_box_predictor方法代碼示例

本文整理匯總了Python中object_detection.builders.box_predictor_builder.build_convolutional_box_predictor方法的典型用法代碼示例。如果您正苦於以下問題：Python box_predictor_builder.build_convolutional_box_predictor方法的具體用法？Python box_predictor_builder.build_convolutional_box_predictor怎麽用？Python box_predictor_builder.build_convolutional_box_predictor使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.builders.box_predictor_builder的用法示例。

在下文中一共展示了box_predictor_builder.build_convolutional_box_predictor方法的6個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: test_get_boxes_for_five_aspect_ratios_per_location

# 需要導入模塊: from object_detection.builders import box_predictor_builder [as 別名]
# 或者: from object_detection.builders.box_predictor_builder import build_convolutional_box_predictor [as 別名]
def test_get_boxes_for_five_aspect_ratios_per_location(self):
    def graph_fn(image_features):
      conv_box_predictor = (
          box_predictor_builder.build_convolutional_box_predictor(
              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])

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:30，代碼來源:convolutional_box_predictor_test.py

示例2: test_get_boxes_for_one_aspect_ratio_per_location

# 需要導入模塊: from object_detection.builders import box_predictor_builder [as 別名]
# 或者: from object_detection.builders.box_predictor_builder import build_convolutional_box_predictor [as 別名]
def test_get_boxes_for_one_aspect_ratio_per_location(self):
    def graph_fn(image_features):
      conv_box_predictor = (
          box_predictor_builder.build_convolutional_box_predictor(
              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])

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:29，代碼來源:convolutional_box_predictor_test.py

示例3: test_get_multi_class_predictions_for_five_aspect_ratios_per_location

# 需要導入模塊: from object_detection.builders import box_predictor_builder [as 別名]
# 或者: from object_detection.builders.box_predictor_builder import build_convolutional_box_predictor [as 別名]
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_builder.build_convolutional_box_predictor(
              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])

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:35，代碼來源:convolutional_box_predictor_test.py

示例4: test_get_predictions_with_feature_maps_of_dynamic_shape

# 需要導入模塊: from object_detection.builders import box_predictor_builder [as 別名]
# 或者: from object_detection.builders.box_predictor_builder import build_convolutional_box_predictor [as 別名]
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_box_predictor(
            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)
    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/Conv2d_0_1x1_32/biases',
        'BoxPredictor/Conv2d_0_1x1_32/weights',
        'BoxPredictor/BoxEncodingPredictor/biases',
        'BoxPredictor/BoxEncodingPredictor/weights',
        'BoxPredictor/ClassPredictor/biases',
        'BoxPredictor/ClassPredictor/weights'])
    self.assertEqual(expected_variable_set, actual_variable_set)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:49，代碼來源:convolutional_box_predictor_test.py

示例5: test_use_depthwise_convolution

# 需要導入模塊: from object_detection.builders import box_predictor_builder [as 別名]
# 或者: from object_detection.builders.box_predictor_builder import build_convolutional_box_predictor [as 別名]
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_box_predictor(
            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,
            dropout_keep_prob=0.8,
            kernel_size=1,
            box_code_size=4,
            use_dropout=True,
            use_depthwise=True))
    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)
    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/Conv2d_0_1x1_32/biases',
        'BoxPredictor/Conv2d_0_1x1_32/weights',
        'BoxPredictor/BoxEncodingPredictor_depthwise/biases',
        'BoxPredictor/BoxEncodingPredictor_depthwise/depthwise_weights',
        'BoxPredictor/BoxEncodingPredictor/biases',
        'BoxPredictor/BoxEncodingPredictor/weights',
        'BoxPredictor/ClassPredictor_depthwise/biases',
        'BoxPredictor/ClassPredictor_depthwise/depthwise_weights',
        'BoxPredictor/ClassPredictor/biases',
        'BoxPredictor/ClassPredictor/weights'])
    self.assertEqual(expected_variable_set, actual_variable_set)

開發者ID:ahmetozlu，項目名稱:vehicle_counting_tensorflow，代碼行數:53，代碼來源:convolutional_box_predictor_test.py

示例6: test_use_depthwise_convolution

# 需要導入模塊: from object_detection.builders import box_predictor_builder [as 別名]
# 或者: from object_detection.builders.box_predictor_builder import build_convolutional_box_predictor [as 別名]
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_box_predictor(
            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,
            dropout_keep_prob=0.8,
            kernel_size=3,
            box_code_size=4,
            use_dropout=True,
            use_depthwise=True))
    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)
    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/Conv2d_0_1x1_32/biases',
        'BoxPredictor/Conv2d_0_1x1_32/weights',
        'BoxPredictor/BoxEncodingPredictor_depthwise/biases',
        'BoxPredictor/BoxEncodingPredictor_depthwise/depthwise_weights',
        'BoxPredictor/BoxEncodingPredictor/biases',
        'BoxPredictor/BoxEncodingPredictor/weights',
        'BoxPredictor/ClassPredictor_depthwise/biases',
        'BoxPredictor/ClassPredictor_depthwise/depthwise_weights',
        'BoxPredictor/ClassPredictor/biases',
        'BoxPredictor/ClassPredictor/weights'])
    self.assertEqual(expected_variable_set, actual_variable_set)

開發者ID:tensorflow，項目名稱:models，代碼行數:53，代碼來源:convolutional_box_predictor_tf1_test.py

注：本文中的object_detection.builders.box_predictor_builder.build_convolutional_box_predictor方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。