code: def setUp(self):
      super(FasterRCNNNetworkTest, self).setUp()
      # Setup
      self.config = EasyDict(
          {
              "train": {
                  "debug": True,
                  "seed": None,
              },
              "model": {
                  "network": {"num_classes": 20, "with_rcnn": True},
                  "anchors": {
                      "base_size": 256,
                      "scales": [0.5, 1, 2],
                      "ratios": [0.5, 1, 2],
                      "stride": 16,
                  },
                  "loss": {
                      "rpn_cls_loss_weight": 1.0,
                      "rpn_reg_loss_weights": 2.0,
                      "rcnn_cls_loss_weight": 1.0,
                      "rcnn_reg_loss_weights": 2.0,
                  },
                  "base_network": {
                      "architecture": "vgg_16",
                      "trainable": True,
                      "endpoint": "conv5/conv5_1",
                      "download": False,
                      "fine_tune_from": "conv4/conv4_2",
                      "freeze_tail": False,
                      "use_tail": True,
                      "arg_scope": {
                          "weight_decay": 0.0005,
                      },
                  },
                  "rcnn": {
                      "enabled": True,
                      "layer_sizes": [4096, 4096],
                      "dropout_keep_prob": 1.0,
                      "activation_function": "relu6",
                      "l2_regularization_scale": 0.0005,
                      "l1_sigma": 3.0,
                      "use_mean": False,
                      "target_normalization_variances": [1.0, 1.0],
                      "rcnn_initializer": {
                          "type": "variance_scaling_initializer",
                          "factor": 1.0,
                          "uniform": True,
                          "mode": "FAN_AVG",
                      },
                      "bbox_initializer": {
                          "type": "variance_scaling_initializer",
                          "factor": 1.0,
                          "uniform": True,
                          "mode": "FAN_AVG",
                      },
                      "cls_initializer": {
                          "type": "variance_scaling_initializer",
                          "factor": 1.0,
                          "uniform": True,
                          "mode": "FAN_AVG",
                      },
                      "roi": {
                          "pooling_mode": "crop",
                          "pooled_width": 7,
                          "pooled_height": 7,
                          "padding": "VALID",
                      },
                      "proposals": {
                          "class_max_detections": 100,
                          "class_nms_threshold": 0.6,
                          "total_max_detections": 300,
                          "min_prob_threshold": 0.0,
                      },
                      "target": {
                          "foreground_fraction": 0.25,
                          "minibatch_size": 64,
                          "foreground_threshold": 0.5,
                          "background_threshold_high": 0.5,
                          "background_threshold_low": 0.1,
                      },
                  },
                  "rpn": {
                      "num_channels": 512,
                      "kernel_shape": [3, 3],
                      "rpn_initializer": {
                          "type": "variance_scaling_initializer",
                          "factor": 1.0,
                          "mode": "FAN_AVG",
                          "uniform": True,
                      },
                      "cls_initializer": {
                          "type": "truncated_normal_initializer",
                          "mean": 0.0,
                          "stddev": 0.01,
                      },
                      "bbox_initializer": {
                          "type": "truncated_normal_initializer",
                          "mean": 0.0,
                          "stddev": 0.01,
                      },
                      "activation_function": "relu6",
                      "l2_regularization_scale": 0.0005,
                      "l1_sigma": 3.0,
                      "proposals": {
                          "pre_nms_top_n": 12000,
                          "post_nms_top_n": 2000,
                          "nms_threshold": 0.6,
                          "min_size": 0,
                          "clip_after_nms": False,
                          "filter_outside_anchors": False,
                          "apply_nms": True,
                          "min_prob_threshold": 0.0,
                      },
                      "target": {
                          "allowed_border": 0,
                          "clobber_positives": False,
                          "foreground_threshold": 0.7,
                          "background_threshold_high": 0.3,
                          "background_threshold_low": 0.0,
                          "foreground_fraction": 0.5,
                          "minibatch_size": 256,
                      },
                  },
              },
          }
      )
  
      self.image_size = (600, 800)
      self.image = np.random.randint(
          low=0, high=255, size=(600, 800, 3)
      )
      self.gt_boxes = np.array(
          [
              [10, 10, 26, 28, 1],
              [10, 10, 20, 22, 1],
              [10, 11, 20, 21, 1],
              [19, 30, 31, 33, 1],
          ]
      )
      tf.reset_default_graph()
  
file_path: luminoth/luminoth/models/fasterrcnn/fasterrcnn_test.py
imports: import numpy
  import tensorflow
  from easydict import EasyDict
  from luminoth.models.fasterrcnn import FasterRCNN
  from luminoth.utils.bbox_transform import clip_boxes
label_desc: Use image node_id 1 for calling the FasterRCNNNetworkTest obj's underlying member method code with example usage: obj.setUp() without return types