[FasterRCNN] Let viz not depend on coco

examples/FasterRCNN/coco.py

@@ -15,11 +15,13 @@ from tensorpack.utils.timer import timed_operation
 from tensorpack.utils.argtools import log_once
 
 from pycocotools.coco import COCO
+import config
 
 
 __all__ = ['COCODetection', 'COCOMeta']
 
 COCO_NUM_CATEGORY = 80
+config.NUM_CLASS = COCO_NUM_CATEGORY + 1
 
 
 class _COCOMeta(object):
@@ -49,6 +51,7 @@ class _COCOMeta(object):
             v: i + 1 for i, v in enumerate(cat_ids)}
         self.class_id_to_category_id = {
             v: k for k, v in self.category_id_to_class_id.items()}
+        config.CLASS_NAMES = self.class_names
 
 
 COCOMeta = _COCOMeta()

examples/FasterRCNN/config.py

@@ -9,6 +9,7 @@ BASEDIR = '/path/to/your/COCO/DIR'
 TRAIN_DATASET = ['train2014', 'valminusminival2014']
 VAL_DATASET = 'minival2014'   # only support evaluation on one dataset
 NUM_CLASS = 81
+CLASS_NAMES = []  # NUM_CLASS strings
 
 # basemodel ----------------------
 RESNET_NUM_BLOCK = [3, 4, 6, 3]     # resnet50

examples/FasterRCNN/utils/box_ops.py

@@ -12,6 +12,7 @@ This file is modified from
 https://github.com/tensorflow/models/blob/master/object_detection/core/box_list_ops.py
 """
 
+
 @under_name_scope()
 def area(boxes):
     """
@@ -73,7 +74,7 @@ def get_iou_callable():
     """
     Get a pairwise box iou callable.
     """
-    with tf.device('/cpu:0'):
+    with tf.Graph().as_default(), tf.device('/cpu:0'):
         A = tf.placeholder(tf.float32, shape=[None, 4])
         B = tf.placeholder(tf.float32, shape=[None, 4])
         iou = pairwise_iou(A, B)

examples/FasterRCNN/viz.py

@@ -8,8 +8,8 @@ import numpy as np
 from tensorpack.utils import viz
 from tensorpack.utils.palette import PALETTE_RGB
 
-from coco import COCOMeta
 from utils.box_ops import get_iou_callable
+import config
 
 
 def draw_annotation(img, boxes, klass, is_crowd=None):
@@ -18,13 +18,13 @@ def draw_annotation(img, boxes, klass, is_crowd=None):
     if is_crowd is not None:
         assert len(boxes) == len(is_crowd)
         for cls, crd in zip(klass, is_crowd):
-            clsname = COCOMeta.class_names[cls]
+            clsname = config.CLASS_NAMES[cls]
             if crd == 1:
                 clsname += ';Crowd'
             labels.append(clsname)
     else:
         for cls in klass:
-            labels.append(COCOMeta.class_names[cls])
+            labels.append(config.CLASS_NAMES[cls])
     img = viz.draw_boxes(img, boxes, labels)
     return img
 
@@ -59,7 +59,7 @@ def draw_predictions(img, boxes, scores):
         return img
     labels = scores.argmax(axis=1)
     scores = scores.max(axis=1)
-    tags = ["{},{:.2f}".format(COCOMeta.class_names[lb], score) for lb, score in zip(labels, scores)]
+    tags = ["{},{:.2f}".format(config.CLASS_NAMES[lb], score) for lb, score in zip(labels, scores)]
     return viz.draw_boxes(img, boxes, tags)
 
 
@@ -74,7 +74,7 @@ def draw_final_outputs(img, results):
     tags = []
     for label, _, score in results:
         tags.append(
-            "{},{:.2f}".format(COCOMeta.class_names[label], score))
+            "{},{:.2f}".format(config.CLASS_NAMES[label], score))
     boxes = np.asarray([x.box for x in results])
     return viz.draw_boxes(img, boxes, tags)