[FasterRCNN] clip final boxes in the graph

examples/FasterRCNN/eval.py

@@ -12,7 +12,7 @@ from pycocotools.coco import COCO
 from pycocotools.cocoeval import COCOeval
 
 from coco import COCOMeta
-from common import clip_boxes, CustomResize
+from common import CustomResize
 import config
 
 DetectionResult = namedtuple(
@@ -43,7 +43,6 @@ def detect_one_image(img, model_func):
     scale = (resized_img.shape[0] * 1.0 / img.shape[0] + resized_img.shape[1] * 1.0 / img.shape[1]) / 2
     boxes, probs, labels = model_func(resized_img)
     boxes = boxes / scale
-    boxes = clip_boxes(boxes, img.shape[:2])
 
     results = [DetectionResult(*args) for args in zip(labels, boxes, probs)]
     return results

examples/FasterRCNN/model.py

@@ -14,6 +14,19 @@ from utils.box_ops import pairwise_iou
 import config
 
 
+@under_name_scope()
+def clip_boxes(boxes, window, name=None):
+    """
+    Args:
+        boxes: nx4, xyxy
+        window: [h, w]
+    """
+    boxes = tf.maximum(boxes, 0.0)
+    m = tf.tile(tf.reverse(window, [0]), [2])    # (4,)
+    boxes = tf.minimum(boxes, tf.to_float(m), name=name)
+    return boxes
+
+
 @layer_register(log_shape=True)
 def rpn_head(featuremap, channel, num_anchors):
     """
@@ -171,13 +184,6 @@ def generate_rpn_proposals(boxes, scores, img_shape):
         PRE_NMS_TOPK = config.TEST_PRE_NMS_TOPK
         POST_NMS_TOPK = config.TEST_POST_NMS_TOPK
 
-    @under_name_scope()
-    def clip_boxes(boxes, window):
-        boxes = tf.maximum(boxes, 0.0)
-        m = tf.tile(tf.reverse(window, [0]), [2])    # (4,)
-        boxes = tf.minimum(boxes, tf.to_float(m))
-        return boxes
-
     topk = tf.minimum(PRE_NMS_TOPK, tf.size(scores))
     topk_scores, topk_indices = tf.nn.top_k(scores, k=topk, sorted=False)
     topk_boxes = tf.gather(boxes, topk_indices)

examples/FasterRCNN/train.py

@@ -25,10 +25,10 @@ from coco import COCODetection
 from basemodel import (
     image_preprocess, pretrained_resnet_conv4, resnet_conv5)
 from model import (
-    decode_bbox_target, encode_bbox_target,
+    clip_boxes, decode_bbox_target, encode_bbox_target,
     rpn_head, rpn_losses,
-    generate_rpn_proposals, sample_fast_rcnn_targets,
-    roi_align, fastrcnn_head, fastrcnn_losses, fastrcnn_predictions)
+    generate_rpn_proposals, sample_fast_rcnn_targets, roi_align,
+    fastrcnn_head, fastrcnn_losses, fastrcnn_predictions)
 from data import (
     get_train_dataflow, get_eval_dataflow,
     get_all_anchors)
@@ -140,7 +140,7 @@ class Model(ModelDesc):
             decoded_boxes = decode_bbox_target(
                 fastrcnn_box_logits /
                 tf.constant(config.FASTRCNN_BBOX_REG_WEIGHTS), anchors)
-            decoded_boxes = tf.identity(decoded_boxes, name='fastrcnn_all_boxes')
+            decoded_boxes = clip_boxes(decoded_boxes, tf.shape(image)[:2], name='fastrcnn_all_boxes')
 
             # indices: Nx2. Each index into (#proposal, #category)
             pred_indices, final_probs = fastrcnn_predictions(decoded_boxes, label_probs)