Do not mutate cfg.TEST.SCALE, cfg.TEST.MAX_SIZE

Reviewed By: ir413

Differential Revision: D7148427

fbshipit-source-id: 25b755c75ddc59cff7a1dc5a5cddb139c44f7cbf

configs/test_time_aug/e2e_mask_rcnn_R-50-FPN_2x.yaml

@@ -43,7 +43,7 @@ TEST:
   NMS: 0.5
   RPN_PRE_NMS_TOP_N: 1000  # Per FPN level
   RPN_POST_NMS_TOP_N: 1000
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/35857389/12_2017_baselines/e2e_faster_rcnn_R-50-FPN_2x.yaml.01_37_22.KSeq0b5q/output/train/coco_2014_train%3Acoco_2014_valminusminival/generalized_rcnn/model_final.pkl
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/35859007/12_2017_baselines/e2e_mask_rcnn_R-50-FPN_2x.yaml.01_49_07.By8nQcCH/output/train/coco_2014_train:coco_2014_valminusminival/generalized_rcnn/model_final.pkl
 
   # -- Test time augmentation example -- #
   BBOX_AUG:

configs/test_time_aug/keypoint_rcnn_R-50-FPN_1x.yaml

@@ -47,7 +47,7 @@ TEST:
   SCALE: 800
   MAX_SIZE: 1333
   NMS: 0.5
-  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/37651887/12_2017_baselines/keypoint_rcnn_R-50-FPN_s1x.yaml.20_01_40.FDjUQ7VX/output/train/keypoints_coco_2014_train%3Akeypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
+  WEIGHTS: https://s3-us-west-2.amazonaws.com/detectron/37651887/12_2017_baselines/keypoint_rcnn_R-50-FPN_s1x.yaml.20_01_40.FDjUQ7VX/output/train/keypoints_coco_2014_train:keypoints_coco_2014_valminusminival/generalized_rcnn/model_final.pkl
 
   # -- Test time augmentation example -- #
   BBOX_AUG:

lib/core/rpn_generator.py

@@ -187,7 +187,7 @@ def im_proposals(model, im):
     """Generate RPN proposals on a single image."""
     inputs = {}
     inputs['data'], im_scale, inputs['im_info'] = \
-        blob_utils.get_image_blob_for_inference(im)
+        blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
     for k, v in inputs.items():
         workspace.FeedBlob(core.ScopedName(k), v.astype(np.float32, copy=False))
     workspace.RunNet(model.net.Proto().name)

lib/core/test_retinanet.py

@@ -77,7 +77,7 @@ def im_detect_bbox(model, im, timers=None):
     A = cfg.RETINANET.SCALES_PER_OCTAVE * len(cfg.RETINANET.ASPECT_RATIOS)
     inputs = {}
     inputs['data'], im_scale, inputs['im_info'] = \
-        blob_utils.get_image_blob_for_inference(im)
+        blob_utils.get_image_blob(im, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE)
     cls_probs, box_preds = [], []
     for lvl in range(k_min, k_max + 1):
         suffix = 'fpn{}'.format(lvl)

lib/roi_data/minibatch.py

@@ -107,8 +107,8 @@ def _get_image_blob(roidb):
         im, im_scale = blob_utils.prep_im_for_blob(
             im, cfg.PIXEL_MEANS, target_size, cfg.TRAIN.MAX_SIZE
         )
-        im_scales.append(im_scale[0])
-        processed_ims.append(im[0])
+        im_scales.append(im_scale)
+        processed_ims.append(im)
 
     # Create a blob to hold the input images
     blob = blob_utils.im_list_to_blob(processed_ims)

lib/utils/blob.py

@@ -37,8 +37,8 @@ from caffe2.proto import caffe2_pb2
 from core.config import cfg
 
 
-def get_image_blob_for_inference(im):
-    """Converts an image into a network input.
+def get_image_blob(im, target_scale, target_max_size):
+    """Convert an image into a network input.
 
     Arguments:
         im (ndarray): a color image in BGR order
@@ -49,7 +49,7 @@ def get_image_blob_for_inference(im):
         im_info (ndarray)
     """
     processed_im, im_scale = prep_im_for_blob(
-        im, cfg.PIXEL_MEANS, cfg.TEST.SCALE, cfg.TEST.MAX_SIZE
+        im, cfg.PIXEL_MEANS, target_scale, target_max_size
     )
     blob = im_list_to_blob(processed_im)
     # NOTE: this height and width may be larger than actual scaled input image