Update for Pruning tool, include merged 2007 and 2012 VOC dataset sup…

…port
foss-for-synopsys-dwc-arc-processors · May 25, 2018 · 8e494b4 · 8e494b4
1 parent 781a917
commit 8e494b4
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Showing 5 changed files with 13 additions and 3 deletions.
diff --git a/lib/datasets/factory.py b/lib/datasets/factory.py
@@ -14,7 +14,7 @@
 import numpy as np
 
 # Set up voc_<year>_<split> using selective search "fast" mode
-for year in ['2007', '2012']:
+for year in ['2007', '2012', '0712']:
     for split in ['train', 'val', 'trainval', 'test']:
         name = 'voc_{}_{}'.format(year, split)
         __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))

diff --git a/lib/datasets/voc_eval.py b/lib/datasets/voc_eval.py
@@ -145,6 +145,12 @@ def voc_eval(detpath,
     # sort by confidence
     sorted_ind = np.argsort(-confidence)
     sorted_scores = np.sort(-confidence)
+    ###FIX added
+    if (len(sorted_ind)<1):
+      rec = 0
+      prec = 0
+      ap = 0
+      return rec, prec, ap
     BB = BB[sorted_ind, :]
     image_ids = [image_ids[x] for x in sorted_ind]
 

diff --git a/lib/fast_rcnn/config.py b/lib/fast_rcnn/config.py
@@ -41,7 +41,7 @@
 __C.TRAIN.MAX_SIZE = 1000
 
 # Images to use per minibatch
-__C.TRAIN.IMS_PER_BATCH = 2
+__C.TRAIN.IMS_PER_BATCH = 1
 
 # Minibatch size (number of regions of interest [ROIs])
 __C.TRAIN.BATCH_SIZE = 128
@@ -97,7 +97,7 @@
 __C.TRAIN.ASPECT_GROUPING = True
 
 # Use RPN to detect objects
-__C.TRAIN.HAS_RPN = False
+__C.TRAIN.HAS_RPN = True
 # IOU >= thresh: positive example
 __C.TRAIN.RPN_POSITIVE_OVERLAP = 0.7
 # IOU < thresh: negative example

diff --git a/lib/fast_rcnn/train.py b/lib/fast_rcnn/train.py
@@ -16,6 +16,7 @@
 
 from caffe.proto import caffe_pb2
 import google.protobuf as pb2
+import google.protobuf.text_format
 
 class SolverWrapper(object):
     """A simple wrapper around Caffe's solver.

diff --git a/lib/rpn/proposal_target_layer.py b/lib/rpn/proposal_target_layer.py
@@ -124,6 +124,8 @@ def _get_bbox_regression_labels(bbox_target_data, num_classes):
         cls = clss[ind]
         start = 4 * cls
         end = start + 4
+        start=int(start)
+        end=int(end)
         bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
         bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
     return bbox_targets, bbox_inside_weights
@@ -161,6 +163,7 @@ def _sample_rois(all_rois, gt_boxes, fg_rois_per_image, rois_per_image, num_clas
     # Guard against the case when an image has fewer than fg_rois_per_image
     # foreground RoIs
     fg_rois_per_this_image = min(fg_rois_per_image, fg_inds.size)
+    fg_rois_per_this_image=int(fg_rois_per_this_image)
     # Sample foreground regions without replacement
     if fg_inds.size > 0:
         fg_inds = npr.choice(fg_inds, size=fg_rois_per_this_image, replace=False)