misc cleanup

tools/reval.py

 #!/usr/bin/env python
 
 import _init_paths
-import fast_rcnn as frc
+from fast_rcnn.test import apply_nms
 from fast_rcnn.config import cfg
 from datasets.factory import get_imdb
 import cPickle
@@ -36,7 +36,7 @@ def from_dets(imdb_name, output_dir):
         dets = cPickle.load(f)
 
     print 'Applying NMS to all detections'
-    nms_dets = frc.test.apply_nms(dets, cfg.TEST.NMS)
+    nms_dets = apply_nms(dets, cfg.TEST.NMS)
 
     print 'Evaluating detections'
     imdb.evaluate_detections(nms_dets, output_dir)

tools/test_net.py

@@ -8,7 +8,7 @@
 # --------------------------------------------------------
 
 import _init_paths
-import fast_rcnn as frc
+from fast_rcnn.test import test_net
 from fast_rcnn.config import cfg, cfg_from_file
 from datasets.factory import get_imdb
 import caffe
@@ -47,7 +47,6 @@ def parse_args():
     args = parser.parse_args()
     return args
 
-
 if __name__ == '__main__':
     args = parse_args()
 
@@ -76,4 +75,4 @@ if __name__ == '__main__':
         if 'cleanup' in imdb.config:
             imdb.config['cleanup'] = False
 
-    frc.test.test_net(net, imdb)
+    test_net(net, imdb)

tools/train_net.py

@@ -65,11 +65,11 @@ if __name__ == '__main__':
     if args.gpu_id is not None:
         caffe.set_device(args.gpu_id)
 
-    imdb_train = get_imdb(args.imdb_name)
-    print 'Loaded dataset `{:s}` for training'.format(imdb_train.name)
-    roidb = get_training_roidb(imdb_train)
+    imdb = get_imdb(args.imdb_name)
+    print 'Loaded dataset `{:s}` for training'.format(imdb.name)
+    roidb = get_training_roidb(imdb)
 
-    output_dir = get_output_dir(imdb_train, None)
+    output_dir = get_output_dir(imdb, None)
     print 'Output will be saved to `{:s}`'.format(output_dir)
 
     train_net(args.solver, roidb, output_dir,

tools/train_svms.py

@@ -327,17 +327,16 @@ if __name__ == '__main__':
     out = os.path.splitext(os.path.basename(args.caffemodel))[0] + '_svm'
     out_dir = os.path.dirname(args.caffemodel)
 
-    imdb_train = get_imdb(args.imdb_name)
-    print 'Loaded dataset `{:s}` for training'.format(imdb_train.name)
+    imdb = get_imdb(args.imdb_name)
+    print 'Loaded dataset `{:s}` for training'.format(imdb.name)
 
     # enhance roidb to contain flipped examples
     if cfg.TRAIN.USE_FLIPPED:
         print 'Appending horizontally-flipped training examples...'
-        imdb_train.append_flipped_roidb()
+        imdb.append_flipped_roidb()
         print 'done'
 
-    trainer = SVMTrainer(net, imdb_train)
-    trainer.train()
+    SVMTrainer(net, imdb).train()
 
     filename = '{}/{}.caffemodel'.format(out_dir, out)
     net.save(filename)