fix link.

snxmv · Sep 14, 2017 · d726834 · d726834
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diff --git a/README.md b/README.md
@@ -34,7 +34,7 @@ Approximate *baseline* [setup](https://github.com/endernewton/tf-faster-rcnn/blo
   - For COCO, we find the performance improving with more iterations (VGG16 350k/490k: 26.9, 600k/790k: 28.3, 900k/1190k: 29.5), and potentially better performance can be achieved with even more iterations.
   - For Resnets, we fix the first block (total 4) when fine-tuning the network, and only use ``crop_and_resize`` to resize the RoIs (7x7) without max-pool (which I find useless especially for COCO). The final feature maps are average-pooled for classification and regression. All batch normalization parameters are fixed. Weight decay is set to Renset101 default 1e-4. Learning rate for biases is not doubled.
   - For approximate [FPN](https://arxiv.org/abs/1612.03144) baseline setup we simply resize the image with 800 pixels, add 32^2 anchors, and take 1000 proposals during testing.
-  - Check out [here](http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](http://gs11655.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ) for the latest models, including longer COCO VGG16 models and Resnet ones.
+  - Check out [here](http://ladoga.graphics.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](http://xinlei.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/)/[here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ) for the latest models, including longer COCO VGG16 models and Resnet ones.
 
 ### Additional features
 Additional features not mentioned in the [report](https://arxiv.org/pdf/1702.02138.pdf) are added to make research life easier:
@@ -99,7 +99,7 @@ If you find it useful, the ``data/cache`` folder created on my side is also shar
   ./data/scripts/fetch_faster_rcnn_models.sh
   ```
   **Note**: if you cannot download the models through the link, or you want to try more models, you can check out the following solutions and optionally update the downloading script:
-  - Another server [here](http://gs11655.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/).
+  - Another server [here](http://xinlei.sp.cs.cmu.edu/xinleic/tf-faster-rcnn/).
   - Google drive [here](https://drive.google.com/open?id=0B1_fAEgxdnvJSmF3YUlZcHFqWTQ).
 
 2. Create a folder and a softlink to use the pre-trained model

diff --git a/experiments/scripts/test_faster_rcnn.sh b/experiments/scripts/test_faster_rcnn.sh
@@ -22,6 +22,13 @@ case ${DATASET} in
     ANCHORS="[8,16,32]"
     RATIOS="[0.5,1,2]"
     ;;
+  pascal_voc_diff)
+    TRAIN_IMDB="voc_2007_trainval"
+    TEST_IMDB="voc_2007_test_diff"
+    ITERS=70000
+    ANCHORS="[8,16,32]"
+    RATIOS="[0.5,1,2]"
+    ;;
   pascal_voc_0712)
     TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval"
     TEST_IMDB="voc_2007_test"

diff --git a/experiments/scripts/train_faster_rcnn.sh b/experiments/scripts/train_faster_rcnn.sh
@@ -23,6 +23,14 @@ case ${DATASET} in
     ANCHORS="[8,16,32]"
     RATIOS="[0.5,1,2]"
     ;;
+  pascal_voc_diff)
+    TRAIN_IMDB="voc_2007_trainval"
+    TEST_IMDB="voc_2007_test_diff"
+    STEPSIZE="[50000]"
+    ITERS=70000
+    ANCHORS="[8,16,32]"
+    RATIOS="[0.5,1,2]"
+    ;;
   pascal_voc_0712)
     TRAIN_IMDB="voc_2007_trainval+voc_2012_trainval"
     TEST_IMDB="voc_2007_test"
@@ -51,35 +59,35 @@ echo Logging output to "$LOG"
 
 set +x
 if [[ ! -z  ${EXTRA_ARGS_SLUG}  ]]; then
-    NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt
+  NET_FINAL=output/${NET}/${TRAIN_IMDB}/${EXTRA_ARGS_SLUG}/${NET}_faster_rcnn_iter_${ITERS}.ckpt
 else
-    NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt
+  NET_FINAL=output/${NET}/${TRAIN_IMDB}/default/${NET}_faster_rcnn_iter_${ITERS}.ckpt
 fi
 set -x
 
 if [ ! -f ${NET_FINAL}.index ]; then
-    if [[ ! -z  ${EXTRA_ARGS_SLUG}  ]]; then
-        CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \
-            --weight data/imagenet_weights/${NET}.ckpt \
-            --imdb ${TRAIN_IMDB} \
-            --imdbval ${TEST_IMDB} \
-            --iters ${ITERS} \
-            --cfg experiments/cfgs/${NET}.yml \
-            --tag ${EXTRA_ARGS_SLUG} \
-            --net ${NET} \
-            --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \
-            TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS}
-    else
-        CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \
-            --weight data/imagenet_weights/${NET}.ckpt \
-            --imdb ${TRAIN_IMDB} \
-            --imdbval ${TEST_IMDB} \
-            --iters ${ITERS} \
-            --cfg experiments/cfgs/${NET}.yml \
-            --net ${NET} \
-            --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \
-            TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS}
-    fi
+  if [[ ! -z  ${EXTRA_ARGS_SLUG}  ]]; then
+    CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \
+      --weight data/imagenet_weights/${NET}.ckpt \
+      --imdb ${TRAIN_IMDB} \
+      --imdbval ${TEST_IMDB} \
+      --iters ${ITERS} \
+      --cfg experiments/cfgs/${NET}.yml \
+      --tag ${EXTRA_ARGS_SLUG} \
+      --net ${NET} \
+      --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \
+      TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS}
+  else
+    CUDA_VISIBLE_DEVICES=${GPU_ID} time python ./tools/trainval_net.py \
+      --weight data/imagenet_weights/${NET}.ckpt \
+      --imdb ${TRAIN_IMDB} \
+      --imdbval ${TEST_IMDB} \
+      --iters ${ITERS} \
+      --cfg experiments/cfgs/${NET}.yml \
+      --net ${NET} \
+      --set ANCHOR_SCALES ${ANCHORS} ANCHOR_RATIOS ${RATIOS} \
+      TRAIN.STEPSIZE ${STEPSIZE} ${EXTRA_ARGS}
+  fi
 fi
 
 ./experiments/scripts/test_faster_rcnn.sh $@
diff --git a/lib/datasets/factory.py b/lib/datasets/factory.py
@@ -22,6 +22,11 @@
     name = 'voc_{}_{}'.format(year, split)
     __sets[name] = (lambda split=split, year=year: pascal_voc(split, year))
 
+for year in ['2007', '2012']:
+  for split in ['train', 'val', 'trainval', 'test']:
+    name = 'voc_{}_{}_diff'.format(year, split)
+    __sets[name] = (lambda split=split, year=year: pascal_voc(split, year, use_diff=True))
+
 # Set up coco_2014_<split>
 for year in ['2014']:
   for split in ['train', 'val', 'minival', 'valminusminival', 'trainval']:

diff --git a/lib/datasets/pascal_voc.py b/lib/datasets/pascal_voc.py
@@ -24,12 +24,14 @@
 
 
 class pascal_voc(imdb):
-  def __init__(self, image_set, year, devkit_path=None):
-    imdb.__init__(self, 'voc_' + year + '_' + image_set)
+  def __init__(self, image_set, year, use_diff=False):
+    name = 'voc_' + year + '_' + image_set
+    if use_diff:
+      name += '_diff'
+    imdb.__init__(self, name)
     self._year = year
     self._image_set = image_set
-    self._devkit_path = self._get_default_path() if devkit_path is None \
-      else devkit_path
+    self._devkit_path = self._get_default_path()
     self._data_path = os.path.join(self._devkit_path, 'VOC' + self._year)
     self._classes = ('__background__',  # always index 0
                      'aeroplane', 'bicycle', 'bird', 'boat',
@@ -48,7 +50,7 @@ def __init__(self, image_set, year, devkit_path=None):
     # PASCAL specific config options
     self.config = {'cleanup': True,
                    'use_salt': True,
-                   'use_diff': False,
+                   'use_diff': use_diff,
                    'matlab_eval': False,
                    'rpn_file': None}
 
@@ -241,7 +243,7 @@ def _do_python_eval(self, output_dir='output'):
       filename = self._get_voc_results_file_template().format(cls)
       rec, prec, ap = voc_eval(
         filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
-        use_07_metric=use_07_metric)
+        use_07_metric=use_07_metric, use_diff=self.config['use_diff'])
       aps += [ap]
       print(('AP for {} = {:.4f}'.format(cls, ap)))
       with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:

diff --git a/lib/datasets/voc_eval.py b/lib/datasets/voc_eval.py
@@ -72,7 +72,8 @@ def voc_eval(detpath,
              classname,
              cachedir,
              ovthresh=0.5,
-             use_07_metric=False):
+             use_07_metric=False,
+             use_diff=False):
   """rec, prec, ap = voc_eval(detpath,
                               annopath,
                               imagesetfile,
@@ -133,7 +134,10 @@ def voc_eval(detpath,
   for imagename in imagenames:
     R = [obj for obj in recs[imagename] if obj['name'] == classname]
     bbox = np.array([x['bbox'] for x in R])
-    difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
+    if use_diff:
+      difficult = np.array([False for x in R]).astype(np.bool)
+    else:
+      difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
     det = [False] * len(R)
     npos = npos + sum(~difficult)
     class_recs[imagename] = {'bbox': bbox,