Fix some error when runing with Synopsys Caffe, but still not running

Fixed error:
1. AttributeError: 'ProposalLayer' object has no attribute 'param_str_'
2. pre_nms_topN = cfg[cfg_key].RPN_PRE_NMS_TOP_N, KeyError: '1'
3. cached dataset not removed error, which will cause the test phase accuracy calculation fail

Reference:
rbgirshick#219

We need to manually remove the cached dataset in data/cache and data/VOCdevkit0712/annotations_cache when you change the train or test list in data/VOCdevkit0712/VOC0712/ImageSets/Main/

Current still facing issue
I0530 09:49:44.784348  8794 sgd_solver.cpp:144] Iteration 80, lr = 0.0005
F0530 09:50:07.775352  8794 io.cpp:87] Check failed: proto.SerializeToOstream(&output)
*** Check failure stack trace: ***
Aborted (core dumped)

Signed-off-by: Huaqi Fang <[email protected]>

lib/datasets/voc_eval.py

@@ -122,7 +122,11 @@ def voc_eval(detpath,
     # extract gt objects for this class
     class_recs = {}
     npos = 0
+    #print "cachedir : {}".format(cachedir)
+    #print "imagenames " +str(imagenames)
     for imagename in imagenames:
+        #for obj in recs[imagename]:
+        #    print "Rec " +str(obj)
         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)

lib/roi_data_layer/layer.py

@@ -84,7 +84,7 @@ def setup(self, bottom, top):
         """Setup the RoIDataLayer."""
 
         # parse the layer parameter string, which must be valid YAML
-        layer_params = yaml.load(self.param_str_)
+        layer_params = yaml.load(self.param_str)
 
         self._num_classes = layer_params['num_classes']
 

lib/rpn/anchor_target_layer.py

@@ -24,7 +24,7 @@ class AnchorTargetLayer(caffe.Layer):
     """
 
     def setup(self, bottom, top):
-        layer_params = yaml.load(self.param_str_)
+        layer_params = yaml.load(self.param_str)
         anchor_scales = layer_params.get('scales', (8, 16, 32))
         self._anchors = generate_anchors(scales=np.array(anchor_scales))
         self._num_anchors = self._anchors.shape[0]

lib/rpn/proposal_layer.py

@@ -23,7 +23,7 @@ class ProposalLayer(caffe.Layer):
 
     def setup(self, bottom, top):
         # parse the layer parameter string, which must be valid YAML
-        layer_params = yaml.load(self.param_str_)
+        layer_params = yaml.load(self.param_str)
 
         self._feat_stride = layer_params['feat_stride']
         anchor_scales = layer_params.get('scales', (8, 16, 32))
@@ -61,7 +61,7 @@ def forward(self, bottom, top):
         assert bottom[0].data.shape[0] == 1, \
             'Only single item batches are supported'
 
-        cfg_key = str(self.phase) # either 'TRAIN' or 'TEST'
+        cfg_key = 'TEST' if self.phase == 1 else 'TRAIN' # either 'TRAIN' or 'TEST'
         pre_nms_topN  = cfg[cfg_key].RPN_PRE_NMS_TOP_N
         post_nms_topN = cfg[cfg_key].RPN_POST_NMS_TOP_N
         nms_thresh    = cfg[cfg_key].RPN_NMS_THRESH

lib/rpn/proposal_target_layer.py

@@ -22,7 +22,7 @@ class ProposalTargetLayer(caffe.Layer):
     """
 
     def setup(self, bottom, top):
-        layer_params = yaml.load(self.param_str_)
+        layer_params = yaml.load(self.param_str)
         self._num_classes = layer_params['num_classes']
 
         # sampled rois (0, x1, y1, x2, y2)

lib/setup.py

@@ -30,21 +30,24 @@ def locate_cuda():
     Returns a dict with keys 'home', 'nvcc', 'include', and 'lib64'
     and values giving the absolute path to each directory.
 
-    Starts by looking for the CUDAHOME env variable. If not found, everything
+    Starts by looking for the CUDAHOME or CUDA_HOME env variable. If not found, everything
     is based on finding 'nvcc' in the PATH.
     """
 
-    # first check if the CUDAHOME env variable is in use
+    # first check if the CUDAHOME or CUDA_HOME env variable is in use
     if 'CUDAHOME' in os.environ:
         home = os.environ['CUDAHOME']
         nvcc = pjoin(home, 'bin', 'nvcc')
+    elif 'CUDA_HOME' in os.environ:
+        home = os.environ['CUDA_HOME']
+        nvcc = pjoin(home, 'bin', 'nvcc')
     else:
         # otherwise, search the PATH for NVCC
         default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
         nvcc = find_in_path('nvcc', os.environ['PATH'] + os.pathsep + default_path)
         if nvcc is None:
             raise EnvironmentError('The nvcc binary could not be '
-                'located in your $PATH. Either add it to your path, or set $CUDAHOME')
+                'located in your $PATH. Either add it to your path, or set $CUDA_HOME or $CUDAHOME')
         home = os.path.dirname(os.path.dirname(nvcc))
 
     cudaconfig = {'home':home, 'nvcc':nvcc,