add softmax on anchor fix #67

data/dataset.py

@@ -64,7 +64,7 @@ def preprocess(img, min_size=600, max_size=1000):
     scale2 = max_size / max(H, W)
     scale = min(scale1, scale2)
     img = img / 255.
-    img = sktsf.resize(img, (C, H * scale, W * scale), mode='reflect')
+    img = sktsf.resize(img, (C, H * scale, W * scale), mode='reflect',anti_aliasing=False)
     # both the longer and shorter should be less than
     # max_size and min_size
     if opt.caffe_pretrain:

model/region_proposal_network.py

@@ -112,8 +112,9 @@ class RegionProposalNetwork(nn.Module):
         rpn_locs = rpn_locs.permute(0, 2, 3, 1).contiguous().view(n, -1, 4)
         rpn_scores = self.score(h)
         rpn_scores = rpn_scores.permute(0, 2, 3, 1).contiguous()
+        rpn_softmax_scores = F.softmax(rpn_scores, dim=3)
         rpn_fg_scores = \
-            rpn_scores.view(n, hh, ww, n_anchor, 2)[:, :, :, :, 1].contiguous()
+            rpn_softmax_scores.view(n, hh, ww, n_anchor, 2)[:, :, :, :, 1].contiguous()
         rpn_fg_scores = rpn_fg_scores.view(n, -1)
         rpn_scores = rpn_scores.view(n, -1, 2)
 

train.py

 from __future__ import  absolute_import
+# though cupy is not used but without this line, it raise errors...
+import cupy as cp
 import os
 
 import ipdb

utils/array_tool.py

@@ -26,4 +26,4 @@ def scalar(data):
     if isinstance(data, np.ndarray):
         return data.reshape(1)[0]
     if isinstance(data, t.Tensor):
-        return data.view(1)[0].item()
\ No newline at end of file
+        return data.item()
\ No newline at end of file

utils/vis_tool.py

@@ -175,7 +175,7 @@ class Visualizer(object):
     """
 
     def __init__(self, env='default', **kwargs):
-        self.vis = visdom.Visdom(env=env, **kwargs)
+        self.vis = visdom.Visdom(env=env, use_incoming_socket=False, **kwargs)
         self._vis_kw = kwargs
 
         # e.g.('loss',23) the 23th value of loss