fixed memory leak for RoIPool Module

.gitignore

@@ -93,3 +93,4 @@ ENV/
 
 .idea
 extension-ffi
+demo_mot.py

faster_rcnn/faster_rcnn.py

@@ -12,7 +12,7 @@
 
 import network
 from network import Conv2d, FC
-# from roi_pool import RoIPool
+# from roi_pooling.modules.roi_pool_py import RoIPool
 from roi_pooling.modules.roi_pool import RoIPool
 from vgg16 import VGG16
 
@@ -114,17 +114,7 @@ def __init__(self):
 
  def forward(self, image):
  features, rois, im_info = self.rpn(image)
- t = Timer()
- t.tic()
  pooled_features = self.roi_pool(features, rois)
- roi_pooling_t = t.toc()
- print('roi pooling spend: {}s'.format(roi_pooling_t))
-
- # loss = pooled_features.mean()
- # print loss
- # loss.backward()
- # print torch.max(features.grad)
- # print features.grad.size()
 
  x = pooled_features.view(pooled_features.size()[0], -1)
  x = self.fc6(x)
@@ -184,34 +174,3 @@ def detect(self, image, thr=0.3):
  self.interpret_faster_rcnn(cls_prob, bbox_pred, rois, im_info, image.shape, min_score=thr)
  return pred_boxes, scores, classes
 
-if __name__ == '__main__':
- def main():
- import os
- im_file = 'demo/004545.jpg'
- image = cv2.imread(im_file)
-
- detector = FasterRCNN()
- network.load_net('/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
- detector.cuda()
- print('load model successfully!')
-
- # network.save_net(r'/media/longc/Data/models/VGGnet_fast_rcnn_iter_70000.h5', detector)
- # print('save model succ')
-
- t = Timer()
- t.tic()
- dets, scores, classes = detector.detect(image, 0.3)
- runtime = t.toc()
- print('total spend: {}s'.format(runtime))
-
- im2show = np.copy(image)
- for i, det in enumerate(dets):
- if scores[i] < 0.3:
- continue
- det = tuple(int(x) for x in det)
- cv2.rectangle(im2show, det[0:2], det[2:4], (255, 205, 51), 2)
- cv2.putText(im2show, '%s: %.3f' % (classes[i], scores[i]), (det[0], det[1] + 15), cv2.FONT_HERSHEY_PLAIN,
- 1.0, (0, 0, 255), thickness=1)
- cv2.imwrite(os.path.join('demo', 'out.jpg'), im2show)
-
- main()

faster_rcnn/roi_pooling/functions/roi_pool.py

@@ -23,7 +23,7 @@ def forward(self, features, rois):
  _features = features.permute(0, 2, 3, 1)
  roi_pooling.roi_pooling_forward(self.pooled_height, self.pooled_width, self.spatial_scale,
  _features, rois, output)
- output = output.cuda()
+ # output = output.cuda()
  else:
  output = output.cuda()
  argmax = argmax.cuda()

faster_rcnn/roi_pooling/modules/roi_pool.py

@@ -1,12 +1,14 @@
 from torch.nn.modules.module import Module
-# from functions.roi_pool import RoIPoolFunction
 from ..functions.roi_pool import RoIPoolFunction
 
 
 class RoIPool(Module):
  def __init__(self, pooled_height, pooled_width, spatial_scale):
  super(RoIPool, self).__init__()
- self.roi_pool = RoIPoolFunction(pooled_height, pooled_width, spatial_scale)
+
+ self.pooled_width = int(pooled_width)
+ self.pooled_height = int(pooled_height)
+ self.spatial_scale = float(spatial_scale)
 
  def forward(self, features, rois):
- return self.roi_pool(features, rois)
+ return RoIPoolFunction(self.pooled_height, self.pooled_width, self.spatial_scale)(features, rois)