示例#1
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文件: layer.py 项目: CV-IP/RstarCNN
 def run(self):
     print 'BlobFetcher started'
     while True:
         db_inds = self._get_next_minibatch_inds()
         minibatch_db = [self._roidb[i] for i in db_inds]
         blobs = get_minibatch(minibatch_db, self._num_classes)
         self._queue.put(blobs)
示例#2
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 def run(self):
     print 'BlobFetcher started'
     while True:
         db_inds = self._get_next_minibatch_inds()
         minibatch_db = [self._roidb[i] for i in db_inds]
         blobs = get_minibatch(minibatch_db)
         self._queue.put(blobs)
示例#3
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文件: predict.py 项目: cjcchen/ML
def predict():
    #imdb, roidb = combined_roidb('voc_2007_trainval')
    imdb, roidb = combined_roidb('voc_2007_test', is_training=False)
    print imdb.classes
    #from data_layer.layer import RoIDataLayer
    #data_layer = RoIDataLayer(roidb, imdb.num_classes)
    #for _ in range(10):
    #  blobs = data_layer.forward()
    #  print blobs

    sv = Predict(imdb.num_classes)
    ids=0
    for roi in roidb:
        print "====================================================================="
        print roi
        blobs=get_minibatch([roi], imdb.num_classes)
        image = blobs['data']
        gt_boxes = blobs['gt_boxes']
        im_info = blobs['im_info']
        clss_score, clss_bbox = sv.predict(image, im_info)
        for i,(score, bbox) in enumerate(zip(clss_score, clss_bbox)):
            if len(score)>0:
                print ("class:",i+1, imdb.classes[i+1])
                print "score:",score
                print "bbox:",bbox
        ids+=1
        if ids > 10:
            break
    def __getitem__(self, item):
        minibatch_db = [self.roidb[item]]
        blobs = get_minibatch(minibatch_db, self.num_classes)
        data = blobs['data']
        im_info = blobs['im_info']
        if self.training:
            gt_boxes = blobs['gt_boxes']
        else:
            gt_boxes = torch.FloatTensor([0, 0, 0, 0, 0])

        return data[0].transpose(2, 0, 1), im_info.reshape([3]), gt_boxes
示例#5
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    def _get_next_minibatch(self):
        """Return the blobs to be used for the next minibatch.

        If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a
        separate process and made available through self._blob_queue.
        """
        if cfg.TRAIN.USE_PREFETCH:
            return self._blob_queue.get()
        else:
            db_inds = self._get_next_minibatch_inds()
            minibatch_db = [self._roidb[i] for i in db_inds]
            return get_minibatch(minibatch_db)
示例#6
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文件: layer.py 项目: CV-IP/RstarCNN
    def _get_next_minibatch(self):
        """Return the blobs to be used for the next minibatch.

        If cfg.TRAIN.USE_PREFETCH is True, then blobs will be computed in a
        separate process and made available through self._blob_queue.
        """
        if cfg.TRAIN.USE_PREFETCH:
            return self._blob_queue.get()
        else:
            db_inds = self._get_next_minibatch_inds()
            minibatch_db = [self._roidb[i] for i in db_inds]
            return get_minibatch(minibatch_db, self._num_classes)
示例#7
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 def run(self):
     print 'BlobFetcher started'
     while True:
         minibatch_inds = self._get_next_minibatch_inds()
         minibatch_img_paths = \
             [self._db.get_img_path(self._db.train_ind[
                 i if i < len(self._db.train_ind) else i - len(self._db.train_ind)])
              for i in minibatch_inds]
         minibatch_labels = \
             [self._db.labels[self._db.train_ind[
                  i if i < len(self._db.train_ind) else i - len(self._db.train_ind)]]
              for i in minibatch_inds]
         minibatch_flip = \
             [0 if i < len(self._db.train_ind) else 1
              for i in minibatch_inds]
         blobs = get_minibatch(minibatch_img_paths, minibatch_labels,
                               minibatch_flip, self._db.flip_attr_pairs,
                               self._weight)
         self._queue.put(blobs)
示例#8
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    def run(self):
        print 'BlobFetcher started'
        while True:
            minibatch_inds, img_ratio = self._get_next_minibatch_inds()

            if len(minibatch_inds) < cfg.TRAIN.BATCH_SIZE:
                continue

            #print "The ratio of this blob is: %d" % img_ratio
            #print "Size of minibatch %d" % len(minibatch_inds)
            #print minibatch_inds
            #print
            minibatch_img_paths = \
                [self._db.get_img_path(self._db.train_ind[i])
                 for i in minibatch_inds]
            minibatch_labels = \
                [self._db.labels[self._db.train_ind[i]]
                 for i in minibatch_inds]
            minibatch_flip = \
                np.random.random_integers(0, 1, len(minibatch_inds))
            blobs = get_minibatch(minibatch_img_paths, minibatch_labels,
                                  minibatch_flip, self._db.flip_attr_pairs,
                                  self._weight, img_ratio)
            self._queue.put(blobs)
示例#9
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文件: layer.py 项目: cjcchen/ML
 def _get_next_minibatch(self):
     db_inds = self._get_next_minibatch_inds()
     minibatch_db = [self._roidb[i] for i in db_inds]
     return get_minibatch(minibatch_db, self._num_classes)
示例#10
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	def _get_next_minibatch(self):
		db_inds = self._get_next_minibatch_inds()
		# minibatch_db = [self._db[i] for i in db_inds]
		return get_minibatch(self._db, db_inds, self._labels)