TypeError: 'numpy.float64' object cannot be interpreted as an index

在訓練stage1 rpn時，出現'numpy.float64' object cannot be interpreted as an index 的提示錯誤，幾乎全部的博客中都指出，須要更換numpy 的版本，照作以後，出現ImportError: numpy.core.multiarray failed to import，這個問題又是numpy不匹配形成的，這樣就造成了惡性循環，因此，能夠考慮從根源上解決'numpy.float64' object cannot be interpreted as an index

TypeError: 'numpy.float64' object cannot be interpreted as an index 

1) /home/xxx/py-faster-rcnn/lib/roi_data_layer/minibatch.py

將第26行：fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
改成：fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image).astype(np.int)
第174，175行改成：

for ind in inds:
cls = clss[ind]
start =int( 4 * cls)
end = int(start + 4)
bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS

2) /home/xxx/py-faster-rcnn/lib/datasets/ds_utils.py

將第12行：hashes = np.round(boxes * scale).dot(v)
改成：hashes = np.round(boxes * scale).dot(v).astype(np.int)

3) /home/xxx/py-faster-rcnn/lib/fast_rcnn/test.py

將第129行： hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v)
改成： hashes = np.round(blobs['rois'] * cfg.DEDUP_BOXES).dot(v).astype(np.int)

4) /home/xxx/py-faster-rcnn/lib/rpn/proposal_target_layer.py

將第60行：fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image)
改成：fg_rois_per_image = np.round(cfg.TRAIN.FG_FRACTION * rois_per_image).astype(np.int)

 解決完上一個問題後，又出現 TypeError: slice indices must be integers or None or have an __index__ method的問題，若是沒有改變numpy的版本，
修改 /home/XXX/py-faster-rcnn/lib/rpn/proposal_target_layer.py，轉到123行：

for ind in inds:
        cls = clss[ind]
        start = 4 * cls
        end = start + 4
        bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
        bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
    return bbox_targets, bbox_inside_weights

這裏的ind，start，end都是 numpy.int 類型，這種類型的數據不能做爲索引，因此必須對其進行強制類型轉換，轉化結果以下：

for ind in inds:
        ind = int(ind)
        cls = clss[ind]
        start = int(4 * cos)
        end = int(start + 4)
        bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
        bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
    return bbox_targets, bbox_inside_weight