[GitHub] MinZhaoLin edited a comment on issue #9920: Asynchronous Issue on CustomOP and mxnet.image.ImageDetIter

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MinZhaoLin edited a comment on issue #9920: Asynchronous Issue on CustomOP and mxnet.image.ImageDetIter
URL: https://github.com/apache/incubator-mxnet/issues/9920#issuecomment-415649816
 
 
   @wkcn 
   我是一开始定义了自己的softmax层后，出现了这个issue的报错src/io/image_io.cc:186: Check failed: inputs[0].ctx().dev_mask() == Context::kCPU (2 vs. 1) Only supports cpu input，
   然后根据这个issue改了之后，就是修改Imge.py之后，在AccMetric里面就出现了src/ndarray/ndarray_function.cu:43: Check failed: to->type_flag == from.type_flag_ (0 vs. 3) Source and target must have the same data type when copying across devices.这个问题
   这个问题具体是在pred_label = pred_label.asnumpy().astype('int32').flatten()的时候出现的，然后当我尝试在这句话前面（某些地方，有些地方可以，有些地方也会报错）将pred_label直接print输出的时候，他是有时会正常运行不会报错，但是每次当pred_label调用到asnumpy()的时候就会报这个错
   `class GenderAccMetric(mx.metric.EvalMetric):
     def __init__(self):
       self.axis = 1
       super(GenderAccMetric, self).__init__(
           'acc', axis=self.axis,
           output_names=None, label_names=None)
       self.losses = []
       self.count = 0
   
     def update(self, _labels, preds):
       self.count+=1
       #print("in gender AccMetric\n")
       #print("label is {}\n".format(_labels[2])) 
       #print("preds is {}\n".format(preds[3]))  当输出label和preds时，有时会正常运行
       labels = [_labels[2]]
       _preds = [preds[3]] #use softmax output
       for label, pred_label in zip(labels, _preds):
           #print("pred_label before if is {}\n".format(pred_label)) 在这里输出也会报错
           pred_label = pred_label.as_in_context(label.context) 加上了这句话还是不行
           if pred_label.shape != label.shape:
               #pred_label = mx.ndarray.array(_pred_label, ctx=mx.current_context())其中一个AccMetric在这里进行转换就解决了问题，但剩下的两个不行
               pred_label = mx.ndarray.argmax(pred_label, axis=self.axis)
           pred_label = pred_label.asnumpy().astype('int32').flatten()
           label = label.asnumpy()
           if label.ndim==2:
             label = label[:,0]
           label = label.astype('int32').flatten()
           assert label.shape==pred_label.shape
           self.sum_metric += (pred_label.flat == label.flat).sum()
           self.num_inst += len(pred_label.flat)`
   
   所以总的来说，我感到非常奇怪和困扰，按照你说的方法改了还是不行，我感觉是只要是pred调用了asnumpy（）就会报错
   

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