jiashu-zhu opened a new issue #16365: Exception: unknown storage type: -1 URL: https://github.com/apache/incubator-mxnet/issues/16365 ## Description I encounter a the exception "Exception: unknown storage type: -1" when I use my focal loss ## my focal loss the shape of out_data[0] is (batch_size, 2, anchor_num) the shape of in_data[1] is (batch_size, anchor_num) ``` class FocalLossOperator(mx.operator.CustomOp): def __init__(self, gamma, alpha): super(FocalLossOperator, self).__init__() self.gamma = gamma self.alpha = alpha def forward(self, is_train, req, in_data, out_data, aux): #print('forward') #print(in_data[0].shape) y = mx.nd.exp(in_data[0] - mx.nd.max_axis(in_data[0], axis=1).reshape((in_data[0].shape[0], 1, -1))) y /= mx.nd.sum(y, axis=1).reshape((in_data[0].shape[0],1, -1)) self.assign(out_data[0], req[0], y) def backward(self, req, out_grad, in_data, out_data, in_grad, aux): y_numpy = out_data[0].asnumpy().transpose((0,2,1)) label_numpy = in_data[1].asnumpy() y_numpy = y_numpy.reshape((-1,2)) label_numpy = label_numpy.reshape((-1)) #print(len(np.where(label_numpy == -1)[0])) indices = np.where(label_numpy == -1)[0] label_numpy[indices] = 0 self.pro_truth = mx.nd.array(y_numpy[np.arange(y_numpy.shape[0]), label_numpy.astype(np.int)]) print(len(indices)) # i!=j pro_truth = (self.pro_truth + 1e-14).reshape((self.pro_truth.shape[0], 1)) grad = self.alpha * mx.nd.power(1 - pro_truth, self.gamma - 1) * \ (self.gamma * (-1 * pro_truth * mx.nd.array(y_numpy)) * mx.nd.log(pro_truth) + mx.nd.array(y_numpy) * (1 - pro_truth)) # i==j pro_truth = self.pro_truth + 1e-14 grad_numpy = grad.asnumpy() grad_numpy[np.arange(y_numpy.shape[0]), label_numpy.astype(np.int)] = ( self.alpha * mx.nd.power(1 - pro_truth, self.gamma) * ( self.gamma * pro_truth * mx.nd.log(pro_truth) + pro_truth - 1)).asnumpy() grad_numpy /= label_numpy.shape[0] grad_numpy[indices,:] = 0 #grad_numpy = grad_numpy.reshape((out_data[0].shape[0],-1,out_data[0].shape[1])).transpose((0,2,1)) grad = mx.nd.array(grad_numpy) grad = grad.reshape(out_data[0].shape[0],-1,out_data[0].shape[1]).transpose((0,2,1)) self.assign(in_grad[0], req[0], grad) @mx.operator.register('FocalLoss') class FocalLossProp(mx.operator.CustomOpProp): def __init__(self, gamma, alpha): super(FocalLossProp, self).__init__(need_top_grad=False) self.gamma = float(gamma) self.alpha = float(alpha) def list_arguments(self): return ['data', 'labels'] def list_outputs(self): return ['output'] def infer_shape(self, in_shape): data_shape = in_shape[0] labels_shape = in_shape[1] out_shape = data_shape return [data_shape, labels_shape], [out_shape], [] def create_operator(self, ctx, shapes, dtypes): return FocalLossOperator(self.gamma, self.alpha) ``` ## Error Message: Error in CustomOp.backward: Traceback (most recent call last): File "/home/anaconda2/lib/python2.7/site-packages/mxnet/operator.py", line 1020, in backward_entry stype=stype)) File "/home/anaconda2/lib/python2.7/site-packages/mxnet/ndarray/sparse.py", line 1187, in _ndarray_cls raise Exception("unknown storage type: %s"%stype) Exception: unknown storage type: -1 terminate called after throwing an instance of 'dmlc::Error' what(): [12:17:03] src/operator/custom/custom.cc:418: Check failed: reinterpret_cast<CustomOpFBFunc>(params.info->callbacks[kCustomOpBackward])( ptrs.size(), const_cast<void**>(ptrs.data()), const_cast<int*>(tags.data()), reinterpret_cast<const int*>(req.data()), static_cast<int>(ctx.is_train), params.info->contexts[kCustomOpBackward]) Stack trace returned 8 entries: [bt] (0) /home/anaconda2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x40b29a) [0x7feccd0c829a] [bt] (1) /home/anaconda2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x40b8b1) [0x7feccd0c88b1] [bt] (2) /home/anaconda2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x6c6239) [0x7feccd383239] [bt] (3) /home/anaconda2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x6e1020) [0x7feccd39e020] [bt] (4) /home/anaconda2/lib/python2.7/site-packages/mxnet/libmxnet.so(+0x6c7078) [0x7feccd384078] [bt] (5) /home/anaconda2/bin/../lib/libstdc++.so.6(+0xafc5c) [0x7fed70a50c5c] [bt] (6) /lib/x86_64-linux-gnu/libpthread.so.0(+0x76ba) [0x7fed78a076ba] [bt] (7) /lib/x86_64-linux-gnu/libc.so.6(clone+0x6d) [0x7fed7802d41d]
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