bartekkuncer commented on a change in pull request #20741:
URL: https://github.com/apache/incubator-mxnet/pull/20741#discussion_r749279822
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File path: src/operator/contrib/adaptive_avg_pooling.cc
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@@ -199,7 +202,53 @@ void AdaptiveAvgPoolUpdateGradInput(mshadow::Stream<cpu>*
s,
}
}
-DMLC_REGISTER_PARAMETER(AdaptiveAvgPoolParam);
+#if MXNET_USE_ONEDNN == 1
+bool SupportDNNLAveragePooling(const NDArray& in_data, const NDArray&
out_data) {
+ for (int64_t idx = 2; idx < in_data.shape().ndim(); ++idx) {
+ const int s1 = in_data.shape()[idx];
+ const int s2 = out_data.shape()[idx];
+ if (s2 == 0) {
+ return false;
+ }
+ if (s1 % s2 != 0) {
+ return false;
+ }
+ }
+ const int IH = in_data.shape()[2];
+ const int IW = in_data.shape()[3];
+ const int OH = out_data.shape()[2];
+ const int OW = out_data.shape()[3];
+ const int strides_H = floor((IH << 1) / OH) - floor(IH / OH);
+ const int strides_W = floor((IW << 1) / OW) - floor(IW / OW);
+ const int kernel_H = ceil((IH << 1) / OH) - floor(IH / OH);
+ const int kernel_W = ceil((IW << 1) / OW) - floor(IW / OW);
+ const int pad_l_top = (strides_H * (OH - 1) + kernel_H - IH) / 2;
+ const int pad_l_left = (strides_W * (OW - 1) + kernel_W - IW) / 2;
+ return pad_l_top == 0 && pad_l_left == 0;
+}
+
+void AdaptiveAvgPoolComputeExCPU(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ CHECK_EQ(inputs.size(), 1U);
+ CHECK_EQ(outputs.size(), 1U);
+ /*
+ OneDNN doesn't support adaptive pooling.
Review comment:
oneDNN*
Also is this comment up-to-date?
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