mozga-intel commented on a change in pull request #19896:
URL: https://github.com/apache/incubator-mxnet/pull/19896#discussion_r619036259
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File path: src/operator/contrib/adaptive_avg_pooling.cc
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@@ -197,6 +198,91 @@
num_threads(engine::OpenMP::Get()->GetRecommendedOMPThreadCount())
}
}
+#if MXNET_USE_MKLDNN == 1
+bool CanMKLDNNSupportAveragePooling(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_1 = floor((IH << 1) / OH) - floor(IH / OH);
+ const int strides_2 = floor((IW << 1) / OW) - floor(IW / OW);
+ const int kernel_1 = ceil((IH << 1) / OH) - floor(IH / OH);
+ const int kernel_2 = ceil((IW << 1) / OW) - floor(IW / OW);
+ const int pad_l_top = (strides_1 * (OH - 1) + kernel_1 - IH) / 2;
+ const int pad_l_left = (strides_2 * (OW - 1) + kernel_2 - 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);
+ /*
+ DNNL doesn't support adaptive pooling.
Review comment:
Done.
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