From: Summer Deng <summerdeng@fb.com>
Date: Mon, 15 Apr 2019 23:43:58 +0000 (-0700)
Subject: Add NHWC order support in the cost inference function of 3d conv (#19170)
X-Git-Tag: accepted/tizen/6.5/unified/20211028.231830~226
X-Git-Url: http://review.tizen.org/git/?a=commitdiff_plain;h=84b264b17dd59d9db8a1afc88f2509af74cbfdfe;p=platform%2Fupstream%2Fpytorch.git

Add NHWC order support in the cost inference function of 3d conv (#19170)

Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/19170

As title
The quantized resnext3d model in production got the following failures without the fix:

```
 Caffe2 operator Int8ConvRelu logging error: [enforce fail at conv_pool_op_base.h:463] order == StorageOrder::NCHW. 1 vs 2. Conv3D only supports NCHW on the production quantized model
```

Reviewed By: jspark1105

Differential Revision: D14894276

fbshipit-source-id: ef97772277f322ed45215e382c3b4a3702e47e59
---

diff --git a/caffe2/operators/conv_pool_op_base.h b/caffe2/operators/conv_pool_op_base.h
index 5fb07d2..c1dd289 100644
--- a/caffe2/operators/conv_pool_op_base.h
+++ b/caffe2/operators/conv_pool_op_base.h
@@ -460,15 +460,25 @@ class ConvPoolOpBase : public Operator<Context> {
     N = X.dims(0);
     if (X.dims_size() == 5) {
       // 3D convolution
-      CAFFE_ENFORCE_EQ(order, StorageOrder::NCHW, "Conv3D only supports NCHW");
-      Y_t = Y.dims(2);
-      Y_h = Y.dims(3);
-      Y_w = Y.dims(4);
-      kernel_t = W.dims(2);
-      kernel_h = W.dims(3);
-      kernel_w = W.dims(4);
-      in_channels = W.dims(1);
-      out_channels = W.dims(0);
+      if (order == StorageOrder::NHWC) {
+        Y_t = Y.dims(1);
+        Y_h = Y.dims(2);
+        Y_w = Y.dims(3);
+        kernel_t = W.dims(1);
+        kernel_h = W.dims(2);
+        kernel_w = W.dims(3);
+        in_channels = W.dims(4);
+        out_channels = W.dims(0);
+      } else {
+        Y_t = Y.dims(2);
+        Y_h = Y.dims(3);
+        Y_w = Y.dims(4);
+        kernel_t = W.dims(2);
+        kernel_h = W.dims(3);
+        kernel_w = W.dims(4);
+        in_channels = W.dims(1);
+        out_channels = W.dims(0);
+      }
     } else if (X.dims_size() == 4) {
       // 2D convolution
       CAFFE_ENFORCE_EQ(W.dims_size(), 4, "Conv2D should have 4D filter tensor");