From b7d83241191232fe7b4ee960daa1bded18acd725 Mon Sep 17 00:00:00 2001
From: Ciyong Chen <ciyong.chen@intel.com>
Date: Sat, 2 Mar 2019 16:56:17 +0800
Subject: [PATCH] change to use mxnet::Tuple and update tests

---
 src/operator/subgraph/mkldnn/mkldnn_fc.cc | 14 +++++++-------
 tests/python/mkl/test_subgraph.py         |  4 ++--
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/src/operator/subgraph/mkldnn/mkldnn_fc.cc b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
index cfb5df32793c..885781e0beb2 100644
--- a/src/operator/subgraph/mkldnn/mkldnn_fc.cc
+++ b/src/operator/subgraph/mkldnn/mkldnn_fc.cc
@@ -109,7 +109,7 @@ void SgMKLDNNFCOp::Forward(const OpContext &ctx,
   NDArray data = in_data[fullc::kData];
   NDArray weight = in_data[fullc::kWeight];
   NDArray output = out_data[fullc::kOut];
-  const TShape &ishape = data.shape();
+  const mxnet::TShape &ishape = data.shape();
   if (mkldnn_param.quantized && ishape.ndim() != 2) {
     CHECK(default_param.flatten)
       << "QuantizedFullyConnected only supports flatten=true when ishape.ndim() != 2 for now.";
@@ -265,12 +265,12 @@ static inline void FillBaseInputOutputInfo(const FullyConnectedParam &param,
 }
 
 static bool SgMKLDNNFCInferShape(const nnvm::NodeAttrs &attrs,
-                                 std::vector<TShape> *in_shapes,
-                                 std::vector<TShape> *out_shapes) {
+                                 mxnet::ShapeVector *in_shapes,
+                                 mxnet::ShapeVector *out_shapes) {
   auto const &full_param = nnvm::get<MKLDNNFCFullParam>(attrs.parsed);
   if (full_param.mkldnn_param.quantized) {
-    std::vector<TShape> base_in_shapes;
-    std::vector<TShape> base_out_shapes;
+    mxnet::ShapeVector base_in_shapes;
+    mxnet::ShapeVector base_out_shapes;
     FillBaseInputOutputInfo(full_param.default_param, &base_in_shapes, &base_out_shapes,
                             in_shapes, out_shapes);
     bool ret = DefaultSubgraphOpShape(attrs, &base_in_shapes, &base_out_shapes);
@@ -368,7 +368,7 @@ static bool SgMKLDNNFCStorageType(const nnvm::NodeAttrs &attrs,
 
 static OpStatePtr CreateSgMKLDNNFCState(const nnvm::NodeAttrs &attrs,
                                         Context ctx,
-                                        const std::vector<TShape> &in_shapes,
+                                        const mxnet::ShapeVector &in_shapes,
                                         const std::vector<int> &in_types) {
   return OpStatePtr::Create<SgMKLDNNFCOp>(attrs);
 }
@@ -414,7 +414,7 @@ NNVM_REGISTER_OP(_sg_mkldnn_fully_connected)
 .set_attr_parser(SgMKLDNNFCParamParser)
 .set_attr<nnvm::FListInputNames>("FListInputNames", SgMKLDNNFCListInputNames)
 .set_attr<nnvm::FListOutputNames>("FListOutputNames", SgMKLDNNFCListOutputNames)
-.set_attr<nnvm::FInferShape>("FInferShape", SgMKLDNNFCInferShape)
+.set_attr<mxnet::FInferShape>("FInferShape", SgMKLDNNFCInferShape)
 .set_attr<nnvm::FInferType>("FInferType", SgMKLDNNFCInferType)
 .set_attr<FInferStorageType>("FInferStorageType", SgMKLDNNFCStorageType)
 .set_attr<FCreateOpState>("FCreateOpState", CreateSgMKLDNNFCState)
diff --git a/tests/python/mkl/test_subgraph.py b/tests/python/mkl/test_subgraph.py
index d0579de1df39..469bd3895384 100644
--- a/tests/python/mkl/test_subgraph.py
+++ b/tests/python/mkl/test_subgraph.py
@@ -62,7 +62,7 @@ def check_qsym_calibrated(qsym, out_type, name='conv'):
     if k.find('_quantize') != -1:
       assert v['out_type'] == out_type
     if k.find(quantized_op_name) != -1:
-      if name == 'fc' and 'fuse_dequantize' in v:
+      if name == 'fc' and 'enable_float_output' in v:
         continue
       assert 'min_calib_range' in v
       assert 'max_calib_range' in v
@@ -155,7 +155,7 @@ def check_fusion(sym, data_shape, attrs_op, name='conv', check_quantization=True
   for k, v in sym_sg.attr_dict().items():
     if k.find(op_name) != -1:
       for attr_op in attrs_op:
-        assert v[attr_op] == 'true'
+        assert v[attr_op] in ['true', 'True']
 
   arg_shapes, _, aux_shapes = sym.infer_shape()
   arg_array = [mx.nd.random.uniform(-1, 1, shape=shape) for shape in arg_shapes]