fixes

src/include/migraphx/operators.hpp

@@ -285,12 +285,12 @@ struct transpose
     int output_alias(const std::vector<shape>&) const { return 0; }
 };
 
-// The contiguous operator takes a non-standard input tensor and returns
-// the same tensor but in standard form. For example, if input tensor A which has lens = (4,5)
-// is first transposed, i.e. lens = (5,4), this tensor's data layout remained the same
-// during the transpose operation; only it's shape lengths and strides were changed.
-// This leaves the tensor in a non-standard form. The contiguous operator copies the
-// underlying data such that resulting tensor is returned to a standard form.
+/// The contiguous operator takes a non-standard input tensor and returns
+/// the same tensor but in standard form. For example, if input tensor A which has lens = (4,5)
+/// is first transposed, i.e. lens = (5,4), this tensor's data layout remained the same
+/// during the transpose operation; only it's shape lengths and strides were changed.
+/// This leaves the tensor in a non-standard form. The contiguous operator copies the
+/// underlying data such that resulting tensor is returned to a standard form.
 struct contiguous
 {
     std::string name() const { return "contiguous"; }
@@ -716,14 +716,14 @@ struct flatten
     int output_alias(const std::vector<shape>&) const { return 0; }
 };
 
-// The broadcast operator performs the numpy-style broadcasting of an axis of a given tensor. This
-// is achieved primarily by setting the stride of the broadcasted axis to zero. Linear indicies are
-// computed from multi-indicies by computing the inner product on the multi-index with the strides.
-// For example, if we have a tensor A(2,3) it has lengths of (2,3) and strides of (3,1). If we want
-// to compute the linear offset that corresponds to the element on the 2nd row (i = 1) and 3rd
-// column (j = 2), we compute the following inner product (1,2) dot (3, 1) = 1*3 + 2*1 = 5. It is
-// obvious from there that we can negate the effects of a given axis by setting the stride of that
-// axis to zero.
+/// The broadcast operator performs the numpy-style broadcasting of an axis of a given tensor. This
+/// is achieved primarily by setting the stride of the broadcasted axis to zero. Linear indicies are
+/// computed from multi-indicies by computing the inner product on the multi-index with the strides.
+/// For example, if we have a tensor A(2,3) it has lengths of (2,3) and strides of (3,1). If we want
+/// to compute the linear offset that corresponds to the element on the 2nd row (i = 1) and 3rd
+/// column (j = 2), we compute the following inner product (1,2) dot (3, 1) = 1*3 + 2*1 = 5. It is
+/// obvious from there that we can negate the effects of a given axis by setting the stride of that
+/// axis to zero.
 struct broadcast
 {
     uint64_t axis = 0;

src/onnx/onnx.cpp

@@ -215,7 +215,7 @@ struct onnx_parser
                 MIGRAPH_THROW("auto_pad and padding cannot be specified simultaneously");
             }
 
-            if(s.find("SAME") >= 0)
+            if(s.find("SAME") != std::string::npos)
             {
                 op.padding_mode = op::convolution::same;
             }
@@ -481,14 +481,6 @@ struct onnx_parser
         onnx::ModelProto model;
         if(model.ParseFromIstream(&is))
         {
-            auto str_toupper = [](std::string s) {
-                std::transform(
-                    s.begin(), s.end(), s.begin(), [](unsigned char c) { return std::toupper(c); });
-                return s;
-            };
-            auto producer_name = str_toupper(model.producer_name());
-            std::cout << producer_name << std::endl;
-
             if(model.has_graph())
             {
                 this->parse_graph(model.graph());