formatting

src/tf/tf.cpp

@@ -291,7 +291,7 @@ struct tf_parser
                 l0 = prog.add_instruction(op::transpose{{0, 3, 1, 2}}, l0);
         }
         auto l1 = args[1];
-        if (is_nhwc)
+        if(is_nhwc)
             l1 = prog.add_instruction(op::transpose{{3, 2, 0, 1}}, args[1]);
         return prog.add_instruction(op, {l0, l1});
     }
@@ -412,9 +412,9 @@ struct tf_parser
         auto l0_dims = l0->get_shape().lens();
         if(op.axes.empty()) // no squeeze_dims provided, remove any dim that equals 1
         {
-            for (size_t i = 0; i < l0_dims.size(); i++)
+            for(size_t i = 0; i < l0_dims.size(); i++)
             {
-                if (l0_dims.at(i) == 1)
+                if(l0_dims.at(i) == 1)
                 {
                     op.axes.push_back(i);
                 }
@@ -680,8 +680,7 @@ struct tf_parser
             return literal{{shape::half_type, dims}, data_uint16};
         }
         case tensorflow::DataType::DT_DOUBLE:
-            return literal{
-                {shape::double_type, dims}, get_data_vals(t.double_val(), shape_size)};
+            return literal{{shape::double_type, dims}, get_data_vals(t.double_val(), shape_size)};
         case tensorflow::DataType::DT_UINT32: throw std::runtime_error("");
         case tensorflow::DataType::DT_UINT64: throw std::runtime_error("");
         case tensorflow::DataType::DT_COMPLEX64: throw std::runtime_error("");
@@ -726,7 +725,8 @@ struct tf_parser
     }
 
     template <class T>
-    static std::vector<T> get_data_vals(const google::protobuf::RepeatedField<T>& data, const std::size_t& shape_size)
+    static std::vector<T> get_data_vals(const google::protobuf::RepeatedField<T>& data,
+                                        const std::size_t& shape_size)
     {
         std::vector<T> data_vals(shape_size);
         // check if shape has enough data values given existing fields

test/tf/tf_test.cpp

@@ -39,7 +39,8 @@ TEST_CASE(batchnorm_test)
     float momentum = 0.9f;
 
     migraphx::program p;
-    migraphx::op::batch_norm_inference op{epsilon, momentum, migraphx::op::batch_norm_inference::spatial};
+    migraphx::op::batch_norm_inference op{
+        epsilon, momentum, migraphx::op::batch_norm_inference::spatial};
     migraphx::shape s0{migraphx::shape::float_type, {32}};
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 16, 16, 32}});
     l0      = p.add_instruction(migraphx::op::transpose{{0, 3, 1, 2}}, l0);
@@ -54,7 +55,6 @@ TEST_CASE(batchnorm_test)
     auto prog = migraphx::parse_tf("batchnorm_test.pb", true);
 
     EXPECT(p == prog);
-
 }
 
 TEST_CASE(biasadd_test)
@@ -107,10 +107,10 @@ TEST_CASE(conv_test)
 
     migraphx::op::convolution op;
     op.padding_mode = migraphx::op::padding_mode_t::same;
-    op.stride = {1,1};
-    op.dilation = {1,1};
-    auto l2 = p.add_instruction(migraphx::op::transpose{{0,3,1,2}}, l0);
-    auto l3 = p.add_instruction(migraphx::op::transpose{{3,2,0,1}}, l1);
+    op.stride       = {1, 1};
+    op.dilation     = {1, 1};
+    auto l2         = p.add_instruction(migraphx::op::transpose{{0, 3, 1, 2}}, l0);
+    auto l3         = p.add_instruction(migraphx::op::transpose{{3, 2, 0, 1}}, l1);
     p.add_instruction(op, l2, l3);
     auto prog = migraphx::parse_tf("conv_test.pb", true);
 
@@ -164,8 +164,8 @@ TEST_CASE(reshape_test)
     auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {16}});
     migraphx::shape s0{migraphx::shape::int32_type, {4}};
     // in tf, the second arg is a literal that contains new dimensions
-    p.add_literal(migraphx::literal{s0, {1,1,1,16}});
-    p.add_instruction(migraphx::op::reshape{{1,1,1,16}}, l0);
+    p.add_literal(migraphx::literal{s0, {1, 1, 1, 16}});
+    p.add_instruction(migraphx::op::reshape{{1, 1, 1, 16}}, l0);
     auto prog = migraphx::parse_tf("reshape_test.pb", false);
 
     EXPECT(p == prog);
@@ -187,7 +187,7 @@ TEST_CASE(softmax_test)
 TEST_CASE(squeeze_test)
 {
     migraphx::program p;
-    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1,2,3,1}});
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {1, 2, 3, 1}});
     p.add_instruction(migraphx::op::squeeze{{0, 3}}, l0);
     auto prog = migraphx::parse_tf("squeeze_test.pb", false);