formatting

src/tf/tf.cpp

@@ -56,9 +56,10 @@ struct tf_parser
         std::vector<T> new_axes;
         if(is_nhwc)
         {
-            std::transform(axes.begin(), axes.end(), std::back_inserter(new_axes), [&](size_t axis) {
-                return parse_axis(axis);
-            });
+            std::transform(axes.begin(),
+                           axes.end(),
+                           std::back_inserter(new_axes),
+                           [&](size_t axis) { return parse_axis(axis); });
         }
         return new_axes;
     }
@@ -334,14 +335,13 @@ struct tf_parser
         return prog.add_instruction(op, {args[0], weights});
     }
 
-    instruction_ref parse_mean(const std::string&,
-                                  attribute_map attributes,
-                                  std::vector<instruction_ref> args)
+    instruction_ref
+    parse_mean(const std::string&, attribute_map attributes, std::vector<instruction_ref> args)
     {
-        
-        auto axes = parse_axes(args[1]->eval().get<int32_t>().to_vector());
+
+        auto axes      = parse_axes(args[1]->eval().get<int32_t>().to_vector());
         bool keep_dims = attributes.at("keep_dims").b();
-        std::vector<int32_t> hw_axes{2,3};
+        std::vector<int32_t> hw_axes{2, 3};
         if(axes == hw_axes and keep_dims)
         {
             op::pooling op{"average"};
@@ -358,23 +358,23 @@ struct tf_parser
     {
         size_t ndims = args.front()->get_shape().lens().size();
 
-        // in tf, the paddings are arranged as a 2d shape (ndims, 2), 
-        // the last dim contains the left padding and right padding respectively 
+        // in tf, the paddings are arranged as a 2d shape (ndims, 2),
+        // the last dim contains the left padding and right padding respectively
         std::vector<std::pair<int32_t, int32_t>> pad_per_dim(ndims);
         auto tf_padding = args[1]->eval().get<int32_t>().to_vector();
-        for(size_t i = 0; i < 2*ndims; i+= 2)
+        for(size_t i = 0; i < 2 * ndims; i += 2)
         {
-            pad_per_dim[i/2].first  = tf_padding[i];
-            pad_per_dim[i/2].second = tf_padding[i+1];
+            pad_per_dim[i / 2].first  = tf_padding[i];
+            pad_per_dim[i / 2].second = tf_padding[i + 1];
         }
         reorder_data(pad_per_dim);
 
         op::pad op;
-        std::vector<int64_t> pads(ndims*2);
-        for (size_t i = 0; i < ndims; i++)
+        std::vector<int64_t> pads(ndims * 2);
+        for(size_t i = 0; i < ndims; i++)
         {
-            pads[i]       = pad_per_dim[i].first;
-            pads[i+ndims] = pad_per_dim[i].second;
+            pads[i]         = pad_per_dim[i].first;
+            pads[i + ndims] = pad_per_dim[i].second;
         }
         op.pads = pads;
         return prog.add_instruction(op, args.front());