Formatting

src/fwd_conv_batchnorm_rewrite.cpp

@@ -8,11 +8,13 @@
 namespace migraph {
 void fwd_conv_batchnorm_rewrite::apply(program& p) const
 {
-    for (auto ins : iterator_for(p))
+    for(auto ins : iterator_for(p))
+    {
+        if(ins->op.name() == "batch_norm_inference")
         {
-        if (ins->op.name() == "batch_norm_inference") {
             auto ins_prev = ins->arguments[0];
-            if (ins_prev->op.name() == "convolution") {
+            if(ins_prev->op.name() == "convolution")
+            {
                 // Get scale, bias, mean, variance from instruction_ref
                 auto gamma    = ins->arguments[1]->lit;
                 auto bias     = ins->arguments[2]->lit;
@@ -32,19 +34,27 @@ void fwd_conv_batchnorm_rewrite::apply(program& p) const
                 argument new_weights{weights.get_shape()};
                 argument new_bias{bias.get_shape()};
                 visit_all(weights, gamma, bias, mean, variance, new_weights, new_bias)(
-                    [&](auto weights2, auto gamma2, auto bias2, auto mean2, 
-                        auto variance2, auto new_weights2, auto new_bias2) {
+                    [&](auto weights2,
+                        auto gamma2,
+                        auto bias2,
+                        auto mean2,
+                        auto variance2,
+                        auto new_weights2,
+                        auto new_bias2) {
                         dfor(out_channels, in_channels, height, width)(
                             [&](std::size_t k, std::size_t c, std::size_t h, std::size_t w) {
-                        new_weights2(k, c, h, w) = gamma2(k) / std::sqrt(variance2(k)+epsilon) * weights2(k, c, h, w); 
-                        new_bias2(k, c, h, w) = bias2(k) - (mean2(k) / std::sqrt(variance2(k)+epsilon)); 
+                                new_weights2(k, c, h, w) = gamma2(k) /
+                                                           std::sqrt(variance2(k) + epsilon) *
+                                                           weights2(k, c, h, w);
+                                new_bias2(k, c, h, w) =
+                                    bias2(k) - (mean2(k) / std::sqrt(variance2(k) + epsilon));
                             });
                     });
                 // Replace convolution instruction with updated weights
                 auto l_weights = p.add_literal({weights.get_shape(), new_weights.data()});
                 auto l_bias    = p.add_literal({bias.get_shape(), new_bias.data()});
-                auto c = p.replace_instruction(ins_prev, conv_op, 
-                    {ins_prev->arguments[0], l_weights});
+                auto c =
+                    p.replace_instruction(ins_prev, conv_op, {ins_prev->arguments[0], l_weights});
                 p.replace_instruction(ins, add{}, {c, l_bias});
             }
         }