fix(aten::batchnorm|aten::view): Fix converter implementation for

dynamic inputs

Signed-off-by: Naren Dasan <naren@narendasan.com>
Signed-off-by: Naren Dasan <narens@nvidia.com>

Author: narendasan

core/conversion/converters/impl/batch_norm.cpp

@@ -19,12 +19,24 @@ auto batch_norm_registrations TRTORCH_UNUSED = RegisterNodeConversionPatterns()
       auto orig_shape = input->getDimensions();
       auto shape = util::toVec(orig_shape);
       auto options = torch::TensorOptions().dtype(torch::kFloat32);
-      auto gamma = args[1].unwrapToTensor(at::full({shape}, 1, {options}));
-      auto beta = args[2].unwrapToTensor(at::full({shape}, 1, {options}));
-      auto mean = args[3].unwrapToTensor(at::full({shape}, 0, {options}));
-      auto var = args[4].unwrapToTensor(at::full({shape}, 0, {options}));
+
+      torch::Tensor gamma, beta, mean, var;
+
+      if (ctx->input_is_dynamic) {
+        gamma = args[1].unwrapToTensor();
+        beta = args[2].unwrapToTensor();
+        mean = args[3].unwrapToTensor();
+        var = args[4].unwrapToTensor();
+      } else {
+        gamma = args[1].unwrapToTensor(at::full({shape}, 1, {options}));
+        beta = args[2].unwrapToTensor(at::full({shape}, 1, {options}));
+        mean = args[3].unwrapToTensor(at::full({shape}, 0, {options}));
+        var = args[4].unwrapToTensor(at::full({shape}, 0, {options}));
+      }
+
       auto eps = args[7].unwrapToDouble(1e-5f);
 
+
       LOG_DEBUG("momentum disregarded");
       LOG_DEBUG("training disregarded");
       LOG_DEBUG("cudnn disregarded");

core/conversion/converters/impl/shuffle.cpp

@@ -9,7 +9,7 @@ namespace converters {
 namespace impl {
 namespace {
 
-static auto shuffle_registrations = RegisterNodeConversionPatterns()
+static auto shuffle_registrations TRTORCH_UNUSED = RegisterNodeConversionPatterns()
   .pattern({
     "aten::flatten.using_ints(Tensor self, int start_dim=0, int end_dim=-1) -> (Tensor)",
     [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
@@ -50,12 +50,10 @@ static auto shuffle_registrations = RegisterNodeConversionPatterns()
     [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
       auto in = args[0].ITensor();
       auto in_shape = util::toVec(in->getDimensions());
-      auto ex_tensor = torch::rand(in_shape);
-      auto new_shape = ex_tensor.view(args[1].unwrapToIntList().vec()).sizes();
 
       auto shuffle = ctx->net->addShuffle(*in);
       TRTORCH_CHECK(shuffle, "Unable to create shuffle layer from node: " << *n);
-      shuffle->setReshapeDimensions(util::toDims(new_shape));
+      shuffle->setReshapeDimensions(util::toDims(args[1].unwrapToIntList().vec()));
       shuffle->setName(util::node_info(n).c_str());
 
       auto out_tensor = ctx->AssociateValueAndTensor(n->outputs()[0], shuffle->getOutput(0));