feat(//core/conversion/converters/impl): all function schemas for upsample_nearest

Signed-off-by: Abhiram Iyer <[email protected]>
Signed-off-by: Abhiram Iyer <[email protected]>

Author: abhi-iyer

Diff for: core/conversion/converters/impl/interpolate.cpp

@@ -2,6 +2,8 @@
 #include "core/util/prelude.h"
 #include "core/conversion/converters/converters.h"
 
+#include <csignal>
+
 namespace trtorch {
 namespace core {
 namespace conversion {
@@ -11,15 +13,69 @@ namespace {
 
 auto interpolate_registrations = RegisterNodeConversionPatterns()
     .pattern({
+        "aten::upsample_nearest1d(Tensor self, int[1] output_size, float? scales=None) -> (Tensor)",
+        [](ConversionCtx* ctx, const torch::jit::Node*n, args& args) -> bool {
+            TRTORCH_ASSERT(args[0].IValue()->isTensor(), "Input expected to be of type Tensor");
+
+            auto in = args[0].ITensor();
+            auto in_shape = util::toVec(in->getDimensions());
+
+            // Case 1: user uses output size and not scales
+            if (!args[1].IValue()->isNone() && args[2].IValue()->isNone()) {
+                auto output_size = util::toDims(args[1].unwrapToIntList());
+
+                TRTORCH_ASSERT(output_size.nbDims == 1, "aten::upsample_nearest1d input Tensor and output size dimension mismatch");
+            } else {
+                LOG_DEBUG("scale factor parameters not supported yet.");
+            }
+
+            return true;
+        }
+    }).pattern({
         "aten::upsample_nearest2d(Tensor self, int[2] output_size, float? scales_h=None, float? scales_w=None) -> (Tensor)",
         [](ConversionCtx* ctx, const torch::jit::Node* n, args& args) -> bool {
+//            std::raise(SIGINT);
+            TRTORCH_ASSERT(args[0].IValue()->isTensor(), "Input expected to be of type Tensor");
+
+            auto in = args[0].ITensor();
+            auto in_shape = util::toVec(in->getDimensions());
+
+            // Case 1: user uses output_size and not scales_h, scales_w
+            if (!args[1].IValue()->isNone() && args[2].IValue()->isNone() && args[3].IValue()->isNone()){
+                auto output_size = util::toDims(args[1].unwrapToIntList());
+
+                TRTORCH_ASSERT( (output_size.nbDims == 1 || output_size.nbDims == 2), "aten::upsample_nearest2d input Tensor and output size dimension mismatch");
+
+                nvinfer1::ILayer* new_layer;
+
+
+        
+                //util::toDims(args[1].unwrapToIntList());
+
+            } else {
+                LOG_DEBUG("scale factor parameters not supported yet.");
+            }
+
+            return true;
+        }
+    }).pattern({
+        "aten::upsample_nearest3d(Tensor self, int[3] output_size, float? scales_d=None, float? scales_h=None, float? scales_w=None) -> (Tensor)",
+        [](ConversionCtx* ctx, const torch::jit::Node*n, args& args) -> bool {
+            TRTORCH_ASSERT(args[0].IValue()->isTensor(), "Input expected to be of type Tensor");
+
             auto in = args[0].ITensor();
+            auto in_shape = util::toVec(in->getDimensions());
 
-            auto shape = util::toVec(in->getDimensions());
+            // Case 1: user uses output size and not scales_d, scales_h, scales_w
+            if (!args[1].IValue()->isNone() && args[2].IValue()->isNone() && args[3].IValue()->isNone() && args[4].IValue()->isNone()) {
+                auto output_size = util::toDims(args[1].unwrapToIntList());
 
-            LOG_DEBUG("Shape of input is" << in);
+                TRTORCH_ASSERT( (output_size.nbDims == 1 || output_size.nbDims == 3), "aten::upsample_nearest3d input Tensor and output size dimension mismatch");
+                
 
-            std::cout << "TEST!" << std::endl;
+            } else {
+                LOG_DEBUG("scale factor parameters not supported yet.");
+            }
 
             return true;
         }