From d9c0e84d651078e867f483405fc7d60af73b39f3 Mon Sep 17 00:00:00 2001
From: Abhiram Iyer <abhi.iyer.ai@gmail.com>
Date: Fri, 19 Jun 2020 12:06:48 -0700
Subject: [PATCH] fix(): fixed FP16 bug, fixed README, addressed some other PR
 comments Signed-off-by: Abhiram Iyer <abhirami@nvidia.com>

Signed-off-by: Abhiram Iyer <abhi.iyer.ai@gmail.com>
---
 README.md                                     |  2 --
 .../converters/impl/interpolate.cpp           |  2 --
 .../impl/plugins/interpolate_plugin.cpp       | 28 ++++++++-----------
 3 files changed, 12 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index ddfd85824c..97e2be3a6e 100644
--- a/README.md
+++ b/README.md
@@ -205,8 +205,6 @@ You can register a converter for your op using the `NodeConverterRegistry` insid
 
 ## Known Limitations
 
-- You cannot use both Adaptive Pooling in PyTorch and also use TRTorch Dynamic input shape (follow [#49](https://github.com/NVIDIA/TRTorch/issues/49) for the latest on the issue)
-
 ## Structure of the repo
 
 | Component     | Description                                                  |
diff --git a/core/conversion/converters/impl/interpolate.cpp b/core/conversion/converters/impl/interpolate.cpp
index 90abfa5996..0ed3cc0402 100755
--- a/core/conversion/converters/impl/interpolate.cpp
+++ b/core/conversion/converters/impl/interpolate.cpp
@@ -5,8 +5,6 @@
 #include "NvInfer.h"
 #include "NvInferRuntimeCommon.h"
 
-#include <tuple>
-
 namespace trtorch {
 namespace core {
 namespace conversion {
diff --git a/core/conversion/converters/impl/plugins/interpolate_plugin.cpp b/core/conversion/converters/impl/plugins/interpolate_plugin.cpp
index 7ebf5d18ea..df7fb5a9b5 100755
--- a/core/conversion/converters/impl/plugins/interpolate_plugin.cpp
+++ b/core/conversion/converters/impl/plugins/interpolate_plugin.cpp
@@ -134,15 +134,20 @@ size_t InterpolatePlugin::getSerializationSize() const {
 }
 
 bool InterpolatePlugin::supportsFormatCombination(int pos, const nvinfer1::PluginTensorDesc* inOut, int nbInputs, int nbOutputs) {
-    if (inOut->format != nvinfer1::TensorFormat::kLINEAR) {
-        return false;
-    } 
+    TRTORCH_ASSERT(0 <= pos && pos <= 1, "There should be exactly 2 connections to the plugin - 1 input, 1 output");
+    TRTORCH_ASSERT(nbInputs == 1, "Expected a single tensor as input to interpolate plugin");
+    TRTORCH_ASSERT(nbOutputs == 1, "Expected a single tensor as output to interpolate plugin");
 
-    if (inOut->type == DataType::kINT32 || inOut->type == DataType::kINT8) {
-        return false;
+    const PluginTensorDesc& in = inOut[0];
+
+    if (pos == 0) {
+        return (in.type == nvinfer1::DataType::kFLOAT) && (in.format == nvinfer1::TensorFormat::kLINEAR);
     }
 
-    return true;
+    // pos == 1, accessing information about output tensor
+    const PluginTensorDesc& out = inOut[1];
+    
+    return (in.type == out.type) && (in.format == out.format);
 }
 
 void InterpolatePlugin::configurePlugin(const nvinfer1::DynamicPluginTensorDesc* in, int nbInputs, const nvinfer1::DynamicPluginTensorDesc* out, int nbOutputs) {
@@ -156,16 +161,7 @@ size_t InterpolatePlugin::getWorkspaceSize(const nvinfer1::PluginTensorDesc* inp
 int InterpolatePlugin::enqueue(const nvinfer1::PluginTensorDesc* inputDesc, const nvinfer1::PluginTensorDesc* outputDesc, const void *const *inputs, 
                                                                                                         void *const *outputs, void *workspace, 
                                                                                                         cudaStream_t stream) {
-    at::Tensor input;
-
-    if (mode == "adaptive_pool2d") {
-        // use dynamically inferred input shape (for pooling)
-        input = at::from_blob((void*) inputs[0], util::toVec(inputDesc->dims), [](void*){}, tensor_options);
-    } else {
-        // use precomputed input shape (for interpolation/upsampling)
-        input = at::from_blob((void*) inputs[0], in_shape, [](void*){}, tensor_options);
-    }
-
+    at::Tensor input = at::from_blob((void*) inputs[0], util::toVec(inputDesc->dims), [](void*){}, tensor_options);
     at::Tensor output = at::from_blob(outputs[0], out_shape, [](void*){}, tensor_options);
 
     at::cuda::CUDAStream torch_stream = at::cuda::getStreamFromPool();