message: fix(//cpp/ptq): Default version of the app should not resize images
diff: @@ -35,7 +35,6 @@ struct Resize : public torch::data::transforms::TensorTransform<torch::Tensor> {
  torch::jit::Module compile_int8_model(const std::string& data_dir, torch::jit::Module& mod) {
  auto calibration_dataset = datasets::CIFAR10(data_dir, datasets::CIFAR10::Mode::kTest)
  .use_subset(320)
  - .map(Resize({300, 300}))
  .map(torch::data::transforms::Normalize<>({0.4914, 0.4822, 0.4465},
  {0.2023, 0.1994, 0.2010}))
  .map(torch::data::transforms::Stack<>());
  @@ -48,7 +47,7 @@ torch::jit::Module compile_int8_model(const std::string& data_dir, torch::jit::M
  auto calibrator = trtorch::ptq::make_int8_calibrator(std::move(calibration_dataloader), calibration_cache_file, true);
  - std::vector<std::vector<int64_t>> input_shape = {{32, 3, 300, 300}};
  + std::vector<std::vector<int64_t>> input_shape = {{32, 3, 32, 32}};
  /// Configure settings for compilation
  auto extra_info = trtorch::ExtraInfo({input_shape});
  /// Set operating precision to INT8
  @@ -99,7 +98,6 @@ int main(int argc, const char* argv[]) {
  /// Dataloader moved into calibrator so need another for inference
  auto eval_dataset = datasets::CIFAR10(data_dir, datasets::CIFAR10::Mode::kTest)
  - .map(Resize({300, 300}))
  .map(torch::data::transforms::Normalize<>({0.4914, 0.4822, 0.4465},
  {0.2023, 0.1994, 0.2010}))
  .map(torch::data::transforms::Stack<>());
  @@ -131,7 +129,7 @@ int main(int argc, const char* argv[]) {
  if (images.sizes()[0] < 32) {
  /// To handle smaller batches util Optimization profiles work with Int8
  auto diff = 32 - images.sizes()[0];
  - auto img_padding = torch::zeros({diff, 3, 300, 300}, {torch::kCUDA});
  + auto img_padding = torch::zeros({diff, 3, 32, 32}, {torch::kCUDA});
  auto target_padding = torch::zeros({diff}, {torch::kCUDA});
  images = torch::cat({images, img_padding}, 0);
  targets = torch::cat({targets, target_padding}, 0);
  @@ -152,7 +150,7 @@ int main(int argc, const char* argv[]) {
  std::cout << "Accuracy of quantized model on test set: " << 100 * (correct / total) << "%" << std::endl;
  /// Time execution in JIT-FP32 and TRT-INT8
  - std::vector<std::vector<int64_t>> dims = {{32, 3, 300, 300}};
  + std::vector<std::vector<int64_t>> dims = {{32, 3, 32, 32}};
  auto jit_runtimes = benchmark_module(mod, dims[0]);
  print_avg_std_dev("JIT model FP32", jit_runtimes, dims[0][0]);
  