fix(//cpp/ptq): Default version of the app should not resize images

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

cpp/ptq/main.cpp

@@ -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]);