MAINT: clean code.

oddkiva · Dec 21, 2023 · a94e1ab · a94e1ab
1 parent 8036076
commit a94e1ab
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Showing 3 changed files with 11 additions and 13 deletions.
diff --git a/cpp/test/Shakti/Cuda/TensorRT/test_neuralnetworks_tensorrt_inference_engine.cpp b/cpp/test/Shakti/Cuda/TensorRT/test_neuralnetworks_tensorrt_inference_engine.cpp
@@ -36,9 +36,9 @@ BOOST_AUTO_TEST_SUITE(TestTensorRT)
 BOOST_AUTO_TEST_CASE(test_inference_engine)
 {
   // Load the network on the host device (CPU).
-  const auto data_dir_path = fs::canonical(fs::path{src_path("data")});
-  const auto yolov4_tiny_dirpath =
-      data_dir_path / "trained_models" / "yolov4-tiny";
+  const auto model_dir_path =
+      fs::canonical(fs::path{src_path("trained_models")});
+  const auto yolov4_tiny_dirpath = model_dir_path / "yolov4-tiny";
 
   // Convert it into a TensorRT network object.
   auto serialized_net = trt::convert_yolo_v4_network_from_darknet(

diff --git a/cpp/test/Shakti/Cuda/TensorRT/test_neuralnetworks_tensorrt_onnx_parser.cpp b/cpp/test/Shakti/Cuda/TensorRT/test_neuralnetworks_tensorrt_onnx_parser.cpp
@@ -32,8 +32,6 @@ namespace sara = DO::Sara;
 namespace shakti = DO::Shakti;
 namespace trt = shakti::TensorRT;
 
-namespace nvonnx = nvonnxparser;
-
 
 template <typename T, int N>
 using PinnedTensor = sara::Tensor_<T, N, shakti::PinnedMemoryAllocator>;
@@ -101,9 +99,9 @@ BOOST_AUTO_TEST_CASE(test_yolox_tiny_onnx_conversion_to_trt_serialized_engine)
   //
   // It is available here:
   // https://yolox.readthedocs.io/en/latest/demo/onnx_readme.html#download-onnx-models
-  const auto data_dir_path = fs::canonical(fs::path{src_path("data")});
-  const auto yolox_tiny_onnx_filepath =
-      data_dir_path / "trained_models" / "yolox_tiny.onnx";
+  const auto model_dir_path =
+      fs::canonical(fs::path{src_path("trained_models")});
+  const auto yolox_tiny_onnx_filepath = model_dir_path / "yolox_tiny.onnx";
   BOOST_CHECK(fs::exists(yolox_tiny_onnx_filepath));
 
   // Instantiate an ONNX parser and read the ONNX model file.

diff --git a/python/oddkiva/shakti/inference/darknet/torch_layers.py b/python/oddkiva/shakti/inference/darknet/torch_layers.py
@@ -53,10 +53,10 @@ def forward(self, x):
 
     def load_weights(self, weights_file: Path):
         pass
-        # with open(weights_file, 'rb') as fp:
-        #     w_data = fp.read(conv.weight.shape.numel() * 4)
-        #     conv.weight.data.copy_(torch.from_numpy
-        #     conv.bias.data = fp.read(conv.bias.shape.numel() * 4)
+    # with open(weights_file, 'rb') as fp:
+    #     w_data = fp.read(conv.weight.shape.numel() * 4)
+    #     conv.weight.data.copy_(torch.from_numpy
+    #     conv.bias.data = fp.read(conv.bias.shape.numel() * 4)
 
 
 class MaxPool(nn.Module):
@@ -209,7 +209,7 @@ def forward(self, x):
         # P[object] and P[class|object] probabilities.
         for box in range(0, 3):
             c_begin = box * num_box_features + 4
-            c_end = (box + 1) * num_box_features 
+            c_end = (box + 1) * num_box_features
             y[:, c_begin:c_end, :, :] = nn.Sigmoid(x[:, c_begin:c_end, :, :])
 
         return y