Fixed some spelling/typos (#276)

deployment/libtorch/README.md

@@ -59,8 +59,8 @@ The LibTorch inference for `yolort`, both GPU and CPU are supported.
 1. Now, you can infer your own images.
 
    ```bash
-   ./yolo_inference [--input_source ../../../test/assets/zidane.jpg]
-                    [--checkpoint ../yolov5n.torchscript.pt]
-                    [--labelmap ../../../notebooks/assets/coco.names]
-                    [--gpu]  # GPU switch, which is optional, and set False as default
+   ./yolort_torch [--input_source ../../../test/assets/zidane.jpg]
+                  [--checkpoint ../yolov5n.torchscript.pt]
+                  [--labelmap ../../../notebooks/assets/coco.names]
+                  [--gpu]  # GPU switch, which is optional, and set False as default
    ```

deployment/libtorch/main.cpp

@@ -172,9 +172,9 @@ int main(int argc, char* argv[]) {
 
   // Run once to warm up
   std::cout << "Run once on empty image" << std::endl;
-  auto img_dumy = torch::rand({3, 416, 320}, options);
+  auto img_dummy = torch::rand({3, 416, 320}, options);
 
-  images.push_back(img_dumy);
+  images.push_back(img_dummy);
   inputs.push_back(images);
 
   auto output = module.forward(inputs);

deployment/tensorrt/README.md

@@ -8,7 +8,7 @@ The TensorRT inference for `yolort`, support CUDA only.
 
 ## Usage
 
-1. Create build director and cmake config.
+1. Create build directory and cmake config.
 
    ```bash
    mkdir -p build/ && cd build/

notebooks/export-relay-inference-tvm.ipynb

@@ -336,7 +336,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Varify the Inference Output on TVM backend"
+    "## Verify the Inference Output on TVM backend"
    ]
   },
   {

notebooks/how-to-align-with-ultralytics-yolov5.ipynb

@@ -324,7 +324,7 @@
    "id": "4f3f7c09",
    "metadata": {},
    "source": [
-    "## Varify the detection results between yolort and ultralytics"
+    "## Verify the detection results between yolort and ultralytics"
    ]
   },
   {

notebooks/inference-pytorch-export-libtorch.ipynb

@@ -113,7 +113,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Varify the PyTorch backend inference results"
+    "## Verify the PyTorch backend inference results"
    ]
   },
   {
@@ -375,7 +375,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Varify the Inference Output on LibTorch backend"
+    "## Verify the Inference Output on LibTorch backend"
    ]
   },
   {

notebooks/onnx-graphsurgeon-inference-tensorrt.ipynb

@@ -618,7 +618,7 @@
    "id": "00196f91-2b49-4b9d-8be7-aa8aea11c0ee",
    "metadata": {},
    "source": [
-    "## Varify the detection results between yolort and TensorRT"
+    "## Verify the detection results between yolort and TensorRT"
    ]
   },
   {

test/test_onnx.py

@@ -59,10 +59,10 @@ def run_model(
             with torch.no_grad():
                 if isinstance(test_inputs, Tensor) or isinstance(test_inputs, list):
                     test_inputs = (test_inputs,)
-                test_ouputs = model(*test_inputs)
-                if isinstance(test_ouputs, Tensor):
-                    test_ouputs = (test_ouputs,)
-            self.ort_validate(onnx_io, test_inputs, test_ouputs, tolerate_small_mismatch)
+                test_outputs = model(*test_inputs)
+                if isinstance(test_outputs, Tensor):
+                    test_outputs = (test_outputs,)
+            self.ort_validate(onnx_io, test_inputs, test_outputs, tolerate_small_mismatch)
 
     def ort_validate(self, onnx_io, inputs, outputs, tolerate_small_mismatch=False):
 

tools/export_model.py

@@ -75,7 +75,7 @@ def export_onnx(
     Args:
         model (nn.Module): The model to be exported.
         inputs (Tuple[torch.Tensor]): The inputs to the model.
-        export_onnx_path (str): A string containg a file name. A binary Protobuf
+        export_onnx_path (str): A string containing a file name. A binary Protobuf
             will be written to this file.
         dynamic_axes (dict): A dictionary of dynamic axes.
         input_names (str): A names list of input names.
@@ -110,7 +110,7 @@ def simplify_onnx(onnx_path, input_shapes):
     # Load onnx mode
     onnx_model = onnx.load(onnx_path)
 
-    # Simlify the ONNX model
+    # Simplify the ONNX model
     model_sim, check = onnxsim.simplify(
         onnx_model,
         input_shapes=input_shapes,

yolort/models/backbone_utils.py

@@ -13,7 +13,7 @@ class BackboneWithPAN(nn.Module):
     Adds a PAN on top of a model.
     Internally, it uses torchvision.models._utils.IntermediateLayerGetter to
     extract a submodel that returns the feature maps specified in return_layers.
-    The same limitations of IntermediatLayerGetter apply here.
+    The same limitations of IntermediateLayerGetter apply here.
 
     Args:
         backbone (nn.Module)

yolort/relaying/trace_wrapper.py

@@ -40,7 +40,7 @@ def get_trace_module(
     input_shape: Tuple[int, int] = (416, 416),
 ):
     """
-    Get the tarcing of a given model function.
+    Get the tracing of a given model function.
 
     Example:
 

yolort/runtime/trt_helper.py

@@ -129,7 +129,7 @@ def __init__(
 
         model.load_state_dict(model_info["state_dict"])
         self.model = model
-        self.num_clases = num_classes
+        self.num_classes = num_classes
 
     @torch.no_grad()
     def forward(self, inputs: Tensor) -> Tuple[Tensor, Tensor]:

yolort/runtime/yolo_graphsurgeon.py

@@ -68,7 +68,7 @@ def __init__(
 
         # Fold constants via ONNX-GS that PyTorch2ONNX may have missed
         self.graph.fold_constants()
-        self.num_classes = model.num_clases
+        self.num_classes = model.num_classes
         self.batch_size = 1
 
     def infer(self):

yolort/utils/image_utils.py

@@ -296,7 +296,7 @@ def anchor_match_visualize(images, targets, indices, anchors, pred):
             )
 
             # The anchors need to restore the offset.
-            # In eacy layer there has at most 3x3=9 anchors for matching.
+            # In each layer there has at most 3x3=9 anchors for matching.
             anchor_restored = restore_anchor(anchor, grid_x, grid_y, stride, pred[i].shape, image_sizes)
 
             # visualize positive anchor
@@ -309,13 +309,13 @@ def anchor_match_visualize(images, targets, indices, anchors, pred):
     return images_with_anchor
 
 
-def overlay_bbox(image, bboxs_list, color=None, thickness=2, font_scale=0.3, with_mask=False):
+def overlay_bbox(image, bboxes_list, color=None, thickness=2, font_scale=0.3, with_mask=False):
     """
     Visualize bbox in object detection by drawing rectangle.
 
     Args:
         image: numpy.ndarray.
-        bboxs_list: list: [pts_xyxy, prob, id]: label or prediction.
+        bboxes_list: list: [pts_xyxy, prob, id]: label or prediction.
         color: tuple.
         thickness: int.
         font_scale: float.
@@ -329,7 +329,7 @@ def overlay_bbox(image, bboxs_list, color=None, thickness=2, font_scale=0.3, wit
     txt = ""
     COLORS = color_list()  # list of COLORS
 
-    for bbox in bboxs_list:
+    for bbox in bboxes_list:
         if len(bbox) == 5:
             txt = "{:.3f}".format(bbox[4])
         elif len(bbox) == 6: