Merge pull request #6 from wisesight/feature/support-inf1

Feature/support inf1

docs/build_docs.py

@@ -63,7 +63,6 @@ def prepare_docs_markdown(clone_repos=True):
 
 def update_page_title(file_path: Path, new_title: str):
     """Update the title of an HTML file."""
-
     # Read the content of the file
     with open(file_path, encoding="utf-8") as file:
         content = file.read()

docs/overrides/javascript/extra.js

@@ -1,7 +1,9 @@
 // Function that applies light/dark theme based on the user's preference
 const applyAutoTheme = () => {
   // Determine the user's preferred color scheme
-  const prefersLight = window.matchMedia("(prefers-color-scheme: light)").matches;
+  const prefersLight = window.matchMedia(
+    "(prefers-color-scheme: light)",
+  ).matches;
   const prefersDark = window.matchMedia("(prefers-color-scheme: dark)").matches;
 
   // Apply the appropriate attributes based on the user's preference
@@ -17,15 +19,31 @@ const applyAutoTheme = () => {
 // Function that checks and applies light/dark theme based on the user's preference (if auto theme is enabled)
 function checkAutoTheme() {
   // Array of supported language codes -> each language has its own palette (stored in local storage)
-  const supportedLangCodes = ["en", "zh", "ko", "ja", "ru", "de", "fr", "es", "pt", "it", "tr", "vi", "nl"];
+  const supportedLangCodes = [
+    "en",
+    "zh",
+    "ko",
+    "ja",
+    "ru",
+    "de",
+    "fr",
+    "es",
+    "pt",
+    "it",
+    "tr",
+    "vi",
+    "nl",
+  ];
   // Get the URL path
   const path = window.location.pathname;
   // Extract the language code from the URL (assuming it's in the format /xx/...)
   const langCode = path.split("/")[1];
   // Check if the extracted language code is in the supported languages
   const isValidLangCode = supportedLangCodes.includes(langCode);
   // Construct the local storage key based on the language code if valid, otherwise default to the root key
-  const localStorageKey = isValidLangCode ? `/${langCode}/.__palette` : "/.__palette";
+  const localStorageKey = isValidLangCode
+    ? `/${langCode}/.__palette`
+    : "/.__palette";
   // Retrieve the palette from local storage using the constructed key
   const palette = localStorage.getItem(localStorageKey);
   if (palette) {
@@ -41,8 +59,12 @@ function checkAutoTheme() {
 checkAutoTheme();
 
 // Re-run the function when the user's preference changes (when the user changes their system theme)
-window.matchMedia("(prefers-color-scheme: light)").addEventListener("change", checkAutoTheme);
-window.matchMedia("(prefers-color-scheme: dark)").addEventListener("change", checkAutoTheme);
+window
+  .matchMedia("(prefers-color-scheme: light)")
+  .addEventListener("change", checkAutoTheme);
+window
+  .matchMedia("(prefers-color-scheme: dark)")
+  .addEventListener("change", checkAutoTheme);
 
 // Re-run the function when the palette changes (e.g. user switched from dark theme to auto theme)
 // ! We can't use window.addEventListener("storage", checkAutoTheme) because it will NOT be triggered on the current tab
@@ -61,9 +83,15 @@ if (autoThemeInput) {
 }
 
 // Add iframe navigation
-window.onhashchange = function() {
-    window.parent.postMessage({
-        type: 'navigation',
-        hash: window.location.pathname + window.location.search + window.location.hash
-    }, '*');
+window.onhashchange = function () {
+  window.parent.postMessage(
+    {
+      type: "navigation",
+      hash:
+        window.location.pathname +
+        window.location.search +
+        window.location.hash,
+    },
+    "*",
+  );
 };

docs/overrides/main.html

@@ -1,8 +1,6 @@
 <!--Ultralytics YOLO 🚀, AGPL-3.0 license-->
 
-{% extends "base.html" %}
-
-{% block announce %}
+{% extends "base.html" %} {% block announce %}
 <a
   href="https://github.com/ultralytics/ultralytics/releases/tag/v8.2.0"
   target="_blank"

docs/overrides/partials/comments.html

@@ -2,50 +2,51 @@
 <h2 id="__comments">{{ lang.t("meta.comments") }}</h2>
 
 <!-- Insert Giscus code snippet from https://giscus.app/ here -->
-<script async
-        crossorigin="anonymous"
-        data-category="Docs"
-        data-category-id="DIC_kwDOH-jzvc4CWLkL"
-        data-emit-metadata="0"
-        data-input-position="top"
-        data-lang="en"
-        data-loading="lazy"
-        data-mapping="pathname"
-        data-reactions-enabled="1"
-        data-repo="ultralytics/ultralytics"
-        data-repo-id="R_kgDOH-jzvQ"
-        data-strict="1"
-        data-theme="preferred_color_scheme"
-        src="https://giscus.app/client.js">
-</script>
+<script
+  async
+  crossorigin="anonymous"
+  data-category="Docs"
+  data-category-id="DIC_kwDOH-jzvc4CWLkL"
+  data-emit-metadata="0"
+  data-input-position="top"
+  data-lang="en"
+  data-loading="lazy"
+  data-mapping="pathname"
+  data-reactions-enabled="1"
+  data-repo="ultralytics/ultralytics"
+  data-repo-id="R_kgDOH-jzvQ"
+  data-strict="1"
+  data-theme="preferred_color_scheme"
+  src="https://giscus.app/client.js"
+></script>
 
 <!-- Synchronize Giscus theme with palette -->
 <script>
-    var giscus = document.querySelector("script[src*=giscus]")
+  var giscus = document.querySelector("script[src*=giscus]");
 
-    /* Set palette on initial load */
-    var palette = __md_get("__palette")
-    if (palette && typeof palette.color === "object") {
-      var theme = palette.color.scheme === "slate" ? "dark" : "light"
-      giscus.setAttribute("data-theme", theme)
-    }
+  /* Set palette on initial load */
+  var palette = __md_get("__palette");
+  if (palette && typeof palette.color === "object") {
+    var theme = palette.color.scheme === "slate" ? "dark" : "light";
+    giscus.setAttribute("data-theme", theme);
+  }
 
-    /* Register event handlers after documented loaded */
-    document.addEventListener("DOMContentLoaded", function() {
-      var ref = document.querySelector("[data-md-component=palette]")
-      ref.addEventListener("change", function() {
-        var palette = __md_get("__palette")
-        if (palette && typeof palette.color === "object") {
-          var theme = palette.color.scheme === "slate" ? "dark" : "light"
+  /* Register event handlers after documented loaded */
+  document.addEventListener("DOMContentLoaded", function () {
+    var ref = document.querySelector("[data-md-component=palette]");
+    ref.addEventListener("change", function () {
+      var palette = __md_get("__palette");
+      if (palette && typeof palette.color === "object") {
+        var theme = palette.color.scheme === "slate" ? "dark" : "light";
 
-          /* Instruct Giscus to change theme */
-          var frame = document.querySelector(".giscus-frame")
-          frame.contentWindow.postMessage(
-            { giscus: { setConfig: { theme } } },
-            "https://giscus.app"
-          )
-        }
-      })
-    })
+        /* Instruct Giscus to change theme */
+        var frame = document.querySelector(".giscus-frame");
+        frame.contentWindow.postMessage(
+          { giscus: { setConfig: { theme } } },
+          "https://giscus.app",
+        );
+      }
+    });
+  });
 </script>
 {% endif %}

docs/overrides/partials/source-file.html

@@ -3,24 +3,20 @@
 <!-- taken from
 https://github.com/squidfunk/mkdocs-material/blob/master/src/partials/source-file.html -->
 
-<br>
+<br />
 <div class="md-source-file">
-    <small>
+  <small>
+    <!-- mkdocs-git-revision-date-localized-plugin -->
+    {% if page.meta.git_revision_date_localized %} 📅 {{
+    lang.t("source.file.date.updated") }}: {{
+    page.meta.git_revision_date_localized }} {% if
+    page.meta.git_creation_date_localized %}
+    <br />
+    🎂 {{ lang.t("source.file.date.created") }}: {{
+    page.meta.git_creation_date_localized }} {% endif %}
 
-        <!-- mkdocs-git-revision-date-localized-plugin -->
-        {% if page.meta.git_revision_date_localized %}
-        📅 {{ lang.t("source.file.date.updated") }}:
-        {{ page.meta.git_revision_date_localized }}
-        {% if page.meta.git_creation_date_localized %}
-        <br/>
-        🎂 {{ lang.t("source.file.date.created") }}:
-        {{ page.meta.git_creation_date_localized }}
-        {% endif %}
-
-        <!-- mkdocs-git-revision-date-plugin -->
-        {% elif page.meta.revision_date %}
-        📅 {{ lang.t("source.file.date.updated") }}:
-        {{ page.meta.revision_date }}
-        {% endif %}
-    </small>
+    <!-- mkdocs-git-revision-date-plugin -->
+    {% elif page.meta.revision_date %} 📅 {{ lang.t("source.file.date.updated")
+    }}: {{ page.meta.revision_date }} {% endif %}
+  </small>
 </div>

examples/YOLOv8-Action-Recognition/action_recognition.py

@@ -199,7 +199,6 @@ def __call__(self, sequences: torch.Tensor) -> torch.Tensor:
         Returns:
             torch.Tensor: The model's output.
         """
-
         input_ids = self.processor(text=self.labels, return_tensors="pt", padding=True)["input_ids"].to(self.device)
 
         inputs = {"pixel_values": sequences, "input_ids": input_ids}

examples/YOLOv8-ONNXRuntime/main.py

@@ -48,7 +48,6 @@ def draw_detections(self, img, box, score, class_id):
         Returns:
             None
         """
-
         # Extract the coordinates of the bounding box
         x1, y1, w, h = box
 
@@ -118,7 +117,6 @@ def postprocess(self, input_image, output):
         Returns:
             numpy.ndarray: The input image with detections drawn on it.
         """
-
         # Transpose and squeeze the output to match the expected shape
         outputs = np.transpose(np.squeeze(output[0]))
 

examples/YOLOv8-OpenCV-int8-tflite-Python/main.py

@@ -28,7 +28,6 @@ def __init__(
 
     def __call__(self, labels=None, image=None):
         """Return updated labels and image with added border."""
-
         if labels is None:
             labels = {}
         img = labels.get("img") if image is None else image
@@ -77,7 +76,6 @@ def __call__(self, labels=None, image=None):
 
     def _update_labels(self, labels, ratio, padw, padh):
         """Update labels."""
-
         labels["instances"].convert_bbox(format="xyxy")
         labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
         labels["instances"].scale(*ratio)
@@ -96,7 +94,6 @@ def __init__(self, tflite_model, input_image, confidence_thres, iou_thres):
             confidence_thres: Confidence threshold for filtering detections.
             iou_thres: IoU (Intersection over Union) threshold for non-maximum suppression.
         """
-
         self.tflite_model = tflite_model
         self.input_image = input_image
         self.confidence_thres = confidence_thres
@@ -121,7 +118,6 @@ def draw_detections(self, img, box, score, class_id):
         Returns:
             None
         """
-
         # Extract the coordinates of the bounding box
         x1, y1, w, h = box
 
@@ -160,7 +156,6 @@ def preprocess(self):
         Returns:
             image_data: Preprocessed image data ready for inference.
         """
-
         # Read the input image using OpenCV
         self.img = cv2.imread(self.input_image)
 
@@ -189,7 +184,6 @@ def postprocess(self, input_image, output):
         Returns:
             numpy.ndarray: The input image with detections drawn on it.
         """
-
         boxes = []
         scores = []
         class_ids = []
@@ -234,7 +228,6 @@ def main(self):
         Returns:
             output_img: The output image with drawn detections.
         """
-
         # Create an interpreter for the TFLite model
         interpreter = tflite.Interpreter(model_path=self.tflite_model)
         self.model = interpreter

examples/YOLOv8-SAHI-Inference-Video/yolov8_sahi.py

@@ -22,7 +22,6 @@ def run(weights="yolov8n.pt", source="test.mp4", view_img=False, save_img=False,
         save_img (bool): Save results.
         exist_ok (bool): Overwrite existing files.
     """
-
     # Check source path
     if not Path(source).exists():
         raise FileNotFoundError(f"Source path '{source}' does not exist.")

examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py

@@ -21,7 +21,6 @@ def __init__(self, onnx_model):
         Args:
             onnx_model (str): Path to the ONNX model.
         """
-
         # Build Ort session
         self.session = ort.InferenceSession(
             onnx_model,
@@ -57,7 +56,6 @@ def __call__(self, im0, conf_threshold=0.4, iou_threshold=0.45, nm=32):
             segments (List): list of segments.
             masks (np.ndarray): [N, H, W], output masks.
         """
-
         # Pre-process
         im, ratio, (pad_w, pad_h) = self.preprocess(im0)
 
@@ -90,7 +88,6 @@ def preprocess(self, img):
             pad_w (float): width padding in letterbox.
             pad_h (float): height padding in letterbox.
         """
-
         # Resize and pad input image using letterbox() (Borrowed from Ultralytics)
         shape = img.shape[:2]  # original image shape
         new_shape = (self.model_height, self.model_width)
@@ -170,7 +167,7 @@ def postprocess(self, preds, im0, ratio, pad_w, pad_h, conf_threshold, iou_thres
     def masks2segments(masks):
         """
         It takes a list of masks(n,h,w) and returns a list of segments(n,xy) (Borrowed from
-        https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L750)
+        https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L750).
 
         Args:
             masks (numpy.ndarray): the output of the model, which is a tensor of shape (batch_size, 160, 160).
@@ -192,7 +189,7 @@ def masks2segments(masks):
     def crop_mask(masks, boxes):
         """
         It takes a mask and a bounding box, and returns a mask that is cropped to the bounding box. (Borrowed from
-        https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L599)
+        https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L599).
 
         Args:
             masks (Numpy.ndarray): [n, h, w] tensor of masks.
@@ -210,7 +207,7 @@ def crop_mask(masks, boxes):
     def process_mask(self, protos, masks_in, bboxes, im0_shape):
         """
         Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher quality
-        but is slower. (Borrowed from https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L618)
+        but is slower. (Borrowed from https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L618).
 
         Args:
             protos (numpy.ndarray): [mask_dim, mask_h, mask_w].
@@ -233,7 +230,7 @@ def process_mask(self, protos, masks_in, bboxes, im0_shape):
     def scale_mask(masks, im0_shape, ratio_pad=None):
         """
         Takes a mask, and resizes it to the original image size. (Borrowed from
-        https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L305)
+        https://github.com/ultralytics/ultralytics/blob/465df3024f44fa97d4fad9986530d5a13cdabdca/ultralytics/utils/ops.py#L305).
 
         Args:
             masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
@@ -277,7 +274,6 @@ def draw_and_visualize(self, im, bboxes, segments, vis=False, save=True):
         Returns:
             None
         """
-
         # Draw rectangles and polygons
         im_canvas = im.copy()
         for (*box, conf, cls_), segment in zip(bboxes, segments):