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Ultralytics Refactor https://ultralytics.com/actions (#17701)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
pull/17705/head^2
Paula Derrenger 3 days ago committed by GitHub
parent 77c3c0aaac
commit d670bcc2b9
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GPG Key ID: B5690EEEBB952194
8 changed files with 33 additions and 45 deletions
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  1. 2
      README.md
  2. 2
      README.zh-CN.md
  3. 2
      docs/en/hub/models.md
  4. 2
      docs/en/index.md
  5. 4
      docs/overrides/javascript/extra.js
  6. 18
      ultralytics/nn/tasks.py
  7. 24
      ultralytics/solutions/object_counter.py
  8. 2
      ultralytics/utils/torch_utils.py

2
README.md
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@ -8,7 +8,7 @@
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://www.pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
<a href="https://pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>

2
README.zh-CN.md
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@ -8,7 +8,7 @@
<div>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://www.pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
<a href="https://pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>

2
docs/en/hub/models.md
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@ -66,7 +66,7 @@ In this step, you have to choose the project in which you want to create your mo
!!! info
You can read more about the available [YOLO models](https://docs.ultralytics.com/models) and architectures in our documentation.
You can read more about the available [YOLO models](https://docs.ultralytics.com/models/) and architectures in our documentation.
By default, your model will use a pre-trained model (trained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset) to reduce training time. You can change this behavior and tweak your model's configuration by opening the **Advanced Model Configuration** accordion.

2
docs/en/index.md
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@ -20,7 +20,7 @@ keywords: Ultralytics, YOLO, YOLO11, object detection, image segmentation, deep
<br>
<br>
<a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yaml/badge.svg" alt="Ultralytics CI"></a>
<a href="https://www.pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
<a href="https://pepy.tech/projects/ultralytics"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
<a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
<a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>

4
docs/overrides/javascript/extra.js
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@ -30,7 +30,9 @@ checkAutoTheme();
document.addEventListener("DOMContentLoaded", () => {
const autoThemeInput = document.getElementById("__palette_1");
autoThemeInput?.addEventListener("click", () => {
if (autoThemeInput.checked) setTimeout(checkAutoTheme);
if (autoThemeInput.checked) {
setTimeout(checkAutoTheme);
}
});
});

18
ultralytics/nn/tasks.py
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@ -960,10 +960,8 @@ def parse_model(d, ch, verbose=True): # model_dict, input_channels(3)
m = getattr(torch.nn, m[3:]) if "nn." in m else globals()[m] # get module
for j, a in enumerate(args):
if isinstance(a, str):
try:
with contextlib.suppress(ValueError):
args[j] = locals()[a] if a in locals() else ast.literal_eval(a)
except ValueError:
pass
n = n_ = max(round(n * depth), 1) if n > 1 else n # depth gain
if m in {
Classify,
@ -1141,24 +1139,16 @@ def guess_model_task(model):
# Guess from model cfg
if isinstance(model, dict):
try:
with contextlib.suppress(Exception):
return cfg2task(model)
except Exception:
pass
# Guess from PyTorch model
if isinstance(model, nn.Module): # PyTorch model
for x in "model.args", "model.model.args", "model.model.model.args":
try:
with contextlib.suppress(Exception):
return eval(x)["task"]
except Exception:
pass
for x in "model.yaml", "model.model.yaml", "model.model.model.yaml":
try:
with contextlib.suppress(Exception):
return cfg2task(eval(x))
except Exception:
pass
for m in model.modules():
if isinstance(m, Segment):
return "segment"

24
ultralytics/solutions/object_counter.py
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@ -80,9 +80,8 @@ class ObjectCounter(BaseSolution):
else: # Moving left
self.out_count += 1
self.classwise_counts[self.names[cls]]["OUT"] += 1
else:
# Horizontal region: Compare y-coordinates to determine direction
if current_centroid[1] > prev_position[1]: # Moving downward
elif current_centroid[1] > prev_position[1]: # Moving downward
self.in_count += 1
self.classwise_counts[self.names[cls]]["IN"] += 1
else: # Moving upward
@ -94,23 +93,20 @@ class ObjectCounter(BaseSolution):
polygon = self.Polygon(self.region)
if polygon.contains(self.Point(current_centroid)):
# Determine motion direction for vertical or horizontal polygons
region_width = max([p[0] for p in self.region]) - min([p[0] for p in self.region])
region_height = max([p[1] for p in self.region]) - min([p[1] for p in self.region])
if region_width < region_height: # Vertical-oriented polygon
if current_centroid[0] > prev_position[0]: # Moving right
region_width = max(p[0] for p in self.region) - min(p[0] for p in self.region)
region_height = max(p[1] for p in self.region) - min(p[1] for p in self.region)
if (
region_width < region_height
and current_centroid[0] > prev_position[0]
or region_width >= region_height
and current_centroid[1] > prev_position[1]
): # Moving right
self.in_count += 1
self.classwise_counts[self.names[cls]]["IN"] += 1
else: # Moving left
self.out_count += 1
self.classwise_counts[self.names[cls]]["OUT"] += 1
else: # Horizontal-oriented polygon
if current_centroid[1] > prev_position[1]: # Moving downward
self.in_count += 1
self.classwise_counts[self.names[cls]]["IN"] += 1
else: # Moving upward
self.out_count += 1
self.classwise_counts[self.names[cls]]["OUT"] += 1
self.counted_ids.append(track_id)
def store_classwise_counts(self, cls):

2
ultralytics/utils/torch_utils.py
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@ -675,7 +675,7 @@ def profile(input, ops, n=10, device=None, max_num_obj=0):
torch.randn(
x.shape[0],
max_num_obj,
int(sum([(x.shape[-1] / s) * (x.shape[-2] / s) for s in m.stride.tolist()])),
int(sum((x.shape[-1] / s) * (x.shape[-2] / s) for s in m.stride.tolist())),
device=device,
dtype=torch.float32,
)

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