Code Refactor `ruff check --fix --extend-select I` (#13672)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
pull/13674/head
Glenn Jocher 9 months ago committed by GitHub
parent c8514a6754
commit 6227d8f8a1
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  1. 18
      .github/workflows/publish.yml
  2. 4
      docs/en/reference/utils/callbacks/mlflow.md
  3. 12
      ultralytics/cfg/datasets/lvis.yaml
  4. 2
      ultralytics/data/explorer/gui/dash.py
  5. 8
      ultralytics/nn/tasks.py
  6. 10
      ultralytics/utils/callbacks/mlflow.py

@ -88,7 +88,7 @@ jobs:
GITHUB_TOKEN = os.getenv('GITHUB_TOKEN')
CURRENT_TAG = os.getenv('CURRENT_TAG')
PREVIOUS_TAG = os.getenv('PREVIOUS_TAG')
# Check for required environment variables
if not all([OPENAI_AZURE_API_KEY, OPENAI_AZURE_ENDPOINT, OPENAI_AZURE_API_VERSION, GITHUB_TOKEN, CURRENT_TAG, PREVIOUS_TAG]):
print(OPENAI_AZURE_API_KEY)
@ -98,24 +98,24 @@ jobs:
print(CURRENT_TAG)
print(PREVIOUS_TAG)
raise ValueError("One or more required environment variables are missing.")
latest_tag = f"v{CURRENT_TAG}"
previous_tag = f"v{PREVIOUS_TAG}"
repo = 'ultralytics/ultralytics'
headers = {"Authorization": f"token {GITHUB_TOKEN}", "Accept": "application/vnd.github.v3.diff"}
# Get the diff between the tags
url = f"https://api.github.com/repos/{repo}/compare/{previous_tag}...{latest_tag}"
response = requests.get(url, headers=headers)
diff = response.text if response.status_code == 200 else f"Failed to get diff: {response.content}"
# Set up OpenAI client
client = openai.AzureOpenAI(
api_key=OPENAI_AZURE_API_KEY,
api_version=OPENAI_AZURE_API_VERSION,
azure_endpoint=OPENAI_AZURE_ENDPOINT
)
# Prepare messages for OpenAI completion
messages = [
{
@ -131,17 +131,17 @@ jobs:
f"\n\nHere's the release diff:\n\n{diff[:96000]}",
}
]
try:
completion = client.chat.completions.create(model="gpt-4o-2024-05-13", messages=messages)
summary = completion.choices[0].message.content.strip()
except openai.error.OpenAIError as e:
print(f"Failed to generate summary: {e}")
raise
# Get the latest commit message
commit_message = subprocess.run(['git', 'log', '-1', '--pretty=%B'], check=True, text=True, capture_output=True).stdout.split("\n")[0].strip()
# Prepare release data
release = {
'tag_name': latest_tag,
@ -150,7 +150,7 @@ jobs:
'draft': False,
'prerelease': False
}
# Create the release on GitHub
release_url = f"https://api.github.com/repos/{repo}/releases"
release_response = requests.post(release_url, headers=headers, data=json.dumps(release))

@ -11,6 +11,10 @@ keywords: MLflow, Ultralytics YOLO, logging, metrics, parameters, model artifact
<br><br>
## ::: ultralytics.utils.callbacks.mlflow.sanitize_dict
<br><br>
## ::: ultralytics.utils.callbacks.mlflow.on_pretrain_routine_end
<br><br>

@ -221,8 +221,7 @@ names:
204: cape
205: cappuccino/coffee cappuccino
206: car/car automobile/auto/auto automobile/automobile
207: railcar/railcar part of a train/railway car/railway car part of a train/railroad
car/railroad car part of a train
207: railcar/railcar part of a train/railway car/railway car part of a train/railroad car/railroad car part of a train
208: elevator car
209: car battery/automobile battery
210: identity card
@ -241,8 +240,7 @@ names:
223: cast/plaster cast/plaster bandage
224: cat
225: cauliflower
226: cayenne/cayenne spice/cayenne pepper/cayenne pepper spice/red pepper/red pepper
spice
226: cayenne/cayenne spice/cayenne pepper/cayenne pepper spice/red pepper/red pepper spice
227: CD player
228: celery
229: cellular telephone/cellular phone/cellphone/mobile phone/smart phone
@ -258,8 +256,7 @@ names:
239: chessboard
240: chicken/chicken animal
241: chickpea/garbanzo
242: chili/chili vegetable/chili pepper/chili pepper vegetable/chilli/chilli vegetable/chilly/chilly
vegetable/chile/chile vegetable
242: chili/chili vegetable/chili pepper/chili pepper vegetable/chilli/chilli vegetable/chilly/chilly vegetable/chile/chile vegetable
243: chime/gong
244: chinaware
245: crisp/crisp potato chip/potato chip
@ -1061,8 +1058,7 @@ names:
1041: sweater
1042: sweatshirt
1043: sweet potato
1044: swimsuit/swimwear/bathing suit/swimming costume/bathing costume/swimming trunks/bathing
trunks
1044: swimsuit/swimwear/bathing suit/swimming costume/bathing costume/swimming trunks/bathing trunks
1045: sword
1046: syringe
1047: Tabasco sauce

@ -259,7 +259,7 @@ def layout():
with col2:
similarity_form(selected_imgs)
display_labels = st.checkbox("Labels", value=False, key="display_labels")
st.checkbox("Labels", value=False, key="display_labels")
utralytics_explorer_docs_callback()

@ -293,8 +293,12 @@ class DetectionModel(BaseModel):
if isinstance(m, Detect): # includes all Detect subclasses like Segment, Pose, OBB, WorldDetect
s = 256 # 2x min stride
m.inplace = self.inplace
forward = lambda x: self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
def _forward(x):
"""Performs a forward pass through the model, handling different Detect subclass types accordingly."""
return self.forward(x)[0] if isinstance(m, (Segment, Pose, OBB)) else self.forward(x)
m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward
self.stride = m.stride
m.bias_init() # only run once
else:

@ -34,11 +34,13 @@ try:
from pathlib import Path
PREFIX = colorstr("MLflow: ")
SANITIZE = lambda x: {k.replace("(", "").replace(")", ""): float(v) for k, v in x.items()}
except (ImportError, AssertionError):
mlflow = None
def sanitize_dict(x):
"""Sanitize dictionary keys by removing parentheses and converting values to floats."""
return {k.replace("(", "").replace(")", ""): float(v) for k, v in x.items()}
def on_pretrain_routine_end(trainer):
"""
@ -88,8 +90,8 @@ def on_train_epoch_end(trainer):
if mlflow:
mlflow.log_metrics(
metrics={
**SANITIZE(trainer.lr),
**SANITIZE(trainer.label_loss_items(trainer.tloss, prefix="train")),
**sanitize_dict(trainer.lr),
**sanitize_dict(trainer.label_loss_items(trainer.tloss, prefix="train")),
},
step=trainer.epoch,
)
@ -98,7 +100,7 @@ def on_train_epoch_end(trainer):
def on_fit_epoch_end(trainer):
"""Log training metrics at the end of each fit epoch to MLflow."""
if mlflow:
mlflow.log_metrics(metrics=SANITIZE(trainer.metrics), step=trainer.epoch)
mlflow.log_metrics(metrics=sanitize_dict(trainer.metrics), step=trainer.epoch)
def on_train_end(trainer):

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