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ultralytics 8.0.200 move on_val_start callback for training (#5790)

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Co-authored-by: Myyura <zz940521@gmail.com>
This commit is contained in:
Glenn Jocher 2023-10-18 14:53:58 +01:00 committed by GitHub
parent 437b4306d2
commit b9b0fd8bf4
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4 changed files with 14 additions and 6 deletions
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2
.github/workflows/ci.yaml vendored
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@ -247,7 +247,7 @@ jobs:
CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
Conda:
if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule' || github.event.inputs.conda == 'true')
if: github.repository == 'ultralytics/ultralytics' && (github.event_name == 'schedule_disabled' || github.event.inputs.conda == 'true')
runs-on: ${{ matrix.os }}
strategy:
fail-fast: false

14
examples/YOLOv8-ONNXRuntime-CPP/inference.cpp
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@ -175,10 +175,18 @@ char *DCSP_CORE::TensorProcess(clock_t &starttime_1, cv::Mat &iImg, N &blob, std
std::vector<int> class_ids;
std::vector<float> confidences;
std::vector<cv::Rect> boxes;
cv::Mat rowData(signalResultNum, strideNum, CV_32F, output);
rowData = rowData.t();
float *data = (float *) rowData.data;
cv::Mat rawData;
if (modelType == 1) {
// FP32
rawData = cv::Mat(signalResultNum, strideNum, CV_32F, output);
} else {
// FP16
rawData = cv::Mat(signalResultNum, strideNum, CV_16F, output);
rawData.convertTo(rawData, CV_32F);
}
rawData = rawData.t();
float *data = (float *) rawData.data;
float x_factor = iImg.cols / 640.;
float y_factor = iImg.rows / 640.;

2
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.199'
__version__ = '8.0.200'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM

2
ultralytics/engine/validator.py
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@ -119,7 +119,6 @@ class BaseValidator:
model.eval()
else:
callbacks.add_integration_callbacks(self)
self.run_callbacks('on_val_start')
model = AutoBackend(model or self.args.model,
device=select_device(self.args.device, self.args.batch),
dnn=self.args.dnn,
@ -152,6 +151,7 @@ class BaseValidator:
model.eval()
model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup
self.run_callbacks('on_val_start')
dt = Profile(), Profile(), Profile(), Profile()
bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
self.init_metrics(de_parallel(model))