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TensorRT detect.py inference fix #9581

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merged 5 commits into from
Sep 25, 2022
Merged

TensorRT detect.py inference fix #9581

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819ab91
Update
glenn-jocher Sep 25, 2022
06f8025
Update ci-testing.yml
glenn-jocher Sep 25, 2022
efb81f2
Update ci-testing.yml
glenn-jocher Sep 25, 2022
a9f6885
Segment fix
glenn-jocher Sep 25, 2022
2629dec
Segment fix
glenn-jocher Sep 25, 2022
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6 changes: 6 additions & 0 deletions .github/workflows/ci-testing.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -44,6 +44,12 @@ jobs:
- name: Benchmark SegmentationModel
run: |
python benchmarks.py --data coco128-seg.yaml --weights ${{ matrix.model }}-seg.pt --img 320 --hard-fail 0.22
- name: Test predictions
run: |
python export.py --weights ${{ matrix.model }}-cls.pt --include onnx --img 224
python detect.py --weights ${{ matrix.model }}.onnx --img 320
python segment/predict.py --weights ${{ matrix.model }}-seg.onnx --img 320
python classify/predict.py --weights ${{ matrix.model }}-cls.onnx --img 224

Tests:
timeout-minutes: 60
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3 changes: 2 additions & 1 deletion classify/predict.py
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Expand Up @@ -89,14 +89,15 @@ def run(
imgsz = check_img_size(imgsz, s=stride) # check image size

# Dataloader
bs = 1 # batch_size
if webcam:
view_img = check_imshow()
dataset = LoadStreams(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
bs = len(dataset)
elif screenshot:
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
else:
dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
bs = len(dataset) # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs

# Run inference
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3 changes: 2 additions & 1 deletion detect.py
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Expand Up @@ -97,14 +97,15 @@ def run(
imgsz = check_img_size(imgsz, s=stride) # check image size

# Dataloader
bs = 1 # batch_size
if webcam:
view_img = check_imshow()
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset)
elif screenshot:
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset) # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs

# Run inference
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5 changes: 3 additions & 2 deletions segment/predict.py
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Original file line number Diff line number Diff line change
Expand Up @@ -100,14 +100,15 @@ def run(
imgsz = check_img_size(imgsz, s=stride) # check image size

# Dataloader
bs = 1 # batch_size
if webcam:
view_img = check_imshow()
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset)
elif screenshot:
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
bs = len(dataset) # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs

# Run inference
Expand Down Expand Up @@ -179,7 +180,7 @@ def run(
c = int(cls) # integer class
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
if save_crop:
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)

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