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Simplify val.py benchmark mode with speed mode (#9674) 

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pull/9654/head^2
Glenn Jocher 2022-10-02 13:37:54 +02:00 committed by GitHub
parent 8a19437690
commit 1158a50abd
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3 changed files with 4 additions and 6 deletions
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4
benchmarks.py
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@ -81,10 +81,10 @@ def run(
# Validate # Validate
if model_type == SegmentationModel: if model_type == SegmentationModel:
result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half) result = val_seg(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls)) metric = result[0][7] # (box(p, r, map50, map), mask(p, r, map50, map), *loss(box, obj, cls))
else: # DetectionModel: else: # DetectionModel:
result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='benchmark', half=half) result = val_det(data, w, batch_size, imgsz, plots=False, device=device, task='speed', half=half)
metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls)) metric = result[0][3] # (p, r, map50, map, *loss(box, obj, cls))
speed = result[2][1] # times (preprocess, inference, postprocess) speed = result[2][1] # times (preprocess, inference, postprocess)
y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference y.append([name, round(file_size(w), 1), round(metric, 4), round(speed, 2)]) # MB, mAP, t_inference

3
segment/val.py
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@ -210,8 +210,7 @@ def run(
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
f'classes). Pass correct combination of --weights and --data that are trained together.' f'classes). Pass correct combination of --weights and --data that are trained together.'
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
pad = 0.0 if task in ('speed', 'benchmark') else 0.5 pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
rect = False if task == 'benchmark' else pt # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task], dataloader = create_dataloader(data[task],
imgsz, imgsz,

3
val.py
View File

@ -169,8 +169,7 @@ def run(
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \ assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
f'classes). Pass correct combination of --weights and --data that are trained together.' f'classes). Pass correct combination of --weights and --data that are trained together.'
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
pad = 0.0 if task in ('speed', 'benchmark') else 0.5 pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
rect = False if task == 'benchmark' else pt # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task], dataloader = create_dataloader(data[task],
imgsz, imgsz,