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Performance improvement by caching the model #24

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Performance improvement by caching the model #24

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2bcab78
Performance improvement by caching the model
Feb 28, 2023
39dddf4
Fix: mmcv version issue
Apr 8, 2023
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22 changes: 17 additions & 5 deletions scripts/ddetailer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,8 @@
from basicsr.utils.download_util import load_file_from_url

dd_models_path = os.path.join(models_path, "mmdet")
segm_model = None
bbox_model = None

def list_models(model_path):
model_list = modelloader.load_models(model_path=model_path, ext_filter=[".pth"])
Expand Down Expand Up @@ -45,9 +47,9 @@ def startup():
from launch import is_installed, run
if not is_installed("mmdet"):
python = sys.executable
run(f'"{python}" -m pip install -U openmim', desc="Installing openmim", errdesc="Couldn't install openmim")
run(f'"{python}" -m mim install mmcv-full', desc=f"Installing mmcv-full", errdesc=f"Couldn't install mmcv-full")
run(f'"{python}" -m pip install mmdet', desc=f"Installing mmdet", errdesc=f"Couldn't install mmdet")
run(f'"{python}" -m pip install --upgrade -U openmim==0.3.7', desc="Installing openmim", errdesc="Couldn't install openmim")
run(f'"{python}" -m mim install --upgrade mmcv-full==1.7.1', desc=f"Installing mmcv-full", errdesc=f"Couldn't install mmcv-full")
run(f'"{python}" -m pip install --upgrade mmdet==2.28.2', desc=f"Installing mmdet", errdesc=f"Couldn't install mmdet")

if (len(list_models(dd_models_path)) == 0):
print("No detection models found, downloading...")
Expand Down Expand Up @@ -476,10 +478,15 @@ def inference(image, modelname, conf_thres, label):
return results

def inference_mmdet_segm(image, modelname, conf_thres, label):
global segm_model
model_checkpoint = modelpath(modelname)
model_config = os.path.splitext(model_checkpoint)[0] + ".py"
model_device = get_device()
model = init_detector(model_config, model_checkpoint, device=model_device)
if segm_model is not None:
model = segm_model
else:
model = init_detector(model_config, model_checkpoint, device=model_device)
segm_model = model
mmdet_results = inference_detector(model, np.array(image))
bbox_results, segm_results = mmdet_results
dataset = modeldataset(modelname)
Expand All @@ -504,10 +511,15 @@ def inference_mmdet_segm(image, modelname, conf_thres, label):
return results

def inference_mmdet_bbox(image, modelname, conf_thres, label):
global bbox_model
model_checkpoint = modelpath(modelname)
model_config = os.path.splitext(model_checkpoint)[0] + ".py"
model_device = get_device()
model = init_detector(model_config, model_checkpoint, device=model_device)
if bbox_model is not None:
model = bbox_model
else:
model = init_detector(model_config, model_checkpoint, device=model_device)
bbox_model = model
results = inference_detector(model, np.array(image))
cv2_image = np.array(image)
cv2_image = cv2_image[:, :, ::-1].copy()
Expand Down