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ultralytics 8.0.40 TensorRT metadata and Results visualizer (#1014)

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This commit is contained in:
Glenn Jocher
2023-02-17 20:06:06 +01:00
committed by GitHub
parent e799592718
commit 9047d737f4
40 changed files with 576 additions and 280 deletions
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7
ultralytics/yolo/v8/classify/predict.py
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@ -18,11 +18,12 @@ class ClassificationPredictor(BasePredictor):
img = img.half() if self.model.fp16 else img.float() # uint8 to fp16/32
return img
def postprocess(self, preds, img, orig_img, classes=None):
def postprocess(self, preds, img, orig_img):
results = []
for i, pred in enumerate(preds):
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
results.append(Results(probs=pred, orig_shape=shape[:2]))
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
results.append(Results(probs=pred.softmax(0), orig_img=orig_img, names=self.model.names))
return results
def write_results(self, idx, results, batch):

7
ultralytics/yolo/v8/detect/predict.py
View File

@ -19,7 +19,7 @@ class DetectionPredictor(BasePredictor):
img /= 255 # 0 - 255 to 0.0 - 1.0
return img
def postprocess(self, preds, img, orig_img, classes=None):
def postprocess(self, preds, img, orig_img):
preds = ops.non_max_suppression(preds,
self.args.conf,
self.args.iou,
@ -29,9 +29,10 @@ class DetectionPredictor(BasePredictor):
results = []
for i, pred in enumerate(preds):
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
shape = orig_img.shape
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
results.append(Results(boxes=pred, orig_shape=shape[:2]))
results.append(Results(boxes=pred, orig_img=orig_img, names=self.model.names))
return results
def write_results(self, idx, results, batch):

10
ultralytics/yolo/v8/segment/predict.py
View File

@ -10,7 +10,7 @@ from ultralytics.yolo.v8.detect.predict import DetectionPredictor
class SegmentationPredictor(DetectionPredictor):
def postprocess(self, preds, img, orig_img, classes=None):
def postprocess(self, preds, img, orig_img):
# TODO: filter by classes
p = ops.non_max_suppression(preds[0],
self.args.conf,
@ -22,9 +22,11 @@ class SegmentationPredictor(DetectionPredictor):
results = []
proto = preds[1][-1] if len(preds[1]) == 3 else preds[1] # second output is len 3 if pt, but only 1 if exported
for i, pred in enumerate(p):
shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape
orig_img = orig_img[i] if isinstance(orig_img, list) else orig_img
shape = orig_img.shape
if not len(pred):
results.append(Results(boxes=pred[:, :6], orig_shape=shape[:2])) # save empty boxes
results.append(Results(boxes=pred[:, :6], orig_img=orig_img,
names=self.model.names)) # save empty boxes
continue
if self.args.retina_masks:
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
@ -32,7 +34,7 @@ class SegmentationPredictor(DetectionPredictor):
else:
masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], img.shape[2:], upsample=True) # HWC
pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], shape).round()
results.append(Results(boxes=pred[:, :6], masks=masks, orig_shape=shape[:2]))
results.append(Results(boxes=pred[:, :6], masks=masks, orig_img=orig_img, names=self.model.names))
return results
def write_results(self, idx, results, batch):

13
ultralytics/yolo/v8/segment/val.py
View File

@ -28,19 +28,8 @@ class SegmentationValidator(DetectionValidator):
return batch
def init_metrics(self, model):
val = self.data.get(self.args.split, '') # validation path
self.is_coco = isinstance(val, str) and val.endswith(f'coco{os.sep}val2017.txt') # is COCO dataset
self.class_map = ops.coco80_to_coco91_class() if self.is_coco else list(range(1000))
self.args.save_json |= self.is_coco and not self.training # run on final val if training COCO
self.names = model.names
self.nc = len(model.names)
self.metrics.names = self.names
self.metrics.plot = self.args.plots
self.confusion_matrix = ConfusionMatrix(nc=self.nc)
super().init_metrics(model)
self.plot_masks = []
self.seen = 0
self.jdict = []
self.stats = []
if self.args.save_json:
check_requirements('pycocotools>=2.0.6')
self.process = ops.process_mask_upsample # more accurate