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[rename] - preprocess-batch -> preprocess, preprocess_preds -> postprocess (#42)
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@ -54,7 +54,7 @@ class BaseValidator:
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self.batch_i = batch_i
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# pre-process
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with dt[0]:
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batch = self.preprocess_batch(batch)
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batch = self.preprocess(batch)
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# inference
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with dt[1]:
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@ -69,7 +69,7 @@ class BaseValidator:
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# pre-process predictions
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with dt[3]:
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preds = self.preprocess_preds(preds)
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preds = self.postprocess(preds)
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self.update_metrics(preds, batch)
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@ -89,10 +89,10 @@ class BaseValidator:
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return stats
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def preprocess_batch(self, batch):
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def preprocess(self, batch):
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return batch
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def preprocess_preds(self, preds):
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def postprocess(self, preds):
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return preds
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def init_metrics(self):
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@ -8,7 +8,7 @@ class ClassificationValidator(BaseValidator):
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def init_metrics(self, model):
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self.correct = torch.tensor([])
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def preprocess_batch(self, batch):
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def preprocess(self, batch):
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batch["img"] = batch["img"].to(self.device)
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batch["cls"] = batch["cls"].to(self.device)
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return batch
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@ -28,7 +28,7 @@ class SegmentationValidator(BaseValidator):
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self.class_map = None
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self.targets = None
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def preprocess_batch(self, batch):
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def preprocess(self, batch):
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batch["img"] = batch["img"].to(self.device, non_blocking=True)
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batch["img"] = (batch["img"].half() if self.args.half else batch["img"].float()) / 225
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batch["bboxes"] = batch["bboxes"].to(self.device)
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@ -66,7 +66,7 @@ class SegmentationValidator(BaseValidator):
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return ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', "R", "mAP50", "mAP50-95)", "Mask(P",
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"R", "mAP50", "mAP50-95)")
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def preprocess_preds(self, preds):
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def postprocess(self, preds):
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p = ops.non_max_suppression(preds[0],
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self.args.conf_thres,
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self.args.iou_thres,
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