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onnx_eval.py

@@ -78,8 +78,10 @@ class DetectionValidator:
             batch = self.preprocess(batch)
 
             # inference
-            outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: batch["img"].cpu().numpy()})
-            outputs = [torch.tensor(item).to(self.device) for item in outputs]
+            # outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: batch["img"].cpu().numpy()})
+            outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: batch["img"].permute(0, 2, 3, 1).cpu().numpy()})
+            # outputs = [torch.tensor(item).to(self.device) for item in outputs]
+            outputs = [torch.tensor(item).permute(0, 3, 1, 2).to(self.device) for item in outputs]
             preds = post_process(outputs)
 
             # pre-process predictions

onnx_inference.py

@@ -133,8 +133,10 @@ if __name__ == '__main__':
         im = preprocess(im)
         if len(im.shape) == 3:
             im = im[None]
-        outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.cpu().numpy()})
-        outputs = [torch.tensor(item) for item in outputs]
+        # outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.cpu().numpy()})
+        # outputs = [torch.tensor(item) for item in outputs]
+        outputs = onnx_model.run(None, {onnx_model.get_inputs()[0].name: im.permute(0, 2, 3, 1).cpu().numpy()})
+        outputs = [torch.tensor(item).permute(0, 3, 1, 2) for item in outputs]
         preds = post_process(outputs)
         preds = non_max_suppression(
             preds, 0.25, 0.7, agnostic=False, max_det=300, classes=None

yolov8m_qat.onnx

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3b770e88b358ad24cc60e7b8bbc00b09bb1e0308f65f45cdcea2a1dfc1301077
-size 103874610
+oid sha256:119038397368b01fee9ad8adcc62061babcf2e2dd417be1946d5bfccb07eb65f
+size 103874987