python TFlite or onnx inference script about super-gradients HOT 6 CLOSED

import requests
from PIL import Image
from io import BytesIO
import numpy as np

# Get PIL image
image = Image.open("./img.jpg")

import torchvision.transforms as transforms
import torch

preprocess = transforms.Compose([
    transforms.Resize([640, 640]),
    transforms.PILToTensor()
])

# Run preprocess on image. unsqueeze for [Batch x Channels x Width x Height] format
transformed_image = preprocess(image).float().unsqueeze(0)
# print(transformed_image)
transformed_image

from super_gradients.training import models
from super_gradients.common.object_names import Models

# Get pretrained model from super-gradients repository.
model = models.get(Models.YOLO_NAS_S, pretrained_weights="coco", num_classes=80)

model.eval()

# Predict using SG model
with torch.no_grad():
  raw_predictions = model(transformed_image)

print(raw_predictions[0][0].shape)
print(raw_predictions[0][1].shape)
print(raw_predictions)


from super_gradients.training.models.detection_models.yolo_base import YoloPostPredictionCallback
predictions = YoloPostPredictionCallback(conf=0.1, iou=0.4)(raw_predictions)[0].numpy()

File "~/yolo_pretrained_model_predict.py", line 40, in <module>
    predictions = YoloPostPredictionCallback(conf=0.1, iou=0.4)(raw_predictions)[0].numpy()
  File "/home/mcw/anaconda3/envs/yolo_nas/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "~/site-packages/super_gradients/training/models/detection_models/yolo_base.py", line 96, in forward
    nms_result = non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, with_confidence=self.with_confidence)
  File "~/site-packages/super_gradients/training/utils/detection_utils.py", line 249, in non_max_suppression
    candidates_above_thres = prediction[..., 4] > conf_thres  # filter by confidence
TypeError: tuple indices must be integers or slices, not tuple