mmdeploy - ERROR - `mmdeploy.backend.tensorrt.onnx2tensorrt.onnx2tensorrt` with Call id: 1 failed. exit

Hi, did you build MMDeploy from the source? Is there a file mmdeploy/lib/libmmdeploy_tensorrt_ops.so?

It worked. Here’s the final code:

from mmdeploy.backend.tensorrt import load_tensorrt_plugin
load_tensorrt_plugin()

import numpy as np
import os
import pycuda.driver as cuda
import pycuda.autoinit
import tensorrt as trt

from collections import OrderedDict, namedtuple
import torch
import cv2

device = 'cuda:0'
TRT_LOGGER = trt.Logger()

# Filenames of TensorRT plan file and input/output images.
engine_file = "/home/sort/ved/sort/apple_l2/mmdeploy_out/end2end.engine"
input_file = '/home/sort/ved/sort/apple_l2/data/val/images/970000000001.0.jpg'

# For torchvision models, input images are loaded in to a range of [0, 1] and
# normalized using mean = [0.485, 0.456, 0.406] and stddev = [0.229, 0.224, 0.225].
def preprocess(image):
    # Mean normalization
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    image_width = image.shape[1]
    image_height = image.shape[0]
    mean = np.array([0.485, 0.456, 0.406]).astype('float32')
    stddev = np.array([0.229, 0.224, 0.225]).astype('float32')
    image = (image.astype('float32') / float(255.0) - mean) / stddev
    image = np.moveaxis(image, 2, 0)
    image = np.expand_dims(image, axis=0)
    return image

def postprocess(bindings):
    results = bindings['dets'].data
    labels = bindings['labels'].data
    # masks = bindings['y.60'].data
    masks = bindings['masks'].data

    bboxes = results[0,:,:-1]
    scores = results[0,:,-1]
    labels = labels[0, :]
    masks = masks[0, :, :, :]

    masks = masks[scores > 0.3].cpu().detach().numpy()
    bboxes = bboxes[scores > 0.3].cpu().detach().numpy()
    labels = labels[scores > 0.3].cpu().detach().numpy()
    scores = scores[scores > 0.3].cpu().detach().numpy()
    return [bboxes, scores, labels, masks]

Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
logger = trt.Logger(trt.Logger.INFO)
with open(engine_file, 'rb') as f, trt.Runtime(logger) as runtime:
    model = runtime.deserialize_cuda_engine(f.read())
bindings = OrderedDict()
for index in range(model.num_bindings):
    name = model.get_binding_name(index)
    dtype = trt.nptype(model.get_binding_dtype(index))
    shape = tuple(model.get_binding_shape(index))
    data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device)
    bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr()))
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
context = model.create_execution_context()

image_cv = cv2.imread(input_file)
image = torch.from_numpy(preprocess(image_cv)).to(device)
binding_addrs['input'] = int(image.data_ptr())
stream = cuda.Stream()
context.execute_async_v2(bindings=list(binding_addrs.values()), stream_handle=stream.handle)
stream.synchronize()
bboxes, scores, labels, masks = postprocess(bindings)