onnxのバッチサイズをUNK(None)に書き換えたうえで別のバッチサイズを指定してモデルを再生成するワークアラウンドの一例

docker run --gpus all -it --rm \
-v `pwd`:/home/user/workdir \
ghcr.io/pinto0309/openvino2tensorflow:latest

# https://github.com/GeekAlexis/FastMOT
gdown --id 1-kXZpA6y8pNbDMMD7N--IWIjwqqnAIGZ -O yolov4_crowdhuman.onnx

python3 -m onnxsim yolov4_crowdhuman.onnx yolov4_crowdhuman.onnx

import onnx
import os
import struct

from argparse import ArgumentParser

def rebatch(infile, outfile, batch_size):
    model = onnx.load(infile)
    graph = model.graph

    # Change batch size in input, output and value_info
    for tensor in list(graph.input) + list(graph.value_info) + list(graph.output):
        tensor.type.tensor_type.shape.dim[0].dim_param = batch_size

    # Set dynamic batch size in reshapes (-1)
    for node in  graph.node:
        if node.op_type != 'Reshape':
            continue
        for init in graph.initializer:
            # node.input[1] is expected to be a reshape
            if init.name != node.input[1]:
                continue
            # Shape is stored as a list of ints
            if len(init.int64_data) > 0:
                # This overwrites bias nodes' reshape shape but should be fine
                init.int64_data[0] = -1
            # Shape is stored as bytes
            elif len(init.raw_data) > 0:
                shape = bytearray(init.raw_data)
                struct.pack_into('q', shape, 0, -1)
                init.raw_data = bytes(shape)

    onnx.save(model, outfile)

if __name__ == '__main__':
    parser = ArgumentParser('Replace batch size with \'N\'')
    parser.add_argument('infile')
    parser.add_argument('outfile')
    args = parser.parse_args()

    rebatch(args.infile, args.outfile, 'N')

python3 batchsize_clear.py yolov4_crowdhuman.onnx yolov4_crowdhuman.onnx

python3 -m onnxsim --input-shape="1,3,512,512" yolov4_crowdhuman.onnx yolov4_crowdhuman.onnx

onnx2trt yolov4_crowdhuman.onnx -o yolov4_crowdhuman.trt -b 1 -d 16 -v