def bev_pool_v2(    depth: Tensor,    feat: Tensor,    ranks_depth: Tensor,    ranks_feat: Tensor,    ranks_bev: Tensor,    interval_starts: Tensor,    interval_lengths: Tensor,    bev_feat_shape,):    """BEVPoolv2 implementation for Lift-Splat-Shoot view transformation.    This impl is same as following except the layout of inout feature:    https://github.com/HuangJunJie2017/BEVDet/blob/dev3.0/mmdet3d/ops/bev_pool_v2/bev_pool.py    Args:        depth (Tensor[b, n, d, h, w]): Input depth.        feat (Tensor[b, n, c, h, w]): Input features.        ranks_depth (Tensor[n_points]): Depth index of points.        ranks_feat (Tensor[n_points]): Feat index of points.        ranks_bev (Tensor[n_points]): Output index of points.        interval_starts (Tensor[n_pillars]): Starting position in ranks_xxx for each pooled point.  # noqa: E501        interval_lengths (Tensor[n_pillars]): How many points in each pooled point.  # noqa: E501        bev_feat_shape: Output shape in [b, z_out, h_out, w_out, c] or            [z_out, h_out, w_out] or [h_out, w_out] format.            When z_out is not given, its value will be 1 by default.    Returns:        Tensor[b, c, z_out, h_out, w_out]: Output features.    """    if len(bev_feat_shape) not in (2, 3, 5):        raise ValueError("Illegal bev_feat_shape length")    if len(bev_feat_shape) < 5:        bev_feat_shape = tuple(bev_feat_shape)        if len(bev_feat_shape) == 2:            bev_feat_shape = (1,) + bev_feat_shape        b = feat.size(0)        c = feat.size(2)        bev_feat_shape = (b,) + tuple(bev_feat_shape) + (c,)    if has_torch_function((depth, feat)):        return handle_torch_function(            bev_pool_v2,            (depth, feat),            depth,            feat,            ranks_depth,            ranks_feat,            ranks_bev,            interval_starts,            interval_lengths,            bev_feat_shape, )    x = torch.ops.horizon.bev_pool_v2(        depth,        feat,        ranks_depth,        ranks_feat,        ranks_bev,        interval_starts,        interval_lengths,        bev_feat_shape,    )    return x

import copyimport torchimport torch.nn as nnimport numpy as npfrom horizon_plugin_pytorch.nn.bev_pool_v2 import BevPoolV2from horizon_plugin_pytorch.quantization.hbdk4 import exportfrom torch.quantization import DeQuantStubfrom horizon_plugin_pytorch.quantization import (    QuantStub,    set_fake_quantize,    FakeQuantState,)from horizon_plugin_pytorch.quantization.qconfig_template import default_calibration_qconfig_setterfrom horizon_plugin_pytorch.quantization.prepare import prepare, PrepareMethodfrom horizon_plugin_pytorch.march import March, set_marchfrom hbdk4.compiler import convert, compile, savedef load_input(b, d, h_out, w_out, c):    #load 真实输入    #b:batch    #d:depth数    #h_out, w_out:输出特征图大小    #c:通道数    depth = torch.Tensor(np.load("real_inputs/depth.npy"))    feat = torch.Tensor(np.load("real_inputs/feat.npy"))    ranks_depth = torch.Tensor(np.load("real_inputs/new_ranks_depth.npy")).type(torch.int32) #    ranks_feat = torch.Tensor(np.load("real_inputs/new_ranks_feat.npy")).type(torch.int32)    ranks_bev = torch.Tensor(np.load("real_inputs/new_ranks_bev.npy")).type(torch.int32)    interval_starts = torch.Tensor(np.load("real_inputs/new_interval_starts.npy")).type(torch.int32)    interval_lengths = torch.Tensor(np.load("real_inputs/new_interval_lengths.npy")).type(torch.int32)    bev_feat_shape = (b, d, h_out, w_out, c)    return depth, feat, ranks_depth, ranks_feat, ranks_bev, interval_starts, interval_lengths, bev_feat_shape#step1；构建复现浮点模型class SimpleBEVModel(nn.Module):    def __init__(self,bev_feat_shape):        super(SimpleBEVModel, self).__init__()        self.bev_feat_shape = bev_feat_shape        self.bev_pool = BevPoolV2(self.bev_feat_shape)        self.quant1 = QuantStub()        self.quant2 = QuantStub()        self.dequant = DeQuantStub()        _, _, self.ranks_depth, self.ranks_feat, self.ranks_bev, self.interval_starts, self.interval_lengths, _ = load_input(1, 1,640, 128, 64)    def forward(self, data):        depth = data["depth"]        feat = data["feat"]        #step2:改造模型        #在输入/输出分别插入QuantStub和DeQuantStub        depth = self.quant1(depth)        feat = self.quant2(feat)        #调用BevPoolV2算子        bev_feat = self.bev_pool(depth, feat, self.ranks_depth, self.ranks_feat, self.ranks_bev, self.interval_starts, self.interval_lengths)        print("output shape:",bev_feat.shape)        bev_feat = self.dequant(bev_feat)        return bev_featif name == '__main__':
    b, d, h_out, w_out, c=1,1,640, 128, 64    depth, feat, ranks_depth, ranks_feat, ranks_bev, interval_starts, interval_lengths, bev_feat_shape = load_input(        b, d, h_out, w_out, c    )    print(f"Depth shape: {depth.shape} {depth.dtype}")    print(f"Feat shape: {feat.shape} {feat.dtype}")    print(f"Ranks depth shape: {ranks_depth.shape} {ranks_depth.dtype}")    print(f"Ranks feat shape: {ranks_feat.shape} {ranks_feat.dtype}")    print(f"Ranks bev shape: {ranks_bev.shape} {ranks_bev.dtype}")    print(f"Interval starts shape: {interval_starts.shape} {interval_starts.dtype}")    print(f"Interval lengths shape: {interval_lengths.shape} {interval_lengths.dtype}")    print(f"BEV feat shape: {bev_feat_shape}")
    model = SimpleBEVModel(bev_feat_shape)    example_inputs = dict(        depth=depth,        feat=feat,    )    import logging    logging.basicConfig(filename='error.log', level=logging.ERROR)    try:        res_float = model(example_inputs)        pass    except Exception as e:        logging.error("An error occurred: %s", e, exc_info=True)    #配置march    set_march(March.NASH_M)    #step3：将浮点模型 prepare为伪量化模型    calib_model = prepare(        copy.deepcopy(model),        example_inputs=(example_inputs,),        qconfig_setter=(            default_calibration_qconfig_setter,        ),        method=PrepareMethod.JIT_STRIP,    )    calib_model.eval()    set_fake_quantize(calib_model, FakeQuantState.CALIBRATION)    res_calib = calib_model(example_inputs)    #step4：export出 qat.bc    qat_hbir = export(        calib_model,        example_inputs,        name="bevpool",    )    save(qat_hbir,"bevpoolv2_qat.bc")    #step5：将qat.bc convert为 quantized.bc    quanti_hbir = convert(qat_hbir, "nash-e")    save(quanti_hbir, "bevpoolv2_quantized.bc")    compile(        quanti_hbir,        path="bevpoolv2.hbm",        march='nash-e',        opt=2,        jobs=64,        balance=100,        progress_bar=True,    )