Insert image_convert op. Change input parameter type.
    Args:        * mode (str): Specify conversion mode, optional values are "nv12"(default) and "gray".
    Returns:        List of newly inserted function arguments which is also the inputs of inserted image convert op
    Raises:        ValueError when this argument is no longer valid
    Note:        To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage
    Example:        module = load("model.bc")        func = module[0]        res = func.inputs[0].insert_image_convert("nv12")

Insert image_convert op. Change input parameter type.
    Args:        * mode (str): Specify conversion mode, optional values are "skip"(default, same as None), "yuvbt601full2rgb", "yuvbt601full2bgr", "yuvbt601video2rgb" and "yuvbt601video2bgr".
    Returns:        List of newly inserted function arguments which is also the inputs of inserted image preprocess op
    Raises:        ValueError when this argument is no longer valid
    Note:        To avoid the new insertion operator not running in some conversion passes, it is recommended to call the insert_xxx api before the convert stage
    Example:        module = load("model.bc")        func = module[0]        res = func.inputs[0].insert_image_preprocess("yuvbt601full2rgb", 255, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225], True)

mean = [0.485]std = [0.229]func = qat_bc[0]
for input in func.flatten_inputs[::-1]:    split_inputs = input.insert_split(dim=0)    for split_input in reversed(split_inputs):        node = split_input.insert_transpose([0, 3, 1, 2])        node = node.insert_image_preprocess(mode="skip",                                divisor=255,                                mean=mean,                                std=std,                                is_signed=True)        node.insert_image_convert(mode="gray")

import torchimport torch.nn as nnimport torch.nn.functional as Ffrom horizon_plugin_pytorch import set_march, Marchset_march(March.NASH_M)from horizon_plugin_pytorch.quantization import prepare, set_fake_quantize, FakeQuantStatefrom horizon_plugin_pytorch.quantization import QuantStubfrom horizon_plugin_pytorch.quantization.hbdk4 import exportfrom horizon_plugin_pytorch.quantization.qconfig_template import calibration_8bit_weight_16bit_act_qconfig_setter, default_calibration_qconfig_setterfrom horizon_plugin_pytorch.quantization.qconfig import get_qconfig, MSEObserver, MinMaxObserverfrom horizon_plugin_pytorch.dtype import qint8, qint16from torch.quantization import DeQuantStubfrom hbdk4.compiler import statistics, save, load,visualize,compile,convert, hbm_perf
class SimpleConvNet(nn.Module):    def __init__(self):        super(SimpleConvNet, self).__init__()        # 第一个节点：输入通道 1，输出通道 16，卷积核 3x3        self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1)        # 后续添加一个池化层和一个全连接层        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)        self.fc = nn.Linear(16 * 14 * 14, 10)  # 假设输入图像为 28x28        self.quant = QuantStub()        self.dequant = DeQuantStub()
    def forward(self, x):        x = self.quant(x)        x = self.conv1(x)      # 卷积层        x = F.relu(x)          # 激活        x = self.pool(x)       # 池化        x = x.view(x.size(0), -1)  # Flatten        x = self.fc(x)         # 全连接层输出        x = self.dequant(x)        return x
# 构造模型model = SimpleConvNet()
# 构造一个假输入：batch_size=4，单通道，28x28 图像example_input = torch.randn(4, 1, 28, 28)output = model(example_input)
print("输出 shape:", output.shape)  # torch.Size([4, 10])
calib_model = prepare(model.eval(), example_input,                       qconfig_setter=(                          default_calibration_qconfig_setter,                          ),                      )
calib_model.eval()set_fake_quantize(calib_model, FakeQuantState.CALIBRATION)calib_model(example_input)
calib_model.eval()                            set_fake_quantize(calib_model, FakeQuantState.VALIDATION)calib_out = calib_model(example_input)print("calib输出数据:", calib_out)
qat_bc = export(calib_model, example_input)mean = [0.485]std = [0.229]func = qat_bc[0]
for input in func.flatten_inputs[::-1]:    split_inputs = input.insert_split(dim=0)    for split_input in reversed(split_inputs):        node = split_input.insert_transpose([0, 3, 1, 2])        node = node.insert_image_preprocess(mode="skip",                                divisor=255,                                mean=mean,                                std=std,                                is_signed=True)        node.insert_image_convert(mode="gray")
quantized_bc = convert(qat_bc, "nash-m")hbir_func = quantized_bc.functions[0]hbir_func.remove_io_op(op_types = ["Dequantize","Quantize"])visualize(quantized_bc, "model_result/quantized_batch4.onnx")statistics(quantized_bc)params = {'jobs': 64, 'balance': 100, 'progress_bar': True,          'opt': 2,'debug': True, "advice": 0.0}hbm_path="model_result/batch4-gray.hbm"print("start to compile")compile(quantized_bc, march="nash-m", path=hbm_path, **params)print("end to compile")