Pytorch:CNN节点类型的NNAPI转换不在加法器映射中

Question

我正在尝试使用新的torch.backends.prepare.convert_model_to_nnapi（）函数为Android设备上的nnapi准备PyTorch CNN模型。我基本上是在学习教程。这是我的密码：

#Prepare the trained model for NNAPI on Android device
import sys
import os
import torch
import torch.utils.bundled_inputs
import torch.utils.mobile_optimizer
import torch.backends._nnapi.prepare
import torchvision.models.quantization.mobilenet
from pathlib import Path

#import a function from a different module that returns the model in eval mode, loading the state dict from a checkpoint file (pretrained)
from common.model import trace_model


# This script supports 3 modes of quantization:
# - "none": Fully floating-point model.
# - "core": Quantize the core of the model, but wrap it a
#    quantizer/dequantizer pair, so the interface uses floating point.
# - "full": Quantize the model, and use quantized tensors
#   for input and output.
#
# "none" maintains maximum accuracy
# "core" sacrifices some accuracy for performance,
# but maintains the same interface.
# "full" maximized performance (with the same accuracy as "core"),
# but requires the application to use quantized tensors.
#
# There is a fourth option, not supported by this script,
# where we include the quant/dequant steps as NNAPI operators.
def make_mymodel_nnapi(output_dir_path, quantize_mode):
    quantize_core, quantize_iface = {
        "none": (False, False),
        "core": (True, False),
        "full": (True, True),
    }[quantize_mode]

#get the model. Ignore layers
    model, layers = trace_model() 

#Commented this section out because model I'm using doesn't have fuse_model() fxn.
    # Fuse BatchNorm operators in the floating point model.
    # (Quantized models already have this done.)
    # Remove dropout for this inference-only use case.
    #if not quantize_core:
    #    model.fuse_model()

    
    #assert type(model.classifier[0]) == torch.nn.Dropout

    #model.classifier[0] = torch.nn.Identity()

#create some input based on my CNN's shape
    input_float = torch.zeros(2, 272, 17, 2)
    input_tensor = input_float

    
    # If we're doing a quantized model, we need to trace only the quantized core.
    # So capture the quantizer and dequantizer, use them to prepare the input,
    # and replace them with identity modules so we can trace without them.
    if quantize_core:
        quantizer = model.quant
        dequantizer = model.dequant
        model.quant = torch.nn.Identity()
        model.dequant = torch.nn.Identity()
        input_tensor = quantizer(input_float)


    
    # Many NNAPI backends prefer NHWC tensors, so convert our input to channels_last,
    # and set the "nnapi_nhwc" attribute for the converter.
    input_tensor = input_tensor.contiguous(memory_format=torch.channels_last)
    input_tensor.nnapi_nhwc = True

    # Trace the model.  NNAPI conversion only works with TorchScript models,
    # and traced models are more likely to convert successfully than scripted.
    with torch.no_grad():
        traced = torch.jit.trace(model, input_tensor)
       

#ERROR IN THIS FUNCTION
#Prepare model for NNAPI
    nnapi_model = torch.backends._nnapi.prepare.convert_model_to_nnapi(traced, input_tensor)




if __name__ == "__main__":
    for quantize_mode in ["none", "core", "full"]:
        make_mymodel_nnapi(Path(os.environ["HOME"]) / "mobilenetv2-nnapi", quantize_mode)

我得到的是convert_model_to_nnapi（）函数中的一个错误：

Traceback (most recent call last):
  File "trace_model_nnapi.py", line 131, in <module>
    make_mymodel_nnapi(Path(os.environ["HOME"]) / "mobilenetv2-nnapi", quantize_mode)
  File "trace_model_nnapi.py", line 89, in make_mymodel_nnapi
    nnapi_model = torch.backends._nnapi.prepare.convert_model_to_nnapi(traced, input_tensor)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/prepare.py", line 169, in convert_model_to_nnapi
    ser_model, used_weights, inp_mem_fmts, out_mem_fmts = serialize_model(model, inputs)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/serializer.py", line 1363, in serialize_model
    return _NnapiSerializer(config).serialize_model(module, inputs)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/serializer.py", line 520, in serialize_model
    self.add_node(node)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/serializer.py", line 641, in add_node
    raise Exception("Unsupported node kind (%r) in node %r" % (node.kind(), node))
Exception: Unsupported node kind ('aten::contiguous') in node %x.2 : Tensor = aten::contiguous(%x.1, %39) # /home/nodog/docs/asp/mymodel/common/model.py:267:0

如果我注释掉代码中的“许多NNAPI后端更喜欢NHWC张量”部分：

Traceback (most recent call last):
  File "trace_model_nnapi.py", line 131, in <module>
    make_mymodel_nnapi(Path(os.environ["HOME"]) / "mobilenetv2-nnapi", quantize_mode)
  File "trace_model_nnapi.py", line 89, in make_mymodel_nnapi
    nnapi_model = torch.backends._nnapi.prepare.convert_model_to_nnapi(traced, input_tensor)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/prepare.py", line 169, in convert_model_to_nnapi
    ser_model, used_weights, inp_mem_fmts, out_mem_fmts = serialize_model(model, inputs)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/serializer.py", line 1363, in serialize_model
    return _NnapiSerializer(config).serialize_model(module, inputs)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/serializer.py", line 520, in serialize_model
    self.add_node(node)
  File "/home/nodog/.local/lib/python3.8/site-packages/torch/backends/_nnapi/serializer.py", line 641, in add_node
    raise Exception("Unsupported node kind (%r) in node %r" % (node.kind(), node))
Exception: Unsupported node kind ('aten::size') in node %40 : int = aten::size(%x.1, %39) # /home/nodog/docs/asp/mymodel/common/model.py:269:0

似乎serializer.py中的ADDER_映射（看起来是节点类型列表）中不包括“aten:：size”和“aten:：Continental”。有没有人知道我是否做错了什么，或者某些功能还没有添加到NNAPI系统中？ 顺便说一下，我使用的CNN模型有Conv1d、Batchnorm、Dropout和Relu层

谢谢