pytorch遇到的一些关键字记录-问题记录

RuntimeError: Failed to export an ONNX attribute, since it's not constant, please try to make things (e.g., kernel size) static if possible 

Traceback (most recent call last):
  File "/home/spple/pycharm-2018.3.2/helpers/pydev/pydevd.py", line 1741, in 
    main()
  File "/home/spple/pycharm-2018.3.2/helpers/pydev/pydevd.py", line 1735, in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "/home/spple/pycharm-2018.3.2/helpers/pydev/pydevd.py", line 1135, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "/home/spple/pycharm-2018.3.2/helpers/pydev/_pydev_imps/_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "/home/spple/paddle/vis-project/pytorch_inference/pytorch1.1.0_inference.py", line 113, in 
    main()
  File "/home/spple/paddle/vis-project/pytorch_inference/pytorch1.1.0_inference.py", line 77, in main
    hl_graph = hl.build_graph(model, temp_input)
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/hiddenlayer/graph.py", line 141, in build_graph
    import_graph(g, model, args)
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/hiddenlayer/pytorch_builder.py", line 71, in import_graph
    torch.onnx._optimize_trace(trace, torch.onnx.OperatorExportTypes.ONNX)
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/__init__.py", line 40, in _optimize_trace
    trace.set_graph(utils._optimize_graph(trace.graph(), operator_export_type))
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/utils.py", line 188, in _optimize_graph
    graph = torch._C._jit_pass_onnx(graph, operator_export_type)
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/__init__.py", line 50, in _run_symbolic_function
    return utils._run_symbolic_function(*args, **kwargs)
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/utils.py", line 589, in _run_symbolic_function
    return fn(g, *inputs, **attrs)
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/symbolic.py", line 130, in wrapper
    args = [_parse_arg(arg, arg_desc) for arg, arg_desc in zip(args, arg_descriptors)]
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/symbolic.py", line 130, in 
    args = [_parse_arg(arg, arg_desc) for arg, arg_desc in zip(args, arg_descriptors)]
  File "/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/symbolic.py", line 90, in _parse_arg
    raise RuntimeError("Failed to export an ONNX attribute, "
RuntimeError: Failed to export an ONNX attribute, since it's not constant, please try to make things (e.g., kernel size) static if possible

转换onnx出问题，有一次是因为

def calc_tf_padding(x,
                    kernel_size,
                    stride=1,
                    dilation=1):
    """
    Calculate TF-same like padding size.

    Parameters:
    ----------
    x : tensor
        Input tensor.
    kernel_size : int
        Convolution window size.
    stride : int, default 1
        Strides of the convolution.
    dilation : int, default 1
        Dilation value for convolution layer.

    Returns
    -------
    tuple of 4 int
        The size of the padding.
    """
    height, width = x.size()[2:]
    oh = math.ceil(height / stride)
    ow = math.ceil(width / stride)
    pad_h = max((oh - 1) * stride + (kernel_size - 1) * dilation + 1 - height, 0)
    pad_w = max((ow - 1) * stride + (kernel_size - 1) * dilation + 1 - width, 0)
    return pad_h // 2, pad_h - pad_h // 2, pad_w // 2, pad_w - pad_w // 2

改为：

    return (int(pad_h // 2), int(pad_h - pad_h // 2), int(pad_w // 2), int(pad_w - pad_w // 2))

---

---

---

还有一次： 

    raise RuntimeError("Failed to export an ONNX attribute, "
RuntimeError: Failed to export an ONNX attribute, since it's not constant, please try to make things (e.g., kernel size) static if possible

怀疑是使用了

return F.interpolate(input=x, scale_factor=self.scale_factor, mode='bilinear', align_corners=True)

所以打开 

/home/spple/anaconda3/envs/pytorch1_1_0--py3_6--cuda9_0/lib/python3.6/site-packages/torch/onnx/symbolic.py

find:
upsample_nearest2d
 

改为：

'''
@parse_args('v', 'is', 'i')
def upsample_bilinear2d(g, input, output_size, align_corners):
    if align_corners:
        return _unimplemented("upsample_bilinear2d", "align_corners == True")
    height_scale = float(output_size[-2]) / input.type().sizes()[-2]
    width_scale = float(output_size[-1]) / input.type().sizes()[-1]
    scales = g.op("Constant", value_t=torch.tensor([1., 1., height_scale,
                                                    width_scale]))
    return g.op("Upsample", input, scales,
                mode_s="linear")
'''
def upsample_bilinear2d(g, input, output_size, align_corners):
    output_size = _maybe_get_const(output_size, 'is')
    if _is_value(output_size):
        offset = 2
        input_length = len(input.type().sizes())
        offsets = g.op("Constant", value_t=torch.tensor([1. for i in range(offset)]))
        dividend = g.op("Cast", output_size, to_i=cast_pytorch_to_onnx["Float"])
        divisor = g.op(
            "Slice",
            g.op("Shape", input),
            axes_i=[0], 
            ends_i=[input_length],
            starts_i=[offset]
        )
        divisor = g.op("Cast", divisor, to_i=cast_pytorch_to_onnx["Float"])
        scale_dims = g.op("Div", dividend, divisor)
        scales = g.op("Concat", offsets, scale_dims, axis_i=0)
    else:
        height_scale = float(output_size[-2]) / input.type().sizes()[-2]
        width_scale = float(output_size[-1]) / input.type().sizes()[-1]
        scales = g.op("Constant", value_t=torch.tensor([1., 1., height_scale, width_scale]))
    return g.op("Upsample", input, scales, mode_s="linear")

然后就可以了

不过推荐换到最新版，最新版已经解决了这个问题。pytorch1.2.0 

---

 

        self.classifier = nn.Sequential(
            nn.Dropout(0.2),
            nn.Linear(self.last_channel, n_class),
        )

 pytorch中的命名方式：

如果是train

classifier.1.weight

classifier.1.bias

如果是eval()

classifier.0.weight

classifier.0.bias

如果我想把这个结构改成：

classifier.weight

classifier.bias

结构应该是训练完以后，把网络修改为

self.classifier = nn.Linear(self.last_channel, n_class)

模型载入方式也改变了：

self.model = FairNasC(n_class=class_num)
self.model.eval()

state_dict = torch.load(ckpt_path)
model_dict = self.model.state_dict()
state_dict = {k: v for k, v in state_dict.items() if k.find("num_batches_tracked")<0 }
state_dict = {k: v for k, v in state_dict.items() if k.find("classifier.1.")<0 }
state_dict = {k: v for k, v in state_dict.items() if k in model_dict}
for k, v in state_dict.items():
    print(k)
self.model.load_state_dict(state_dict)

其中：

state_dict = {k: v for k, v in state_dict.items() if k.find("num_batches_tracked")<0 }

是因为pytorch1.1.0训练出来的模型，BN层结构与pytorch0.3.0不一样，所以进行删除多余结构

 

 

 

 

if use_cuda:
    inputs, targets = inputs.cuda(), targets.cuda(async=True)
inputs, targets = torch.autograd.Variable(inputs), torch.autograd.Variable(targets)

https://blog.csdn.net/amds123/article/details/70144995

https://blog.csdn.net/qq_18644873/article/details/88216904 

Pytorch常见问题、PyTorch 0.4新版本升级指南no_grad、cuda(async=True)

https://blog.csdn.net/qq_18644873/article/details/88216904

 

PyTorch 0.4新版本 升级指南 no_grad

https://blog.csdn.net/jacke121/article/details/80597759