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我正在尝试使用火炬实现对角 BiLSTM。我准备了 CBAM 模型，还有本文中提到的空间门。我定义 conv2d 层的代码如下。

def Conv2D(name, input_dim, output_dim, filter_size, inputs, mask_type=None, he_init=False):
    """
    inputs.shape: (batch size, height, width, input_dim)
    mask_type: None, 'a', 'b'
    output.shape: (batch size, height, width, output_dim)
    """
    def uniform(stdev, size):
        return np.random.uniform(
            low=-stdev * np.sqrt(3),
            high=stdev * np.sqrt(3),
            size=size
        ).astype(numpy.float32)

    filters_init = uniform(
        1./np.sqrt(input_dim * filter_size * filter_size),
        # output dim, input dim, height, width
        (output_dim, input_dim, filter_size, filter_size)
    )

    if he_init:
        filters_init *= lib.floatX(np.sqrt(2.))

    if mask_type is not None:
        filters_init *= lib.floatX(np.sqrt(2.))

    filters = lib.param(
        name+'.Filters',
        filters_init
    )

    if mask_type is not None:
        mask = np.ones(
            (output_dim, input_dim, filter_size, filter_size), 
            dtype=np.float32
        )
        center = filter_size//2
        for i in range(filter_size):
            for j in range(filter_size):
                    if (j > center) or (j==center and i > center):
                        mask[:, :, j, i] = 0.
        for i in range(N_CHANNELS):
            for j in range(N_CHANNELS):
                if (mask_type=='a' and i >= j) or (mask_type=='b' and i > j):
                    mask[
                        j::N_CHANNELS,
                        i::N_CHANNELS,
                        center,
                        center
                    ] = 0.

        filters = filters * mask

    inputs = inputs.permute(0, 3, 1, 2)
    #print(inputs)
    inps = torch.cat((torch.max(inputs, 1)[0].unsqueeze(1), torch.mean(inputs, 1).unsqueeze(1)), dim=1)
    print(inps)
    result = torch.nn.Conv2d(inps, 1, 7, stride=1)
    biases = lib.param(
        name+'.Biases',
        np.zeros(output_dim, dtype=np.float32)
    )
    result = result + biases[None, :, None, None]

    return result.permute(0, 2, 3, 1)

但是当我尝试将张量从 torch.nn 传递给 conv2d 层时，我收到以下错误。

我试图在 inps 变量中打印张量，它们看起来像这样

tensor([[[[ 2.1475e+00,  2.2656e+00,  2.2285e+00,  ...,  2.1634e+00,
            1.9802e+00,  2.0768e+00],
          [ 1.1065e-01,  9.3942e-02,  1.3884e-01,  ...,  6.6712e-02,
            9.9830e-02,  1.4429e-01]],

         [[ 1.6910e+00,  1.5110e+00,  1.5579e+00,  ...,  1.0768e+00,
            1.5984e+00,  1.6736e+00],
          [-8.2758e-02, -1.3184e-02,  5.0098e-02,  ...,  2.2589e-03,
            5.8106e-02,  7.3571e-03]]],


        [[[ 2.2599e+00,  2.3655e+00,  2.2511e+00,  ...,  2.5398e+00,
            2.0128e+00,  1.9834e+00],
          [ 9.4436e-02,  9.2293e-02,  1.5296e-01,  ...,  5.4408e-02,
            9.8131e-02,  1.3627e-01]],

         [[ 1.6812e+00,  1.4765e+00,  1.5793e+00,  ...,  1.1819e+00,
            1.6332e+00,  1.6482e+00],
          [-8.4564e-02, -1.1611e-02,  5.6016e-02,  ...,  1.8283e-03,
            5.0835e-02,  1.7487e-02]]],


        [[[ 2.2437e+00,  2.4011e+00,  2.4019e+00,  ...,  2.4042e+00,
            2.0544e+00,  2.2374e+00],
          [ 8.5540e-02,  9.6436e-02,  1.3502e-01,  ...,  7.4034e-02,
            1.0697e-01,  1.4066e-01]],

         [[ 1.6898e+00,  1.4664e+00,  1.5747e+00,  ...,  1.0820e+00,
            1.6203e+00,  1.7650e+00],
          [-7.5721e-02, -1.1245e-02,  5.4568e-02,  ..., -5.5246e-03,
            5.6962e-02,  9.4589e-03]]],


        ...,


        [[[ 2.1059e+00,  2.3265e+00,  2.4311e+00,  ...,  2.4247e+00,
            1.9939e+00,  2.0536e+00],
          [ 9.6550e-02,  1.0173e-01,  1.4329e-01,  ...,  6.5086e-02,
            1.0176e-01,  1.3881e-01]],

         [[ 1.6375e+00,  1.5037e+00,  1.5442e+00,  ...,  1.1553e+00,
            1.6443e+00,  1.6747e+00],
          [-8.6096e-02, -1.2071e-02,  5.6651e-02,  ..., -1.4265e-03,
            5.6373e-02,  1.3945e-02]]],


        [[[ 2.3196e+00,  2.1638e+00,  2.1018e+00,  ...,  2.3779e+00,
            1.9471e+00,  1.9064e+00],
          [ 9.8306e-02,  9.1398e-02,  1.5183e-01,  ...,  6.4398e-02,
            1.0629e-01,  1.4232e-01]],

         [[ 1.6620e+00,  1.5137e+00,  1.5810e+00,  ...,  1.0661e+00,
            1.5326e+00,  1.6870e+00],
          [-8.2823e-02, -8.9255e-03,  5.7103e-02,  ..., -3.1661e-03,
            5.9011e-02,  6.9708e-03]]],


        [[[ 2.2586e+00,  2.3127e+00,  2.1126e+00,  ...,  2.3692e+00,
            2.0004e+00,  2.0361e+00],
          [ 8.9194e-02,  9.7919e-02,  1.4491e-01,  ...,  8.3260e-02,
            1.0935e-01,  1.4324e-01]],

         [[ 1.7048e+00,  1.5188e+00,  1.6082e+00,  ...,  1.1044e+00,
            1.5624e+00,  1.7280e+00],
          [-8.0997e-02, -8.0655e-03,  6.0712e-02,  ..., -4.3486e-03,
            5.7900e-02,  8.8437e-03]]]], grad_fn=<CatBackward>)

RuntimeError：具有多个值的张量的布尔值不明确

请帮帮我。我是手电筒的新手，希望得到任何答案。

4

1 回答 1

0

您需要先声明 conv 层，然后通过它传递数据。

myconv = torch.nn.Conv2d(in_channels=2 out_channels=7, kernel_size=1, stride=1) 
# NOTE: Switched in_channels 1->2, and added a kernel size argument ... please change this as needed for your purposes!
result = myconv(inps)

但是请myconv在其他地方声明，因为每次都重新声明它对训练不起作用——它会重置权重。不确定您的网络是如何构造的，但这需要在其他地方初始化并在此处使用。

于 2019-07-04T19:09:07.187 回答