卷积神经网络的中多维卷积代码

以下是一个三维卷积的示例代码，其中输入张量的形状为(batch_size, in_depth, in_height, in_width)，卷积核的形状为(out_depth, in_depth, kernel_height, kernel_width, kernel_depth)，输出张量的形状为(batch_size, out_depth, out_height, out_width)：

import numpy as np

def conv3d(input_tensor, kernel, bias, strides, padding):
    batch_size, in_depth, in_height, in_width = input_tensor.shape
    out_depth, _, kernel_height, kernel_width, kernel_depth = kernel.shape
    stride_depth, stride_height, stride_width = strides
    
    # 计算输出张量的形状
    if padding == 'SAME':
        out_height = int(np.ceil(float(in_height) / float(stride_height)))
        out_width = int(np.ceil(float(in_width) / float(stride_width)))
        out_depth = out_depth
    else:
        out_height = int(np.ceil(float(in_height - kernel_height + 1) / float(stride_height)))
        out_width = int(np.ceil(float(in_width - kernel_width + 1) / float(stride_width)))
        out_depth = int(np.ceil(float(in_depth - kernel_depth + 1) / float(stride_depth)))
    
    # 初始化输出张量
    output_tensor = np.zeros((batch_size, out_depth, out_height, out_width))
    
    # 对输入张量进行填充
    if padding == 'SAME':
        pad_depth = max((out_depth - 1) * stride_depth + kernel_depth - in_depth, 0)
        pad_height = max((out_height - 1) * stride_height + kernel_height - in_height, 0)
        pad_width = max((out_width - 1) * stride_width + kernel_width - in_width, 0)
        pad_top = pad_depth // 2
        pad_bottom = pad_depth - pad_top
        pad_left = pad_width // 2
        pad_right = pad_width - pad_left
        pad_front = pad_height // 2
        pad_back = pad_height - pad_front
        input_tensor = np.pad(input_tensor, ((0, 0), (pad_top, pad_bottom), (pad_front, pad_back), (pad_left, pad_right)), mode='constant')
    
    # 执行卷积操作
    for b in range(batch_size):
        for d in range(out_depth):
            for i in range(out_height):
                for j in range(out_width):
                    for fd in range(kernel_depth):
                        for fh in range(kernel_height):
                            for fw in range(kernel_width):
                                input_depth = d * stride_depth + fd
                                input_row = i * stride_height + fh
                                input_col = j * stride_width + fw
                                output_tensor[b, d, i, j] += input_tensor[b, input_depth, input_row, input_col] * kernel[d, :, fh, fw, fd]
                    output_tensor[b, d, i, j] += bias[d]
    
    return output_tensor