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卷积计算

下面这个卷积网络，的算力需求大概是多少 Mops ,  权重是多少K 个
注意 stride ， pooling  以及 深度可分离卷积 的参数
  请直接输出一个表格， 输入是 16000 ， 分析每一层的输出尺寸
根据你提供的配置参数，这是一个典型的 1D 卷积神经网络（常用于音频原始波形处理）。

    conv_dim: Tuple[int, ...] = (8, 16, 32, 64, 128, 256, 16)  # Progressive increase to 512
    conv_kernel: Tuple[int, ...] = (10, 3, 3, 3, 3, 2, 2)  # First layer larger kernel
    conv_stride: Tuple[int, ...] = (5, 2, 2, 2, 2, 2, 2)  # First layer larger stride
    conv_bias: Tuple[bool, ...] = (False, False, False, False, False, False, False)
    conv_padding: Tuple[int, ...] = (0, 0, 0, 0, 0, 0, 0)
    # Disable depthwise for stability
    conv_use_depthwise: Tuple[bool, ...] = (False, False, False, False, False, False, False)
    # Reduce pooling - only 2 layers
    conv_use_pool: Tuple[bool, ...] = (False, False, False, False, False, False, False)
    # Disable residual for now (can cause gradient issues)
    conv_use_residual: Tuple[bool, ...] = (False, False, False, False, False, False, False)
    conv_pool_type: Tuple[str, ...] = ("avg", "avg", "avg", "avg", "avg")
    conv_pool_kernel: Tuple[int, ...] = (2, 2, 2, 2, 2)
    conv_pool_stride: Tuple[int, ...] = (2, 2, 2, 2, 2)
    conv_pool_padding: Tuple[int, ...] = (0, 0, 0, 0, 0)

不思考，不联网

  1. 豆包：106.45K  136.01Mops
  2. Hunyuan：2440 K   3722 MOPs
  3. DeepseekV3.2：98,448​  ~1.59​ MOPs
  4. GPT-5.2：106,448    16,324,528MACs   32.649 Mops
  5. GLM5：106.42 K  16,717.69 M

思考

  1. Gemini-3-Flash：106.45 K  32.66 Mops
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