spikingjelly.activation_based.functional package ================================================== .. warning:: 卷积-批归一化融合函数已弃用。用户可以使用 `PyTorch的fuse_conv_bn_eval `_ 来实现相同的功能。 Functions for conv-bn fusion have been deprecated. Use `PyTorch's fuse_conv_bn_eval `_ to achieve the same functionality. .. note:: **API稳定性说明** SpikingJelly ``0.0.0.1.0`` 对 ``functional`` 模块的内部实现进行了重构：原来的 ``functional.py`` 文件已被拆分并重组为 ``functional/`` 包，以提升代码的模块化程度和可维护性。 **该改动不会影响对外公开的 API。** 我们强烈建议用户仍然通过 ``functional`` 这一顶层命名空间来访问相关功能，而不是从具体的内部子模块中进行导入。 ``functional`` 层级下的导入路径被视为稳定的公共接口；更深层的子模块仅作为内部实现细节，未来可能发生变化。 .. code:: python from spikingjelly.activation_based.functional import reset_net # 推荐 ✅ from spikingjelly.activation_based.functional.net_config import reset_net # 不推荐 ❌ **API Stability Notice** We have refactored the internal implementation of the ``functional`` module. The original ``functional.py`` file has been reorganized into a package ( ``functional/`` ) for better modularity and maintainability. **This change does not affect the public API.** Users are strongly encouraged to continue accessing layers directly from the ``functional`` namespace, rather than importing from specific internal submodules. Import paths under ``functional`` are considered part of the stable public API, while deeper submodule paths are treated as implementation details and may change in future releases. .. code:: python from spikingjelly.activation_based.functional import reset_net # recommended ✅ from spikingjelly.activation_based.functional.net_config import reset_net # not recommended ❌ Network Configuration Functions +++++++++++++++++++++++++++++++++++ 这些函数帮助用户统一设置网络中每个 **子模块的配置** ，如步进模式、后端等。 ---- These functions help users set **configurations for each submodule** in a network, such as step mode and backend. .. list-table:: * - :func:`reset_net ` - Reset the state of a network. * - :func:`set_step_mode ` - Set the step mode for a network. * - :func:`set_backend ` - Set the computational backend for a network. * - :func:`detach_net ` - Detach the network's parameters from the computation graph. .. toctree:: :hidden: spikingjelly.activation_based.functional.net_config Forward Functions ++++++++++++++++++++++++++ SpikingJelly 的 **前向传播函数** 实现了 SNN 的多步前向传播逻辑。 ---- SpikingJelly's **forward functions** provide multi-step forward propagation logic for SNNs. .. list-table:: * - :func:`multi_step_forward ` - Forward pass for stateful modules in multi-step mode. * - :func:`t_last_multi_step_forward ` - Multi-step forward. The time dimension is placed at the last. * - :func:`chunk_multi_step_forward ` - Multi-step forward pass with chunked processing. * - :func:`seq_to_ann_forward ` - Forward pass for stateless modules in multi-step mode. * - :func:`t_last_seq_to_ann_forward ` - Seq-to-ann forward. The time dimension is placed at the last. .. toctree:: :hidden: spikingjelly.activation_based.functional.forward Loss Functions +++++++++++++++ 适用于 SNN 的 **损失函数** 实现。 ---- **Loss functions** suitable for SNNs. .. list-table:: * - :func:`kernel_dot_product ` - Kernel dot product implementation. * - :func:`spike_similar_loss ` - Spike similarity loss. * - :func:`temporal_efficient_training_cross_entropy ` - TET loss. .. toctree:: :hidden: spikingjelly.activation_based.functional.loss Online Learning Pipelines +++++++++++++++++++++++++++++++ **在线学习** 的辅助函数。 ---- Auxiliary functions for **online learning** . .. list-table:: * - :func:`fptt_online_training_init_w_ra ` - Initialize weight for FPTT. * - :func:`fptt_online_training ` - Online training with FPTT. * - :func:`ottt_online_training ` - Online training with OTTT or SLTT. .. toctree:: :hidden: spikingjelly.activation_based.functional.online_learning Miscellaneous +++++++++++++++++++++ 其他辅助 **工具函数** 。 ---- Other auxiliary **tool functions** . .. list-table:: * - :func:`set_threshold_margin ` - Set the threshold margin for classification layers. * - :func:`redundant_one_hot ` - Convert labels to redundant one-hot encoding. * - :func:`first_spike_index ` - Find the index of the first spike in a spike train. * - :func:`kaiming_normal_conv_linear_weight ` - Initialize weights with Kaiming Normal initialization. * - :func:`delay ` - ``y[t] = x[t - delay_steps]`` . .. toctree:: :hidden: spikingjelly.activation_based.functional.misc