480 lines
17 KiB
Python
480 lines
17 KiB
Python
import torch
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from network.SpikeLayer import SpikeLayer
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from network.HDynamicLayer import HDynamicLayer
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from network.calculate_output_size import calculate_output_size
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from network.SortSpikesLayer import SortSpikesLayer
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class SbSLayer(torch.nn.Module):
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_epsilon_xy: torch.Tensor | None = None
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_epsilon_xy_use: bool
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_epsilon_0: float
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_weights: torch.nn.parameter.Parameter
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_weights_exists: bool = False
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_kernel_size: list[int]
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_stride: list[int]
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_dilation: list[int]
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_padding: list[int]
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_output_size: torch.Tensor
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_number_of_spikes: int
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_number_of_cpu_processes: int
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_number_of_neurons: int
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_number_of_input_neurons: int
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_epsilon_xy_intitial: float
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_h_initial: torch.Tensor | None = None
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_w_trainable: bool
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_last_grad_scale: torch.nn.parameter.Parameter
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_keep_last_grad_scale: bool
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_disable_scale_grade: bool
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_forgetting_offset: float
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_weight_noise_range: list[float]
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_skip_gradient_calculation: bool
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_is_pooling_layer: bool
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_input_size: list[int]
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_output_layer: bool = False
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_local_learning: bool = False
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device: torch.device
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default_dtype: torch.dtype
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_gpu_tuning_factor: int
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_max_grad_weights: torch.Tensor | None = None
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_number_of_grad_weight_contributions: float = 0.0
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last_input_store: bool = False
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last_input_data: torch.Tensor | None = None
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_cooldown_after_number_of_spikes: int = -1
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_reduction_cooldown: float = 1.0
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_layer_id: int = -1
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spike_full_layer_input_distribution: bool = False
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def __init__(
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self,
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number_of_input_neurons: int,
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number_of_neurons: int,
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input_size: list[int],
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forward_kernel_size: list[int],
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number_of_spikes: int,
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epsilon_xy_intitial: float = 0.1,
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epsilon_xy_use: bool = False,
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epsilon_0: float = 1.0,
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weight_noise_range: list[float] = [0.0, 1.0],
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is_pooling_layer: bool = False,
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strides: list[int] = [1, 1],
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dilation: list[int] = [0, 0],
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padding: list[int] = [0, 0],
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number_of_cpu_processes: int = 1,
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w_trainable: bool = False,
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keep_last_grad_scale: bool = False,
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disable_scale_grade: bool = True,
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forgetting_offset: float = -1.0,
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skip_gradient_calculation: bool = False,
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device: torch.device | None = None,
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default_dtype: torch.dtype | None = None,
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gpu_tuning_factor: int = 10,
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layer_id: int = -1,
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cooldown_after_number_of_spikes: int = -1,
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reduction_cooldown: float = 1.0,
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) -> None:
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super().__init__()
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assert device is not None
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assert default_dtype is not None
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self.device = device
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self.default_dtype = default_dtype
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self._w_trainable = bool(w_trainable)
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self._keep_last_grad_scale = bool(keep_last_grad_scale)
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self._skip_gradient_calculation = bool(skip_gradient_calculation)
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self._disable_scale_grade = bool(disable_scale_grade)
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self._epsilon_xy_intitial = float(epsilon_xy_intitial)
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self._stride = strides
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self._dilation = dilation
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self._padding = padding
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self._kernel_size = forward_kernel_size
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self._number_of_input_neurons = int(number_of_input_neurons)
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self._number_of_neurons = int(number_of_neurons)
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self._epsilon_0 = float(epsilon_0)
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self._number_of_cpu_processes = int(number_of_cpu_processes)
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self._number_of_spikes = int(number_of_spikes)
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self._weight_noise_range = weight_noise_range
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self._is_pooling_layer = bool(is_pooling_layer)
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self._cooldown_after_number_of_spikes = int(cooldown_after_number_of_spikes)
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self.reduction_cooldown = float(reduction_cooldown)
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self._layer_id = layer_id
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self._epsilon_xy_use = epsilon_xy_use
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assert len(input_size) == 2
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self._input_size = input_size
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# The GPU hates me...
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# Too many SbS threads == bad
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# Thus I need to limit them...
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# (Reminder: We cannot access the mini-batch size here,
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# which is part of the GPU thread size calculation...)
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self._last_grad_scale = torch.nn.parameter.Parameter(
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torch.tensor(-1.0, dtype=self.default_dtype),
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requires_grad=True,
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)
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self._forgetting_offset = float(forgetting_offset)
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self._output_size = calculate_output_size(
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value=input_size,
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kernel_size=self._kernel_size,
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stride=self._stride,
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dilation=self._dilation,
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padding=self._padding,
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)
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self.set_h_init_to_uniform()
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self.spike_generator = SpikeLayer(
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number_of_spikes=self._number_of_spikes,
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number_of_cpu_processes=self._number_of_cpu_processes,
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device=self.device,
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)
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self.h_dynamic = HDynamicLayer(
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output_size=self._output_size.tolist(),
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output_layer=self._output_layer,
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local_learning=self._local_learning,
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number_of_cpu_processes=number_of_cpu_processes,
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w_trainable=w_trainable,
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skip_gradient_calculation=skip_gradient_calculation,
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device=device,
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default_dtype=self.default_dtype,
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gpu_tuning_factor=gpu_tuning_factor,
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)
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assert len(input_size) >= 2
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self.spikes_sorter = SortSpikesLayer(
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kernel_size=self._kernel_size,
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input_shape=[
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self._number_of_input_neurons,
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int(input_size[0]),
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int(input_size[1]),
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],
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output_size=self._output_size.clone(),
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strides=self._stride,
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dilation=self._dilation,
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padding=self._padding,
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number_of_cpu_processes=number_of_cpu_processes,
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)
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# TODO: TEST
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if layer_id == 0:
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self.spike_full_layer_input_distribution = True
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# ###############################################################
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# Initialize the weights
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# ###############################################################
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if self._is_pooling_layer is True:
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self.weights = self._make_pooling_weights()
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else:
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assert len(self._weight_noise_range) == 2
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weights = torch.empty(
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(
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int(self._kernel_size[0])
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* int(self._kernel_size[1])
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* int(self._number_of_input_neurons),
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int(self._number_of_neurons),
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),
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dtype=self.default_dtype,
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device=self.device,
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)
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torch.nn.init.uniform_(
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weights,
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a=float(self._weight_noise_range[0]),
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b=float(self._weight_noise_range[1]),
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)
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self.weights = weights
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####################################################################
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# Variables in and out #
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####################################################################
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def get_epsilon_t(self, number_of_spikes: int):
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"""Generates the time series of the basic epsilon."""
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t = (
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torch.arange(
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0, number_of_spikes, dtype=self.default_dtype, device=self.device
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)
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+ 1
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)
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# torch.ones((number_of_spikes), dtype=self.default_dtype, device=self.device
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epsilon_t: torch.Tensor = t ** (-1.0 / 2.0)
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if (self._cooldown_after_number_of_spikes < number_of_spikes) and (
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self._cooldown_after_number_of_spikes >= 0
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):
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epsilon_t[
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self._cooldown_after_number_of_spikes : number_of_spikes
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] /= self._reduction_cooldown
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return epsilon_t
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@property
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def weights(self) -> torch.Tensor | None:
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if self._weights_exists is False:
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return None
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else:
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return self._weights
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@weights.setter
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def weights(self, value: torch.Tensor):
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assert value is not None
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assert torch.is_tensor(value) is True
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assert value.dim() == 2
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temp: torch.Tensor = (
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value.detach()
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.clone(memory_format=torch.contiguous_format)
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.type(dtype=self.default_dtype)
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.to(device=self.device)
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)
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temp /= temp.sum(dim=0, keepdim=True, dtype=self.default_dtype)
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if self._weights_exists is False:
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self._weights = torch.nn.parameter.Parameter(temp, requires_grad=True)
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self._weights_exists = True
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else:
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self._weights.data = temp
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@property
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def h_initial(self) -> torch.Tensor | None:
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return self._h_initial
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@h_initial.setter
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def h_initial(self, value: torch.Tensor):
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assert value is not None
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assert torch.is_tensor(value) is True
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assert value.dim() == 1
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assert value.dtype == self.default_dtype
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self._h_initial = (
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value.detach()
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.clone(memory_format=torch.contiguous_format)
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.type(dtype=self.default_dtype)
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.to(device=self.device)
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.requires_grad_(False)
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)
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def update_pre_care(self):
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if self._weights.grad is not None:
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assert self._number_of_grad_weight_contributions > 0
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self._weights.grad /= self._number_of_grad_weight_contributions
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self._number_of_grad_weight_contributions = 0.0
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def update_after_care(self, threshold_weight: float):
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if self._w_trainable is True:
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self.norm_weights()
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self.threshold_weights(threshold_weight)
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self.norm_weights()
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def after_batch(self, new_state: bool = False):
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if self._keep_last_grad_scale is True:
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self._last_grad_scale.data = self._last_grad_scale.grad
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self._keep_last_grad_scale = new_state
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self._last_grad_scale.grad = torch.zeros_like(self._last_grad_scale.grad)
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####################################################################
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# Helper functions #
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####################################################################
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def _make_pooling_weights(self) -> torch.Tensor:
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"""For generating the pooling weights."""
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assert self._number_of_neurons is not None
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assert self._kernel_size is not None
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weights: torch.Tensor = torch.zeros(
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(
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int(self._kernel_size[0]),
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int(self._kernel_size[1]),
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int(self._number_of_neurons),
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int(self._number_of_neurons),
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),
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dtype=self.default_dtype,
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device=self.device,
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)
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for i in range(0, int(self._number_of_neurons)):
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weights[:, :, i, i] = 1.0
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weights = weights.moveaxis(-1, 0).moveaxis(-1, 1)
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weights = torch.nn.functional.unfold(
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input=weights,
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kernel_size=(int(self._kernel_size[0]), int(self._kernel_size[1])),
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dilation=(1, 1),
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padding=(0, 0),
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stride=(1, 1),
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).squeeze()
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weights = torch.moveaxis(weights, 0, 1)
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return weights
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def set_h_init_to_uniform(self) -> None:
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assert self._number_of_neurons > 2
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self.h_initial: torch.Tensor = torch.full(
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(self._number_of_neurons,),
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(1.0 / float(self._number_of_neurons)),
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dtype=self.default_dtype,
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device=self.device,
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)
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def norm_weights(self) -> None:
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assert self._weights_exists is True
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temp: torch.Tensor = (
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self._weights.data.detach()
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.clone(memory_format=torch.contiguous_format)
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.type(dtype=self.default_dtype)
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.to(device=self.device)
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)
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temp /= temp.sum(dim=0, keepdim=True, dtype=self.default_dtype)
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self._weights.data = temp
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def threshold_weights(self, threshold: float) -> None:
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assert self._weights_exists is True
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assert threshold >= 0
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torch.clamp(
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self._weights.data,
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min=float(threshold),
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max=None,
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out=self._weights.data,
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)
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####################################################################
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# Forward #
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####################################################################
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def forward(
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self,
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input: torch.Tensor,
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labels: torch.Tensor | None = None,
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) -> torch.Tensor:
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# Are we happy with the input?
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assert input is not None
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assert torch.is_tensor(input) is True
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assert input.dim() == 4
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assert input.dtype == self.default_dtype
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assert input.shape[1] == self._number_of_input_neurons
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assert input.shape[2] == self._input_size[0]
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assert input.shape[3] == self._input_size[1]
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# Are we happy with the rest of the network?
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assert self._epsilon_0 is not None
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assert self._h_initial is not None
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assert self._forgetting_offset is not None
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assert self._weights_exists is True
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assert self._weights is not None
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# Convolution of the input...
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# Well, this is a convoltion layer
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# there needs to be convolution somewhere
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input_convolved = torch.nn.functional.fold(
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torch.nn.functional.unfold(
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input.requires_grad_(True),
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kernel_size=(int(self._kernel_size[0]), int(self._kernel_size[1])),
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dilation=(int(self._dilation[0]), int(self._dilation[1])),
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padding=(int(self._padding[0]), int(self._padding[1])),
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stride=(int(self._stride[0]), int(self._stride[1])),
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),
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output_size=tuple(self._output_size.tolist()),
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kernel_size=(1, 1),
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dilation=(1, 1),
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padding=(0, 0),
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stride=(1, 1),
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)
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# We might need the convolved input for other layers
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# let us keep it for the future
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if self.last_input_store is True:
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self.last_input_data = input_convolved.detach().clone()
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self.last_input_data /= self.last_input_data.sum(dim=1, keepdim=True)
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else:
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self.last_input_data = None
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epsilon_t_0: torch.Tensor = (
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(self.get_epsilon_t(self._number_of_spikes) * self._epsilon_0)
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.type(input.dtype)
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.to(input.device)
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)
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if (self._epsilon_xy is None) and (self._epsilon_xy_use is True):
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self._epsilon_xy = torch.full(
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(
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input_convolved.shape[1],
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input_convolved.shape[2],
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input_convolved.shape[3],
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),
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float(self._epsilon_xy_intitial),
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dtype=self.default_dtype,
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device=self.device,
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)
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if self._epsilon_xy_use is True:
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assert self._epsilon_xy is not None
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# In the case somebody tried to replace the matrix with wrong dimensions
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assert self._epsilon_xy.shape[0] == input_convolved.shape[1]
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assert self._epsilon_xy.shape[1] == input_convolved.shape[2]
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assert self._epsilon_xy.shape[2] == input_convolved.shape[3]
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else:
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assert self._epsilon_xy is None
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if self.spike_full_layer_input_distribution is False:
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spike = self.spike_generator(input_convolved, int(self._number_of_spikes))
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else:
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input_shape = input.shape
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input = (
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input.reshape(
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(input_shape[0], input_shape[1] * input_shape[2] * input_shape[3])
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)
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.unsqueeze(-1)
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.unsqueeze(-1)
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)
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spike_unsorted = self.spike_generator(input, int(self._number_of_spikes))
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input = (
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input.squeeze(-1)
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.squeeze(-1)
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.reshape(
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(input_shape[0], input_shape[1], input_shape[2], input_shape[3])
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)
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)
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spike = self.spikes_sorter(spike_unsorted).to(device=input_convolved.device)
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output = self.h_dynamic(
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input=input_convolved,
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spike=spike,
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epsilon_xy=self._epsilon_xy,
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epsilon_t_0=epsilon_t_0,
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weights=self._weights,
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h_initial=self._h_initial,
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last_grad_scale=self._last_grad_scale,
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labels=labels,
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keep_last_grad_scale=self._keep_last_grad_scale,
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disable_scale_grade=self._disable_scale_grade,
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forgetting_offset=self._forgetting_offset,
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)
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self._number_of_grad_weight_contributions += (
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output.shape[0] * output.shape[-2] * output.shape[-1]
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)
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return output
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