diff --git a/.gitignore b/.gitignore index 71aadef8d..c78b91a8e 100644 --- a/.gitignore +++ b/.gitignore @@ -23,4 +23,5 @@ dist/* logs/* .pytest_cache/* .vscode/* +.claude/ data/* \ No newline at end of file diff --git a/.pytest_cache/CACHEDIR.TAG b/.pytest_cache/CACHEDIR.TAG deleted file mode 100644 index fce15ad7e..000000000 --- a/.pytest_cache/CACHEDIR.TAG +++ /dev/null @@ -1,4 +0,0 @@ -Signature: 8a477f597d28d172789f06886806bc55 -# This file is a cache directory tag created by pytest. -# For information about cache directory tags, see: -# https://bford.info/cachedir/spec.html diff --git a/.pytest_cache/README.md b/.pytest_cache/README.md deleted file mode 100644 index b89018ced..000000000 --- a/.pytest_cache/README.md +++ /dev/null @@ -1,8 +0,0 @@ -# pytest cache directory # - -This directory contains data from the pytest's cache plugin, -which provides the `--lf` and `--ff` options, as well as the `cache` fixture. - -**Do not** commit this to version control. - -See [the docs](https://docs.pytest.org/en/stable/how-to/cache.html) for more information. diff --git a/.pytest_cache/v/cache/lastfailed b/.pytest_cache/v/cache/lastfailed deleted file mode 100644 index 9e26dfeeb..000000000 --- a/.pytest_cache/v/cache/lastfailed +++ /dev/null @@ -1 +0,0 @@ -{} \ No newline at end of file diff --git a/.pytest_cache/v/cache/nodeids b/.pytest_cache/v/cache/nodeids deleted file mode 100644 index 067eab053..000000000 --- a/.pytest_cache/v/cache/nodeids +++ /dev/null @@ -1,23 +0,0 @@ -[ - "test/analysis/test_analyzers.py::TestAnalyzer::test_init", - "test/analysis/test_analyzers.py::TestAnalyzer::test_plot_runs", - "test/conversion/test_conversion.py::test_conversion_1", - "test/conversion/test_conversion.py::test_conversion_2", - "test/encoding/test_encoding.py::TestEncodings::test_bernoulli", - "test/encoding/test_encoding.py::TestEncodings::test_bernoulli_loader", - "test/encoding/test_encoding.py::TestEncodings::test_multidim_bernoulli", - "test/encoding/test_encoding.py::TestEncodings::test_poisson", - "test/encoding/test_encoding.py::TestEncodings::test_poisson_loader", - "test/models/test_models.py::TestDiehlAndCook2015::test_init", - "test/models/test_models.py::TestTwoLayerNetwork::test_init", - "test/network/test_learning.py::TestLearningRules::test_hebbian", - "test/network/test_learning.py::TestLearningRules::test_mstdp", - "test/network/test_learning.py::TestLearningRules::test_mstdpet", - "test/network/test_learning.py::TestLearningRules::test_post_pre", - "test/network/test_learning.py::TestLearningRules::test_rmax", - "test/network/test_learning.py::TestLearningRules::test_weight_dependent_post_pre", - "test/network/test_network.py::TestNetwork::test_add_objects", - "test/network/test_network.py::TestNetwork::test_empty", - "test/network/test_nodes.py::TestNodes::test_init", - "test/network/test_nodes.py::TestNodes::test_transfer" -] \ No newline at end of file diff --git a/.pytest_cache/v/cache/stepwise b/.pytest_cache/v/cache/stepwise deleted file mode 100644 index 0637a088a..000000000 --- a/.pytest_cache/v/cache/stepwise +++ /dev/null @@ -1 +0,0 @@ -[] \ No newline at end of file diff --git a/bindsnet/evaluation/evaluation.py b/bindsnet/evaluation/evaluation.py index 39f6ae720..62ae2885c 100644 --- a/bindsnet/evaluation/evaluation.py +++ b/bindsnet/evaluation/evaluation.py @@ -25,7 +25,8 @@ def assign_labels( ``assign_labels()`` call. :param alpha: Rate of decay of label assignments. :return: Tuple of class assignments, per-class spike proportions, and per-class - firing rates. + firing rates. Neurons that never fired are assigned ``-1`` (unassigned) so + they do not bias predictions toward class ``0``. """ n_neurons = spikes.size(2) @@ -35,10 +36,10 @@ def assign_labels( # Sum over time dimension (spike ordering doesn't matter). spikes = spikes.sum(1) - + for i in range(n_labels): # Create mask. - mask = (labels == i) + mask = labels == i # Count the number of samples with this label. n_labeled = mask.sum().float() @@ -50,24 +51,23 @@ def assign_labels( # Compute proportions of spike activity per class. total_activity = rates.sum(1, keepdim=True) - proportions = torch.where(total_activity > 0, rates / total_activity, torch.zeros_like(rates)) + proportions = torch.where( + total_activity > 0, rates / total_activity, torch.zeros_like(rates) + ) # Noise for random tie breaking. - eps = 1e-6 # Small enough not to distort real decisions + eps = 1e-6 # Small enough not to distort real decisions noise = eps * torch.randn_like(proportions) - + # Neuron assignments are the labels they fire most for. assignments = torch.argmax(proportions + noise, dim=1) - # Uniform assignment for silent neurons - silent_mask = total_activity.squeeze() == 0 - n_silent = silent_mask.sum() + # Neurons that never fired have no class preference; mark them with -1 so + # they are masked out of downstream voting (issue #736: silent neurons must + # not default to a vote for class 0). Downstream schemes only match + # ``assignments == i`` for i >= 0, so -1 neurons are excluded. + assignments[rates.sum(1) == 0] = -1 - if n_silent > 0: - assignments[silent_mask] = torch.randint( - 0, n_labels, (n_silent,), device=spikes.device - ) - return assignments, proportions, rates @@ -118,7 +118,9 @@ def all_activity( :param assignments: A vector of shape ``(n_neurons,)`` of neuron label assignments. :param n_labels: The number of target labels in the data. :return: Predictions tensor of shape ``(n_samples,)`` resulting from the "all - activity" classification scheme. + activity" classification scheme. Samples that elicit no activity from any + assigned neuron are predicted as ``-1`` (abstain) rather than defaulting to + class ``0``. """ n_samples = spikes.size(0) @@ -140,7 +142,12 @@ def all_activity( rates[:, i] = torch.sum(spikes[:, indices], 1) / n_assigns # Predictions are arg-max of layer-wise firing rates. - return torch.sort(rates, dim=1, descending=True)[1][:, 0] + predictions = torch.sort(rates, dim=1, descending=True)[1][:, 0] + + # Abstain (-1) on samples with no activity, avoiding a biased vote for class 0. + predictions[rates.sum(1) == 0] = -1 + + return predictions def proportion_weighting( @@ -161,7 +168,9 @@ def proportion_weighting( proportions of neuron spiking activity. :param n_labels: The number of target labels in the data. :return: Predictions tensor of shape ``(n_samples,)`` resulting from the "proportion - weighting" classification scheme. + weighting" classification scheme. Samples that elicit no weighted activity from + any assigned neuron are predicted as ``-1`` (abstain) rather than defaulting to + class ``0``. """ n_samples = spikes.size(0) @@ -187,6 +196,9 @@ def proportion_weighting( # Predictions are arg-max of layer-wise firing rates. predictions = torch.sort(rates, dim=1, descending=True)[1][:, 0] + # Abstain (-1) on samples with no activity, avoiding a biased vote for class 0. + predictions[rates.sum(1) == 0] = -1 + return predictions diff --git a/bindsnet/learning/learning.py b/bindsnet/learning/learning.py index 0ffad89cd..a0a8f027d 100644 --- a/bindsnet/learning/learning.py +++ b/bindsnet/learning/learning.py @@ -5,6 +5,7 @@ import numpy as np import torch import torch.nn.functional as F +import torch.nn.grad as conv_grad from torch.nn.modules.utils import _pair from bindsnet.utils import im2col_indices @@ -21,6 +22,34 @@ LocalConnection3D, ) +_CONV_WEIGHT_GRAD = { + 1: conv_grad.conv1d_weight, + 2: conv_grad.conv2d_weight, + 3: conv_grad.conv3d_weight, +} + + +def _conv_point_eligibility(connection, p_plus, p_minus, source_s, target_s, dim): + # language=rst + """ + Florian (2007) point eligibility for a convolutional connection, computed as + the convolution weight-gradient so it matches the forward weight layout for + any number of channels / stride / padding / dilation. Returned batch-summed + with shape ``connection.w.shape``. + + ``eligibility[o, i, *k] = sum_b sum_p P+[b, i, rf(p,k)] * post[b, o, p]`` + `` + pre[b, i, rf(p,k)] * P-[b, o, p]`` + """ + grad_fn = _CONV_WEIGHT_GRAD[dim] + kw = dict( + stride=connection.stride, + padding=connection.padding, + dilation=connection.dilation, + ) + return grad_fn(p_plus, connection.w.shape, target_s, **kw) + grad_fn( + source_s, connection.w.shape, p_minus, **kw + ) + class LearningRule(ABC): # language=rst @@ -530,7 +559,7 @@ def _conv3d_connection_update(self, **kwargs) -> None: ) target_x = self.target.x.view(batch_size, out_channels, -1) source_s = F.pad( - self.source.s, + self.source.s.float(), (padding[0], padding[0], padding[1], padding[1], padding[2], padding[2]), ) source_s = ( @@ -1009,7 +1038,7 @@ def _conv3d_connection_update(self, **kwargs) -> None: ) target_x = self.target.x.view(batch_size, out_channels, -1) source_s = F.pad( - self.source.s, + self.source.s.float(), (padding[0], padding[0], padding[1], padding[1], padding[2], padding[2]), ) source_s = ( @@ -1412,7 +1441,7 @@ def _conv3d_connection_update(self, **kwargs) -> None: ) target_x = self.target.x.view(batch_size, out_channels, -1) source_s = F.pad( - self.source.s, + self.source.s.float(), (padding[0], padding[0], padding[1], padding[1], padding[2], padding[2]), ) source_s = ( @@ -1500,6 +1529,11 @@ def __init__( self.tc_plus = torch.tensor(kwargs.get("tc_plus", 20.0)) self.tc_minus = torch.tensor(kwargs.get("tc_minus", 20.0)) + # If True, the reward at step t modulates the eligibility that already + # includes the spikes at step t (exact Florian 2007 timing). If False + # (default, backward-compatible) the eligibility is applied with a + # one-timestep lag. Currently honoured by the ``Connection`` path. + self.zero_lag = kwargs.get("zero_lag", False) def _connection_update(self, **kwargs) -> None: # language=rst @@ -1554,22 +1588,31 @@ def _connection_update(self, **kwargs) -> None: else: a_minus = torch.tensor(a_minus, device=self.connection.w.device) - # Compute weight update based on the eligibility value of the past timestep. + # Update P^+/P^- traces and the point eligibility for this timestep. + def _update_traces_and_eligibility(): + self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) + self.p_plus += a_plus * source_s + self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) + self.p_minus += a_minus * target_s + self.eligibility = torch.bmm( + self.p_plus.unsqueeze(2), target_s.unsqueeze(1) + ) + torch.bmm(source_s.unsqueeze(2), self.p_minus.unsqueeze(1)) + + # With zero_lag, fold in the current spikes before applying the update, + # so reward(t) multiplies eligibility(t) exactly as in Florian 2007. + if self.zero_lag: + _update_traces_and_eligibility() + + # Compute weight update based on the (current or previous) eligibility. update = self.reduction(reward * self.eligibility, dim=0) if self.connection.w.is_sparse: update = update.to_sparse() self.connection.w += self.nu[0] * update - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s - - # Calculate point eligibility value. - self.eligibility = torch.bmm( - self.p_plus.unsqueeze(2), target_s.unsqueeze(1) - ) + torch.bmm(source_s.unsqueeze(2), self.p_minus.unsqueeze(1)) + # Default (backward-compatible): the eligibility computed here is applied + # on the next timestep (one-step lag). + if not self.zero_lag: + _update_traces_and_eligibility() super().update() @@ -1868,82 +1911,37 @@ def _local_connection3d_update(self, **kwargs) -> None: def _conv1d_connection_update(self, **kwargs) -> None: # language=rst """ - MSTDP learning rule for ``Conv1dConnection`` subclass of ``AbstractConnection`` - class. - - Keyword arguments: - - :param Union[float, torch.Tensor] reward: Reward signal from reinforcement - learning task. - :param float a_plus: Learning rate (post-synaptic). - :param float a_minus: Learning rate (pre-synaptic). + MSTDP learning rule for ``Conv1dConnection`` subclass of + ``AbstractConnection`` class. """ - batch_size = self.source.batch_size - - # Initialize eligibility. - if not hasattr(self, "eligibility"): - self.eligibility = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device - ) - - # Parse keyword arguments. - reward = kwargs["reward"] - a_plus = torch.tensor( - kwargs.get("a_plus", 1.0), device=self.connection.w.device - ) - a_minus = torch.tensor( - kwargs.get("a_minus", -1.0), device=self.connection.w.device - ) - - # Compute weight update based on the eligibility value of the past timestep. - update = reward * self.eligibility - self.connection.w += self.nu[0] * torch.sum(update, dim=0) - - out_channels, in_channels, kernel_size = self.connection.w.size() - padding, stride = self.connection.padding, self.connection.stride - - # Initialize P^+ and P^-. - if not hasattr(self, "p_plus"): - self.p_plus = torch.zeros( - batch_size, *self.source.shape, device=self.connection.w.device - ) - self.p_plus = F.pad(self.p_plus, _pair(padding)) - self.p_plus = self.p_plus.unfold(-1, kernel_size, stride).reshape( - batch_size, -1, in_channels * kernel_size - ) - - if not hasattr(self, "p_minus"): - self.p_minus = torch.zeros( - batch_size, *self.target.shape, device=self.connection.w.device - ) - self.p_minus = self.p_minus.view(batch_size, out_channels, -1).float() + self._conv_connection_update(1, **kwargs) - # Reshaping spike occurrences. - source_s = F.pad(self.source.s.float(), _pair(padding)) - source_s = source_s.unfold(-1, kernel_size, stride).reshape( - batch_size, -1, in_channels * kernel_size - ) - target_s = self.target.s.view(batch_size, out_channels, -1).float() - - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s - - # Calculate point eligibility value. - self.eligibility = torch.bmm(target_s, self.p_plus) + torch.bmm( - self.p_minus, source_s - ) - self.eligibility = self.eligibility.view(self.connection.w.size()) + def _conv2d_connection_update(self, **kwargs) -> None: + # language=rst + """ + MSTDP learning rule for ``Conv2dConnection`` subclass of + ``AbstractConnection`` class. + """ + self._conv_connection_update(2, **kwargs) - super().update() + def _conv3d_connection_update(self, **kwargs) -> None: + # language=rst + """ + MSTDP learning rule for ``Conv3dConnection`` subclass of + ``AbstractConnection`` class. + """ + self._conv_connection_update(3, **kwargs) - def _conv2d_connection_update(self, **kwargs) -> None: + def _conv_connection_update(self, dim: int, **kwargs) -> None: # language=rst """ - MSTDP learning rule for ``Conv2dConnection`` subclass of ``AbstractConnection`` - class. + Shared MSTDP update for 1D/2D/3D convolutional connections. + + Traces are kept in neuron space and the point eligibility is computed as + a convolution weight-gradient (see ``_conv_point_eligibility``), so it + matches the forward weight layout and supports batches. Storing traces in + im2col space previously aliased overlapping kernel positions and + double-counted them. Keyword arguments: @@ -1954,85 +1952,17 @@ def _conv2d_connection_update(self, **kwargs) -> None: """ batch_size = self.source.batch_size - # Initialize eligibility. - if not hasattr(self, "eligibility"): - self.eligibility = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device - ) - - # Parse keyword arguments. - reward = kwargs["reward"] - a_plus = torch.tensor( - kwargs.get("a_plus", 1.0), device=self.connection.w.device - ) - a_minus = torch.tensor( - kwargs.get("a_minus", -1.0), device=self.connection.w.device - ) - - # Compute weight update based on the eligibility value of the past timestep. - update = reward * self.eligibility - self.connection.w += self.nu[0] * torch.sum(update, dim=0) - - out_channels, _, kernel_height, kernel_width = self.connection.w.size() - padding, stride = self.connection.padding, self.connection.stride - - # Initialize P^+ and P^-. if not hasattr(self, "p_plus"): self.p_plus = torch.zeros( batch_size, *self.source.shape, device=self.connection.w.device ) - self.p_plus = im2col_indices( - self.p_plus, kernel_height, kernel_width, padding=padding, stride=stride - ) if not hasattr(self, "p_minus"): self.p_minus = torch.zeros( batch_size, *self.target.shape, device=self.connection.w.device ) - self.p_minus = self.p_minus.view(batch_size, out_channels, -1).float() - - # Reshaping spike occurrences. - source_s = im2col_indices( - self.source.s.float(), - kernel_height, - kernel_width, - padding=padding, - stride=stride, - ) - target_s = self.target.s.view(batch_size, out_channels, -1).float() - - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s - - # Calculate point eligibility value. - self.eligibility = torch.bmm( - target_s, self.p_plus.permute((0, 2, 1)) - ) + torch.bmm(self.p_minus, source_s.permute((0, 2, 1))) - self.eligibility = self.eligibility.view(self.connection.w.size()) - - super().update() - - def _conv3d_connection_update(self, **kwargs) -> None: - # language=rst - """ - MSTDP learning rule for ``Conv3dConnection`` subclass of ``AbstractConnection`` - class. - - Keyword arguments: - - :param Union[float, torch.Tensor] reward: Reward signal from reinforcement - learning task. - :param float a_plus: Learning rate (post-synaptic). - :param float a_minus: Learning rate (pre-synaptic). - """ - batch_size = self.source.batch_size - - # Initialize eligibility. if not hasattr(self, "eligibility"): self.eligibility = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device + *self.connection.w.shape, device=self.connection.w.device ) # Parse keyword arguments. @@ -2044,79 +1974,27 @@ def _conv3d_connection_update(self, **kwargs) -> None: kwargs.get("a_minus", -1.0), device=self.connection.w.device ) - # Compute weight update based on the eligibility value of the past timestep. - update = reward * self.eligibility - self.connection.w += self.nu[0] * torch.sum(update, dim=0) - - ( - out_channels, - in_channels, - kernel_depth, - kernel_height, - kernel_width, - ) = self.connection.w.size() - padding, stride = self.connection.padding, self.connection.stride + source_s = self.source.s.float() + target_s = self.target.s.float() - # Initialize P^+ and P^-. - if not hasattr(self, "p_plus"): - self.p_plus = torch.zeros( - batch_size, *self.source.shape, device=self.connection.w.device - ) - self.p_plus = F.pad( - self.p_plus, - ( - padding[0], - padding[0], - padding[1], - padding[1], - padding[2], - padding[2], - ), - ) - self.p_plus = ( - self.p_plus.unfold(-3, kernel_width, stride[0]) - .unfold(-3, kernel_height, stride[1]) - .unfold(-3, kernel_depth, stride[2]) - .reshape( - batch_size, - -1, - in_channels * kernel_width * kernel_height * kernel_depth, - ) - ) - if not hasattr(self, "p_minus"): - self.p_minus = torch.zeros( - batch_size, *self.target.shape, device=self.connection.w.device + def _update_traces_and_eligibility(): + self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) + self.p_plus += a_plus * source_s + self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) + self.p_minus += a_minus * target_s + self.eligibility = _conv_point_eligibility( + self.connection, self.p_plus, self.p_minus, source_s, target_s, dim ) - self.p_minus = self.p_minus.view(batch_size, out_channels, -1).float() - # Reshaping spike occurrences. - source_s = F.pad( - self.source.s, - (padding[0], padding[0], padding[1], padding[1], padding[2], padding[2]), - ) - source_s = ( - source_s.unfold(-3, kernel_width, stride[0]) - .unfold(-3, kernel_height, stride[1]) - .unfold(-3, kernel_depth, stride[2]) - .reshape( - batch_size, - -1, - in_channels * kernel_width * kernel_height * kernel_depth, - ) - ) - target_s = self.target.s.view(batch_size, out_channels, -1).float() + # With zero_lag, fold in the current spikes before the weight update. + if self.zero_lag: + _update_traces_and_eligibility() - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s + # Weight update from the (current or previous) batch-summed eligibility. + self.connection.w += self.nu[0] * reward * self.eligibility - # Calculate point eligibility value. - self.eligibility = torch.bmm(target_s, self.p_plus) + torch.bmm( - self.p_minus, source_s - ) - self.eligibility = self.eligibility.view(self.connection.w.size()) + if not self.zero_lag: + _update_traces_and_eligibility() super().update() @@ -2183,6 +2061,11 @@ def __init__( self.tc_plus = torch.tensor(kwargs.get("tc_plus", 20.0)) self.tc_minus = torch.tensor(kwargs.get("tc_minus", 20.0)) self.tc_e_trace = torch.tensor(kwargs.get("tc_e_trace", 25.0)) + # If True, the current spikes are folded into the eligibility before the + # eligibility trace is integrated (exact Florian 2007 timing). If False + # (default, backward-compatible) a one-timestep lag is kept. Currently + # honoured by the ``Connection`` path. + self.zero_lag = kwargs.get("zero_lag", False) def _connection_update(self, **kwargs) -> None: # language=rst @@ -2197,23 +2080,29 @@ def _connection_update(self, **kwargs) -> None: :param float a_plus: Learning rate (post-synaptic). :param float a_minus: Learning rate (pre-synaptic). """ - # Initialize eligibility, eligibility trace, P^+, and P^-. + batch_size = self.source.batch_size + + # Initialize eligibility, eligibility trace, P^+, and P^- (batch-aware). if not hasattr(self, "p_plus"): - self.p_plus = torch.zeros((self.source.n), device=self.source.s.device) + self.p_plus = torch.zeros( + batch_size, self.source.n, device=self.source.s.device + ) if not hasattr(self, "p_minus"): - self.p_minus = torch.zeros((self.target.n), device=self.target.s.device) + self.p_minus = torch.zeros( + batch_size, self.target.n, device=self.target.s.device + ) if not hasattr(self, "eligibility"): self.eligibility = torch.zeros( - *self.connection.w.shape, device=self.connection.w.device + batch_size, *self.connection.w.shape, device=self.connection.w.device ) if not hasattr(self, "eligibility_trace"): self.eligibility_trace = torch.zeros( - *self.connection.w.shape, device=self.connection.w.device + batch_size, *self.connection.w.shape, device=self.connection.w.device ) # Reshape pre- and post-synaptic spikes. - source_s = self.source.s.view(-1).float() - target_s = self.target.s.view(-1).float() + source_s = self.source.s.view(batch_size, -1).float() + target_s = self.target.s.view(batch_size, -1).float() # Parse keyword arguments. reward = kwargs["reward"] @@ -2224,27 +2113,38 @@ def _connection_update(self, **kwargs) -> None: kwargs.get("a_minus", -1.0), device=self.connection.w.device ) + # Update P^+/P^- traces and the point eligibility for this timestep. + def _update_traces_and_eligibility(): + self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) + self.p_plus += a_plus * source_s + self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) + self.p_minus += a_minus * target_s + self.eligibility = torch.bmm( + self.p_plus.unsqueeze(2), target_s.unsqueeze(1) + ) + torch.bmm(source_s.unsqueeze(2), self.p_minus.unsqueeze(1)) + + # With zero_lag, fold in the current spikes before integrating the + # eligibility trace (exact Florian 2007 timing). + if self.zero_lag: + _update_traces_and_eligibility() + # Calculate value of eligibility trace based on the value # of the point eligibility value of the past timestep. self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) self.eligibility_trace += self.eligibility / self.tc_e_trace - update = self.nu[0] * self.connection.dt * reward * self.eligibility_trace + # Compute weight update, reducing over the minibatch dimension. + update = self.reduction( + self.nu[0] * self.connection.dt * reward * self.eligibility_trace, dim=0 + ) if self.connection.w.is_sparse: update = update.to_sparse() - # Compute weight update. self.connection.w += update - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s - - # Calculate point eligibility value. - self.eligibility = torch.outer(self.p_plus, target_s) + torch.outer( - source_s, self.p_minus - ) + # Default (backward-compatible): the eligibility computed here is applied + # on the next timestep (one-step lag). + if not self.zero_lag: + _update_traces_and_eligibility() super().update() @@ -2272,7 +2172,7 @@ def _local_connection1d_update(self, **kwargs) -> None: if not hasattr(self, "eligibility_trace"): self.eligibility_trace = torch.zeros( - *self.connection.w.shape, device=self.connection.w.device + batch_size, *self.connection.w.shape, device=self.connection.w.device ) # Parse keyword arguments. @@ -2284,11 +2184,12 @@ def _local_connection1d_update(self, **kwargs) -> None: kwargs.get("a_minus", -1.0), device=self.connection.w.device ) - # Calculate value of eligibility trace based on the value - # of the point eligibility value of the past timestep. + # Integrate the eligibility trace from the point eligibility of the + # previous timestep (decay then accumulate). self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) + self.eligibility_trace += self.eligibility / self.tc_e_trace - # Compute weight update. + # Compute weight update, reducing over the minibatch dimension. update = reward * self.eligibility_trace self.connection.w += self.nu[0] * self.connection.dt * torch.sum(update, dim=0) @@ -2371,7 +2272,7 @@ def _local_connection2d_update(self, **kwargs) -> None: if not hasattr(self, "eligibility_trace"): self.eligibility_trace = torch.zeros( - *self.connection.w.shape, device=self.connection.w.device + batch_size, *self.connection.w.shape, device=self.connection.w.device ) # Parse keyword arguments. @@ -2383,11 +2284,12 @@ def _local_connection2d_update(self, **kwargs) -> None: kwargs.get("a_minus", -1.0), device=self.connection.w.device ) - # Calculate value of eligibility trace based on the value - # of the point eligibility value of the past timestep. + # Integrate the eligibility trace from the point eligibility of the + # previous timestep (decay then accumulate). self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) + self.eligibility_trace += self.eligibility / self.tc_e_trace - # Compute weight update. + # Compute weight update, reducing over the minibatch dimension. update = reward * self.eligibility_trace self.connection.w += self.nu[0] * self.connection.dt * torch.sum(update, dim=0) @@ -2482,7 +2384,7 @@ def _local_connection3d_update(self, **kwargs) -> None: if not hasattr(self, "eligibility_trace"): self.eligibility_trace = torch.zeros( - *self.connection.w.shape, device=self.connection.w.device + batch_size, *self.connection.w.shape, device=self.connection.w.device ) # Parse keyword arguments. @@ -2494,11 +2396,12 @@ def _local_connection3d_update(self, **kwargs) -> None: kwargs.get("a_minus", -1.0), device=self.connection.w.device ) - # Calculate value of eligibility trace based on the value - # of the point eligibility value of the past timestep. + # Integrate the eligibility trace from the point eligibility of the + # previous timestep (decay then accumulate). self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) + self.eligibility_trace += self.eligibility / self.tc_e_trace - # Compute weight update. + # Compute weight update, reducing over the minibatch dimension. update = reward * self.eligibility_trace self.connection.w += self.nu[0] * self.connection.dt * torch.sum(update, dim=0) @@ -2573,89 +2476,35 @@ def _conv1d_connection_update(self, **kwargs) -> None: """ MSTDPET learning rule for ``Conv1dConnection`` subclass of ``AbstractConnection`` class. - - Keyword arguments: - - :param Union[float, torch.Tensor] reward: Reward signal from reinforcement - learning task. - :param float a_plus: Learning rate (post-synaptic). - :param float a_minus: Learning rate (pre-synaptic). """ - batch_size = self.source.batch_size - - # Initialize eligibility and eligibility trace. - if not hasattr(self, "eligibility"): - self.eligibility = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device - ) - if not hasattr(self, "eligibility_trace"): - self.eligibility_trace = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device - ) - - # Parse keyword arguments. - reward = kwargs["reward"] - a_plus = torch.tensor( - kwargs.get("a_plus", 1.0), device=self.connection.w.device - ) - a_minus = torch.tensor( - kwargs.get("a_minus", -1.0), device=self.connection.w.device - ) - - # Calculate value of eligibility trace based on the value - # of the point eligibility value of the past timestep. - self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) - - # Compute weight update. - update = reward * self.eligibility_trace - self.connection.w += self.nu[0] * self.connection.dt * torch.sum(update, dim=0) - - out_channels, in_channels, kernel_size = self.connection.w.size() - padding, stride = self.connection.padding, self.connection.stride - - # Initialize P^+ and P^-. - if not hasattr(self, "p_plus"): - self.p_plus = torch.zeros( - batch_size, *self.source.shape, device=self.connection.w.device - ) - self.p_plus = F.pad(self.p_plus.float(), _pair(padding)) - self.p_plus = self.p_plus.unfold(-1, kernel_size, stride).reshape( - batch_size, -1, in_channels * kernel_size - ) - if not hasattr(self, "p_minus"): - self.p_minus = torch.zeros( - batch_size, *self.target.shape, device=self.connection.w.device - ) - self.p_minus = self.p_minus.view(batch_size, out_channels, -1).float() - - # Reshaping spike occurrences. - source_s = F.pad(self.source.s.float(), _pair(padding)) - source_s = source_s.unfold(-1, kernel_size, stride).reshape( - batch_size, -1, in_channels * kernel_size - ) - target_s = ( - self.target.s.permute(1, 2, 0).view(batch_size, out_channels, -1).float() - ) - - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s - - # Calculate point eligibility value. - self.eligibility = torch.bmm(target_s, self.p_plus) + torch.bmm( - self.p_minus, source_s - ) - self.eligibility = self.eligibility.view(self.connection.w.size()) - - super().update() + self._conv_connection_update(1, **kwargs) def _conv2d_connection_update(self, **kwargs) -> None: # language=rst """ MSTDPET learning rule for ``Conv2dConnection`` subclass of ``AbstractConnection`` class. + """ + self._conv_connection_update(2, **kwargs) + + def _conv3d_connection_update(self, **kwargs) -> None: + # language=rst + """ + MSTDPET learning rule for ``Conv3dConnection`` subclass of + ``AbstractConnection`` class. + """ + self._conv_connection_update(3, **kwargs) + + def _conv_connection_update(self, dim: int, **kwargs) -> None: + # language=rst + """ + Shared MSTDPET update for 1D/2D/3D convolutional connections. + + Traces are kept in neuron space and the point eligibility is computed as + a convolution weight-gradient (see ``_conv_point_eligibility``), so it + matches the forward weight layout and supports batches. Storing traces in + im2col space previously aliased overlapping kernel positions and + double-counted them. Keyword arguments: @@ -2666,99 +2515,21 @@ def _conv2d_connection_update(self, **kwargs) -> None: """ batch_size = self.source.batch_size - # Initialize eligibility and eligibility trace. - if not hasattr(self, "eligibility"): - self.eligibility = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device - ) - if not hasattr(self, "eligibility_trace"): - self.eligibility_trace = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device - ) - - # Parse keyword arguments. - reward = kwargs["reward"] - a_plus = torch.tensor( - kwargs.get("a_plus", 1.0), device=self.connection.w.device - ) - a_minus = torch.tensor( - kwargs.get("a_minus", -1.0), device=self.connection.w.device - ) - - # Calculate value of eligibility trace based on the value - # of the point eligibility value of the past timestep. - self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) - - # Compute weight update. - update = reward * self.eligibility_trace - self.connection.w += self.nu[0] * self.connection.dt * torch.sum(update, dim=0) - - out_channels, _, kernel_height, kernel_width = self.connection.w.size() - padding, stride = self.connection.padding, self.connection.stride - - # Initialize P^+ and P^-. if not hasattr(self, "p_plus"): self.p_plus = torch.zeros( batch_size, *self.source.shape, device=self.connection.w.device ) - self.p_plus = im2col_indices( - self.p_plus, kernel_height, kernel_width, padding=padding, stride=stride - ) if not hasattr(self, "p_minus"): self.p_minus = torch.zeros( batch_size, *self.target.shape, device=self.connection.w.device ) - self.p_minus = self.p_minus.view(batch_size, out_channels, -1).float() - - # Reshaping spike occurrences. - source_s = im2col_indices( - self.source.s.float(), - kernel_height, - kernel_width, - padding=padding, - stride=stride, - ) - target_s = ( - self.target.s.permute(1, 2, 3, 0).view(batch_size, out_channels, -1).float() - ) - - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s - - # Calculate point eligibility value. - self.eligibility = torch.bmm( - target_s, self.p_plus.permute((0, 2, 1)) - ) + torch.bmm(self.p_minus, source_s.permute((0, 2, 1))) - self.eligibility = self.eligibility.view(self.connection.w.size()) - - super().update() - - def _conv3d_connection_update(self, **kwargs) -> None: - # language=rst - """ - MSTDPET learning rule for ``Conv3dConnection`` subclass of - ``AbstractConnection`` class. - - Keyword arguments: - - :param Union[float, torch.Tensor] reward: Reward signal from reinforcement - learning task. - :param float a_plus: Learning rate (post-synaptic). - :param float a_minus: Learning rate (pre-synaptic). - """ - batch_size = self.source.batch_size - - # Initialize eligibility and eligibility trace. if not hasattr(self, "eligibility"): self.eligibility = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device + *self.connection.w.shape, device=self.connection.w.device ) if not hasattr(self, "eligibility_trace"): self.eligibility_trace = torch.zeros( - batch_size, *self.connection.w.shape, device=self.connection.w.device + *self.connection.w.shape, device=self.connection.w.device ) # Parse keyword arguments. @@ -2770,87 +2541,31 @@ def _conv3d_connection_update(self, **kwargs) -> None: kwargs.get("a_minus", -1.0), device=self.connection.w.device ) - # Calculate value of eligibility trace based on the value - # of the point eligibility value of the past timestep. - self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) - - # Compute weight update. - update = reward * self.eligibility_trace - self.connection.w += self.nu[0] * self.connection.dt * torch.sum(update, dim=0) - - ( - out_channels, - in_channels, - kernel_depth, - kernel_height, - kernel_width, - ) = self.connection.w.size() - padding, stride = self.connection.padding, self.connection.stride - - # Initialize P^+ and P^-. - if not hasattr(self, "p_plus"): - self.p_plus = torch.zeros( - batch_size, *self.source.shape, device=self.connection.w.device - ) - self.p_plus = F.pad( - self.p_plus, - ( - padding[0], - padding[0], - padding[1], - padding[1], - padding[2], - padding[2], - ), - ) - self.p_plus = ( - self.p_plus.unfold(-3, kernel_width, stride[0]) - .unfold(-3, kernel_height, stride[1]) - .unfold(-3, kernel_depth, stride[2]) - .reshape( - batch_size, - -1, - in_channels * kernel_width * kernel_height * kernel_depth, - ) - ) - if not hasattr(self, "p_minus"): - self.p_minus = torch.zeros( - batch_size, *self.target.shape, device=self.connection.w.device - ) - self.p_minus = self.p_minus.view(batch_size, out_channels, -1).float() + source_s = self.source.s.float() + target_s = self.target.s.float() - # Reshaping spike occurrences. - source_s = F.pad( - self.source.s, - (padding[0], padding[0], padding[1], padding[1], padding[2], padding[2]), - ) - source_s = ( - source_s.unfold(-3, kernel_width, stride[0]) - .unfold(-3, kernel_height, stride[1]) - .unfold(-3, kernel_depth, stride[2]) - .reshape( - batch_size, - -1, - in_channels * kernel_width * kernel_height * kernel_depth, + def _update_traces_and_eligibility(): + self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) + self.p_plus += a_plus * source_s + self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) + self.p_minus += a_minus * target_s + self.eligibility = _conv_point_eligibility( + self.connection, self.p_plus, self.p_minus, source_s, target_s, dim ) - ) - target_s = ( - self.target.s.permute(1, 2, 3, 4, 0) - .view(batch_size, out_channels, -1) - .float() - ) - # Update P^+ and P^- values. - self.p_plus *= torch.exp(-self.connection.dt / self.tc_plus) - self.p_plus += a_plus * source_s - self.p_minus *= torch.exp(-self.connection.dt / self.tc_minus) - self.p_minus += a_minus * target_s + # With zero_lag, fold in the current spikes before the weight update. + if self.zero_lag: + _update_traces_and_eligibility() - # Calculate point eligibility value. - self.eligibility = torch.bmm(target_s, self.p_plus) + torch.bmm( - self.p_minus, source_s + # Integrate the eligibility trace and apply the weight update. + self.eligibility_trace *= torch.exp(-self.connection.dt / self.tc_e_trace) + self.eligibility_trace += self.eligibility / self.tc_e_trace + self.connection.w += ( + self.nu[0] * self.connection.dt * reward * self.eligibility_trace ) - self.eligibility = self.eligibility.view(self.connection.w.size()) + + if not self.zero_lag: + _update_traces_and_eligibility() super().update() diff --git a/examples/mnist/eth_mnist.py b/examples/mnist/eth_mnist.py index 4ad4aa6cf..f5e6184b9 100644 --- a/examples/mnist/eth_mnist.py +++ b/examples/mnist/eth_mnist.py @@ -1,347 +1,347 @@ -import argparse -import os -from time import time as t - -import matplotlib.pyplot as plt -import numpy as np -import torch -from torchvision import transforms -from tqdm import tqdm - -from bindsnet.analysis.plotting import ( - plot_assignments, - plot_input, - plot_performance, - plot_spikes, - plot_voltages, - plot_weights, -) -from bindsnet.datasets import MNIST -from bindsnet.encoding import PoissonEncoder -from bindsnet.evaluation import all_activity, assign_labels, proportion_weighting -from bindsnet.models import DiehlAndCook2015 -from bindsnet.network.monitors import Monitor -from bindsnet.utils import get_square_assignments, get_square_weights - -parser = argparse.ArgumentParser() -parser.add_argument("--seed", type=int, default=0) -parser.add_argument("--n_neurons", type=int, default=100) -parser.add_argument("--n_epochs", type=int, default=1) -parser.add_argument("--n_test", type=int, default=10000) -parser.add_argument("--n_train", type=int, default=60000) -parser.add_argument("--n_workers", type=int, default=-1) -parser.add_argument("--exc", type=float, default=22.5) -parser.add_argument("--inh", type=float, default=120) -parser.add_argument("--theta_plus", type=float, default=0.05) -parser.add_argument("--time", type=int, default=250) -parser.add_argument("--dt", type=int, default=1.0) -parser.add_argument("--intensity", type=float, default=128) -parser.add_argument("--progress_interval", type=int, default=10) -parser.add_argument("--update_interval", type=int, default=250) -parser.add_argument("--train", dest="train", action="store_true") -parser.add_argument("--test", dest="train", action="store_false") -parser.add_argument("--plot", dest="plot", action="store_true") -parser.add_argument("--gpu", dest="gpu", action="store_true") -parser.set_defaults(plot=True, gpu=True) - -args = parser.parse_args() - -seed = args.seed -n_neurons = args.n_neurons -n_epochs = args.n_epochs -n_test = args.n_test -n_train = args.n_train -n_workers = args.n_workers -exc = args.exc -inh = args.inh -theta_plus = args.theta_plus -time = args.time -dt = args.dt -intensity = args.intensity -progress_interval = args.progress_interval -update_interval = args.update_interval -train = args.train -plot = args.plot -gpu = args.gpu - -# Sets up Gpu use -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -if gpu and torch.cuda.is_available(): - torch.cuda.manual_seed_all(seed) -else: - torch.manual_seed(seed) - device = "cpu" - if gpu: - gpu = False - -torch.set_num_threads(os.cpu_count() - 1) -print("Running on Device = ", device) - -# Determines number of workers to use -if n_workers == -1: - n_workers = 0 # gpu * 4 * torch.cuda.device_count() - -if not train: - update_interval = n_test - -n_sqrt = int(np.ceil(np.sqrt(n_neurons))) -start_intensity = intensity - -# Build network. -network = DiehlAndCook2015( - n_inpt=784, - n_neurons=n_neurons, - exc=exc, - inh=inh, - dt=dt, - norm=78.4, - theta_plus=theta_plus, - inpt_shape=(1, 28, 28), -) - -# Directs network to GPU -if gpu: - network.to("cuda") - -# Load MNIST data. -train_dataset = MNIST( - PoissonEncoder(time=time, dt=dt), - None, - root=os.path.join("..", "..", "data", "MNIST"), - download=True, - train=True, - transform=transforms.Compose( - [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] - ), -) - -# Record spikes during the simulation. -spike_record = torch.zeros((update_interval, int(time / dt), n_neurons), device=device) - -# Neuron assignments and spike proportions. -n_classes = 10 -assignments = -torch.ones(n_neurons, device=device) -proportions = torch.zeros((n_neurons, n_classes), device=device) -rates = torch.zeros((n_neurons, n_classes), device=device) - -# Sequence of accuracy estimates. -accuracy = {"all": [], "proportion": []} - -# Voltage recording for excitatory and inhibitory layers. -exc_voltage_monitor = Monitor( - network.layers["Ae"], ["v"], time=int(time / dt), device=device -) -inh_voltage_monitor = Monitor( - network.layers["Ai"], ["v"], time=int(time / dt), device=device -) -network.add_monitor(exc_voltage_monitor, name="exc_voltage") -network.add_monitor(inh_voltage_monitor, name="inh_voltage") - -# Set up monitors for spikes and voltages -spikes = {} -for layer in set(network.layers): - spikes[layer] = Monitor( - network.layers[layer], state_vars=["s"], time=int(time / dt), device=device - ) - network.add_monitor(spikes[layer], name="%s_spikes" % layer) - -voltages = {} -for layer in set(network.layers) - {"X"}: - voltages[layer] = Monitor( - network.layers[layer], state_vars=["v"], time=int(time / dt), device=device - ) - network.add_monitor(voltages[layer], name="%s_voltages" % layer) - -inpt_ims, inpt_axes = None, None -spike_ims, spike_axes = None, None -weights_im = None -assigns_im = None -perf_ax = None -voltage_axes, voltage_ims = None, None - -# Train the network. -print("\nBegin training.\n") -start = t() -for epoch in range(n_epochs): - labels = [] - - if epoch % progress_interval == 0: - print("Progress: %d / %d (%.4f seconds)" % (epoch, n_epochs, t() - start)) - start = t() - - # Create a dataloader to iterate and batch data - dataloader = torch.utils.data.DataLoader( - train_dataset, batch_size=1, shuffle=True, num_workers=n_workers, pin_memory=gpu - ) - - for step, batch in enumerate(tqdm(dataloader)): - if step > n_train: - break - # Get next input sample. - inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} - if gpu: - inputs = {k: v.cuda() for k, v in inputs.items()} - - if step % update_interval == 0 and step > 0: - # Convert the array of labels into a tensor - label_tensor = torch.tensor(labels, device=device) - - # Get network predictions. - all_activity_pred = all_activity( - spikes=spike_record, assignments=assignments, n_labels=n_classes - ) - proportion_pred = proportion_weighting( - spikes=spike_record, - assignments=assignments, - proportions=proportions, - n_labels=n_classes, - ) - - # Compute network accuracy according to available classification strategies. - accuracy["all"].append( - 100 - * torch.sum(label_tensor.long() == all_activity_pred).item() - / len(label_tensor) - ) - accuracy["proportion"].append( - 100 - * torch.sum(label_tensor.long() == proportion_pred).item() - / len(label_tensor) - ) - - print( - "\nAll activity accuracy: %.2f (last), %.2f (average), %.2f (best)" - % ( - accuracy["all"][-1], - np.mean(accuracy["all"]), - np.max(accuracy["all"]), - ) - ) - print( - "Proportion weighting accuracy: %.2f (last), %.2f (average), %.2f" - " (best)\n" - % ( - accuracy["proportion"][-1], - np.mean(accuracy["proportion"]), - np.max(accuracy["proportion"]), - ) - ) - - # Assign labels to excitatory layer neurons. - assignments, proportions, rates = assign_labels( - spikes=spike_record, - labels=label_tensor, - n_labels=n_classes, - rates=rates, - ) - - labels = [] - - labels.append(batch["label"]) - - # Run the network on the input. - network.run(inputs=inputs, time=time) - - # Get voltage recording. - exc_voltages = exc_voltage_monitor.get("v") - inh_voltages = inh_voltage_monitor.get("v") - - # Add to spikes recording. - spike_record[step % update_interval] = spikes["Ae"].get("s").squeeze() - - # Optionally plot various simulation information. - if plot: - image = batch["image"].view(28, 28) - inpt = inputs["X"].view(time, 784).sum(0).view(28, 28) - input_exc_weights = network.connections[("X", "Ae")].pipeline[0].value - square_weights = get_square_weights( - input_exc_weights.view(784, n_neurons), n_sqrt, 28 - ) - square_assignments = get_square_assignments(assignments, n_sqrt) - spikes_ = {layer: spikes[layer].get("s") for layer in spikes} - voltages = {"Ae": exc_voltages, "Ai": inh_voltages} - inpt_axes, inpt_ims = plot_input( - image, inpt, label=batch["label"], axes=inpt_axes, ims=inpt_ims - ) - spike_ims, spike_axes = plot_spikes(spikes_, ims=spike_ims, axes=spike_axes) - weights_im = plot_weights(square_weights, im=weights_im) - assigns_im = plot_assignments(square_assignments, im=assigns_im) - perf_ax = plot_performance(accuracy, x_scale=update_interval, ax=perf_ax) - voltage_ims, voltage_axes = plot_voltages( - voltages, ims=voltage_ims, axes=voltage_axes, plot_type="line" - ) - - plt.pause(1e-8) - - network.reset_state_variables() # Reset state variables. - -print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) -print("Training complete.\n") - -# Load MNIST data. -test_dataset = MNIST( - PoissonEncoder(time=time, dt=dt), - None, - root=os.path.join("..", "..", "data", "MNIST"), - download=True, - train=False, - transform=transforms.Compose( - [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] - ), -) - -# Sequence of accuracy estimates. -accuracy = {"all": 0, "proportion": 0} - -# Record spikes during the simulation. -spike_record = torch.zeros((1, int(time / dt), n_neurons), device=device) - -# Train the network. -print("\nBegin testing\n") -network.train(mode=False) -start = t() - -pbar = tqdm(total=n_test) -for step, batch in enumerate(test_dataset): - if step >= n_test: - break - # Get next input sample. - inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} - if gpu: - inputs = {k: v.cuda() for k, v in inputs.items()} - - # Run the network on the input. - network.run(inputs=inputs, time=time) - - # Add to spikes recording. - spike_record[0] = spikes["Ae"].get("s").squeeze() - - # Convert the array of labels into a tensor - label_tensor = torch.tensor(batch["label"], device=device) - - # Get network predictions. - all_activity_pred = all_activity( - spikes=spike_record, assignments=assignments, n_labels=n_classes - ) - proportion_pred = proportion_weighting( - spikes=spike_record, - assignments=assignments, - proportions=proportions, - n_labels=n_classes, - ) - - # Compute network accuracy according to available classification strategies. - accuracy["all"] += float(torch.sum(label_tensor.long() == all_activity_pred).item()) - accuracy["proportion"] += float( - torch.sum(label_tensor.long() == proportion_pred).item() - ) - - network.reset_state_variables() # Reset state variables. - pbar.set_description_str("Test progress: ") - pbar.update() - -print("\nAll activity accuracy: %.2f" % (accuracy["all"] / n_test)) -print("Proportion weighting accuracy: %.2f \n" % (accuracy["proportion"] / n_test)) - -print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) -print("Testing complete.\n") +import argparse +import os +from time import time as t + +import matplotlib.pyplot as plt +import numpy as np +import torch +from torchvision import transforms +from tqdm import tqdm + +from bindsnet.analysis.plotting import ( + plot_assignments, + plot_input, + plot_performance, + plot_spikes, + plot_voltages, + plot_weights, +) +from bindsnet.datasets import MNIST +from bindsnet.encoding import PoissonEncoder +from bindsnet.evaluation import all_activity, assign_labels, proportion_weighting +from bindsnet.models import DiehlAndCook2015 +from bindsnet.network.monitors import Monitor +from bindsnet.utils import get_square_assignments, get_square_weights + +parser = argparse.ArgumentParser() +parser.add_argument("--seed", type=int, default=0) +parser.add_argument("--n_neurons", type=int, default=100) +parser.add_argument("--n_epochs", type=int, default=1) +parser.add_argument("--n_test", type=int, default=10000) +parser.add_argument("--n_train", type=int, default=60000) +parser.add_argument("--n_workers", type=int, default=-1) +parser.add_argument("--exc", type=float, default=22.5) +parser.add_argument("--inh", type=float, default=120) +parser.add_argument("--theta_plus", type=float, default=0.05) +parser.add_argument("--time", type=int, default=250) +parser.add_argument("--dt", type=int, default=1.0) +parser.add_argument("--intensity", type=float, default=128) +parser.add_argument("--progress_interval", type=int, default=10) +parser.add_argument("--update_interval", type=int, default=250) +parser.add_argument("--train", dest="train", action="store_true") +parser.add_argument("--test", dest="train", action="store_false") +parser.add_argument("--plot", dest="plot", action="store_true") +parser.add_argument("--gpu", dest="gpu", action="store_true") +parser.set_defaults(plot=True, gpu=True) + +args = parser.parse_args() + +seed = args.seed +n_neurons = args.n_neurons +n_epochs = args.n_epochs +n_test = args.n_test +n_train = args.n_train +n_workers = args.n_workers +exc = args.exc +inh = args.inh +theta_plus = args.theta_plus +time = args.time +dt = args.dt +intensity = args.intensity +progress_interval = args.progress_interval +update_interval = args.update_interval +train = args.train +plot = args.plot +gpu = args.gpu + +# Sets up Gpu use +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +if gpu and torch.cuda.is_available(): + torch.cuda.manual_seed_all(seed) +else: + torch.manual_seed(seed) + device = "cpu" + if gpu: + gpu = False + +torch.set_num_threads(os.cpu_count() - 1) +print("Running on Device = ", device) + +# Determines number of workers to use +if n_workers == -1: + n_workers = 0 # gpu * 4 * torch.cuda.device_count() + +if not train: + update_interval = n_test + +n_sqrt = int(np.ceil(np.sqrt(n_neurons))) +start_intensity = intensity + +# Build network. +network = DiehlAndCook2015( + n_inpt=784, + n_neurons=n_neurons, + exc=exc, + inh=inh, + dt=dt, + norm=78.4, + theta_plus=theta_plus, + inpt_shape=(1, 28, 28), +) + +# Directs network to GPU +if gpu: + network.to("cuda") + +# Load MNIST data. +train_dataset = MNIST( + PoissonEncoder(time=time, dt=dt), + None, + root=os.path.join("..", "..", "data", "MNIST"), + download=True, + train=True, + transform=transforms.Compose( + [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] + ), +) + +# Record spikes during the simulation. +spike_record = torch.zeros((update_interval, int(time / dt), n_neurons), device=device) + +# Neuron assignments and spike proportions. +n_classes = 10 +assignments = -torch.ones(n_neurons, device=device) +proportions = torch.zeros((n_neurons, n_classes), device=device) +rates = torch.zeros((n_neurons, n_classes), device=device) + +# Sequence of accuracy estimates. +accuracy = {"all": [], "proportion": []} + +# Voltage recording for excitatory and inhibitory layers. +exc_voltage_monitor = Monitor( + network.layers["Ae"], ["v"], time=int(time / dt), device=device +) +inh_voltage_monitor = Monitor( + network.layers["Ai"], ["v"], time=int(time / dt), device=device +) +network.add_monitor(exc_voltage_monitor, name="exc_voltage") +network.add_monitor(inh_voltage_monitor, name="inh_voltage") + +# Set up monitors for spikes and voltages +spikes = {} +for layer in set(network.layers): + spikes[layer] = Monitor( + network.layers[layer], state_vars=["s"], time=int(time / dt), device=device + ) + network.add_monitor(spikes[layer], name="%s_spikes" % layer) + +voltages = {} +for layer in set(network.layers) - {"X"}: + voltages[layer] = Monitor( + network.layers[layer], state_vars=["v"], time=int(time / dt), device=device + ) + network.add_monitor(voltages[layer], name="%s_voltages" % layer) + +inpt_ims, inpt_axes = None, None +spike_ims, spike_axes = None, None +weights_im = None +assigns_im = None +perf_ax = None +voltage_axes, voltage_ims = None, None + +# Train the network. +print("\nBegin training.\n") +start = t() +for epoch in range(n_epochs): + labels = [] + + if epoch % progress_interval == 0: + print("Progress: %d / %d (%.4f seconds)" % (epoch, n_epochs, t() - start)) + start = t() + + # Create a dataloader to iterate and batch data + dataloader = torch.utils.data.DataLoader( + train_dataset, batch_size=1, shuffle=True, num_workers=n_workers, pin_memory=gpu + ) + + for step, batch in enumerate(tqdm(dataloader)): + if step > n_train: + break + # Get next input sample. + inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} + if gpu: + inputs = {k: v.cuda() for k, v in inputs.items()} + + if step % update_interval == 0 and step > 0: + # Convert the array of labels into a tensor + label_tensor = torch.tensor(labels, device=device) + + # Get network predictions. + all_activity_pred = all_activity( + spikes=spike_record, assignments=assignments, n_labels=n_classes + ) + proportion_pred = proportion_weighting( + spikes=spike_record, + assignments=assignments, + proportions=proportions, + n_labels=n_classes, + ) + + # Compute network accuracy according to available classification strategies. + accuracy["all"].append( + 100 + * torch.sum(label_tensor.long() == all_activity_pred).item() + / len(label_tensor) + ) + accuracy["proportion"].append( + 100 + * torch.sum(label_tensor.long() == proportion_pred).item() + / len(label_tensor) + ) + + print( + "\nAll activity accuracy: %.2f (last), %.2f (average), %.2f (best)" + % ( + accuracy["all"][-1], + np.mean(accuracy["all"]), + np.max(accuracy["all"]), + ) + ) + print( + "Proportion weighting accuracy: %.2f (last), %.2f (average), %.2f" + " (best)\n" + % ( + accuracy["proportion"][-1], + np.mean(accuracy["proportion"]), + np.max(accuracy["proportion"]), + ) + ) + + # Assign labels to excitatory layer neurons. + assignments, proportions, rates = assign_labels( + spikes=spike_record, + labels=label_tensor, + n_labels=n_classes, + rates=rates, + ) + + labels = [] + + labels.append(batch["label"]) + + # Run the network on the input. + network.run(inputs=inputs, time=time) + + # Get voltage recording. + exc_voltages = exc_voltage_monitor.get("v") + inh_voltages = inh_voltage_monitor.get("v") + + # Add to spikes recording. + spike_record[step % update_interval] = spikes["Ae"].get("s").squeeze() + + # Optionally plot various simulation information. + if plot: + image = batch["image"].view(28, 28) + inpt = inputs["X"].view(time, 784).sum(0).view(28, 28) + input_exc_weights = network.connections[("X", "Ae")].pipeline[0].value + square_weights = get_square_weights( + input_exc_weights.view(784, n_neurons), n_sqrt, 28 + ) + square_assignments = get_square_assignments(assignments, n_sqrt) + spikes_ = {layer: spikes[layer].get("s") for layer in spikes} + voltages = {"Ae": exc_voltages, "Ai": inh_voltages} + inpt_axes, inpt_ims = plot_input( + image, inpt, label=batch["label"], axes=inpt_axes, ims=inpt_ims + ) + spike_ims, spike_axes = plot_spikes(spikes_, ims=spike_ims, axes=spike_axes) + weights_im = plot_weights(square_weights, im=weights_im) + assigns_im = plot_assignments(square_assignments, im=assigns_im) + perf_ax = plot_performance(accuracy, x_scale=update_interval, ax=perf_ax) + voltage_ims, voltage_axes = plot_voltages( + voltages, ims=voltage_ims, axes=voltage_axes, plot_type="line" + ) + + plt.pause(1e-8) + + network.reset_state_variables() # Reset state variables. + +print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - start)) +print("Training complete.\n") + +# Load MNIST data. +test_dataset = MNIST( + PoissonEncoder(time=time, dt=dt), + None, + root=os.path.join("..", "..", "data", "MNIST"), + download=True, + train=False, + transform=transforms.Compose( + [transforms.ToTensor(), transforms.Lambda(lambda x: x * intensity)] + ), +) + +# Sequence of accuracy estimates. +accuracy = {"all": 0, "proportion": 0} + +# Record spikes during the simulation. +spike_record = torch.zeros((1, int(time / dt), n_neurons), device=device) + +# Train the network. +print("\nBegin testing\n") +network.train(mode=False) +start = t() + +pbar = tqdm(total=n_test) +for step, batch in enumerate(test_dataset): + if step >= n_test: + break + # Get next input sample. + inputs = {"X": batch["encoded_image"].view(int(time / dt), 1, 1, 28, 28)} + if gpu: + inputs = {k: v.cuda() for k, v in inputs.items()} + + # Run the network on the input. + network.run(inputs=inputs, time=time) + + # Add to spikes recording. + spike_record[0] = spikes["Ae"].get("s").squeeze() + + # Convert the array of labels into a tensor + label_tensor = torch.tensor(batch["label"], device=device) + + # Get network predictions. + all_activity_pred = all_activity( + spikes=spike_record, assignments=assignments, n_labels=n_classes + ) + proportion_pred = proportion_weighting( + spikes=spike_record, + assignments=assignments, + proportions=proportions, + n_labels=n_classes, + ) + + # Compute network accuracy according to available classification strategies. + accuracy["all"] += float(torch.sum(label_tensor.long() == all_activity_pred).item()) + accuracy["proportion"] += float( + torch.sum(label_tensor.long() == proportion_pred).item() + ) + + network.reset_state_variables() # Reset state variables. + pbar.set_description_str("Test progress: ") + pbar.update() + +print("\nAll activity accuracy: %.2f" % (accuracy["all"] / n_test)) +print("Proportion weighting accuracy: %.2f \n" % (accuracy["proportion"] / n_test)) + +print("Progress: %d / %d (%.4f seconds)" % (epoch + 1, n_epochs, t() - 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">=3.11,<3.14" diff --git a/test/network/test_mstdp_florian.py b/test/network/test_mstdp_florian.py new file mode 100644 index 000000000..c675709de --- /dev/null +++ b/test/network/test_mstdp_florian.py @@ -0,0 +1,488 @@ +# language=rst +""" +Validation tests for the reward-modulated learning rules ``MSTDP`` and +``MSTDPET`` against a from-scratch implementation of the discrete +`Florian (2007) `_ +equations. + +Motivated by issue #217 / #140 (P-traces had been written as a state +assignment instead of an exponentially-decaying trace). These tests pin the +behaviour of the ``Connection``, ``LocalConnection`` and ``Conv`` paths for +both rules against a from-scratch Florian reference (point eligibility, weight +update, batch handling, STDP sign, and the ``zero_lag`` timing option). + +Reference (i = presynaptic/source, j = postsynaptic/target): + P+_i(t) = P+_i(t-dt) * exp(-dt/tc_plus) + a_plus * pre_i(t) + P-_j(t) = P-_j(t-dt) * exp(-dt/tc_minus) + a_minus * post_j(t) + zeta_ij(t) = P+_i(t) * post_j(t) + pre_i(t) * P-_j(t) + MSTDP: dw_ij(t) = nu * r(t) * zeta_ij(t) + MSTDPET: e_ij(t) = e_ij(t-dt)*exp(-dt/tc_e) + zeta_ij(t-dt)/tc_e + dw_ij(t) = nu * dt * r(t) * e_ij(t) +""" + +import itertools + +import pytest +import torch + +from bindsnet.learning import MSTDP, MSTDPET +from bindsnet.network.nodes import Input, LIFNodes +from bindsnet.network.topology import ( + Connection, + Conv3dConnection, + LocalConnection1D, + LocalConnection2D, + LocalConnection3D, + Conv1dConnection, + Conv2dConnection, +) + +TOL = 1e-5 + + +# --------------------------------------------------------------------------- # +# From-scratch Florian (2007) reference implementations. # +# --------------------------------------------------------------------------- # +def _florian_mstdp( + pre, post, rewards, nu, dt, tc_plus, tc_minus, a_plus=1.0, a_minus=-1.0, lag=True +): + T, npre, npost = pre.shape[0], pre.shape[1], post.shape[1] + p_plus = torch.zeros(npre) + p_minus = torch.zeros(npost) + elig = torch.zeros(npre, npost) + w = torch.zeros(npre, npost) + w_hist, pplus_hist = [], [] + for t in range(T): + if lag: + w = w + nu * rewards[t] * elig + p_plus = p_plus * torch.exp(torch.tensor(-dt / tc_plus)) + a_plus * pre[t] + p_minus = p_minus * torch.exp(torch.tensor(-dt / tc_minus)) + a_minus * post[t] + elig = torch.outer(p_plus, post[t]) + torch.outer(pre[t], p_minus) + if not lag: + w = w + nu * rewards[t] * elig + w_hist.append(w.clone()) + pplus_hist.append(p_plus.clone()) + return torch.stack(w_hist), torch.stack(pplus_hist) + + +def _florian_mstdpet( + pre, post, rewards, nu, dt, tc_plus, tc_minus, tc_e, a_plus=1.0, a_minus=-1.0 +): + T, npre, npost = pre.shape[0], pre.shape[1], post.shape[1] + p_plus = torch.zeros(npre) + p_minus = torch.zeros(npost) + elig = torch.zeros(npre, npost) + etrace = torch.zeros(npre, npost) + w = torch.zeros(npre, npost) + w_hist = [] + for t in range(T): + etrace = etrace * torch.exp(torch.tensor(-dt / tc_e)) + elig / tc_e + w = w + nu * dt * rewards[t] * etrace + p_plus = p_plus * torch.exp(torch.tensor(-dt / tc_plus)) + a_plus * pre[t] + p_minus = p_minus * torch.exp(torch.tensor(-dt / tc_minus)) + a_minus * post[t] + elig = torch.outer(p_plus, post[t]) + torch.outer(pre[t], p_minus) + w_hist.append(w.clone()) + return torch.stack(w_hist) + + +# --------------------------------------------------------------------------- # +# Helpers to drive a Connection with explicit, controlled spike trains. # +# --------------------------------------------------------------------------- # +def _make_conn(npre, npost, rule, batch=1, nu=1e-2, dt=1.0, **kw): + src = Input(n=npre) + tgt = LIFNodes(n=npost) + src.batch_size = batch + tgt.batch_size = batch + src.s = torch.zeros(batch, npre, dtype=torch.bool) + tgt.s = torch.zeros(batch, npost, dtype=torch.bool) + conn = Connection(src, tgt, nu=nu, update_rule=rule, reduction=torch.sum, **kw) + conn.dt = dt + with torch.no_grad(): + conn.w.zero_() + return conn + + +def _drive(conn, pre, post, rewards): + """pre/post: [T, batch, n]; returns weight history [T, npre, npost].""" + T, batch = pre.shape[0], pre.shape[1] + w_hist, pplus_hist, elig_hist = [], [], [] + for t in range(T): + conn.source.s = pre[t].bool().view(batch, -1) + conn.target.s = post[t].bool().view(batch, -1) + conn.update(reward=rewards[t]) + w_hist.append(conn.w.detach().clone()) + ur = conn.update_rule + pplus_hist.append(ur.p_plus.detach().clone()) + elig_hist.append(ur.eligibility.detach().clone()) + return torch.stack(w_hist), torch.stack(pplus_hist), torch.stack(elig_hist) + + +class TestMSTDPFlorian: + """Numerical validation of MSTDP / MSTDPET on ``Connection``.""" + + def test_pplus_trace_is_exponential_decay(self): + # Regression for issue #140: a single pre-spike must leave an + # exponentially-decaying P+ trace, not a divergent state update. + T, dt, tc = 40, 1.0, 20.0 + pre = torch.zeros(T, 1, 1) + pre[0, 0, 0] = 1.0 + post = torch.zeros(T, 1, 1) + rew = torch.ones(T) + conn = _make_conn(1, 1, MSTDP, dt=dt, tc_plus=tc, tc_minus=tc) + _, pplus, _ = _drive(conn, pre, post, rew) + expected = torch.exp(-torch.arange(T).float() / tc) + assert (pplus.view(T) - expected).abs().max().item() < TOL + + def test_mstdp_matches_florian(self): + torch.manual_seed(1) + T, npre, npost = 60, 4, 3 + pre = torch.bernoulli(torch.full((T, 1, npre), 0.2)) + post = torch.bernoulli(torch.full((T, 1, npost), 0.2)) + rew = torch.randn(T) * 0.5 + 1.0 + nu, dt, tcp, tcm = 1e-2, 1.0, 20.0, 22.0 + conn = _make_conn(npre, npost, MSTDP, nu=nu, dt=dt, tc_plus=tcp, tc_minus=tcm) + w_b, _, _ = _drive(conn, pre, post, rew) + w_f, _ = _florian_mstdp(pre[:, 0], post[:, 0], rew, nu, dt, tcp, tcm, lag=True) + assert (w_b - w_f).abs().max().item() < TOL + + def test_mstdpet_matches_florian(self): + torch.manual_seed(2) + T, npre, npost = 60, 4, 3 + pre = torch.bernoulli(torch.full((T, 1, npre), 0.2)) + post = torch.bernoulli(torch.full((T, 1, npost), 0.2)) + rew = torch.randn(T) * 0.5 + 1.0 + nu, dt, tcp, tcm, tce = 1e-2, 1.0, 20.0, 22.0, 25.0 + conn = _make_conn( + npre, + npost, + MSTDPET, + nu=nu, + dt=dt, + tc_plus=tcp, + tc_minus=tcm, + tc_e_trace=tce, + ) + w_b, _, _ = _drive(conn, pre, post, rew) + w_f = _florian_mstdpet(pre[:, 0], post[:, 0], rew, nu, dt, tcp, tcm, tce) + assert (w_b - w_f).abs().max().item() < TOL + + def test_stdp_causality_sign(self): + # pre-before-post must potentiate; post-before-pre must depress. + T, dt, tc = 10, 1.0, 20.0 + + def elig_at_second_spike(pre_first): + pre = torch.zeros(T, 1, 1) + post = torch.zeros(T, 1, 1) + if pre_first: + pre[2, 0, 0] = 1.0 + post[5, 0, 0] = 1.0 + else: + post[2, 0, 0] = 1.0 + pre[5, 0, 0] = 1.0 + conn = _make_conn(1, 1, MSTDP, nu=1.0, dt=dt, tc_plus=tc, tc_minus=tc) + _, _, elig = _drive(conn, pre, post, torch.ones(T)) + return elig[5].view(()).item() + + assert elig_at_second_spike(pre_first=True) > 0.0 + assert elig_at_second_spike(pre_first=False) < 0.0 + + def test_zero_lag_matches_florian_no_lag(self): + # With zero_lag=True, MSTDP must reproduce the un-lagged Florian update. + torch.manual_seed(3) + T, npre, npost = 50, 4, 3 + pre = torch.bernoulli(torch.full((T, 1, npre), 0.2)) + post = torch.bernoulli(torch.full((T, 1, npost), 0.2)) + rew = torch.randn(T) * 0.5 + 1.0 + nu, dt, tcp, tcm = 1e-2, 1.0, 20.0, 22.0 + conn = _make_conn( + npre, npost, MSTDP, nu=nu, dt=dt, tc_plus=tcp, tc_minus=tcm, zero_lag=True + ) + w_b, _, _ = _drive(conn, pre, post, rew) + w_f, _ = _florian_mstdp(pre[:, 0], post[:, 0], rew, nu, dt, tcp, tcm, lag=False) + assert (w_b - w_f).abs().max().item() < TOL + + @pytest.mark.parametrize("rule", [MSTDP, MSTDPET]) + def test_batch_equals_sum_of_samples(self, rule): + # Batched update with reduction=sum must equal the sum of the + # independent per-sample updates. + torch.manual_seed(7) + T, npre, npost, B = 40, 5, 4, 3 + pre = torch.bernoulli(torch.full((T, B, npre), 0.25)) + post = torch.bernoulli(torch.full((T, B, npost), 0.25)) + rew = torch.ones(T) + + conn = _make_conn(npre, npost, rule, batch=B) + w_batch, _, _ = _drive(conn, pre, post, rew) + + w_sum = torch.zeros_like(w_batch[-1]) + for b in range(B): + cb = _make_conn(npre, npost, rule, batch=1) + wb, _, _ = _drive(cb, pre[:, b : b + 1], post[:, b : b + 1], rew) + w_sum = w_sum + wb[-1] + + assert (w_batch[-1] - w_sum).abs().max().item() < TOL + assert w_batch[-1].abs().sum().item() > 0.0 # actually learned + + def test_conv3d_runs_without_dtype_error(self): + # Regression: conv3d source spikes must be cast to float (issue: bmm + # dtype mismatch). Both rules should run a few steps without error. + for rule in (MSTDP, MSTDPET): + src = Input(shape=[1, 8, 8, 8]) + tgt = LIFNodes(shape=[4, 6, 6, 6]) + src.batch_size = 1 + tgt.batch_size = 1 + conn = Conv3dConnection( + src, tgt, kernel_size=3, stride=1, nu=1e-2, update_rule=rule + ) + from bindsnet.network import Network + + net = Network(dt=1.0, batch_size=1) + net.add_layer(src, name="in") + net.add_layer(tgt, name="out") + net.add_connection(conn, source="in", target="out") + inp = torch.bernoulli(torch.rand(5, 1, 1, 8, 8, 8)).byte() + net.run(inputs={"in": inp}, time=5, reward=1.0) # must not raise + + +def _drive_shaped(conn, pre, post): + """Drive a conv/local connection with explicitly-shaped spike trains. + + pre/post: [T, batch, *node_shape]. Returns (weight_change, [eligibility...]). + """ + T = pre.shape[0] + w0 = conn.w.detach().clone() + eligs = [] + for t in range(T): + conn.source.s = pre[t].bool() + conn.target.s = post[t].bool() + conn.update(reward=1.0) + eligs.append(conn.update_rule.eligibility.detach().clone()) + return conn.w.detach() - w0, eligs + + +_LOCAL_CASES = [ + (LocalConnection1D, [1, 12], [4, 10]), + (LocalConnection2D, [1, 8, 8], [4, 6, 6]), + (LocalConnection3D, [1, 6, 6, 6], [4, 4, 4, 4]), +] + + +def _make_local(lc, in_shape, out_shape, rule, batch): + src = Input(shape=in_shape) + tgt = LIFNodes(shape=out_shape) + src.batch_size = batch + tgt.batch_size = batch + src.s = torch.zeros(batch, *in_shape, dtype=torch.bool) + tgt.s = torch.zeros(batch, *out_shape, dtype=torch.bool) + conn = lc( + src, + tgt, + kernel_size=3, + stride=1, + n_filters=4, + nu=1e-2, + update_rule=rule, + reduction=torch.sum, + ) + conn.dt = 1.0 + with torch.no_grad(): + conn.w.zero_() + return conn + + +class TestLocalMSTDPET: + """LocalConnection MSTDPET is validated against the (sound) Local MSTDP. + + The local-connection trace init uses ``.repeat`` (a copy), so it does not + suffer the conv overlap-aliasing bug; these variants are correct. + """ + + @pytest.mark.parametrize("lc,in_shape,out_shape", _LOCAL_CASES) + def test_local_mstdpet_learns(self, lc, in_shape, out_shape): + torch.manual_seed(0) + T = 25 + pre = torch.bernoulli(torch.full((T, 1, *in_shape), 0.3)) + post = torch.bernoulli(torch.full((T, 1, *out_shape), 0.3)) + conn = _make_local(lc, in_shape, out_shape, MSTDPET, 1) + dW, _ = _drive_shaped(conn, pre, post) + assert dW.abs().sum().item() > 0.0 + + @pytest.mark.parametrize("lc,in_shape,out_shape", _LOCAL_CASES) + def test_local_mstdpet_eligibility_matches_mstdp(self, lc, in_shape, out_shape): + # MSTDPET must compute exactly the same point eligibility as MSTDP. + torch.manual_seed(1) + T = 20 + pre = torch.bernoulli(torch.full((T, 1, *in_shape), 0.3)) + post = torch.bernoulli(torch.full((T, 1, *out_shape), 0.3)) + _, e_et = _drive_shaped( + _make_local(lc, in_shape, out_shape, MSTDPET, 1), pre, post + ) + _, e_mp = _drive_shaped( + _make_local(lc, in_shape, out_shape, MSTDP, 1), pre, post + ) + err = max((a - b).abs().max().item() for a, b in zip(e_et, e_mp)) + assert err < TOL + + @pytest.mark.parametrize("lc,in_shape,out_shape", _LOCAL_CASES) + def test_local_mstdpet_batch_equals_sum(self, lc, in_shape, out_shape): + torch.manual_seed(2) + T, B = 20, 3 + pre = torch.bernoulli(torch.full((T, B, *in_shape), 0.3)) + post = torch.bernoulli(torch.full((T, B, *out_shape), 0.3)) + dW_b, _ = _drive_shaped( + _make_local(lc, in_shape, out_shape, MSTDPET, B), pre, post + ) + dW_s = torch.zeros_like(dW_b) + for b in range(B): + d, _ = _drive_shaped( + _make_local(lc, in_shape, out_shape, MSTDPET, 1), + pre[:, b : b + 1], + post[:, b : b + 1], + ) + dW_s = dW_s + d + assert (dW_b - dW_s).abs().max().item() < TOL + + +import itertools + +_CONV_CASES = [ + (1, Conv1dConnection, [2, 8], [3, 6]), + (2, Conv2dConnection, [1, 8, 8], [3, 6, 6]), + (3, Conv3dConnection, [1, 6, 6, 6], [2, 4, 4, 4]), +] + + +def _conv_florian_ref(Pp, Pm, pre, post, wshape, dim, stride=1): + """Batch-summed n-d Florian conv point eligibility, computed from scratch. + + eligibility[o, i, *k] = sum_b sum_p P+[b,i, p*stride+k] * post[b,o,p] + + pre[b,i, p*stride+k] * P-[b,o,p] + """ + Co, Ci = wshape[0], wshape[1] + ksz = wshape[2:] + B = pre.shape[0] + Lout = post.shape[2:] + ref = torch.zeros(*wshape) + for b in range(B): + for co in range(Co): + for ci in range(Ci): + for k in itertools.product(*[range(x) for x in ksz]): + acc = 0.0 + for p in itertools.product(*[range(x) for x in Lout]): + idx = tuple(p[d] * stride + k[d] for d in range(dim)) + acc += ( + Pp[(b, ci) + idx] * post[(b, co) + p] + + pre[(b, ci) + idx] * Pm[(b, co) + p] + ) + ref[(co, ci) + k] += acc + return ref + + +def _make_conv(conn_cls, in_shape, out_shape, rule, batch, tc=20.0): + src = Input(shape=in_shape) + tgt = LIFNodes(shape=out_shape) + src.batch_size = batch + tgt.batch_size = batch + src.s = torch.zeros(batch, *in_shape, dtype=torch.bool) + tgt.s = torch.zeros(batch, *out_shape, dtype=torch.bool) + conn = conn_cls( + src, + tgt, + kernel_size=3, + stride=1, + nu=1e-2, + update_rule=rule, + tc_plus=tc, + tc_minus=tc, + ) + conn.dt = 1.0 + with torch.no_grad(): + conn.w.zero_() + return conn + + +class TestConvMSTDP: + """Conv MSTDP/MSTDPET validated against the from-scratch Florian conv rule. + + Traces are kept in neuron space and the eligibility is the convolution + weight-gradient, so it is correct for any channels/stride and supports + batches (the previous im2col-space trace double-counted overlapping + kernel positions). + """ + + @pytest.mark.parametrize("dim,conn_cls,in_shape,out_shape", _CONV_CASES) + def test_conv_mstdp_eligibility_matches_florian( + self, dim, conn_cls, in_shape, out_shape + ): + torch.manual_seed(0) + tc = 20.0 + conn = _make_conv(conn_cls, in_shape, out_shape, MSTDP, 1, tc=tc) + T = 5 + Pp = torch.zeros(1, *in_shape) + Pm = torch.zeros(1, *out_shape) + decay = torch.exp(torch.tensor(-1.0 / tc)) + max_err = 0.0 + for _ in range(T): + pre = torch.bernoulli(torch.full((1, *in_shape), 0.5)) + post = torch.bernoulli(torch.full((1, *out_shape), 0.5)) + conn.source.s = pre.bool() + conn.target.s = post.bool() + conn.update(reward=1.0) + Pp = Pp * decay + pre + Pm = Pm * decay - post + ref = _conv_florian_ref(Pp, Pm, pre, post, tuple(conn.w.shape), dim) + max_err = max( + max_err, (conn.update_rule.eligibility - ref).abs().max().item() + ) + assert max_err < 1e-4 # float32 accumulation tolerance + + @pytest.mark.parametrize("dim,conn_cls,in_shape,out_shape", _CONV_CASES) + def test_conv_mstdpet_eligibility_matches_mstdp( + self, dim, conn_cls, in_shape, out_shape + ): + torch.manual_seed(1) + T = 12 + pre = torch.bernoulli(torch.full((T, 1, *in_shape), 0.3)) + post = torch.bernoulli(torch.full((T, 1, *out_shape), 0.3)) + _, e_et = _drive_shaped( + _make_conv(conn_cls, in_shape, out_shape, MSTDPET, 1), pre, post + ) + _, e_mp = _drive_shaped( + _make_conv(conn_cls, in_shape, out_shape, MSTDP, 1), pre, post + ) + err = max((a - b).abs().max().item() for a, b in zip(e_et, e_mp)) + assert err < TOL + + @pytest.mark.parametrize("dim,conn_cls,in_shape,out_shape", _CONV_CASES) + @pytest.mark.parametrize("rule", [MSTDP, MSTDPET]) + def test_conv_learns(self, dim, conn_cls, in_shape, out_shape, rule): + torch.manual_seed(2) + T = 25 + pre = torch.bernoulli(torch.full((T, 1, *in_shape), 0.3)) + post = torch.bernoulli(torch.full((T, 1, *out_shape), 0.3)) + dW, _ = _drive_shaped( + _make_conv(conn_cls, in_shape, out_shape, rule, 1), pre, post + ) + assert dW.abs().sum().item() > 0.0 + + @pytest.mark.parametrize("dim,conn_cls,in_shape,out_shape", _CONV_CASES) + @pytest.mark.parametrize("rule", [MSTDP, MSTDPET]) + def test_conv_batch_equals_sum(self, dim, conn_cls, in_shape, out_shape, rule): + # Conv eligibility is batch-summed, so a batched run equals the sum of + # the per-sample runs (scalar reward). + torch.manual_seed(3) + T, B = 15, 3 + pre = torch.bernoulli(torch.full((T, B, *in_shape), 0.3)) + post = torch.bernoulli(torch.full((T, B, *out_shape), 0.3)) + dW_b, _ = _drive_shaped( + _make_conv(conn_cls, in_shape, out_shape, rule, B), pre, post + ) + dW_s = torch.zeros_like(dW_b) + for b in range(B): + d, _ = _drive_shaped( + _make_conv(conn_cls, in_shape, out_shape, rule, 1), + pre[:, b : b + 1], + post[:, b : b + 1], + ) + dW_s = dW_s + d + assert (dW_b - dW_s).abs().max().item() < 1e-4