Merge pull request #484 from BindsNET/hananel

Encoding -> single

bindsnet/analysis/plotting.py

@@ -115,8 +115,7 @@ def plot_spikes(
         for i, datum in enumerate(spikes.items()):
             spikes = (
                 datum[1][
-                    time[0] : time[1],
-                    n_neurons[datum[0]][0] : n_neurons[datum[0]][1],
+                    time[0] : time[1], n_neurons[datum[0]][0] : n_neurons[datum[0]][1]
                 ]
                 .detach()
                 .clone()
@@ -144,19 +143,13 @@ def plot_spikes(
         for ax in axes:
             ax.set_aspect("auto")
 
-        plt.setp(
-            axes,
-            xticks=[],
-            xlabel="Simulation time",
-            ylabel="Neuron index",
-        )
+        plt.setp(axes, xticks=[], xlabel="Simulation time", ylabel="Neuron index")
         plt.tight_layout()
     else:
         for i, datum in enumerate(spikes.items()):
             spikes = (
                 datum[1][
-                    time[0] : time[1],
-                    n_neurons[datum[0]][0] : n_neurons[datum[0]][1],
+                    time[0] : time[1], n_neurons[datum[0]][0] : n_neurons[datum[0]][1]
                 ]
                 .detach()
                 .clone()
@@ -424,10 +417,7 @@ def plot_assignments(
         else:
             color = plt.get_cmap("RdBu", len(classes) + 1)
             im = ax.matshow(
-                locals_assignments,
-                cmap=color,
-                vmin=-1.5,
-                vmax=len(classes) - 0.5,
+                locals_assignments, cmap=color, vmin=-1.5, vmax=len(classes) - 0.5
             )
 
         div = make_axes_locatable(ax)
@@ -616,9 +606,7 @@ def plot_voltages(
                     ):
                         ims.append(
                             axes.axhline(
-                                y=thresholds[v[0]].item(),
-                                c="r",
-                                linestyle="--",
+                                y=thresholds[v[0]].item(), c="r", linestyle="--"
                             )
                         )
                 else:
@@ -635,13 +623,7 @@ def plot_voltages(
                         )
                     )
 
-                args = (
-                    v[0],
-                    n_neurons[v[0]][0],
-                    n_neurons[v[0]][1],
-                    time[0],
-                    time[1],
-                )
+                args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1])
                 plt.title("%s voltages for neurons (%d - %d) from t = %d to %d " % args)
                 plt.xlabel("Time (ms)")
 
@@ -670,9 +652,7 @@ def plot_voltages(
                     ):
                         ims.append(
                             axes[i].axhline(
-                                y=thresholds[v[0]].item(),
-                                c="r",
-                                linestyle="--",
+                                y=thresholds[v[0]].item(), c="r", linestyle="--"
                             )
                         )
                 else:
@@ -688,13 +668,7 @@ def plot_voltages(
                             cmap=cmap,
                         )
                     )
-                args = (
-                    v[0],
-                    n_neurons[v[0]][0],
-                    n_neurons[v[0]][1],
-                    time[0],
-                    time[1],
-                )
+                args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1])
                 axes[i].set_title(
                     "%s voltages for neurons (%d - %d) from t = %d to %d " % args
                 )
@@ -736,13 +710,7 @@ def plot_voltages(
                         .T,
                         cmap=cmap,
                     )
-                args = (
-                    v[0],
-                    n_neurons[v[0]][0],
-                    n_neurons[v[0]][1],
-                    time[0],
-                    time[1],
-                )
+                args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1])
                 axes.set_title(
                     "%s voltages for neurons (%d - %d) from t = %d to %d " % args
                 )
@@ -776,13 +744,7 @@ def plot_voltages(
                         .T,
                         cmap=cmap,
                     )
-                args = (
-                    v[0],
-                    n_neurons[v[0]][0],
-                    n_neurons[v[0]][1],
-                    time[0],
-                    time[1],
-                )
+                args = (v[0], n_neurons[v[0]][0], n_neurons[v[0]][1], time[0], time[1])
                 axes[i].set_title(
                     "%s voltages for neurons (%d - %d) from t = %d to %d " % args
                 )

bindsnet/datasets/alov300.py

@@ -138,10 +138,7 @@ def get_sample(self, idx):
         bbox_curr_gt = BoundingBox(currbb[0], currbb[1], currbb[2], currbb[3])
         bbox_gt_recentered = BoundingBox(0, 0, 0, 0)
         bbox_gt_recentered = bbox_curr_gt.recenter(
-            rand_search_location,
-            edge_spacing_x,
-            edge_spacing_y,
-            bbox_gt_recentered,
+            rand_search_location, edge_spacing_x, edge_spacing_y, bbox_gt_recentered
         )
         curr_sample["image"] = rand_search_region
         curr_sample["bb"] = bbox_gt_recentered.get_bb_list()

bindsnet/datasets/preprocess.py

@@ -129,10 +129,7 @@ def shift_crop_training_sample(sample, bb_params):
     bbox_curr_gt = BoundingBox(currbb[0], currbb[1], currbb[2], currbb[3])
     bbox_gt_recentered = BoundingBox(0, 0, 0, 0)
     bbox_gt_recentered = bbox_curr_gt.recenter(
-        rand_search_location,
-        edge_spacing_x,
-        edge_spacing_y,
-        bbox_gt_recentered,
+        rand_search_location, edge_spacing_x, edge_spacing_y, bbox_gt_recentered
     )
     output_sample["image"] = rand_search_region
     output_sample["bb"] = bbox_gt_recentered.get_bb_list()
@@ -155,12 +152,9 @@ def crop_sample(sample):
     opts = {}
     image, bb = sample["image"], sample["bb"]
     orig_bbox = BoundingBox(bb[0], bb[1], bb[2], bb[3])
-    (
-        output_image,
-        pad_image_location,
-        edge_spacing_x,
-        edge_spacing_y,
-    ) = cropPadImage(orig_bbox, image)
+    (output_image, pad_image_location, edge_spacing_x, edge_spacing_y) = cropPadImage(
+        orig_bbox, image
+    )
     new_bbox = BoundingBox(0, 0, 0, 0)
     new_bbox = new_bbox.recenter(
         pad_image_location, edge_spacing_x, edge_spacing_y, new_bbox
@@ -198,8 +192,7 @@ def cropPadImage(bbox_tight, image):
     output_height = max(math.ceil(bbox_tight.compute_output_height()), roi_height)
     if image.ndim > 2:
         output_image = np.zeros(
-            (int(output_height), int(output_width), image.shape[2]),
-            dtype=image.dtype,
+            (int(output_height), int(output_width), image.shape[2]), dtype=image.dtype
         )
     else:
         output_image = np.zeros(
@@ -392,8 +385,7 @@ def shift(
         ):
             if shift_motion_model:
                 width_scale_factor = max(
-                    min_scale,
-                    min(max_scale, sample_exp_two_sides(lambda_scale_frac)),
+                    min_scale, min(max_scale, sample_exp_two_sides(lambda_scale_frac))
                 )
             else:
                 rand_num = sample_rand_uniform()
@@ -410,8 +402,7 @@ def shift(
         ):
             if shift_motion_model:
                 height_scale_factor = max(
-                    min_scale,
-                    min(max_scale, sample_exp_two_sides(lambda_scale_frac)),
+                    min_scale, min(max_scale, sample_exp_two_sides(lambda_scale_frac))
                 )
             else:
                 rand_num = sample_rand_uniform()
@@ -464,8 +455,7 @@ def shift(
                 new_y_temp = center_y + rand_num * (2 * new_height) - new_height
 
             new_center_y = min(
-                image.shape[0] - new_height / 2,
-                max(new_height / 2, new_y_temp),
+                image.shape[0] - new_height / 2, max(new_height / 2, new_y_temp)
             )
             first_time_y = False
             num_tries_y = num_tries_y + 1

bindsnet/encoding/encodings.py

@@ -1,7 +1,6 @@
 from typing import Optional
 
 import torch
-import numpy as np
 
 
 def single(
@@ -27,10 +26,10 @@ def single(
     """
     time = int(time / dt)
     shape = list(datum.shape)
-    datum = np.copy(datum)
-    quantile = np.quantile(datum, 1 - sparsity)
-    s = np.zeros([time, *shape], device=device)
-    s[0] = np.where(datum > quantile, np.ones(shape), np.zeros(shape))
+    datum = torch.tensor(datum)
+    quantile = torch.quantile(datum, 1 - sparsity)
+    s = torch.zeros([time, *shape], device=device)
+    s[0] = torch.where(datum > quantile, torch.ones(shape), torch.zeros(shape))
     return torch.Tensor(s).byte()
 
 

bindsnet/models/models.py

@@ -405,9 +405,7 @@ def __init__(
         w = w / w.max()
         w = (w * self.max_inhib) + self.start_inhib
         recurrent_output_conn = Connection(
-            source=self.layers["Y"],
-            target=self.layers["Y"],
-            w=w,
+            source=self.layers["Y"], target=self.layers["Y"], w=w
         )
         self.add_connection(recurrent_output_conn, source="Y", target="Y")
 

bindsnet/network/topology.py

@@ -344,8 +344,7 @@ def __init__(
 
         self.w = Parameter(w, requires_grad=False)
         self.b = Parameter(
-            kwargs.get("b", torch.zeros(self.out_channels)),
-            requires_grad=False,
+            kwargs.get("b", torch.zeros(self.out_channels)), requires_grad=False
         )
 
     def compute(self, s: torch.Tensor) -> torch.Tensor:

examples/mnist/batch_eth_mnist.py

@@ -12,11 +12,7 @@
 from bindsnet import ROOT_DIR
 from bindsnet.datasets import MNIST, DataLoader
 from bindsnet.encoding import PoissonEncoder
-from bindsnet.evaluation import (
-    all_activity,
-    proportion_weighting,
-    assign_labels,
-)
+from bindsnet.evaluation import all_activity, proportion_weighting, assign_labels
 from bindsnet.models import DiehlAndCook2015
 from bindsnet.network.monitors import Monitor
 from bindsnet.utils import get_square_weights, get_square_assignments
@@ -201,9 +197,7 @@
 
             # Get network predictions.
             all_activity_pred = all_activity(
-                spikes=spike_record,
-                assignments=assignments,
-                n_labels=n_classes,
+                spikes=spike_record, assignments=assignments, n_labels=n_classes
             )
             proportion_pred = proportion_weighting(
                 spikes=spike_record,

examples/mnist/supervised_mnist.py

@@ -13,11 +13,7 @@
 from bindsnet.models import DiehlAndCook2015
 from bindsnet.network.monitors import Monitor
 from bindsnet.utils import get_square_assignments, get_square_weights
-from bindsnet.evaluation import (
-    all_activity,
-    proportion_weighting,
-    assign_labels,
-)
+from bindsnet.evaluation import all_activity, proportion_weighting, assign_labels
 from bindsnet.analysis.plotting import (
     plot_input,
     plot_assignments,
@@ -183,11 +179,7 @@
 
         print(
             "\nAll activity accuracy: %.2f (last), %.2f (average), %.2f (best)"
-            % (
-                accuracy["all"][-1],
-                np.mean(accuracy["all"]),
-                np.max(accuracy["all"]),
-            )
+            % (accuracy["all"][-1], np.mean(accuracy["all"]), np.max(accuracy["all"]))
         )
         print(
             "Proportion weighting accuracy: %.2f (last), %.2f (average), %.2f (best)\n"
@@ -233,11 +225,7 @@
         voltages = {"Ae": exc_voltages, "Ai": inh_voltages}
 
         inpt_axes, inpt_ims = plot_input(
-            image.sum(1).view(28, 28),
-            inpt,
-            label=label,
-            axes=inpt_axes,
-            ims=inpt_ims,
+            image.sum(1).view(28, 28), inpt, label=label, axes=inpt_axes, ims=inpt_ims
         )
         spike_ims, spike_axes = plot_spikes(
             {layer: spikes[layer].get("s").view(time, 1, -1) for layer in spikes},