Merge pull request #400 from BindsNET/hananel

Configuration update and small cosmetic changes

Author: Hananel-Hazan

.pre-commit-config.yaml

@@ -3,4 +3,4 @@ repos:
     rev: master
     hooks:
     - id: black
-      language_version: python3.8
+      language_version: python3.8

Dockerfile

@@ -3,9 +3,9 @@ FROM arwineap/docker-ubuntu-python3.6
 RUN apt-get update
 
 # Install latest version of python3
-RUN apt install -y python3.6
+RUN apt install -y python3.8
 RUN pip install --upgrade setuptools
-RUN apt-get install -y python3.6-dev build-essential
+RUN apt-get install -y python3.8-dev build-essential
 RUN apt-get install -y libblas-dev libatlas-base-dev
 
 # Upgrade pip
@@ -15,14 +15,11 @@ RUN apt-get install -y python3-tk
 RUN apt install -y libglib2.0-0
 RUN apt install -y libsm6 libxext6
 
-# Install bindsnet and dependencies
-RUN pip install -U bindsnet
-
 # Install git
 RUN apt-get install -y git-core
 
 # Install vim
-RUN apt install -y vim 
+RUN apt install -y vim
 RUN pip install jupyter -U && pip install jupyterlab
 
 # Bind python3.6 to python
@@ -36,3 +33,5 @@ RUN mkdir working
 RUN chmod 777 \/working
 ENTRYPOINT ["/working"]
 
+# Install bindsnet and dependencies
+RUN pip install -U git+https://github.com/BindsNET/bindsnet.git

bindsnet/analysis/plotting.py

@@ -588,7 +588,7 @@ def plot_voltages(
                             .cpu()
                             .numpy()[
                                 time[0] : time[1],
-                                n_neurons[v[0]][0]: n_neurons[v[0]][1],
+                                n_neurons[v[0]][0] : n_neurons[v[0]][1],
                             ]
                         )
                     )
@@ -608,7 +608,7 @@ def plot_voltages(
                             .cpu()
                             .numpy()[
                                 time[0] : time[1],
-                                    n_neurons[v[0]][0] : n_neurons[v[0]][1],
+                                n_neurons[v[0]][0] : n_neurons[v[0]][1],
                             ]
                             .T,
                             cmap=cmap,
@@ -685,7 +685,7 @@ def plot_voltages(
                         v[1]
                         .cpu()
                         .numpy()[
-                           time[0] : time[1],  n_neurons[v[0]][0] : n_neurons[v[0]][1]
+                            time[0] : time[1], n_neurons[v[0]][0] : n_neurons[v[0]][1]
                         ]
                     )
                     if thresholds is not None and thresholds[v[0]].size() == torch.Size(

bindsnet/datasets/davis.py

@@ -204,7 +204,7 @@ def _check_directories(self):
                 self._download()
             else:
                 raise FileNotFoundError(
-                    f"DAVIS not found in the specified directory, download it from "
+                    "DAVIS not found in the specified directory, download it from "
                     f"{self.DATASET_WEB} or add download=True to your call"
                 )
         if not os.path.exists(os.path.join(self.imagesets_path, f"{self.subset}.txt")):

bindsnet/datasets/spoken_mnist.py

@@ -103,7 +103,10 @@ def _get_train(self, split: float = 0.8) -> Tuple[torch.Tensor, torch.Tensor]:
                 # Serialize image data on disk for next time.
                 torch.save((audio, labels), open(path, "wb"))
             else:
-                msg = "Dataset not found on disk; specify 'download=True' to allow downloads."
+                msg = (
+                    "Dataset not found on disk; specify 'download=True' to allow"
+                    " downloads."
+                )
                 raise FileNotFoundError(msg)
         else:
             if not os.path.isdir(path):
@@ -148,7 +151,10 @@ def _get_test(self, split: float = 0.8) -> Tuple[torch.Tensor, List[torch.Tensor
                 # Serialize image data on disk for next time.
                 torch.save((audio, labels), open(path, "wb"))
             else:
-                msg = "Dataset not found on disk; specify 'download=True' to allow downloads."
+                msg = (
+                    "Dataset not found on disk; specify 'download=True' to allow"
+                    " downloads."
+                )
                 raise FileNotFoundError(msg)
         else:
             if not os.path.isdir(path):

bindsnet/network/nodes.py

@@ -507,16 +507,16 @@ def forward(self, x: torch.Tensor) -> None:
         self.v = self.decay * (self.v - self.rest) + self.rest
 
         # Integrate inputs.
-        x.masked_fill_(self.refrac_count > 0, 0.0) # OPTIM 2
+        x.masked_fill_(self.refrac_count > 0, 0.0)
+        
         # Decrement refractory counters.
-        self.refrac_count -= self.dt  # OPTIM 1
+        self.refrac_count -= self.dt
 
-        self.v += x # interlaced
+        self.v += x  # interlaced
 
         # Check for spiking neurons.
         self.s = self.v >= self.thresh
 
-
         # Refractoriness and voltage reset.
         self.refrac_count.masked_fill_(self.s, self.refrac)
         self.v.masked_fill_(self.s, self.reset)

bindsnet/pipeline/action.py

@@ -26,9 +26,10 @@ def select_multinomial(pipeline: EnvironmentPipeline, **kwargs) -> int:
     output = pipeline.network.layers[output]
     action_space = pipeline.env.action_space
 
-    assert (
-        output.n % action_space.n == 0
-    ), f"Output layer size of {output.n} is not divisible by action space size of {action_space.n}."
+    assert output.n % action_space.n == 0, (
+        f"Output layer size of {output.n} is not divisible by action space size of"
+        f" {action_space.n}."
+    )
 
     pop_size = int(output.n / action_space.n)
     spikes = output.s

examples/mnist/reservoir.py

@@ -63,12 +63,12 @@
 
 # Sets up Gpu use
 if gpu and torch.cuda.is_available():
-    device = 'cuda'
+    device = "cuda"
     torch.cuda.set_device(device)
     # torch.set_default_tensor_type('torch.cuda.FloatTensor')
 else:
     torch.manual_seed(seed)
-    device = 'cpu'
+    device = "cpu"
     if gpu:
         gpu = False
 
@@ -129,7 +129,7 @@
 for (i, dataPoint) in pbar:
     if i > n_iters:
         break
-    datum = dataPoint["encoded_image"].view(int(time/dt), 1, 1, 28, 28).to(device)
+    datum = dataPoint["encoded_image"].view(int(time / dt), 1, 1, 28, 28).to(device)
     label = dataPoint["label"]
     pbar.set_description_str("Train progress: (%d / %d)" % (i, n_iters))
 
@@ -140,7 +140,7 @@
 
         inpt_axes, inpt_ims = plot_input(
             dataPoint["image"].view(28, 28),
-            datum.view(int(time/dt), 784).sum(0).view(28, 28),
+            datum.view(int(time / dt), 784).sum(0).view(28, 28),
             label=label,
             axes=inpt_axes,
             ims=inpt_ims,