Merge pull request #518 from danielgafni/isort-and-autoflake-refactor

Applied isort and autoflake; Converted all relative imports to absolute

.pre-commit-config.yaml

@@ -1,8 +1,20 @@
 repos:
 -   repo: local
     hooks:
+    -   id: isort
+        name: isort
+        entry: isort . --settings-file pyproject.toml
+        language: system
+        pass_filenames: false
     -   id: black
         name: black
         entry: black .
         language: system
         pass_filenames: false
+    -   id: autoflake
+        name: autoflake
+        entry: autoflake
+        language: system
+        types: [ python ]
+        args: [ --in-place, --remove-all-unused-imports, --remove-duplicate-keys ]
+        files: ^bindsnet/|test/

bindsnet/__init__.py

@@ -1,18 +1,35 @@
 from pathlib import Path
 
-from . import (
-    utils,
-    network,
-    models,
+from bindsnet import (
     analysis,
-    preprocessing,
+    conversion,
     datasets,
     encoding,
-    pipeline,
-    learning,
-    evaluation,
     environment,
-    conversion,
+    evaluation,
+    learning,
+    models,
+    network,
+    pipeline,
+    preprocessing,
+    utils,
 )
 
 ROOT_DIR = Path(__file__).parents[0].parents[0]
+
+
+__all__ = [
+    "utils",
+    "network",
+    "models",
+    "analysis",
+    "preprocessing",
+    "datasets",
+    "encoding",
+    "pipeline",
+    "learning",
+    "evaluation",
+    "environment",
+    "conversion",
+    "ROOT_DIR",
+]

bindsnet/analysis/__init__.py

@@ -1 +1,3 @@
-from . import plotting, visualization, pipeline_analysis
+from bindsnet.analysis import pipeline_analysis, plotting, visualization
+
+__all__ = ["plotting", "visualization", "pipeline_analysis"]

bindsnet/analysis/dotTrace_plotter.py

@@ -1,8 +1,8 @@
-import numpy as np
 import glob
 import sys
 
 import matplotlib.pyplot as plt
+import numpy as np
 
 # Define grid dimensions globally
 ROWS = 28

bindsnet/analysis/pipeline_analysis.py

@@ -8,8 +8,8 @@
 from tensorboardX import SummaryWriter
 from torchvision.utils import make_grid
 
-from .plotting import plot_spikes, plot_voltages, plot_conv2d_weights
 from ..utils import reshape_conv2d_weights
+from .plotting import plot_conv2d_weights, plot_spikes, plot_voltages
 
 
 class PipelineAnalyzer(ABC):
@@ -25,7 +25,6 @@ def finalize_step(self) -> None:
         """
         Flush the output from the current step.
         """
-        pass
 
     @abstractmethod
     def plot_obs(self, obs: torch.Tensor, tag: str = "obs", step: int = None) -> None:
@@ -38,7 +37,6 @@ def plot_obs(self, obs: torch.Tensor, tag: str = "obs", step: int = None) -> Non
         :param tag: A unique tag to associate the data with.
         :param step: The step of the pipeline.
         """
-        pass
 
     @abstractmethod
     def plot_reward(
@@ -57,7 +55,6 @@ def plot_reward(
         :param tag: A unique tag to associate the data with.
         :param step: The step of the pipeline.
         """
-        pass
 
     @abstractmethod
     def plot_spikes(
@@ -75,7 +72,6 @@ def plot_spikes(
         :param tag: A unique tag to associate the data with.
         :param step: The step of the pipeline.
         """
-        pass
 
     @abstractmethod
     def plot_voltages(
@@ -96,7 +92,6 @@ def plot_voltages(
         :param tag: A unique tag to associate the data with.
         :param step: The step of the pipeline.
         """
-        pass
 
     @abstractmethod
     def plot_conv2d_weights(
@@ -110,7 +105,6 @@ def plot_conv2d_weights(
         :param tag: A unique tag to associate the data with.
         :param step: The step of the pipeline.
         """
-        pass
 
 
 class MatplotlibAnalyzer(PipelineAnalyzer):
@@ -313,7 +307,6 @@ def finalize_step(self) -> None:
         """
         No-op for ``TensorboardAnalyzer``.
         """
-        pass
 
     def plot_obs(self, obs: torch.Tensor, tag: str = "obs", step: int = None) -> None:
         # language=rst

bindsnet/analysis/plotting.py

@@ -1,15 +1,15 @@
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
+from typing import Dict, List, Optional, Sized, Tuple, Union
 
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
 from matplotlib.axes import Axes
-from matplotlib.image import AxesImage
-from torch.nn.modules.utils import _pair
 from matplotlib.collections import PathCollection
+from matplotlib.image import AxesImage
 from mpl_toolkits.axes_grid1 import make_axes_locatable
-from typing import Tuple, List, Optional, Sized, Dict, Union
+from torch.nn.modules.utils import _pair
 
-from ..utils import reshape_locally_connected_weights, reshape_conv2d_weights
+from bindsnet.utils import reshape_conv2d_weights, reshape_locally_connected_weights
 
 plt.ion()
 

bindsnet/analysis/visualization.py

@@ -1,9 +1,9 @@
-import torch
-import numpy as np
-import matplotlib.pyplot as plt
-import matplotlib.animation as animation
+from typing import List, Optional, Tuple
 
-from typing import List, Tuple, Optional
+import matplotlib.animation as animation
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
 
 
 def plot_weights_movie(ws: np.ndarray, sample_every: int = 1) -> None:

bindsnet/conversion/__init__.py

@@ -1,10 +1,21 @@
-from .conversion import (
-    Permute,
+from bindsnet.conversion.conversion import (
+    ConstantPad2dConnection,
     FeatureExtractor,
-    SubtractiveResetIFNodes,
     PassThroughNodes,
+    Permute,
     PermuteConnection,
-    ConstantPad2dConnection,
-    data_based_normalization,
+    SubtractiveResetIFNodes,
     ann_to_snn,
+    data_based_normalization,
 )
+
+__all__ = [
+    "Permute",
+    "FeatureExtractor",
+    "SubtractiveResetIFNodes",
+    "PassThroughNodes",
+    "PermuteConnection",
+    "ConstantPad2dConnection",
+    "data_based_normalization",
+    "ann_to_snn",
+]

bindsnet/conversion/conversion.py

@@ -1,19 +1,16 @@
-import torch
+from copy import deepcopy
+from typing import Dict, Optional, Sequence, Union
+
 import numpy as np
+import torch
 import torch.nn as nn
-import torch.nn.functional as F
-
 from torch.nn.modules.utils import _pair
 
-from copy import deepcopy
-from typing import Union, Sequence, Optional, Tuple, Dict, Iterable
-
 import bindsnet.network.nodes as nodes
 import bindsnet.network.topology as topology
-
+from bindsnet.conversion.nodes import PassThroughNodes, SubtractiveResetIFNodes
+from bindsnet.conversion.topology import ConstantPad2dConnection, PermuteConnection
 from bindsnet.network import Network
-from .nodes import SubtractiveResetIFNodes, PassThroughNodes
-from .topology import PermuteConnection, ConstantPad2dConnection
 
 
 class Permute(nn.Module):

bindsnet/conversion/nodes.py

@@ -1,4 +1,4 @@
-from typing import Optional, Iterable, Union
+from typing import Iterable, Optional, Union
 
 import torch
 

bindsnet/conversion/topology.py

@@ -1,4 +1,4 @@
-from typing import Optional, Iterable, Union, Tuple
+from typing import Iterable, Optional, Tuple, Union
 
 import torch
 import torch.nn.functional as F

bindsnet/datasets/__init__.py

@@ -1,11 +1,9 @@
-from .torchvision_wrapper import create_torchvision_dataset_wrapper
-from .spoken_mnist import SpokenMNIST
-from .davis import Davis
-from .alov300 import ALOV300
-
-from .collate import time_aware_collate
-from .dataloader import DataLoader
-
+from bindsnet.datasets.alov300 import ALOV300
+from bindsnet.datasets.collate import time_aware_collate
+from bindsnet.datasets.dataloader import DataLoader
+from bindsnet.datasets.davis import Davis
+from bindsnet.datasets.spoken_mnist import SpokenMNIST
+from bindsnet.datasets.torchvision_wrapper import create_torchvision_dataset_wrapper
 
 CIFAR10 = create_torchvision_dataset_wrapper("CIFAR10")
 CIFAR100 = create_torchvision_dataset_wrapper("CIFAR100")
@@ -31,3 +29,44 @@
 SVHN = create_torchvision_dataset_wrapper("SVHN")
 VOCDetection = create_torchvision_dataset_wrapper("VOCDetection")
 VOCSegmentation = create_torchvision_dataset_wrapper("VOCSegmentation")
+
+
+__all__ = [
+    "torchvision_wrapper",
+    "create_torchvision_dataset_wrapper",
+    "spoken_mnist",
+    "SpokenMNIST",
+    "davis",
+    "Davis",
+    "preprocess",
+    "alov300",
+    "ALOV300",
+    "collate",
+    "time_aware_collate",
+    "dataloader",
+    "DataLoader",
+    "CIFAR10",
+    "CIFAR100",
+    "Cityscapes",
+    "CocoCaptions",
+    "CocoDetection",
+    "DatasetFolder",
+    "EMNIST",
+    "FakeData",
+    "FashionMNIST",
+    "Flickr30k",
+    "Flickr8k",
+    "ImageFolder",
+    "KMNIST",
+    "LSUN",
+    "LSUNClass",
+    "MNIST",
+    "Omniglot",
+    "PhotoTour",
+    "SBU",
+    "SEMEION",
+    "STL10",
+    "SVHN",
+    "VOCDetection",
+    "VOCSegmentation",
+]

bindsnet/datasets/alov300.py

@@ -1,24 +1,20 @@
 import os
 import sys
 import time
-import zipfile
 import warnings
-from glob import glob
+import zipfile
 from urllib.request import urlretrieve
-from typing import Optional, Tuple, List, Iterable
 
 import cv2
-import torch
 import numpy as np
-from PIL import Image
 from torch.utils.data import Dataset
 
 from bindsnet.datasets.preprocess import (
-    cropPadImage,
     BoundingBox,
-    crop_sample,
     Rescale,
     bgr2rgb,
+    crop_sample,
+    cropPadImage,
 )
 
 warnings.filterwarnings("ignore")

bindsnet/datasets/collate.py

@@ -7,10 +7,10 @@
 Modifications exist to have [time, batch, n_0, ... n_k] instead of batch in dimension 0.
 """
 
-import torch
-from torch._six import string_classes
 import collections
 
+import torch
+from torch._six import string_classes
 from torch.utils.data._utils import collate as pytorch_collate
 
 

bindsnet/datasets/dataloader.py

@@ -1,6 +1,6 @@
 import torch
 
-from .collate import time_aware_collate
+from bindsnet.datasets.collate import time_aware_collate
 
 
 class DataLoader(torch.utils.data.DataLoader):

bindsnet/datasets/davis.py

@@ -1,14 +1,14 @@
 import os
+import shutil
 import sys
 import time
-import shutil
 import zipfile
-from glob import glob
 from collections import defaultdict
+from glob import glob
 from urllib.request import urlretrieve
 
-import torch
 import numpy as np
+import torch
 from PIL import Image
 from tqdm import tqdm
 

bindsnet/datasets/preprocess.py

@@ -1,10 +1,9 @@
 import math
 import random
-import warnings
 
 import cv2
-import torch
 import numpy as np
+import torch
 from torchvision import transforms