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danielgafnidanielgafni
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applied isort; converted all relative imports to absolute
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‎.pre-commit-config.yaml

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

‎bindsnet/__init__.py

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@@ -1,18 +1,35 @@
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from pathlib import Path
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3-
from . import (
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utils,
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network,
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models,
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from bindsnet import (
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analysis,
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preprocessing,
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conversion,
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datasets,
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encoding,
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pipeline,
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learning,
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evaluation,
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environment,
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conversion,
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evaluation,
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learning,
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models,
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network,
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pipeline,
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preprocessing,
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utils,
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)
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ROOT_DIR = Path(__file__).parents[0].parents[0]
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__all__ = [
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"utils",
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"network",
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"models",
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"analysis",
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"preprocessing",
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"datasets",
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"encoding",
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"pipeline",
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"learning",
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"evaluation",
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"environment",
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"conversion",
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"ROOT_DIR",
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]

‎bindsnet/analysis/__init__.py

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

‎bindsnet/analysis/dotTrace_plotter.py

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import numpy as np
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import glob
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import sys
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import matplotlib.pyplot as plt
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import numpy as np
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# Define grid dimensions globally
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ROWS = 28

‎bindsnet/analysis/pipeline_analysis.py

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

‎bindsnet/analysis/plotting.py

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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from typing import Dict, List, Optional, Sized, Tuple, Union
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3+
import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from matplotlib.axes import Axes
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from matplotlib.image import AxesImage
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from torch.nn.modules.utils import _pair
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from matplotlib.collections import PathCollection
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from matplotlib.image import AxesImage
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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from typing import Tuple, List, Optional, Sized, Dict, Union
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from torch.nn.modules.utils import _pair
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from ..utils import reshape_locally_connected_weights, reshape_conv2d_weights
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from bindsnet.utils import reshape_conv2d_weights, reshape_locally_connected_weights
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plt.ion()
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‎bindsnet/analysis/visualization.py

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

‎bindsnet/conversion/__init__.py

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from .conversion import (
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Permute,
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from bindsnet.conversion.conversion import (
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ConstantPad2dConnection,
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FeatureExtractor,
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SubtractiveResetIFNodes,
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PassThroughNodes,
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Permute,
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PermuteConnection,
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ConstantPad2dConnection,
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data_based_normalization,
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SubtractiveResetIFNodes,
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ann_to_snn,
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data_based_normalization,
1010
)
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12+
__all__ = [
13+
"Permute",
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"FeatureExtractor",
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"SubtractiveResetIFNodes",
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"PassThroughNodes",
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"PermuteConnection",
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"ConstantPad2dConnection",
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"data_based_normalization",
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"ann_to_snn",
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]

‎bindsnet/conversion/conversion.py

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@@ -1,19 +1,16 @@
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import torch
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from copy import deepcopy
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from typing import Dict, Optional, Sequence, Union
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24
import numpy as np
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import torch
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import torch.nn as nn
4-
import torch.nn.functional as F
5-
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from torch.nn.modules.utils import _pair
78

8-
from copy import deepcopy
9-
from typing import Union, Sequence, Optional, Tuple, Dict, Iterable
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import bindsnet.network.nodes as nodes
1210
import bindsnet.network.topology as topology
13-
11+
from bindsnet.conversion.nodes import PassThroughNodes, SubtractiveResetIFNodes
12+
from bindsnet.conversion.topology import ConstantPad2dConnection, PermuteConnection
1413
from bindsnet.network import Network
15-
from .nodes import SubtractiveResetIFNodes, PassThroughNodes
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from .topology import PermuteConnection, ConstantPad2dConnection
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class Permute(nn.Module):

‎bindsnet/conversion/nodes.py

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from typing import Optional, Iterable, Union
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from typing import Iterable, Optional, Union
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import torch
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‎bindsnet/conversion/topology.py

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from typing import Optional, Iterable, Union, Tuple
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from typing import Iterable, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F

‎bindsnet/datasets/__init__.py

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from .torchvision_wrapper import create_torchvision_dataset_wrapper
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from .spoken_mnist import SpokenMNIST
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from .davis import Davis
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from .alov300 import ALOV300
5-
6-
from .collate import time_aware_collate
7-
from .dataloader import DataLoader
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1+
from bindsnet.datasets.alov300 import ALOV300
2+
from bindsnet.datasets.collate import time_aware_collate
3+
from bindsnet.datasets.dataloader import DataLoader
4+
from bindsnet.datasets.davis import Davis
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from bindsnet.datasets.spoken_mnist import SpokenMNIST
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from bindsnet.datasets.torchvision_wrapper import create_torchvision_dataset_wrapper
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CIFAR10 = create_torchvision_dataset_wrapper("CIFAR10")
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CIFAR100 = create_torchvision_dataset_wrapper("CIFAR100")
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SVHN = create_torchvision_dataset_wrapper("SVHN")
3230
VOCDetection = create_torchvision_dataset_wrapper("VOCDetection")
3331
VOCSegmentation = create_torchvision_dataset_wrapper("VOCSegmentation")
32+
33+
34+
__all__ = [
35+
"torchvision_wrapper",
36+
"create_torchvision_dataset_wrapper",
37+
"spoken_mnist",
38+
"SpokenMNIST",
39+
"davis",
40+
"Davis",
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"preprocess",
42+
"alov300",
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"ALOV300",
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"collate",
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"time_aware_collate",
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"dataloader",
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"DataLoader",
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"CIFAR10",
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"CIFAR100",
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"Cityscapes",
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"CocoCaptions",
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"CocoDetection",
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"DatasetFolder",
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"EMNIST",
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"FakeData",
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"FashionMNIST",
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"Flickr30k",
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"Flickr8k",
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"ImageFolder",
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"KMNIST",
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"LSUN",
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"LSUNClass",
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"MNIST",
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"Omniglot",
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"PhotoTour",
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"SBU",
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"SEMEION",
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"STL10",
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"SVHN",
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"VOCDetection",
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"VOCSegmentation",
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]

‎bindsnet/datasets/alov300.py

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import os
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import sys
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import time
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import zipfile
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import warnings
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from glob import glob
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import zipfile
76
from urllib.request import urlretrieve
8-
from typing import Optional, Tuple, List, Iterable
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108
import cv2
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import torch
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import numpy as np
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from PIL import Image
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from torch.utils.data import Dataset
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1612
from bindsnet.datasets.preprocess import (
17-
cropPadImage,
1813
BoundingBox,
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crop_sample,
2014
Rescale,
2115
bgr2rgb,
16+
crop_sample,
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cropPadImage,
2218
)
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2420
warnings.filterwarnings("ignore")

‎bindsnet/datasets/collate.py

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Modifications exist to have [time, batch, n_0, ... n_k] instead of batch in dimension 0.
88
"""
99

10-
import torch
11-
from torch._six import string_classes
1210
import collections
1311

12+
import torch
13+
from torch._six import string_classes
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from torch.utils.data._utils import collate as pytorch_collate
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‎bindsnet/datasets/dataloader.py

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import torch
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3-
from .collate import time_aware_collate
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from bindsnet.datasets.collate import time_aware_collate
44

55

66
class DataLoader(torch.utils.data.DataLoader):

‎bindsnet/datasets/davis.py

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import os
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import shutil
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import sys
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import time
4-
import shutil
55
import zipfile
6-
from glob import glob
76
from collections import defaultdict
7+
from glob import glob
88
from urllib.request import urlretrieve
99

10-
import torch
1110
import numpy as np
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import torch
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from PIL import Image
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from tqdm import tqdm
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‎bindsnet/datasets/preprocess.py

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import math
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import random
3-
import warnings
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import cv2
6-
import torch
75
import numpy as np
6+
import torch
87
from torchvision import transforms
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