bindsnet.datasets package

Submodules

bindsnet.datasets.alov300 module

class bindsnet.datasets.alov300.ALOV300(root, transform, input_size, download=False)[source]

Bases: torch.utils.data.dataset.Dataset

Class to read the ALOV dataset

Parameters:
  • root – Path to the ALOV folder that contains JPEGImages, annotations, etc. folders.
  • input_size – The input size of network that is using this data, for rescaling.
  • download – Specify whether to download the dataset if it is not present.
  • num_samples – Number of samples to pass to the batch
DATASET_WEB = 'http://alov300pp.joomlafree.it/dataset-resources.html'
VOID_LABEL = 255
get_bb(ann)[source]

Parses ALOV annotation and returns bounding box in the format: [left, upper, width, height]

get_orig_sample(idx, i=1)[source]

Returns original image with bounding box at a specific index. Range of valid index: [0, self.len-1].

get_sample(idx)[source]

Returns sample without transformation for visualization.

Sample consists of resized previous and current frame with target which is passed to the network. Bounding box values are normalized between 0 and 1 with respect to the target frame and then scaled by factor of 10.

progress(count, block_size, total_size)[source]
show(idx, is_current=1)[source]

Helper function to display image at a particular index with grounttruth bounding box.

Arguments:
idx: index is_current: 0 for previous frame and 1 for current frame
show_sample(idx)[source]

Helper function to display sample, which is passed to GOTURN. Shows previous frame and current frame with bounding box.

bindsnet.datasets.collate module

This code is directly pulled from the pytorch version found at:

https://github.com/pytorch/pytorch/blob/master/torch/utils/data/_utils/collate.py

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

bindsnet.datasets.collate.safe_worker_check()[source]

Method to check to use shared memory.

bindsnet.datasets.collate.time_aware_collate(batch)[source]

Puts each data field into a tensor with dimensions [time, batch size, ...]

Interpretation of dimensions being input: - 0 dim (,) - (1, batch_size, 1) - 1 dim (time,) - (time, batch_size, 1) - >2 dim (time, n_0, …) - (time, batch_size, n_0, …)

bindsnet.datasets.dataloader module

class bindsnet.datasets.dataloader.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=<function time_aware_collate>, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None)[source]

Bases: torch.utils.data.dataloader.DataLoader

bindsnet.datasets.davis module

class bindsnet.datasets.davis.Davis(root, task='unsupervised', subset='train', sequences='all', resolution='480p', size=(600, 480), codalab=False, download=False, num_samples: int = -1)[source]

Bases: torch.utils.data.dataset.Dataset

Class to read the DAVIS dataset :param root: Path to the DAVIS folder that contains JPEGImages, Annotations,

etc. folders.
Parameters:
  • task – Task to load the annotations, choose between semi-supervised or unsupervised.
  • subset – Set to load the annotations
  • sequences – Sequences to consider, ‘all’ to use all the sequences in a set.
  • resolution – Specify the resolution to use the dataset, choose between ‘480’ and ‘Full-Resolution’
  • download – Specify whether to download the dataset if it is not present
  • num_samples – Number of samples to pass to the batch
DATASET_WEB = 'https://davischallenge.org/davis2017/code.html'
RESOLUTION_OPTIONS = ['480p', 'Full-Resolution']
SUBSET_OPTIONS = ['train', 'val', 'test-dev', 'test-challenge']
TASKS = ['semi-supervised', 'unsupervised']
VOID_LABEL = 255
get_all_images(sequence)[source]
get_all_masks(sequence, separate_objects_masks=False)[source]
get_frames(sequence)[source]
get_sequences()[source]
static progress(count, block_size, total_size)[source]

Simple progress indicator for the download of the dataset.

bindsnet.datasets.preprocess module

class bindsnet.datasets.preprocess.BoundingBox(x1, y1, x2, y2)[source]

Bases: object

compute_output_height()[source]
compute_output_width()[source]
edge_spacing_x()[source]
edge_spacing_y()[source]
get_bb_list()[source]
get_center_x()[source]
get_center_y()[source]
get_height()[source]
get_width()[source]
print_bb()[source]
recenter(search_loc, edge_spacing_x, edge_spacing_y, bbox_gt_recentered)[source]
scale(image)[source]
shift(image, lambda_scale_frac, lambda_shift_frac, min_scale, max_scale, shift_motion_model, bbox_rand)[source]
uncenter(raw_image, search_location, edge_spacing_x, edge_spacing_y)[source]
unscale(image)[source]
class bindsnet.datasets.preprocess.NormalizeToTensor[source]

Bases: object

Returns torch tensor normalized images.

class bindsnet.datasets.preprocess.Rescale(output_size)[source]

Bases: object

Rescale image and bounding box. Args:

output_size (tuple or int): Desired output size. If int, square crop is made.
bindsnet.datasets.preprocess.bgr2rgb(image)[source]
bindsnet.datasets.preprocess.binary_image(image: numpy.ndarray) → numpy.ndarray[source]

Converts input image into black and white (binary)

Parameters:image – Gray-scaled image.
Returns:Black and white image.
bindsnet.datasets.preprocess.computeCropPadImageLocation(bbox_tight, image)[source]
bindsnet.datasets.preprocess.crop(image: numpy.ndarray, x1: int, x2: int, y1: int, y2: int) → numpy.ndarray[source]

Crops an image given coordinates of cropping box.

Parameters:
  • image – 3-dimensional image.
  • x1 – Left x coordinate.
  • x2 – Right x coordinate.
  • y1 – Bottom y coordinate.
  • y2 – Top y coordinate.
Returns:

Image cropped using coordinates (x1, x2, y1, y2).

bindsnet.datasets.preprocess.cropPadImage(bbox_tight, image)[source]
bindsnet.datasets.preprocess.crop_sample(sample)[source]

Given a sample image with bounding box, this method returns the image crop at the bounding box location with twice the width and height for context.

bindsnet.datasets.preprocess.gray_scale(image: numpy.ndarray) → numpy.ndarray[source]

Converts RGB image into grayscale.

Parameters:image – RGB image.
Returns:Gray-scaled image.
bindsnet.datasets.preprocess.sample_exp_two_sides(lambda_)[source]
bindsnet.datasets.preprocess.sample_rand_uniform()[source]
bindsnet.datasets.preprocess.shift_crop_training_sample(sample, bb_params)[source]

Given an image with bounding box, this method randomly shifts the box and generates a training example. It returns current image crop with shifted box (with respect to current image).

bindsnet.datasets.preprocess.subsample(image: numpy.ndarray, x: int, y: int) → numpy.ndarray[source]

Scale the image to (x, y).

Parameters:
  • image – Image to be rescaled.
  • x – Output value for image’s x dimension.
  • y – Output value for image’s y dimension.
Returns:

Re-scaled image.

bindsnet.datasets.spoken_mnist module

class bindsnet.datasets.spoken_mnist.SpokenMNIST(path: str, download: bool = False, shuffle: bool = True, train: bool = True, split: float = 0.8, num_samples: int = -1)[source]

Bases: torch.utils.data.dataset.Dataset

Handles loading and saving of the Spoken MNIST audio dataset (link).

Constructor for the SpokenMNIST object. Makes the data directory if it doesn’t already exist.

Parameters:
  • path – Pathname of directory in which to store the dataset.
  • download – Whether or not to download the dataset (requires internet connection).
  • shuffle – Whether to randomly permute order of dataset.
  • train – Load training split if true else load test split
  • split – Train, test split; in range (0, 1).
  • num_samples – Number of samples to pass to the batch
digit = 9
example = 49
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n_files = 1500
process_data(file_names: Iterable[str]) → Tuple[List[torch.Tensor], torch.Tensor][source]

Opens files of Spoken MNIST data and processes them into numpy arrays.

Parameters:file_names – Names of the files containing Spoken MNIST audio to load.
Returns:Processed Spoken MNIST audio and label data.
speaker = 'theo'
test_pickle = 'test.pt'
train_pickle = 'train.pt'
url = 'https://github.com/Jakobovski/free-spoken-digit-dataset/archive/master.zip'

bindsnet.datasets.torchvision_wrapper module

bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper(ds_type)[source]

Creates wrapper classes for datasets that output (image, label) from __getitem__. This applies to all of the datasets inside of torchvision.

Module contents

bindsnet.datasets.create_torchvision_dataset_wrapper(ds_type)[source]

Creates wrapper classes for datasets that output (image, label) from __getitem__. This applies to all of the datasets inside of torchvision.

class bindsnet.datasets.SpokenMNIST(path: str, download: bool = False, shuffle: bool = True, train: bool = True, split: float = 0.8, num_samples: int = -1)[source]

Bases: torch.utils.data.dataset.Dataset

Handles loading and saving of the Spoken MNIST audio dataset (link).

Constructor for the SpokenMNIST object. Makes the data directory if it doesn’t already exist.

Parameters:
  • path – Pathname of directory in which to store the dataset.
  • download – Whether or not to download the dataset (requires internet connection).
  • shuffle – Whether to randomly permute order of dataset.
  • train – Load training split if true else load test split
  • split – Train, test split; in range (0, 1).
  • num_samples – Number of samples to pass to the batch
digit = 9
example = 49
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n_files = 1500
process_data(file_names: Iterable[str]) → Tuple[List[torch.Tensor], torch.Tensor][source]

Opens files of Spoken MNIST data and processes them into numpy arrays.

Parameters:file_names – Names of the files containing Spoken MNIST audio to load.
Returns:Processed Spoken MNIST audio and label data.
speaker = 'theo'
test_pickle = 'test.pt'
train_pickle = 'train.pt'
url = 'https://github.com/Jakobovski/free-spoken-digit-dataset/archive/master.zip'
class bindsnet.datasets.Davis(root, task='unsupervised', subset='train', sequences='all', resolution='480p', size=(600, 480), codalab=False, download=False, num_samples: int = -1)[source]

Bases: torch.utils.data.dataset.Dataset

Class to read the DAVIS dataset :param root: Path to the DAVIS folder that contains JPEGImages, Annotations,

etc. folders.
Parameters:
  • task – Task to load the annotations, choose between semi-supervised or unsupervised.
  • subset – Set to load the annotations
  • sequences – Sequences to consider, ‘all’ to use all the sequences in a set.
  • resolution – Specify the resolution to use the dataset, choose between ‘480’ and ‘Full-Resolution’
  • download – Specify whether to download the dataset if it is not present
  • num_samples – Number of samples to pass to the batch
DATASET_WEB = 'https://davischallenge.org/davis2017/code.html'
RESOLUTION_OPTIONS = ['480p', 'Full-Resolution']
SUBSET_OPTIONS = ['train', 'val', 'test-dev', 'test-challenge']
TASKS = ['semi-supervised', 'unsupervised']
VOID_LABEL = 255
get_all_images(sequence)[source]
get_all_masks(sequence, separate_objects_masks=False)[source]
get_frames(sequence)[source]
get_sequences()[source]
static progress(count, block_size, total_size)[source]

Simple progress indicator for the download of the dataset.

class bindsnet.datasets.ALOV300(root, transform, input_size, download=False)[source]

Bases: torch.utils.data.dataset.Dataset

Class to read the ALOV dataset

Parameters:
  • root – Path to the ALOV folder that contains JPEGImages, annotations, etc. folders.
  • input_size – The input size of network that is using this data, for rescaling.
  • download – Specify whether to download the dataset if it is not present.
  • num_samples – Number of samples to pass to the batch
DATASET_WEB = 'http://alov300pp.joomlafree.it/dataset-resources.html'
VOID_LABEL = 255
get_bb(ann)[source]

Parses ALOV annotation and returns bounding box in the format: [left, upper, width, height]

get_orig_sample(idx, i=1)[source]

Returns original image with bounding box at a specific index. Range of valid index: [0, self.len-1].

get_sample(idx)[source]

Returns sample without transformation for visualization.

Sample consists of resized previous and current frame with target which is passed to the network. Bounding box values are normalized between 0 and 1 with respect to the target frame and then scaled by factor of 10.

progress(count, block_size, total_size)[source]
show(idx, is_current=1)[source]

Helper function to display image at a particular index with grounttruth bounding box.

Arguments:
idx: index is_current: 0 for previous frame and 1 for current frame
show_sample(idx)[source]

Helper function to display sample, which is passed to GOTURN. Shows previous frame and current frame with bounding box.

bindsnet.datasets.time_aware_collate(batch)[source]

Puts each data field into a tensor with dimensions [time, batch size, ...]

Interpretation of dimensions being input: - 0 dim (,) - (1, batch_size, 1) - 1 dim (time,) - (time, batch_size, 1) - >2 dim (time, n_0, …) - (time, batch_size, n_0, …)

class bindsnet.datasets.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=<function time_aware_collate>, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None)[source]

Bases: torch.utils.data.dataloader.DataLoader

bindsnet.datasets.CIFAR10

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.CIFAR100

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.Cityscapes

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.CocoCaptions

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.CocoDetection

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.DatasetFolder

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.EMNIST

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.FakeData

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.FashionMNIST

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.Flickr30k

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.Flickr8k

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.ImageFolder

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.KMNIST

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.LSUN

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.LSUNClass

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.MNIST

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.Omniglot

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.PhotoTour

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.SBU

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.SEMEION

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.STL10

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.SVHN

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.VOCDetection

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper

bindsnet.datasets.VOCSegmentation

alias of bindsnet.datasets.torchvision_wrapper.create_torchvision_dataset_wrapper.<locals>.TorchvisionDatasetWrapper