#!/user/bin/env python
import numpy as np
import h5py
import os
from typing import Callable, Optional
from tonic.dataset import Dataset
from tonic.io import make_structured_array
import requests
from tqdm import tqdm
[docs]class NTIDIGITS18(Dataset):
"""`N-TIDIGITS18 Dataset <https://docs.google.com/document/d/1Uxe7GsKKXcy6SlDUX4hoJVAC0-UkH-8kr5UXp0Ndi1M/edit?tab=t.0#heading=h.sbnu5gtazqjq/>`_
Cochlea Spike Dataset.
::
@article{anumula2018feature,
title={Feature representations for neuromorphic audio spike streams},
author={Anumula, Jithendar and Neil, Daniel and Delbruck, Tobi and Liu, Shih-Chii},
journal={Frontiers in neuroscience},
volume={12},
pages={23},
year={2018},
publisher={Frontiers Media SA}
}
Parameters:
save_to (string): Location to save files to on disk. Will put files in an 'hsd' subfolder.
train (bool): If True, uses training subset, otherwise testing subset.
single_digits (bool): If True, only returns samples with single digits (o, 1, 2, 3, 4, 5, 6, 7, 8, 9, z), with class 0 for 'o' and 11 for 'z'.
transform (callable, optional): A callable of transforms to apply to the data.
target_transform (callable, optional): A callable of transforms to apply to the targets/labels.
Returns:
A dataset object that can be indexed or iterated over. One sample returns a tuple of (events, targets).
"""
base_url = "https://www.dropbox.com/scl/fi/1x4lxt9yyw25sc3tez8oi/n-tidigits.hdf5?e=2&rlkey=w8gi5udvib2zqzosusa5tr3wq&dl=1"
filename = "n-tidigits.hdf5"
file_md5 = "360a2d11e5429555c9197381cf6b58e0"
folder_name = ""
sensor_size = (64, 1, 1)
dtype = np.dtype([("t", int), ("x", int), ("p", int)])
ordering = dtype.names
class_map = {"o": 0,
"1": 1,
"2": 2,
"3": 3,
"4": 4,
"5": 5,
"6": 6,
"7": 7,
"8": 8,
"9": 9,
"z": 10}
def __init__(
self,
save_to: str,
train: bool = True,
single_digits=False,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
):
super().__init__(
save_to,
transform=transform,
target_transform=target_transform,
)
self.url = self.base_url
# load the data
self.file_path = os.path.join(self.location_on_system, self.filename)
if not self._check_exists():
self.download()
self.data = h5py.File(self.file_path, 'r')
self.partition = "train" if train else "test"
self.single_indices = [i for i in range(len(self.data[f"{self.partition}_labels"])) if
len(self.data[f"{self.partition}_labels"][i].decode().split("-")[-1]) == 1]
self._samples = [x.decode() for x in self.data[f"{self.partition}_labels"]]
self.single_digits = single_digits
if single_digits:
self._samples = [self._samples[i] for i in self.single_indices]
self.labels = [x.decode().split("-")[-1] for x in self.data[f"{self.partition}_labels"]]
[docs] def download(self) -> None:
response = requests.get(self.base_url, stream=True)
if response.status_code == 200:
print("Downloading N-TIDIGITS from Dropbox at {}...".format(self.base_url))
file_size = int(response.headers.get('Content-Length', 0)) # get total file size in bytes
chunk_size = 8192
os.makedirs(self.location_on_system, exist_ok=True)
# Initialize progress bar
with open(os.path.join(self.location_on_system, self.filename), 'wb') as f, tqdm(
total=file_size,
unit='B',
unit_scale=True,
desc="Downloading",
ascii=True
) as pbar:
for chunk in response.iter_content(chunk_size=chunk_size):
if chunk: # filter out keep-alive new chunks
f.write(chunk)
pbar.update(len(chunk))
else:
print("Failed to download N-TIDIGITS from Dropbox. Please try again later.")
response.raise_for_status()
[docs] def __getitem__(self, index):
sample_id = self._samples[index]
x = np.asarray(self.data[f"{self.partition}_addresses"][sample_id])
t = np.asarray(self.data[f"{self.partition}_timestamps"][sample_id])
events = make_structured_array(
t * 1e6,
x,
1,
dtype=self.dtype,
)
target = sample_id.split("-")[-1]
if self.single_digits:
assert len(target) == 1, "Single digit samples requested, but target is not single digit."
target = self.class_map[target]
if self.transform is not None:
events = self.transform(events)
if self.target_transform is not None:
target = self.target_transform(target)
return events, target
[docs] def __len__(self):
return len(self._samples)
def _check_exists(self):
return (
self._is_file_present()
)
