import os
import shutil
from collections.abc import Callable
import numpy as np
from tonic.dataset import Dataset
from tonic.download_utils import extract_archive
[docs]class POKERDVS(Dataset):
"""`POKER-DVS <http://www2.imse-cnm.csic.es/caviar/POKERDVS.html>`_
Events have (txyp) ordering.
::
@article{serrano2015poker,
title={Poker-DVS and MNIST-DVS. Their history, how they were made, and other details},
author={Serrano-Gotarredona, Teresa and Linares-Barranco, Bernab{\'e}},
journal={Frontiers in neuroscience},
volume={9},
pages={481},
year={2015},
publisher={Frontiers}
}
Parameters:
save_to (string): Location to save files to on disk.
train (bool): If True, uses training subset, otherwise testing subset.
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.
transforms (callable, optional): A callable of transforms that is applied to both data and
labels at the same time.
"""
train_filename = "pips_train.tar.gz"
test_filename = "pips_test.tar.gz"
train_md5 = "412bcfb96826e4fcb290558e8c150aae"
test_md5 = "eef2bf7d0d3defae89a6fa98b07c17af"
classes = ["cl", "he", "di", "sp"]
int_classes = dict(zip(classes, range(4), strict=False))
sensor_size = (35, 35, 2)
dtype = np.dtype([("t", int), ("x", int), ("y", int), ("p", int)])
ordering = dtype.names
def __init__(
self,
save_to: str,
train: bool = True,
transform: Callable | None = None,
target_transform: Callable | None = None,
transforms: Callable | None = None,
):
super().__init__(
save_to,
transform=transform,
target_transform=target_transform,
transforms=transforms,
)
self.train = train
if train:
self.file_md5 = self.train_md5
self.filename = self.train_filename
self.folder_name = "pips_train"
else:
self.file_md5 = self.test_md5
self.filename = self.test_filename
self.folder_name = "pips_test"
if not self._check_exists():
self._copy_and_extract_local_data()
file_path = os.path.join(self.location_on_system, self.folder_name)
for path, dirs, files in os.walk(file_path):
files.sort()
for file in files:
if file.endswith("npy"):
self.data.append(np.load(os.path.join(path, file)))
self.targets.append(self.int_classes[path[-2:]])
[docs] def __getitem__(self, index):
"""
Returns:
a tuple of (events, target) where target is the index of the target class.
"""
events, target = self.data[index], self.targets[index]
events = np.lib.recfunctions.unstructured_to_structured(events, self.dtype)
if self.transform is not None:
events = self.transform(events)
if self.target_transform is not None:
target = self.target_transform(target)
if self.transforms is not None:
events, target = self.transforms(events, target)
return events, target
[docs] def __len__(self):
return len(self.data)
def _copy_and_extract_local_data(self):
"""Copy and extract data from local datasets directory."""
# Get the path to the datasets directory
datasets_dir = os.path.dirname(os.path.abspath(__file__))
source_file = os.path.join(datasets_dir, self.filename)
if not os.path.exists(source_file):
raise FileNotFoundError(
f"Local data file {self.filename} not found in {datasets_dir}. "
"Please ensure the POKER data files are in the datasets directory."
)
# Copy the file to the target location
target_file = os.path.join(self.location_on_system, self.filename)
# Ensure the target directory exists
os.makedirs(self.location_on_system, exist_ok=True)
shutil.copy2(source_file, target_file)
# Extract the archive
extract_archive(target_file, self.location_on_system)
# Remove the copied archive file to save space
os.remove(target_file)
def _check_exists(self):
return (
self._is_file_present()
and self._folder_contains_at_least_n_files_of_type(20, ".npy")
)
