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NMNIST tutorial is added #67

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ff9030a
NMNIST tutorial is added
ahmetakman Apr 4, 2022
dad9dfa
Merge branch 'main' into ahmetakman
PhilippPlank May 30, 2023
0eddb9c
Merge branch 'main' into ahmetakman
mgkwill Jul 25, 2023
4e48e46
Merge branch 'main' into ahmetakman
mgkwill Nov 13, 2023
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101 changes: 101 additions & 0 deletions tutorials/lava/lib/dl/netx/nmnist/Trained/accuracy.txt
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# Train Test
0.801933 0.945200
0.907750 0.949300
0.921467 0.948500
0.929283 0.961600
0.935350 0.965100
0.940217 0.962600
0.943083 0.964600
0.947050 0.967700
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0.969117 0.980800
0.970300 0.981400
0.969217 0.981800
0.970117 0.982600
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0.971133 0.981800
0.971833 0.981800
0.970983 0.984200
0.972217 0.982600
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0.975167 0.983400
0.975767 0.984000
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0.976917 0.984800
0.976200 0.984000
0.977100 0.984700
0.977050 0.984100
0.976767 0.983800
0.976400 0.985800
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0.978333 0.985600
0.978650 0.985400
0.977933 0.985100
0.978017 0.986000
0.978067 0.985300
0.978667 0.985900
0.978567 0.986700
0.978917 0.986100
0.979300 0.986300
0.978850 0.984000
0.978617 0.985300
0.978733 0.984900
0.979117 0.985800
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0.979083 0.985500
0.979800 0.985600
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0.980517 0.986200
0.980217 0.986200
0.980033 0.985300
0.980183 0.986100
0.981033 0.984900
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0.981250 0.985000
0.980783 0.985300
0.981417 0.986400
0.981300 0.986400
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101 changes: 101 additions & 0 deletions tutorials/lava/lib/dl/netx/nmnist/Trained/loss.txt
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# Train Test
0.392544 0.176659
0.239416 0.167550
0.210520 0.149321
0.194289 0.125025
0.177958 0.109535
0.171833 0.118700
0.164164 0.111476
0.158248 0.100817
0.152601 0.100551
0.149008 0.098966
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0.138394 0.083000
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0.132844 0.086755
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0.125912 0.079843
0.122580 0.083085
0.118639 0.074248
0.117358 0.073422
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0.109428 0.075555
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0.094452 0.062046
0.090770 0.059484
0.092471 0.075752
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0.063753 0.040203
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117 changes: 117 additions & 0 deletions tutorials/lava/lib/dl/netx/nmnist/nmnist.py
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# Copyright (C) 2022 Intel Corporation
# SPDX-License-Identifier: BSD-3-Clause
import glob
import os
import zipfile
import h5py
import numpy as np
import matplotlib.pyplot as plt
import torch

import lava.lib.dl.slayer as slayer


def augment(event):
x_shift = 4
y_shift = 4
theta = 10
xjitter = np.random.randint(2*x_shift) - x_shift
yjitter = np.random.randint(2*y_shift) - y_shift
ajitter = (np.random.rand() - 0.5) * theta / 180 * 3.141592654
sin_theta = np.sin(ajitter)
cos_theta = np.cos(ajitter)
event.x = event.x * cos_theta - event.y * sin_theta + xjitter
event.y = event.x * sin_theta + event.y * cos_theta + yjitter
return event


class NMNISTDataset():
"""NMNIST dataset method

Parameters
----------
path : str, optional
path of dataset root, by default 'data'
train : bool, optional
train/test flag, by default True
sampling_time : int, optional
sampling time of event data, by default 1
sample_length : int, optional
length of sample data, by default 300
transform : None or lambda or fx-ptr, optional
transformation method. None means no transform. By default Noney.
download : bool, optional
enable/disable automatic download, by default True
"""
def __init__(
self, path='data',
train=True,
sampling_time=1, sample_length=300,
transform=None, download=True,
):
super(NMNISTDataset, self).__init__()
self.path = path

if train:
data_path = path + '/Train'
source = 'https://www.dropbox.com/sh/tg2ljlbmtzygrag/'\
'AABlMOuR15ugeOxMCX0Pvoxga/Train.zip'
else:
data_path = path + '/Test'
source = 'https://www.dropbox.com/sh/tg2ljlbmtzygrag/'\
'AADSKgJ2CjaBWh75HnTNZyhca/Test.zip'

if download is True:
attribution_text = '''
NMNIST dataset is freely available here:
https://www.garrickorchard.com/datasets/n-mnist

(c) Creative Commons:
Orchard, G.; Cohen, G.; Jayawant, A.; and Thakor, N.
"Converting Static Image Datasets to Spiking Neuromorphic Datasets Using
Saccades",
Frontiers in Neuroscience, vol.9, no.437, Oct. 2015
'''.replace(' '*12, '')
if train is True:
print(attribution_text)

if len(glob.glob(f'{data_path}/')) == 0: # dataset does not exist
print(
f'NMNIST {"training" if train is True else "testing"} '
'dataset is not available locally.'
)
print('Attempting download (This will take a while) ...')
os.system(f'wget {source} -P {self.path}/ -q --show-progress')
print('Extracting files ...')
with zipfile.ZipFile(data_path + '.zip') as zip_file:
for member in zip_file.namelist():
zip_file.extract(member, self.path)
print('Download complete.')
else:
assert len(glob.glob(f'{data_path}/')) == 0, \
f'Dataset does not exist. Either set download=True '\
f'or download it from '\
f'https://www.garrickorchard.com/datasets/n-mnist '\
f'to {data_path}/'

self.samples = glob.glob(f'{data_path}/*/*.bin') #TODO

self.sampling_time = sampling_time
self.num_time_bins = int(sample_length/sampling_time)
self.transform = transform

def __getitem__(self, i):
filename = self.samples[i]
label = int(filename.split('/')[-2])
event = slayer.io.read_2d_spikes(filename)
if self.transform is not None:
event = self.transform(event)
spike = event.fill_tensor(
np.zeros((2, 34, 34, self.num_time_bins)),
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I guess the only difference between the dataloader here and the dataloader for training is this line. So I would suggest deriving this NMNISTdataset class from tutorials.lava.lib.dl.slayer.nmnist.NMNISTdataset and just override the getitem(). It would just be conversion of spike from torch to numpy. spike = spike_torch.cpu().data.numpy()

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Oh, I see; I did not know that NMNIST is included in the library. I just took the NMNIST slayer tutorial as the reference. Thanks for the advice.

sampling_time=self.sampling_time,
)
return spike.reshape(-1, self.num_time_bins), label

def __len__(self):
return len(self.samples)

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