File size: 2,963 Bytes
e4cdd5f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | """DVS128 Gesture dataset loader for neuromorphic benchmarks.
Uses the `tonic` library for event camera data loading and transforms.
128x128 pixels x 2 polarities -> downsampled to 32x32 = 2048 input channels.
11 gesture classes.
Requires: pip install tonic
"""
import os
import numpy as np
try:
import torch
from torch.utils.data import Dataset
except ImportError:
raise ImportError("PyTorch required: pip install torch")
try:
import tonic
import tonic.transforms as transforms
except ImportError:
raise ImportError("tonic required: pip install tonic")
N_CHANNELS = 2048 # 32x32x2 (downsampled from 128x128x2)
N_CLASSES = 11 # gesture classes
SENSOR_SIZE = (128, 128, 2)
DS_FACTOR = 4 # downsample 128->32
DS_SIZE = (32, 32, 2)
def get_dvs_transform(dt=10e-3, duration=1.5):
"""Build tonic transform pipeline: downsample -> bin to frames."""
n_bins = int(duration / dt)
return transforms.Compose([
transforms.Downsample(spatial_factor=1.0 / DS_FACTOR),
transforms.ToFrame(
sensor_size=DS_SIZE,
n_time_bins=n_bins,
),
])
class DVSGestureDataset(Dataset):
"""PyTorch Dataset wrapper for DVS128 Gesture.
Each sample is converted to a dense frame tensor (T, 2048) via tonic transforms.
"""
def __init__(self, data_dir="data/dvs_gesture", train=True, dt=10e-3, duration=1.5):
transform = get_dvs_transform(dt=dt, duration=duration)
self._tonic_ds = tonic.datasets.DVSGesture(
save_to=data_dir,
train=train,
transform=transform,
)
self.n_bins = int(duration / dt)
self.dt = dt
self.duration = duration
def __len__(self):
return len(self._tonic_ds)
def __getitem__(self, idx):
frames, label = self._tonic_ds[idx]
# frames shape from tonic: (T, 2, 32, 32) or (T, C, H, W)
# Flatten spatial dims: (T, 2*32*32) = (T, 2048)
frames = np.array(frames, dtype=np.float32)
if frames.ndim == 4:
T = frames.shape[0]
frames = frames.reshape(T, -1)
elif frames.ndim == 3:
T = frames.shape[0]
frames = frames.reshape(T, -1)
# Clip to n_bins
if frames.shape[0] > self.n_bins:
frames = frames[:self.n_bins]
elif frames.shape[0] < self.n_bins:
pad = np.zeros((self.n_bins - frames.shape[0], frames.shape[1]), dtype=np.float32)
frames = np.concatenate([frames, pad], axis=0)
# Binarize (any event count > 0 = spike)
frames = (frames > 0).astype(np.float32)
return torch.from_numpy(frames), int(label)
def collate_fn(batch):
"""Collate with uniform time length."""
inputs, labels = zip(*batch)
return torch.stack(inputs), torch.tensor(labels, dtype=torch.long)
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