File size: 6,597 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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 | """Surrogate gradient SNN training for DVS128 Gesture benchmark.
Trains a 2-layer feedforward SNN (2048 -> hidden -> 11) using the same
SubtractiveLIF neuron model from shd_train.py.
Usage:
python dvs_train.py --data-dir data/dvs_gesture --epochs 80 --hidden 512
"""
import os
import sys
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
sys.path.insert(0, os.path.dirname(__file__))
from dvs_loader import DVSGestureDataset, collate_fn, N_CHANNELS, N_CLASSES
from shd_train import SubtractiveLIF, surrogate_spike
class DVSSNN(nn.Module):
"""2-layer SNN for DVS Gesture classification.
2048 (32x32x2 input) -> hidden (LIF) -> 11 (output integrator)
"""
def __init__(self, n_input=N_CHANNELS, n_hidden=512, n_output=N_CLASSES,
threshold=1.0, leak=0.003):
super().__init__()
self.n_hidden = n_hidden
self.n_output = n_output
self.fc1 = nn.Linear(n_input, n_hidden, bias=False)
self.fc2 = nn.Linear(n_hidden, n_output, bias=False)
self.fc_rec = nn.Linear(n_hidden, n_hidden, bias=False)
self.lif1 = SubtractiveLIF(n_hidden, threshold=threshold, leak=leak)
self.output_leak = leak * 0.5
nn.init.xavier_uniform_(self.fc1.weight, gain=0.1)
nn.init.xavier_uniform_(self.fc2.weight, gain=0.3)
nn.init.orthogonal_(self.fc_rec.weight, gain=0.1)
def forward(self, x):
batch, T, _ = x.shape
device = x.device
v1 = torch.zeros(batch, self.n_hidden, device=device)
v2 = torch.zeros(batch, self.n_output, device=device)
spk1 = torch.zeros(batch, self.n_hidden, device=device)
out_sum = torch.zeros(batch, self.n_output, device=device)
for t in range(T):
I1 = self.fc1(x[:, t]) + self.fc_rec(spk1)
v1, spk1 = self.lif1(I1, v1)
I2 = self.fc2(spk1)
v2 = v2 + I2 - self.output_leak
v2 = torch.clamp(v2, min=0.0)
out_sum = out_sum + v2
return out_sum / T
def train_epoch(model, loader, optimizer, device):
model.train()
total_loss = 0.0
correct = 0
total = 0
for inputs, labels in loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
output = model(inputs)
loss = F.cross_entropy(output, labels)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
total_loss += loss.item() * inputs.size(0)
correct += (output.argmax(1) == labels).sum().item()
total += inputs.size(0)
return total_loss / total, correct / total
@torch.no_grad()
def evaluate(model, loader, device):
model.eval()
total_loss = 0.0
correct = 0
total = 0
for inputs, labels in loader:
inputs, labels = inputs.to(device), labels.to(device)
output = model(inputs)
loss = F.cross_entropy(output, labels)
total_loss += loss.item() * inputs.size(0)
correct += (output.argmax(1) == labels).sum().item()
total += inputs.size(0)
return total_loss / total, correct / total
def main():
parser = argparse.ArgumentParser(description="Train SNN on DVS Gesture")
parser.add_argument("--data-dir", default="data/dvs_gesture")
parser.add_argument("--epochs", type=int, default=80)
parser.add_argument("--batch-size", type=int, default=32)
parser.add_argument("--lr", type=float, default=5e-4)
parser.add_argument("--hidden", type=int, default=512)
parser.add_argument("--threshold", type=float, default=1.0)
parser.add_argument("--leak", type=float, default=0.003)
parser.add_argument("--dt", type=float, default=10e-3,
help="Time bin width (10ms -> 150 bins for 1.5s)")
parser.add_argument("--duration", type=float, default=1.5)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--save", default="dvs_model.pt")
args = parser.parse_args()
torch.manual_seed(args.seed)
np.random.seed(args.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Device: {device}")
print("Loading DVS Gesture dataset (first load downloads ~1.5GB)...")
train_ds = DVSGestureDataset(args.data_dir, train=True,
dt=args.dt, duration=args.duration)
test_ds = DVSGestureDataset(args.data_dir, train=False,
dt=args.dt, duration=args.duration)
train_loader = DataLoader(
train_ds, batch_size=args.batch_size, shuffle=True,
collate_fn=collate_fn, num_workers=0, pin_memory=True)
test_loader = DataLoader(
test_ds, batch_size=args.batch_size, shuffle=False,
collate_fn=collate_fn, num_workers=0, pin_memory=True)
print(f"Train: {len(train_ds)}, Test: {len(test_ds)}, "
f"Time bins: {train_ds.n_bins} (dt={args.dt*1000:.1f}ms)")
model = DVSSNN(
n_hidden=args.hidden,
threshold=args.threshold,
leak=args.leak,
).to(device)
n_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
print(f"Model: {N_CHANNELS}->{args.hidden}->{N_CLASSES}, {n_params:,} params")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, args.epochs)
best_acc = 0.0
for epoch in range(args.epochs):
train_loss, train_acc = train_epoch(model, train_loader, optimizer, device)
test_loss, test_acc = evaluate(model, test_loader, device)
scheduler.step()
if test_acc > best_acc:
best_acc = test_acc
torch.save({
'epoch': epoch,
'model_state_dict': model.state_dict(),
'test_acc': test_acc,
'args': vars(args),
}, args.save)
lr = optimizer.param_groups[0]['lr']
print(f"Epoch {epoch+1:3d}/{args.epochs} | "
f"Train: {train_loss:.4f} / {train_acc*100:.1f}% | "
f"Test: {test_loss:.4f} / {test_acc*100:.1f}% | "
f"LR={lr:.2e} | Best={best_acc*100:.1f}%")
print(f"\nDone. Best test accuracy: {best_acc*100:.1f}%")
print(f"Model saved to {args.save}")
if __name__ == "__main__":
main()
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