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import torch
from torch.utils.data import DataLoader
from neurobench.datasets import SpeechCommands
from neurobench.processors.preprocessors import S2SPreProcessor
from neurobench.processors.postprocessors import ChooseMaxCount
from neurobench.models import NeuroBenchModel
from neurobench.models import SNNTorchModel
from neurobench.metrics.workload import (
ActivationSparsity,
SynapticOperations,
ClassificationAccuracy,
)
from neurobench.metrics.static import (
Footprint,
ConnectionSparsity,
)
from neurobench.benchmarks import Benchmark
from torch import nn
import snntorch as snn
from snntorch import surrogate
beta = 0.5 #this does nothing with the current model
device = torch.device("cpu")
spike_grad = surrogate.fast_sigmoid()
net = nn.Sequential(
nn.Flatten(),
nn.Linear(20, 256),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Linear(256, 256),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Linear(256, 256),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True),
nn.Linear(256, 35),
snn.Leaky(beta=beta, spike_grad=spike_grad, init_hidden=True, output=True),
)
test_set = SpeechCommands(path="data/SpeechCommands/", subset="testing")
test_set_loader = DataLoader(test_set, batch_size=500, shuffle=True)
net.load_state_dict(torch.load("examples/gsc/model_data/s2s_gsc_snntorch", map_location=device))
print("weight before noise: ", net[1].weight[0][0].item())
#adds noise for 'simulating' memristors
# not perfect ik but just testing it out
noise = 0.05
with torch.no_grad():
for param in net.parameters():
param.add_(torch.randn_like(param) * noise)
print("weight after noise: ", net[1].weight[0][0].item())
model = SNNTorchModel(net)
preprocessors = [S2SPreProcessor(device=device)]
postprocessors = [ChooseMaxCount()]
static_metrics = [Footprint, ConnectionSparsity]
workload_metrics = [ClassificationAccuracy, ActivationSparsity, SynapticOperations]
print("Layer 1 Beta:", net[2].beta)
print("Layer 2 Beta:", net[4].beta)
benchmark = Benchmark(
model,
test_set_loader,
preprocessors,
postprocessors,
[static_metrics, workload_metrics]
)
results = benchmark.run()
print(results)
# Energy calulations
ENERGY_PER_MAC = 0.9e-12
ENERGY_PER_AC = 0.1e-12
macs = results['SynapticOperations']['Effective_MACs']
acs = results['SynapticOperations']['Effective_ACs']
total_energy = (macs * ENERGY_PER_MAC) + (acs * ENERGY_PER_AC)
print("Energy Report:")
print(f"Total Operations: {macs} MACS, {acs} acs ")
print(f"Calculated Energy cost: {total_energy} joules per batch")
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