1 files changed, 70 insertions, 0 deletions
diff --git a/neurobench_testing/examples/gsc/benchmark_ann.py b/neurobench_testing/examples/gsc/benchmark_ann.py
new file mode 100644
index 0000000..6f71a1e
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+++ b/neurobench_testing/examples/gsc/benchmark_ann.py
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+import os
+import torch
+
+from torch.utils.data import DataLoader
+import torchaudio
+
+from neurobench.datasets import SpeechCommands
+
+from neurobench.models import TorchModel
+from neurobench.benchmarks import Benchmark
+
+from neurobench.processors.abstract import NeuroBenchPreProcessor, NeuroBenchPostProcessor
+
+from neurobench.metrics.workload import (
+    ActivationSparsity,
+    SynapticOperations,
+    ClassificationAccuracy
+)
+from neurobench.metrics.static import (
+    Footprint,
+    ConnectionSparsity,
+)
+
+from ANN import M5
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+file_path = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(file_path, "model_data/m5_ann")
+data_dir = os.path.join(file_path, "../../data/speech_commands") # data in repo root dir
+
+test_set = SpeechCommands(path=data_dir, subset="testing")
+
+test_set_loader = DataLoader(test_set, batch_size=500, shuffle=True)
+
+net = M5()
+net.load_state_dict(torch.load(model_path, map_location=device))
+
+class resample(NeuroBenchPreProcessor):
+	def __init__(self):
+		self.resample = torchaudio.transforms.Resample(orig_freq=16000, new_freq=8000).to(device)
+
+	def __call__(self, dataset):
+		inputs = dataset[0].permute(0, 2, 1)
+		inputs = self.resample(inputs)
+		return (inputs, dataset[1])
+
+preprocessors = [resample()]
+
+class  convert_to_label(NeuroBenchPostProcessor):
+
+	def __call__(self, output):
+		return output.argmax(dim=-1).squeeze()
+
+postprocessors = [convert_to_label()]
+
+## Define model ##
+model = TorchModel(net)
+
+static_metrics = [Footprint, ConnectionSparsity]
+workload_metrics = [ClassificationAccuracy, ActivationSparsity, SynapticOperations]
+
+benchmark = Benchmark(model, test_set_loader, preprocessors, postprocessors, [static_metrics, workload_metrics])
+results = benchmark.run(device=device)
+print(results)
+
+# Results:
+# {'Footprint': 109228, 'ConnectionSparsity': 0.0, 
+# 'ClassificationAccuracy': 0.8653339412687909, 'ActivationSparsity': 0.3854464619019532, 
+# 'SynapticOperations': {'Effective_MACs': 1728071.1701953658, 'Effective_ACs': 0.0, 'Dense': 1880256.0}}
+\ No newline at end of file