readout layer trained

verified with time-series forecasting task and plotted

1 files changed, 0 insertions, 70 deletions

diff --git a/neurobench_testing/examples/gsc/benchmark_ann.py b/neurobench_testing/examples/gsc/benchmark_ann.py
deleted file mode 100644
index 6f71a1e..0000000
--- a/neurobench_testing/examples/gsc/benchmark_ann.py
+++ /dev/null
@@ -1,70 +0,0 @@
-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