readout layer trained

verified with time-series forecasting task and plotted

1 files changed, 0 insertions, 169 deletions

diff --git a/neurobench_testing/memTorch_testing.py b/neurobench_testing/memTorch_testing.py
deleted file mode 100644
index 1992e05..0000000
--- a/neurobench_testing/memTorch_testing.py
+++ /dev/null
@@ -1,169 +0,0 @@
-import sys
-import torch
-import torch.nn as nn
-import snntorch as snn
-from snntorch import surrogate
-
-from torch.utils.data import DataLoader, Subset
-
-from neurobench.models import SNNTorchModel
-from neurobench.benchmarks import Benchmark, benchmark
-from neurobench.datasets import SpeechCommands
-from neurobench.metrics.workload import (
-        ActivationSparsity,
-        SynapticOperations,
-        ClassificationAccuracy,
-)
-
-from neurobench.metrics.static import (
-        Footprint,
-        ConnectionSparsity,
-)
-
-from neurobench.processors.preprocessors import S2SPreProcessor
-from neurobench.processors.postprocessors import ChooseMaxCount
-
-import copy
-from memtorch.mn.Module import patch_model
-from memtorch.map.Parameter import naive_map 
-from memtorch.bh.memristor import VTEAM
-from memtorch.map.Input import naive_scale
-
-beta = 0.9
-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),
-)
-
-vteam_params = {
-        'time_series_resolution': 1e-3,
-        'r_on': 50,
-        'r_off': 1000,
-}
-
-net.load_state_dict(torch.load("examples/gsc/model_data/s2s_gsc_snntorch", map_location=device))
-
-patched_net = patch_model(
-        copy.deepcopy(net),
-        memristor_model=VTEAM,
-        memristor_model_params=vteam_params,
-        module_parameters_to_patch=[torch.nn.Linear],
-        mapping_routine=naive_map,
-        transistor=True,
-        tile_shape=(128, 128),
-        max_input_voltage=0.3,
-        scaling_routine=naive_scale,
-        ADC_resolution=16,
-        use_bindings=True,
-        verbose=True,
-)
-
-static_metrics = [Footprint, ConnectionSparsity]
-workload_metrics = [ActivationSparsity, SynapticOperations, ClassificationAccuracy]
-
-test_set = SpeechCommands(path="data/SpeechCommands/", subset="testing")
-
-#shorten data set so I can actually run it lol
-tiny_indices = list(range(10)) 
-tiny_test_set = Subset(test_set, tiny_indices)
-test_set_loader = DataLoader(tiny_test_set, batch_size=16, shuffle=True)
-
-pre_processor = [S2SPreProcessor(device=device)]
-post_processor = [ChooseMaxCount()]
-
-model = SNNTorchModel(net)
-benchmark = Benchmark(
-        model,
-        test_set_loader,
-        pre_processor,
-        post_processor,
-        [static_metrics, workload_metrics]
-)
-
-print("\n --Checking signal strength--")
-dummy_input = torch.randn(2, 20).to(device)
-
-try:
-    raw_signal = patched_net(dummy_input)
-
-    if isinstance(raw_signal, tuple) and len(raw_signal) > 1:
-        voltage_signal = raw_signal[0]
-        print("Checking Neuron voltages")
-    else:
-        voltage_signal = raw_signal
-        print("Checking RAW OUTPUTS")
-
-
-    print(f"Signal Max: {raw_signal.max().item():.8f}")
-    print(f"Signal Min: {raw_signal.min().item():.8f}")
-    print(f"Signal Mean: {raw_signal.mean().item():.8f}")
-
-except Exception as e:
-    print("Error getting signal:", e)
-
-sys.exit()
-
-results = benchmark.run()
-print("\n\n----- IDEAL BENCHMARK -----")
-print(f"Footprint: {results['Footprint']}")
-print(f"Connection Sparsity: {results['ConnectionSparsity']}")
-print(f"Activation Sparsity: {results['ActivationSparsity']}")
-print(f"Synaptic Operations: {results['SynapticOperations']}")
-print(f"Classification Accuracy: {results['ClassificationAccuracy']}\n")
-
-#energy calculations
-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\n\n")
-
-
-model = SNNTorchModel(patched_net)
-benchmark = Benchmark(
-        model,
-        test_set_loader,
-        pre_processor,
-        post_processor,
-        [static_metrics, workload_metrics]
-)
-
-
-with torch.no_grad(): #makes sure its in inference mode
-    #otherwise you get memory leaks : ( 
-    results = benchmark.run()
-    
-print("\n\n----- MEMRISTOR BENCHMARK -----")
-print(f"Footprint: {results['Footprint']}")
-print(f"Connection Sparsity: {results['ConnectionSparsity']}")
-print(f"Activation Sparsity: {results['ActivationSparsity']}")
-print(f"Synaptic Operations: {results['SynapticOperations']}")
-print(f"Classification Accuracy: {results['ClassificationAccuracy']}")
-
-#energy calculations
-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")