def test_resample_r_off_r_on(debug_networks):
networks = debug_networks
for network in networks:
with pytest.raises(Exception):
patched_network = patch_model(
copy.deepcopy(network),
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={
'r_off': memtorch.bh.StochasticParameter(loc=1, scale=0),
'r_on': memtorch.bh.StochasticParameter(loc=1, scale=0)
},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.SingleColumn)
with pytest.raises(Exception):
patched_network = patch_model(
copy.deepcopy(network),
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={
'r_off': 1,
'r_on': 1
},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.SingleColumn)
def debug_patched_networks_(tile_shape, quant_method):
if quant_method is not None:
ADC_resolution = 8
else:
ADC_resolution = None
networks = debug_networks
device = torch.device("cpu" if "cpu" in
memtorch.__version__ else "cuda")
patched_networks = []
for network in networks:
patched_networks.append(
patch_model(
network,
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={"time_series_resolution": 0.1},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.SingleColumn,
tile_shape=tile_shape,
max_input_voltage=1.0,
ADC_resolution=ADC_resolution,
quant_method=quant_method,
))
return patched_networks
def test_networks():
for network in networks:
patched_network = patch_model(
copy.deepcopy(network),
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.SingleColumn)
patched_network.tune_()
def test_schemes(debug_networks):
networks = debug_networks
for scheme in memtorch.bh.Scheme:
for network in networks:
patched_network = patch_model(copy.deepcopy(network),
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=scheme)
assert patched_network.layer.crossbars is not None
def debug_patched_networks(debug_networks):
networks = debug_networks
device = torch.device('cpu' if 'cpu' in memtorch.__version__ else 'cuda')
patched_networks = []
for network in networks:
patched_networks.append(
patch_model(network,
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.SingleColumn))
return patched_networks
def test_networks(debug_networks, tile_shape, quant_method):
networks = debug_networks
if quant_method is not None:
ADC_resolution = 8
else:
ADC_resolution = None
for network in networks:
patched_network = patch_model(copy.deepcopy(network),
memristor_model=memtorch.bh.memristor.LinearIonDrift,
memristor_model_params={},
module_parameters_to_patch=[type(network.layer)],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.SingleColumn,
tile_shape=tile_shape,
max_input_voltage=1.0,
ADC_resolution=ADC_resolution,
quant_method=quant_method)
patched_network.tune_()
patched_network.disable_legacy()
r_off = 300000
reference_memristor_params = {
'time_series_resolution': 1e-10,
'r_off': r_off,
'r_on': r_on
}
model = MobileNetV2().to(device)
model.load_state_dict(torch.load('trained_model.pt'), strict=False)
model.eval()
patched_model = patch_model(
model,
memristor_model=reference_memristor,
memristor_model_params=reference_memristor_params,
module_parameters_to_patch=[torch.nn.Linear, torch.nn.Conv2d],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
scheme=memtorch.bh.Scheme.DoubleColumn,
tile_shape=(128, 128),
max_input_voltage=0.3,
ADC_resolution=8,
ADC_overflow_rate=0.,
quant_method='linear')
del model
patched_model.tune_()
times_to_reprogram = 10**np.arange(1, 10, dtype=np.float64)
v_stop_values = np.linspace(1.3, 1.9, 10, endpoint=True)
df = pd.DataFrame(columns=['times_reprogramed', 'v_stop', 'test_set_accuracy'])
for time_to_reprogram in times_to_reprogram:
cycle_count = time_to_reprogram
for v_stop in v_stop_values:
print('time_to_reprogram: %f, v_stop: %f' %
from memtorch.mn.Module import patch_model
from memtorch.map.Parameter import naive_map
from memtorch.bh.crossbar.Program import naive_program
from conv_net import ConvNet
from model import Model
# Create new reference memristor
reference_memristor = memtorch.bh.memristor.VTEAM
reference_memristor_params = {"time_series_resolution": 1e-10}
memristor = reference_memristor(**reference_memristor_params)
memristor.plot_hysteresis_loop()
memristor_model = ConvNet()
memristor_model.load_state_dict(torch.load("model.ckpt"), strict=False)
patched_model = patch_model(copy.deepcopy(memristor_model),
memristor_model=reference_memristor,
memristor_model_params=reference_memristor_params,
module_parameters_to_patch=[torch.nn.Linear],
mapping_routine=naive_map,
transistor=True,
programming_routine=None,
tile_shape=(128, 128),
max_input_voltage=1.0,
ADC_resolution=8,
ADC_overflow_rate=0.,
quant_method='linear')
print("Hello world")
