def default_validate_gate(gate_base_dir, validation_processor_configs):
model = torch.load(
os.path.join(gate_base_dir, 'reproducibility', 'model.pt'),
map_location=torch.device(TorchUtils.get_accelerator_type()))
results = torch.load(
os.path.join(gate_base_dir, 'reproducibility', "results.pickle"),
map_location=torch.device(TorchUtils.get_accelerator_type()))
experiment_configs = load_configs(
os.path.join(gate_base_dir, 'reproducibility', 'configs.yaml'))
results_dir = init_dirs(gate_base_dir, is_main=True)
criterion = manager.get_criterion(experiment_configs["algorithm"])
waveform_transforms = transforms.Compose([
PlateausToPoints(
experiment_configs['processor']["data"]['waveform']
), # Required to remove plateaus from training because the perceptron cannot accept less than 10 values for each gate
PointsToPlateaus(validation_processor_configs["data"]["waveform"])
])
validate_gate(model,
results,
validation_processor_configs,
criterion,
results_dir=results_dir,
transforms=waveform_transforms)
def validate_vcdim(vcdim_base_dir, validation_processor_configs, is_main=True):
base_dir = init_dirs(vcdim_base_dir, is_main=is_main)
dirs = [
os.path.join(vcdim_base_dir, o) for o in os.listdir(vcdim_base_dir)
if os.path.isdir(os.path.join(vcdim_base_dir, o))
]
for d in dirs:
if os.path.split(d)[1] != "validation":
gate_dir = create_directory(
os.path.join(base_dir,
d.split(os.path.sep)[-1]))
model = torch.load(os.path.join(d, 'reproducibility', 'model.pt'),
map_location=torch.device(
TorchUtils.get_accelerator_type()))
results = torch.load(
os.path.join(d, 'reproducibility', "results.pickle"),
map_location=torch.device(TorchUtils.get_accelerator_type()))
experiment_configs = load_configs(
os.path.join(d, 'reproducibility', "configs.yaml"))
#results_dir = init_dirs(d, is_main=is_main)
criterion = manager.get_criterion(experiment_configs["algorithm"])
waveform_transforms = transforms.Compose([
PlateausToPoints(
experiment_configs['processor']["data"]['waveform']
), # Required to remove plateaus from training because the perceptron cannot accept less than 10 values for each gate
PointsToPlateaus(
validation_processor_configs["data"]["waveform"])
])
# validate_gate(os.path.join(d, "reproducibility"), base_dir, is_main=False)
validate_gate(model,
results,
validation_processor_configs,
criterion,
results_dir=gate_dir,
transforms=waveform_transforms,
is_main=False)
plt.xlabel("Accuracy values")
plt.ylabel("Counts")
plt.savefig(os.path.join(save_dir, "accuracy_histogram_" + label + "." + extension))
if show_plots:
plt.show()
if __name__ == "__main__":
from torchvision import transforms
from brainspy.utils import manager
from brainspy.utils.io import load_configs
from brainspy.utils.transforms import DataToTensor, DataToVoltageRange
from brainspy.processors.dnpu import DNPU
V_MIN = [-1.2, -1.2]
V_MAX = [0.6, 0.6]
transforms = transforms.Compose(
[DataToVoltageRange(V_MIN, V_MAX, -1, 1), DataToTensor(torch.device('cpu'))]
)
configs = load_configs("configs/ring.yaml")
criterion = manager.get_criterion(configs["algorithm"])
algorithm = manager.get_algorithm(configs["algorithm"])
search_solution(configs, DNPU, criterion, algorithm, transforms=transforms)
