import re
if __name__ == '__main__':
device = get_device()
system_train = 'two_spins'
system_test = 'two_spins'
if system_test in ['ospm', 'two_spins']:
size = 16
elif system_test in ['os']:
size = 4
else:
raise ValueError('unsupported test system')
path_train = get_path(
) + f'/dl/datasets/floquet_lindbladian/{system_train}'
path_test = get_path() + f'/dl/datasets/floquet_lindbladian/{system_test}'
suffix_train = 'ampl(0.5000_0.5000_200)_freq(0.0500_0.0500_200)_phase(0.0000_0.0000_0)'
suffix_test = 'ampl(0.5000_0.5000_200)_freq(0.0500_0.0500_200)_phase(1.5708_0.0000_0)'
# Models to choose from [resnet, resnet50_2D, alexnet, vgg, squeezenet, densenet, inception]
model_name = "resnet"
feature_type = 'eval'
transforms_type = 'regular'
label_type = 'log'
model_dir = f'{path_train}/{model_name}_{feature_type}_{transforms_type}_{label_type}_{suffix_train}'
num_classes = 1
import os
if __name__ == '__main__':
device = get_device()
system = 'two_spins'
if system in ['ospm', 'two_spins']:
size = 16
elif system in ['os']:
size = 4
else:
raise ValueError('unsupported test system')
input_size = 224
path = get_path() + f'/dl/datasets/floquet_lindbladian/{system}'
num_points = 200
suffix = f'ampl(0.5000_0.5000_{num_points})_freq(0.0500_0.0500_{num_points})_phase(0.0000_0.0000_0)'
feature_type = 'eval'
transforms_type = 'noNorm'
label_type = 'log'
if transforms_type == 'regular':
transforms_regular = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((input_size, input_size)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])