data_set_parameters = {
'data_set_dir': './data/pathfinder_natural_images_2',
}
train_parameters = {
'train_batch_size': 32,
'test_batch_size': 1,
'learning_rate': 1e-4,
'num_epochs': 50,
'gaussian_reg_weight': 1e-5,
'gaussian_reg_sigma': 10,
}
# Create Model
cont_int_layer = new_piech_models.CurrentSubtractInhibitLayer(
lateral_e_size=15, lateral_i_size=15, n_iters=5)
# cont_int_layer = new_control_models.ControlMatchParametersLayer(
# lateral_e_size=15, lateral_i_size=15)
# cont_int_layer = new_control_models.ControlMatchIterationsLayer(
# lateral_e_size=15, lateral_i_size=15, n_iters=5)
scale_down_input_to_contour_integration_layer = 4
net = new_piech_models.BinaryClassifierResnet50(cont_int_layer)
main(net,
train_params=train_parameters,
data_set_params=data_set_parameters,
base_results_store_dir='./results/pathfinder',
cont_int_scale=scale_down_input_to_contour_integration_layer)
# ----------------------------------------------------------------------
data_set_parameters = {
'data_set_dir': "./data/channel_wise_optimal_full14_frag7",
'train_subset_size': 20000,
'test_subset_size': 2000
}
for lr in lr_arr:
print("Processing learning rate = {} {}".format(lr, '*' * 40))
base_results_dir = './results/analyze_lr_rate/lr_{}'.format(lr)
cont_int_layer = new_piech_models.CurrentSubtractInhibitLayer(
lateral_e_size=15,
lateral_i_size=15,
n_iters=5,
use_recurrent_batch_norm=True)
# cont_int_layer = new_piech_models.CurrentDivisiveInhibitLayer(
# lateral_e_size=15, lateral_i_size=15, n_iters=5, use_recurrent_batch_norm=True)
# cont_int_layer = new_control_models.ControlMatchParametersLayer(
# lateral_e_size=15, lateral_i_size=15)
model = new_piech_models.ContourIntegrationResnet50(cont_int_layer)
train_parameters = {
'random_seed': random_seed,
'train_batch_size': 32,
'test_batch_size': 32,
'learning_rate': lr,
plt.title("Input")
if __name__ == "__main__":
# -----------------------------------------------------------------------------------
# Init
# -----------------------------------------------------------------------------------
random_seed = 10
plt.ion()
torch.manual_seed(random_seed)
np.random.seed(random_seed)
data_dir = "./data/channel_wise_optimal_full14_frag7"
cont_int_layer = new_piech_models.CurrentSubtractInhibitLayer(
lateral_e_size=15,
lateral_i_size=15,
n_iters=5,
store_recurrent_acts=True)
net = new_piech_models.ContourIntegrationResnet50(cont_int_layer)
saved_model = \
'./results/positive_weights' \
'/ContourIntegrationResnet50_CurrentSubtractInhibitLayer_20201220_194703_baseline' \
'/best_accuracy.pth'
dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net.load_state_dict(torch.load(saved_model, map_location=dev))
device = dev
# -----------------------------------------------------------------------------------
