Random_Cov.py

'''
Copyright 2020 Amanpreet Singh,
               Martin Bauer,
               Sarang Joshi

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

'''



from ICNN import *
import torch
from Train import *
from Datasets import *
from Distributions import *
import os
from Loss_Function import *
from Random_Support import *
from sqrtm import sqrtm
import argparse

parser = argparse.ArgumentParser(description='Random_Covariance_Experiments')

parser.add_argument('--Num_Samples', type=int, default=10000)
parser.add_argument('--GPU', type=str, default="0")
parser.add_argument('--Run', type=int, default=1)
parser.add_argument('--Dim', type=int, default=2)

args = parser.parse_args()

# Specify the GPU to use.
os.environ["CUDA_VISIBLE_DEVICES"] = args.GPU

# PARAMETERS for Network
hidden_layers = 5
npl = 128
epochs = 100

dim = args.Dim
run = args.Run

samples = args.Num_Samples

# Regularization constant
k = 0

# Variable to save model in multiples of this
save_after_e = 10

Period_Boundary_Flag = False
CINN_Flag = True
deformation_flag = False

# Function g
func_g = gauss_md_2

matrix_file_path = './Models/Random_Cov/{}d/Samples_{}k/Run_{}/'.format(dim, samples/1000, run)
model_path = './Models/Random_Cov/{}d/Samples_{}k/Run_{}/'.format(dim, samples/1000, run)
model_name = 'model_hl_' + str(hidden_layers) + '_npl_' + str(npl) + '_k_' + str(k) + '/'

os.makedirs(model_path + model_name, exist_ok=True)

# Initial model to start the training from.
init_model_path = './Models/Softplus_11/Initial/{0}d/'.format(dim)
init_model_name = 'model_hl_' + str(hidden_layers) + '_npl_' + str(npl) + '_10000/e_10000.pth'

init_model_path = init_model_path + init_model_name

mu, cov = get_random_matrices(dim)
write_matrix_to_file(mu, cov, matrix_file_path)


# Specify the data set that you want to train
train_data = mdGaussianDataset_torch(samples, mu, cov, dim)

# Define the model and put that on the GPU
model = ICNN(dim, 1, hidden_layers, npl)
model = model.cuda()

# Define the Loss function you want to impose
criterion = IdentityLoss

# Train the network
model = train_network(train_data, model, KLLoss, criterion, epochs, None, func_g, None, k, save_after_e,
                      model_path + model_name, init_weights_flag=False, initial_path=init_model_path,
                      periodic_Flag=Period_Boundary_Flag, cinn_flag=CINN_Flag, deformation_flag=deformation_flag, mu=mu,
                      cov=cov, w2_flag=True)

# For debugging
print("All done")