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run.py
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run.py
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import matplotlib.pyplot as plt
import numpy as np
import sys
import torch
from time import time
import datetime as dt
from tqdm import tqdm
import src.mace.CSE_0D.dataset as ds
import src.mace.train as train
import src.mace.test as test
import src.mace.mace as mace
from src.mace.loss import Loss
import src.mace.loss as loss
import src.mace.utils as utils
from src.mace.input import Input
specs_dict, idx_specs = utils.get_specs()
start = time()
now = dt.datetime.now()
name = str(now.strftime("%Y%m%d")+'_'+now.strftime("%H%M%S"))
path = '/STER/silkem/MACE/models/CSE_0D/'+name
## ================================================== INPUT ========
## ADJUST THESE PARAMETERS FOR DIFFERENT MODELS
## READ INPUT FILE
arg = sys.argv[1]
infile = '/STER/silkem/MACE/input/'+arg+'.in'
input = Input(infile, name)
input.print()
utils.makeOutputDir(path)
utils.makeOutputDir(path+'/nn')
meta = input.make_meta(path)
## ================================================== SETUP ========
## Set up PyTorch
cuda = False
DEVICE = torch.device("cuda" if cuda else "cpu")
batch_size = 1
kwargs = {'num_workers': 1, 'pin_memory': True}
## Load train & test data sets
traindata, testdata, data_loader, test_loader = ds.get_data(dt_fract=input.dt_fract,
nb_samples=input.nb_samples, batch_size=batch_size,
nb_test=input.nb_test,kwargs=kwargs)
## Make model
model = mace.Solver(n_dim=input.n_dim, p_dim=4,z_dim = input.z_dim,
nb_hidden=input.nb_hidden, ae_type=input.ae_type,
scheme=input.scheme, nb_evol=input.nb_evol,
path = path,
DEVICE = DEVICE,
lr=input.lr )
num_params = utils.count_parameters(model)
print(f'\nThe model has {num_params} trainable parameters')
## ================================================== TRAIN ========
## ------------- PART 1: unnormalised losses ----------------
norm, fract = loss.initialise()
## Make loss objects
trainloss = Loss(norm, fract, input.losstype)
testloss = Loss(norm, fract, input.losstype)
## Train
tic = time()
train.train(model,
data_loader, test_loader,
end_epochs = input.ini_epochs,
trainloss=trainloss, testloss=testloss,
start_time = start)
toc = time()
train_time1 = toc-tic
## ------------- PART 2: normalised losses, but reinitialise model ----------------
## Change the ratio of losses via the fraction
print('\n\n>>> Continue with normalised losses.')
fract = input.get_facts()
trainloss.change_fract(fract)
testloss.change_fract(fract)
## Normalise the losses
new_norm = trainloss.normalise()
testloss.change_norm(new_norm)
## Continue training
tic = time()
train.train(model,
data_loader, test_loader,
start_epochs = input.ini_epochs, end_epochs = input.nb_epochs,
trainloss=trainloss, testloss=testloss,
start_time = start)
toc = time()
train_time2 = toc-tic
train_time = train_time1 + train_time2
## ================================================== SAVE ========
## losses
trainloss.save(path+'/train')
testloss.save(path+'/valid')
## dataset characteristics
min_max = np.stack((traindata.mins, traindata.maxs), axis=1)
np.save(path+'/minmax', min_max)
## model
torch.save(model.state_dict(),path+'/nn/nn.pt')
## status
np.save(path+'/train/status', model.get_status('train')) # type: ignore
np.save(path +'/valid/status', model.get_status('test') ) # type: ignore
fig_loss = loss.plot(trainloss, testloss, len = input.nb_epochs)
plt.savefig(path+'/loss.png')
stop = time()
overhead_time = (stop-start)-train_time
## updating meta file
input.update_meta(traindata, train_time, overhead_time, path)
## ================================================== TEST ========
input.print()
## Test the model on the test samples
print('\n\n>>> Testing model on',len(traindata.testpath),'test samples ...')
sum_err_step = 0
sum_err_evol = 0
step_calctime = list()
evol_calctime = list()
for i in tqdm(range(len(traindata.testpath))):
# print(i+1,end='\r')
testpath = traindata.testpath[i]
err_test, err_evol, step_time, evol_time,n, n_hat, n_evol = test.test_model(model,testpath, meta, printing = False)
sum_err_step += err_test
sum_err_evol += err_evol
step_calctime.append(step_time)
evol_calctime.append(evol_time)
utils.makeOutputDir(path+'/test')
np.save(path+ '/test/sum_err_step.npy', np.array(sum_err_step/len(traindata.testpath)))
np.save(path+ '/test/sum_err_evol.npy', np.array(sum_err_evol/len(traindata.testpath)))
np.save(path+ '/test/calctime_evol.npy', evol_calctime)
np.save(path+ '/test/calctime_step.npy', step_calctime)
print('\nAverage error:')
print(' Step:', np.round(sum_err_step,3))
print(' Evolution:', np.round(sum_err_evol,3))
print('(following Eq. 23 of Maes et al., 2024)')
stop = time()
print('\n>>> FULLY DONE!')
total_time = stop-start
if total_time < 60:
print('Total time [secs]:', np.round(total_time,2))
if total_time >= 60:
print('Total time [mins]:', np.round(total_time/60,2))
if total_time >= 3600:
print('Total time [hours]:', np.round(total_time/3600,2))
print('Output saved in:', path,'\n')