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run2_biaxial.py
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# Testing 3 Models:
# 1. DEEPONET
# 2. PODDEEPONET
# 3. SNO
################ Importing Libraries ####################################
from models.deeponet.deeponet import DeepONet
from models.deeponet.deeponet import PODDeepONet
from models.sno.sno2d import *
import torch
from trainer import Trainer
from utils import utilities, transforms
from loader.dataloader import *
from loader.loader_2d import *
from utils.utilities import set_seed
import argparse
#########################################################################
############### Setting the Device and Random SEED ######################
parser = argparse.ArgumentParser()
parser.add_argument("--seed", default=0, help = "Input Experiment Random Seed")
args = parser.parse_args()
random_seed = int(args.seed)
set_seed(random_seed)
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
#########################################################################
###################### Dataset Params ###################################
PATH = 'data/Biaxial_N70000_D28.npz'
training_data_resolution = 28
grid_size = 28
ntrain = 50000
nval = 10000
ntest = 10000
batch_size = 20
#########################################################################
##################### Generate Data-Loaders #############################
loader = npzloader(path=PATH)
x_train, y_train, x_val, y_val, x_test, y_test = loader.split(ntrain, nval, ntest)
x_normalizer = utilities.UnitGaussianNormalizer(x_train)
y_normalizer = utilities.UnitGaussianNormalizer(y_train)
# train loader obj
train_obj = DataLoader_2D(X=x_train, y=y_train, n=ntrain, res=training_data_resolution, \
grid_size=grid_size, batch_size=batch_size, x_normalizer=x_normalizer)
# val loader obj
val_obj = DataLoader_2D(X=x_val, y=y_val, n=nval, res=training_data_resolution, \
grid_size=grid_size, batch_size=batch_size, x_normalizer=x_normalizer)
# test loader obj
test_obj = DataLoader_2D(X=x_test, y=y_test, n=ntest, res=training_data_resolution, \
grid_size=grid_size, batch_size=batch_size, x_normalizer=x_normalizer)
# dataloaders with grid info
train_grid_loader = train_obj.get_loader()
val_grid_loader = val_obj.get_loader()
test_grid_loader = test_obj.get_loader()
grid = train_obj.get_grid()
#########################################################################
################## Creating the Models ##################################
models = {
# 'SNO': fSNO2d(sizes=([1, 20, 20, 20], [(28, 101, 101, 101, 101, 28), (15, 51, 51, 51, 51, 15), (20, 20, 20, 20, 20, 20)], [20, 20, 1]),
# out_shape=[28,28,1]).to(device),
# 'DeepONet': DeepONet(BNET_ARCH=(1, (28,28)), TNET_ARCH=(2, [256, 256, 256, 256, 256], 28), \
# CONST_Y_LOC=grid.to(device), modes=256).to(device),
'PODDeepONet': PODDeepONet(BNET_ARCH=(1, (28,28)), normalized_y_train=y_train, modes=32).to(device),
}
#########################################################################
################## HyperParameters for Training #########################
hyperparameters = {
'lr': 1e-4,
'weight_decay': 1e-4,
'step_size': 100,
'gamma': 0.5,
'optimizer': 'Adam',
'scheduler': 'StepLR',
'loss_fn': 'RelL2',
'loss_metric': 'MSE',
'batch_size': batch_size,
'random_seed': random_seed,
}
#########################################################################
############# Create the Trainer, Fit Dataset and Test ##################
out_transform = transforms.OutTransforms(y_normalizer, device=device).stdTransform
for model_name in models:
model = models[model_name]
if model_name == 'PODDeepONet':
out_transform = transforms.OutTransforms(y_normalizer, device=device, modes=32, y_mean=model.__getMean__()).podnetTransform
if model_name == 'DeepONet':
hyperparameters['lr'] = 1e-4
trainer = Trainer(model_name=f"Benchmark_2+{model_name}+Biaxial", model=model, hyperparams=hyperparameters, \
output_transform=out_transform, device=device)
trainer.fit(train_dataloader=train_grid_loader, val_dataloader=val_grid_loader, test_dataloader=test_grid_loader)
#########################################################################