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main.py
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main.py
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import argparse
import numpy as np
import torch
from bruges.filters import wavelets
from os.path import isdir
import os
from core.models import inverse_model, forward_model
from torch.utils import data
from core.functions import *
from torch import nn, optim
from datetime import datetime
import matplotlib.pyplot as plt
from tqdm import tqdm
import wget
import hashlib
#Manual seeds for reproducibility
random_seed=30
torch.manual_seed(random_seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(random_seed)
np.random.seed(random_seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def get_data(args, test=False):
#Loading data
try:
data_dic = np.load("data.npy",allow_pickle=True).item()
except FileNotFoundError:
print("Data file not found. Downloading the data..")
url= "https://www.dropbox.com/s/qdbepx2jzz9jd9l/data.npy?raw=1"
wget.download(url,"./")
assert hashlib.md5(open("./data.npy", "rb").read()).hexdigest()=="1fc229e7b7042829b8a834e6850ec9e5", "Data file checksum did not match. Redownload the data file"
data_dic = np.load("data.npy",allow_pickle=True).item()
seismic_data = data_dic["synth_seismic_15db_noise"]
elastic_impedance_data = data_dic["elastic_impedance"]
assert seismic_data.shape[1]==len(args.incident_angles) ,'Data dimensions are not consistent with incident angles. Got {} incident angles and {} in data dimensions'.format(len(args.incident_angles),seismic_data.shape[1])
assert seismic_data.shape[1]==elastic_impedance_data.shape[1] ,'Data dimensions are not consistent. Got {} channels for seismic data and {} for elastic elastic impedance dimensions'.format(seismic_data.shape[1],elastic_impedance_data.shape[1])
seismic_mean = torch.tensor(np.mean(seismic_data,axis=(0,-1),keepdims=True)).float()
seismic_std = torch.tensor(np.std(seismic_data,axis=(0,-1),keepdims=True)).float()
elastic_mean= torch.tensor(np.mean(elastic_impedance_data, keepdims=True)).float()
elastic_std = torch.tensor(np.std(elastic_impedance_data,keepdims=True)).float()
seismic_data = torch.tensor(seismic_data).float()
elastic_impedance_data = torch.tensor(elastic_impedance_data).float()
if torch.cuda.is_available():
seismic_data = seismic_data.cuda()
elastic_impedance_data = elastic_impedance_data.cuda()
seismic_mean = seismic_mean.cuda()
seismic_std = seismic_std.cuda()
elastic_mean = elastic_mean.cuda()
elastic_std = elastic_std.cuda()
seismic_normalization = Normalization(mean_val=seismic_mean,
std_val=seismic_std)
elastic_normalization = Normalization(mean_val=elastic_mean,
std_val=elastic_std)
seismic_data = seismic_normalization.normalize(seismic_data)
elastic_impedance_data = elastic_normalization.normalize(elastic_impedance_data)
if not test:
num_samples = seismic_data.shape[0]
indecies = np.arange(0,num_samples)
train_indecies = indecies[(np.linspace(0,len(indecies)-1,args.num_train_wells)).astype(int)]
train_data = data.Subset(data.TensorDataset(seismic_data,elastic_impedance_data), train_indecies)
train_loader = data.DataLoader(train_data, batch_size=args.batch_size, shuffle=False)
unlabeled_loader = data.DataLoader(data.TensorDataset(seismic_data), batch_size=args.batch_size, shuffle=True)
return train_loader, unlabeled_loader, seismic_normalization, elastic_normalization
else:
test_loader = data.DataLoader(data.TensorDataset(seismic_data,elastic_impedance_data), batch_size=args.batch_size, shuffle=False, drop_last=False)
return test_loader, seismic_normalization, elastic_normalization
def get_models(args):
if args.test_checkpoint is None:
inverse_net = inverse_model(in_channels=len(args.incident_angles), nonlinearity=args.nonlinearity)
else:
try:
inverse_net = torch.load(args.test_checkpoint)
except FileNotFoundError:
print("No checkpoint found at '{}'- Please specify the model for testing".format(args.test_checkpoint))
exit()
#Set up forward model
# For wavelet info, refer to https://github.com/agile-geoscience/bruges/blob/master/bruges/filters/wavelets.py
# For simpicity, the same wavlet is used for all incident angles
wavelet, wavelet_time = wavelets.ormsby(args.wavelet_duration, args.dt,args.f, return_t=True)
wavelet = torch.tensor(wavelet).unsqueeze(dim=0).unsqueeze(dim=0).float()
forward_net = forward_model(wavelet=wavelet)
if torch.cuda.is_available():
inverse_net.cuda()
forward_net.cuda()
return inverse_net, forward_net
def train(args):
#writer = SummaryWriter()
train_loader, unlabeled_loader, seismic_normalization, elastic_normalization = get_data(args)
inverse_net, forward_net = get_models(args)
inverse_net.train()
criterion = nn.MSELoss()
optimizer = inverse_net.optimizer
#make a direcroty to save models if it doesn't exist
if not isdir("checkpoints"):
os.mkdir("checkpoints")
print("Training the model")
best_loss = np.inf
for epoch in tqdm(range(args.max_epoch)):
train_loss = []
train_property_corr = []
train_property_r2 = []
for x,y in train_loader:
optimizer.zero_grad()
y_pred = inverse_net(x)
property_loss = criterion(y_pred,y)
corr, r2 = metrics(y_pred.detach(),y.detach())
train_property_corr.append(corr)
train_property_r2.append(r2)
if args.beta!=0:
#loading unlabeled data
try:
x_u = next(unlabeled)[0]
except:
unlabeled = iter(unlabeled_loader)
x_u = next(unlabeled)[0]
y_u_pred = inverse_net(x_u)
y_u_pred = elastic_normalization.unnormalize(y_u_pred)
x_u_rec = forward_net(y_u_pred)
x_u_rec = seismic_normalization.normalize(x_u_rec)
seismic_loss = criterion(x_u_rec,x_u)
else:
seismic_loss=0
loss = args.alpha*property_loss + args.beta*seismic_loss
loss.backward()
optimizer.step()
train_loss.append(loss.detach().clone())
torch.save(inverse_net,"./checkpoints/{}".format(args.session_name))
def test(args):
#make a direcroty to save precited sections
if not isdir("output_images"):
os.mkdir("output_images")
test_loader, seismic_normalization, elastic_normalization = get_data(args, test=True)
if args.test_checkpoint is None:
args.test_checkpoint = "./checkpoints/{}".format(args.session_name)
inverse_net, forward_net = get_models(args)
criterion = nn.MSELoss(reduction="sum")
predicted_impedance = []
true_impedance = []
test_property_corr = []
test_property_r2 = []
inverse_net.eval()
print("\nTesting the model\n")
with torch.no_grad():
test_loss = []
for x,y in test_loader:
y_pred = inverse_net(x)
property_loss = criterion(y_pred,y)/np.prod(y.shape)
corr, r2 = metrics(y_pred.detach(),y.detach())
test_property_corr.append(corr)
test_property_r2.append(r2)
x_rec = forward_net(elastic_normalization.unnormalize(y_pred))
x_rec = seismic_normalization.normalize(x_rec)
seismic_loss = criterion(x_rec, x)/np.prod(x.shape)
loss = args.alpha*property_loss + args.beta*seismic_loss
test_loss.append(loss.item())
true_impedance.append(y)
predicted_impedance.append(y_pred)
display_results(test_loss, test_property_corr, test_property_r2, args, header="Test")
predicted_impedance = torch.cat(predicted_impedance, dim=0)
true_impedance = torch.cat(true_impedance, dim=0)
predicted_impedance = elastic_normalization.unnormalize(predicted_impedance)
true_impedance = elastic_normalization.unnormalize(true_impedance)
if torch.cuda.is_available():
predicted_impedance = predicted_impedance.cpu()
true_impedance = true_impedance.cpu()
predicted_impedance = predicted_impedance.numpy()
true_impedance = true_impedance.numpy()
#diplaying estimated section
cols = ['{}'.format(col) for col in ['Predicted EI','True EI', 'Absolute difference']]
rows = [r'$\theta=$ {}$^\circ$'.format(row) for row in args.incident_angles]
fig, axes = plt.subplots(nrows=len(args.incident_angles), ncols=3)
for i, theta in enumerate(args.incident_angles):
axes[i][0].imshow(predicted_impedance[:,i].T, cmap='rainbow',aspect=0.5, vmin=true_impedance.min(), vmax=true_impedance.max())
axes[i][0].axis('off')
axes[i][1].imshow(true_impedance[:,i].T, cmap='rainbow',aspect=0.5,vmin=true_impedance.min(), vmax=true_impedance.max())
axes[i][1].axis('off')
axes[i][2].imshow(abs(true_impedance[:,i].T-predicted_impedance[:,i].T), cmap='gray',aspect=0.5)
axes[i][2].axis('off')
pad = 10 # in points
for ax, row in zip(axes[:,0], rows):
ax.annotate(row,xy=(0,0.5), xytext=(-pad,0), xycoords='axes fraction', textcoords='offset points', ha='right', va='center')
for ax, col in zip(axes[0], cols):
ax.annotate(col, xy=(0.5, 1), xytext=(0, pad),
xycoords='axes fraction', textcoords='offset points', ha='center', va='baseline')
fig.tight_layout()
plt.savefig("./output_images/{}.png".format(args.test_checkpoint.split("/")[-1]))
plt.show()
if __name__ == '__main__':
## Arguments and parameters
parser = argparse.ArgumentParser()
parser.add_argument('-num_train_wells', type=int, default=10, help="Number of EI traces from the model to be used for validation")
parser.add_argument('-max_epoch', type=int, default=500, help="maximum number of training epochs")
parser.add_argument('-batch_size', type=int, default=40,help="Batch size for training")
parser.add_argument('-alpha', type=float, default=1, help="weight of property loss term")
parser.add_argument('-beta', type=float, default=1, help="weight of seismic loss term")
parser.add_argument('-test_checkpoint', type=str, action="store", default=None,help="path to model to test on. When this flag is used, no training is performed")
parser.add_argument('-session_name', type=str, action="store", default=datetime.now().strftime('%b%d_%H%M%S'),help="name of the session to be ised in saving the model")
parser.add_argument('-nonlinearity', action="store", type=str, default="tanh",help="Type of nonlinearity for the CNN [tanh, relu]", choices=["tanh","relu"])
## Do not change these values unless you use the code on a different data and edit the code accordingly
parser.add_argument('-dt', type=float, default=1e-3, help='Time resolution in seconds')
parser.add_argument('-wavelet_duration', type=float, default=0.2, help='wavelet duration in seconds')
parser.add_argument('-f', default="5, 10, 60, 80", help="Frequency of wavelet. if multiple frequencies use , to seperate them with no spaces, e.g., -f \"5,10,60,80\"", type=lambda x: np.squeeze(np.array(x.split(",")).astype(float)))
parser.add_argument('-resolution_ratio', type=int, default=6, action="store",help="resolution mismtach between seismic and EI")
parser.add_argument('-incident_angles', type=float, default=np.arange(0, 30+ 1, 10), help="Incident angles of the input seismic and EI")
args = parser.parse_args()
if args.test_checkpoint is not None:
test(args)
else:
train(args)
test(args)