# Data
print("Loading data...")
# df = pd.read_pickle('./data/sample_slice_LS_BP.pkl')
df = pd.read_pickle('./data/sample_slice_LS_BP_3T.pkl')
X = df.loc[:, ['b0', 'b10', 'b20', 'b60', 'b150', 'b300', 'b500', 'b1000']].values.astype('float32')
# normalize dw signal
S0 = X[:,0].copy()
for i in range(len(hp.b_values)):
X[:,i] = X[:,i]/S0
X = X[:,1:]
print("Done")
# Network
b_values_no0 = torch.FloatTensor(hp.b_values[1:])
net = Net(b_values_no0).to(device)
# Restore variables from disk
# net.load_state_dict(torch.load(hp.logdir + 'sample_slice/final_model.pt', map_location=device))
net.load_state_dict(torch.load(hp.logdir + 'sample_slice/final_model_3T.pt', map_location=device))
# evaluate on test data
net.eval()
with torch.no_grad():
X_pred, Dp_pred, Dt_pred, Fp_pred = net(torch.from_numpy(X.astype(np.float32)))
df['Dp_NN'] = Dp_pred.numpy()
df['Dt_NN'] = Dt_pred.numpy()
df['Fp_NN'] = Fp_pred.numpy()
# df.to_pickle('./data/sample_slice_LS_BP_NN.pkl')
import torch
from phantom_train_NN import Net
from data_load import *
import matplotlib.pyplot as plt
from time import time
from pdb import set_trace as bp
if __name__ == '__main__':
# CUDA for PyTorch
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
torch.backends.cudnn.benchmark = True
# Network
b_values_no0 = torch.FloatTensor(hp.b_values[1:])
net = Net(b_values_no0).to(device)
# Restore variables from disk
net.load_state_dict(
torch.load(hp.logdir + 'phantom/final_model.pt', map_location=device))
Dp_pred = np.zeros((hp.num_samples, 10))
Dt_pred = np.zeros((hp.num_samples, 10))
Fp_pred = np.zeros((hp.num_samples, 10))
Dp_error = np.zeros((hp.num_samples, 10))
Dt_error = np.zeros((hp.num_samples, 10))
Fp_error = np.zeros((hp.num_samples, 10))
time_start = time()
for noise_sd in range(15, 165, 15):
# Training data
print("Loading training data...")
# df = pd.read_pickle('./data/sample_slice.pkl')
df = pd.read_pickle('./data/sample_slice_3T.pkl')
X = df.loc[:, ['b0', 'b10', 'b20', 'b60', 'b150', 'b300', 'b500', 'b1000']].values.astype('float32')
# normalize dw signal
S0 = X[:,0].copy()
for i in range(len(hp.b_values)):
X[:,i] = X[:,i]/S0
X = X[:,1:]
trainloader, num_batches = get_trainloader(X)
print("Done")
# Network
b_values_no0 = torch.FloatTensor(hp.b_values[1:])
net = Net(b_values_no0).to(device)
# Restore variables from disk
net.load_state_dict(torch.load(hp.logdir + 'phantom/final_model_Dp02.pt', map_location=device))
# Loss function and optimizer
criterion = nn.MSELoss().to(device)
optimizer = optim.Adam(net.parameters(), lr = 0.001)
# Best loss
best = 1e16
num_bad_epochs = 0
# Train
for epoch in range(hp.num_epochs):
print("-----------------------------------------------------------------")
