def rand_bool():
return bool(np.random.randint(0, 2))
#Test the batch-functions
if __name__ == '__main__':
from data import readSEGY, readLabels, get_slice
import tensorboard
import numpy as np
data, data_info = readSEGY('F3/data.segy')
train_coordinates = {'1': np.expand_dims(np.array([50, 50, 50]), 1)}
logger = tensorboard.TBLogger('log', 'batch test')
[batch, labels] = get_random_batch(data, train_coordinates, 65, 32)
logger.log_images('normal', batch)
[batch, labels] = get_random_batch(data,
train_coordinates,
65,
32,
random_flip=True)
logger.log_images('flipping', batch)
[batch, labels] = get_random_batch(data,
train_coordinates,
65,
32,
import tensorboard
import numpy as np
from utils import *
#This is the network definition proposed in the paper
#Parameters
dataset_name = 'F3'
im_size = 65
batch_size = 32 #If you have a GPU with little memory, try reducing this to 16 (may degrade results)
use_gpu = True #Switch to toggle the use of GPU or not
log_tensorboard = True #Log progress on tensor board
if log_tensorboard: logger = tensorboard.TBLogger('log','Train')
#See the texture_net.py file for the network configuration
from texture_net import TextureNet
network = TextureNet()
#Loss function
cross_entropy = nn.CrossEntropyLoss() #Softmax function is included
#Optimizer to control step size in gradient descent
optimizer = torch.optim.Adam(network.parameters())
#Transfer model to gpu
if use_gpu:
network = network.cuda()
#Load trained model (run train.py to create trained
network = TextureNet()
network.load_state_dict(torch.load('F3/saved_model.pt'))
if use_gpu: network = network.cuda()
network.eval()
# We can set the interpretation resolution to save time.
# The interpretation is then conducted over every n-th sample and
# then resized to the full size of the input data
resolution = 16
##########################################################################
slice = 'inline' #Inline, crossline, timeslice or full
slice_no = 339
#Log to tensorboard
logger = tensorboard.TBLogger('log', 'Test')
logger.log_images(slice + '_' + str(slice_no),
get_slice(data, data_info, slice, slice_no),
cm='gray')
""" Plot extracted features, class probabilities and salt-predictions for slice """
#features (attributes) from layer 5
im = interpret(network.f5, data, data_info, slice, slice_no, im_size,
resolution)
logger.log_images(slice + '_' + str(slice_no) + ' _f5', im)
#features from layer 4
im = interpret(network.f4, data, data_info, slice, slice_no, im_size,
resolution)
logger.log_images(slice + '_' + str(slice_no) + ' _f4', im)
#Class "probabilities"