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test_gan.py
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test_gan.py
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from __future__ import division
from sklearn.metrics import f1_score
from eval.evaluation_mrbrain import evaluate
from lib.operations import *
from lib.utils import *
from preprocess.preprocess_mrbrains import *
F = tf.app.flags.FLAGS
# Function to save predicted images as .nii.gz file in results folder
def save_image(direc, i, num):
img = nib.Nifti1Image(i, None)
imgname = 'result_' + str(num) + '.nii.gz'
nib.save(img, os.path.join(direc, imgname))
# Same discriminator network as in model file
def trained_dis_network(patch, reuse=False):
"""
Parameters:
* patch - input image for the network
* reuse - boolean variable to reuse weights
Returns:
* softmax of logits
"""
with tf.variable_scope('D') as scope:
if reuse:
scope.reuse_variables()
h0 = lrelu(conv3d_WN(patch, 32, name='d_h0_conv'))
h1 = lrelu(conv3d_WN(h0, 32, name='d_h1_conv'))
p1 = avg_pool3D(h1)
h2 = lrelu(conv3d_WN(p1, 64, name='d_h2_conv'))
h3 = lrelu(conv3d_WN(h2, 64, name='d_h3_conv'))
p3 = avg_pool3D(h3)
h4 = lrelu(conv3d_WN(p3, 128, name='d_h4_conv'))
h5 = lrelu(conv3d_WN(h4, 128, name='d_h5_conv'))
p5 = avg_pool3D(h5)
h6 = lrelu(conv3d_WN(p5, 256, name='d_h6_conv'))
h7 = lrelu(conv3d_WN(h6, 256, name='d_h7_conv'))
up1 = deconv3d_WN(h7, 256, name='d_up1_deconv')
up1 = tf.concat([h5, up1], 4)
h8 = lrelu(conv3d_WN(up1, 128, name='d_h8_conv'))
h9 = lrelu(conv3d_WN(h8, 128, name='d_h9_conv'))
up2 = deconv3d_WN(h9, 128, name='d_up2_deconv')
up2 = tf.concat([h3, up2], 4)
h10 = lrelu(conv3d_WN(up2, 64, name='d_h10_conv'))
h11 = lrelu(conv3d_WN(h10, 64, name='d_h11_conv'))
up3 = deconv3d_WN(h11, 64, name='d_up3_deconv')
up3 = tf.concat([h1, up3], 4)
h12 = lrelu(conv3d_WN(up3, 32, name='d_h12_conv'))
h13 = lrelu(conv3d_WN(h12, 32, name='d_h13_conv'))
h14 = conv3d_WN(h13, F.num_classes, name='d_h14_conv')
return tf.nn.softmax(h14)
"""
Function to test the model and evaluate the predicted images
Parameters:
* patch_shape - shape of the patch
* extraction_step - stride while extracting patches
"""
def test(patch_shape, extraction_step):
with tf.Graph().as_default():
test_patches = tf.placeholder(tf.float32, [F.batch_size, patch_shape[0], patch_shape[1],
patch_shape[2], F.num_mod], name='real_patches')
# Define the network
output_soft = trained_dis_network(test_patches, reuse=None)
# To convert from one hat form
output = tf.argmax(output_soft, axis=-1)
print("Output Patch Shape:", output.get_shape())
# To load the saved checkpoint
saver = tf.train.Saver()
with tf.Session() as sess:
try:
load_model(F.best_checkpoint_dir, sess, saver)
print(" Checkpoint loaded succesfully!....\n")
except:
print(" [!] Checkpoint loading failed!....\n")
return
# Get patches from test images
patches_test, labels_test = preprocess_dynamic_lab(F.data_directory,
F.num_classes, extraction_step, patch_shape,
F.number_train_images, validating=F.training,
testing=F.testing,
num_images_testing=F.number_test_images)
total_batches = int(patches_test.shape[0] / F.batch_size)
# Array to store the prediction results
predictions_test = np.zeros((patches_test.shape[0], patch_shape[0], patch_shape[1],
patch_shape[2]))
print("max and min of patches_test:", np.min(patches_test), np.max(patches_test))
print("Total number of Batches: ", total_batches)
# Batch wise prediction
for batch in range(total_batches):
patches_feed = patches_test[batch * F.batch_size:(batch + 1) * F.batch_size, :, :, :, :]
preds = sess.run(output, feed_dict={test_patches: patches_feed})
predictions_test[batch * F.batch_size:(batch + 1) * F.batch_size, :, :, :] += preds
print(("Processed_batch:[%8d/%8d]") % (batch, total_batches))
print("All patches Predicted")
print("Shape of predictions_test, min and max:", predictions_test.shape, np.min(predictions_test),
np.max(predictions_test))
# To stitch the image back
images_pred = recompose3D_overlap(predictions_test, 220, 220, 48, extraction_step[0],
extraction_step[1], extraction_step[2])
print("Shape of Predicted Output Groundtruth Images:", images_pred.shape,
np.min(images_pred), np.max(images_pred),
np.mean(images_pred), np.mean(labels_test))
# To save the images
#test_idx = [7,14]
test_idx = [1, 14]
for i in range(F.number_test_images):
save_image(F.results_dir, images_pred[i], test_idx[i])
# Evaluation
pred2d = np.reshape(images_pred, (images_pred.shape[0] * 220 * 220 * 48))
lab2d = np.reshape(labels_test, (labels_test.shape[0] * 220 * 220 * 48))
# F1_score = f1_score(lab2d, pred2d, [0, 1, 2, 3, 4, 5, 6, 7, 8], average=None)
# print("Testing Dice Coefficient.... ")
# print("Background:", F1_score[0])
# print("Cortical Gray Matter:", F1_score[1])
# print("Basal ganglia:", F1_score[2])
# print("White matter:", F1_score[3])
# print("White matter lesions:", F1_score[4])
# print("Cerebrospinal fluid in the extracerebral space:", F1_score[5])
# print("Ventricles:", F1_score[6])
# print("Cerebellum:", F1_score[7])
# print("Brain stem:", F1_score[8])
print(test_idx)
for i in range(F.number_test_images):
print("Test Image : "+ str(test_idx[i]))
evaluate(os.path.join(F.results_dir, 'result_'+str(test_idx[i])+'.nii.gz'),
os.path.join(F.data_directory + "/test/"+str(test_idx[i]), 'segm.nii.gz'))
return