def test(object_folder, model_path, filename_list, result_path, flags,
he_dcis_segmentation_path, igpu):
checkpoint_dir = os.path.join(object_folder, 'checkpoint')
mat_contents = matlab.load(
os.path.join(checkpoint_dir, 'network_stats.mat'))
mean_image = np.float32(mat_contents['mean_image'])
variance_image = np.float32(mat_contents['variance_image'])
###########################################################
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152
os.environ["CUDA_VISIBLE_DEVICES"] = igpu
###########################################################
with tf.Graph().as_default(), tf.device(flags['gpu']):
keep_prob = tf.placeholder(tf.float32)
# Place holder for patches
images_test = tf.placeholder(tf.float32,
shape=(np.hstack([
flags['test_batch_size'],
flags['size_input_patch']
])))
# Network
with tf.variable_scope("network") as scope:
logits_test, parameters = tf_model.inference(
images_test, keep_prob, flags)
# Saver and initialisation
saver = tf.train.Saver()
init = tf.initialize_all_variables()
# config = tf.ConfigProto(allow_soft_placement=True)
# config.gpu_options.allow_growth = True
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=flags['gpu_memory_fraction'])
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
with tf.Session(config=config) as sess:
# Initialise and load variables
sess.run(init)
saver.restore(sess, model_path)
result_dir = result_path
routine.create_dir(result_dir)
for iImage, file in enumerate(filename_list):
start_time = time.time()
file = file[0]
basename = os.path.basename(file)
basename = os.path.splitext(basename)[0]
savename = os.path.join(result_dir, basename + '.png')
if not os.path.exists(savename):
result = batch_processing(file, sess, logits_test,
parameters, images_test,
keep_prob, mean_image,
variance_image, flags,
he_dcis_segmentation_path)
# matlab.save(savename,{'mask':result})
# sio.savemat(savename,{'mask':result})
KSimage.imwrite(result, savename)
duration = time.time() - start_time
print(
'Finish segmenting cells on the H&E image of sample %d out of %d samples (%.2f sec)'
% (iImage + 1, len(filename_list), duration))
def testWSI(object_folder, model_path, directory, flags):
"""
testWSI segments all of the WSIs in a given directory
param: object_folder
param: model_path
param: directory
param: gpu
param: gpu_memory_fraction
param: test_batch_size
param: size_input_patch
return: writes segmentation result to corresponding segmentation result directory
"""
checkpoint_dir = os.path.join(object_folder, 'checkpoint')
mat_contents = matlab.load(
os.path.join(checkpoint_dir, 'network_stats.mat'))
mean_image = np.float32(mat_contents['mean_image'])
variance_image = np.float32(mat_contents['variance_image'])
startTime = time.time()
with tf.Graph().as_default(), tf.device(flags['gpu']):
keep_prob = tf.placeholder(tf.float32)
# Place holder for patches
images_test = tf.placeholder(tf.float32,
shape=(np.hstack([
flags['test_batch_size'],
flags['size_input_patch']
])))
# Network
with tf.variable_scope("network") as scope:
logits_test, parameters = tf_model.inference(
images_test, keep_prob, flags)
# Saver and initialisation
saver = tf.train.Saver()
init = tf.global_variables_initializer()
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=flags['gpu_memory_fraction'])
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
with tf.Session(config=config) as sess:
# Initialise and load variables
sess.run(init)
saver.restore(sess, model_path)
print("Current directory: " + str(directory))
result_dir = os.path.join(directory + '_cellSeg')
create_dir(result_dir)
filename_list = glob.glob(
os.path.join(directory, '*.png')
) #the main statement, returns all the files in the directory
print("Num Files: " + str(len(filename_list)))
for file in filename_list:
print(file)
basename = os.path.basename(file)
basename = os.path.splitext(basename)[0]
savename = os.path.join(result_dir, basename + '.png')
if not os.path.exists(savename) and not (
'mask' in file or 'thumbnail'
in file): #TODO: should account for mask and thumbnail
result = batch_processing(file, sess, logits_test,
parameters, images_test,
keep_prob, mean_image,
variance_image, flags)
KSimage.imwrite(result, savename)
print("Total Time: " + str(time.time() - startTime))
def define_graph(object_folder, checkpoint_folder, flags):
#keys = ['HE', 'DAPI', 'label']
keys = list(flags['dict_path'].keys())
keys.remove('group')
# global step
global_step = tf.Variable(0, trainable=False, name='global_step')
# Epoch counter
curr_epoch = tf.Variable(0, trainable=False, name='curr_epoch')
update_curr_epoch = tf.assign(curr_epoch, tf.add(curr_epoch, tf.constant(1)))
# drop out
keep_prob = tf.placeholder(tf.float32)
# random field brightness
# random_field = tf.placeholder_with_default(
# np.ones(shape = (flags['size_input_patch'][0],flags['size_input_patch'][1],1), dtype = np.float32),
# shape = (flags['size_input_patch'][0],flags['size_input_patch'][1],1))
# network stats
mat_contents = matlab.load(os.path.join(checkpoint_folder, 'network_stats.mat'))
# Get images
# out_content_train - dictionary containing 32,512,512,3
# labels - dictionary with pair containing 32,
out_content_train, train_labels = tf_model_input.inputs(object_folder, 'train', flags, mat_contents)
out_content_val, val_labels = tf_model_input.inputs(object_folder, 'val', flags, mat_contents)
images_train = out_content_train['HE']
#labels_train = out_content_train['labels']
#weights_train = out_content_train['weights']
images_val = out_content_val['HE']
#labels_val = out_content_val['labels']
#weights_val = out_content_val['weights']
# Build a Graph that computes the logits predictions from the inference model.
# sigmoid_all_train - 32,4 for 4 different classes with batch 32
with tf.variable_scope("network") as scope:
sigmoid_all_train, parameters = tf_model.inference(images_train, keep_prob, flags)
scope.reuse_variables()
sigmoid_all_val, _ = tf_model.inference(images_val, keep_prob, flags)
# Get model weights
counts = matlab.load(os.path.join(checkpoint_folder, 'counts.mat'))
# Calculate loss.
loss_train = tf_model.loss(sigmoid_all_train, train_labels, curr_epoch, flags, counts)
loss_val = tf_model.loss(sigmoid_all_val, val_labels, curr_epoch, flags, counts)
# Accuracy train
predict_train = tf.squeeze(tf.argmax(sigmoid_all_train, dimension=1))
actual_train = tf.squeeze(train_labels['HE'])
accuracy_train_output = tf_model.accuracy(predict_train, actual_train, flags)
# Accuracy val
predict_val = tf.squeeze(tf.argmax(sigmoid_all_val, dimension=1))
actual_val = tf.squeeze(train_labels['HE'])
accuracy_val_output = tf_model.accuracy(predict_val, actual_val, flags)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = tf_model.train(loss_train['avg_cross_entropy_log_loss'], global_step, parameters, flags)
return {'global_step': global_step,
'curr_epoch': curr_epoch,
'update_curr_epoch': update_curr_epoch,
'keep_prob': keep_prob,
'loss_train': loss_train,
'loss_val': loss_val,
'predict_train': predict_train,
'actual_train': actual_train,
'predict_val': predict_val,
'actual_val': actual_val,
'train_op': train_op,
'accuracy_train_output': accuracy_train_output,
'accuracy_val_output': accuracy_val_output,
'parameters': parameters,
'out_content_train': out_content_train,
'out_content_val': out_content_val,
'sigmoid_all_train': sigmoid_all_train,
'sigmoid_all_val': sigmoid_all_val
# 'random_field': random_field
}
def test(object_folder, model_path, filename_list, flags):
"""
test uses either whole image segmentation or patch based
segmentation to segment an entire directory of test images
param: object_folder
param: model_path
param: filename_list
param: gpu
param: gpu_memory_fraction
return: writes segmentation result to appropriate image file
"""
checkpoint_dir = os.path.join(object_folder, 'checkpoint')
mat_contents = matlab.load(
os.path.join(checkpoint_dir, 'network_stats.mat'))
mean_image = np.float32(mat_contents['mean_image'])
variance_image = np.float32(mat_contents['variance_image'])
startTime = time.time()
with tf.Graph().as_default(), tf.device(flags['gpu']):
keep_prob = tf.placeholder(tf.float32)
# Place holder for patches
images_test = tf.placeholder(tf.float32,
shape=(np.hstack([
flags['test_batch_size'],
flags['size_input_patch']
])))
# Network
with tf.variable_scope("network") as scope:
logits_test, parameters = tf_model.inference(
images_test, keep_prob, flags)
# Saver and initialisation
saver = tf.train.Saver()
init = tf.initialize_all_variables()
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=flags['gpu_memory_fraction'])
config = tf.ConfigProto(allow_soft_placement=True,
gpu_options=gpu_options)
with tf.Session(config=config) as sess:
# Initialise and load variables
sess.run(init)
saver.restore(sess, model_path)
result_dir = os.path.join(object_folder, 'result')
routine.create_dir(result_dir)
print("FILENAME LIST", filename_list)
for iImage, file in enumerate(filename_list):
file = file[0]
basename = os.path.basename(file)
basename = os.path.splitext(basename)[0]
savename = os.path.join(result_dir, basename + '.png')
if not os.path.exists(savename):
print('processing image %d/%d' %
(iImage + 1, len(filename_list)))
result = batch_processing(file, sess, logits_test,
parameters, images_test,
keep_prob, mean_image,
variance_image, flags)
KSimage.imwrite(result, savename)
print("Total Time: " + str(time.time() - startTime))
def define_graph(object_folder,checkpoint_folder, flags):
"""
define_graph defines the computational graph in tensorflow
which represents the entire network
param: object_folder
param: checkpoint_folder
param: flags
return: train operation dictionary
"""
# global step
global_step = tf.Variable(0, trainable=False, name='global_step')
# Epoch counter
curr_epoch = tf.Variable(0, trainable=False, name='curr_epoch')
update_curr_epoch = tf.assign(curr_epoch, tf.add(curr_epoch, tf.constant(1)))
# drop out
keep_prob = tf.placeholder(tf.float32)
# Load network stats
mat_contents = matlab.load(os.path.join(checkpoint_folder, 'network_stats.mat'))
mean_img = np.float32(mat_contents['mean_image'])
variance_img = np.float32(mat_contents['variance_image'])
if mean_img.ndim == 2:
mean_img = np.expand_dims(mean_img, axis=2)
if variance_img.ndim == 2:
variance_img = np.expand_dims(variance_img, axis=2)
mean_image = tf.Variable(mean_img, trainable=False, name='mean_image')
variance_image = tf.Variable(variance_img, trainable=False, name='variance_image')
# Get images and labels.
out_content_train = tf_model_input.inputs(mean_image, variance_image, object_folder, 'train', flags)
out_content_val = tf_model_input.inputs(mean_image, variance_image, object_folder, 'val', flags)
images_train = out_content_train['images']
labels_train = out_content_train['labels']
weights_train = out_content_train['weights']
images_val = out_content_val['images']
labels_val = out_content_val['labels']
weights_val = out_content_val['weights']
# Build a Graph that computes the logits predictions from the inference model.
with tf.variable_scope("network") as scope:
sigmoid_all_train, parameters = tf_model.inference(images_train, keep_prob, flags)
scope.reuse_variables()
sigmoid_all_val, _ = tf_model.inference(images_val, keep_prob, flags)
# Calculate loss
loss_train = tf_model.loss(sigmoid_all_train, labels_train, weights_train, curr_epoch, flags)
loss_val = tf_model.loss(sigmoid_all_val, labels_val, weights_val, curr_epoch, flags)
# Accuracy train
predict_train = tf.squeeze(tf.argmax(sigmoid_all_train, dimension=3))
actual_train = tf.squeeze(labels_train)
accuracy_train_output = tf_model.accuracy(predict_train, actual_train, flags)
# Accuracy val
predict_val = tf.squeeze(tf.argmax(sigmoid_all_val, dimension=3))
actual_val = tf.squeeze(labels_val)
accuracy_val_output = tf_model.accuracy(predict_val, actual_val, flags)
# Build a Graph that trains the model with one batch of examples and updates the model parameters.
train_op = tf_model.train(loss_train['avg_cross_entropy_log_loss'], global_step, parameters, flags)
return {'global_step':global_step,
'curr_epoch':curr_epoch,
'update_curr_epoch':update_curr_epoch,
'keep_prob':keep_prob,
'loss_train':loss_train,
'loss_val':loss_val,
'predict_train':predict_train,
'actual_train':actual_train,
'predict_val':predict_val,
'actual_val':actual_val,
'train_op':train_op,
'accuracy_train_output':accuracy_train_output,
'accuracy_val_output':accuracy_val_output,
'parameters':parameters,
'out_content_train':out_content_train,
'out_content_val':out_content_val,
'sigmoid_all_train':sigmoid_all_train,
'sigmoid_all_val':sigmoid_all_val
# 'random_field': random_field
}