def run_for_resnet_train():
from Net.BaseNet.ResNet.resnet_train import train
phase_name = 'ART'
traindatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROIMulti/train'
valdatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROIMulti/val'
val_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path=valdatapath)
train_dataset = ValDataSet(
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path=traindatapath)
x = tf.placeholder(tf.float32,
shape=[
None, sub_Config.IMAGE_W, sub_Config.IMAGE_H,
sub_Config.IMAGE_CHANNEL
],
name='input_x')
y_ = tf.placeholder(tf.float32, shape=[
None,
])
is_training = tf.placeholder('bool', [], name='is_training')
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_boolean(
'use_bn', True, 'use batch normalization. otherwise use biases')
logits = inference_small(x,
is_training=is_training,
num_classes=sub_Config.OUTPUT_NODE,
use_bias=FLAGS.use_bn,
num_blocks=3)
train(train_generator=train_dataset,
val_generator=val_dataset,
logits=logits,
images_tensor=x,
labeles=y_)
if load_model_path:
saver.restore(sess, load_model_path)
validation_images, validation_labels = dataset.images, dataset.labels
validation_images = changed_shape(validation_images, [
len(validation_images), sub_Config.IMAGE_W, sub_Config.IMAGE_W, 1
])
validation_accuracy, logits = sess.run([accuracy_tensor, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
_, _, _, error_indexs, error_record = calculate_acc_error(
logits=np.argmax(logits, 1), label=validation_labels, show=True)
print 'accuracy is %g' % \
(validation_accuracy)
return error_indexs, error_record
if __name__ == '__main__':
dataset = ValDataSet(
data_path=
'/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val',
phase='ART',
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
shuffle=False)
error_indexs, error_record = val(
dataset,
load_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/LeNet/model_finetuing/model_art/',
save_model_path=None)
dataset.show_error_name(error_indexs, error_record)
validation_images = changed_shape(validation_images, [
len(validation_images), sub_Config.IMAGE_W, sub_Config.IMAGE_W, 1
])
validation_accuracy, validation_loss, logits = sess.run(
[accuracy_tensor, loss_, y],
feed_dict={
x: validation_images,
y_: validation_labels
})
_, _, _, error_indexs, error_record = calculate_acc_error(
logits=np.argmax(logits, 1), label=validation_labels, show=True)
print 'validation loss value is %g, accuracy is %g' % \
(validation_loss, validation_accuracy)
return error_indexs, error_record
if __name__ == '__main__':
phase_name = 'ART'
state = ''
val_dataset = ValDataSet(
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path=
'/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val')
error_indexs, error_record = val(
val_dataset,
load_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/ResNet/models/fine_tuning/2/'
)
val_dataset.show_error_name(error_indexs, error_record, copy=False)
use_bias=FLAGS.use_bn,
num_blocks=3)
train(train_generator=train_dataset,
val_generator=val_dataset,
logits=logits,
images_tensor=x,
labeles=y_)
if __name__ == '__main__':
phase_name = 'ART'
state = ''
traindatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROIMulti/train'
valdatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROIMulti/val'
val_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path=valdatapath)
train_dataset = ValDataSet(
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path=traindatapath)
train(
train_dataset,
val_dataset,
# load_model_path='/home/give/PycharmProjects/MedicalImage/Net/BaseNet/ResNet/models/fine_tuning/5-64/21001/',
load_model_path=None,
save_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/ResNet/models/fine_tuning/5-64'
)
label=validation_labels,
)
val_writer.add_summary(summary, i)
print 'step is %d,training loss value is %g, accuracy is %g ' \
'validation loss value is %g, accuracy is %g, binary_acc is %g' % \
(i, loss_value, accuracy_value, validation_loss, validation_accuracy, binary_acc)
writer.close()
val_writer.close()
if __name__ == '__main__':
phase_name = 'ART'
state = ''
val_dataset = ValDataSet(
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI'
+ state + '/val')
train_dataset = ValDataSet(
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
# data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI' + state +'/train'
data_path=
'/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI_Augmented/train'
)
train(
train_dataset,
val_dataset,
load_model_path=None,
save_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/LeNet/model_finetuing/model_'
label=validation_labels,
show=True
)
val_writer.add_summary(summary, global_step_value)
print 'step is %d,training loss value is %g, accuracy is %g ' \
'validation loss value is, accuracy is %g' % \
(global_step_value, loss_value, accuracy_value, validation_accuracy)
writer.close()
val_writer.close()
if __name__ == '__main__':
phase_name = 'ART'
# state = '_Expand'
state = ''
val_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
shuffle=False,
category_number=sub_Config.OUTPUT_NODE,
data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val')
print 'val label is '
# print val_dataset.labels
train_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
shuffle=False,
category_number=sub_Config.OUTPUT_NODE,
data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROIMulti/train')
# print np.shape(train_dataset.labels)
train(
train_dataset,
val_dataset,
# load_model_path='/home/give/PycharmProjects/MedicalImage/Net/BaseNet/LeNet/model_finetuing/2/art/',
load_model_path=None,
show=True
)
binary_acc = acc_binary_acc(
logits=np.argmax(logits, 1),
label=validation_labels,
)
val_writer.add_summary(summary, i)
print 'step is %d,training loss value is %g, accuracy is %g ' \
'validation loss value is %g, accuracy is %g, binary_acc is %g' % \
(i, loss_value, accuracy_value, validation_loss, validation_accuracy, binary_acc)
writer.close()
val_writer.close()
if __name__ == '__main__':
phase_name = 'ART'
state = ''
val_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI' + state +'/val')
train_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=sub_Config.OUTPUT_NODE,
data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROIAugmented/train'
)
train(
train_dataset,
val_dataset,
load_model_path=None,
save_model_path='/home/give/PycharmProjects/MedicalImage/Net/BaseNet/LeNet/model_finetuing/model_'
+ phase_name.lower() + state + '/'
)
label=validation_labels,
)
val_writer.add_summary(summary, global_step_value)
print 'step is %d,training loss value is %g, accuracy is %g ' \
'validation loss value is %g, accuracy is %g, binary_acc is %g' % \
(global_step_value, loss_value, accuracy_value, validation_loss, validation_accuracy, binary_acc)
writer.close()
val_writer.close()
if __name__ == '__main__':
phase_name = 'ART'
state = ''
traindatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/train'
valdatapath = '/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/val'
val_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=2,
shuffle=True,
data_path=valdatapath
)
train_dataset = ValDataSet(new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
phase=phase_name,
category_number=2,
data_path=traindatapath,
shuffle=True,
)
train(
train_dataset,
val_dataset,
load_model_path=None,
save_model_path='/home/give/PycharmProjects/MedicalImage/Net/BaseNet/ResNet/models/fine_tuning/2-128/'
)
validation_accuracy, features_value = sess.run(
[accuracy_tensor, features],
feed_dict={
x: validation_images,
y_: validation_labels
})
print validation_accuracy
return features_value
if __name__ == '__main__':
phase = 'pv'
state = 'train'
dataset = ValDataSet(
data_path='/home/give/Documents/dataset/MedicalImage/MedicalImage/ROI/'
+ state,
phase=phase.upper(),
new_size=[sub_Config.IMAGE_W, sub_Config.IMAGE_H],
shuffle=False)
features = val(
dataset,
load_model_path=
'/home/give/PycharmProjects/MedicalImage/Net/BaseNet/LeNet/model_finetuing/model_'
+ phase + '/',
save_model_path=None)
np.save(
'/home/give/PycharmProjects/MedicalImage/Net/data/' + state + '_' +
phase + '.npy', features)
np.save(
'/home/give/PycharmProjects/MedicalImage/Net/data/' + state + '_' +
phase + '_label.npy', dataset.labels)
print np.shape(features)