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2D-dense-unet.py
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2D-dense-unet.py
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from __future__ import print_function
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# The GPU id to use, usually either "0" or "1"
# os.environ["CUDA_VISIBLE_DEVICES"]="0"
import keras.models as models
from skimage.transform import resize
from skimage.io import imsave
import numpy as np
np.random.seed(1337)
import tensorflow as tf
tf.set_random_seed(1337)
from keras.models import Model
from keras.layers import Input, concatenate, Conv2D, MaxPooling2D, Conv2DTranspose, AveragePooling3D, ZeroPadding3D
from keras.optimizers import RMSprop, Adam, SGD, Adagrad, Adadelta
from keras.callbacks import ModelCheckpoint, CSVLogger
from keras import backend as K
from keras.regularizers import l2
from keras.utils import plot_model
from data2D import load_train_data, load_test_data, preprocess_squeeze
K.set_image_data_format('channels_last')
project_name = '2D-Dense-Unet'
img_rows = 400
img_cols = 400
img_depth = 1
smooth = 1.
def dice_coef(y_true, y_pred):
y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
def dice_coef_loss(y_true, y_pred):
return -dice_coef(y_true, y_pred)
def get_unet():
inputs = Input((img_rows, img_cols, 1))
conv11 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
conc11 = concatenate([inputs, conv11], axis=3)
conv12 = Conv2D(32, (3, 3), activation='relu', padding='same')(conc11)
conc12 = concatenate([inputs, conv12], axis=3)
pool1 = MaxPooling2D(pool_size=(2, 2))(conc12)
conv21 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conc21 = concatenate([pool1, conv21], axis=3)
conv22 = Conv2D(64, (3, 3), activation='relu', padding='same')(conc21)
conc22 = concatenate([pool1, conv22], axis=3)
pool2 = MaxPooling2D(pool_size=(2, 2))(conc22)
conv31 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conc31 = concatenate([pool2, conv31], axis=3)
conv32 = Conv2D(128, (3, 3), activation='relu', padding='same')(conc31)
conc32 = concatenate([pool2, conv32], axis=3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conc32)
conv41 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conc41 = concatenate([pool3, conv41], axis=3)
conv42 = Conv2D(256, (3, 3), activation='relu', padding='same')(conc41)
conc42 = concatenate([pool3, conv42], axis=3)
pool4 = MaxPooling2D(pool_size=(2, 2))(conc42)
conv51 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conc51 = concatenate([pool4, conv51], axis=3)
conv52 = Conv2D(512, (3, 3), activation='relu', padding='same')(conc51)
conc52 = concatenate([pool4, conv52], axis=3)
up6 = concatenate([Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same')(conc52), conc42], axis=3)
conv61 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conc61 = concatenate([up6, conv61], axis=3)
conv62 = Conv2D(256, (3, 3), activation='relu', padding='same')(conc61)
conc62 = concatenate([up6, conv62], axis=3)
up7 = concatenate([Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(conc62), conv32], axis=3)
conv71 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conc71 = concatenate([up7, conv71], axis=3)
conv72 = Conv2D(128, (3, 3), activation='relu', padding='same')(conc71)
conc72 = concatenate([up7, conv72], axis=3)
up8 = concatenate([Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(conc72), conv22], axis=3)
conv81 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conc81 = concatenate([up8, conv81], axis=3)
conv82 = Conv2D(64, (3, 3), activation='relu', padding='same')(conc81)
conc82 = concatenate([up8, conv82], axis=3)
up9 = concatenate([Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(conc82), conv12], axis=3)
conv91 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conc91 = concatenate([up9, conv91], axis=3)
conv92 = Conv2D(32, (3, 3), activation='relu', padding='same')(conc91)
conc92 = concatenate([up9, conv92], axis=3)
conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conc92)
model = Model(inputs=[inputs], outputs=[conv10])
model.summary()
#plot_model(model, to_file='model.png')
model.compile(optimizer=Adam(lr=1e-5, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.000000199), loss='binary_crossentropy', metrics=['accuracy'])
return model
def train():
print('-'*30)
print('Loading and preprocessing train data...')
print('-'*30)
imgs_train, imgs_mask_train = load_train_data()
imgs_mask_train = imgs_mask_train.astype('float32')
imgs_train = imgs_train.astype('float32')
imgs_mask_train /= 255. # scale masks to [0, 1]
imgs_train /= 255. # scale masks to [0, 1]
print('-'*30)
print('Creating and compiling model...')
print('-'*30)
model = get_unet()
weight_dir = 'weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model_checkpoint = ModelCheckpoint(os.path.join(weight_dir, project_name + '.h5'), monitor='val_loss', save_best_only=True)
log_dir = 'logs'
if not os.path.exists(log_dir):
os.mkdir(log_dir)
csv_logger = CSVLogger(os.path.join(log_dir, project_name + '.txt'), separator=',', append=False)
print('-'*30)
print('Fitting model...')
print('-'*30)
model.fit(imgs_train, imgs_mask_train, batch_size=1, epochs=50, verbose=1, shuffle=True, validation_split=0.10, callbacks=[model_checkpoint, csv_logger])
print('-'*30)
print('Training finished')
print('-'*30)
def predict():
print('-'*30)
print('Loading and preprocessing test data...')
print('-'*30)
imgs_test = load_test_data()
imgs_test = imgs_test.astype('float32')
imgs_test /= 255. # scale masks to [0, 1]
print('-'*30)
print('Loading saved weights...')
print('-'*30)
model = get_unet()
weight_dir = 'weights'
if not os.path.exists(weight_dir):
os.mkdir(weight_dir)
model.load_weights(os.path.join(weight_dir, project_name + '.h5'))
print('-'*30)
print('Predicting masks on test data...')
print('-'*30)
imgs_mask_test = model.predict(imgs_test, batch_size=1, verbose=1)
npy_mask_dir = 'test_mask_npy'
if not os.path.exists(npy_mask_dir):
os.mkdir(npy_mask_dir)
np.save(os.path.join(npy_mask_dir, project_name + '_mask.npy'), imgs_mask_test)
print('-' * 30)
print('Saving predicted masks to files...')
print('-' * 30)
imgs_mask_test = preprocess_squeeze(imgs_mask_test)
# imgs_mask_test /= 1.7
imgs_mask_test = np.around(imgs_mask_test, decimals=0)
imgs_mask_test = (imgs_mask_test * 255.).astype(np.uint8)
count_visualize = 1
count_processed = 0
count_processed = 0
pred_dir = 'preds/'
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
pred_dir = os.path.join('preds/', project_name)
if not os.path.exists(pred_dir):
os.mkdir(pred_dir)
for x in range(0, imgs_mask_test.shape[0]):
imsave(os.path.join(pred_dir, 'pred_' + str(f"{count_processed:03}") + '.png'), imgs_mask_test[x])
count_processed += 1
if (count_processed % 100) == 0:
print('Done: {0}/{1} test images'.format(count_processed, imgs_mask_test.shape[0]))
print('-'*30)
print('Prediction finished')
print('-'*30)
if __name__ == '__main__':
train()
predict()