# 2、初始化和启动线程
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=sess, coord=coord)
model.restore(model_restore_name)
#3、测试图片
index = 0
for i in range(test_data_number // batch_size):
pics, pics_masks = sess.run([images, labels]) # 取出一个batchsize的图片
pics = pics / 255
# 3、计算耗时
since = time.time()
pre = model.predict(pics)
seconds = time.time() - since
pre_list = np.split(pre[0], batch_size, axis=0)
pres = np.squeeze(pre_list, axis=0)
for img, label, predict in zip(pics, pics_masks, pres):
img = img * 255
label = label * 255
predict = predict * 255
label = cv2.cvtColor(label, cv2.COLOR_GRAY2BGR)
predict = cv2.cvtColor(predict, cv2.COLOR_GRAY2BGR)
hstack = np.hstack((img, label, predict))
loss=bce_dice_loss,
metrics=[bce_dice_loss, recall_score, precision_score, rocauc_score])
ckpt = ModelCheckpoint('.model.hdf5',
save_best_only=True,
monitor='val_rocauc_score',
mode='max')
model.fit_generator(train_generator,
steps_per_epoch=len(xtr) / 6,
epochs=50,
validation_data=val_generator,
validation_steps=len(xval) / batch_size,
callbacks=[ckpt])
model.load_weights('.model.hdf5')
preds_test = model.predict(X_test, verbose=1)
preds_test_upsampled = []
for i in range(len(preds_test)):
preds_test_upsampled.append(
cv2.resize(preds_test[i], (sizes_test[i][1], sizes_test[i][0])))
test_ids = next(os.walk(test_path))[1]
new_test_ids = []
rles = []
for n, id_ in enumerate(test_ids):
rle = list(prob_to_rles(preds_test_upsampled[n]))
rles.extend(rle)
new_test_ids.extend([id_] * len(rle))
sub = pd.DataFrame()
sub['ImageId'] = new_test_ids