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predict.py
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predict.py
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from model import *
from get_data import *
import tensorflow as tf
class predict(object):
def __init__(self):
print('begin predicting')
self.im_height = 512
self.im_width = 512
def get_iou(self, A, B):
batch_size = A.shape[0]
metric = []
for batch in range(batch_size):
t, p = A[batch] > 0, B[batch] > 0
intersection = np.logical_and(t, p)
union = np.logical_or(t, p)
iou = (np.sum(intersection > 0) + 1e-10) / (np.sum(union > 0) + 1e-10)
if iou >= 0.5:
metric.append(1)
else:
metric.append(0)
return np.sum(metric,dtype=np.float64)
def get_iou_metric(self,label, preds_test):
return tf.py_func(self.get_iou, [label, preds_test > 0.5], tf.float64)
def predict_accuracy(self,path,train=True):
X_test, y_test = get_data(path, self.im_height,self.im_width, train)
input_img = Input((self.im_height, self.im_width, 1), name='img')
model = get_unet(input_img, n_filters=16, batchnorm=True)
model.load_weights('model-unet.h5')
sess = tf.Session()
sess.run(tf.global_variables_initializer())
size = 20
total_accuracy = 0
for i in range(0, len(X_test), size):
print('i=', i)
label = y_test[i:i+size]
preds_test = model.predict(X_test[i:i + size])
accuracy = self.get_iou_metric(label, preds_test)
total_accuracy += accuracy
ave_accuracy = sess.run(total_accuracy / len(X_test))
return ave_accuracy
if __name__=='__main__':
path_test = './input/test/'
ave_accuracy = predict().predict_accuracy(path=path_test,train=True)
print('ave_accuracy = ',ave_accuracy)