def test_makePredictions(self):
cn.trainNetwork(5, self.testfile, self.trainfile)
cn.makePredictions(self.testfile)
rows, cols = load_extension.getDims(self.testfile)
predimage = 'prediction.tif'
testrows, testcols = load_extension.getDims(predimage)
self.assertEqual((rows, cols), (testrows, testcols))
self.assertIs(type(testrows), int)
self.assertIs(type(testcols), int)
def test_makePredictions(self):
cn.trainNetwork(5, self.testfile, self.trainfile)
cn.makePredictions(self.testfile)
rows, cols = load_extension.getDims(self.testfile)
predimage = 'prediction.tif'
testrows, testcols = load_extension.getDims(predimage)
self.assertEqual((rows, cols), (testrows, testcols))
self.assertIs(type(testrows), int)
self.assertIs(type(testcols), int)
def test_trainNetwork(self):
cn.trainNetwork(1, self.testfile, self.trainfile)
with open('net.pickle', 'rb') as f:
net_pretrain = pickle.load(f)
self.assertIsNotNone(net_pretrain)
def test_trainNetwork(self):
cn.trainNetwork(1, self.testfile, self.trainfile)
with open('net.pickle', 'rb') as f:
net_pretrain = pickle.load(f)
self.assertIsNotNone(net_pretrain)
