test_views.py 文件源码

def test_keras_export(self):
        tests = open(os.path.join(settings.BASE_DIR, 'tests', 'unit', 'keras_app',
                                  'keras_export_test.json'), 'r')
        response = json.load(tests)
        tests.close()
        net = yaml.safe_load(json.dumps(response['net']))
        net = {'l0': net['Input'], 'l1': net['BatchNorm'], 'l2': net['Scale']}
        net['l0']['connection']['output'].append('l1')
        # Test 1
        inp = data(net['l0'], '', 'l0')['l0']
        temp = batch_norm(net['l1'], [inp], 'l1', 'l2', net['l2'])
        model = Model(inp, temp['l2'])
        self.assertEqual(model.layers[1].__class__.__name__, 'BatchNormalization')
        # Test 2
        net['l2']['params']['filler'] = 'VarianceScaling'
        net['l2']['params']['bias_filler'] = 'VarianceScaling'
        inp = data(net['l0'], '', 'l0')['l0']
        temp = batch_norm(net['l1'], [inp], 'l1', 'l2', net['l2'])
        model = Model(inp, temp['l2'])
        self.assertEqual(model.layers[1].__class__.__name__, 'BatchNormalization')
        # Test 3
        inp = data(net['l0'], '', 'l0')['l0']
        temp = batch_norm(net['l1'], [inp], 'l1', 'l0', net['l0'])
        model = Model(inp, temp['l1'])
        self.assertEqual(model.layers[1].__class__.__name__, 'BatchNormalization')