Fix keras model loading issue with loading model with KerasH5 (#664)

* Fix keras model loading issue with loading model with KerasH5

* Add unit test for KerasH5 loader

* comment on h5py version causing KerasH5 loading issue

Author: calad0i

hls4ml/converters/keras_to_hls.py

@@ -232,7 +232,10 @@ def keras_to_hls(config):
             if model_arch is None:
                 raise ValueError('No model found in config file.')
             else:
-                model_arch = json.loads(model_arch.decode('utf-8'))
+                # model_arch is string by default since h5py 3.0.0, keeping this condition for compatibility.
+                if isinstance(model_arch, bytes): 
+                    model_arch = model_arch.decode('utf-8')
+                model_arch = json.loads(model_arch) 
         reader = KerasFileReader(config)
     else:
         raise ValueError('No model found in config file.')

test/pytest/test_keras_h5_loader.py

@@ -0,0 +1,37 @@
+import pytest
+import hls4ml
+import tensorflow as tf
+import numpy as np
+from pathlib import Path
+
+
+test_root_path = Path(__file__).parent
+
+test_root_path = Path('/tmp')
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Quartus'])
+def test_keras_h5_loader(backend):
+    input_shape = (10,)
+    model = tf.keras.models.Sequential([
+        tf.keras.layers.InputLayer(input_shape=input_shape),
+        tf.keras.layers.Activation(activation='relu'),
+    ])
+
+    hls_config = hls4ml.utils.config_from_keras_model(model, granularity='name')
+
+    config = {'OutputDir': 'KerasH5_loader_test',
+              'ProjectName': 'KerasH5_loader_test',
+              'Backend': backend,
+              'ClockPeriod': 25.0,
+              'IOType': 'io_parallel',
+              'HLSConfig': hls_config,
+              'KerasH5': str(test_root_path / 'KerasH5_loader_test.h5'),
+              'output_dir': str(test_root_path / 'KerasH5_loader_test')}
+
+    model.save(config['KerasH5'])
+    hls_model = hls4ml.converters.keras_to_hls(config)
+    hls_model.compile()
+    data = np.random.rand(1000, 10).astype(np.float32)
+    pred = hls_model.predict(data)
+    np.testing.assert_allclose(pred, model.predict(data), rtol=5e-3, atol=5e-3)