Use smaller model in Conv1D test

Author: vloncar

hls4ml/backends/quartus/quartus_backend.py

@@ -254,6 +254,8 @@ def init_conv1d(self, layer):
         # - Winograd - use Winograd, if possible
         layer.set_attr('implementation', layer.model.config.get_layer_config_value(layer, 'Implementation', 'combination'))
 
+        layer.set_attr('n_partitions', 1) #TODO Not used yet as there is no codegen implementation of CNNs for Quartus backend
+
     @layer_optimizer(Conv2D)
     def init_conv2d(self, layer):
         # This can happen if we assign weights of Dense layer to 1x1 Conv2D
@@ -280,6 +282,8 @@ def init_conv2d(self, layer):
         # - im2col - specifically use im2col
         # - Winograd - use Winograd, if possible
         layer.set_attr('implementation', layer.model.config.get_layer_config_value(layer, 'Implementation', 'combination'))
+
+        layer.set_attr('n_partitions', 1) #TODO Not used yet as there is no codegen implementation of CNNs for Quartus backend
 
     @layer_optimizer(LSTM)
     def init_lstm(self, layer):

test/pytest/test_conv1d.py

@@ -10,16 +10,16 @@
 
 @pytest.fixture(scope='module')
 def data():
-    X = np.random.rand(100,100,7)
+    X = np.random.rand(100,10,4)
     return X
 
 @pytest.fixture(scope='module')
 def keras_model():
-    model_path = example_model_path / 'keras/KERAS_conv1d.json'
+    model_path = example_model_path / 'keras/KERAS_conv1d_small.json'
     with model_path.open('r') as f:
         jsons = f.read()
     model = model_from_json(jsons)
-    model.load_weights(example_model_path / 'keras/KERAS_conv1d_weights.h5')
+    model.load_weights(example_model_path / 'keras/KERAS_conv1d_small_weights.h5')
     return model
 
 @pytest.fixture