add globalpooling1d testing; add missing include in nnet_conv_stream.h

Author: jmitrevs

hls4ml/templates/vivado/nnet_utils/nnet_conv_stream.h

@@ -4,6 +4,7 @@
 #include "ap_shift_reg.h"
 #include "nnet_common.h"
 #include "hls_stream.h"
+#include "nnet_dense.h"
 
 namespace nnet {
 

test/pytest/test_globalpooling1d.py

@@ -0,0 +1,59 @@
+import pytest
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import GlobalAveragePooling1D, GlobalMaxPooling1D
+import numpy as np
+import hls4ml
+
+
+in_shape = 8
+in_feat = 4
+atol = 5e-3
+
+@pytest.fixture(scope='module')
+def data():
+    X = np.random.rand(100, in_shape, in_feat)
+    return X
+
+
+@pytest.fixture(scope='module')
+def keras_model_max():
+    model = Sequential()
+    model.add(GlobalMaxPooling1D(input_shape=(in_shape, in_feat)))
+    model.compile()
+    return model
+
+@pytest.fixture(scope='module')
+def keras_model_ave():
+    model = Sequential()
+    model.add(GlobalAveragePooling1D(input_shape=(in_shape, in_feat)))
+    model.compile()
+    return model
+
+  
+@pytest.mark.parametrize("configuration", [("ave", 'io_parallel'),
+                                            ("ave", 'io_stream'),
+                                            ("max", 'io_parallel'),
+                                            ("max", 'io_stream')])
+def test_global_pool1d(keras_model_max, keras_model_ave, data, configuration):
+    model_type, io_type = configuration
+    if model_type == "ave":
+        model = keras_model_ave
+    else:
+        model = keras_model_max
+    config = hls4ml.utils.config_from_keras_model(model, default_precision='ap_fixed<32,1>',
+                                                  granularity='name')
+    if model_type == "ave":
+        config['LayerName']['global_average_pooling1d']['Precision']='ap_fixed<32,6>'
+
+    hls_model = hls4ml.converters.convert_from_keras_model(model,
+                                                           hls_config=config,
+                                                           io_type=io_type,
+                                                           output_dir=f'hls4ml_globalplool1d_{model_type}_{io_type}',
+                                                           part='xcvu9p-flgb2104-2-i')
+    hls_model.compile()
+    
+
+    # Predict
+    y_keras = np.squeeze(model.predict(data))
+    y_hls = hls_model.predict(data)
+    np.testing.assert_allclose(y_keras, y_hls, rtol=0, atol=atol, verbose=True)