remove obsolete np.int and np.float (fastmachinelearning#703)

Author: jmitrevs

hls4ml/writer/quartus_writer.py

@@ -195,7 +195,7 @@ def write_project_cpp(self, model):
                     newline += 'hls_max_concurrency(0)\n'
                     newline += f'hls_component_ii({self.get_max_reuse_factor(model)})\n'
                 clock_mhz = 1000 / (model.config.get_config_value('ClockPeriod'))
-                newline += f'hls_scheduler_target_fmax_mhz({np.ceil(clock_mhz).astype(np.int)})\n'
+                newline += f'hls_scheduler_target_fmax_mhz({np.ceil(clock_mhz).astype(int)})\n'
 
             # In io_parallel, an output (struct) is returned from the top-level function
             # Therefore, it needs to be initialised before returning
@@ -342,7 +342,7 @@ def write_project_header(self, model):
                     newline += 'hls_max_concurrency(0)\n'
                     newline += f'hls_component_ii({self.get_max_reuse_factor(model)})\n'
                 clock_mhz = 1000 / (model.config.get_config_value('ClockPeriod'))
-                newline += f'hls_scheduler_target_fmax_mhz({np.ceil(clock_mhz).astype(np.int)})\n'
+                newline += f'hls_scheduler_target_fmax_mhz({np.ceil(clock_mhz).astype(int)})\n'
 
             # For io_stream, no inputs/outputs are instantiated, as they are passed by reference
             # For io_parallel, input/output structs are required

test/pytest/test_embed.py

@@ -1,49 +1,51 @@
-import pytest
-import hls4ml
-import numpy as np
 from pathlib import Path
+
+import numpy as np
+import pytest
+from tensorflow.keras.layers import Embedding, Input
 from tensorflow.keras.models import Model
-from tensorflow.keras.layers import Input, Embedding
+
+import hls4ml
 
 test_root_path = Path(__file__).parent
 
+
 @pytest.fixture(scope='module')
 def data():
     X = np.random.randint(10, size=(32, 100))
     return X
 
+
 @pytest.fixture(scope='module')
 def keras_model():
     inputs = Input(shape=(100,), name='embedding_input')
     embedding = Embedding(13, 8, input_length=100, name='embedding')(inputs)
     model = Model(inputs=inputs, outputs=embedding)
     return model
 
+
 @pytest.fixture
 @pytest.mark.parametrize('backend', ['Vivado', 'Quartus'])
 @pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
 def hls_model(keras_model, backend, io_type):
-    hls_config = hls4ml.utils.config_from_keras_model(keras_model,
-                                                      default_precision='ap_fixed<16,6>',
-                                                      granularity='name')
+    hls_config = hls4ml.utils.config_from_keras_model(keras_model, default_precision='ap_fixed<16,6>', granularity='name')
     hls_config['LayerName']['embedding_input']['Precision']['result'] = 'ap_uint<4>'
     out_dir = str(test_root_path / 'hls4mlprj_embed_{}_{}').format(backend, io_type)
-    hls_model = hls4ml.converters.convert_from_keras_model(keras_model,
-                                                           backend=backend,
-                                                           hls_config=hls_config,
-                                                           io_type=io_type,
-                                                           output_dir=out_dir)
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        keras_model, backend=backend, hls_config=hls_config, io_type=io_type, output_dir=out_dir
+    )
 
     hls_model.compile()
     return hls_model
 
+
 @pytest.mark.parametrize('backend', ['Vivado', 'Quartus'])
 @pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
 def test_embedding_accuracy(data, keras_model, hls_model):
     X = data
     model = keras_model
     # model under test predictions and accuracy
     y_keras = model.predict(X)
-    y_hls4ml   = hls_model.predict(X.astype(np.float)).reshape(y_keras.shape)
+    y_hls4ml = hls_model.predict(X.astype(float)).reshape(y_keras.shape)
     # "accuracy" of hls4ml predictions vs keras
     np.testing.assert_allclose(y_keras, y_hls4ml, rtol=0, atol=1e-03, verbose=True)