From 10dd8924fd9b8f9de940c2da007010e6a529a0cb Mon Sep 17 00:00:00 2001 From: Jan-Frederik Schulte Date: Tue, 29 Apr 2025 14:51:13 -0400 Subject: [PATCH 1/2] round inputs for dense unrolled RNN tests --- hls4ml/converters/onnx_to_hls.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/hls4ml/converters/onnx_to_hls.py b/hls4ml/converters/onnx_to_hls.py index 0f7662c35e..e754bf339e 100644 --- a/hls4ml/converters/onnx_to_hls.py +++ b/hls4ml/converters/onnx_to_hls.py @@ -260,7 +260,7 @@ def parse_onnx_model(onnx_model): sanitize_layer_name(layer) print(f"Layer name: {layer['name']}, layer type: {layer['class_name']}, current shape: {input_shapes}") layer_list.append(layer) - + print(output_layers) return layer_list, input_layers, output_layers From 89399ca0cf6ec9ae2f4f1ae4e0f6ee960018d218 Mon Sep 17 00:00:00 2001 From: Jan-Frederik Schulte Date: Tue, 29 Apr 2025 15:15:57 -0400 Subject: [PATCH 2/2] round inputs for dense unrolled RNN tests --- hls4ml/converters/onnx_to_hls.py | 2 +- test/pytest/test_dense_unrolled.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/hls4ml/converters/onnx_to_hls.py b/hls4ml/converters/onnx_to_hls.py index e754bf339e..0f7662c35e 100644 --- a/hls4ml/converters/onnx_to_hls.py +++ b/hls4ml/converters/onnx_to_hls.py @@ -260,7 +260,7 @@ def parse_onnx_model(onnx_model): sanitize_layer_name(layer) print(f"Layer name: {layer['name']}, layer type: {layer['class_name']}, current shape: {input_shapes}") layer_list.append(layer) - print(output_layers) + return layer_list, input_layers, output_layers diff --git a/test/pytest/test_dense_unrolled.py b/test/pytest/test_dense_unrolled.py index 5d3e8f4acb..9c2f1baeae 100644 --- a/test/pytest/test_dense_unrolled.py +++ b/test/pytest/test_dense_unrolled.py @@ -107,6 +107,7 @@ def test_resource_unrolled_rnn(rnn_layer, backend, io_type, static, reuse_factor # Subtract 0.5 to include negative values input_shape = (12, 8) X = np.random.rand(50, *input_shape) - 0.5 + X = np.round(X * 2**16) * 2**-16 # make it exact ap_fixed<32,16> layer_name = rnn_layer.__name__.lower() keras_model = Sequential()