[Test] add test_convert_to_hls_layers_lfc_w1a1

tests/fpgadataflow/test_convert_to_hls_layers.py

+import os
+
+import brevitas.onnx as bo
+import torch
+from models.LFC import LFC
+
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+import finn.transformation.streamline.absorb as absorb
+from finn.core.modelwrapper import ModelWrapper
+from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
+from finn.transformation.fold_constants import FoldConstants
+from finn.transformation.general import GiveReadableTensorNames, GiveUniqueNodeNames
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import Streamline
+
+export_onnx_path = "test_output_lfc.onnx"
+# TODO get from config instead, hardcoded to Docker path for now
+trained_lfc_checkpoint = (
+    "/workspace/brevitas_cnv_lfc/pretrained_models/LFC_1W1A/checkpoints/best.tar"
+)
+
+
+def test_convert_to_hls_layers_lfc_w1a1():
+    lfc = LFC(weight_bit_width=1, act_bit_width=1, in_bit_width=1)
+    checkpoint = torch.load(trained_lfc_checkpoint, map_location="cpu")
+    lfc.load_state_dict(checkpoint["state_dict"])
+    bo.export_finn_onnx(lfc, (1, 1, 28, 28), export_onnx_path)
+    model = ModelWrapper(export_onnx_path)
+    model = model.transform(InferShapes())
+    model = model.transform(FoldConstants())
+    model = model.transform(GiveUniqueNodeNames())
+    model = model.transform(GiveReadableTensorNames())
+    model = model.transform(Streamline())
+    model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+    model = model.transform(absorb.AbsorbAddIntoMultiThreshold())
+    model = model.transform(absorb.AbsorbMulIntoMultiThreshold())
+    model = model.transform(to_hls.InferBinaryStreamingFCLayer())
+    fc0 = model.graph.node[2]
+    assert fc0.op_type == "StreamingFCLayer_Batch"
+    assert model.get_tensor_shape(fc0.input[0]) == [1, 784, 1]
+    assert model.get_tensor_shape(fc0.input[1]) == [1, 784 * 1024, 1]
+    assert model.get_tensor_shape(fc0.input[2]) == [1, 1024, 1]
+    fc1 = model.graph.node[3]
+    assert fc1.op_type == "StreamingFCLayer_Batch"
+    assert model.get_tensor_shape(fc1.input[0]) == [1, 1024, 1]
+    assert model.get_tensor_shape(fc1.input[1]) == [1, 1024 * 1024, 1]
+    assert model.get_tensor_shape(fc1.input[2]) == [1, 1024, 1]
+    fc2 = model.graph.node[4]
+    assert fc2.op_type == "StreamingFCLayer_Batch"
+    assert model.get_tensor_shape(fc2.input[0]) == [1, 1024, 1]
+    assert model.get_tensor_shape(fc2.input[1]) == [1, 1024 * 1024, 1]
+    assert model.get_tensor_shape(fc2.input[2]) == [1, 1024, 1]
+    os.remove(export_onnx_path)