diff --git a/tests/end2end/test_vitis_end2end_cnv_w1a1.py b/tests/end2end/test_vitis_end2end_cnv_w1a1.py
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index 0000000000000000000000000000000000000000..8630c0c1db17048ab9336c3ffbf3fa9170073068
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+# Copyright (c) 2020, Xilinx
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# * Neither the name of FINN nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+import os
+import pytest
+import numpy as np
+
+# as of Feb'20 there is a bug that segfaults ONNX shape inference if we
+# import pytorch before onnx, so we make sure to import onnx first
+import onnx # NOQA
+import finn.transformation.fpgadataflow.convert_to_hls_layers as to_hls
+import finn.transformation.streamline.absorb as absorb
+from finn.core.onnx_exec import execute_onnx
+from finn.custom_op.registry import getCustomOp
+from finn.transformation.bipolar_to_xnor import ConvertBipolarMatMulToXnorPopcount
+from finn.transformation.fold_constants import FoldConstants
+
+from finn.transformation.fpgadataflow.create_dataflow_partition import (
+ CreateDataflowPartition,
+)
+from finn.transformation.fpgadataflow.make_deployment import DeployToPYNQ
+from finn.transformation.general import (
+ RemoveUnusedTensors,
+ RemoveStaticGraphInputs,
+ GiveReadableTensorNames,
+ GiveUniqueNodeNames,
+)
+from finn.transformation.infer_shapes import InferShapes
+from finn.transformation.streamline import Streamline
+from finn.util.basic import alveo_part_map, alveo_default_platform
+from finn.util.test import get_test_model_trained, load_test_checkpoint_or_skip
+from finn.transformation.fpgadataflow.annotate_resources import AnnotateResources
+from finn.transformation.fpgadataflow.vitis_build import VitisBuild
+import pkg_resources as pk
+from finn.transformation.double_to_single_float import DoubleToSingleFloat
+from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
+from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
+from finn.transformation.streamline.reorder import MakeMaxPoolNHWC
+from finn.transformation.infer_data_layouts import InferDataLayouts
+from finn.transformation.fpgadataflow.annotate_cycles import AnnotateCycles
+import warnings
+
+build_dir = "/tmp/" + os.environ["FINN_INST_NAME"]
+test_alveo_board = os.getenv("ALVEO_BOARD", default="U250")
+test_fpga_part = alveo_part_map[test_alveo_board]
+test_platform = alveo_default_platform[test_alveo_board]
+target_clk_ns = 10
+mem_mode = "decoupled"
+
+
+def test_end2end_vitis_cnv_w1a1_export():
+ import brevitas.onnx as bo
+
+ tfc = get_test_model_trained("CNV", 1, 1)
+ bo.export_finn_onnx(
+ tfc, (1, 3, 32, 32), build_dir + "/end2end_vitis_cnv_w1a1_export.onnx"
+ )
+
+
+def test_end2end_vitis_cnv_w1a1_import_and_tidy():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_export.onnx"
+ )
+ model = model.transform(DoubleToSingleFloat())
+ model = model.transform(InferShapes())
+ model = model.transform(FoldConstants())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(GiveReadableTensorNames())
+ model = model.transform(RemoveStaticGraphInputs())
+ model.save(build_dir + "/end2end_vitis_cnv_w1a1_tidy.onnx")
+
+
+def test_end2end_vitis_cnv_w1a1_streamline():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_tidy.onnx"
+ )
+ model = model.transform(Streamline())
+ model = model.transform(LowerConvsToMatMul())
+ model = model.transform(MakeMaxPoolNHWC())
+ model = model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
+ model = model.transform(ConvertBipolarMatMulToXnorPopcount())
+ model = model.transform(Streamline())
+ model = model.transform(RemoveUnusedTensors())
+ model.save(build_dir + "/end2end_vitis_cnv_w1a1_streamlined.onnx")
+
+
+def test_end2end_vitis_cnv_w1a1_convert_to_hls_layers():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_streamlined.onnx"
+ )
+ model = model.transform(to_hls.InferBinaryStreamingFCLayer(mem_mode))
+ model = model.transform(to_hls.InferQuantizedStreamingFCLayer(mem_mode))
+ model = model.transform(to_hls.InferConvInpGen())
+ model = model.transform(to_hls.InferStreamingMaxPool())
+ model = model.transform(GiveUniqueNodeNames())
+ model = model.transform(RemoveCNVtoFCFlatten())
+ model = model.transform(InferDataLayouts())
+ model.save(build_dir + "/end2end_vitis_cnv_w1a1_hls_layers.onnx")
+
+
+def test_end2end_vitis_cnv_w1a1_create_dataflow_partition():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_hls_layers.onnx"
+ )
+ parent_model = model.transform(CreateDataflowPartition())
+ parent_model.save(build_dir + "/end2end_vitis_cnv_w1a1_dataflow_parent.onnx")
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ dataflow_model_filename = sdp_node.get_nodeattr("model")
+ dataflow_model = load_test_checkpoint_or_skip(dataflow_model_filename)
+ dataflow_model.save(build_dir + "/end2end_vitis_cnv_w1a1_dataflow_model.onnx")
+
+
+def test_end2end_vitis_cnv_w1a1_fold():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_dataflow_model.onnx"
+ )
+ fc_layers = model.get_nodes_by_op_type("StreamingFCLayer_Batch")
+ # each tuple is (PE, SIMD, in_fifo_depth) for a layer
+ folding = [
+ (16, 3, 256),
+ (32, 32, 256),
+ (16, 32, 256),
+ (16, 32, 256),
+ (4, 32, 214),
+ (1, 32, 2),
+ (1, 4, 126),
+ (1, 8, 62),
+ (5, 1, 6),
+ ]
+ for fcl, (pe, simd, ififodepth) in zip(fc_layers, folding):
+ fcl_inst = getCustomOp(fcl)
+ fcl_inst.set_nodeattr("PE", pe)
+ fcl_inst.set_nodeattr("SIMD", simd)
+ fcl_inst.set_nodeattr("inFIFODepth", ififodepth)
+
+ swg_layers = model.get_nodes_by_op_type("ConvolutionInputGenerator")
+ swg_idepth = [2, 51, 9, 106, 2, 2]
+ for i in range(len(swg_layers)):
+ swg_inst = getCustomOp(swg_layers[i])
+ simd = folding[i][1]
+ swg_inst.set_nodeattr("SIMD", simd)
+ swg_inst.set_nodeattr("inFIFODepth", swg_idepth[i])
+ model = model.transform(AnnotateResources("estimate"))
+ model = model.transform(AnnotateCycles())
+ model.save(build_dir + "/end2end_vitis_cnv_w1a1_folded.onnx")
+
+
+@pytest.mark.slow
+@pytest.mark.vivado
+def test_end2end_vitis_cnv_w1a1_build():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_folded.onnx"
+ )
+ model = model.transform(VitisBuild(test_fpga_part, target_clk_ns, test_platform))
+ warnings.warn(
+ "Post-synthesis resources (excluding shell): "
+ + model.get_metadata_prop("res_total_synth")
+ )
+ model.save(build_dir + "/end2end_vitis_cnv_w1a1_build.onnx")
+
+
+def test_end2end_vitis_cnv_w1a1_deploy_on_pynq():
+ model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_build.onnx"
+ )
+ try:
+ ip = os.environ["ALVEO_IP"] # no fault for this one; skip if not defined
+ if ip == "":
+ pytest.skip("Alveo host IP address not specified")
+ username = os.getenv("ALVEO_USERNAME", "xilinx")
+ password = os.getenv("ALVEO_PASSWORD", "xilinx")
+ port = os.getenv("ALVEO_PORT", 22)
+ target_dir = os.getenv("ALVEO_TARGET_DIR", "/home/xilinx/finn")
+ model = model.transform(DeployToPYNQ(ip, port, username, password, target_dir))
+ # save the model to be able to link it to the parent
+ model.save(build_dir + "/end2end_vitis_cnv_w1a1_pynq_deploy.onnx")
+ except KeyError:
+ pytest.skip("Alveo host IP address not specified")
+
+
+def test_end2end_vitis_cnv_w1a1_run_on_pynq():
+ # use the streamlined model as the "golden" model for right answers
+ golden = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_streamlined.onnx"
+ )
+ iname = golden.graph.input[0].name
+ oname = golden.graph.output[0].name
+ # load one of the test vectors
+ fn = pk.resource_filename("finn", "data/cifar10/cifar10-test-data-class3.npz")
+ input_tensor = np.load(fn)["arr_0"].astype(np.float32)
+ input_tensor = input_tensor / 255
+ assert input_tensor.shape == (1, 3, 32, 32)
+ x = input_tensor
+ # x = np.zeros(ishape, dtype=np.float32)
+ # run using FINN-based execution
+ ret_golden = execute_onnx(golden, {iname: x}, True)
+ y_golden = ret_golden[oname]
+ # set up parent+child graph to test
+ # we'll use models from the previous step as the child model
+ parent_model = load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_dataflow_parent.onnx"
+ )
+ iname = parent_model.graph.input[0].name
+ oname = parent_model.graph.output[0].name
+ try:
+ ip = os.environ["ALVEO_IP"] # NOQA
+ if ip == "":
+ pytest.skip("Alveo host IP address not specified")
+ # produce results with cppsim
+ sdp_node = parent_model.get_nodes_by_op_type("StreamingDataflowPartition")[0]
+ sdp_node = getCustomOp(sdp_node)
+ load_test_checkpoint_or_skip(
+ build_dir + "/end2end_vitis_cnv_w1a1_pynq_deploy.onnx"
+ )
+ sdp_node.set_nodeattr(
+ "model", build_dir + "/end2end_vitis_cnv_w1a1_pynq_deploy.onnx"
+ )
+ ret = execute_onnx(parent_model, {iname: x}, True)
+ y = ret[oname]
+ assert np.isclose(y, y_golden).all()
+ assert np.argmax(y) == 3
+
+ except KeyError:
+ pytest.skip("Alveo host IP address not specified")