[Data] add test data for end2end UNSW-NB15 MLP

includes trained model + Python validation script

src/finn/qnn-data/cybsec-mlp/validate-unsw-nb15.py

+# Copyright (c) 2020 Xilinx, Inc.
+# 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 Xilinx 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 argparse
+from driver import io_shape_dict
+from driver_base import FINNExampleOverlay
+import numpy as np
+
+
+def make_unsw_nb15_test_batches(bsize, dataset_root, limit_batches):
+    unsw_nb15_data = np.load(dataset_root + "/unsw_nb15_binarized.npz")["test"][:82000]
+    test_imgs = unsw_nb15_data[:, :-1]
+    test_labels = unsw_nb15_data[:, -1]
+    n_batches = int(test_imgs.shape[0] / bsize)
+    if limit_batches == -1:
+        limit_batches = n_batches
+    test_imgs = test_imgs.reshape(n_batches, bsize, -1)[:limit_batches]
+    test_labels = test_labels.reshape(n_batches, bsize)[:limit_batches]
+    return (test_imgs, test_labels)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="Validate top-1 accuracy for FINN-generated accelerator"
+    )
+    parser.add_argument("--batchsize", help="samples per batch", type=int, default=1000)
+    parser.add_argument(
+        "--platform", help="Target platform: zynq-iodma alveo", default="zynq-iodma"
+    )
+    parser.add_argument(
+        "--bitfile",
+        help='name of bitfile (i.e. "resizer.bit")',
+        default="../bitfile/finn-accel.bit",
+    )
+    parser.add_argument(
+        "--dataset_root", help="dataset root dir for download/reuse", default="."
+    )
+    parser.add_argument(
+        "--limit_batches", help="number of batches, -1 for max", type=int, default=-1
+    )
+    # parse arguments
+    args = parser.parse_args()
+    bsize = args.batchsize
+    bitfile = args.bitfile
+    platform = args.platform
+    dataset_root = args.dataset_root
+    limit_batches = args.limit_batches
+
+    print("Loading dataset...")
+    (test_imgs, test_labels) = make_unsw_nb15_test_batches(
+        bsize, dataset_root, limit_batches
+    )
+
+    ok = 0
+    nok = 0
+    n_batches = test_imgs.shape[0]
+    total = n_batches * bsize
+
+    print("Initializing driver, flashing bitfile...")
+
+    driver = FINNExampleOverlay(
+        bitfile_name=bitfile,
+        platform=platform,
+        io_shape_dict=io_shape_dict,
+        batch_size=bsize,
+    )
+
+    n_batches = int(total / bsize)
+
+    print("Starting...")
+
+    for i in range(n_batches):
+        inp = np.pad(test_imgs[i].astype(np.float32), [(0, 0), (0, 7)], mode="constant")
+        exp = test_labels[i].astype(np.float32)
+        inp = 2 * inp - 1
+        exp = 2 * exp - 1
+        out = driver.execute(inp)
+        matches = np.count_nonzero(out.flatten() == exp.flatten())
+        nok += bsize - matches
+        ok += matches
+        print("batch %d / %d : total OK %d NOK %d" % (i + 1, n_batches, ok, nok))
+
+    acc = 100.0 * ok / (total)
+    print("Final accuracy: %f" % acc)