[GitHub] [incubator-tvm] mbaret commented on a change in pull request #6355: [BYOC][ETHOSN] Introduce further operator support

[GitBox <gi...@apache.org>]

mbaret commented on a change in pull request #6355:
URL: https://github.com/apache/incubator-tvm/pull/6355#discussion_r484366666



##########
File path: tests/python/contrib/test_ethosn/test_networks.py
##########
@@ -0,0 +1,143 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Ethos-N integration end-to-end network tests"""
+
+import pytest
+pytest.importorskip('tflite')
+pytest.importorskip('tensorflow')
+
+from tvm import relay
+from tvm.relay.op.contrib.ethosn import ethosn_available, Available
+from tvm.contrib import download
+import tvm.relay.testing.tf as tf_testing
+import tflite.Model
+from . import infrastructure as tei
+
+
+def _get_tflite_model(tflite_model_path, inputs_dict, dtype):
+    with open(tflite_model_path, 'rb') as f:
+        tflite_model_buffer = f.read()
+
+    try:
+        tflite_model = tflite.Model.Model.GetRootAsModel(tflite_model_buffer, 0)
+    except AttributeError:
+        tflite_model = tflite.Model.GetRootAsModel(tflite_model_buffer, 0)
+    shape_dict = {}
+    dtype_dict = {}
+    for input in inputs_dict:
+        input_shape = inputs_dict[input]
+        shape_dict[input] = input_shape
+        dtype_dict[input] = dtype
+
+    return relay.frontend.from_tflite(
+        tflite_model,
+        shape_dict=shape_dict,
+        dtype_dict=dtype_dict,
+    )
+
+
+def _test_image_network(model_url, model_sub_path, input_dict, compile_hash, output_count, run=True, host_ops=0, npu_partitions=1):
+    if not ethosn_available():
+        return
+
+    def get_model():
+        if model_url[-3:] in ("tgz", "zip"):
+            model_path = tf_testing.get_workload_official(
+                model_url,
+                model_sub_path,
+            )
+        else:
+            model_path = download.download_testdata(
+                model_url,
+                model_sub_path,
+            )
+        return _get_tflite_model(model_path, input_dict, 'uint8')
+
+    outputs = []
+    inputs = {}
+    for input_name in input_dict:
+        input_shape = input_dict[input_name]
+        inputs[input_name] = tei.get_real_image(input_shape[1], input_shape[2])
+
+    for npu in [False, True]:
+        mod, params = get_model()
+        graph, lib, params = tei.build(mod, params, npu=npu, expected_host_ops=host_ops, npu_partitions=npu_partitions)
+        if npu:
+            tei.assert_lib_hash(lib, compile_hash)
+        if run:
+            outputs.append(tei.run(graph, lib, params, inputs, output_count, npu=npu))
+
+    if run:
+        tei.verify(outputs, 1, verify_saturation=False)
+
+
+def test_mobilenet_v1():
+    hw = ethosn_available()
+    _test_image_network(
+        model_url="https://storage.googleapis.com/download.tensorflow.org/" \
+                  "models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
+        model_sub_path="mobilenet_v1_1.0_224_quant.tflite",
+        input_dict={"input": (1, 224, 224, 3)},
+        compile_hash="81637c89339201a07dc96e3b5dbf836a",

Review comment:
       This is created by hashing the library output we get when compiling the model. We create the hash at a 'known good' point in time (i.e. when we run the HW tests internally and see everything is correct). The thinking here is that this provides a quick way to test whether changes in TVM have resulted in a behavioural change to our codegen.
   
   We will remain responsible for updating these hashes while no public hardware is available. Should it become available, we'll no longer need the hashes as we can just test execution correctness directly.




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