[GitHub] [incubator-mxnet] marcoabreu commented on a change in pull request #15344: [MXNET-1086] added sub and mul to ONNX->TensorRT conversion

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

marcoabreu commented on a change in pull request #15344: [MXNET-1086] added sub and mul to ONNX->TensorRT conversion
URL: https://github.com/apache/incubator-mxnet/pull/15344#discussion_r297538918
 
 

 ##########
 File path: tests/python/tensorrt/test_ops.py
 ##########
 @@ -0,0 +1,68 @@
+# 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.
+
+from mxnet.test_utils import assert_almost_equal
+import mxnet as mx
+import numpy as np
+import os
+
+def check_elementwise_random(op='sum', shape=(1, 3, 224, 224)):
+    """
+    Check elementwise operators with vanilla/TensorRT executors with uniform random tensors
+    """
+    a = mx.sym.Variable('a')
+    b = mx.sym.Variable('b')
+    if op == 'sum':
+        sym = a + b
+    elif op == 'sub':
+        sym = a - b
+    elif op == 'mul':
+        sym = a * b
+
+    a_data = mx.ndarray.random.uniform(shape=shape, ctx=mx.gpu())
+    b_data = mx.ndarray.random.uniform(shape=shape, ctx=mx.gpu())
+
+    executor = sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
+                               grad_req='null', force_rebind=True)
+    y = executor.forward(is_train=False, a=a_data, b=b_data)
+    trt_sym = sym.get_backend_symbol('TensorRT')
+    original_precision_value = mx.contrib.tensorrt.get_use_fp16()
+    try:
+        mx.contrib.tensorrt.set_use_fp16(True)
+        executor = trt_sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
+                                       grad_req='null', force_rebind=True)
+        y_trt = executor.forward(is_train=False, a=a_data, b=b_data)
+        mx.contrib.tensorrt.set_use_fp16(False)
+        executor = trt_sym.simple_bind(ctx=mx.gpu(), a=shape, b=shape,
+                                       grad_req='null', force_rebind=True)
+        y_trt_fp32 = executor.forward(is_train=False, a=a_data, b=b_data)
+        assert_almost_equal(y[0].asnumpy(), y_trt[0].asnumpy(), 1e-1, 1e-2)
+        assert_almost_equal(y[0].asnumpy(), y_trt_fp32[0].asnumpy(), 1e-4, 1e-4)
+    finally:
+        mx.contrib.tensorrt.set_use_fp16(original_precision_value)
+
+
+def test_elementwise():
 
 Review comment:
   with_seed missing

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