what would onnx runtime do?

3 files changed, 70 insertions, 0 deletions

diff --git a/eval/src/tests/tensor/onnx_wrapper/float_to_int8.onnx b/eval/src/tests/tensor/onnx_wrapper/float_to_int8.onnx
new file mode 100644
index 00000000000..cde81d428bd
--- /dev/null
+++ b/eval/src/tests/tensor/onnx_wrapper/float_to_int8.onnx
@@ -0,0 +1,12 @@
+float_to_int8.py:P
+
+inout"Cast*	
+to 

float_to_int8Z
+in
+
+

+b
+out
+
+
+B
\ No newline at end of file
diff --git a/eval/src/tests/tensor/onnx_wrapper/float_to_int8.py b/eval/src/tests/tensor/onnx_wrapper/float_to_int8.py
new file mode 100755
index 00000000000..2a8e47b3ffa
--- /dev/null
+++ b/eval/src/tests/tensor/onnx_wrapper/float_to_int8.py
@@ -0,0 +1,23 @@
+# Copyright Verizon Media. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
+import onnx
+from onnx import helper, TensorProto
+
+IN = helper.make_tensor_value_info('in', TensorProto.FLOAT, [7])
+OUT = helper.make_tensor_value_info('out', TensorProto.INT8, [7])
+
+nodes = [
+    helper.make_node(
+        'Cast',
+        ['in'],
+        ['out'],
+        to=getattr(TensorProto, 'INT8'),
+    ),
+]
+graph_def = helper.make_graph(
+    nodes,
+    'float_to_int8',
+    [IN],
+    [OUT],
+)
+model_def = helper.make_model(graph_def, producer_name='float_to_int8.py', opset_imports=[onnx.OperatorSetIdProto(version=13)])
+onnx.save(model_def, 'float_to_int8.onnx')
diff --git a/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp b/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp
index 4fd527a7dfb..9b44dd7519e 100644
--- a/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp
+++ b/eval/src/tests/tensor/onnx_wrapper/onnx_wrapper_test.cpp
@@ -26,6 +26,7 @@ std::string dynamic_model = source_dir + "/dynamic.onnx";
 std::string int_types_model = source_dir + "/int_types.onnx";
 std::string guess_batch_model = source_dir + "/guess_batch.onnx";
 std::string unstable_types_model = source_dir + "/unstable_types.onnx";
+std::string float_to_int8_model = source_dir + "/float_to_int8.onnx";
 
 void dump_info(const char *ctx, const std::vector<TensorInfo> &info) {
     fprintf(stderr, "%s:\n", ctx);
@@ -401,4 +402,38 @@ TEST(OnnxTest, converted_unstable_types) {
     //-------------------------------------------------------------------------
 }
 
+TEST(OnnxTest, inspect_float_to_int8_conversion) {
+    Onnx model(float_to_int8_model, Onnx::Optimize::ENABLE);
+    ASSERT_EQ(model.inputs().size(), 1);
+    ASSERT_EQ(model.outputs().size(), 1);
+
+    ValueType in_type = ValueType::from_spec("tensor<float>(a[7])");
+    const float my_nan = std::numeric_limits<float>::quiet_NaN();
+    const float my_inf = std::numeric_limits<float>::infinity();
+    std::vector<float> in_values({-my_inf, -142, -42, my_nan, 42, 142, my_inf});
+    DenseValueView in(in_type, TypedCells(in_values));
+
+    Onnx::WirePlanner planner;
+    EXPECT_TRUE(planner.bind_input_type(in_type, model.inputs()[0]));
+    EXPECT_EQ(planner.make_output_type(model.outputs()[0]).to_spec(), "tensor<int8>(d0[7])");
+
+    auto wire_info = planner.get_wire_info(model);
+    Onnx::EvalContext ctx(model, wire_info);
+
+    const Value &out = ctx.get_result(0);
+    EXPECT_EQ(out.type().to_spec(), "tensor<int8>(d0[7])");
+    //-------------------------------------------------------------------------
+    ctx.bind_param(0, in);
+    ctx.eval();
+    auto cells = out.cells();
+    ASSERT_EQ(cells.type, CellType::INT8);
+    ASSERT_EQ(cells.size, 7);
+    auto out_values = cells.typify<Int8Float>();
+    for (size_t i = 0; i < 7; ++i) {
+        fprintf(stderr, "convert(float->int8): '%g' -> '%d'\n",
+                in_values[i], out_values[i].get_bits());
+    }
+    //-------------------------------------------------------------------------
+}
+
 GTEST_MAIN_RUN_ALL_TESTS()