unsafe void Run(KeyValuePair <Operation, Tensor>[] inputs, TF_Output[] outputs, int[] expected_results)
{
var csession = new CSession(host_graph_, s_);
ASSERT_EQ(TF_OK, s_.Code, s_.Message);
csession.SetInputs(inputs);
csession.SetOutputs(outputs);
csession.Run(s_);
ASSERT_EQ(TF_OK, s_.Code, s_.Message);
for (int i = 0; i < expected_results.Length; ++i)
{
var output = csession.output_tensor(i);
ASSERT_TRUE(output != IntPtr.Zero);
EXPECT_EQ(TF_DataType.TF_INT32, c_api.TF_TensorType(output));
EXPECT_EQ(0, c_api.TF_NumDims(output));
ASSERT_EQ(sizeof(int), (int)c_api.TF_TensorByteSize(output));
var output_contents = c_api.TF_TensorData(output);
EXPECT_EQ(expected_results[i], *(int *)output_contents.ToPointer());
}
}
private void RunGraphsAndCompareOutputs(TF_Output[] grad_outputs, TF_Output[] expected_grad_outputs)
{
var csession = new CSession(graph_, s_);
var expected_csession = new CSession(expected_graph_, s_);
var grad_outputs_vec = grad_outputs;
csession.SetOutputs(grad_outputs_vec);
csession.Run(s_);
ASSERT_EQ(TF_OK, TF_GetCode(s_));
var out0 = csession.output_tensor(0);
var out1 = csession.output_tensor(1);
var expected_grad_outputs_vec = expected_grad_outputs;
expected_csession.SetOutputs(expected_grad_outputs_vec);
expected_csession.Run(s_);
ASSERT_EQ(TF_OK, TF_GetCode(s_));
var expected_out0 = expected_csession.output_tensor(0);
var expected_out1 = expected_csession.output_tensor(1);
//CompareTensors(out0, expected_out0);
//CompareTensors(out1, expected_out1);
}
public void Session()
{
lock (Locks.ProcessWide)
{
var s = new Status();
var graph = new Graph().as_default();
// Make a placeholder operation.
var feed = c_test_util.Placeholder(graph, s);
// Make a constant operation with the scalar "2".
var two = c_test_util.ScalarConst(2, graph, s);
// Add operation.
var add = c_test_util.Add(feed, two, graph, s);
var csession = new CSession(graph, s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
// Run the graph.
var inputs = new Dictionary <Operation, Tensor>();
inputs.Add(feed, new Tensor(3));
csession.SetInputs(inputs);
var outputs = new TF_Output[] { new TF_Output(add, 0) };
csession.SetOutputs(outputs);
csession.Run(s);
Tensor outTensor = csession.output_tensor(0);
EXPECT_EQ(TF_DataType.TF_INT32, outTensor.dtype);
EXPECT_EQ(0, outTensor.NDims);
ASSERT_EQ((ulong)sizeof(uint), outTensor.bytesize);
var output_contents = outTensor.ToArray <int>();
EXPECT_EQ(3 + 2, output_contents[0]);
// Add another operation to the graph.
var neg = c_test_util.Neg(add, graph, s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
// Run up to the new operation.
inputs = new Dictionary <Operation, Tensor>();
inputs.Add(feed, new Tensor(7));
csession.SetInputs(inputs);
outputs = new TF_Output[] { new TF_Output(neg, 0) };
csession.SetOutputs(outputs);
csession.Run(s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
outTensor = csession.output_tensor(0);
ASSERT_TRUE(outTensor != IntPtr.Zero);
EXPECT_EQ(TF_DataType.TF_INT32, outTensor.dtype);
EXPECT_EQ(0, outTensor.NDims); // scalar
ASSERT_EQ((ulong)sizeof(uint), outTensor.bytesize);
output_contents = outTensor.ToArray <int>();
EXPECT_EQ(-(7 + 2), output_contents[0]);
// Clean up
csession.CloseAndDelete(s);
ASSERT_EQ(TF_Code.TF_OK, s.Code);
}
}