private void TestGradientsSuccess(bool grad_inputs_provided)
{
var inputs = new TF_Output[2];
var outputs = new TF_Output[1];
var grad_outputs = new TF_Output[2];
var expected_grad_outputs = new TF_Output[2];
BuildSuccessGraph(inputs, outputs);
BuildExpectedGraph(grad_inputs_provided, expected_grad_outputs);
AddGradients(grad_inputs_provided, "gradients", inputs, 2, outputs, 1,
grad_outputs);
EXPECT_EQ(TF_OK, TF_GetCode(s_));
// Compare that the graphs match.
GraphDef expected_gdef;
GraphDef gdef;
EXPECT_TRUE(GetGraphDef(expected_graph_, out expected_gdef));
EXPECT_TRUE(GetGraphDef(graph_, out gdef));
// Assert.IsTrue(expected_gdef.ToString().Equals(gdef.ToString()));
// Compare that the output of the gradients of both graphs match.
RunGraphsAndCompareOutputs(grad_outputs, expected_grad_outputs);
}
private void BuildSuccessGraph(TF_Output[] inputs, TF_Output[] outputs)
{
// Construct the following graph:
// |
// z|
// |
// MatMul
// / \
// ^ ^
// | |
// x| y|
// | |
// | |
// Const_0 Const_1
//
var const0_val = new float[] { 1.0f, 2.0f, 3.0f, 4.0f };
var const1_val = new float[] { 1.0f, 0.0f, 0.0f, 1.0f };
var const0 = FloatConst2x2(graph_, s_, const0_val, "Const_0");
var const1 = FloatConst2x2(graph_, s_, const1_val, "Const_1");
var matmul = MatMul(graph_, s_, const0, const1, "MatMul");
inputs[0] = new TF_Output(const0, 0);
inputs[1] = new TF_Output(const1, 0);
outputs[0] = new TF_Output(matmul, 0);
EXPECT_EQ(TF_OK, TF_GetCode(s_));
}
private void TestGradientsSuccess(bool grad_inputs_provided)
{
var inputs = new TF_Output[2];
var outputs = new TF_Output[1];
var grad_outputs = new TF_Output[2];
var expected_grad_outputs = new TF_Output[2];
BuildSuccessGraph(inputs, outputs);
BuildExpectedGraph(grad_inputs_provided, expected_grad_outputs);
AddGradients(grad_inputs_provided, string.Empty, inputs, 2, outputs, 1,
grad_outputs);
// EXPECT_EQ(TF_OK, TF_GetCode(s_));
// Compare that the graphs match.
GraphDef expected_gdef;
GraphDef gdef;
EXPECT_TRUE(GetGraphDef(expected_graph_, out expected_gdef));
EXPECT_TRUE(GetGraphDef(graph_, out gdef));
//TF_EXPECT_GRAPH_EQ(expected_gdef, gdef);
// Compare that the output of the gradients of both graphs match.
RunGraphsAndCompareOutputs(grad_outputs, expected_grad_outputs);
}
private void TestGradientsSuccess(bool grad_inputs_provided)
{
var inputs = new TF_Output[2];
var outputs = new TF_Output[1];
var grad_outputs = new TF_Output[2];
var expected_grad_outputs = new TF_Output[2];
BuildSuccessGraph(inputs, outputs);
}
private void BuildExpectedGraph(bool grad_inputs_provided, TF_Output[] expected_grad_outputs)
{
// The expected graph looks like this if grad_inputs_provided.
// If grad_inputs_provided is false, Const_0 will be a OnesLike op.
// ^ ^
// dy| dx| // MatMul Gradient Graph
// | |
// MatMul_2 MatMul_1
// ^ ^ ^ ^
// | |----------| |
// | ^ |
// | dz| |
// | | |
// | Const_3 |
// | |
// | ^ |
// | z| | // MatMul Forward Graph
// | | |
// | MatMul |
// | / \ |
// | ^ ^ |
// | | | |
// |---x| y|----|
// | |
// | |
// Const_0 Const_1
//
float[] const0_val = { 1.0f, 2.0f, 3.0f, 4.0f };
float[] const1_val = { 1.0f, 0.0f, 0.0f, 1.0f };
var const0 = FloatConst2x2(expected_graph_, s_, const0_val, "Const_0");
var const1 = FloatConst2x2(expected_graph_, s_, const1_val, "Const_1");
var matmul = MatMul(expected_graph_, s_, const0, const1, "MatMul");
Operation const3;
if (grad_inputs_provided)
{
float[] const3_val = { 1.0f, 1.0f, 1.0f, 1.0f };
const3 = FloatConst2x2(expected_graph_, s_, const3_val, "GradInputs");
}
else
{
const3 = OnesLike(expected_graph_, s_, matmul, "gradients/OnesLike");
}
var matmul1 = MatMul(expected_graph_, s_, const3, const1,
"gradients/MatMul", false, true);
var matmul2 = MatMul(expected_graph_, s_, const0, const3,
"gradients/MatMul_1", true, false);
expected_grad_outputs[0] = new TF_Output(matmul1, 0);
expected_grad_outputs[1] = new TF_Output(matmul2, 0);
}
public static Operation Neg(Operation n, Graph graph, Status s, string name = "neg")
{
OperationDescription desc = c_api.TF_NewOperation(graph, "Neg", name);
var neg_input = new TF_Output(n, 0);
c_api.TF_AddInput(desc, neg_input);
var op = c_api.TF_FinishOperation(desc, s);
s.Check();
return(op);
}
/*void TestGradientsError(bool grad_inputs_provided)
* {
* var inputs = new TF_Output[1];
* var outputs = new TF_Output[1];
* var grad_outputs = new TF_Output[1];
*
* BuildErrorGraph(inputs, outputs);
*
* AddGradients(grad_inputs_provided, nullptr, inputs, 1, outputs, 1,
* grad_outputs);
*
* string expected_msg =
* "No gradient defined for op: TestOpWithNoGradient. Please see "
* "https://www.tensorflow.org/code/"
* "tensorflow/cc/gradients/README.md"
* " for instructions on how to add C++ gradients.";
* EXPECT_EQ(expected_msg, TF_Message(s_));
* }*/
private void AddGradients(bool grad_inputs_provided, string prefix, TF_Output[] inputs, int ninputs,
TF_Output[] outputs, int noutputs, TF_Output[] grad_outputs)
{
if (grad_inputs_provided)
{
var grad_inputs = new TF_Output[1];
float[] grad_inputs_val = { 1.0f, 1.0f, 1.0f, 1.0f };
var grad_inputs_op = FloatConst2x2(graph_, s_, grad_inputs_val, "GradInputs");
grad_inputs[0] = new TF_Output(grad_inputs_op, 0);
c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
ninputs, grad_inputs, s_, grad_outputs);
}
else
{
c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
ninputs, null, s_, grad_outputs);
}
}
public static Operation Add(Operation l, Operation r, Graph graph, Status s, string name = "add")
{
var desc = c_api.TF_NewOperation(graph, "AddN", name);
var inputs = new TF_Output[]
{
new TF_Output(l, 0),
new TF_Output(r, 0),
};
c_api.TF_AddInputList(desc, inputs, inputs.Length);
var op = c_api.TF_FinishOperation(desc, s);
s.Check();
return(op);
}
/*void TestGradientsError(bool grad_inputs_provided)
* {
* var inputs = new TF_Output[1];
* var outputs = new TF_Output[1];
* var grad_outputs = new TF_Output[1];
*
* BuildErrorGraph(inputs, outputs);
*
* AddGradients(grad_inputs_provided, nullptr, inputs, 1, outputs, 1,
* grad_outputs);
*
* string expected_msg =
* "No gradient defined for op: TestOpWithNoGradient. Please see "
* "https://www.tensorflow.org/code/"
* "tensorflow/cc/gradients/README.md"
* " for instructions on how to add C++ gradients.";
* EXPECT_EQ(expected_msg, TF_Message(s_));
* }*/
private void AddGradients(bool grad_inputs_provided, string prefix, TF_Output[] inputs, int ninputs,
TF_Output[] outputs, int noutputs, TF_Output[] grad_outputs)
{
if (grad_inputs_provided)
{
var grad_inputs = new TF_Output[1];
float[] grad_inputs_val = { 1.0f, 1.0f, 1.0f, 1.0f };
var grad_inputs_op = FloatConst2x2(graph_, s_, grad_inputs_val, "GradInputs");
grad_inputs[0] = new TF_Output(grad_inputs_op, 0);
IntPtr[] handles = new IntPtr[2] {
IntPtr.Zero, IntPtr.Zero
};
c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
ninputs, grad_inputs, s_, handles);
var op = new Operation(handles[0]);
}
else
{
//c_api.TF_AddGradientsWithPrefix(graph_, prefix, outputs, noutputs, inputs,
//ninputs, null, s_, grad_outputs);
}
}
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);
}
}
public void SetShape()
{
var s = new Status();
var graph = new Graph().as_default();
var feed = c_test_util.Placeholder(graph, s);
var feed_out_0 = new TF_Output(feed, 0);
// Fetch the shape, it should be completely unknown.
int num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(-1, num_dims);
// Set the shape to be unknown, expect no change.
c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle);
EXPECT_EQ(-1, num_dims);
// Set the shape to be 2 x Unknown
long[] dims = { 2, -1 };
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
num_dims = c_api.TF_GraphGetTensorNumDims(graph, feed_out_0, s.Handle);
EXPECT_EQ(2, num_dims);
// Get the dimension vector appropriately.
var returned_dims = new long[dims.Length];
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
Assert.IsTrue(Enumerable.SequenceEqual(dims, returned_dims));
// Set to a new valid shape: [2, 3]
dims[1] = 3;
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, dims.Length, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
// Fetch and see that the new value is returned.
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
Assert.IsTrue(Enumerable.SequenceEqual(dims, returned_dims));
// Try to set 'unknown' with unknown rank on the shape and see that
// it doesn't change.
c_api.TF_GraphSetTensorShape(graph, feed_out_0, null, -1, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, (int)returned_dims[0]);
EXPECT_EQ(3, (int)returned_dims[1]);
// Try to set 'unknown' with same rank on the shape and see that
// it doesn't change.
dims[0] = -1;
dims[1] = -1;
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, num_dims, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(2, num_dims);
EXPECT_EQ(2, (int)returned_dims[0]);
EXPECT_EQ(3, (int)returned_dims[1]);
// Try to fetch a shape with the wrong num_dims
c_api.TF_GraphGetTensorShape(graph, feed_out_0, returned_dims, 5, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT);
// Try to set an invalid shape (cannot change 2x3 to a 2x5).
dims[1] = 5;
c_api.TF_GraphSetTensorShape(graph, feed_out_0, dims, 2, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_INVALID_ARGUMENT);
// Test for a scalar.
var three = c_test_util.ScalarConst(3, graph, s);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
var three_out_0 = new TF_Output(three, 0);
num_dims = c_api.TF_GraphGetTensorNumDims(graph, three_out_0, s.Handle);
Assert.IsTrue(s.Code == TF_Code.TF_OK);
EXPECT_EQ(0, num_dims);
c_api.TF_GraphGetTensorShape(graph, feed_out_0, null, num_dims, s.Handle);
//Assert.IsTrue(s.Code == TF_Code.TF_OK);
// graph.Dispose();
s.Dispose();
}
protected void TF_AddInput(OperationDescription desc, TF_Output input)
{
c_api.TF_AddInput(desc, input);
}
