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C++ gradients for MaxPool3D, AvgPool and AvgPool3D #17222

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Feb 26, 2018
Merged

C++ gradients for MaxPool3D, AvgPool and AvgPool3D #17222

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64 changes: 64 additions & 0 deletions tensorflow/cc/gradients/nn_grad.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -196,6 +196,70 @@ Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op,
}
REGISTER_GRADIENT_OP("MaxPoolV2", MaxPoolGradV2Helper);

Status MaxPool3DGradHelper(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
std::vector<int32> ksize;
std::vector<int32> strides;
string padding;
string data_format;
auto attrs = op.output(0).node()->attrs();
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
MaxPool3DGrad::Attrs grad_attrs;
grad_attrs.DataFormat(data_format);
auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0],
ksize, strides, padding, grad_attrs);
grad_outputs->push_back(dx);
return scope.status();
}
REGISTER_GRADIENT_OP("MaxPool3D", MaxPool3DGradHelper);

Status AvgPoolGradHelper(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
std::vector<int32> ksize;
std::vector<int32> strides;
string padding;
string data_format;
auto attrs = op.output(0).node()->attrs();
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
internal::AvgPoolGrad::Attrs grad_attrs;
grad_attrs.DataFormat(data_format);
auto dx =
internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0],
ksize, strides, padding, grad_attrs);
grad_outputs->push_back(dx);
return scope.status();
}
REGISTER_GRADIENT_OP("AvgPool", AvgPoolGradHelper);

Status AvgPool3DGradHelper(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
std::vector<int32> ksize;
std::vector<int32> strides;
string padding;
string data_format;
auto attrs = op.output(0).node()->attrs();
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "ksize", &ksize));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "strides", &strides));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding));
TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format));
AvgPool3DGrad::Attrs grad_attrs;
grad_attrs.DataFormat(data_format);
auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0],
ksize, strides, padding, grad_attrs);
grad_outputs->push_back(dx);
return scope.status();
}
REGISTER_GRADIENT_OP("AvgPool3D", AvgPool3DGradHelper);

Status LRNGradHelper(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs){
Expand Down
44 changes: 41 additions & 3 deletions tensorflow/cc/gradients/nn_grad_test.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -31,8 +31,11 @@ using ops::Elu;
using ops::L2Loss;
using ops::LogSoftmax;
using ops::LRN;
using ops::AvgPool;
using ops::AvgPool3D;
using ops::MaxPool;
using ops::MaxPoolV2;
using ops::MaxPool3D;
using ops::Placeholder;
using ops::Relu;
using ops::Relu6;
Expand Down Expand Up @@ -70,9 +73,9 @@ class NNGradTest : public ::testing::Test {

// Sets tensor with random values, ensuring that the max value is largest by
// a reasonable amount.
// This is an issue for MaxPool and MaxPoolV2, in which perturbations by the
// numeric gradient computation in the gradient checker can change the max
// value if values are too close together.
// This is an issue for MaxPool, MaxPoolV2 and MaxPool3D, in which
// perturbations by the numeric gradient computation in the gradient checker
// can change the max value if values are too close together.
template <typename T>
void SetRandomValuesWithBumpedMax(Tensor* tensor) {
auto tensor_flat = tensor->flat<T>();
Expand Down Expand Up @@ -203,6 +206,41 @@ TEST_F(NNGradTest, MaxPoolGradV2Helper) {
RunTest(x, x_init_value, y, y_shape);
}

TEST_F(NNGradTest, MaxPool3DGradHelper) {
TensorShape x_shape({1, 3, 3, 3, 1});
TensorShape y_shape({1, 1, 1, 1, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
// Setup window and strides so that we only do one MaxPool3D.
const std::vector<int> ksize{1, 3, 3, 3, 1};
const std::vector<int> strides{1, 3, 3, 3, 1};
auto y = MaxPool3D(scope_, x, ksize, strides, "VALID");
Tensor x_init_value = Tensor(DT_FLOAT, x_shape);
SetRandomValuesWithBumpedMax<float>(&x_init_value);
RunTest(x, x_init_value, y, y_shape);
}

TEST_F(NNGradTest, AvgPoolGradHelper) {
TensorShape x_shape({1, 2, 2, 1});
TensorShape y_shape({1, 1, 1, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
// Setup window and strides so that we only do one AvgPool.
const std::vector<int> ksize{1, 2, 2, 1};
const std::vector<int> strides{1, 2, 2, 1};
auto y = AvgPool(scope_, x, ksize, strides, "SAME");
RunTest(x, x_shape, y, y_shape);
}

TEST_F(NNGradTest, AvgPool3DGradHelper) {
TensorShape x_shape({1, 3, 3, 3, 1});
TensorShape y_shape({1, 1, 1, 1, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
// Setup window and strides so that we only do one AvgPool3D.
const std::vector<int> ksize{1, 3, 3, 3, 1};
const std::vector<int> strides{1, 3, 3, 3, 1};
auto y = AvgPool3D(scope_, x, ksize, strides, "SAME");
RunTest(x, x_shape, y, y_shape);
}

TEST_F(NNGradTest, LRN){
TensorShape x_shape({1, 1, 2, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape));
Expand Down