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Conv2DGrad & MaxPoolGradHelper #12665

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Sep 7, 2017
Merged

Conv2DGrad & MaxPoolGradHelper #12665

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c30ef18
Conv2DGrad, MaxPoolGradHelper
bpiel Aug 28, 2017
5619b05
MaxPoolGradV2Helper
bpiel Aug 29, 2017
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1 change: 1 addition & 0 deletions tensorflow/cc/BUILD
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Expand Up @@ -305,6 +305,7 @@ cc_library(
":cc_ops",
":cc_ops_internal",
":grad_op_registry",
":gradients",
],
alwayslink = 1,
)
Expand Down
81 changes: 81 additions & 0 deletions tensorflow/cc/gradients/nn_grad.cc
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Expand Up @@ -18,6 +18,7 @@ limitations under the License.
#include "tensorflow/cc/ops/standard_ops.h"

#include "tensorflow/cc/framework/grad_op_registry.h"
#include "tensorflow/cc/framework/gradients.h"

namespace tensorflow {
namespace ops {
Expand Down Expand Up @@ -118,6 +119,86 @@ Status BiasAddGradHelper(const Scope& scope, const Operation& op,
}
REGISTER_GRADIENT_OP("BiasAdd", BiasAddGradHelper);

Status Conv2DGrad(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
string data_format;
string padding;
std::vector<int32> strides;
bool use_cudnn_on_gpu;
auto attrs = op.output(0).node()->attrs();
GetNodeAttr(attrs, "data_format", &data_format);
GetNodeAttr(attrs, "padding", &padding);
GetNodeAttr(attrs, "strides", &strides);
GetNodeAttr(attrs, "use_cudnn_on_gpu", &use_cudnn_on_gpu);
Conv2DBackpropInput::Attrs input_attrs;
input_attrs.DataFormat(data_format);
input_attrs.UseCudnnOnGpu(use_cudnn_on_gpu);
auto dx_1 = Conv2DBackpropInput(scope, Shape(scope, op.input(0)),
op.input(1), grad_inputs[0],
strides, padding, input_attrs);
grad_outputs->push_back(dx_1);
Conv2DBackpropFilter::Attrs filter_attrs;
filter_attrs.DataFormat(data_format);
filter_attrs.UseCudnnOnGpu(use_cudnn_on_gpu);
auto dx_2 = Conv2DBackpropFilter(scope, op.input(0),
Shape(scope, op.input(1)), grad_inputs[0],
strides, padding, filter_attrs);
grad_outputs->push_back(dx_2);
return scope.status();
}
REGISTER_GRADIENT_OP("Conv2D", Conv2DGrad);

Status MaxPoolGradHelper(const Scope& scope, const Operation& op,
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I am not sure what the status of MaxPool and MaxPoolV2 is, but my guess is that MaxPoolV2 is the one more commonly used now. You may want to also add that gradient

tensorflow/tensorflow/python/ops/nn_grad.py

Line 545 in 86d9171

@ops.RegisterGradient("MaxPoolV2")

If you feel like it you can add MaxPoolGradGrad and MaxPoolGradGradV2, but I doubt these are blocking your models.

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Ah, didn't know about MaxPoolV2. Added it.

const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
string data_format;
string padding;
std::vector<int32> strides;
std::vector<int32> ksize;
auto attrs = op.output(0).node()->attrs();
GetNodeAttr(attrs, "data_format", &data_format);
GetNodeAttr(attrs, "ksize", &ksize);
GetNodeAttr(attrs, "padding", &padding);
GetNodeAttr(attrs, "strides", &strides);
internal::MaxPoolGrad::Attrs grad_attrs;
grad_attrs.DataFormat(data_format);
auto dx = internal::MaxPoolGrad(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("MaxPool", MaxPoolGradHelper);

Status MaxPoolGradV2Helper(const Scope& scope, const Operation& op,
const std::vector<Output>& grad_inputs,
std::vector<Output>* grad_outputs) {
string data_format;
string padding;
auto attrs = op.output(0).node()->attrs();
GetNodeAttr(attrs, "data_format", &data_format);
GetNodeAttr(attrs, "padding", &padding);
MaxPoolGradV2::Attrs grad_attrs;
grad_attrs.DataFormat(data_format);
auto dx = MaxPoolGradV2(scope, op.input(0),
op.output(0),
grad_inputs[0],
op.input(1),
op.input(2),
padding,
grad_attrs);
grad_outputs->push_back(dx);
grad_outputs->push_back(NoGradient());
grad_outputs->push_back(NoGradient());
return scope.status();
}
REGISTER_GRADIENT_OP("MaxPoolV2", MaxPoolGradV2Helper);



} // anonymous namespace
} // namespace ops
} // namespace tensorflow
27 changes: 27 additions & 0 deletions tensorflow/cc/gradients/nn_grad_test.cc
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Expand Up @@ -139,5 +139,32 @@ TEST_F(NNGradTest, BiasAddGradHelper) {
RunTest({x,bias}, {shape, bias_shape}, {y}, {shape});
}

TEST_F(NNGradTest, Conv2DGrad) {
TensorShape shape({1, 2, 2, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
Tensor filter = test::AsTensor<float>({0.5f}, {1, 1, 1, 1});
const std::vector<int> strides{1, 1, 1, 1};
auto y = Conv2D(scope_, x, filter, strides, "SAME");
RunTest(x, shape, y, shape);
}

TEST_F(NNGradTest, MaxPoolGradHelper) {
TensorShape shape({1, 2, 2, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
const std::vector<int> ksize{1, 2, 2, 1};
const std::vector<int> strides{1, 1, 1, 1};
auto y = MaxPool(scope_, x, ksize, strides, "SAME");
RunTest(x, shape, y, shape);
}

TEST_F(NNGradTest, MaxPoolGradV2Helper) {
TensorShape shape({1, 2, 2, 1});
auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape));
Tensor ksize = test::AsTensor<int>({1, 2, 2, 1}, {4});
Tensor strides = test::AsTensor<int>({1, 1, 1, 1}, {4});
auto y = MaxPoolV2(scope_, x, ksize, strides, "SAME");
RunTest(x, shape, y, shape);
}

} // namespace
} // namespace tensorflow