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#include "caffe2/operators/conv_op.h"
#include "caffe2/operators/conv_op_impl.h"
#include "caffe2/operators/conv_pool_op_base.h"
namespace caffe2 {
std::vector<TensorShape> TensorInferenceForConvGradient(
const OperatorDef& def,
const std::vector<TensorShape>& in) {
CAFFE_ENFORCE_EQ(in.size(), 3U, "ConvGradient requires 3 inputs");
if (in[0].unknown_shape()) {
std::vector<TensorShape> out(1);
out[0].set_unknown_shape(true);
return out;
}
ArgumentHelper helper(def);
const auto no_bias = helper.GetSingleArgument<int>("no_bias", 0);
const auto n_outputs = def.output_size();
vector<TensorShape> out(n_outputs);
// FILTER_GRAD has the same shape as FILTER
out[0] = in[1];
if (!no_bias) {
vector<int64_t> bias_shape = {in[1].dims(0)};
out[1] = CreateTensorShape(bias_shape, in[1].data_type());
}
if (n_outputs == 3 || (no_bias && n_outputs == 2)) {
// INPUT_GRAD has the same shape as INPUT
out[out.size() - 1] = in[0];
}
return out;
}
OpSchema::Cost CostInferenceForConvGradient(
const OperatorDef& def,
const vector<TensorShape>& inputs) {
CAFFE_ENFORCE_EQ(inputs.size(), 3U, "ConvGradient requires 3 inputs");
ArgumentHelper helper(def);
const auto order =
StringToStorageOrder(helper.GetSingleArgument<string>("order", "NCHW"));
const auto no_bias = helper.GetSingleArgument<int>("no_bias", 0);
const auto n_outputs = def.output_size();
const auto& outputs = TensorInferenceForConvGradient(def, inputs);
const auto& X = inputs[0];
const auto& filter = inputs[1];
const auto& dY = inputs[2];
const auto N = X.dims(0);
const auto M = filter.dims(0);
const auto C =
(order == StorageOrder::NCHW ? X.dims(1) : X.dims(X.dims_size() - 1));
const auto output_image_size =
(order == StorageOrder::NCHW
? nElemFromDim(dY, 2)
: nElemBetweenDim(dY, 1, dY.dims_size() - 1));
auto kernel_elem =
(order == StorageOrder::NCHW
? nElemFromDim(filter, 2)
: nElemBetweenDim(filter, 1, filter.dims_size() - 1));
struct OpSchema::Cost c;
c.flops = N * 2 * M * kernel_elem * C * output_image_size;
if (!no_bias) {
c.flops += N * (M * output_image_size);
}
if (n_outputs == 3 || (no_bias && n_outputs == 2)) {
c.flops += N * 2 * M * kernel_elem * C * output_image_size;
}
c.bytes_read = (nElemFromDim(X) + nElemFromDim(filter) + nElemFromDim(dY)) *
sizeof(float);
for (auto i = 0; i < n_outputs; i++) {
c.bytes_written += nElemFromDim(outputs[i]) * sizeof(float);
}
c.params_bytes = nElemFromDim(filter) * sizeof(float);
return c;
}
REGISTER_CPU_OPERATOR(ConvGradient, ConvGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(ConvGradient)
.NumInputs(2, 3)
.NumOutputs(1, 3)
.TensorInferenceFunction(TensorInferenceForConvGradient)
.CostInferenceFunction(CostInferenceForConvGradient);
REGISTER_CPU_OPERATOR(Conv1DGradient, ConvGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(Conv1DGradient).NumInputs(2, 3).NumOutputs(1, 3);
REGISTER_CPU_OPERATOR(Conv2DGradient, ConvGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(Conv2DGradient).NumInputs(2, 3).NumOutputs(1, 3);
REGISTER_CPU_OPERATOR(Conv3DGradient, ConvGradientOp<float, CPUContext>);
OPERATOR_SCHEMA(Conv3DGradient).NumInputs(2, 3).NumOutputs(1, 3);
class GetConvGradient : public GradientMakerBase {
using GradientMakerBase::GradientMakerBase;
vector<OperatorDef> GetGradientDefs() override {
CAFFE_ENFORCE(def_.input_size() == 3 || def_.input_size() == 2);
ArgumentHelper argsHelper(def_);
// NOLINTNEXTLINE(modernize-use-bool-literals)
auto compute_dX = !argsHelper.GetSingleArgument<bool>("no_gradient_to_input", 0);
if (def_.input_size() == 3) {
if (compute_dX) {
return SingleGradientDef(
def_.type() + "Gradient",
"",
vector<string>{I(0), I(1), GO(0)},
vector<string>{GI(1), GI(2), GI(0)});
} else {
return SingleGradientDef(
def_.type() + "Gradient",
"",
vector<string>{I(0), I(1), GO(0)},
vector<string>{GI(1), GI(2)});
}
} else {
if (compute_dX) {
return SingleGradientDef(
def_.type() + "Gradient",
"",
vector<string>{I(0), I(1), GO(0)},
vector<string>{GI(1), GI(0)},
vector<Argument>{MakeArgument<int>("no_bias", 1)});
} else {
return SingleGradientDef(
def_.type() + "Gradient",
"",
vector<string>{I(0), I(1), GO(0)},
vector<string>{GI(1)},
vector<Argument>{MakeArgument<int>("no_bias", 1)});
}
}
}
};
REGISTER_GRADIENT(Conv, GetConvGradient);
REGISTER_GRADIENT(Conv1D, GetConvGradient);
REGISTER_GRADIENT(Conv2D, GetConvGradient);
REGISTER_GRADIENT(Conv3D, GetConvGradient);
} // namespace caffe2
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