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@ -17,90 +17,66 @@ limitations under the License. */
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namespace paddle {
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namespace operators {
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class ReshapeOp : public framework::OperatorWithKernel {
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public:
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ReshapeOp(const std::string &type, const framework::VariableNameMap &inputs,
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const framework::VariableNameMap &outputs,
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const framework::AttributeMap &attrs)
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: OperatorWithKernel(type, inputs, outputs, attrs) {}
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void InferShape(framework::InferShapeContext *ctx) const override {
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// input check
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of ReshapeOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of ReshapeOp should not be null.");
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auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
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PADDLE_ENFORCE(shape.size() > 0, "Attr(shape) shouldn't be empty.");
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auto x_dims = ctx->GetInputDim("X");
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std::vector<size_t> neg_dims_idx;
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// set some dimension to -1 if it is unknown
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const int unknown_size = -1;
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for (size_t i = 0; i < shape.size(); ++i) {
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PADDLE_ENFORCE(shape[i] > 0 || shape[i] == unknown_size,
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"Each dimension of Attr(shape) must be positive or %d.",
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unknown_size);
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if (shape[i] == unknown_size) {
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neg_dims_idx.push_back(i);
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PADDLE_ENFORCE(neg_dims_idx.size() <= 1,
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"Only one dimension of Attr(shape) can be unknown.");
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}
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}
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int64_t capacity =
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std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>());
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int64_t in_size = framework::product(x_dims);
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if (neg_dims_idx.size() == 1) {
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// dim infer
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shape[neg_dims_idx[0]] = in_size / (-capacity);
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// recalculate capacity
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capacity = shape[neg_dims_idx[0]] * (-capacity);
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}
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// capacity check
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PADDLE_ENFORCE(capacity == in_size,
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"The size of Input(X) mismatches with Attr(shape).");
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// resize output
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std::vector<int64_t> shape_int64(shape.size(), 0);
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std::transform(shape.begin(), shape.end(), shape_int64.begin(),
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[](int a) { return static_cast<int64_t>(a); });
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auto out_dims = framework::make_ddim(shape_int64);
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ctx->SetOutputDim("Out", out_dims);
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if (shape[0] == x_dims[0]) {
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// Only pass LoD when the first dimension is equal between
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// output and input.
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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}
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};
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class ReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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ReshapeOpMaker(OpProto *proto, OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "The input tensor of reshape operator.");
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AddOutput("Out", "The output tensor of reshape operator.");
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AddAttr<std::vector<int>>("shape",
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"(vector<int>) "
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"Target shape of reshape operator.");
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AddInput("X", "(Tensor). The input tensor of reshape operator.");
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AddInput("Shape",
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"(Tensor<int32>, optional). If provided, reshape according to "
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"this given shape. That is to say it has a higher priority than "
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"the shape attribute, while the shape attribute still should be "
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"set correctly to gurantee shape inference in compile time.")
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.AsDispensable();
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AddOutput("Out", "(Tensor). The output tensor of reshape operator.");
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AddAttr<std::vector<int>>(
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"shape", "(std::vector<int>) Target shape of reshape operator.");
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AddAttr<bool>("inplace",
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"Change the source tensor's shape without copy memory.")
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.SetDefault(true);
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"(default: false) Change the source tensor's shape without "
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"memory copy. When Attr(inplace) is set true, the output "
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"tensor shares memory with Input(X), otherwise, a new output "
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"tensor is created, and its data are copied from Input(x).")
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.SetDefault(false);
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AddComment(R"DOC(
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Reshape Operator.
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Reshape Input(X) into the shape specified by Attr(shape).
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Reshape Input(X) into the shape specified by Attr(shape) or Input(Shape). The
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data in Input(X) are unchanged.
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Examples:
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An example:
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Given a 2-D tensor X with 2 rows and 2 columns : [[1, 2], [3, 4]]
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1. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
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specified by Attr(shape) is [6, 8], the reshape operator will transform Input(X)
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into a 2-D tensor with shape [6, 8] and leaving Input(X)'s data unchanged.
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and target shape = [1, 4], the reshape operator will transform
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the tensor X into a 2-D tensor: [[1, 2, 3, 4]]
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2. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
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specified by Attr(shape) is [2, 3, -1, 2], the reshape operator will transform
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Input(X) into a 4-D tensor with shape [2, 3, 4, 2] and leaving Input(X)'s data
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unchanged. In this case, one and only dimension of Attr(shape) can be set to -1,
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the value of this dimension is inferred from the total element number of
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Input(X) and remaining dimensions.
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3. Given a 3-D tensor Input(X) with a shape [2, 4, 6], and the target shape
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specified by Attr(shape) is [-1, 0, 3, 2], the reshape operator will transform
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Input(X) into a 4-D tensor with shape [2, 4, 3, 2] and leaving Input(X)'s data
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unchanged. In this case, besides -1, 0 means the actual dimension value is going
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to be copied from the corresponding dimension of Input(X).
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Note:
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1. One and only one dimension in Attr(shape) can be set -1. In this case,
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the actual dimension value will be infered from the total element number of
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Input(X) and remaining dimensions.
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2. More than one dimensions in Attr(shape) can be set to 0, which means the real
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dimension value will be copied from Input(X) at runtime. Note that the index of
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0 can not exceed Rank(X). For example, Input(X) is a 3-D tensor with shape
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[2, 3, 4], Attr(shape) = [2, 3, 2, 0] is an invalid input.
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3. Input(Shape) has a higher priority than Attr(shape) if it is provided, while
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Attr(shape) still should be set correctly to gurantee shape inference in
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compile-time.
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One dimension in the target shape can be set -1, representing that its
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size is unknown. In this case, the real dimension will be infered from
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the original shape of Input(X) and other dimensions in the target shape.
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)DOC");
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}
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};
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@ -119,6 +95,14 @@ class ReshapeGradOp : public framework::OperatorWithKernel {
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"Input(Out@GRAD) shouldn't be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext &ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
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ctx.device_context());
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}
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};
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} // namespace operators
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guosheng
7 years ago