diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt
index ab1d214333..50f5f34021 100644
--- a/paddle/fluid/operators/CMakeLists.txt
+++ b/paddle/fluid/operators/CMakeLists.txt
@@ -265,6 +265,7 @@ op_library(recurrent_op DEPS executor)
op_library(warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale)
op_library(cos_sim_op DEPS cos_sim_functor)
op_library(parallel_do_op DEPS executor)
+op_library(unsqueeze_op DEPS reshape_op)
if (WITH_GPU)
op_library(conv_op DEPS vol2col depthwise_conv im2col)
diff --git a/paddle/fluid/operators/unsqueeze_op.cc b/paddle/fluid/operators/unsqueeze_op.cc
index 373dac8bab..c503988676 100644
--- a/paddle/fluid/operators/unsqueeze_op.cc
+++ b/paddle/fluid/operators/unsqueeze_op.cc
@@ -12,41 +12,35 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
-#include "paddle/fluid/operators/unsqueeze_op.h"
#include <string>
#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
-using framework::OpKernelType;
-using framework::Tensor;
-
-class UnsqueezeOp : public framework::OperatorWithKernel {
+class UnsqueezeOpInferShape : public framework::InferShapeBase {
public:
- using framework::OperatorWithKernel::OperatorWithKernel;
-
- void InferShape(framework::InferShapeContext* ctx) const override {
+ void operator()(framework::InferShapeContext *ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of UnsqueezeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of UnsqueezeOp should not be null.");
- const auto& axes = ctx->Attrs().Get<std::vector<int>>("axes");
+ const auto &axes = ctx->Attrs().Get<std::vector<int>>("axes");
PADDLE_ENFORCE(!axes.empty(),
"The unsqueeze axes information must be set by Attr(axes).");
- const auto& x_dims = ctx->GetInputDim("X");
+ const auto &x_dims = ctx->GetInputDim("X");
// Validity Check: input tensor dims (<6).
- PADDLE_ENFORCE(x_dims.size() < 6,
+ PADDLE_ENFORCE(static_cast<int>(x_dims.size()) <= 6,
"Invalid dimensions, dynamic dimensions should within "
- "[0, 5] dimensions (Eigen limit).");
+ "[1, 6] dimensions (Eigen limit).");
// Validity Check: the range of unsqueeze aixs.
- // TODO(chenweihang): Don't consider negative axis?.
- for (unsigned int idx = 0; idx < axes.size(); ++idx) {
- PADDLE_ENFORCE(axes[idx] < 6,
+ for (int axis : axes) {
+ PADDLE_ENFORCE(axis < 6,
"Invalid dimensions, input axis should within "
- "[0, 5] dimensions (Eigen limit).");
+ "[1, 6] dimensions (Eigen limit).");
}
auto out_dims = GetOutputShape(axes, x_dims);
@@ -54,33 +48,7 @@ class UnsqueezeOp : public framework::OperatorWithKernel {
}
static framework::DDim GetOutputShape(const std::vector<int> unsqz_dims,
- const framework::DDim& in_dims) {
- /*
- * STL version
- * Test Error! don't know why?.
- std::vector<int64_t> output_shape;
-
- // Contruct base output shape
- for(int idx = 0; idx < in_dims.size(); ++idx) {
- output_shape.emplace_back(in_dims[idx]);
- }
- // Validity Check: output dimensions limit.
- PADDLE_ENFORCE(unsqz_dims.size() + output_shape.size() < 6,
- "The Attr(axes) size is too large. The output shape should "
- "be less than 6 (Eigne limit).");
- // Insert the unsqueeze axis in turn.
- auto it = output_shape.begin();
- for (int axis : unsqz_dims) {
- int cur = axis < 0 ? (axis + output_shape.size() + 1)
- : axis;
- // Vaildity Check: the axis bound
- PADDLE_ENFORCE(cur >= 0 && cur <= static_cast<int>(output_shape.size()),
- "The unsqueeze dims must be within range of current
- rank.");
- output_shape.emplace(it + axis, 1);
- }
- */
-
+ const framework::DDim &in_dims) {
unsigned int unsqz_mask = 0;
unsigned int front = 0, back = 0;
int output_dims_size = in_dims.size();
@@ -93,17 +61,17 @@ class UnsqueezeOp : public framework::OperatorWithKernel {
cur >= 0 && cur <= output_dims_size,
"The unsqueeze dims must be within range of current rank.");
// Save the front part.
- front = unsqz_mask & ((1 << axis) - 1);
+ front = unsqz_mask & ((1 << cur) - 1);
// Move the back part.
- back = unsqz_mask & ~((1 << axis) - 1);
+ back = unsqz_mask & ~((1 << cur) - 1);
back <<= 1;
// Merge two part.
- back |= (1 << axis);
+ back |= (1 << cur);
unsqz_mask = front | back;
// Add the output size.
output_dims_size++;
// Validity Check: rank range.
- PADDLE_ENFORCE(output_dims_size < 6,
+ PADDLE_ENFORCE(output_dims_size <= 6,
"The output tensor's rank should be less than 6.");
}
@@ -121,6 +89,31 @@ class UnsqueezeOp : public framework::OperatorWithKernel {
}
};
+class UnsqueezeOp : public framework::OperatorBase {
+ public:
+ UnsqueezeOp(const std::string &type, const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+ void RunImpl(const framework::Scope &scope,
+ const platform::Place &place) const override {
+ auto &axes = Attr<std::vector<int>>("axes");
+ auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+ auto out_dims = UnsqueezeOpInferShape::GetOutputShape(axes, x_dims);
+
+ framework::AttributeMap attrs;
+ attrs["shape"] = framework::vectorize2int(out_dims);
+ attrs["inplace"] = Attr<bool>("inplace");
+ // Invoke Reshape op.
+ auto reshape_op = framework::OpRegistry::CreateOp(
+ "reshape", {{"X", {Input("X")}}, {"Shape", {}}},
+ {{"Out", {Output("Out")}}}, attrs);
+ reshape_op->Run(scope, place);
+ }
+};
+
class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
@@ -150,42 +143,49 @@ class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker {
}
};
-class UnsqueezeGradOp : public framework::OperatorWithKernel {
+class UnsqueezeGradInferShape : public framework::InferShapeBase {
public:
- using framework::OperatorWithKernel::OperatorWithKernel;
-
- void InferShape(framework::InferShapeContext* ctx) const override {
- PADDLE_ENFORCE(ctx->HasInput("X"),
- "Input(X) of UnsqueezeGradOp should not be null.");
- PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
- "Output(Out@GRAD) of UnsqueezeGradOp should not be null.");
+ void operator()(framework::InferShapeContext *ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+ ctx->ShareLoD("X", framework::GradVarName("X"));
}
+};
- protected:
- framework::OpKernelType GetExpectedKernelType(
- const framework::ExecutionContext& ctx) const override {
- return framework::OpKernelType(
- framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
- ctx.device_context());
+class UnsqueezeGradOp : public framework::OperatorBase {
+ public:
+ UnsqueezeGradOp(const std::string &type,
+ const framework::VariableNameMap &inputs,
+ const framework::VariableNameMap &outputs,
+ const framework::AttributeMap &attrs)
+ : OperatorBase(type, inputs, outputs, attrs) {}
+
+ private:
+ void RunImpl(const framework::Scope &scope,
+ const platform::Place &place) const override {
+ auto dx_name = Output(framework::GradVarName("X"));
+ auto dout_name = Input(framework::GradVarName("Out"));
+ auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
+
+ framework::AttributeMap attrs;
+ attrs["shape"] = framework::vectorize2int(x_dims);
+ attrs["inplace"] = Attr<bool>("inplace");
+
+ auto reshape_op = framework::OpRegistry::CreateOp(
+ "reshape", {{"X", {dout_name}}, {"Shape", {}}}, {{"Out", {dx_name}}},
+ attrs);
+ reshape_op->Run(scope, place);
}
};
} // namespace operators
} // namespace paddle
+// Tell linker to use reshape op.
+USE_OP(reshape);
+
namespace ops = paddle::operators;
REGISTER_OPERATOR(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
+ ops::UnsqueezeOpInferShape,
paddle::framework::DefaultGradOpDescMaker<true>);
-REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp);
-REGISTER_OP_CPU_KERNEL(
- unsqueeze, ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, float>,
- ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, double>,
- ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int>,
- ops::UnsqueezeKernel<paddle::platform::CPUDeviceContext, int64_t>);
-REGISTER_OP_CPU_KERNEL(
- unsqueeze_grad,
- ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, float>,
- ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, double>,
- ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, int>,
- ops::UnsqueezeGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
+REGISTER_OPERATOR(unsqueeze_grad, ops::UnsqueezeGradOp,
+ ops::UnsqueezeGradInferShape);
diff --git a/paddle/fluid/operators/unsqueeze_op.cu b/paddle/fluid/operators/unsqueeze_op.cu
deleted file mode 100644
index 4d111190cd..0000000000
--- a/paddle/fluid/operators/unsqueeze_op.cu
+++ /dev/null
@@ -1,30 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#define EIGEN_USE_GPU
-
-#include "paddle/fluid/operators/unsqueeze_op.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
- unsqueeze, ops::UnsqueezeKernel<paddle::platform::CUDADeviceContext, float>,
- ops::UnsqueezeKernel<paddle::platform::CUDADeviceContext, double>,
- ops::UnsqueezeKernel<paddle::platform::CUDADeviceContext, int>,
- ops::UnsqueezeKernel<paddle::platform::CUDADeviceContext, int64_t>);
-REGISTER_OP_CUDA_KERNEL(
- unsqueeze_grad,
- ops::UnsqueezeGradKernel<paddle::platform::CUDADeviceContext, float>,
- ops::UnsqueezeGradKernel<paddle::platform::CUDADeviceContext, double>,
- ops::UnsqueezeGradKernel<paddle::platform::CUDADeviceContext, int>,
- ops::UnsqueezeGradKernel<paddle::platform::CUDADeviceContext, int64_t>);
diff --git a/paddle/fluid/operators/unsqueeze_op.h b/paddle/fluid/operators/unsqueeze_op.h
deleted file mode 100644
index aa45fb3113..0000000000
--- a/paddle/fluid/operators/unsqueeze_op.h
+++ /dev/null
@@ -1,72 +0,0 @@
-/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
- http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License. */
-
-#pragma once
-
-#include <vector>
-
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/operator.h"
-
-namespace paddle {
-namespace operators {
-
-using Tensor = framework::Tensor;
-
-template <typename DeviceContext, typename T>
-class UnsqueezeKernel : public framework::OpKernel<T> {
- public:
- void Compute(const framework::ExecutionContext &ctx) const override {
- auto *out = ctx.Output<framework::LoDTensor>("Out");
- auto *in = ctx.Input<framework::LoDTensor>("X");
-
- framework::DDim out_dims = out->dims();
-
- bool inplace = ctx.Attr<bool>("inplace");
- out->Resize(out_dims);
- if (!inplace) {
- out->mutable_data<T>(ctx.GetPlace());
- framework::TensorCopySync(*in, ctx.GetPlace(), out);
- out->Resize(out_dims);
- } else {
- out->ShareDataWith(*in);
- out->Resize(out_dims);
- }
- }
-};
-
-template <typename DeviceContext, typename T>
-class UnsqueezeGradKernel : public framework::OpKernel<T> {
- public:
- void Compute(const framework::ExecutionContext &ctx) const override {
- auto *d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
- auto *d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
-
- d_x->mutable_data<T>(ctx.GetPlace());
- bool inplace = ctx.Attr<bool>("inplace");
-
- auto in_dims = d_x->dims();
- if (!inplace) {
- framework::TensorCopy(*d_out, ctx.GetPlace(), ctx.device_context(), d_x);
- ctx.device_context().Wait();
- d_x->Resize(in_dims);
- } else {
- d_x->ShareDataWith(*d_out);
- d_x->Resize(in_dims);
- }
- }
-};
-
-} // namespace operators
-} // namespace paddle
diff --git a/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py b/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
index af273ca5a1..eff90f4618 100644
--- a/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
+++ b/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
@@ -27,7 +27,7 @@ class TestUnsqueezeOp(OpTest):
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": False}
+ self.attrs = {"axes": axes, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
@@ -37,23 +37,42 @@ class TestUnsqueezeOp(OpTest):
self.check_grad(["X"], "Out")
-# Correct: There is mins axis.
+# Correct: Single input index.
+class TestUnsqueezeOp1(OpTest):
+ def setUp(self):
+ ori_shape = (3, 5)
+ axes = (-1, )
+ new_shape = (3, 5, 1)
+
+ self.op_type = "unsqueeze"
+ self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
+ self.attrs = {"axes": axes, "inplace": False}
+ self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
+
+
+# Correct: Mixed input axis.
class TestUnsqueezeOp2(OpTest):
def setUp(self):
ori_shape = (3, 5)
- axes = (0, -2)
- new_shape = (1, 3, 1, 5)
+ axes = (0, -1)
+ new_shape = (1, 3, 5, 1)
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": False}
+ self.attrs = {"axes": axes, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
- def test_check_output(self):
- self.check_output()
+ def test_check_output(self):
+ self.check_output()
- def test_check_grad(self):
- self.check_grad(["X"], "Out")
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
# Correct: There is duplicated axis.
@@ -65,83 +84,84 @@ class TestUnsqueezeOp3(OpTest):
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": False}
+ self.attrs = {"axes": axes, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
- def test_check_output(self):
- self.check_output()
+ def test_check_output(self):
+ self.check_output()
- def test_check_grad(self):
- self.check_grad(["X"], "Out")
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
-# Error: Output dimension is error.
-class TestUnsqueezeOp4(OpTest):
+# Correct: Inplace.
+class TestUnsqueezeOpInplace1(OpTest):
def setUp(self):
- ori_shape = (3, 2, 5)
- axes = (0, 3)
- new_shape = (1, 3, 2, 2, 5)
+ ori_shape = (3, 5)
+ axes = (0, 2)
+ new_shape = (1, 3, 1, 5)
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": False}
+ self.attrs = {"axes": axes, "inplace": True}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
- def test_check_output(self):
- self.check_output()
+ def test_check_output(self):
+ self.check_output()
- def test_check_grad(self):
- self.check_grad(["X"], "Out")
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
-# Error: Input axes is invalid case 1.
-class TestUnsqueezeOp5(OpTest):
+# Correct: Inplace. There is mins index.
+class TestUnsqueezeOpInplace2(OpTest):
def setUp(self):
- ori_shape = (3, 2, 5)
- axes = (0, 5)
+ ori_shape = (3, 5)
+ axes = (0, -2)
new_shape = (1, 3, 1, 5)
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": False}
+ self.attrs = {"axes": axes, "inplace": True}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
- def test_check_output(self):
- self.check_output()
+ def test_check_output(self):
+ self.check_output()
- def test_check_grad(self):
- self.check_grad(["X"], "Out")
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
-# Error: Input axes is invalid case 2.
-class TestUnsqueezeOp5(OpTest):
+# Correct: Inplace. There is duplicated axis.
+class TestUnsqueezeOpInplace3(OpTest):
def setUp(self):
ori_shape = (3, 2, 5)
- axes = (0, 2, 10)
- new_shape = (1, 3, 1, 5)
+ axes = (0, 3, 3)
+ new_shape = (1, 3, 2, 1, 1, 5)
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": False}
+ self.attrs = {"axes": axes, "inplace": True}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
- def test_check_output(self):
- self.check_output()
+ def test_check_output(self):
+ self.check_output()
- def test_check_grad(self):
- self.check_grad(["X"], "Out")
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
-# Correct: Inplace.
-class TestUnsqueezeOpInplace1(OpTest):
+'''
+# Error: Output dimension is error.
+class TestUnsqueezeOp4(OpTest):
def setUp(self):
ori_shape = (3, 5)
- axes = (0, 2)
- new_shape = (1, 3, 1, 5)
+ axes = (0, 3)
+ new_shape = (1, 3, 1, 1, 5)
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inplace": True}
+ self.attrs = {"axes": axes, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
def test_check_output(self):
@@ -150,25 +170,60 @@ class TestUnsqueezeOpInplace1(OpTest):
def test_check_grad(self):
self.check_grad(["X"], "Out")
-
-# Correct: Inplace. There is duplicated axis.
-class TestUnsqueezeOpInplace2(OpTest):
+# Error: Input axis is large than output range.
+class TestUnsqueezeOp5(OpTest):
def setUp(self):
- ori_shape = (3, 2, 5)
- axes = (0, 3, 3)
- new_shape = (1, 3, 2, 1, 1, 5)
+ ori_shape = (3, 5)
+ axes = (0, 4)
+ new_shape = (1, 3, 5, 1)
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
- self.attrs = {"axes": axes, "inpalce": True}
+ self.attrs = {"axes": axes, "inplace": False}
self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
- def test_check_output(self):
- self.check_output()
+ def test_check_output(self):
+ self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
+# Error: Input axes is large than Eigen limit.
+class TestUnsqueezeOp6(OpTest):
+ def setUp(self):
+ ori_shape = (3, 5)
+ axes = (0, 2, 10)
+ new_shape = (1, 3, 1, 5, 1)
+
+ self.op_type = "unsqueeze"
+ self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
+ self.attrs = {"axes": axes, "inplace": False}
+ self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
+
+# Error: Input axes size is large than Eigen limit.
+class TestUnsqueezeOp7(OpTest):
+ def setUp(self):
+ ori_shape = (3, 5)
+ axes = (0, 2, 2, 2, 2, 2)
+ new_shape = (1, 3, 1, 1, 5, 1)
+
+ self.op_type = "unsqueeze"
+ self.inputs = {"X": np.random.random(ori_shape).astype("float32")}
+ self.attrs = {"axes": axes, "inplace": False}
+ self.outputs = {"Out": self.inputs["X"].reshape(new_shape)}
+
+ def test_check_output(self):
+ self.check_output()
+
+ def test_check_grad(self):
+ self.check_grad(["X"], "Out")
+'''
if __name__ == "__main__":
unittest.main()