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Merge pull request #11897 from chenwhql/unsqueeze_op

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Add Unsqueeze operator and unit testing
guochaorong-patch-1
Yibing Liu 7 years ago committed by GitHub
parent 99cdfed436 938319bbd2
commit 1617fe2edd
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1
paddle/fluid/operators/CMakeLists.txt
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@ -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)
op_library(squeeze_op DEPS reshape_op)
if (WITH_GPU)

191
paddle/fluid/operators/unsqueeze_op.cc
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@ -0,0 +1,191 @@
/* Copyright (c) 2018 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. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
class UnsqueezeOpInferShape : public framework::InferShapeBase {
public:
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 &x_dims = ctx->GetInputDim("X");
// Validity Check: input tensor dims (<6).
PADDLE_ENFORCE(x_dims.size() <= 6,
"Invalid dimensions, the rank of Input(X) "
"should be in the range of [1, 6] (Eigen limit)");
auto out_dims = GetOutputShape(axes, x_dims);
ctx->SetOutputDim("Out", out_dims);
if (x_dims[0] == out_dims[0]) {
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx->ShareLoD("X", "Out");
}
}
static framework::DDim GetOutputShape(const std::vector<int> unsqz_dims,
const framework::DDim &in_dims) {
int output_size = in_dims.size() + static_cast<int>(unsqz_dims.size());
int cur_output_size = in_dims.size();
std::vector<int64_t> output_shape(output_size, 0);
// Validity Check: rank range.
PADDLE_ENFORCE(output_size <= 6,
"The output tensor's rank should be less than 6.");
for (int axis : unsqz_dims) {
int cur = axis < 0 ? axis + cur_output_size + 1 : axis;
// Vaildity Check: the axis bound
PADDLE_ENFORCE(
cur >= 0 && cur <= cur_output_size,
"The unsqueeze dims must be within range of current rank.");
// Move old axis, and insert new axis
for (int i = cur_output_size; i >= cur; --i) {
if (output_shape[i] == 1) {
// Move axis
output_shape[i + 1] = 1;
output_shape[i] = 0;
}
}
output_shape[cur] = 1;
// Add the output size.
cur_output_size++;
}
// Make output shape
for (int in_idx = 0, out_idx = 0; out_idx < output_size; ++out_idx) {
if (output_shape[out_idx] == 0) {
output_shape[out_idx] = in_dims[in_idx++];
}
}
return framework::make_ddim(output_shape);
}
};
class UnsqueezeOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
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 {
AddInput("X", "(Tensor). The input tensor of unsqueeze operator.");
AddOutput("Out", "(Tensor). The output tensor of unsqueeze operator.");
AddAttr<std::vector<int>>("axes",
"(std::vector<int>). List of integers,"
" indicating the dimensions to be inserted")
.AddCustomChecker([](const std::vector<int> &axes) {
PADDLE_ENFORCE(!axes.empty(),
"Invalid axes, The unsqueeze axes is empty.");
// Validity Check: axes dims (<6).
PADDLE_ENFORCE(static_cast<int>(axes.size()) < 6,
"Invalid dimensions, dynamic dimensions should be "
"within [1, 6] dimensions (Eigen limit).");
// Validity Check: the range of unsqueeze aixs.
for (int axis : axes) {
PADDLE_ENFORCE(axis < 6,
"Invalid dimensions, input axis should be"
" within [1, 6] dimensions (Eigen limit).");
}
});
AddAttr<bool>(
"inplace",
"(default: false) Unsqueeze the source tensor's shape without "
"memory copy. When Attr(inplace) is set true, the output "
"tensor shares memory with Input(X), otherwise, a new output "
"tensor is created, and its data are copied from Input(x).")
.SetDefault(false);
AddComment(R"DOC(
Unsqueeze Operator.
Insert single-dimensional entries to the shape of a tensor.
Takes one required argument axes, a list of dimensions that will be inserted.
Dimension indices in axes are as seen in the output tensor.
For example:
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueeze(tensor, axes=[0, 4]) has shape [1, 3, 4, 5, 1]
)DOC");
}
};
class UnsqueezeGradInferShape : public framework::InferShapeBase {
public:
void operator()(framework::InferShapeContext *ctx) const override {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", framework::GradVarName("X"));
}
};
class UnsqueezeGradOp : public framework::OperatorBase {
public:
using OperatorBase::OperatorBase;
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,
ops::UnsqueezeGradInferShape);

111
python/paddle/fluid/tests/unittests/test_unsqueeze_op.py
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@ -0,0 +1,111 @@
# Copyright (c) 2018 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.
import unittest
import numpy as np
from op_test import OpTest
# Correct: General.
class TestUnsqueezeOp(OpTest):
def setUp(self):
self.init_test_case()
self.op_type = "unsqueeze"
self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")}
self.init_attrs()
self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)}
def test_check_output(self):
self.check_output()
def test_check_grad(self):
self.check_grad(["X"], "Out")
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (1, 2)
self.new_shape = (3, 1, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": False}
# Correct: Single input index.
class TestUnsqueezeOp1(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (-1, )
self.new_shape = (3, 5, 1)
# Correct: Mixed input axis.
class TestUnsqueezeOp2(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (0, -1)
self.new_shape = (1, 3, 5, 1)
# Correct: There is duplicated axis.
class TestUnsqueezeOp3(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 2, 5)
self.axes = (0, 3, 3)
self.new_shape = (1, 3, 2, 1, 1, 5)
# Correct: Reversed axes.
class TestUnsqueezeOp4(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 2, 5)
self.axes = (3, 1, 1)
self.new_shape = (3, 1, 1, 2, 5, 1)
# Correct: Inplace.
class TestUnsqueezeOpInplace1(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (0, 2)
self.new_shape = (1, 3, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inplace. There is mins index.
class TestUnsqueezeOpInplace2(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 5)
self.axes = (0, -2)
self.new_shape = (1, 3, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
# Correct: Inplace. There is duplicated axis.
class TestUnsqueezeOpInplace3(TestUnsqueezeOp):
def init_test_case(self):
self.ori_shape = (3, 2, 5)
self.axes = (0, 3, 3)
self.new_shape = (1, 3, 2, 1, 1, 5)
def init_attrs(self):
self.attrs = {"axes": self.axes, "inplace": True}
if __name__ == "__main__":
unittest.main()
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