Add Unsqueeze Operator Framework, not finshed

paddle/fluid/operators/unsqueeze_op.cc

+/* 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. */
+
+#include "paddle/fluid/operators/unsqueeze_op.h"
+#include <string>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+using framework::OpKernelType;
+using framework::Tensor;
+
+class UnsqueezeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(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& x_dims = ctx->GetInputDim("X");
+    const auto& axes = ctx->Attrs().Get<std::vector<int>>("axes");
+    // Check output tensor dims (<9).
+    PADDLE_ENFORCE_LE(x_dims.size() + axes.size(), 9,
+                      "Invalid dimnesions, dynamic dimensions must have "
+                      "between [1, 9] dimensions.");
+    // Check the range of unsqueeze aixs.
+    for (int a : axes) {
+      PADDLE_ENFORCE_LT(a, static_cast<int64_t>(x_dims.size() + axes.size()),
+                        "The axis must be less than output tensor's rank.");
+    }
+
+    auto out_dims = GetOutputShape(axes, x_dims);
+    ctx->SetOutputDim("Out", out_dims);
+  }
+
+  static framework::DDim GetOutputShape(const std::vector<int> unsqueeze_dims,
+                                        const framework::DDim& in_dims) {
+    int out_dims_size = in_dims.size() + unsqueeze_dims.size();
+    bool should_unsqueeze[9] = {false};
+
+    // Determines the dimensions should be unsqueezed in output tensor after.
+    for (unsigned int idx = 0; idx < unsqueeze_dims.size(); ++idx) {
+      int current = unsqueeze_dims[idx] < 0
+                        ? unsqueeze_dims[idx] + out_dims_size
+                        : unsqueeze_dims[idx];
+      // Check current index.
+      PADDLE_ENFORCE_GE(current, 0,
+                        "Invaild axis, negative axis is out of range.");
+      should_unsqueeze[idx] = true;
+    }
+
+    // Make output dimensions
+    std::vector<int64_t> output_shape(out_dims_size, 0);
+    for (int in_idx = 0, out_idx = 0; out_idx < out_dims_size; ++out_idx) {
+      if (!should_unsqueeze[out_idx]) {
+        output_shape[out_idx] = in_dims[in_idx++];
+      } else {
+        output_shape[out_idx] = 1;
+      }
+    }
+
+    return framework::make_ddim(output_shape);
+  }
+};
+
+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 positive integers,"
+                              " indicate the dimensions to be inserted");
+    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 UnsqueezeGradOp : public framework::OperatorWithKernel {
+ 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.");
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("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());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(unsqueeze, ops::UnsqueezeOp, ops::UnsqueezeOpMaker,
+                  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>);

paddle/fluid/operators/unsqueeze_op.cu

+/* 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(
+    squeeze, 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(
+    squeeze_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>);

paddle/fluid/operators/unsqueeze_op.h

+/* 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

python/paddle/fluid/tests/unittests/test_unsqueeze_op.py

+#   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 TestSqueezeOp1(OpTest):
+    def setUp(self):
+        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.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: There is mins axis.
+class TestSqueezeOp2(OpTest):
+    def setUp(self):
+        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.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: Inplace.
+class TestUnsqueezeOpInplace1(OpTest):
+    def setUp(self):
+        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, "inplace": True}
+        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: Inplace. There is mins axis.
+class TestUnsqueezeOpInplace2(OpTest):
+    def setUp(self):
+        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": True}
+        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()