Add crop op

paddle/framework/ddim.cc

@@ -292,5 +292,13 @@ DDim flatten_to_2d(const DDim& src, int num_col_dims) {
 
 DDim flatten_to_1d(const DDim& src) { return make_ddim({product(src)}); }
 
+DDim stride(const DDim& ddim) {
+  std::vector<int64_t> strides(ddim.size());
+  strides[ddim.size() - 1] = 1;
+  for (int i = ddim.size() - 2; i >= 0; --i) {
+    strides[i] = strides[i + 1] * ddim[i + 1];
+  }
+  return framework::make_ddim(strides);
+}
 }  // namespace framework
 }  // namespace paddle

paddle/framework/ddim.h

@@ -121,6 +121,7 @@ DDim flatten_to_2d(const DDim& src, int num_col_dims);
 
 DDim flatten_to_1d(const DDim& src);
 
+DDim stride(const DDim& ddim);
 }  // namespace framework
 }  // namespace paddle
 

paddle/operators/crop_op.cc

@@ -0,0 +1,139 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/crop_op.h"
+#include <boost/lexical_cast.hpp>
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+using framework::LoDTensor;
+
+class CropOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"),
+                            "Input(X) of CropOp should not be null.");
+    PADDLE_ENFORCE_NOT_NULL(ctx.OutputVar("Out"),
+                            "Output(Out) of CropOp should not be null.");
+    auto x_dim = ctx.Input<LoDTensor>("X")->dims();
+    auto *y = ctx.Input<LoDTensor>("Y");
+    auto *out = ctx.Output<LoDTensor>("Out");
+    if (y == nullptr) {
+      auto shape = Attr<std::vector<int>>("shape");
+      PADDLE_ENFORCE_EQ(
+          int64_t(shape.size()), x_dim.size(),
+          "Shape size should be equal to dimention size of input tensor.");
+      std::vector<int64_t> tensor_shape(shape.size());
+      for (size_t i = 0; i < shape.size(); ++i) {
+        tensor_shape[i] = static_cast<int64_t>(shape[i]);
+      }
+      out->Resize(framework::make_ddim(tensor_shape));
+    } else {
+      PADDLE_ENFORCE_EQ(framework::arity(x_dim), framework::arity(y->dims()),
+                        "Tensor rank of both CropOp's "
+                        "inputs must be same.");
+      out->Resize(y->dims());
+    }
+  }
+};
+
+class CropOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  CropOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
+      : OpProtoAndCheckerMaker(proto, op_checker) {
+    AddInput("X",
+             "The input of pad op. "
+             "The input should be a k-D tensor(k > 0 and k < 7)");
+    AddInput("Y",
+             "The input used as reference for cropping"
+             " with the same dimension as X. ");
+    AddOutput("Out",
+              "The output of crop op "
+              "with the same dimension as X.");
+    AddAttr<std::vector<int>>("offsets",
+                              "A list<int> describing offsets to be cropped."
+                              "The size of offsets list should be as same as "
+                              "dimension size of  input X.");
+    AddAttr<std::vector<int>>("shape",
+                              "A list<int> describing the shape of output."
+                              "The size of shape list should be as same as "
+                              "dimension size of  input X.")
+        .SetDefault(std::vector<int>());
+    AddComment(R"DOC(
+Crop Operator.
+Crop input into output, as specified by offsets and shape.
+
+There are two ways to set shape: 
+1. referenc input: crop input X as shape as reference input.
+                    The dimension of reference input should 
+                    be as same as input X.
+2. shape list: crop input X by shape described by a list<int>.
+               The size of shape list should be as same as 
+               dimension size of  input X.
+
+The input should be a k-D tensor(k > 0 and k < 7). As an example:
+
+Given:
+
+    X = [[0, 1, 2, 0, 0]
+         [0, 3, 4, 0, 0]
+         [0, 0, 0, 0, 0]]
+
+and 
+
+    offsets = [0, 1]
+
+and
+
+    shape = [2, 2]
+
+then we get 
+
+    Out = [[1, 2],
+           [3, 4]]
+
+)DOC");
+  }
+};
+
+class CropOpGrad : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ protected:
+  void InferShape(const framework::InferShapeContext &ctx) const override {
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar("X"), "Input(X) should not be null");
+    PADDLE_ENFORCE_NOT_NULL(ctx.InputVar(framework::GradVarName("Out")),
+                            "Input(Out@GRAD) should not be null");
+    auto x_dims = ctx.Input<LoDTensor>("X")->dims();
+    auto *x_grad = ctx.Output<LoDTensor>(framework::GradVarName("X"));
+    if (x_grad != nullptr) {
+      x_grad->Resize(x_dims);
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP(crop, ops::CropOp, ops::CropOpMaker, crop_grad, ops::CropOpGrad);
+REGISTER_OP_CPU_KERNEL(crop, ops::CropKernel<float>);
+REGISTER_OP_CPU_KERNEL(crop_grad,
+                       ops::CropGradKernel<paddle::platform::CPUPlace, float>);

paddle/operators/crop_op.cu

@@ -0,0 +1,21 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+   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/operators/crop_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_GPU_KERNEL(crop, ops::CropKernel<float>);
+REGISTER_OP_GPU_KERNEL(crop_grad,
+                       ops::CropGradKernel<paddle::platform::GPUPlace, float>);

paddle/operators/crop_op.h

@@ -0,0 +1,104 @@
+/* Copyright (c) 2016 CropdleCropdle Authors. All Rights Reserve.
+
+   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 "paddle/framework/eigen.h"
+#include "paddle/framework/op_registry.h"
+#include "paddle/operators/strided_memcpy.h"
+
+namespace paddle {
+namespace operators {  // Internal
+
+template <typename T, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+using framework::Tensor;
+
+template <typename T>
+class CropKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* x = context.Input<Tensor>("X");
+    auto* out = context.Output<Tensor>("Out");
+    const T* x_data = x->data<T>();
+    T* out_data = out->mutable_data<T>(context.GetPlace());
+    auto x_stride = framework::stride(x->dims());
+    auto out_stride = framework::stride(out->dims());
+    auto offsets = context.Attr<std::vector<int>>("offsets");
+    PADDLE_ENFORCE_EQ(
+        x->dims().size(), offsets.size(),
+        "Offsets size should be equal to dimension size of input tensor.");
+    int64_t offset = 0;
+    for (int i = 0; i < offsets.size(); ++i) {
+      offset += (x_stride[i] * offsets[i]);
+    }
+    StridedMemcpy<T>(context.device_context(), x_data + offset, x_stride,
+                     out->dims(), out_stride, out_data);
+  }
+};
+
+template <typename Place, typename T, size_t D>
+void CropGradFunction(const framework::ExecutionContext& context) {
+  auto* d_x = context.Output<Tensor>(framework::GradVarName("X"));
+  if (d_x != nullptr) {
+    auto* d_out = context.Input<Tensor>(framework::GradVarName("Out"));
+    d_x->mutable_data<T>(context.GetPlace());
+    auto offsets = context.Attr<std::vector<int>>("offsets");
+    Eigen::array<std::pair<int, int>, D> paddings;
+    for (int i = 0; i < D; ++i) {
+      paddings[i].first = offsets[i];
+      paddings[i].second = d_x->dims()[i] - d_out->dims()[i] - offsets[i];
+    }
+    auto d_x_tensor = EigenTensor<T, D>::From(*d_x);
+    auto d_out_tensor = EigenTensor<T, D>::From(*d_out);
+    d_x_tensor.device(context.GetEigenDevice<Place>()) =
+        d_out_tensor.pad(paddings, 0);
+  }
+}
+
+template <typename Place, typename T>
+class CropGradKernel : public framework::OpKernel {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    size_t rank =
+        context.Input<Tensor>(framework::GradVarName("Out"))->dims().size();
+    switch (rank) {
+      case 1:
+        CropGradFunction<Place, T, 1>(context);
+        break;
+      case 2:
+        CropGradFunction<Place, T, 2>(context);
+        break;
+      case 3:
+        CropGradFunction<Place, T, 3>(context);
+        break;
+      case 4:
+        CropGradFunction<Place, T, 4>(context);
+        break;
+      case 5:
+        CropGradFunction<Place, T, 5>(context);
+        break;
+      case 6:
+        CropGradFunction<Place, T, 6>(context);
+        break;
+      default:
+        PADDLE_THROW(
+            "CropOp only support tensors with no more than 6 dimensions.");
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/v2/framework/tests/test_crop_op.py

@@ -0,0 +1,91 @@
+import unittest
+import numpy as np
+from op_test import OpTest
+
+
+def crop(data, offsets, crop_shape):
+    def indexOf(shape, index):
+        result = []
+        for dim in reversed(shape):
+            result.append(index % dim)
+            index = index / dim
+        return result[::-1]
+
+    result = []
+    for i, value in enumerate(data.flatten()):
+        index = indexOf(data.shape, i)
+        selected = True
+        if len(index) == len(offsets):
+            for j, offset in enumerate(offsets):
+                selected = selected and index[j] >= offset and index[
+                    j] < crop_shape[j] + offset
+            if selected:
+                result.append(value)
+    return np.array(result).reshape(crop_shape)
+
+
+class TestCropOp(OpTest):
+    def setUp(self):
+        self.op_type = "crop"
+        self.crop_by_input = False
+        self.attrs = {}
+        self.initTestCase()
+        self.attrs['offsets'] = self.offsets
+        if self.crop_by_input:
+            self.inputs = {
+                'X': np.random.random(self.x_shape).astype("float32"),
+                'Y': np.random.random(self.crop_shape).astype("float32")
+            }
+        else:
+            self.attrs['shape'] = self.crop_shape
+            self.inputs = {
+                'X': np.random.random(self.x_shape).astype("float32"),
+            }
+        self.outputs = {
+            'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
+        }
+
+    def initTestCase(self):
+        self.x_shape = (8, 8)
+        self.crop_shape = (2, 2)
+        self.offsets = [1, 2]
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad_normal(self):
+        self.check_grad(['X'], 'Out', max_relative_error=0.006)
+
+
+class TestCase1(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (16, 8, 32)
+        self.crop_shape = [2, 2, 3]
+        self.offsets = [1, 5, 3]
+
+
+class TestCase2(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (4, 8)
+        self.crop_shape = [4, 8]
+        self.offsets = [0, 0]
+
+
+class TestCase3(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (4, 8, 16)
+        self.crop_shape = [2, 2, 3]
+        self.offsets = [1, 5, 3]
+        self.crop_by_input = True
+
+
+class TestCase4(TestCropOp):
+    def initTestCase(self):
+        self.x_shape = (4, 4)
+        self.crop_shape = [4, 4]
+        self.offsets = [0, 0]
+        self.crop_by_input = True
+
+
+if __name__ == '__main__':
+    unittest.main()