Add slice op.

paddle/fluid/operators/slice_op.cc

@@ -0,0 +1,128 @@
+/* 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 "paddle/fluid/operators/slice_op.h"
+#include <algorithm>
+#include <vector>
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+class SliceOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input (Input) of slice op should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Out"),
+                   "Output (Out) of slice op should not be null.");
+
+    auto in_dims = ctx->GetInputDim("Input");
+    framework::DDim out_dims(in_dims);
+    auto axes = ctx->Attrs().Get<std::vector<int>>("axes");
+    auto starts = ctx->Attrs().Get<std::vector<int>>("starts");
+    auto ends = ctx->Attrs().Get<std::vector<int>>("ends");
+
+    PADDLE_ENFORCE_EQ(starts.size(), ends.size());
+    PADDLE_ENFORCE_EQ(starts.size(), axes.size());
+    int dim_value, start, end;
+    for (size_t i = 0; i < axes.size(); ++i) {
+      dim_value = out_dims[axes[i]];
+      start = starts[i] < 0 ? (starts[i] + dim_value) : starts[i];
+      end = ends[i] < 0 ? (ends[i] + dim_value) : ends[i];
+      start = std::max(start, 0);
+      end = std::max(end, 0);
+      start = std::min(start, dim_value);
+      end = std::min(end, dim_value);
+      start = std::min(start, end);
+      out_dims[axes[i]] = end - start;
+    }
+    ctx->SetOutputDim("Out", out_dims);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<Tensor>("Input")->type()),
+        ctx.GetPlace());
+  }
+};
+
+class SliceOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Input", "Tensor of data to extract slices from.");
+    AddOutput("Out", "Sliced data tensor.");
+
+    AddAttr<std::vector<int>>(
+        "axes",
+        "(list<int>) Axes that `starts` and `ends` apply to. It's optional."
+        "If not present, will be treated as [0, 1, ..., len(`starts`) - 1].");
+    AddAttr<std::vector<int>>(
+        "starts",
+        "(list<int>) Starting indices of corresponding axis in `axes`");
+    AddAttr<std::vector<int>>(
+        "ends",
+        "(list<int>) Starting indices of corresponding axis in `axes`.");
+
+    AddComment(R"DOC(
+Slice Operator.
+
+Produces a slice of the input tensor along multiple axes. Similar to numpy:
+https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+Slices uses axes, starts and ends attributes to specify the start and 
+end dimension for each axis in the list of axes, it uses this information
+to slice the input data tensor. If a negative value is passed for any of 
+the start or end indices, it represent number of elements before the end 
+of that dimension. If the value passed to start or end is larger than
+the n (the number of elements in this dimension), it represents n. 
+For slicing to the end of a dimension with unknown size, it is recommended 
+to pass in INT_MAX. If axes are omitted, they are set to [0, ..., ndim-1].
+
+    Example 1:
+    Given:
+        data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
+        axes = [0, 1]
+        starts = [1, 0]
+        ends = [2, 3]
+    Then:
+        result = [ [5, 6, 7], ]
+
+    Example 2:
+    Given:
+        data = [ [1, 2, 3, 4], [5, 6, 7, 8], ]
+        starts = [0, 1]
+        ends = [-1, 1000]
+    Then:
+        result = [ [2, 3, 4], ]
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(slice, ops::SliceOp, ops::SliceOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+
+REGISTER_OP_CPU_KERNEL(
+    slice, ops::SliceKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::SliceKernel<paddle::platform::CPUDeviceContext, int64_t>,
+    ops::SliceKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::SliceKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/slice_op.cu

@@ -0,0 +1,22 @@
+/* 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 "paddle/fluid/operators/slice_op.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    slice, ops::SliceKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::SliceKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::SliceKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::SliceKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/slice_op.h

@@ -0,0 +1,88 @@
+/* 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. */
+
+#pragma once
+#include <algorithm>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext, typename T>
+class SliceKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    int rank = ctx.Input<framework::Tensor>("Input")->dims().size();
+    switch (rank) {
+      case 1:
+        SliceCompute<1>(ctx);
+        break;
+      case 2:
+        SliceCompute<2>(ctx);
+        break;
+      case 3:
+        SliceCompute<3>(ctx);
+        break;
+      case 4:
+        SliceCompute<4>(ctx);
+        break;
+      case 5:
+        SliceCompute<5>(ctx);
+        break;
+      case 6:
+        SliceCompute<6>(ctx);
+        break;
+    }
+  }
+
+ private:
+  template <size_t D>
+  void SliceCompute(const framework::ExecutionContext& context) const {
+    auto& place =
+        *context.template device_context<DeviceContext>().eigen_device();
+    auto in = context.Input<framework::Tensor>("Input");
+    auto out = context.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(context.GetPlace());
+    auto out_dims = out->dims();
+    auto in_dims = in->dims();
+    auto axes = context.Attr<std::vector<int>>("axes");
+    auto starts = context.Attr<std::vector<int>>("starts");
+
+    auto offsets = Eigen::array<int, D>();
+    auto extents = Eigen::array<int, D>();
+    for (size_t i = 0; i < D; ++i) {
+      offsets[i] = 0;
+      extents[i] = out_dims[i];
+    }
+    int start;
+    for (size_t i = 0; i < axes.size(); ++i) {
+      start = starts[i];
+      if (start < 0) {
+        start = (start + in_dims[axes[i]]);
+      }
+      start = std::max(start, 0);
+      offsets[axes[i]] = start;
+    }
+    auto in_t =
+        framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
+            *in);
+    auto out_t =
+        framework::EigenTensor<T, D, Eigen::RowMajor, Eigen::DenseIndex>::From(
+            *out);
+    out_t.device(place) = in_t.slice(offsets, extents);
+  }
+};
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/ops.py

@@ -71,6 +71,7 @@
     'cumsum',
     'scatter',
     'sum',
+    'slice',
     'polygon_box_transform',
     'shape',
 ] + __activations__

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

@@ -0,0 +1,62 @@
+#   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
+
+
+class TestSliceOp(OpTest):
+    def setUp(self):
+        self.op_type = "slice"
+        self.config()
+        self.inputs = {'Input': self.input}
+        self.outputs = {'Out': self.out}
+        self.attrs = {
+            'axes': self.axes,
+            'starts': self.starts,
+            'ends': self.ends
+        }
+
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [1, 0, 2]
+        self.ends = [3, 3, 4]
+        self.axes = [0, 1, 2]
+        self.out = self.input[1:3, 0:3, 2:4, :]
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestCase1(TestSliceOp):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-3, 0, 2]
+        self.ends = [3, 100, -1]
+        self.axes = [0, 1, 2]
+        self.out = self.input[-3:3, 0:100, 2:-1, :]
+
+
+class TestCase2(TestSliceOp):
+    def config(self):
+        self.input = np.random.random([3, 4, 5, 6]).astype("float32")
+        self.starts = [-3, 0, 2]
+        self.ends = [3, 100, -1]
+        self.axes = [0, 1, 3]
+        self.out = self.input[-3:3, 0:100, :, 2:-1]
+
+
+if __name__ == '__main__':
+    unittest.main()