Merge pull request #10970 from JiayiFeng/dev_add_random_crop_op

Add random crop op

paddle/fluid/framework/operator.cc

@@ -469,6 +469,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
  protected:
   DDim GetDim(const std::string& name) const override {
     Variable* var = scope_.FindVar(name);
+    PADDLE_ENFORCE_NOT_NULL(var);
     if (var->IsType<LoDTensor>()) {
       return var->Get<LoDTensor>().dims();
     } else if (var->IsType<SelectedRows>()) {

paddle/fluid/operators/random_crop_op.cc

+// 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/random_crop_op.h"
+
+namespace paddle {
+namespace operators {
+
+class RandomCropOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+ 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 RandomCropOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "A batch of instances to random crop.");
+    AddInput("Seed", "The random seed.");
+    AddOutput("Out", "The cropped instance batch.");
+    AddOutput("SeedOut", "The random seed after random cropping.")
+        .AsDispensable();
+    AddAttr<std::vector<int>>("shape", "The shape of a cropped instance.");
+    AddComment(R"DOC(
+      This operator takes a batch of instance, and do random cropping on each instance. 
+      It means that cropping positions differs on each instance, which is determined 
+      by an uniform random generator. All cropped instances have the same shape, which 
+      is determined by the operator's attribute 'shape'.
+    )DOC");
+  }
+};
+
+class RandomCropOpInferShape : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {
+    auto seed_dim = ctx->GetInputDim("Seed");
+    PADDLE_ENFORCE(seed_dim.size() == 1 && seed_dim[0] == 1);
+    auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    auto x_dim = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GT(x_dim.size(), static_cast<int64_t>(shape.size()));
+    auto out_dim = framework::vectorize2int(x_dim);
+    for (size_t i = 1; i <= shape.size(); ++i) {
+      size_t x_i = x_dim.size() - i;
+      size_t shape_i = shape.size() - i;
+      PADDLE_ENFORCE_GE(x_dim[x_i], shape[shape_i]);
+      out_dim[x_i] = shape[shape_i];
+    }
+    ctx->SetOutputDim("Out", framework::make_ddim(out_dim));
+    ctx->SetOutputDim("SeedOut", framework::make_ddim({1}));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace f = paddle::framework;
+REGISTER_OPERATOR(random_crop, ops::RandomCropOp, ops::RandomCropOpMaker,
+                  ops::RandomCropOpInferShape, f::EmptyGradOpMaker);
+
+template <typename T>
+using Kernel = ops::RandomCropKernel<paddle::platform::CPUDeviceContext, T>;
+REGISTER_OP_CPU_KERNEL(random_crop, Kernel<float>, Kernel<int>, Kernel<double>,
+                       Kernel<uint8_t>, Kernel<int16_t>);

paddle/fluid/operators/random_crop_op.cu

+// 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/random_crop_op.h"
+
+namespace ops = paddle::operators;
+template <typename T>
+using Kernel = ops::RandomCropKernel<paddle::platform::CUDADeviceContext, T>;
+REGISTER_OP_CUDA_KERNEL(random_crop, Kernel<float>, Kernel<int>, Kernel<double>,
+                        Kernel<uint8_t>, Kernel<int16_t>);

paddle/fluid/operators/random_crop_op.h

+// 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 <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/detail/safe_ref.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/for_range.h"
+#ifdef PADDLE_WITH_CUDA
+#include <thrust/random.h>
+#endif
+
+namespace paddle {
+namespace operators {
+
+template <typename DeviceContext>
+struct Random;
+
+template <>
+struct Random<platform::CPUDeviceContext> {
+  using Engine = std::minstd_rand;
+
+  template <typename T>
+  using UniformIntDist = std::uniform_int_distribution<T>;
+};
+
+#ifdef PADDLE_WITH_CUDA
+template <>
+struct Random<platform::CUDADeviceContext> {
+  using Engine = thrust::minstd_rand;
+
+  template <typename T>
+  using UniformIntDist = thrust::uniform_int_distribution<T>;
+};
+#endif
+
+template <typename T>
+HOSTDEVICE inline void StridedMemcpy(const T* x, const size_t* x_dims, T* out,
+                                     const size_t* out_dims, int i, int rank,
+                                     size_t prod_x_remain,
+                                     size_t prod_out_remain,
+                                     const size_t* offsets) {
+  size_t x_dim_i = x_dims[i];
+  size_t out_dim_i = out_dims[i];
+  size_t x_stride = prod_x_remain / x_dim_i;
+  size_t out_stride = prod_out_remain / out_dim_i;
+  size_t offset_i = offsets[i];
+
+  if (i == rank - 1) {
+    PADDLE_ASSERT(x_stride == 1 && out_stride == 1);
+    x += offset_i;
+    for (size_t j = 0; j < out_dim_i; ++j) {
+      *out++ = *x++;
+    }
+  } else {
+    x += offset_i * x_stride;
+    for (size_t j = 0; j < out_dim_i; ++j) {
+      StridedMemcpy<T>(x, x_dims, out, out_dims, i + 1, rank, x_stride,
+                       out_stride, offsets);
+      x += x_stride;
+      out += out_stride;
+    }
+  }
+}
+
+template <typename DeviceContext, typename T>
+struct RandomCropFunctor {
+  const T* x_;
+  T* out_;
+  size_t x_dims_[9];
+  size_t out_dims_[9];
+  int num_batchsize_dims_;
+  int rank_;
+  int64_t seed_;
+
+  size_t prod_batchsize_dims_;
+  size_t prod_x_ins_dims_;
+  size_t prod_out_ins_dims_;
+
+  RandomCropFunctor(const T* x, T* out, const framework::DDim& x_dims,
+                    const framework::DDim& out_dims, int num_batchsize_dims,
+                    int64_t seed)
+      : x_(x),
+        out_(out),
+        num_batchsize_dims_(num_batchsize_dims),
+        rank_(x_dims.size()),
+        seed_(seed) {
+    PADDLE_ENFORCE_EQ(x_dims.size(), out_dims.size());
+    PADDLE_ENFORCE_GT(rank_, num_batchsize_dims_);
+    prod_batchsize_dims_ = 1;
+    prod_x_ins_dims_ = 1;
+    prod_out_ins_dims_ = 1;
+    for (size_t i = 0; i < static_cast<size_t>(rank_); ++i) {
+      size_t x_dim_i = x_dims[i];
+      size_t out_dim_i = out_dims[i];
+      x_dims_[i] = x_dim_i;
+      out_dims_[i] = out_dim_i;
+      if (i < static_cast<size_t>(num_batchsize_dims_)) {
+        PADDLE_ENFORCE_EQ(x_dim_i, out_dim_i);
+        prod_batchsize_dims_ *= x_dim_i;
+      } else {
+        prod_x_ins_dims_ *= x_dim_i;
+        prod_out_ins_dims_ *= out_dim_i;
+      }
+    }
+  }
+
+  HOSTDEVICE void operator()(size_t ins_idx) {
+    typename Random<DeviceContext>::Engine engine(seed_);
+    engine.discard(ins_idx * (rank_ - num_batchsize_dims_));
+    size_t offsets[9];
+    for (int i = num_batchsize_dims_; i < rank_; ++i) {
+      typename Random<DeviceContext>::template UniformIntDist<size_t> dist(
+          0, x_dims_[i] - out_dims_[i]);
+      offsets[i - num_batchsize_dims_] = dist(engine);
+    }
+
+    const T* x = x_ + ins_idx * prod_x_ins_dims_;
+    T* out = out_ + ins_idx * prod_out_ins_dims_;
+
+    StridedMemcpy<T>(x, x_dims_ + num_batchsize_dims_, out,
+                     out_dims_ + num_batchsize_dims_, 0,
+                     rank_ - num_batchsize_dims_, prod_x_ins_dims_,
+                     prod_out_ins_dims_, offsets);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class RandomCropKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& ctx) const {
+    auto& seed_tensor = detail::Ref(ctx.Input<framework::LoDTensor>("Seed"));
+    int64_t seed = 0;
+    if (platform::is_cpu_place(seed_tensor.place())) {
+      seed = *seed_tensor.data<int64_t>();
+    } else {
+      LOG(WARNING) << "It is slow to place seed in GPU memory. Please verify "
+                      "your program";
+      framework::LoDTensor cpu_seed;
+      framework::TensorCopySync(seed_tensor, platform::CPUPlace(), &cpu_seed);
+      seed = *cpu_seed.data<int64_t>();
+    }
+    auto shape = ctx.Attr<std::vector<int>>("shape");
+    auto& x = detail::Ref(ctx.Input<framework::LoDTensor>("X"));
+    auto& out = detail::Ref(ctx.Output<framework::LoDTensor>("Out"));
+
+    int num_batchsize_dims = x.dims().size() - shape.size();
+    RandomCropFunctor<DeviceContext, T> functor(
+        x.data<T>(), out.mutable_data<T>(ctx.GetPlace()), x.dims(), out.dims(),
+        num_batchsize_dims, seed);
+    platform::ForRange<DeviceContext> for_range(
+        ctx.template device_context<DeviceContext>(),
+        functor.prod_batchsize_dims_);
+
+    for_range(functor);
+
+    Random<platform::CPUDeviceContext>::Engine engine(seed);
+    engine.discard(functor.prod_batchsize_dims_ *
+                   (functor.rank_ - functor.num_batchsize_dims_));
+    *ctx.Output<framework::LoDTensor>("SeedOut")->mutable_data<int64_t>(
+        platform::CPUPlace()) = engine();
+  }
+};
+
+// TODO(fengjiayi): Backward of random crop op
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/nn.py

@@ -82,6 +82,7 @@ __all__ = [
     'roi_pool',
     'dice_loss',
     'upsampling_bilinear2d',
+    'random_crop',
 ]
 
 
@@ -154,7 +155,8 @@ def fc(input,
     Examples:
         .. code-block:: python
 
-          data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
+          data = fluid.layers.data(
+              name="data", shape=[32, 32], dtype="float32")
           fc = fluid.layers.fc(input=data, size=1000, act="tanh")
     """
 
@@ -349,7 +351,8 @@ def dynamic_lstm(input,
         cell_activation(str): The activation for cell output. Choices = ["sigmoid",
                               "tanh", "relu", "identity"], default "tanh".
         candidate_activation(str): The activation for candidate hidden state.
-                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              Choices = ["sigmoid", "tanh",
+                                  "relu", "identity"],
                               default "tanh".
         dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
         name(str|None): A name for this layer(optional). If set None, the layer
@@ -516,10 +519,12 @@ def dynamic_lstmp(input,
         cell_activation(str): The activation for cell output. Choices = ["sigmoid",
                               "tanh", "relu", "identity"], default "tanh".
         candidate_activation(str): The activation for candidate hidden state.
-                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              Choices = ["sigmoid", "tanh",
+                                  "relu", "identity"],
                               default "tanh".
         proj_activation(str): The activation for projection output.
-                              Choices = ["sigmoid", "tanh", "relu", "identity"],
+                              Choices = ["sigmoid", "tanh",
+                                  "relu", "identity"],
                               default "tanh".
         dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
         name(str|None): A name for this layer(optional). If set None, the layer
@@ -2174,7 +2179,8 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
             fluid.layers.reduce_mean(x)  # [0.4375]
             fluid.layers.reduce_mean(x, dim=0)  # [0.15, 0.25, 0.55, 0.8]
             fluid.layers.reduce_mean(x, dim=-1)  # [0.475, 0.4]
-            fluid.layers.reduce_mean(x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
+            fluid.layers.reduce_mean(
+                x, dim=1, keep_dim=True)  # [[0.475], [0.4]]
 
             # x is a Tensor variable with shape [2, 2, 2] and elements as below:
             #      [[[1.0, 2.0], [3.0, 4.0]],
@@ -2393,7 +2399,8 @@ def split(input, num_or_sections, dim=-1, name=None):
             x0.shape  # [3, 3, 5]
             x1.shape  # [3, 3, 5]
             x2.shape  # [3, 3, 5]
-            x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
+            x0, x1, x2 = fluid.layers.split(
+                x, num_or_sections=[2, 3, 4], dim=1)
             x0.shape  # [3, 2, 5]
             x1.shape  # [3, 3, 5]
             x2.shape  # [3, 4, 5]
@@ -3305,7 +3312,8 @@ def softmax_with_cross_entropy(logits, label, soft_label=False):
             data = fluid.layers.data(name='data', shape=[128], dtype='float32')
             label = fluid.layers.data(name='label', shape=[1], dtype='int64')
             fc = fluid.layers.fc(input=data, size=100)
-            out = fluid.layers.softmax_with_cross_entropy(logits=fc, label=label)
+            out = fluid.layers.softmax_with_cross_entropy(
+                logits=fc, label=label)
     """
     helper = LayerHelper('softmax_with_cross_entropy', **locals())
     softmax = helper.create_tmp_variable(dtype=logits.dtype)
@@ -3352,7 +3360,8 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
         .. code-block:: python
 
             data = fluid.layers.data(name='data', shape=[128], dtype='float32')
-            label = fluid.layers.data(name='label', shape=[100], dtype='float32')
+            label = fluid.layers.data(
+                name='label', shape=[100], dtype='float32')
             fc = fluid.layers.fc(input=data, size=100)
             out = fluid.layers.smooth_l1(x=fc, y=label)
     """
@@ -3674,7 +3683,8 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
     Examples:
         .. code-block:: python
 
-          data = fluid.layers.data(name="data", shape=[3, 112, 112], dtype="float32")
+          data = fluid.layers.data(
+              name="data", shape=[3, 112, 112], dtype="float32")
           lrn = fluid.layers.lrn(input=data)
     """
     helper = LayerHelper('lrn', **locals())
@@ -3983,3 +3993,32 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
         attrs={"out_h": out_h,
                "out_w": out_w})
     return out
+
+
+def random_crop(input, shape, seed=1):
+    helper = LayerHelper("random_crop", **locals())
+    dtype = helper.input_dtype()
+    out = helper.create_tmp_variable(dtype)
+    if isinstance(seed, int):
+        seed_value = seed
+        seed = helper.create_tmp_variable(dtype="int64")
+        helper.append_op(
+            type="fill_constant",
+            inputs={},
+            outputs={"Out": seed},
+            attrs={
+                "dtype": seed.dtype,
+                "shape": [1],
+                "value": float(seed_value)
+            })
+    elif not isinstance(seed, Variable):
+        raise ValueError("'seed' must be a Variable or an int.")
+    seed_out = helper.create_tmp_variable(dtype="int64")
+    helper.append_op(
+        type="random_crop",
+        inputs={"X": input,
+                "Seed": seed},
+        outputs={"Out": out,
+                 "SeedOut": seed_out},
+        attrs={"shape": shape})
+    return out

python/paddle/fluid/tests/unittests/test_random_crop_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
+import paddle.fluid.core as core
+from op_test import OpTest
+
+
+class TestRandomCropOp(OpTest):
+    def setUp(self):
+        to_crop = np.array([[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]] *
+                           5).astype("float32")
+        self.possible_res = [
+            np.array([[1, 2, 3], [5, 6, 7]]), np.array([[2, 3, 4], [6, 7, 8]]),
+            np.array([[5, 6, 7], [9, 10, 11]]),
+            np.array([[6, 7, 8], [10, 11, 12]])
+        ]
+        self.op_type = "random_crop"
+        self.inputs = {'X': to_crop, 'Seed': np.array([10])}
+        self.outputs = {'Out': np.array([]), 'SeedOut': np.array([])}
+        self.attrs = {'shape': [2, 3]}
+
+    def test_check_output(self):
+        self.check_output_customized(self.verify_output)
+
+    def verify_output(self, outs):
+        out = np.array(outs[1])
+        for ins in out[:]:
+            is_equal = [(ins == res).all() for res in self.possible_res]
+            self.assertIn(True, is_equal)
+
+
+if __name__ == "__main__":
+    unittest.main()