Add the argmax op to API 2.0， and update some parameters

* Add the argmax op to API 2.0, test=develop

* Fix the compiler problem in arg_max op, test=develop

* Fix the import meesage in common_ops_import, test=develop

* Fix the default dtype of arg_min_max, test=develop

paddle/fluid/operators/arg_min_max_op_base.h

@@ -38,26 +38,36 @@ struct ArgMinMaxFunctor {};
   struct ArgMinMaxFunctor<DeviceContext, T, Tout, Rank,                       \
                           enum_argminmax_value> {                             \
     void operator()(const DeviceContext& ctx, const framework::LoDTensor& in, \
-                    framework::LoDTensor* out, int64_t axis) {                \
+                    framework::LoDTensor* out, int64_t axis, bool keepdims) { \
       auto in_eigen = framework::EigenTensor<T, Rank>::From(in);              \
+      if (keepdims) {                                                         \
+        auto out_eigen = framework::EigenTensor<Tout, Rank>::From(*out);      \
+        out_eigen.device(*(ctx.eigen_device())) =                             \
+            in_eigen.eigen_op_type(axis).template cast<Tout>();               \
+      } else {                                                                \
         auto out_eigen = framework::EigenTensor<Tout, Rank - 1>::From(*out);  \
         out_eigen.device(*(ctx.eigen_device())) =                             \
             in_eigen.eigen_op_type(axis).template cast<Tout>();               \
       }                                                                       \
+    }                                                                         \
   }
 
 DECLARE_ARG_MIN_MAX_FUNCTOR(argmin, ArgMinMaxType::kArgMin);
 DECLARE_ARG_MIN_MAX_FUNCTOR(argmax, ArgMinMaxType::kArgMax);
 
-template <typename DeviceContext, typename T, typename Tout,
-          ArgMinMaxType EnumArgMinMaxValue>
-class ArgMinMaxKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
+template <typename DeviceContext, typename T, ArgMinMaxType EnumArgMinMaxValue>
+struct VisitDataArgMinMaxFunctor {
+  const framework::ExecutionContext& ctx;
+
+  explicit VisitDataArgMinMaxFunctor(const framework::ExecutionContext& ctx)
+      : ctx(ctx) {}
+  template <typename Tout>
+  void apply() const {
     auto& x = *(ctx.Input<framework::LoDTensor>("X"));
     auto& out = *(ctx.Output<framework::LoDTensor>("Out"));
-    out.mutable_data<Tout>(ctx.GetPlace());
+    out.template mutable_data<Tout>(ctx.GetPlace());
     auto axis = ctx.Attr<int64_t>("axis");
+    auto keepdims = ctx.Attr<bool>("keepdims");
     auto x_rank = x.dims().size();
     if (axis < 0) axis += x_rank;
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
@@ -65,7 +75,7 @@ class ArgMinMaxKernel : public framework::OpKernel<T> {
 #define CALL_ARG_MINMAX_FUNCTOR(rank)                                \
   ArgMinMaxFunctor<DeviceContext, T, Tout, rank, EnumArgMinMaxValue> \
       functor##rank;                                                 \
-  functor##rank(dev_ctx, x, &out, axis)
+  functor##rank(dev_ctx, x, &out, axis, keepdims)
 
     switch (x.dims().size()) {
       case 1:
@@ -97,13 +107,29 @@ class ArgMinMaxKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T, ArgMinMaxType EnumArgMinMaxValue>
+class ArgMinMaxKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& dtype = ctx.Attr<int>("dtype");
+    if (dtype < 0) {
+      framework::VisitDataType(
+          static_cast<framework::proto::VarType::Type>(
+              framework::proto::VarType::INT64),
+          VisitDataArgMinMaxFunctor<DeviceContext, T, EnumArgMinMaxValue>(ctx));
+      return;
+    }
+    framework::VisitDataType(
+        static_cast<framework::proto::VarType::Type>(dtype),
+        VisitDataArgMinMaxFunctor<DeviceContext, T, EnumArgMinMaxValue>(ctx));
+  }
+};
+
 template <typename DeviceContext, typename T>
-using ArgMinKernel =
-    ArgMinMaxKernel<DeviceContext, T, int64_t, ArgMinMaxType::kArgMin>;
+using ArgMinKernel = ArgMinMaxKernel<DeviceContext, T, ArgMinMaxType::kArgMin>;
 
 template <typename DeviceContext, typename T>
-using ArgMaxKernel =
-    ArgMinMaxKernel<DeviceContext, T, int64_t, ArgMinMaxType::kArgMax>;
+using ArgMaxKernel = ArgMinMaxKernel<DeviceContext, T, ArgMinMaxType::kArgMax>;
 
 class ArgMinMaxOp : public framework::OperatorWithKernel {
  public:
@@ -114,14 +140,18 @@ class ArgMinMaxOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null");
     const auto& x_dims = ctx->GetInputDim("X");
     int64_t axis = ctx->Attrs().Get<int64_t>("axis");
+    bool keepdims = ctx->Attrs().Get<bool>("keepdims");
+
     PADDLE_ENFORCE(axis >= -x_dims.size() && axis < x_dims.size(),
                    "'axis' must be inside [-Rank(X), Rank(X))");
 
     auto x_rank = x_dims.size();
     if (axis < 0) axis += x_rank;
-
     std::vector<int64_t> vec;
     for (int64_t i = 0; i < axis; i++) vec.push_back(x_dims[i]);
+    if (keepdims) {
+      vec.push_back(static_cast<int64_t>(1));
+    }
     for (int64_t i = axis + 1; i < x_rank; i++) vec.push_back(x_dims[i]);
     ctx->SetOutputDim("Out", framework::make_ddim(vec));
   }
@@ -137,6 +167,8 @@ class BaseArgMinMaxOpMaker : public framework::OpProtoAndCheckerMaker {
     AddInput("X", "Input tensor.");
     AddOutput("Out", "Output tensor.");
     AddAttr<int64_t>("axis", "The axis in which to compute the arg indics.");
+    AddAttr<bool>("keepdims", "Keep the dim that to reduce.").SetDefault(false);
+    AddAttr<int>("dtype", "Keep the dim that to reduce.").SetDefault(-1);
     AddComment(string::Sprintf(R"DOC(
       %s Operator.
 

python/paddle/__init__.py

@@ -182,7 +182,7 @@ from .tensor.math import tanh  #DEFINE_ALIAS
 from .tensor.manipulation import flip  #DEFINE_ALIAS
 # from .tensor.manipulation import unbind   #DEFINE_ALIAS
 # from .tensor.manipulation import roll   #DEFINE_ALIAS
-# from .tensor.search import argmax   #DEFINE_ALIAS
+from .tensor.search import argmax  #DEFINE_ALIAS
 # from .tensor.search import argmin   #DEFINE_ALIAS
 # from .tensor.search import argsort   #DEFINE_ALIAS
 # from .tensor.search import has_inf   #DEFINE_ALIAS

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

@@ -17,6 +17,9 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
 
 
 class BaseTestCase(OpTest):
@@ -88,5 +91,199 @@ class TestCase4(BaseTestCase):
         self.axis = 0
 
 
+class TestCase3(BaseTestCase):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (3, )
+        self.axis = 0
+
+
+class BaseTestComplex1_1(OpTest):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (4, 5, 6)
+        self.dtype = 'int32'
+        self.axis = 2
+
+    def setUp(self):
+        self.initTestCase()
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
+        if self.op_type == "arg_min":
+            self.outputs = {
+                'Out': np.argmin(
+                    self.x, axis=self.axis).asdtype("int32")
+            }
+        else:
+            self.outputs = {
+                'Out': np.argmax(
+                    self.x, axis=self.axis).asdtype("int32")
+            }
+
+
+class BaseTestComplex1_2(OpTest):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (4, 5, 6)
+        self.dtype = 'int32'
+        self.axis = 2
+
+    def setUp(self):
+        self.initTestCase()
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
+        if self.op_type == "arg_min":
+            self.outputs = {
+                'Out': np.argmin(
+                    self.x, axis=self.axis).asdtype("int32")
+            }
+        else:
+            self.outputs = {
+                'Out': np.argmax(
+                    self.x, axis=self.axis).asdtype("int32")
+            }
+
+
+class BaseTestComplex2_1(OpTest):
+    def initTestCase(self):
+        self.op_type = 'arg_max'
+        self.dims = (4, 5, 6)
+        self.dtype = 'int32'
+        self.axis = 2
+
+    def setUp(self):
+        self.initTestCase()
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
+        self.attrs = {'keep_dims': True}
+        if self.op_type == "arg_min":
+            self.outputs = {
+                'Out': np.argmin(
+                    self.x, axis=self.axis).asdtype("int32").reshape(4, 5, 1)
+            }
+        else:
+            self.outputs = {
+                'Out': np.argmax(
+                    self.x, axis=self.axis).asdtype("int32").reshape(4, 5, 1)
+            }
+
+
+class BaseTestComplex2_2(OpTest):
+    def initTestCase(self):
+        self.op_type = 'arg_min'
+        self.dims = (4, 5, 6)
+        self.dtype = 'int32'
+        self.axis = 2
+
+    def setUp(self):
+        self.initTestCase()
+        self.x = (np.random.random(self.dims)).astype(self.dtype)
+        self.inputs = {'X': self.x}
+        self.attrs = {'axis': self.axis}
+        self.attrs = {'dtype': int(core.VarDesc.VarType.INT32)}
+        self.attrs = {'keep_dims': True}
+        if self.op_type == "arg_min":
+            self.outputs = {
+                'Out': np.argmin(
+                    self.x, axis=self.axis).asdtype("int32").reshape(4, 5, 1)
+            }
+        else:
+            self.outputs = {
+                'Out': np.argmax(
+                    self.x, axis=self.axis).asdtype("int32").reshape(4, 5, 1)
+            }
+
+
+class APT_ArgMaxTest(unittest.TestCase):
+    def test_output_result(self):
+        with fluid.program_guard(fluid.Program()):
+            data1 = fluid.data(name="X", shape=[3, 4], dtype="float32")
+            data2 = fluid.data(name="Y", shape=[3], dtype="int64")
+            out = paddle.argmax(input=data1, out=data2)
+
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            result = exe.run(
+                feed={"X": np.random.rand(3, 4).astype("float32")},
+                fetch_list=[data2, out])
+            self.assertEqual((result[0] == result[1]).all(), True)
+
+    def test_basic(self):
+        with fluid.program_guard(fluid.Program()):
+            data = fluid.data(name="X", shape=[3, 4], dtype="float32")
+            out = paddle.argmax(input=data)
+
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            np_input = np.random.rand(3, 4).astype("float32")
+            expected_result = np.argmax(np_input, axis=1)
+
+            result, = exe.run(feed={"X": np_input}, fetch_list=[out])
+        self.assertEqual((result == expected_result).all(), True)
+
+        with fluid.program_guard(fluid.Program()):
+            data = fluid.data(name="X", shape=[3, 4], dtype="float32")
+            out = paddle.argmax(input=data, axis=0)
+
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            np_input = np.random.rand(3, 4).astype("float32")
+            expected_result = np.argmax(np_input, axis=0)
+
+            result = exe.run(feed={"X": np_input}, fetch_list=[out])
+        self.assertEqual((result == expected_result).all(), True)
+
+        with fluid.program_guard(fluid.Program()):
+            data = fluid.data(name="X", shape=[3, 4], dtype="float32")
+            out = paddle.argmax(input=data, dtype="int32")
+
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            np_input = np.random.rand(3, 4).astype("float32")
+            expected_result = np.argmax(np_input, axis=1).astype(np.int32)
+
+            result = exe.run(feed={"X": np_input}, fetch_list=[out])
+        self.assertEqual((result == expected_result).all(), True)
+
+        with fluid.program_guard(fluid.Program()):
+            data1 = fluid.data(name="X", shape=[3, 4], dtype="float32")
+            data2 = fluid.data(name="Y", shape=[3], dtype="int64")
+            out = paddle.argmax(input=data, out=data2)
+
+            place = fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            result = exe.run(
+                feed={"X": np.random.rand(3, 4).astype("float32")},
+                fetch_list=[data2, out])
+        self.assertEqual((result[0] == result[1]).all(), True)
+
+    def test_name(self):
+        with fluid.program_guard(fluid.Program()):
+            x = fluid.data(name="x", shape=[100], dtype="float32")
+            y_1 = paddle.argmax(x, name='arg_max_res')
+            self.assertEqual(('arg_max_res' in y_1.name), True)
+
+    def test_errors(self):
+        def test_dtype1():
+            with fluid.program_guard(fluid.Program(), fluid.Program()):
+                data = fluid.data(name="data", shape=[10], dtype="float32")
+                paddle.argmax(data, dtype="float32")
+
+        self.assertRaises(TypeError, test_dtype1)
+
+        def test_dtype2():
+            with fluid.program_guard(fluid.Program(), fluid.Program()):
+                data = fluid.data(name="data", shape=[10], dtype="float64")
+                paddle.argmax(data, dtype="float32")
+
+        self.assertRaises(TypeError, test_dtype2)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/tensor/__init__.py

@@ -158,7 +158,7 @@ from .math import tanh  #DEFINE_ALIAS
 from .manipulation import flip  #DEFINE_ALIAS
 # from .manipulation import unbind   #DEFINE_ALIAS
 # from .manipulation import roll   #DEFINE_ALIAS
-# from .search import argmax   #DEFINE_ALIAS
+from .search import argmax  #DEFINE_ALIAS
 # from .search import argmin   #DEFINE_ALIAS
 # from .search import argsort   #DEFINE_ALIAS
 # from .search import has_inf   #DEFINE_ALIAS

python/paddle/tensor/search.py

@@ -28,7 +28,96 @@ __all__ = [
 ]
 
 from paddle.common_ops_import import *
-import warnings
+
+
+def argmax(input, axis=None, dtype=None, out=None, keepdims=False, name=None):
+    """
+    This OP computes the indices of the max elements of the input tensor's
+    element along the provided axis.
+
+    Args:
+        input(Variable): An input N-D Tensor with type float32, float64, int16,
+            int32, int64, uint8.
+        axis(int, optional): Axis to compute indices along. The effective range
+            is [-R, R), where R is Rank(input). when axis<0, it works the same way
+            as axis+R. Default is None, it will use the last dim to select indices of max value.
+        dtype(np.dtype|core.VarDesc.VarType|str): Data type of the output tensor which can
+                    be int32, int64. The default value is None, and it will
+                    return the int64 indices.
+        out(Variable, optional): Optional output which can be any created 
+            Variable that meets the requirements to store the result of operation.
+            if out is None, a new Varibale will be create to store the result. Defalut is None.
+        keepdims(bool, optional): Keep the axis that do the select max.
+        name(str, optional): The name of output variable, normally there is no need for user to set this this property. 
+            Default value is None, the framework set the name of output variable.  
+
+
+    Returns:
+        Variable: A Tensor with data type int64.
+
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            import paddle.fluid as fluid
+            import numpy as np
+
+            in1 = np.array([[[5,8,9,5],
+                            [0,0,1,7],
+                            [6,9,2,4]],
+                            [[5,2,4,2],
+                            [4,7,7,9],
+                            [1,7,0,6]]])
+            with fluid.dygraph.guard():
+                x = fluid.dygraph.to_variable(in1)
+                out1 = paddle.argmax(input=x, axis=-1)
+                out2 = paddle.argmax(input=x, axis=0)
+                out3 = paddle.argmax(input=x, axis=1)
+                out4 = paddle.argmax(input=x, axis=2)
+                out5 = paddle.argmax(input=x, axis=2, keepdims=True)
+                print(out1.numpy())
+                # [[2 3 1]
+                #  [0 3 1]]
+                print(out2.numpy())
+                # [[0 0 0 0]
+                #  [1 1 1 1]
+                #  [0 0 0 1]]
+                print(out3.numpy())
+                # [[2 2 0 1]
+                #  [0 1 1 1]]
+                print(out4.numpy())
+                # [[2 3 1]
+                #  [0 3 1]]
+                print(out5.numpy())
+                #array([[[2],
+                #        [3],
+                #        [1]],
+                #       [[0],
+                #        [3],
+                #        [1]]])
+    """
+    helper = LayerHelper("arg_max", **locals())
+    var_dtype = None
+    attrs = {}
+    if dtype is not None:
+        check_dtype(dtype, 'create data type', ['int32', 'int64'], 'arg_max')
+        var_dtype = convert_np_dtype_to_dtype_(dtype)
+        attrs["dtype"] = var_dtype
+    else:
+        var_dtype = VarDesc.VarType.INT64
+    if out is None:
+        out = helper.create_variable_for_type_inference(var_dtype)
+    if axis is None:
+        axis = -1
+    attrs['keepdims'] = keepdims
+    attrs['axis'] = axis
+    helper.append_op(
+        type='arg_max',
+        inputs={'X': input},
+        outputs={'Out': [out]},
+        attrs=attrs)
+    out.stop_gradient = True
+    return out
 
 
 def sort(input, axis=-1, descending=False, out=None, name=None):