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fix the argmin,argmax op for the paddlepaddle 2.0

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* fix the argmin,argmax op for the paddlepaddle 2.0， add checkPoint for the argmax/argmin
revert-26856-strategy_example2
wawltor 5 years ago committed by GitHub
parent 6129b0e246
commit 0a29fc85d6
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GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 108 additions and 56 deletions
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18
paddle/fluid/operators/arg_max_op.cc
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@ -12,6 +12,7 @@ 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/framework/op_version_registry.h"
#include "paddle/fluid/operators/arg_min_max_op_base.h"
REGISTER_OPERATOR(
@ -31,3 +32,20 @@ REGISTER_OP_CPU_KERNEL(
int16_t>,
paddle::operators::ArgMaxKernel<paddle::platform::CPUDeviceContext,
uint8_t>);
REGISTER_OP_VERSION(arg_max)
.AddCheckpoint(
R"ROC(
Upgrade argmax add a new attribute [flatten] and modify the attribute of dtype)ROC",
paddle::framework::compatible::OpVersionDesc()
.NewAttr("flatten",
"In order to compute the argmax over the flattened array "
"when the "
"argument `axis` in python API is None.",
false)
.ModifyAttr(
"dtype",
"change the default value of dtype, the older version "
"is -1, means return the int64 indices."
"The new version is 3, return the int64 indices directly."
"And supporting the dtype of -1 in new version.",
3));

35
paddle/fluid/operators/arg_min_max_op_base.h
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@ -70,6 +70,8 @@ struct VisitDataArgMinMaxFunctor {
auto axis = ctx.Attr<int64_t>("axis");
auto keepdims = ctx.Attr<bool>("keepdims");
const bool& flatten = ctx.Attr<bool>("flatten");
// paddle do not have the scalar tensor, just return the shape [1] tensor
if (flatten) keepdims = true;
// if flatten, will construct the new dims for the cacluate
framework::DDim x_dims;
@ -164,15 +166,30 @@ class ArgMinMaxOp : public framework::OperatorWithKernel {
platform::errors::InvalidArgument(
"'axis'(%d) must be less than Rank(X)(%d).", axis, x_dims.size()));
auto x_rank = x_dims.size();
if (axis < 0) axis += x_rank;
if (ctx->IsRuntime()) {
const int& dtype = ctx->Attrs().Get<int>("dtype");
if (dtype == framework::proto::VarType::INT32) {
int64_t all_element_num = 0;
if (flatten) {
all_element_num = framework::product(x_dims);
} else {
all_element_num = x_dims[axis];
}
PADDLE_ENFORCE_LE(
all_element_num, INT_MAX,
"The element num of the argmin/argmax input at axis is "
"%d, is larger than int32 maximum value:%d, you must "
"set the dtype of argmin/argmax to 'int64'.",
all_element_num, INT_MAX);
}
}
std::vector<int64_t> vec;
if (flatten) {
// if is flatten, will return the only on element
if (keepdims) {
vec.emplace_back(static_cast<int64_t>(1));
}
vec.emplace_back(static_cast<int64_t>(1));
} else {
auto x_rank = x_dims.size();
if (axis < 0) axis += x_rank;
for (int64_t i = 0; i < axis; i++) vec.emplace_back(x_dims[i]);
if (keepdims) {
vec.emplace_back(static_cast<int64_t>(1));
@ -194,10 +211,14 @@ class BaseArgMinMaxOpMaker : public framework::OpProtoAndCheckerMaker {
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);
AddAttr<bool>("flatten",
"Flatten the input value, and search the min or max indices")
.SetDefault(false);
AddAttr<int>("dtype",
"(int, 3), the dtype of indices, the indices dtype must be "
"int32, int64."
"default dtype is int64, and proto value is 3.")
.SetDefault(3);
AddComment(string::Sprintf(R"DOC(
%s Operator.

18
paddle/fluid/operators/arg_min_op.cc
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@ -12,6 +12,7 @@ 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/framework/op_version_registry.h"
#include "paddle/fluid/operators/arg_min_max_op_base.h"
REGISTER_OPERATOR(
@ -31,3 +32,20 @@ REGISTER_OP_CPU_KERNEL(
int16_t>,
paddle::operators::ArgMinKernel<paddle::platform::CPUDeviceContext,
uint8_t>);
REGISTER_OP_VERSION(arg_min)
.AddCheckpoint(
R"ROC(
Upgrade argmin add a new attribute [flatten] and modify the attribute of dtype)ROC",
paddle::framework::compatible::OpVersionDesc()
.NewAttr("flatten",
"In order to compute the argmin over the flattened array "
"when the "
"argument `axis` in python API is None.",
false)
.ModifyAttr(
"dtype",
"change the default value of dtype, the older version "
"is -1, means return the int64 indices."
"The new version is 3, return the int64 indices directly."
"And supporting the dtype of -1 in new version.",
3));

22
python/paddle/fluid/tests/unittests/test_arg_min_max_v2_op.py
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@ -218,7 +218,7 @@ def create_test_case(op_type):
self.assertTrue("test_arg_api" in result.name)
def run_dygraph(self, place):
paddle.disable_static()
paddle.disable_static(place)
op = eval("paddle.%s" % (op_type))
data_tensor = paddle.to_tensor(self.input_data)
@ -240,7 +240,7 @@ def create_test_case(op_type):
#case 4
result_data = op(data_tensor, axis=-1, keepdim=True)
excepted_data = self.numpy_op(self.input_data, axis=-1)
excepted_data = excepted_data.reshape((10))
excepted_data = excepted_data.reshape((10, 1))
self.assertTrue((result_data.numpy() == excepted_data).all(), True)
#case 5
@ -299,14 +299,28 @@ class TestArgMinMaxOpError(unittest.TestCase):
name="test_argmax", shape=[10], dtype="float32")
output = paddle.argmax(x=data, dtype="float32")
self.assertRaises(ValueError, test_argmax_attr_type)
self.assertRaises(TypeError, test_argmax_attr_type)
def test_argmin_attr_type():
data = paddle.static.data(
name="test_argmax", shape=[10], dtype="float32")
output = paddle.argmin(x=data, dtype="float32")
self.assertRaises(ValueError, test_argmin_attr_type)
self.assertRaises(TypeError, test_argmin_attr_type)
def test_argmax_axis_type():
data = paddle.static.data(
name="test_argmax", shape=[10], dtype="float32")
output = paddle.argmax(x=data, axis=1.2)
self.assertRaises(TypeError, test_argmax_axis_type)
def test_argmin_axis_type():
data = paddle.static.data(
name="test_argmin", shape=[10], dtype="float32")
output = paddle.argmin(x=data, axis=1.2)
self.assertRaises(TypeError, test_argmin_axis_type)
if __name__ == '__main__':

71
python/paddle/tensor/search.py
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@ -18,7 +18,6 @@ from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtyp
from ..fluid import core, layers
# TODO: define searching & indexing functions of a tensor
from ..fluid.layers import argmin #DEFINE_ALIAS
from ..fluid.layers import has_inf #DEFINE_ALIAS
from ..fluid.layers import has_nan #DEFINE_ALIAS
@ -124,7 +123,7 @@ def argsort(x, axis=-1, descending=False, name=None):
return ids
def argmax(x, axis=None, dtype=None, keepdim=False, name=None):
def argmax(x, axis=None, keepdim=False, dtype="int64", name=None):
"""
This OP computes the indices of the max elements of the input tensor's
element along the provided axis.
@ -135,10 +134,10 @@ def argmax(x, axis=None, dtype=None, keepdim=False, name=None):
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is x.ndim. when axis < 0, it works the same way
as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index.
dtype(str): Data type of the output tensor which can
be int32, int64. The default value is None, and it will
return the int64 indices.
keepdim(bool, optional): Keep the axis that selecting max. The defalut value is False.
dtype(str|np.dtype, optional): Data type of the output tensor which can
be int32, int64. The default value is 'int64', and it will
return the int64 indices.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
@ -166,48 +165,39 @@ def argmax(x, axis=None, dtype=None, keepdim=False, name=None):
print(out3.numpy())
# [2 3 1]
"""
if axis is not None and not isinstance(axis, int):
raise TypeError(
"The type of 'axis' must be int or None in argmax, but received %s."
% (type(axis)))
var_dtype = convert_np_dtype_to_dtype_(dtype)
check_dtype(var_dtype, 'dtype', ['int32', 'int64'], 'argmin')
flatten = False
if axis is None:
flatten = True
axis = 0
if in_dygraph_mode():
if dtype != None:
var_dtype = convert_np_dtype_to_dtype_(dtype)
out = core.ops.arg_max(x, 'axis', axis, 'dtype', var_dtype,
'keepdim', keepdim, 'flatten', flatten)
else:
out = core.ops.arg_max(x, 'axis', axis, 'keepdim', keepdim,
'flatten', flatten)
out = core.ops.arg_max(x, 'axis', axis, 'dtype', var_dtype, 'keepdims',
keepdim, 'flatten', flatten)
return out
helper = LayerHelper("argmax", **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
'paddle.argmax')
var_dtype = None
attrs = {}
if dtype is not None:
if dtype not in ['int32', 'int64']:
raise ValueError(
"The value of 'dtype' in argmax op must be int32, int64, but received of {}".
format(dtype))
var_dtype = convert_np_dtype_to_dtype_(dtype)
attrs["dtype"] = var_dtype
else:
var_dtype = VarDesc.VarType.INT64
out = helper.create_variable_for_type_inference(var_dtype)
attrs['keepdims'] = keepdim
attrs['axis'] = axis
attrs['flatten'] = flatten
attrs['dtype'] = var_dtype
helper.append_op(
type='arg_max', inputs={'X': x}, outputs={'Out': [out]}, attrs=attrs)
out.stop_gradient = True
return out
def argmin(x, axis=None, dtype=None, keepdim=False, name=None):
def argmin(x, axis=None, keepdim=False, dtype="int64", name=None):
"""
This OP computes the indices of the min elements of the input tensor's
element along the provided axis.
@ -218,10 +208,10 @@ def argmin(x, axis=None, dtype=None, keepdim=False, name=None):
axis(int, optional): Axis to compute indices along. The effective range
is [-R, R), where R is x.ndim. when axis < 0, it works the same way
as axis + R. Default is None, the input `x` will be into the flatten tensor, and selecting the min value index.
keepdim(bool, optional): Keep the axis that selecting min. The defalut value is False.
dtype(str): Data type of the output tensor which can
be int32, int64. The default value is None, and it will
be int32, int64. The default value is 'int64', and it will
return the int64 indices.
keepdim(bool, optional): Keep the axis that selecting min. The defalut value is False.
name(str, optional): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
@ -249,41 +239,32 @@ def argmin(x, axis=None, dtype=None, keepdim=False, name=None):
print(out3.numpy())
# [0 0 2]
"""
if axis is not None and not isinstance(axis, int):
raise TypeError(
"The type of 'axis' must be int or None in argmin, but received %s."
% (type(axis)))
var_dtype = convert_np_dtype_to_dtype_(dtype)
check_dtype(var_dtype, 'dtype', ['int32', 'int64'], 'argmin')
flatten = False
if axis is None:
flatten = True
axis = 0
if in_dygraph_mode():
if dtype != None:
var_dtype = convert_np_dtype_to_dtype_(dtype)
out = core.ops.arg_min(x, 'axis', axis, 'dtype', var_dtype,
'keepdim', keepdim, 'flatten', flatten)
else:
out = core.ops.arg_min(x, 'axis', axis, 'keepdim', keepdim,
'flatten', flatten)
out = core.ops.arg_min(x, 'axis', axis, 'dtype', var_dtype, 'keepdims',
keepdim, 'flatten', flatten)
return out
helper = LayerHelper("argmin", **locals())
check_variable_and_dtype(
x, 'x', ['float32', 'float64', 'int16', 'int32', 'int64', 'uint8'],
'paddle.argmin')
var_dtype = None
attrs = {}
if dtype is not None:
if dtype not in ['int32', 'int64']:
raise ValueError(
"The value of 'dtype' in argmin op must be int32, int64, but received of {}".
format(dtype))
var_dtype = convert_np_dtype_to_dtype_(dtype)
attrs["dtype"] = var_dtype
else:
var_dtype = VarDesc.VarType.INT64
out = helper.create_variable_for_type_inference(var_dtype)
attrs = {}
attrs['keepdims'] = keepdim
attrs['axis'] = axis
attrs['flatten'] = flatten
attrs['dtype'] = var_dtype
helper.append_op(
type='arg_min', inputs={'X': x}, outputs={'Out': [out]}, attrs=attrs)
out.stop_gradient = True

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