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!10988 Tensor op decoupling stage 1 

From: @ezphlow
Reviewed-by: 
Signed-off-by:
This commit is contained in:
mindspore-ci-bot 2021-01-09 05:12:10 +08:00 committed by Gitee
parent d27ba8ff2a be46ccf721
commit 7957a3b6f5
12 changed files with 249 additions and 68 deletions
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22
mindspore/ccsrc/minddata/dataset/api/CMakeLists.txt
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@ -2,25 +2,27 @@ file(GLOB_RECURSE _CURRENT_SRC_FILES RELATIVE ${CMAKE_CURRENT_SOURCE_DIR} "*.cc"
set_property(SOURCE ${_CURRENT_SRC_FILES} PROPERTY COMPILE_DEFINITIONS SUBMODULE_ID=mindspore::SubModuleId::SM_MD)
if (ENABLE_PYTHON)
add_library(APItoPython OBJECT
python/pybind_register.cc
python/pybind_conversion.cc
python/bindings/dataset/callback/bindings.cc
python/bindings/dataset/core/bindings.cc
python/bindings/dataset/engine/cache/bindings.cc
python/bindings/dataset/engine/datasetops/bindings.cc
python/bindings/dataset/engine/datasetops/source/bindings.cc
python/bindings/dataset/engine/datasetops/source/sampler/bindings.cc
python/bindings/dataset/engine/gnn/bindings.cc
python/bindings/dataset/include/datasets_bindings.cc
python/bindings/dataset/include/iterator_bindings.cc
python/bindings/dataset/include/execute_binding.cc
python/bindings/dataset/include/schema_bindings.cc
python/bindings/dataset/engine/cache/bindings.cc
python/bindings/dataset/core/bindings.cc
python/bindings/dataset/callback/bindings.cc
python/bindings/dataset/kernels/data/bindings.cc
python/bindings/dataset/kernels/bindings.cc
python/bindings/dataset/engine/datasetops/bindings.cc
python/bindings/dataset/engine/datasetops/source/bindings.cc
python/bindings/dataset/engine/gnn/bindings.cc
python/bindings/dataset/kernels/data/bindings.cc
python/bindings/dataset/kernels/image/bindings.cc
python/bindings/dataset/engine/datasetops/source/sampler/bindings.cc
python/bindings/dataset/kernels/ir/bindings.cc
python/bindings/dataset/kernels/ir/image/bindings.cc
python/bindings/dataset/text/bindings.cc
python/bindings/dataset/text/kernels/bindings.cc
python/bindings/mindrecord/include/bindings.cc
python/pybind_conversion.cc
python/pybind_register.cc
)
target_include_directories(APItoPython PRIVATE ${pybind11_INCLUDE_DIRS})
endif ()

37
mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/bindings.cc
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@ -49,42 +49,5 @@ PYBIND_REGISTER(TensorOp, 0, ([](const py::module *m) {
(void)py::class_<TensorOp, std::shared_ptr<TensorOp>>(*m, "TensorOp")
.def("__deepcopy__", [](py::object &t, py::dict memo) { return t; });
}));
PYBIND_REGISTER(ComposeOp, 1, ([](const py::module *m) {
(void)py::class_<ComposeOp, TensorOp, std::shared_ptr<ComposeOp>>(*m, "ComposeOp")
.def(py::init([](const py::list &ops) {
std::vector<std::shared_ptr<TensorOp>> t_ops;
THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
return std::make_shared<ComposeOp>(t_ops);
}));
}));
PYBIND_REGISTER(NoOp, 1, ([](const py::module *m) {
(void)py::class_<NoOp, TensorOp, std::shared_ptr<NoOp>>(
*m, "NoOp", "TensorOp that does nothing, for testing purposes only.")
.def(py::init<>());
}));
PYBIND_REGISTER(RandomChoiceOp, 1, ([](const py::module *m) {
(void)py::class_<RandomChoiceOp, TensorOp, std::shared_ptr<RandomChoiceOp>>(*m, "RandomChoiceOp")
.def(py::init([](const py::list &ops) {
std::vector<std::shared_ptr<TensorOp>> t_ops;
THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
return std::make_shared<RandomChoiceOp>(t_ops);
}));
}));
PYBIND_REGISTER(RandomApplyOp, 1, ([](const py::module *m) {
(void)py::class_<RandomApplyOp, TensorOp, std::shared_ptr<RandomApplyOp>>(*m, "RandomApplyOp")
.def(py::init([](double prob, const py::list &ops) {
std::vector<std::shared_ptr<TensorOp>> t_ops;
THROW_IF_ERROR(PyListToTensorOps(ops, &t_ops));
if (prob < 0 || prob > 1) {
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be within [0,1]."));
}
return std::make_shared<RandomApplyOp>(prob, t_ops);
}));
}));
} // namespace dataset
} // namespace mindspore

91
mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/ir/bindings.cc Normal file
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@ -0,0 +1,91 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* 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 "pybind11/pybind11.h"
#include "minddata/dataset/api/python/pybind_register.h"
#include "minddata/dataset/core/global_context.h"
#include "minddata/dataset/include/vision.h"
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/kernels/py_func_op.h"
namespace mindspore {
namespace dataset {
Status PyListToTensorOperations(const py::list &py_ops, std::vector<std::shared_ptr<TensorOperation>> *ops) {
RETURN_UNEXPECTED_IF_NULL(ops);
for (auto op : py_ops) {
if (py::isinstance<TensorOp>(op)) {
ops->emplace_back(std::make_shared<transforms::PreBuiltOperation>(op.cast<std::shared_ptr<TensorOp>>()));
} else if (py::isinstance<py::function>(op)) {
ops->emplace_back(
std::make_shared<transforms::PreBuiltOperation>(std::make_shared<PyFuncOp>(op.cast<py::function>())));
} else if (py::isinstance<TensorOperation>(op)) {
ops->emplace_back(op.cast<std::shared_ptr<TensorOperation>>());
} else {
RETURN_STATUS_UNEXPECTED("element is neither a TensorOp, TensorOperation nor a pyfunc.");
}
}
CHECK_FAIL_RETURN_UNEXPECTED(!ops->empty(), "TensorOp list is empty.");
for (auto const &op : *ops) {
RETURN_UNEXPECTED_IF_NULL(op);
}
return Status::OK();
}
PYBIND_REGISTER(TensorOperation, 0, ([](const py::module *m) {
(void)py::class_<TensorOperation, std::shared_ptr<TensorOperation>>(*m, "TensorOperation");
py::arg("TensorOperation");
}));
PYBIND_REGISTER(
ComposeOperation, 1, ([](const py::module *m) {
(void)py::class_<transforms::ComposeOperation, TensorOperation, std::shared_ptr<transforms::ComposeOperation>>(
*m, "ComposeOperation")
.def(py::init([](const py::list &ops) {
std::vector<std::shared_ptr<TensorOperation>> t_ops;
THROW_IF_ERROR(PyListToTensorOperations(ops, &t_ops));
auto compose = std::make_shared<transforms::ComposeOperation>(std::move(t_ops));
THROW_IF_ERROR(compose->ValidateParams());
return compose;
}));
}));
PYBIND_REGISTER(RandomChoiceOperation, 1, ([](const py::module *m) {
(void)py::class_<transforms::RandomChoiceOperation, TensorOperation,
std::shared_ptr<transforms::RandomChoiceOperation>>(*m, "RandomChoiceOperation")
.def(py::init([](const py::list &ops) {
std::vector<std::shared_ptr<TensorOperation>> t_ops;
THROW_IF_ERROR(PyListToTensorOperations(ops, &t_ops));
auto random_choice = std::make_shared<transforms::RandomChoiceOperation>(std::move(t_ops));
THROW_IF_ERROR(random_choice->ValidateParams());
return random_choice;
}));
}));
PYBIND_REGISTER(RandomApplyOperation, 1, ([](const py::module *m) {
(void)py::class_<transforms::RandomApplyOperation, TensorOperation,
std::shared_ptr<transforms::RandomApplyOperation>>(*m, "RandomApplyOperation")
.def(py::init([](double prob, const py::list &ops) {
std::vector<std::shared_ptr<TensorOperation>> t_ops;
THROW_IF_ERROR(PyListToTensorOperations(ops, &t_ops));
auto random_apply = std::make_shared<transforms::RandomApplyOperation>(std::move(t_ops), prob);
THROW_IF_ERROR(random_apply->ValidateParams());
return random_apply;
}));
}));
} // namespace dataset
} // namespace mindspore

65
mindspore/ccsrc/minddata/dataset/api/python/bindings/dataset/kernels/ir/image/bindings.cc Normal file
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@ -0,0 +1,65 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* 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 "pybind11/pybind11.h"
#include "pybind11/stl.h"
#include "pybind11/stl_bind.h"
#include "minddata/dataset/api/python/pybind_conversion.h"
#include "minddata/dataset/api/python/pybind_register.h"
#include "minddata/dataset/include/transforms.h"
#include "minddata/dataset/include/vision.h"
#include "minddata/dataset/include/vision_lite.h"
namespace mindspore {
namespace dataset {
PYBIND_REGISTER(
RandomSelectSubpolicyOperation, 1, ([](const py::module *m) {
(void)py::class_<vision::RandomSelectSubpolicyOperation, TensorOperation,
std::shared_ptr<vision::RandomSelectSubpolicyOperation>>(*m, "RandomSelectSubpolicyOperation")
.def(py::init([](const py::list &py_policy) {
std::vector<std::vector<std::pair<std::shared_ptr<TensorOperation>, double>>> cpp_policy;
for (auto &py_sub : py_policy) {
cpp_policy.push_back({});
for (auto handle : py_sub.cast<py::list>()) {
py::tuple tp = handle.cast<py::tuple>();
if (tp.is_none() || tp.size() != 2) {
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "Each tuple in subpolicy should be (op, prob)."));
}
std::shared_ptr<TensorOperation> t_op;
if (py::isinstance<TensorOperation>(tp[0])) {
t_op = (tp[0]).cast<std::shared_ptr<TensorOperation>>();
} else if (py::isinstance<TensorOp>(tp[0])) {
t_op = std::make_shared<transforms::PreBuiltOperation>((tp[0]).cast<std::shared_ptr<TensorOp>>());
} else if (py::isinstance<py::function>(tp[0])) {
t_op = std::make_shared<transforms::PreBuiltOperation>(
std::make_shared<PyFuncOp>((tp[0]).cast<py::function>()));
} else {
THROW_IF_ERROR(
Status(StatusCode::kUnexpectedError, "op is neither a tensorOp, tensorOperation nor a pyfunc."));
}
double prob = (tp[1]).cast<py::float_>();
if (prob < 0 || prob > 1) {
THROW_IF_ERROR(Status(StatusCode::kUnexpectedError, "prob needs to be with [0,1]."));
}
cpp_policy.back().emplace_back(std::make_pair(t_op, prob));
}
}
return std::make_shared<vision::RandomSelectSubpolicyOperation>(cpp_policy);
}));
}));
} // namespace dataset
} // namespace mindspore

22
mindspore/ccsrc/minddata/dataset/api/python/pybind_conversion.cc
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@ -92,13 +92,20 @@ std::vector<std::shared_ptr<TensorOperation>> toTensorOperations(py::list operat
std::shared_ptr<TensorOp> tensor_op;
if (py::isinstance<TensorOp>(op)) {
tensor_op = op.cast<std::shared_ptr<TensorOp>>();
vector.push_back(std::make_shared<transforms::PreBuiltOperation>(tensor_op));
} else if (py::isinstance<py::function>(op)) {
tensor_op = std::make_shared<PyFuncOp>(op.cast<py::function>());
vector.push_back(std::make_shared<transforms::PreBuiltOperation>(tensor_op));
} else {
THROW_IF_ERROR(
[]() { RETURN_STATUS_UNEXPECTED("Error: tensor_op is not recognised (not TensorOp and not pyfunc)."); }());
if (py::isinstance<TensorOperation>(op)) {
vector.push_back(op.cast<std::shared_ptr<TensorOperation>>());
} else {
THROW_IF_ERROR([]() {
RETURN_STATUS_UNEXPECTED(
"Error: tensor_op is not recognised (not TensorOp, TensorOperation and not pyfunc).");
}());
}
}
vector.push_back(std::make_shared<transforms::PreBuiltOperation>(tensor_op));
}
}
return vector;
@ -107,12 +114,15 @@ std::vector<std::shared_ptr<TensorOperation>> toTensorOperations(py::list operat
std::shared_ptr<TensorOperation> toTensorOperation(py::handle operation) {
std::shared_ptr<TensorOperation> op;
std::shared_ptr<TensorOp> tensor_op;
if (py::isinstance<TensorOp>(operation)) {
if (py::isinstance<TensorOperation>(operation)) {
op = operation.cast<std::shared_ptr<TensorOperation>>();
} else if (py::isinstance<TensorOp>(operation)) {
tensor_op = operation.cast<std::shared_ptr<TensorOp>>();
op = std::make_shared<transforms::PreBuiltOperation>(tensor_op);
} else {
THROW_IF_ERROR([]() { RETURN_STATUS_UNEXPECTED("Error: input operation is not a tensor_op."); }());
THROW_IF_ERROR(
[]() { RETURN_STATUS_UNEXPECTED("Error: input operation is not a tensor_op or TensorOperation."); }());
}
op = std::make_shared<transforms::PreBuiltOperation>(tensor_op);
return op;
}

4
mindspore/ccsrc/minddata/dataset/engine/ir/datasetops/map_node.cc
View File

@ -88,7 +88,9 @@ Status MapNode::ValidateParams() {
MS_LOG(ERROR) << err_msg;
RETURN_STATUS_SYNTAX_ERROR(err_msg);
}
for (const auto &op : operations_) {
RETURN_IF_NOT_OK(op->ValidateParams());
}
if (!input_columns_.empty()) {
RETURN_IF_NOT_OK(ValidateDatasetColumnParam("MapNode", "input_columns", input_columns_));
}

4
mindspore/dataset/core/validator_helpers.py
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@ -381,5 +381,5 @@ def check_gnn_list_or_ndarray(param, param_name):
def check_tensor_op(param, param_name):
"""check whether param is a tensor op or a callable Python function"""
if not isinstance(param, cde.TensorOp) and not callable(param):
raise TypeError("{0} is neither a c_transform op (TensorOp) nor a callable pyfunc.".format(param_name))
if not isinstance(param, cde.TensorOp) and not callable(param) and not getattr(param, 'parse', None):
raise TypeError("{0} is neither a c_transform op (TensorOperation) nor a callable pyfunc.".format(param_name))

8
mindspore/dataset/engine/datasets.py
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@ -2332,10 +2332,16 @@ class MapDataset(Dataset):
def parse(self, children=None):
column_order = replace_none(self.column_order, [])
operations = []
for op in self.operations:
if op and getattr(op, 'parse', None):
operations.append(op.parse())
else:
operations.append(op)
cc = self.cache.cache_client if self.cache else None
callbacks = [cb.create_runtime_obj() for cb in self.callbacks] if self.callbacks else []
return cde.MapNode(children[0], self.operations, self.input_columns, self.output_columns, column_order, cc,
return cde.MapNode(children[0], operations, self.input_columns, self.output_columns, column_order, cc,
callbacks).SetNumWorkers(self.num_parallel_workers)
def get_args(self):

39
mindspore/dataset/transforms/c_transforms.py
View File

@ -327,7 +327,7 @@ class Unique(cde.UniqueOp):
>>> # +---------+-----------------+---------+
"""
class Compose(cde.ComposeOp):
class Compose():
"""
Compose a list of transforms into a single transform.
@ -344,10 +344,18 @@ class Compose(cde.ComposeOp):
@check_random_transform_ops
def __init__(self, transforms):
super().__init__(transforms)
self.transforms = transforms
def parse(self):
operations = []
for op in self.transforms:
if op and getattr(op, 'parse', None):
operations.append(op.parse())
else:
operations.append(op)
return cde.ComposeOperation(operations)
class RandomApply(cde.RandomApplyOp):
class RandomApply():
"""
Randomly perform a series of transforms with a given probability.
@ -365,10 +373,20 @@ class RandomApply(cde.RandomApplyOp):
@check_random_transform_ops
def __init__(self, transforms, prob=0.5):
super().__init__(prob, transforms)
self.transforms = transforms
self.prob = prob
def parse(self):
operations = []
for op in self.transforms:
if op and getattr(op, 'parse', None):
operations.append(op.parse())
else:
operations.append(op)
return cde.RandomApplyOperation(self.prob, operations)
class RandomChoice(cde.RandomChoiceOp):
class RandomChoice():
"""
Randomly selects one transform from a list of transforms to perform operation.
@ -385,4 +403,13 @@ class RandomChoice(cde.RandomChoiceOp):
@check_random_transform_ops
def __init__(self, transforms):
super().__init__(transforms)
self.transforms = transforms
def parse(self):
operations = []
for op in self.transforms:
if op and getattr(op, 'parse', None):
operations.append(op.parse())
else:
operations.append(op)
return cde.RandomChoiceOperation(operations)

21
mindspore/dataset/vision/c_transforms.py
View File

@ -1325,14 +1325,14 @@ class UniformAugment(cde.UniformAugOp):
super().__init__(transforms, num_ops)
class RandomSelectSubpolicy(cde.RandomSelectSubpolicyOp):
class RandomSelectSubpolicy():
"""
Choose a random sub-policy from a list to be applied on the input image. A sub-policy is a list of tuples
(op, prob), where op is a TensorOp operation and prob is the probability that this op will be applied. Once
a sub-policy is selected, each op within the subpolicy with be applied in sequence according to its probability.
Args:
policy (list(list(tuple(TensorOp,float))): List of sub-policies to choose from.
policy (list(list(tuple(TensorOp, float))): List of sub-policies to choose from.
Examples:
>>> policy = [[(c_vision.RandomRotation((45, 45)), 0.5),
@ -1346,7 +1346,22 @@ class RandomSelectSubpolicy(cde.RandomSelectSubpolicyOp):
@check_random_select_subpolicy_op
def __init__(self, policy):
super().__init__(policy)
self.policy = policy
def parse(self):
"""
Return a C++ representation of the operator for execution
"""
policy = []
for list_one in self.policy:
policy_one = []
for list_two in list_one:
if hasattr(list_two[0], 'parse'):
policy_one.append((list_two[0].parse(), list_two[1]))
else:
policy_one.append((list_two[0], list_two[1]))
policy.append(policy_one)
return cde.RandomSelectSubpolicyOperation(policy)
class SoftDvppDecodeResizeJpeg(cde.SoftDvppDecodeResizeJpegOp):

2
tests/ut/python/dataset/test_compose.py
View File

@ -63,7 +63,7 @@ def test_compose():
# Test exceptions.
with pytest.raises(TypeError) as error_info:
c_transforms.Compose([1, c_transforms.TypeCast(mstype.int32)])
assert "op_list[0] is neither a c_transform op (TensorOp) nor a callable pyfunc." in str(error_info.value)
assert "op_list[0] is neither a c_transform op (TensorOperation) nor a callable pyfunc." in str(error_info.value)
# Test empty op list
with pytest.raises(ValueError) as error_info:

2
tests/ut/python/dataset/test_random_select_subpolicy.py
View File

@ -41,7 +41,7 @@ def test_random_select_subpolicy():
# test exceptions
assert "policy can not be empty." in test_config([[1, 2, 3]], [])
assert "policy[0] can not be empty." in test_config([[1, 2, 3]], [[]])
assert "op of (op, prob) in policy[1][0] is neither a c_transform op (TensorOp) nor a callable pyfunc" \
assert "op of (op, prob) in policy[1][0] is neither a c_transform op (TensorOperation) nor a callable pyfunc" \
in test_config([[1, 2, 3]], [[(ops.PadEnd([4], 0), 0.5)], [(1, 0.4)]])
assert "prob of (op, prob) policy[1][0] is not within the required interval of (0 to 1)" in test_config([[1]], [
[(ops.Duplicate(), 0)], [(ops.Duplicate(), -0.1)]])