提交 c545f1ed 编写于 作者: S sneaxiy

unify API

test=develop
上级 a8c4324d
......@@ -10,6 +10,9 @@ paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=Non
paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None)
paddle.fluid.program_guard ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.name_scope ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.cuda_places ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.cpu_places ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.cuda_pinned_places ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.Executor.__init__ ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.close ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.Executor.run ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False))
......@@ -44,7 +47,7 @@ paddle.fluid.AsyncExecutor.run ArgSpec(args=['self', 'program', 'data_feed', 'fi
paddle.fluid.AsyncExecutor.save_model ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None)
paddle.fluid.AsyncExecutor.stop ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.CompiledProgram.__init__ ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=None)
paddle.fluid.CompiledProgram.with_data_parallel ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.CompiledProgram.with_data_parallel ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from', 'places'], varargs=None, keywords=None, defaults=(None, None, None, None, None))
paddle.fluid.CompiledProgram.with_inference_optimize ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None)
paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.ExecutionStrategy) -> None
paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.GradientScaleStrategy, arg0: int) -> None
......@@ -58,6 +61,11 @@ paddle.fluid.io.load_params ArgSpec(args=['executor', 'dirname', 'main_program',
paddle.fluid.io.load_persistables ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.io.save_inference_model ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True))
paddle.fluid.io.load_inference_model ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.io.PyReader.__init__ ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable'], varargs=None, keywords=None, defaults=(True, True))
paddle.fluid.io.PyReader.decorate_paddle_reader ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.io.PyReader.decorate_tensor_provider ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.io.PyReader.reset ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.io.PyReader.start ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
paddle.fluid.initializer.ConstantInitializer.__init__ ArgSpec(args=['self', 'value', 'force_cpu'], varargs=None, keywords=None, defaults=(0.0, False))
paddle.fluid.initializer.UniformInitializer.__init__ ArgSpec(args=['self', 'low', 'high', 'seed'], varargs=None, keywords=None, defaults=(-1.0, 1.0, 0))
paddle.fluid.initializer.NormalInitializer.__init__ ArgSpec(args=['self', 'loc', 'scale', 'seed'], varargs=None, keywords=None, defaults=(0.0, 1.0, 0))
......
......@@ -54,7 +54,6 @@ class ReaderBase {
private:
friend class DecoratedReader;
friend class MultiDecoratedReader;
// These methods can be only invoked inside DecoratedReader to record the
// decorating chain.
void InsertDecoratedReader(
......@@ -63,20 +62,15 @@ class ReaderBase {
std::vector<std::weak_ptr<ReaderBase>> decorated_readers_;
};
class DecoratedReaderBase : public ReaderBase {
public:
virtual void RegisterDecorateChain() = 0;
};
class DecoratedReader : public DecoratedReaderBase,
class DecoratedReader : public ReaderBase,
public std::enable_shared_from_this<DecoratedReader> {
public:
explicit DecoratedReader(const std::shared_ptr<ReaderBase>& reader)
: DecoratedReaderBase(), reader_(reader) {
: ReaderBase(), reader_(reader) {
PADDLE_ENFORCE_NOT_NULL(reader_);
}
void RegisterDecorateChain() final {
void RegisterDecorateChain() {
reader_->InsertDecoratedReader(shared_from_this());
}
......@@ -90,41 +84,6 @@ class DecoratedReader : public DecoratedReaderBase,
std::shared_ptr<ReaderBase> reader_;
};
class MultiDecoratedReader
: public DecoratedReaderBase,
public std::enable_shared_from_this<MultiDecoratedReader> {
public:
explicit MultiDecoratedReader(
const std::vector<std::shared_ptr<ReaderBase>>& readers)
: readers_(readers) {
PADDLE_ENFORCE(!readers_.empty());
for (auto& r : readers_) {
PADDLE_ENFORCE_NOT_NULL(r);
}
}
void RegisterDecorateChain() final {
for (auto& r : readers_) {
r->InsertDecoratedReader(shared_from_this());
}
}
protected:
void ShutdownImpl() override {
for (auto& r : readers_) {
r->Shutdown();
}
}
void StartImpl() override {
for (auto& r : readers_) {
r->Start();
}
}
std::vector<std::shared_ptr<ReaderBase>> readers_;
};
// FileReader is just a conceptual class.
class FileReader : public ReaderBase {};
......@@ -173,10 +132,8 @@ class ReaderHolder {
};
template <typename T, typename... ARGS>
inline std::shared_ptr<DecoratedReaderBase> MakeDecoratedReader(
ARGS&&... args) {
std::shared_ptr<DecoratedReaderBase> reader(
new T(std::forward<ARGS>(args)...));
inline std::shared_ptr<DecoratedReader> MakeDecoratedReader(ARGS&&... args) {
std::shared_ptr<DecoratedReader> reader(new T(std::forward<ARGS>(args)...));
reader->RegisterDecorateChain();
return reader;
}
......
......@@ -18,7 +18,6 @@ function(reader_library TARGET_NAME)
endfunction()
cc_library(py_reader SRCS py_reader.cc DEPS reader)
cc_library(compose_reader SRCS compose_reader.cc DEPS reader)
cc_library(buffered_reader SRCS buffered_reader.cc DEPS reader simple_threadpool)
reader_library(open_files_op SRCS open_files_op.cc DEPS buffered_reader)
......@@ -41,7 +40,7 @@ cc_test(reader_blocking_queue_test SRCS reader_blocking_queue_test.cc)
# Export local libraries to parent
# set(READER_LIBRARY ${LOCAL_READER_LIBS} PARENT_SCOPE)
op_library(read_op DEPS py_reader compose_reader buffered_reader)
op_library(read_op DEPS py_reader buffered_reader)
foreach(src ${LOCAL_READER_LIBS})
set(OP_LIBRARY ${src} ${OP_LIBRARY} CACHE INTERNAL "op libs")
......
......@@ -114,11 +114,6 @@ class BlockingQueue {
return queue_.size();
}
void Clear() {
std::lock_guard<std::mutex> lock(mutex_);
queue_.clear();
}
private:
size_t capacity_;
bool speed_test_mode_;
......
// Copyright (c) 2019 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/reader/compose_reader.h"
namespace paddle {
namespace operators {
namespace reader {
ComposeReader::ComposeReader(
const std::vector<std::shared_ptr<framework::ReaderBase>> &readers)
: framework::MultiDecoratedReader(readers) {}
void ComposeReader::ReadNext(std::vector<framework::LoDTensor> *out) {
out->clear();
std::vector<framework::LoDTensor> each_ret;
for (auto &r : readers_) {
r->ReadNext(&each_ret);
out->reserve(out->size() + each_ret.size());
for (auto &data : each_ret) {
out->emplace_back(std::move(data));
}
}
}
} // namespace reader
} // namespace operators
} // namespace paddle
// Copyright (c) 2019 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/reader.h"
namespace paddle {
namespace operators {
namespace reader {
class ComposeReader : public framework::MultiDecoratedReader {
public:
explicit ComposeReader(
const std::vector<std::shared_ptr<framework::ReaderBase>> &readers);
void ReadNext(std::vector<framework::LoDTensor> *out) override;
};
} // namespace reader
} // namespace operators
} // namespace paddle
......@@ -36,43 +36,6 @@ void PyReader::Shutdown() { queue_->Close(); }
void PyReader::Start() { queue_->ReOpen(); }
MultiQueuePyReader::MultiQueuePyReader(
const std::vector<std::shared_ptr<LoDTensorBlockingQueue>>& queues)
: queues_(queues) {
PADDLE_ENFORCE(!queues_.empty());
for (auto& q : queues_) {
PADDLE_ENFORCE_NOT_NULL(q);
}
}
void MultiQueuePyReader::ReadNext(std::vector<framework::LoDTensor>* out) {
auto idx = read_out_idx_.fetch_add(1) % queues_.size();
for (size_t i = 0; i < queues_.size(); ++i) {
*out = queues_[idx]->Pop();
if (!out->empty()) return;
idx = (idx + 1) % queues_.size();
}
}
MultiQueuePyReader::~MultiQueuePyReader() {
for (auto& q : queues_) {
q->Close();
}
}
void MultiQueuePyReader::Shutdown() {
for (auto& q : queues_) {
q->Close();
}
read_out_idx_.store(0, std::memory_order::memory_order_seq_cst);
}
void MultiQueuePyReader::Start() {
for (auto& q : queues_) {
q->ReOpen();
}
}
} // namespace reader
} // namespace operators
} // namespace paddle
......@@ -39,24 +39,6 @@ class PyReader : public framework::FileReader {
std::shared_ptr<LoDTensorBlockingQueue> queue_;
};
class MultiQueuePyReader : public framework::FileReader {
public:
explicit MultiQueuePyReader(
const std::vector<std::shared_ptr<LoDTensorBlockingQueue>>& queues);
void ReadNext(std::vector<framework::LoDTensor>* out) override;
~MultiQueuePyReader();
void Shutdown() override;
void Start() override;
private:
std::vector<std::shared_ptr<LoDTensorBlockingQueue>> queues_;
std::atomic<size_t> read_out_idx_{0};
};
} // namespace reader
} // namespace operators
} // namespace paddle
......@@ -547,11 +547,6 @@ All parameter, weight, gradient are variables in Paddle.
using LoDTensorBlockingQueueHolder =
::paddle::operators::reader::LoDTensorBlockingQueueHolder;
using LockFreeLoDTensorBlockingQueue =
::paddle::operators::reader::LockFreeLoDTensorBlockingQueue;
using LockFreeLoDTensorBlockingQueueHolder =
::paddle::operators::reader::LockFreeLoDTensorBlockingQueueHolder;
py::class_<LoDTensorBlockingQueue, std::shared_ptr<LoDTensorBlockingQueue>>(
m, "LoDTensorBlockingQueue", "")
.def("push",
......@@ -565,20 +560,6 @@ All parameter, weight, gradient are variables in Paddle.
.def("close", &LoDTensorBlockingQueue::Close)
.def("is_closed", &LoDTensorBlockingQueue::IsClosed);
py::class_<LockFreeLoDTensorBlockingQueue,
std::shared_ptr<LockFreeLoDTensorBlockingQueue>>(
m, "LockFreeLoDTensorBlockingQueue", "")
.def("push",
[](LockFreeLoDTensorBlockingQueue &self,
std::vector<framework::LoDTensor> &lod_tensor_vec) {
pybind11::gil_scoped_release release;
return self.Push(std::move(lod_tensor_vec));
})
.def("size", &LockFreeLoDTensorBlockingQueue::Size)
.def("capacity", &LockFreeLoDTensorBlockingQueue::Cap)
.def("close", &LockFreeLoDTensorBlockingQueue::Close)
.def("is_closed", &LockFreeLoDTensorBlockingQueue::IsClosed);
m.def("init_lod_tensor_blocking_queue",
[](Variable &var,
size_t capacity) -> std::shared_ptr<LoDTensorBlockingQueue> {
......@@ -588,15 +569,6 @@ All parameter, weight, gradient are variables in Paddle.
},
py::return_value_policy::copy);
m.def("init_lock_free_lod_tensor_blocking_queue",
[](Variable &var,
size_t capacity) -> std::shared_ptr<LockFreeLoDTensorBlockingQueue> {
auto *holder = var.GetMutable<LockFreeLoDTensorBlockingQueueHolder>();
holder->InitOnce(capacity);
return holder->GetQueue();
},
py::return_value_policy::copy);
py::class_<Scope>(m, "_Scope", R"DOC(
Scope is an association of a name to Variable. All variables belong to Scope.
......@@ -777,8 +749,6 @@ All parameter, weight, gradient are variables in Paddle.
.def("_equals", &IsSamePlace<platform::CUDAPlace, platform::CPUPlace>)
.def("_equals",
&IsSamePlace<platform::CUDAPlace, platform::CUDAPinnedPlace>)
.def("gpu_device_id",
[](platform::CUDAPlace &self) { return self.device; })
.def("__str__", string::to_string<const platform::CUDAPlace &>);
py::class_<paddle::platform::CPUPlace>(m, "CPUPlace")
......
......@@ -17,7 +17,6 @@
#include <vector>
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/operators/reader/buffered_reader.h"
#include "paddle/fluid/operators/reader/compose_reader.h"
#include "paddle/fluid/operators/reader/py_reader.h"
#include "paddle/fluid/platform/place.h"
#include "pybind11/stl.h"
......@@ -82,7 +81,6 @@ class MultiDeviceFeedReader {
void Reset() {
Shutdown();
Start();
ReadAsync();
}
......@@ -117,14 +115,14 @@ class MultiDeviceFeedReader {
}
}
std::shared_ptr<operators::reader::LoDTensorBlockingQueue> queue_;
std::vector<std::string> names_;
std::unique_ptr<::ThreadPool> pool_;
std::shared_ptr<operators::reader::LoDTensorBlockingQueue> queue_;
std::vector<std::unique_ptr<framework::ReaderHolder>> readers_;
std::vector<std::future<bool>> futures_;
std::vector<std::vector<framework::LoDTensor>> ret_;
bool drop_last_;
};
namespace py = pybind11;
......@@ -150,7 +148,7 @@ void BindReader(py::module *module) {
const std::vector<std::string> &names,
const std::vector<platform::Place> &dst_places,
bool use_double_buffer) {
return new MultiDeviceFeedReader(queues, names, dst_places,
return new MultiDeviceFeedReader(queue, names, dst_places,
use_double_buffer);
},
py::return_value_policy::take_ownership);
......
......@@ -486,8 +486,7 @@ def _py_reader(capacity,
lod_levels=None,
name=None,
use_double_buffer=True,
feed_list=None,
lock_free=False):
feed_list=None):
if feed_list is not None:
if not isinstance(feed_list, list):
......@@ -527,11 +526,7 @@ def _py_reader(capacity,
double_buffer_name = "_".join([name, "double_buffer"])
var = global_scope().var(queue_name)
if not lock_free:
feed_queue = core.init_lod_tensor_blocking_queue(var, capacity)
else:
feed_queue = core.init_lock_free_lod_tensor_blocking_queue(var,
capacity)
startup_blk = default_startup_program().current_block()
startup_var = startup_blk.create_var(name=reader_name)
......@@ -644,8 +639,7 @@ def py_reader(capacity,
dtypes,
lod_levels=None,
name=None,
use_double_buffer=True,
lock_free=False):
use_double_buffer=True):
"""
Create a Python reader for data feeding in Python
......@@ -770,8 +764,7 @@ def py_reader(capacity,
dtypes=dtypes,
lod_levels=lod_levels,
name=name,
use_double_buffer=use_double_buffer,
lock_free=lock_free)
use_double_buffer=use_double_buffer)
def create_py_reader_by_data(capacity,
......
......@@ -15,9 +15,11 @@
import core
import six
import threading
from .framework import Program, Variable, program_guard
from .framework import Program, Variable, program_guard, default_main_program, default_startup_program
from .executor import global_scope
from .data_feeder import DataFeeder
import paddle.reader.decorator as decorator
from .layers.io import monkey_patch_reader_methods, _copy_reader_var_, double_buffer
import unique_name
__all__ = ['PyReader']
......@@ -37,30 +39,101 @@ def _convert_places(places):
return ret
class PyReader(Reader):
def __init__(self, feed_list, places, capacity):
class PyReader(object):
unique_name_generator = unique_name.UniqueNameGenerator()
def __init__(self,
feed_list,
capacity,
use_double_buffer=True,
iterable=True):
self._tensor_reader = None
self._thread = None
# TODO(zjl): to support drop_last = False
self._drop_last = True
self._iterable = iterable
self._use_double_buffer = use_double_buffer
self._capacity = capacity
self._feed_list = feed_list
self._var_names = [v.name for v in feed_list]
self._queues = []
self._scope = global_scope()
if not self._iterable:
self._init_non_iterable()
def _init_iterable(self, places):
self._var_names = [v.name for v in self._feed_list]
self._places = _convert_places(places)
self._queue_capacity = capacity
self.queue = core.init_lod_tensor_blocking_queue(core.Variable(),
self._queue_capacity)
self._reader = core.create_py_reader(self._queue, self._var_names,
self._places, self._drop_last)
self._queue = core.init_lod_tensor_blocking_queue(core.Variable(),
self._capacity)
self._reader = core.create_py_reader(
self.queue, self._var_names, self._places, self._use_double_buffer)
def _init_non_iterable(self):
lod_levels = []
dtypes = []
shape_concat = []
ranks = []
shapes = []
for feed_data in self._feed_list:
dtypes.append(feed_data.dtype)
shape_concat.extend(feed_data.shape)
ranks.append(len(feed_data.shape))
shapes.append(feed_data.shape)
lod_levels.append(feed_data.lod_level)
queue_name = PyReader.unique_name_generator('lod_tensor_blocking_queue')
reader_name = PyReader.unique_name_generator('create_py_reader')
double_buffer_name = PyReader.unique_name_generator('double_buffer')
var = self._scope.var(queue_name)
self._queue = core.init_lod_tensor_blocking_queue(var, self._capacity)
startup_blk = default_startup_program().current_block()
startup_var = startup_blk.create_var(name=reader_name)
startup_blk.append_op(
type='create_py_reader',
inputs={'blocking_queue': [queue_name]},
outputs={'Out': [startup_var]},
attrs={
'shape_concat': shape_concat,
'lod_levels': lod_levels,
'ranks': ranks
})
startup_var.desc.set_dtypes(dtypes)
startup_var.persistable = True
main_prog_var = _copy_reader_var_(
default_main_program().current_block(), startup_var)
main_prog_var.stop_gradient = True
main_prog_var.persistable = True
reader = monkey_patch_reader_methods(main_prog_var)
if self._use_double_buffer:
double_buffer_reader = double_buffer(
reader, name=double_buffer_name)
# we return a double buffer reader. However, the reset method comes from
# py_reader.
double_buffer_reader.reset = reader.reset
reader = double_buffer_reader
self._reader = reader
default_main_program().current_block().append_op(
type='read',
inputs={'Reader': [self._reader]},
outputs={'Out': self._feed_list})
@property
def queue(self):
return self._queue
@property
def iterable(self):
return self._iterable
def __call__(self):
assert self.iterable, "PyReader is not iterable"
assert self._tensor_reader is not None, \
"Data source of PyReader has not set yet"
......@@ -80,13 +153,22 @@ class PyReader(Reader):
self._reset()
raise StopIteration
self._start()
return Iterator(self)
def _reset(self):
if self._thread:
self._reader.reset()
self._thread.join()
def start(self):
assert not self._iterable, "start() cannot be called when PyReader is iterable"
self._start()
def reset(self):
assert not self._iterable, "reset() cannot be called when PyReader is iterable"
self._reset()
def _start(self):
def __thread_main__():
for tensors in self._tensor_reader():
array = core.LoDTensorArray()
......@@ -98,16 +180,16 @@ class PyReader(Reader):
array.append(item)
if not self.queue.push(array):
if not self._queue.push(array):
break
self.queue.close()
self._queue.close()
self._thread = threading.Thread(target=__thread_main__)
self._thread.daemon = True
self._thread.start()
def set_numpy_reader(self, reader):
def decorate_paddle_reader(self, reader, places=None):
assert self._tensor_reader is None, \
"Cannot reset the data source of PyReader"
with program_guard(Program(), Program()):
......@@ -119,10 +201,12 @@ class PyReader(Reader):
for slots in paddle_reader():
yield [slots[var.name] for var in self._feed_list]
self.set_tensor_reader(__tensor_reader_impl__)
self.decorate_tensor_provider(__tensor_reader_impl__, places)
def set_tensor_reader(self, reader):
def decorate_tensor_provider(self, reader, places=None):
assert self._tensor_reader is None, \
"Cannot reset the data source of PyReader"
self._tensor_reader = reader
self._reset()
if self._iterable:
assert places is not None, "Places cannot be None when py_reader is iterable"
self._init_iterable(places)
......@@ -31,35 +31,22 @@ def random_reader():
yield image, label
def simple_fc_net(places, use_legacy_py_reader, lock_free=False):
def simple_fc_net(places, use_legacy_py_reader, use_double_buffer):
startup_prog = fluid.Program()
main_prog = fluid.Program()
startup_prog.random_seed = 1
main_prog.random_seed = 1
reader = paddle.batch(random_reader, batch_size=BATCH_SIZE)
with fluid.unique_name.guard():
with fluid.program_guard(main_prog, startup_prog):
if not use_legacy_py_reader:
image = fluid.layers.data(
name='image', shape=[784], dtype='float32')
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
py_reader = fluid.io.PyReader(
feed_list=[image, label],
places=places,
capacity=4,
multi_queue=False)
py_reader.set_numpy_reader(reader)
else:
py_reader = fluid.layers.py_reader(
capacity=4,
shapes=[(-1, 784), (-1, 1)],
dtypes=['float32', 'int64'],
lock_free=lock_free)
image, label = fluid.layers.read_file(py_reader)
py_reader.decorate_paddle_reader(reader)
iterable=not use_legacy_py_reader,
use_double_buffer=use_double_buffer)
hidden = image
for hidden_size in [10, 20, 30]:
hidden = fluid.layers.fc(
......@@ -82,11 +69,19 @@ def simple_fc_net(places, use_legacy_py_reader, lock_free=False):
class TestBase(unittest.TestCase):
def run_main(self, use_legacy_py_reader, with_data_parallel, places):
def run_main(self, use_legacy_py_reader, with_data_parallel, places,
use_double_buffer):
scope = fluid.Scope()
with fluid.scope_guard(scope):
startup_prog, main_prog, py_reader, loss = simple_fc_net(
places, use_legacy_py_reader)
places, use_legacy_py_reader, use_double_buffer)
reader = paddle.batch(random_reader, batch_size=BATCH_SIZE)
ps = places if use_double_buffer else fluid.cpu_places(len(places))
py_reader.decorate_paddle_reader(
reader, places=ps if py_reader.iterable else None)
exe = fluid.Executor(place=places[0])
exe.run(startup_prog)
......@@ -98,7 +93,7 @@ class TestBase(unittest.TestCase):
step = 0
step_list = []
start_t = time.time()
if use_legacy_py_reader:
if not py_reader.iterable:
for _ in six.moves.range(EPOCH_NUM):
step = 0
py_reader.start()
......@@ -107,12 +102,9 @@ class TestBase(unittest.TestCase):
L, = exe.run(program=prog,
fetch_list=[loss],
use_program_cache=True)
# print('runned', step, py_reader.queue.is_closed(), py_reader.queue.size())
step += 1
except fluid.core.EOFException:
# print('try to reset')
py_reader.reset()
# print('reseted')
break
step_list.append(step)
else:
......@@ -125,8 +117,8 @@ class TestBase(unittest.TestCase):
label = item['label']
assert image.shape() == [BATCH_SIZE, 784]
assert label.shape() == [BATCH_SIZE, 1]
assert image._place()._equals(places[i])
assert label._place()._equals(places[i])
assert image._place()._equals(ps[i])
assert label._place()._equals(ps[i])
L, = exe.run(program=prog,
feed=d,
fetch_list=[loss],
......@@ -138,7 +130,7 @@ class TestBase(unittest.TestCase):
scope._remove_from_pool()
return ret
def prepare_places(self, with_data_parallel, with_cpu=False, with_gpu=True):
def prepare_places(self, with_data_parallel, with_cpu=True, with_gpu=True):
places = []
if with_cpu:
places.append([fluid.CPUPlace()])
......@@ -156,21 +148,13 @@ class TestBase(unittest.TestCase):
def test_main(self):
for with_data_parallel in [True, False]:
for p in self.prepare_places(with_data_parallel):
t = []
for use_legacy_py_reader in [
False
]: #[True, False]: #[False, True]:
print(p, use_legacy_py_reader)
for use_double_buffer in [False, True]:
for use_legacy_py_reader in [False, True]:
ret = self.run_main(
use_legacy_py_reader=use_legacy_py_reader,
with_data_parallel=with_data_parallel,
places=p)
ret['legacy'] = use_legacy_py_reader
ret['data_parallel'] = with_data_parallel
ret['places'] = p
t.append([ret['step'], ]) #, ret['places']])
print(t)
places=p,
use_double_buffer=use_double_buffer)
if __name__ == '__main__':
......
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