提交 bc4af386 编写于 作者: L Liufang Sang 提交者: whs

[PaddleSlim] refine slim reader to support dataloader (#20604)

上级 a4753f3a
......@@ -20,7 +20,7 @@ from .... import profiler
from .... import scope_guard
from ....data_feeder import DataFeeder
from ....log_helper import get_logger
from ....reader import PyReader
from ....reader import DataLoaderBase
from ..graph import *
from .config import ConfigFactory
import numpy as np
......@@ -194,8 +194,8 @@ class Context(object):
reader = cached_reader(reader, sampled_rate, self.cache_path,
cached_id)
if isinstance(reader, Variable) or (isinstance(reader, PyReader) and
(not reader.iterable)):
if isinstance(reader, Variable) or (
isinstance(reader, DataLoaderBase) and (not reader.iterable)):
reader.start()
try:
while True:
......@@ -488,8 +488,8 @@ class Compressor(object):
build_strategy=build_strategy)
if isinstance(context.train_reader, Variable) or (
isinstance(context.train_reader,
PyReader) and (not context.train_reader.iterable)):
isinstance(context.train_reader, DataLoaderBase) and
(not context.train_reader.iterable)):
context.train_reader.start()
try:
while True:
......
......@@ -42,7 +42,10 @@ class SlimGraphExecutor(object):
"""
assert isinstance(graph, GraphWrapper)
feed = None
if data is not None:
if data is not None and isinstance(data[0], dict):
# return list = False
feed = data
elif data is not None:
feeder = DataFeeder(
feed_list=list(graph.in_nodes.values()),
place=self.place,
......
# 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.
import os
import shutil
import paddle
import unittest
import paddle.fluid as fluid
from mobilenet import MobileNet
from paddle.fluid.contrib.slim.core import Compressor
from paddle.fluid.contrib.slim.graph import GraphWrapper
class TestReader(unittest.TestCase):
"""
Test API of quantization strategy.
"""
def set_train_reader(self, image, label, place):
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
return train_reader
def set_val_reader(self, image, label, place):
val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
return val_reader
def set_feed_list(self, image, label):
return [('img', image.name), ('label', label.name)]
def quan(self, config_file):
if os.path.exists('./checkpoints_quan'):
shutil.rmtree('./checkpoints_quan')
if not fluid.core.is_compiled_with_cuda():
return
class_dim = 10
image_shape = [1, 28, 28]
train_program = fluid.Program()
startup_program = fluid.Program()
val_program = fluid.Program()
with fluid.program_guard(train_program, startup_program):
with fluid.unique_name.guard():
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
image.stop_gradient = False
label = fluid.layers.data(
name='label', shape=[1], dtype='int64')
out = MobileNet(name='quan').net(input=image,
class_dim=class_dim)
print("out: {}".format(out.name))
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5))
val_program = train_program.clone(for_test=False)
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(startup_program)
val_reader = self.set_val_reader(image, label, place)
val_feed_list = self.set_feed_list(image, label)
val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
acc_top5.name)]
train_reader = self.set_train_reader(image, label, place)
train_feed_list = self.set_feed_list(image, label)
train_fetch_list = [('loss', avg_cost.name)]
com_pass = Compressor(
place,
fluid.global_scope(),
train_program,
train_reader=train_reader,
train_feed_list=train_feed_list,
train_fetch_list=train_fetch_list,
eval_program=val_program,
eval_reader=val_reader,
eval_feed_list=val_feed_list,
eval_fetch_list=val_fetch_list,
train_optimizer=optimizer)
com_pass.config(config_file)
eval_graph = com_pass.run()
class TestReader1(TestReader):
def set_train_reader(self, image, label, place):
loader = fluid.io.DataLoader.from_generator(
feed_list=[image, label], capacity=16, iterable=True)
loader.set_sample_generator(
paddle.dataset.mnist.train(), batch_size=128, places=place)
return loader
def set_val_reader(self, image, label, place):
loader = fluid.io.DataLoader.from_generator(
feed_list=[image, label], capacity=16, iterable=True)
loader.set_sample_generator(
paddle.dataset.mnist.test(), batch_size=128, places=place)
return loader
def test_compression(self):
self.quan("./quantization/compress_1.yaml")
if __name__ == '__main__':
unittest.main()
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