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未验证 提交 2e5831f0 编写于 作者: W whs 提交者： GitHub
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[slim] Refine framework of slim and add filter pruning strategy (#16226) 

* First pr of paddle slim.
1. Add framework of paddle slim
2. Add filter pruning strategy
test=develop

* Rename unitest to tests.
test=develop

* Add prettytable into requirements.
test=develop

* Change in_nodes and out_nodes to odered dict.
test=develop

* Remove distillation.
test=develop

* Fix API.spec
test=develop

* Fix unitest.
test=develop

* Fix unitest in windows.
test=develop

* Fix unitest in windows.
test=develop

* Fix unitest.
test=develop

* Hide some functions.
test=develop

* Fix python import in python3.5
test=develop

* Fix compress pass.
test=develop

* Fix unitest of test_dist_ctr.
test=develop

* Enhence flops.

* use os.path.join

* Fix pickle for python3
Fix log and comments.
test=develop

* 1. Remove feed_reader in compress pass
2. Fix cache reader
3. Rename CompressPass to Compressor
4. Add comments for distiller optimizer
5. Remove unused pruner currently
6. Add some comments.
7. Change API.spec
test=develop

* Fix pruning in python3.
test=develop

* Fix unitest in python3.
test=develop

* Fix format in python3.
test=develop
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Showing with 2545 addition and 548 deletion
paddle/fluid/API.spec
浏览文件 @ 2e5831f0
......@@ -377,23 +377,9 @@ paddle.fluid.contrib.Calibrator.__init__ (ArgSpec(args=['self'], varargs='args',
paddle.fluid.contrib.Calibrator.sample_data (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3b8c85ca1e2cf753cc8c90a6c6992958'))
paddle.fluid.contrib.Calibrator.save_int8_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab'))
paddle.fluid.contrib.build_compressor (ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.CompressPass.__init__ (ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.CompressPass.add_strategy (ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None), ('document', '3bf6010b6f47d3c86df0ec8957be95e0'))
paddle.fluid.contrib.CompressPass.apply (ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None), ('document', 'a92bf85d4b59bd4f2ac1706d7c4899a6'))
paddle.fluid.contrib.ImitationGraph.__init__ (ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.ImitationGraph.all_parameters (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.__init__ (ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.MagnitudePruner.__init__ (ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.MagnitudePruner.prune (ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.RatioPruner.__init__ (ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a325b296a9ca502ee5adb4fc85d'))
paddle.fluid.contrib.RatioPruner.prune (ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)), ('document', '358cbf2978c91028fb96a195a9884645'))
paddle.fluid.contrib.Compressor.__init__ (ArgSpec(args=['self', 'place', 'scope', 'train_program', 'train_reader', 'train_feed_list', 'train_fetch_list', 'eval_program', 'eval_reader', 'eval_feed_list', 'eval_fetch_list', 'teacher_programs', 'checkpoint_path', 'train_optimizer', 'distiller_optimizer'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, [], './checkpoints', None, None)), ('document', '31ae143830c9bf6b43547dd546c5ba80'))
paddle.fluid.contrib.Compressor.config (ArgSpec(args=['self', 'config_file'], varargs=None, keywords=None, defaults=None), ('document', '780d9c007276ccbb95b292400d7807b0'))
paddle.fluid.contrib.Compressor.run (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'c6e43d6a078d307672283c1f36e04fe9'))
paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '2ab36d4f7a564f5f65e455807ad06c67'))
paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '59066bac9db0ac6ce414d05780b7333f'))
paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '74c39c595dc70d6be2f16d8e462d282b'))
......
python/paddle/fluid/contrib/slim/__init__.py
浏览文件 @ 2e5831f0
......@@ -13,13 +13,4 @@
# limitations under the License.
from .core import *
from .graph import *
from .prune import *
__all__ = [
'build_compressor',
'CompressPass',
'ImitationGraph',
'SensitivePruneStrategy',
'MagnitudePruner',
'RatioPruner',
]
__all__ = ['Compressor', ]
python/paddle/fluid/contrib/slim/core/__init__.py
浏览文件 @ 2e5831f0
......@@ -14,11 +14,9 @@
from . import config
from .config import *
from . import compress_pass
from .compress_pass import *
from . import compressor
from .compressor import *
from . import strategy
from .strategy import *
from . import pass_builder
from .pass_builder import *
__all__ = config.__all__ + compress_pass.__all__ + strategy.__all__ + pass_builder.__all__
__all__ = config.__all__ + compressor.__all__ + strategy.__all__
python/paddle/fluid/contrib/slim/core/compress_pass.py 已删除 100644 → 0
浏览文件 @ 18779b5b
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from ....core import CPUPlace
from ..graph import get_executor
__all__ = ['Context', 'CompressPass']
class Context(object):
"""
The context in the process of compression.
Args:
exe: The executor used to execute graph.
graph: The graph to be compressed.
scope: The scope used to execute graph.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self, exe, graph, scope, program_exe=None):
# The total number of epoches to be trained.
self.epoch = 0
# Current epoch
self.epoch_id = 0
# Current batch
self.batch_id = 0
self.exe = exe
self.graph = graph
self.scope = scope
self.program_exe = program_exe
class CompressPass(object):
"""
The pass used to compress model.
Args:
place: The device used in compression.
data_reader: The data_reader used to run graph.
data_feeder: The data_feeder used to run graph.
scope: The scope used to run graph.
metrics: The metrics for evaluating model.
epoch: The total epoches of trainning in compression.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self,
place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
program_exe=None):
self.strategies = []
self.place = CPUPlace() if place is None else place
self.data_reader = data_reader
self.data_feeder = data_feeder
self.scope = scope
self.metrics = metrics
self.epoch = epoch
self.program_exe = program_exe
def add_strategy(self, strategy):
"""
Add a strategy to current compress pass.
Args:
strategy: The strategy to be added into current compress pass.
"""
self.strategies.append(strategy)
self.epoch = max(strategy.end_epoch, self.epoch)
def apply(self, graph):
"""
Compress a model.
Args:
graph: The target graph to be compressed.
"""
self.executor = get_executor(graph, self.place)
context = Context(
self.executor, graph, self.scope, program_exe=self.program_exe)
for strategy in self.strategies:
strategy.on_compress_begin(context)
for epoch in range(self.epoch):
for strategy in self.strategies:
strategy.on_epoch_begin(context)
for data in self.data_reader():
for strategy in self.strategies:
strategy.on_batch_begin(context)
fetches = None
if self.metrics:
fetches = self.metrics.values()
feed = None
if self.data_feeder:
feed = self.data_feeder.feed(data)
results = self.executor.run(graph,
fetches=fetches,
scope=self.scope,
feed=feed)
if results:
print("results: {}".format(
zip(self.metrics.keys(), results)))
for strategy in self.strategies:
strategy.on_batch_end(context)
context.batch_id += 1
for strategy in self.strategies:
strategy.on_epoch_end(context)
context.epoch_id += 1
for strategy in self.strategies:
strategy.on_compress_end(context)
python/paddle/fluid/contrib/slim/core/compressor.py 0 → 100644
浏览文件 @ 2e5831f0
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from ....core import CPUPlace
from .... import compiler
from .... import io
from .... import profiler
from .... import scope_guard
from ....data_feeder import DataFeeder
from ..graph import *
from .config import ConfigFactory
import numpy as np
from collections import Iterable
import time
import os
import logging
import sys
import pickle
import functools
__all__ = ['Context', 'Compressor']
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO)
def cached_reader(reader, sampled_rate, cache_path, cached_id):
"""
Sample partial data from reader and cache them into local file system.
Args:
reader: Iterative data source.
sampled_rate(float): The sampled rate used to sample partial data for evaluation. None means using all data in eval_reader. default: None.
cache_path(str): The path to cache the sampled data.
cached_id(int): The id of dataset sampled. Evaluations with same cached_id use the same sampled dataset. default: 0.
"""
np.random.seed(cached_id)
cache_path = os.path.join(cache_path, str(cached_id))
_logger.debug('read data from: {}'.format(cache_path))
def s_reader():
if os.path.isdir(cache_path):
for file_name in open(os.path.join(cache_path, "list")):
yield np.load(os.path.join(cache_path, file_name.strip()))
else:
os.makedirs(cache_path)
list_file = open(os.path.join(cache_path, "list"), 'w')
batch = 0
dtype = None
for data in reader():
if batch == 0 or (np.random.uniform() < sampled_rate):
np.save(
os.path.join(cache_path, 'batch' + str(batch)), data)
list_file.write('batch' + str(batch) + '.npy\n')
batch += 1
yield data
return s_reader
class Context(object):
"""
The context in the process of compression.
"""
def __init__(self,
place,
scope,
train_graph=None,
train_reader=None,
eval_graph=None,
eval_reader=None,
teacher_graphs=None,
train_optimizer=None,
distiller_optimizer=None):
"""
Args:
place: The device place where the compression job running.
scope: The scope used in compression job.
train_graph: The graph with loss as output node.
eval_graph: The graph used for evaluation.
eval_reader: The data reader used for evaluation.
teacher_graphs: The teacher graphs used in distillation strategies.
train_optimizer: The optimizer used to append backward ops and
optimization ops into train_graph.
distiller_optimizer: The optimizer used by distillation strategies.
"""
# The total number of epoches to be trained.
self.epoch = 0
# Current epoch
self.epoch_id = 0
# Current batch
self.batch_id = 0
self.k_v = {}
self.place = place
self.scope = scope
self.train_graph = train_graph
self.train_reader = train_reader
self.eval_graph = eval_graph
self.eval_reader = eval_reader
self.executor = None
self.teacher_graphs = teacher_graphs
self.train_optimizer = train_optimizer
self.distiller_optimizer = distiller_optimizer
self.optimize_graph = None
self.cache_path = './eval_cache'
self.eval_results = {}
def to_file(self, file_name):
"""
Save the context into file.
"""
data = {}
data['epoch_id'] = self.epoch_id
data['eval_results'] = self.eval_results
with open(file_name, 'wb') as context_file:
pickle.dump(data, context_file)
def from_file(self, file_name):
"""
Load the context from file.
"""
with open(file_name) as context_file:
if sys.version_info < (3, 0):
data = pickle.load(context_file)
else:
data = pickle.load(context_file, encoding='bytes')
self.epoch_id = data['epoch_id']
self.eval_results = data['eval_results']
def eval_converged(self, metric_name, delta=0.001):
"""
Check whether the training has been converged.
Args:
metric_name(str): The metric used to check convergence.
delta(float): '(metric[k] - metric[k-1] / metric[k-1]) < delta'
means that the training has been converged.
Returns:
bool: True means the training has been converged.
"""
# TODO(wanghaoshuang@baidu.com): enhence this method.
if (metric_name not in self.eval_results
) or len(self.eval_results[metric_name]) < 2:
return False
results = self.eval_results[metric_name][-2:]
_logger.info('Latest evaluations: {}'.format(results))
return abs(results[1] - results[0]) / results[0] < delta
def run_eval_graph(self, sampled_rate=None, cached_id=0):
"""
Evaluate the current mode in context.
Args:
sampled_rate(float): The sampled rate used to sample partial data
for evaluation. None means using all data in eval_reader. default: None.
cached_id(int): The id of dataset sampled. Evaluations with same
cached_id use the same sampled dataset. default: 0.
"""
_logger.info('Running evaluation')
assert self.eval_graph is not None
assert self.eval_reader is not None
eval_graph = self.eval_graph.clone(for_test=True)
executor = SlimGraphExecutor(self.place)
results = []
batch_id = 0
s_time = time.time()
reader = self.eval_reader
if sampled_rate:
reader = cached_reader(reader, sampled_rate, self.cache_path,
cached_id)
for data in reader():
result = executor.run(eval_graph, self.scope, data=data)
result = [np.mean(r) for r in result]
results.append(result)
if batch_id % 20 == 0:
_logger.info("batch-{}; {}={}".format(
batch_id, eval_graph.out_nodes.keys(), result))
batch_id += 1
result = np.mean(np.array(results), axis=0)
_logger.info("Final eval result: {}={}".format(
eval_graph.out_nodes.keys(), result))
if not isinstance(result, Iterable):
result = [result]
_logger.info('Finish evaluation')
return result, eval_graph.out_nodes.keys()
def put(self, key, value):
self.k_v[key] = value
def get(self, key):
return self.k_v.get(key)
class Compressor(object):
"""
The pass used to compress model.
"""
def __init__(self,
place,
scope,
train_program,
train_reader=None,
train_feed_list=None,
train_fetch_list=None,
eval_program=None,
eval_reader=None,
eval_feed_list=None,
eval_fetch_list=None,
teacher_programs=[],
checkpoint_path='./checkpoints',
train_optimizer=None,
distiller_optimizer=None):
"""
Args:
place(fluid.Place): The device place where the compression job running.
scope(fluid.core.Scope): The scope used to run graph.
train_program(Program): The main program to be compressed. It must have loss op.
train_reader: The data reader used for training.
train_feed_list(dict): A dict to indicate the input variable of the training program.
The key is user-defined and human-readable name.
The value is the name of Variable.
train_fetch_list(dict): A dict to indicate the output variable of the training program.
The key is user-defined and human-readable name.
The value is the name of Variable.
eval_program(Program): The program used for evaluation.
eval_reader: The data reader used for evaluation.
eval_feed_list(dict): A dict to indicate the input variable of the evaluation program.
The key is user-defined and human-readable name.
The value is the name of Variable.
eval_fetch_list(dict): A dict to indicate the output variable of the evaluation program.
The key is user-defined and human-readable name.
The value is the name of Variable.
teacher_programs: The teacher graphs used in distillation strategies.
train_optimizer: The optimizer used to append backward ops and
optimization ops into train_graph.
distiller_optimizer: The optimizer used by distillation strategies. In distillation strategy,
this optimizer is used to minimize the combined loss of student-net and
teacher-net while train_optimizer is used to minimize loss of
student-net in fine-tune stage.
"""
assert isinstance(
train_feed_list, list
), "train_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]"
assert isinstance(
eval_feed_list, list
), "eval_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]"
self.strategies = []
self.epoch = 0
self.place = CPUPlace() if place is None else place
self.scope = scope
self.train_graph = GraphWrapper(
train_program, in_nodes=train_feed_list, out_nodes=train_fetch_list)
self.eval_graph = GraphWrapper(
eval_program, in_nodes=eval_feed_list, out_nodes=eval_fetch_list)
self.train_reader = train_reader
self.eval_reader = eval_reader
self.teacher_graphs = []
for teacher in teacher_programs:
self.teacher_graphs.append(ImitationGraph(teacher, scope=scope))
self.checkpoint = None
self.checkpoint_path = checkpoint_path
self.eval_epoch = 1
self.train_optimizer = train_optimizer
self.distiller_optimizer = distiller_optimizer
self.init_model = None
def _add_strategy(self, strategy):
"""
Add a strategy to current compress pass.
Args:
strategy: The strategy to be added into current compress pass.
"""
self.strategies.append(strategy)
self.epoch = max(strategy.end_epoch, self.epoch)
def config(self, config_file):
"""
Configure the compress pass from file with yaml format.
Args:
config_file(str): The config file in local file system.
"""
factory = ConfigFactory(config_file)
self.epoch = factory.compressor['epoch']
for strategy in factory.compressor['strategies']:
self._add_strategy(strategy)
if 'checkpoint_path' in factory.compressor:
self.checkpoint_path = factory.compressor['checkpoint_path']
if 'init_model' in factory.compressor:
self.init_model = factory.compressor['init_model']
def _init_model(self, context):
"""
Load model that has been compressed.
"""
if self.init_model and os.path.exists(self.init_model):
exe = SlimGraphExecutor(context.place)
with scope_guard(context.scope):
context.train_graph.load_persistables(self.init_model, exe)
flops = context.eval_graph.flops()
conv_flops = context.eval_graph.flops(only_conv=True)
context.eval_graph.update_param_shape(context.scope)
context.eval_graph.update_groups_of_conv()
_logger.info("conv flops: -{}".format(1 - float(
context.eval_graph.flops(only_conv=True)) / conv_flops))
_logger.info("total flops: -{}".format(1 - float(
context.eval_graph.flops()) / flops))
context.train_graph.update_param_shape(context.scope)
context.train_graph.update_groups_of_conv()
context.train_graph.infer_shape()
_logger.info("Init model from: {}".format(self.init_model))
def _load_checkpoint(self, context):
"""
Load checkpoints from file.
"""
_logger.debug('_load_checkpoint')
strategies = self.strategies
if self.checkpoint_path:
if not os.path.exists(self.checkpoint_path):
_logger.warning("Checkpints path doesn't exist: [{}]".format(
self.checkpoint_path))
return context, strategies
checkpoints = [
dir for dir in os.listdir(self.checkpoint_path)
if os.path.isdir(os.path.join(self.checkpoint_path, dir))
]
_logger.debug('self.checkpoint_path: {}'.format(
self.checkpoint_path))
_logger.info('checkpoints: {}'.format(checkpoints))
if len(checkpoints) > 0:
latest = max([int(ck) for ck in checkpoints])
latest_ck_path = os.path.join(self.checkpoint_path, str(latest))
model_path = os.path.join(latest_ck_path, 'model')
context_path = os.path.join(latest_ck_path, 'context')
strategy_path = os.path.join(latest_ck_path, 'strategies')
if os.path.exists(context_path):
context.from_file(context_path)
context.epoch_id += 1
if os.path.exists(strategy_path):
with open(strategy_path, 'rb') as strategy_file:
if sys.version_info < (3, 0):
strategies = pickle.load(strategy_file)
else:
strategies = pickle.load(
strategy_file, encoding='bytes')
if os.path.exists(model_path):
exe = SlimGraphExecutor(context.place)
with scope_guard(context.scope):
context.optimize_graph.load_persistables(model_path,
exe)
context.optimize_graph.update_param_shape(context.scope)
context.optimize_graph.update_groups_of_conv()
context.eval_graph.update_param_shape(context.scope)
context.eval_graph.update_groups_of_conv()
_logger.info("Loaded params from: {}".format(model_path))
return context, strategies
def _save_checkpoint(self, context):
"""
Save checkpoints to file.
"""
if context.epoch_id % 1 == 0 and self.checkpoint_path:
checkpoint_path = os.path.join(self.checkpoint_path,
str(context.epoch_id))
model_path = os.path.join(checkpoint_path, 'model')
context_path = os.path.join(checkpoint_path, 'context')
strategy_path = os.path.join(checkpoint_path, 'strategies')
if not os.path.isdir(model_path):
os.makedirs(model_path)
exe = SlimGraphExecutor(context.place)
with scope_guard(context.scope):
context.optimize_graph.save_persistables(model_path, exe)
context.to_file(context_path)
with open(strategy_path, 'wb') as strategy_file:
pickle.dump(self.strategies, strategy_file)
_logger.info('Saved checkpoint to: {}'.format(checkpoint_path))
def _train_one_epoch(self, context):
"""
Train one epoch.
"""
executor = SlimGraphExecutor(self.place)
if context.optimize_graph.compiled_graph is None:
context.optimize_graph.compiled_graph = compiler.CompiledProgram(
context.optimize_graph.program).with_data_parallel(
loss_name=context.optimize_graph.out_nodes['loss'])
for data in context.train_reader():
for strategy in self.strategies:
strategy.on_batch_begin(context)
results = executor.run(context.optimize_graph,
context.scope,
data=data)
results = [float(np.mean(result)) for result in results]
if context.batch_id % 20 == 0:
_logger.info("epoch:{}; batch_id:{}; {} = {}".format(
context.epoch_id, context.batch_id,
context.optimize_graph.out_nodes.keys(
), [round(r, 3) for r in results]))
for strategy in self.strategies:
strategy.on_batch_end(context)
context.batch_id += 1
context.batch_id = 0
def _eval(self, context):
"""
Runing evaluation.
"""
results, names = context.run_eval_graph()
for name, result in zip(names, results):
if name not in context.eval_results:
context.eval_results[name] = []
context.eval_results[name].append(result)
def run(self):
"""
Execute compressiong pass.
"""
context = Context(
place=self.place,
scope=self.scope,
train_graph=self.train_graph,
train_reader=self.train_reader,
eval_graph=self.eval_graph,
eval_reader=self.eval_reader,
teacher_graphs=self.teacher_graphs,
train_optimizer=self.train_optimizer,
distiller_optimizer=self.distiller_optimizer)
self.context = context
if self.teacher_graphs:
context.put('teachers', self.teacher_graphs)
self._init_model(context)
if not context.optimize_graph:
if context.train_optimizer:
context.train_optimizer._name = 'train_opt'
context.optimize_graph = context.train_graph.get_optimize_graph(
context.train_optimizer, context.place, context.scope)
else:
context.optimize_graph = context.train_graph
context, self.strategies = self._load_checkpoint(context)
for strategy in self.strategies:
strategy.on_compression_begin(context)
start = context.epoch_id
self._eval(context)
for epoch in range(start, self.epoch):
context.epoch_id = epoch
for strategy in self.strategies:
strategy.on_epoch_begin(context)
self._train_one_epoch(context)
for strategy in self.strategies:
strategy.on_epoch_end(context)
if self.eval_epoch and epoch % self.eval_epoch == 0:
self._eval(context)
self._save_checkpoint(context)
for strategy in self.strategies:
strategy.on_compression_end(context)
return context.eval_graph
python/paddle/fluid/contrib/slim/core/config.py
浏览文件 @ 2e5831f0
......@@ -17,7 +17,7 @@ import funcsigs
import yaml
from collections import OrderedDict
from ..prune import *
from .compress_pass import *
from ..quantization import *
from .strategy import *
__all__ = ['ConfigFactory']
......@@ -29,15 +29,10 @@ class ConfigFactory(object):
def __init__(self, config):
"""Init a factory from configure file."""
self.instances = {}
self.compressor = {}
self.version = None
self._parse_config(config)
def get_compress_pass(self):
"""
Get compress pass from factory.
"""
return self.instance('compress_pass')
def instance(self, name):
"""
Get instance from factory.
......@@ -59,8 +54,16 @@ class ConfigFactory(object):
args = {}
for key in keys:
value = attrs[key]
if isinstance(value, str) and value.lower() == 'none':
value = None
if isinstance(value, str) and value in self.instances:
value = self.instances[value]
if isinstance(value, list):
for i in range(len(value)):
if isinstance(value[i],
str) and value[i] in self.instances:
value[i] = self.instances[value[i]]
args[key] = value
self.instances[name] = class_(**args)
return self.instances.get(name)
......@@ -76,16 +79,23 @@ class ConfigFactory(object):
assert self.version == int(key_values['version'])
# parse pruners
if key == 'pruners' or key == 'strategies':
if key == 'distillers' or key == 'pruners' or key == 'quantizers' or key == 'strategies':
instances = key_values[key]
for name in instances:
self._new_instance(name, instances[name])
if key == 'compress_pass':
compress_pass = self._new_instance(key, key_values[key])
if key == 'compressor':
self.compressor['strategies'] = []
self.compressor['epoch'] = key_values[key]['epoch']
if 'init_model' in key_values[key]:
self.compressor['init_model'] = key_values[key][
'init_model']
self.compressor['checkpoint_path'] = key_values[key][
'checkpoint_path']
if 'strategies' in key_values[key]:
for name in key_values[key]['strategies']:
strategy = self.instance(name)
compress_pass.add_strategy(strategy)
self.compressor['strategies'].append(strategy)
if key == 'include':
for config_file in key_values[key]:
......
python/paddle/fluid/contrib/slim/core/pass_builder.py 已删除 100644 → 0
浏览文件 @ 18779b5b
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from .compress_pass import CompressPass
from .config import ConfigFactory
__all__ = ['build_compressor']
def build_compressor(place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
config=None):
if config is not None:
factory = ConfigFactory(config)
comp_pass = factory.get_compress_pass()
else:
comp_pass = CompressPass()
comp_pass.place = place
comp_pass.data_reader = data_reader
comp_pass.data_feeder = data_feeder
comp_pass.scope = scope
comp_pass.metrics = metrics
comp_pass.epoch = epoch
return comp_pass
python/paddle/fluid/contrib/slim/core/strategy.py
浏览文件 @ 2e5831f0
......@@ -20,7 +20,7 @@ class Strategy(object):
Base class for all strategies.
"""
def __init__(self, start_epoch=0, end_epoch=10):
def __init__(self, start_epoch=0, end_epoch=0):
"""
Args:
start_epoch: The first epoch to apply the strategy.
......@@ -29,7 +29,7 @@ class Strategy(object):
self.start_epoch = start_epoch
self.end_epoch = end_epoch
def on_compress_begin(self, context):
def on_compression_begin(self, context):
pass
def on_epoch_begin(self, context):
......@@ -44,5 +44,5 @@ class Strategy(object):
def on_batch_end(self, context):
pass
def on_compress_end(self, context):
def on_compression_end(self, context):
pass
python/paddle/fluid/contrib/slim/demo/filter_prune/config.yaml 已删除 100644 → 0
浏览文件 @ 18779b5b
version: 1.0
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/demo/filter_prune/demo.py 已删除 100644 → 0
浏览文件 @ 18779b5b
# Copyright (c) 2018 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 paddle.fluid as fluid
import paddle
import os
import sys
from paddle.fluid.contrib.slim import CompressPass
from paddle.fluid.contrib.slim import build_compressor
from paddle.fluid.contrib.slim import ImitationGraph
class LinearModel(object):
def __init__(slef):
pass
def train(self):
train_program = fluid.Program()
startup_program = fluid.Program()
startup_program.random_seed = 10
with fluid.program_guard(train_program, startup_program):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=predict, label=y)
avg_cost = fluid.layers.mean(cost)
eval_program = train_program.clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
eval_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=1)
place = fluid.CPUPlace()
train_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
eval_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(startup_program)
train_metrics = {"loss": avg_cost.name}
eval_metrics = {"loss": avg_cost.name}
graph = ImitationGraph(train_program)
config = './config.yaml'
comp_pass = build_compressor(
place,
data_reader=train_reader,
data_feeder=train_feeder,
scope=fluid.global_scope(),
metrics=train_metrics,
epoch=1,
config=config)
comp_pass.apply(graph)
if __name__ == "__main__":
model = LinearModel()
model.train()
python/paddle/fluid/contrib/slim/graph/__init__.py
浏览文件 @ 2e5831f0
......@@ -14,10 +14,7 @@
from . import executor
from .executor import *
from . import graph
from .graph import *
from . import graph_pass
from .graph_pass import *
from . import graph_wrapper
from .graph_wrapper import *
__all__ = executor.__all__
__all__ += graph.__all__
__all__ += graph_pass.__all__
__all__ += graph_wrapper.__all__
python/paddle/fluid/contrib/slim/graph/executor.py
浏览文件 @ 2e5831f0
......@@ -12,51 +12,46 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
from abc import abstractmethod
from ....compiler import CompiledProgram
from ....data_feeder import DataFeeder
from .... import executor
from .graph import IRGraph, ImitationGraph
from .graph_wrapper import GraphWrapper
__all__ = ['get_executor']
__all__ = ['SlimGraphExecutor']
class GraphExecutor(object):
__metaclass__ = abc.ABCMeta
class SlimGraphExecutor(object):
"""
Wrapper of executor used to run GraphWrapper.
"""
def __init__(self, place):
self.place = place
@abstractmethod
def run(self, graph, feches=None, feed=None):
pass
class IRGraphExecutor(GraphExecutor):
def run(self, grah, fetches, feed=None):
pass
class ImitationGraphExecutor(GraphExecutor):
def __init__(self, place):
super(ImitationGraphExecutor, self).__init__(place)
self.exe = executor.Executor(place)
self.place = place
def run(self, graph, scope=None, fetches=None, feed=None):
assert isinstance(graph, ImitationGraph)
fetch_list = None
if fetches:
fetch_list = [
graph.program.global_block().var(name) for name in fetches
]
results = self.exe.run(graph.program,
def run(self, graph, scope, data=None):
"""
Runing a graph with a batch of data.
Args:
graph(GraphWrapper): The graph to be executed.
scope(fluid.core.Scope): The scope to be used.
data(list<tuple>): A batch of data. Each tuple in this list is a sample.
It will feed the items of tuple to the in_nodes of graph.
Returns:
results(list): A list of result with the same order indicated by graph.out_nodes.
"""
assert isinstance(graph, GraphWrapper)
if data is not None:
feeder = DataFeeder(
feed_list=graph.in_nodes.values(),
place=self.place,
program=graph.program)
feed = feeder.feed(data)
fetch_list = graph.out_nodes.values()
program = graph.compiled_graph if graph.compiled_graph else graph.program
results = self.exe.run(program,
scope=scope,
fetch_list=fetch_list,
feed=feed)
return results
def get_executor(graph, place):
if isinstance(graph, ImitationGraph):
return ImitationGraphExecutor(place)
if isinstance(graph, IRGraph):
return IRGraphExecutor(place)
python/paddle/fluid/contrib/slim/graph/graph.py 已删除 100644 → 0
浏览文件 @ 18779b5b
# Copyright (c) 2018 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.
from __future__ import print_function
import os
import subprocess
from ....framework import Program
from ....framework import Block
from .... import core
__all__ = ['Graph', 'ImitationGraph', 'IRGraph']
class Graph(object):
"""
Base class for all graph.
"""
def __init__(self):
pass
def all_parameters(self):
"""
Return all the parameters in current graph.
"""
pass
class ImitationGraph(Graph):
def __init__(self, program=None):
super(ImitationGraph, self).__init__()
self.program = Program() if program is None else program
def all_parameters(self):
return self.program.global_block().all_parameters()
class IRGraph(Graph):
pass
python/paddle/fluid/contrib/slim/graph/graph_wrapper.py 0 → 100644
浏览文件 @ 2e5831f0
# 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.
from collections import OrderedDict
from .... import io
from .... import compiler
from ....framework import Program
from ....framework import program_guard
from ....framework import Parameter
from ....framework import Variable
from ....executor import Executor
import copy
from collections import Iterable
from ....io import save_inference_model, load_inference_model, save_persistables
import numpy as np
import pickle
import os
__all__ = ['GraphWrapper', 'VarWrapper', 'OpWrapper']
OPTIMIZER_OPS = [
'momentum',
'lars_momentum',
'adagrad',
'adam',
'adamax',
'decayed_adagrad',
'adadelta',
'rmsprop',
]
class VarWrapper(object):
def __init__(self, var, graph):
assert isinstance(var, Variable)
assert isinstance(graph, GraphWrapper)
self._var = var
self._graph = graph
def __eq__(self, v):
"""
Overwrite this function for ...in... syntax in python.
"""
return self._var.name == v._var.name
def name(self):
"""
Get the name of the variable.
"""
return self._var.name
def shape(self):
"""
Get the shape of the varibale.
"""
return self._var.shape
def set_shape(self, shape):
"""
Set the shape of the variable.
"""
self._var.desc.set_shape(shape)
def inputs(self):
"""
Get all the operators that use this variable as output.
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for op in self._graph.ops():
if self in op.all_inputs():
ops.append(op)
return ops
def outputs(self):
"""
Get all the operators that use this variable as input.
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for op in self._graph.ops():
if self in op.all_outputs():
ops.append(op)
return ops
class OpWrapper(object):
def __init__(self, op, graph):
assert isinstance(graph, GraphWrapper)
self._op = op
self._graph = graph
def __eq__(self, op):
"""
Overwrite this function for ...in... syntax in python.
"""
return self.idx() == op.idx()
def all_inputs(self):
"""
Get all the input variables of this operator.
"""
return [
self._graph.var(var_name) for var_name in self._op.input_arg_names
]
def all_outputs(self):
"""
Get all the output variables of this operator.
"""
return [
self._graph.var(var_name) for var_name in self._op.output_arg_names
]
def idx(self):
"""
Get the id of this operator.
"""
return self._op.idx
def type(self):
"""
Get the type of this operator.
"""
return self._op.type
def is_bwd_op(self):
"""
Whether this operator is backward op.
"""
return self.type().endswith('_grad')
def is_opt_op(self):
"""
Whether this operator is optimizer op.
"""
return self.type() in OPTIMIZER_OPS
def inputs(self, name):
"""
Get all the varibales by the input name.
"""
return [self._graph.var(var_name) for var_name in self._op.input(name)]
def outputs(self, name):
"""
Get all the varibales by the output name.
"""
return [self._graph.var(var_name) for var_name in self._op.output(name)]
def set_attr(self, key, value):
"""
Set the value of attribute by attribute's name.
Args:
key(str): the attribute name.
value(bool|int|str|float|list): the value of the attribute.
"""
self._op._set_attr(key, value)
def attr(self, name):
"""
Get the attribute by name.
Args:
name(str): the attribute name.
Returns:
bool|int|str|float|list: The attribute value. The return value
can be any valid attribute type.
"""
return self._op.attr(name)
class GraphWrapper(object):
"""
It is a wrapper of paddle.fluid.framework.IrGraph with some special functions
for paddle slim framework.
"""
def __init__(self, program=None, in_nodes=[], out_nodes=[]):
"""
Args:
program(framework.Program): A program with
in_nodes(dict): A dict to indicate the input nodes of the graph.
The key is user-defined and human-readable name.
The value is the name of Variable.
out_nodes(dict): A dict to indicate the input nodes of the graph.
The key is user-defined and human-readable name.
The value is the name of Variable.
"""
super(GraphWrapper, self).__init__()
self.program = Program() if program is None else program
self.compiled_graph = None
self.in_nodes = OrderedDict(in_nodes)
self.out_nodes = OrderedDict(out_nodes)
self._attrs = OrderedDict()
def all_parameters(self):
"""
Get all the parameters in this graph.
Returns:
list<VarWrapper>: A list of VarWrapper instances.
"""
params = []
for block in self.program.blocks:
for param in block.all_parameters():
params.append(VarWrapper(param, self))
return params
def is_parameter(self, var):
"""
Whether the given variable is parameter.
Args:
var(VarWrapper): The given varibale.
"""
return isinstance(var._var, Parameter)
def is_persistable(self, var):
"""
Whether the given variable is persistable.
Args:
var(VarWrapper): The given varibale.
"""
return var._var.persistable
def compile(self, for_parallel=True, for_test=False):
"""
Compile the program in this wrapper to framework.CompiledProgram for next running.
This function must be called if the program is modified.
Args:
for_parallel(bool): Whether the program to run in data parallel way. default: True.
for_test(bool): Whether the compiled program is used for test.
"""
target = self.program
if for_test:
loss = None
else:
loss = self.out_nodes['loss']
if for_parallel:
# disable memory optimize for stable training
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = False
build_strategy.memory_optimize = False
self.compiled_graph = compiler.CompiledProgram(
target).with_data_parallel(
loss_name=loss, build_strategy=build_strategy)
else:
self.compiled_graph = compiler.CompiledProgram(target)
def ops(self):
"""
Return all operator nodes included in the graph as a set.
"""
ops = []
for block in self.program.blocks:
for op in block.ops:
ops.append(OpWrapper(op, self))
return ops
def vars(self):
"""
Get all the variables.
"""
return [VarWrapper(var, self) for var in self.program.list_vars()]
def var(self, name):
"""
Get the variable by variable name.
"""
return VarWrapper(self.program.global_block().var(name), self)
def clone(self, for_test=False):
"""
Clone a new graph from current graph.
Returns:
(GraphWrapper): The wrapper of a new graph.
"""
return GraphWrapper(
self.program.clone(for_test),
copy.deepcopy(self.in_nodes), copy.deepcopy(self.out_nodes))
def merge(self, graph):
"""
Merge a graph into current graph.
Args:
graph(GraphWrapper): The graph to be merged by current graph.
"""
for var in graph.program.list_vars():
self.program.global_block()._clone_variable(var)
# TODO: parameters should be cloned
for op in graph.ops():
op = op._op
inputs = {}
outputs = {}
attrs = {}
for input_name in op.input_names:
inputs[input_name] = [
self.var(in_var_name)
for in_var_name in op.inputs(input_name)
]
for output_name in op.output_names:
outputs[output_name] = [
self.var(out_var_name)
for out_var_name in op.output(output_name)
]
for attr_name in op.attr_names:
attrs[attr_name] = op.attr(attr_name)
self.program.global_block().append_op(
type=op.type, inputs=inputs, outputs=outputs, attrs=attrs)
def program(self):
"""
Get the program in current wrapper.
"""
return self.program
def pre_ops(self, op):
"""
Get all the previous operators of target operator.
Args:
op(OpWrapper): Target operator..
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for p in self.ops():
for in_var in op.all_inputs():
if in_var in p.all_outputs():
ops.append(p)
return ops
def next_ops(self, op):
"""
Get all the next operators of target operator.
Args:
op(OpWrapper): Target operator..
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for p in self.ops():
for out_var in op.all_outputs():
if out_var in p.all_inputs():
ops.append(p)
return ops
def get_param_by_op(self, op):
"""
Get the parameters used by target operator.
"""
assert isinstance(op, OpWrapper)
params = []
for var in op.all_inputs():
if isinstance(var._var, Parameter):
params.append(var)
assert len(params) > 0
return params
def numel_params(self):
"""
Get the number of elements in all parameters.
"""
ret = 0
for param in self.all_parameters():
ret += np.product(param.shape())
return ret
def get_optimize_graph(self, optimizer, place, scope, no_grad_var_names=[]):
"""
Get a new graph for training by appending some backward operators and optimization operators.
Args:
optimizer: The optimzier used to generate training graph.
place: The place to run the graph.
scope: The scope used to run the graph. Some new variable will be added into this scope.
no_grad_var_names(list<str>): Names of variables that should be ignored while computing gradients. default: [].
Returns:
(GraphWrapper): The wrapper of new graph with backward ops and optimization ops.
"""
graph = self.clone()
startup_program = Program()
with program_guard(
main_program=graph.program, startup_program=startup_program):
target_name = None
if 'loss' in graph.out_nodes:
target_name = graph.out_nodes['loss']
elif 'cost' in graph.out_nodes:
target_name = graph.out_nodes['cost']
target = graph.var(target_name)._var
optimizer.minimize(target, no_grad_set=no_grad_var_names)
exe = Executor(place)
exe.run(program=startup_program, scope=scope)
return graph
def flops(self, only_conv=False):
"""
Get the flops of current graph.
Args:
only_conv: Only calculating the conv layers. default: False.
Returns:
int: The flops of current graph.
"""
flops = 0
for op in self.ops():
if op.type() in ['conv2d', 'depthwise_conv2d']:
filter_shape = op.inputs("Filter")[0].shape()
input_shape = op.inputs("Input")[0].shape()
output_shape = op.outputs("Output")[0].shape()
c_out, c_in, k_h, k_w = filter_shape
_, _, h_out, w_out = output_shape
groups = op.attr("groups")
kernel_ops = k_h * k_w * (c_in / groups)
if len(op.inputs("Bias")) > 0:
with_bias = 1
else:
with_bias = 0
flops += 2 * h_out * w_out * c_out * (kernel_ops + with_bias)
elif op.type() == 'pool2d' and not only_conv:
input_shape = op.inputs("X")[0].shape()
output_shape = op.outputs("Out")[0].shape()
_, c_out, h_out, w_out = output_shape
k_size = op.attr("ksize")
flops += h_out * w_out * c_out * (k_size[0]**2)
elif op.type() == 'mul' and not only_conv:
x_shape = list(op.inputs("X")[0].shape())
y_shape = op.inputs("Y")[0].shape()
if x_shape[0] == -1:
x_shape[0] = 1
flops += 2 * x_shape[0] * x_shape[1] * y_shape[1]
elif op.type() in ['relu', 'sigmoid', 'batch_norm'
] and not only_conv:
input_shape = list(op.inputs("X")[0].shape())
if input_shape[0] == -1:
input_shape[0] = 1
flops += np.product(input_shape)
return flops
def save_persistables(self, path, exe):
"""
Save all the persistable variables into file.
Args:
path(str): The path to save the persistables.
exe(framework.Executor): The executor used to save the persistables.
"""
io.save_persistables(exe.exe, path, main_program=self.program)
def load_persistables(self, path, exe):
"""
Load the persistable variables from file.
Args:
path(str): The path to load the persistables.
exe(framework.Executor): The executor used to load the persistables.
"""
def if_exist(var):
return os.path.exists(os.path.join(path, var.name))
io.load_vars(
exe.exe, path, main_program=self.program, predicate=if_exist)
def update_param_shape(self, scope):
"""
Update the shape of parameters in the graph according to tensors in scope.
It is used after loading pruned parameters from file.
"""
for param in self.all_parameters():
tensor_shape = np.array(scope.find_var(param.name()).get_tensor(
)).shape
param.set_shape(tensor_shape)
def infer_shape(self):
"""
Update the groups of convolution layer according to current filters.
It is used after loading pruned parameters from file.
"""
for op in self.ops():
if op.type() != 'conditional_block':
op._op.desc.infer_shape(op._op.block.desc)
def update_groups_of_conv(self):
for op in self.ops():
if op.type() == 'depthwise_conv2d':
op.set_attr('groups', op.inputs('Filter')[0].shape()[0])
python/paddle/fluid/contrib/slim/prune/prune_strategy.py
浏览文件 @ 2e5831f0
......@@ -13,54 +13,919 @@
# limitations under the License.
from ..core.strategy import Strategy
from ....framework import Program, program_guard
from ..graph import VarWrapper, OpWrapper, GraphWrapper
from ....framework import Program, program_guard, Parameter
from .... import layers
import prettytable as pt
import numpy as np
from scipy.optimize import leastsq
import copy
import re
import os
import pickle
import logging
import sys
__all__ = ['SensitivePruneStrategy', 'PruneStrategy']
__all__ = ['SensitivePruneStrategy', 'UniformPruneStrategy']
logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
_logger = logging.getLogger(__name__)
_logger.setLevel(logging.INFO)
class PruneStrategy(Strategy):
"""
The base class of all pruning strategies.
"""
class SensitivePruneStrategy(Strategy):
def __init__(self,
pruner=None,
start_epoch=0,
end_epoch=10,
delta_rate=0.20,
acc_loss_threshold=0.2,
sensitivities=None):
super(SensitivePruneStrategy, self).__init__(start_epoch, end_epoch)
end_epoch=0,
target_ratio=0.5,
metric_name=None,
pruned_params='conv.*_weights'):
"""
Args:
pruner(slim.Pruner): The pruner used to prune the parameters.
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
target_ratio(float): The flops ratio to be pruned from current model.
metric_name(str): The metric used to evaluate the model.
It should be one of keys in out_nodes of graph wrapper.
pruned_params(str): The pattern str to match the parameter names to be pruned.
"""
super(PruneStrategy, self).__init__(start_epoch, end_epoch)
self.pruner = pruner
self.delta_rate = delta_rate
self.acc_loss_threshold = acc_loss_threshold
self.sensitivities = sensitivities
self.target_ratio = target_ratio
self.metric_name = metric_name
self.pruned_params = pruned_params
self.pruned_list = []
self.backup = {}
self.param_shape_backup = {}
def _eval_graph(self, context, sampled_rate=None, cached_id=0):
"""
Evaluate the current mode in context.
Args:
context(slim.core.Context): The context storing all information used to evaluate the current model.
sampled_rate(float): The sampled rate used to sample partial data for evaluation. None means using all data in eval_reader. default: None.
cached_id(int): The id of dataset sampled. Evaluations with same cached_id use the same sampled dataset. default: 0.
"""
results, names = context.run_eval_graph(sampled_rate, cached_id)
metric = np.mean(results[list(names).index(self.metric_name)])
return metric
class PruneStrategy(Strategy):
def _prune_filters_by_ratio(self,
scope,
params,
ratio,
place,
lazy=False,
only_graph=False):
"""
Pruning filters by given ratio.
Args:
scope(fluid.core.Scope): The scope used to pruning filters.
params(list<VarWrapper>): A list of filter parameters.
ratio(float): The ratio to be pruned.
place(fluid.Place): The device place of filter parameters.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
if params[0].name() in self.pruned_list[0]:
return
param_t = scope.find_var(params[0].name()).get_tensor()
pruned_idx = self.pruner.cal_pruned_idx(
params[0].name(), np.array(param_t), ratio, axis=0)
for param in params:
assert isinstance(param, VarWrapper)
param_t = scope.find_var(param.name()).get_tensor()
if lazy:
self.backup[param.name()] = copy.deepcopy(np.array(param_t))
pruned_param = self.pruner.prune_tensor(
np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy)
if not only_graph:
param_t.set(pruned_param, place)
ori_shape = param.shape()
if param.name() not in self.param_shape_backup:
self.param_shape_backup[param.name()] = copy.deepcopy(
param.shape())
new_shape = list(param.shape())
new_shape[0] = pruned_param.shape[0]
param.set_shape(new_shape)
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
_logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format(
str(param.name()), str(0), str(ori_shape), str(param.shape())))
self.pruned_list[0].append(param.name())
return pruned_idx
def _prune_parameter_by_idx(self,
scope,
params,
pruned_idx,
pruned_axis,
place,
lazy=False,
only_graph=False):
"""
Pruning parameters in given axis.
Args:
scope(fluid.core.Scope): The scope storing paramaters to be pruned.
params(VarWrapper): The parameter to be pruned.
pruned_idx(list): The index of elements to be pruned.
pruned_axis(int): The pruning axis.
place(fluid.Place): The device place of filter parameters.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
if params[0].name() in self.pruned_list[pruned_axis]:
return
for param in params:
assert isinstance(param, VarWrapper)
param_t = scope.find_var(param.name()).get_tensor()
if lazy:
self.backup[param.name()] = copy.deepcopy(np.array(param_t))
pruned_param = self.pruner.prune_tensor(
np.array(param_t), pruned_idx, pruned_axis, lazy=lazy)
if not only_graph:
param_t.set(pruned_param, place)
ori_shape = param.shape()
if param.name() not in self.param_shape_backup:
self.param_shape_backup[param.name()] = copy.deepcopy(
param.shape())
new_shape = list(param.shape())
new_shape[pruned_axis] = pruned_param.shape[pruned_axis]
param.set_shape(new_shape)
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
_logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format(
str(param.name()),
str(pruned_axis), str(ori_shape), str(param.shape())))
self.pruned_list[pruned_axis].append(param.name())
def _forward_search_related_op(self, graph, param):
"""
Forward search operators that will be affected by pruning of param.
Args:
graph(GraphWrapper): The graph to be searched.
param(VarWrapper): The current pruned parameter.
Returns:
list<OpWrapper>: A list of operators.
"""
assert isinstance(param, VarWrapper)
visited = {}
for op in graph.ops():
visited[op.idx()] = False
stack = []
for op in graph.ops():
if (not op.is_bwd_op()) and (param in op.all_inputs()):
stack.append(op)
visit_path = []
while len(stack) > 0:
top_op = stack[len(stack) - 1]
if visited[top_op.idx()] == False:
visit_path.append(top_op)
visited[top_op.idx()] = True
next_ops = None
if top_op.type() == "conv2d" and param not in top_op.all_inputs():
next_ops = None
elif top_op.type() == "mul":
next_ops = None
else:
next_ops = self._get_next_unvisited_op(graph, visited, top_op)
if next_ops == None:
stack.pop()
else:
stack += next_ops
return visit_path
def _get_next_unvisited_op(self, graph, visited, top_op):
"""
Get next unvisited adjacent operators of given operators.
Args:
graph(GraphWrapper): The graph used to search.
visited(list): The ids of operators that has been visited.
top_op: The given operator.
Returns:
list<OpWrapper>: A list of operators.
"""
assert isinstance(top_op, OpWrapper)
next_ops = []
for op in graph.next_ops(top_op):
if (visited[op.idx()] == False) and (not op.is_bwd_op()):
next_ops.append(op)
return next_ops if len(next_ops) > 0 else None
def _get_accumulator(self, graph, param):
"""
Get accumulators of given parameter. The accumulator was created by optimizer.
Args:
graph(GraphWrapper): The graph used to search.
param(VarWrapper): The given parameter.
Returns:
list<VarWrapper>: A list of accumulators which are variables.
"""
assert isinstance(param, VarWrapper)
params = []
for op in param.outputs():
if op.is_opt_op():
for out_var in op.all_outputs():
if graph.is_persistable(out_var) and out_var.name(
) != param.name():
params.append(out_var)
return params
def _forward_pruning_ralated_params(self,
graph,
scope,
param,
place,
ratio=None,
pruned_idxs=None,
lazy=False,
only_graph=False):
"""
The strategy that pruning weights by threshold or ratio iteratively.
Pruning all the parameters affected by the pruning of given parameter.
Args:
graph(GraphWrapper): The graph to be searched.
scope(fluid.core.Scope): The scope storing paramaters to be pruned.
param(VarWrapper): The given parameter.
place(fluid.Place): The device place of filter parameters.
ratio(float): The target ratio to be pruned.
pruned_idx(list): The index of elements to be pruned.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
assert isinstance(
graph,
GraphWrapper), "graph must be instance of slim.core.GraphWrapper"
assert isinstance(
param, VarWrapper), "param must be instance of slim.core.VarWrapper"
if param.name() in self.pruned_list[0]:
return
related_ops = self._forward_search_related_op(graph, param)
if ratio is None:
assert pruned_idxs is not None
self._prune_parameter_by_idx(
scope, [param] + self._get_accumulator(graph, param),
pruned_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
else:
pruned_idxs = self._prune_filters_by_ratio(
scope, [param] + self._get_accumulator(graph, param),
ratio,
place,
lazy=lazy,
only_graph=only_graph)
corrected_idxs = pruned_idxs[:]
for idx, op in enumerate(related_ops):
if op.type() == "conv2d" and (param not in op.all_inputs()):
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
conv_param = in_var
self._prune_parameter_by_idx(
scope, [conv_param] + self._get_accumulator(
graph, conv_param),
corrected_idxs,
pruned_axis=1,
place=place,
lazy=lazy,
only_graph=only_graph)
if op.type() == "depthwise_conv2d":
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
conv_param = in_var
self._prune_parameter_by_idx(
scope, [conv_param] + self._get_accumulator(
graph, conv_param),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
elif op.type() == "elementwise_add":
# pruning bias
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
bias_param = in_var
self._prune_parameter_by_idx(
scope, [bias_param] + self._get_accumulator(
graph, bias_param),
pruned_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
elif op.type() == "mul": # pruning fc layer
fc_input = None
fc_param = None
for in_var in op.all_inputs():
if graph.is_parameter(in_var):
fc_param = in_var
else:
fc_input = in_var
idx = []
feature_map_size = fc_input.shape()[2] * fc_input.shape()[3]
range_idx = np.array(range(feature_map_size))
for i in corrected_idxs:
idx += list(range_idx + i * feature_map_size)
corrected_idxs = idx
self._prune_parameter_by_idx(
scope, [fc_param] + self._get_accumulator(graph, fc_param),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
elif op.type() == "concat":
concat_inputs = op.all_inputs()
last_op = related_ops[idx - 1]
for out_var in last_op.all_outputs():
if out_var in concat_inputs:
concat_idx = concat_inputs.index(out_var)
offset = 0
for ci in range(concat_idx):
offset += concat_inputs[ci].shape()[1]
corrected_idxs = [x + offset for x in pruned_idxs]
elif op.type() == "batch_norm":
bn_inputs = op.all_inputs()
mean = bn_inputs[2]
variance = bn_inputs[3]
alpha = bn_inputs[0]
beta = bn_inputs[1]
self._prune_parameter_by_idx(
scope, [mean] + self._get_accumulator(graph, mean),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
self._prune_parameter_by_idx(
scope, [variance] + self._get_accumulator(graph, variance),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
self._prune_parameter_by_idx(
scope, [alpha] + self._get_accumulator(graph, alpha),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
self._prune_parameter_by_idx(
scope, [beta] + self._get_accumulator(graph, beta),
corrected_idxs,
pruned_axis=0,
place=place,
lazy=lazy,
only_graph=only_graph)
def _prune_parameters(self,
graph,
scope,
params,
ratios,
place,
lazy=False,
only_graph=False):
"""
Pruning the given parameters.
Args:
graph(GraphWrapper): The graph to be searched.
scope(fluid.core.Scope): The scope storing paramaters to be pruned.
params(list<str>): A list of parameter names to be pruned.
ratios(list<float>): A list of ratios to be used to pruning parameters.
place(fluid.Place): The device place of filter parameters.
pruned_idx(list): The index of elements to be pruned.
lazy(bool): True means setting the pruned elements to zero.
False means cutting down the pruned elements.
only_graph(bool): True means only modifying the graph.
False means modifying graph and variables in scope.
"""
_logger.debug('\n################################')
_logger.debug('# pruning parameters #')
_logger.debug('################################\n')
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
_logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format('parameter', 'axis',
'from', 'to'))
assert len(params) == len(ratios)
self.pruned_list = [[], []]
for param, ratio in zip(params, ratios):
assert isinstance(param, str) or isinstance(param, unicode)
param = graph.var(param)
self._forward_pruning_ralated_params(
graph,
scope,
param,
place,
ratio=ratio,
lazy=lazy,
only_graph=only_graph)
ops = param.outputs()
for op in ops:
if op.type() == 'conv2d':
brother_ops = self._search_brother_ops(graph, op)
for broher in brother_ops:
for p in graph.get_param_by_op(broher):
self._forward_pruning_ralated_params(
graph,
scope,
p,
place,
ratio=ratio,
lazy=lazy,
only_graph=only_graph)
_logger.debug(
'|----------------------------------------+----+------------------------------+------------------------------|'
)
def _search_brother_ops(self, graph, op_node):
"""
Search brother operators that was affected by pruning of given operator.
Args:
graph(GraphWrapper): The graph to be searched.
op_node(OpWrapper): The start node for searching.
Returns:
list<VarWrapper>: A list of operators.
"""
visited = [op_node.idx()]
stack = []
brothers = []
for op in graph.next_ops(op_node):
if (op.type() != 'conv2d') and (op.type() != 'fc') and (
not op._is_bwd_op()):
stack.append(op)
visited.append(op.idx())
while len(stack) > 0:
top_op = stack.pop()
for parent in graph.pre_ops(top_op):
if parent.idx() not in visited and (not parent._is_bwd_op()):
if ((parent.type == 'conv2d') or (parent.type == 'fc')):
brothers.append(parent)
else:
stack.append(parent)
visited.append(parent.idx())
for child in graph.next_ops(top_op):
if (child.type != 'conv2d') and (child.type != 'fc') and (
child.idx() not in visited) and (
not child._is_bwd_op()):
stack.append(child)
visited.append(child.idx())
return brothers
def _prune_graph(self, graph, target_graph):
"""
Pruning parameters of graph according to target graph.
Args:
graph(GraphWrapper): The graph to be pruned.
target_graph(GraphWrapper): The reference graph.
Return: None
"""
count = 1
_logger.debug(
'|----+----------------------------------------+------------------------------+------------------------------|'
)
_logger.debug('|{:^4}|{:^40}|{:^30}|{:^30}|'.format('id', 'parammeter',
'from', 'to'))
for param in target_graph.all_parameters():
var = graph.var(param.name())
ori_shape = var.shape()
var.set_shape(param.shape())
_logger.debug(
'|----+----------------------------------------+------------------------------+------------------------------|'
)
_logger.debug('|{:^4}|{:^40}|{:^30}|{:^30}|'.format(
str(count),
str(param.name()), str(ori_shape), str(param.shape())))
count += 1
_logger.debug(
'|----+----------------------------------------+------------------------------+------------------------------|'
)
class UniformPruneStrategy(PruneStrategy):
"""
The uniform pruning strategy. The parameters will be pruned by uniform ratio.
"""
def __init__(self,
pruner,
mini_batch_pruning_frequency=1,
pruner=None,
start_epoch=0,
end_epoch=10):
super(PruneStrategy, self).__init__(start_epoch, end_epoch)
self.pruner = pruner
self.mini_batch_pruning_frequency = mini_batch_pruning_frequency
def _triger(self, context):
return (context.batch_id % self.mini_batch_pruning_frequency == 0 and
self.start_epoch <= context.epoch_id < self.end_epoch)
def on_batch_end(self, context):
if self._triger(context):
prune_program = Program()
with program_guard(prune_program):
for param in context.graph.all_parameters():
prune_program.global_block().clone_variable(param)
p = prune_program.global_block().var(param.name)
zeros_mask = self.pruner.prune(p)
pruned_param = p * zeros_mask
layers.assign(input=pruned_param, output=param)
context.program_exe.run(prune_program, scope=context.scope)
end_epoch=0,
target_ratio=0.5,
metric_name=None,
pruned_params='conv.*_weights'):
"""
Args:
pruner(slim.Pruner): The pruner used to prune the parameters.
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
target_ratio(float): The flops ratio to be pruned from current model.
metric_name(str): The metric used to evaluate the model.
It should be one of keys in out_nodes of graph wrapper.
pruned_params(str): The pattern str to match the parameter names to be pruned.
"""
super(UniformPruneStrategy, self).__init__(pruner, start_epoch,
end_epoch, target_ratio,
metric_name, pruned_params)
def _get_best_ratios(self, context):
"""
Search a group of ratios for pruning target flops.
"""
_logger.info('_get_best_ratios')
pruned_params = []
for param in context.eval_graph.all_parameters():
if re.match(self.pruned_params, param.name()):
pruned_params.append(param.name())
min_ratio = 0.
max_ratio = 1.
flops = context.eval_graph.flops()
model_size = context.eval_graph.numel_params()
while min_ratio < max_ratio:
ratio = (max_ratio + min_ratio) / 2
_logger.debug(
'-----------Try pruning ratio: {:.2f}-----------'.format(ratio))
ratios = [ratio] * len(pruned_params)
self._prune_parameters(
context.eval_graph,
context.scope,
pruned_params,
ratios,
context.place,
only_graph=True)
pruned_flops = 1 - (float(context.eval_graph.flops()) / flops)
pruned_size = 1 - (float(context.eval_graph.numel_params()) /
model_size)
_logger.debug('Pruned flops: {:.2f}'.format(pruned_flops))
_logger.debug('Pruned model size: {:.2f}'.format(pruned_size))
for param in self.param_shape_backup.keys():
context.eval_graph.var(param).set_shape(self.param_shape_backup[
param])
self.param_shape_backup = {}
if abs(pruned_flops - self.target_ratio) < 1e-2:
break
if pruned_flops > self.target_ratio:
max_ratio = ratio
else:
min_ratio = ratio
_logger.info('Get ratios: {}'.format([round(r, 2) for r in ratios]))
return pruned_params, ratios
def on_epoch_begin(self, context):
if context.epoch_id == self.start_epoch:
params, ratios = self._get_best_ratios(context)
self._prune_parameters(context.optimize_graph, context.scope,
params, ratios, context.place)
model_size = context.eval_graph.numel_params()
flops = context.eval_graph.flops()
_logger.debug('\n################################')
_logger.debug('# pruning eval graph #')
_logger.debug('################################\n')
self._prune_graph(context.eval_graph, context.optimize_graph)
context.optimize_graph.update_groups_of_conv()
context.eval_graph.update_groups_of_conv()
_logger.info(
'------------------finish pruning--------------------------------'
)
_logger.info('Pruned size: {:.2f}'.format(1 - (float(
context.eval_graph.numel_params()) / model_size)))
_logger.info('Pruned flops: {:.2f}'.format(1 - (float(
context.eval_graph.flops()) / flops)))
# metric = self._eval_graph(context)
# _logger.info('Metric after pruning: {:.2f}'.format(metric))
_logger.info(
'------------------UniformPruneStrategy.on_compression_begin finish--------------------------------'
)
class SensitivePruneStrategy(PruneStrategy):
"""
Sensitive pruning strategy. Different pruned ratio was applied on each layer.
"""
def __init__(self,
pruner=None,
start_epoch=0,
end_epoch=0,
delta_rate=0.20,
target_ratio=0.5,
metric_name='top1_acc',
pruned_params='conv.*_weights',
sensitivities_file='./sensitivities.data',
sensitivities={},
num_steps=1,
eval_rate=None):
"""
Args:
pruner(slim.Pruner): The pruner used to prune the parameters.
start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0.
end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 10.
delta_rate(float): The delta used to generate ratios when calculating sensitivities. default: 0.2
target_ratio(float): The flops ratio to be pruned from current model. default: 0.5
metric_name(str): The metric used to evaluate the model.
It should be one of keys in out_nodes of graph wrapper. default: 'top1_acc'
pruned_params(str): The pattern str to match the parameter names to be pruned. default: 'conv.*_weights'.
sensitivities_file(str): The sensitivities file. default: './sensitivities.data'
sensitivities(dict): The user-defined sensitivities. default: {}.
num_steps(int): The number of pruning steps. default: 1.
eval_rate(float): The rate of sampled data used to calculate sensitivities.
None means using all the data. default: None.
"""
super(SensitivePruneStrategy, self).__init__(pruner, start_epoch,
end_epoch, target_ratio,
metric_name, pruned_params)
self.delta_rate = delta_rate
self.pruned_list = []
self.sensitivities = sensitivities
self.sensitivities_file = sensitivities_file
self.backup = {}
self.param_shape_backup = {}
self.num_steps = num_steps
self.eval_rate = eval_rate
self.pruning_step = 1 - pow((1 - target_ratio), 1.0 / self.num_steps)
def _save_sensitivities(self, sensitivities, sensitivities_file):
"""
Save sensitivities into file.
"""
with open(sensitivities_file, 'wb') as f:
pickle.dump(sensitivities, f)
def _load_sensitivities(self, sensitivities_file):
"""
Load sensitivities from file.
"""
sensitivities = {}
if sensitivities_file and os.path.exists(sensitivities_file):
with open(sensitivities_file, 'rb') as f:
if sys.version_info < (3, 0):
sensitivities = pickle.load(f)
else:
sensitivities = pickle.load(f, encoding='bytes')
for param in sensitivities:
sensitivities[param]['pruned_percent'] = [
round(p, 2) for p in sensitivities[param]['pruned_percent']
]
self._format_sensitivities(sensitivities)
return sensitivities
def _format_sensitivities(self, sensitivities):
"""
Print formated sensitivities in debug log level.
"""
tb = pt.PrettyTable()
tb.field_names = ["parameter", "size"] + [
str(round(i, 2))
for i in np.arange(self.delta_rate, 1, self.delta_rate)
]
for param in sensitivities:
if len(sensitivities[param]['loss']) == (len(tb.field_names) - 2):
tb.add_row([param, sensitivities[param]['size']] + [
round(loss, 2) for loss in sensitivities[param]['loss']
])
_logger.debug('\n################################')
_logger.debug('# sensitivities table #')
_logger.debug('################################\n')
_logger.debug(tb)
def _compute_sensitivities(self, context):
"""
Computing the sensitivities of all parameters.
"""
_logger.info("calling _compute_sensitivities.")
self.param_shape_backup = {}
self.backup = {}
cached_id = np.random.randint(1000)
if self.start_epoch == context.epoch_id:
sensitivities_file = self.sensitivities_file
else:
sensitivities_file = self.sensitivities_file + ".epoch" + str(
context.epoch_id)
sensitivities = self._load_sensitivities(sensitivities_file)
for param in context.eval_graph.all_parameters():
if not re.match(self.pruned_params, param.name()):
continue
if param.name() not in sensitivities:
sensitivities[param.name()] = {
'pruned_percent': [],
'loss': [],
'size': param.shape()[0]
}
metric = None
for param in sensitivities.keys():
ratio = self.delta_rate
while ratio < 1:
ratio = round(ratio, 2)
if ratio in sensitivities[param]['pruned_percent']:
_logger.debug('{}, {} has computed.'.format(param, ratio))
ratio += self.delta_rate
continue
if metric is None:
metric = self._eval_graph(context, self.eval_rate,
cached_id)
# prune parameter by ratio
self._prune_parameters(
context.eval_graph,
context.scope, [param], [ratio],
context.place,
lazy=True)
self.pruned_list[0]
# get accuracy after pruning and update self.sensitivities
pruned_metric = self._eval_graph(context, self.eval_rate,
cached_id)
loss = metric - pruned_metric
_logger.info("pruned param: {}; {}; loss={}".format(
param, ratio, loss))
for brother in self.pruned_list[0]:
if re.match(self.pruned_params, brother):
if brother not in sensitivities:
sensitivities[brother] = {
'pruned_percent': [],
'loss': []
}
sensitivities[brother]['pruned_percent'].append(ratio)
sensitivities[brother]['loss'].append(loss)
self._save_sensitivities(sensitivities, sensitivities_file)
# restore pruned parameters
for param_name in self.backup.keys():
param_t = context.scope.find_var(param_name).get_tensor()
param_t.set(self.backup[param_name], context.place)
# pruned_metric = self._eval_graph(context)
self.backup = {}
ratio += self.delta_rate
return sensitivities
def _get_best_ratios(self, context, sensitivities, target_ratio):
"""
Search a group of ratios for pruning target flops.
"""
_logger.info('_get_best_ratios for pruning ratie: {}'.format(
target_ratio))
self.param_shape_backup = {}
self.backup = {}
def func(params, x):
a, b, c, d = params
return a * x * x * x + b * x * x + c * x + d
def error(params, x, y):
return func(params, x) - y
def slove_coefficient(x, y):
init_coefficient = [10, 10, 10, 10]
coefficient, loss = leastsq(error, init_coefficient, args=(x, y))
return coefficient
min_loss = 0.
max_loss = 0.
# step 1: fit curve by sensitivities
coefficients = {}
for param in sensitivities:
losses = np.array([0] * 5 + sensitivities[param]['loss'])
precents = np.array([0] * 5 + sensitivities[param][
'pruned_percent'])
coefficients[param] = slove_coefficient(precents, losses)
loss = np.max(losses)
max_loss = np.max([max_loss, loss])
# step 2: Find a group of ratios by binary searching.
flops = context.eval_graph.flops()
model_size = context.eval_graph.numel_params()
ratios = []
while min_loss < max_loss:
loss = (max_loss + min_loss) / 2
_logger.info(
'-----------Try pruned ratios while acc loss={:.4f}-----------'.
format(loss))
ratios = []
# step 2.1: Get ratios according to current loss
for param in sensitivities:
coefficient = copy.deepcopy(coefficients[param])
coefficient[-1] = coefficient[-1] - loss
roots = np.roots(coefficient)
for root in roots:
min_root = 1
if np.isreal(root) and root > 0 and root < 1:
selected_root = min(root.real, min_root)
ratios.append(selected_root)
_logger.info('Pruned ratios={}'.format(
[round(ratio, 3) for ratio in ratios]))
# step 2.2: Pruning by current ratios
self._prune_parameters(
context.eval_graph,
context.scope,
sensitivities.keys(),
ratios,
context.place,
only_graph=True)
pruned_flops = 1 - (float(context.eval_graph.flops()) / flops)
pruned_size = 1 - (float(context.eval_graph.numel_params()) /
model_size)
_logger.info('Pruned flops: {:.4f}'.format(pruned_flops))
_logger.info('Pruned model size: {:.4f}'.format(pruned_size))
for param in self.param_shape_backup.keys():
context.eval_graph.var(param).set_shape(self.param_shape_backup[
param])
self.param_shape_backup = {}
# step 2.3: Check whether current ratios is enough
if abs(pruned_flops - target_ratio) < 0.015:
break
if pruned_flops > target_ratio:
max_loss = loss
else:
min_loss = loss
return sensitivities.keys(), ratios
def _current_pruning_target(self, context):
'''
Get the target pruning rate in current epoch.
'''
_logger.info('Left number of pruning steps: {}'.format(self.num_steps))
if self.num_steps <= 0:
return None
if (self.start_epoch == context.epoch_id) or context.eval_converged(
self.metric_name, 0.005):
self.num_steps -= 1
return self.pruning_step
def on_epoch_begin(self, context):
current_ratio = self._current_pruning_target(context)
if current_ratio is not None:
sensitivities = self._compute_sensitivities(context)
params, ratios = self._get_best_ratios(context, sensitivities,
current_ratio)
self._prune_parameters(context.optimize_graph, context.scope,
params, ratios, context.place)
self.param_shape_backup = {}
self.backup = {}
model_size = context.eval_graph.numel_params()
flops = context.eval_graph.flops()
_logger.debug('################################')
_logger.debug('# pruning eval graph #')
_logger.debug('################################')
self._prune_graph(context.eval_graph, context.optimize_graph)
context.optimize_graph.update_groups_of_conv()
context.eval_graph.update_groups_of_conv()
context.optimize_graph.compile() # to update the compiled program
context.eval_graph.compile(
for_parallel=False,
for_test=True) # to update the compiled program
_logger.info(
'------------------finish pruning--------------------------------'
)
_logger.info('Pruned size: {:.3f}'.format(1 - (float(
context.eval_graph.numel_params()) / model_size)))
_logger.info('Pruned flops: {:.3f}'.format(1 - (float(
context.eval_graph.flops()) / flops)))
metric = self._eval_graph(context)
_logger.info('Metric after pruning: {:.2f}'.format(metric))
_logger.info(
'------------------SensitivePruneStrategy.on_epoch_begin finish--------------------------------'
)
python/paddle/fluid/contrib/slim/prune/pruner.py
浏览文件 @ 2e5831f0
......@@ -13,9 +13,10 @@
# limitations under the License.
import numpy as np
import collections
from .... import layers
__all__ = ['Pruner', 'MagnitudePruner', 'RatioPruner']
__all__ = ['Pruner', 'StructurePruner']
class Pruner(object):
......@@ -30,54 +31,77 @@ class Pruner(object):
pass
class MagnitudePruner(Pruner):
class StructurePruner(Pruner):
"""
Pruner used to pruning a parameter by threshold.
Pruner used to pruning parameters by groups.
"""
def __init__(self, threshold):
self.threshold = threshold
def prune(self, param, threshold=None):
if threshold is None:
thres = layers.fill_constant(
shape=[1], dtype='float32', value=self.threshold)
else:
thres = threshold
zeros_mask = layers.less_than(x=param, y=thres)
return zeros_mask
class RatioPruner(Pruner):
def __init__(self, pruning_axis, criterions):
"""
Pruner used to pruning a parameter by ratio.
Args:
pruning_axis(dict): The key is the name of parameter to be pruned,
'*' means all the parameters.
The value is the axis to be used. Given a parameter
with shape [3, 4], the result of pruning 50% on aixs 1
is a parameter with shape [3, 2].
criterions(dict): The key is the name of parameter to be pruned,
'*' means all the parameters.
The value is the criterion used to sort groups for pruning.
It only supports 'l1_norm' currently.
"""
self.pruning_axis = pruning_axis
self.criterions = criterions
def __init__(self, ratios=None):
def cal_pruned_idx(self, name, param, ratio, axis=None):
"""
Calculate the index to be pruned on axis by given pruning ratio.
Args:
ratios: dict with pair (paramer_name, pruned_ratio).
name(str): The name of parameter to be pruned.
param(np.array): The data of parameter to be pruned.
ratio(float): The ratio to be pruned.
axis(int): The axis to be used for pruning given parameter.
If it is None, the value in self.pruning_axis will be used.
default: None.
Returns:
list<int>: The indexes to be pruned on axis.
"""
self.ratios = ratios
criterion = self.criterions[
name] if name in self.criterions else self.criterions['*']
if axis is None:
assert self.pruning_axis is not None, "pruning_axis should set if axis is None."
axis = self.pruning_axis[
name] if name in self.pruning_axis else self.pruning_axis['*']
prune_num = int(round(param.shape[axis] * ratio))
reduce_dims = [i for i in range(len(param.shape)) if i != axis]
if criterion == 'l1_norm':
criterions = np.sum(np.abs(param), axis=tuple(reduce_dims))
pruned_idx = criterions.argsort()[:prune_num]
return pruned_idx
def prune(self, param, ratio=None):
def prune_tensor(self, tensor, pruned_idx, pruned_axis, lazy=False):
"""
Pruning a array by indexes on given axis.
Args:
ratio: `ratio=40%` means pruning (1 - 40%) weights to zero.
tensor(numpy.array): The target array to be pruned.
pruned_idx(list<int>): The indexes to be pruned.
pruned_axis(int): The axis of given array to be pruned on.
lazy(bool): True means setting the pruned elements to zero.
False means remove the pruned elements from memory.
default: False.
Returns:
numpy.array: The pruned array.
"""
if ratio is None:
rat = self.ratios[
param.name] if param.name in self.ratios else self.ratios['*']
else:
rat = ratio
if rat < 1.0:
k = max(int(rat * np.prod(param.shape)), 1)
param_vec = layers.reshape(x=param, shape=[1, -1])
param_topk, _ = layers.topk(param_vec, k=k)
threshold = layers.slice(
param_topk, axes=[1], starts=[-1], ends=[k])
threshold = layers.reshape(x=threshold, shape=[1])
zeros_mask = layers.less_than(x=param, y=threshold)
mask = np.zeros(tensor.shape[pruned_axis], dtype=bool)
mask[pruned_idx] = True
def func(data):
return data[~mask]
def lazy_func(data):
data[mask] = 0
return data
if lazy:
return np.apply_along_axis(lazy_func, pruned_axis, tensor)
else:
zeros_mask = layers.ones(param.shape)
return zeros_mask
return np.apply_along_axis(func, pruned_axis, tensor)
python/paddle/fluid/contrib/slim/tests/configs/config.yaml 已删除 100644 → 0
浏览文件 @ 18779b5b
version: 1.0
include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_2'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/tests/configs/filter_pruning.yaml 0 → 100644
浏览文件 @ 2e5831f0
#start_epoch: The 'on_epoch_begin' function will be called in start_epoch. default: 0.
#end_epoch: The 'on_epoch_end' function will be called in end_epoch. default: 10.
#delta_rate: The delta used to generate ratios when calculating sensitivities.
#target_ratio: The flops ratio to be pruned from current model.
#metric_name: The metric used to evaluate the model.
#pruned_params: The pattern str to match the parameter names to be pruned.
#sensitivities_file: The sensitivities file.
#num_steps: The number of pruning steps.
#eval_rate: The rate of sampled data used to calculate sensitivities.
version: 1.0
pruners:
pruner_1:
class: 'StructurePruner'
pruning_axis:
'*': 0
criterions:
'*': 'l1_norm'
strategies:
sensitive_pruning_strategy:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
delta_rate: 0.1
target_ratio: 0.3
num_steps: 1
eval_rate: 0.5
pruned_params: '.*_sep_weights'
sensitivities_file: 'mobilenet_acc_top1_sensitive.data'
metric_name: 'acc_top1'
compressor:
epoch: 120
checkpoint_path: './checkpoints/'
strategies:
- sensitive_pruning_strategy
python/paddle/fluid/contrib/slim/tests/configs/pruners.yaml 已删除 100644 → 0
浏览文件 @ 18779b5b
version: 1.0
pruners:
pruner_2:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/tests/configs/pruners_0.yaml 已删除 100644 → 0
浏览文件 @ 18779b5b
version: 1.0
pruners:
pruner_3:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/graph/graph_pass.py → python/paddle/fluid/contrib/slim/tests/filter_pruning/__init__.py
浏览文件 @ 2e5831f0
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# 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.
......@@ -11,32 +11,3 @@
# 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.
__all__ = ['GraphPass', 'PruneParameterPass']
class GraphPass(object):
"""
Base class for all graph pass.
"""
def __init__(self):
pass
def apply(self, graph):
pass
class PruneParameterPass(GraphPass):
"""
Generate a graph for pruning parameters from target graph.
"""
def __init__(self, pruned_params, thresholds):
super(PruneParameterPass, self).__init__()
self.pruned_params = pruned_params
self.thresholds = thresholds
self.default_threshold = thresholds['*']
def apply(self, graph):
pass
python/paddle/fluid/contrib/slim/tests/filter_pruning/compress.yaml 0 → 100644
浏览文件 @ 2e5831f0
#start_epoch: The 'on_epoch_begin' function will be called in start_epoch. default: 0.
#end_epoch: The 'on_epoch_end' function will be called in end_epoch. default: 10.
#delta_rate: The delta used to generate ratios when calculating sensitivities.
#target_ratio: The flops ratio to be pruned from current model.
#metric_name: The metric used to evaluate the model.
#pruned_params: The pattern str to match the parameter names to be pruned.
#sensitivities_file: The sensitivities file.
#num_steps: The number of pruning steps.
#eval_rate: The rate of sampled data used to calculate sensitivities.
version: 1.0
pruners:
pruner_1:
class: 'StructurePruner'
pruning_axis:
'*': 0
criterions:
'*': 'l1_norm'
strategies:
sensitive_pruning_strategy:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 1
delta_rate: 0.2
target_ratio: 0.08
num_steps: 1
eval_rate: 0.5
pruned_params: 'conv6_sep_weights'
sensitivities_file: 'mobilenet_acc_top1_sensitive.data'
metric_name: 'acc_top1'
compressor:
epoch: 2
checkpoint_path: './checkpoints/'
strategies:
- sensitive_pruning_strategy
python/paddle/fluid/contrib/slim/tests/filter_pruning/mobilenet.py 0 → 100644
浏览文件 @ 2e5831f0
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
__all__ = ['MobileNet']
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class MobileNet():
def __init__(self):
self.params = train_parameters
def net(self, input, class_dim=1000, scale=1.0):
# conv1: 112x112
input = self.conv_bn_layer(
input,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1,
name="conv1")
# 56x56
input = self.depthwise_separable(
input,
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale,
name="conv2_1")
input = self.depthwise_separable(
input,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2,
scale=scale,
name="conv2_2")
# 28x28
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale,
name="conv3_1")
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2,
scale=scale,
name="conv3_2")
# 14x14
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale,
name="conv4_1")
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2,
scale=scale,
name="conv4_2")
# 14x14
for i in range(5):
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
scale=scale,
name="conv5" + "_" + str(i + 1))
# 7x7
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2,
scale=scale,
name="conv5_6")
input = self.depthwise_separable(
input,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1,
scale=scale,
name="conv6")
input = fluid.layers.pool2d(
input=input,
pool_size=0,
pool_stride=1,
pool_type='avg',
global_pooling=True)
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=MSRA(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
return output
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
act='relu',
use_cudnn=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(
initializer=MSRA(), name=name + "_weights"),
bias_attr=False)
bn_name = name + "_bn"
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def depthwise_separable(self,
input,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
name=None):
depthwise_conv = self.conv_bn_layer(
input=input,
filter_size=3,
num_filters=int(num_filters1 * scale),
stride=stride,
padding=1,
num_groups=int(num_groups * scale),
use_cudnn=False,
name=name + "_dw")
pointwise_conv = self.conv_bn_layer(
input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
padding=0,
name=name + "_sep")
return pointwise_conv
python/paddle/fluid/contrib/slim/tests/test_factory.py
浏览文件 @ 2e5831f0
......@@ -12,29 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.fluid.contrib.slim import ConfigFactory
from paddle.fluid.contrib.slim.core import ConfigFactory
import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./configs/config.yaml')
def test_parse_pruning(self):
factory = ConfigFactory('./configs/filter_pruning.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
pruner_1 = factory.instance('pruner_1')
self.assertEquals(pruner_1.pruning_axis['*'], 0)
self.assertEquals(pruner_1.criterions['*'], 'l1_norm')
pruner = factory.instance('pruner_2')
self.assertEquals(pruner.ratios['*'], 0.7)
strategy = factory.instance('sensitive_pruning_strategy')
pruner_1 = strategy.pruner
self.assertEquals(pruner_1.criterions['*'], 'l1_norm')
strategy = factory.instance('strategy_1')
pruner = strategy.pruner
self.assertEquals(pruner.ratios['*'], 0.7)
compress_pass = factory.get_compress_pass()
self.assertEquals(compress_pass.epoch, 100)
strategy = compress_pass.strategies[0]
self.assertEquals(strategy.delta_rate, 0.2)
self.assertEquals(strategy.start_epoch, 0)
self.assertEquals(strategy.sensitivities_file,
'mobilenet_acc_top1_sensitive.data')
if __name__ == '__main__':
......
python/paddle/fluid/contrib/slim/tests/test_filter_pruning.py 0 → 100644
浏览文件 @ 2e5831f0
# 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 paddle
import unittest
import paddle.fluid as fluid
from filter_pruning.mobilenet import MobileNet
from paddle.fluid.contrib.slim.core import Compressor
from paddle.fluid.contrib.slim.graph import GraphWrapper
class TestFilterPruning(unittest.TestCase):
def test_compression(self):
"""
Model: mobilenet_v1
data: mnist
step1: Training one epoch
step2: pruning flops
step3: fine-tune one epoch
step4: check top1_acc.
"""
if not fluid.core.is_compiled_with_cuda():
return
class_dim = 10
image_shape = [1, 28, 28]
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().net(input=image, class_dim=class_dim)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
val_program = fluid.default_main_program().clone(for_test=False)
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))
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
val_feed_list = [('img', image.name), ('label', label.name)]
val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
acc_top5.name)]
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
train_feed_list = [('img', image.name), ('label', label.name)]
train_fetch_list = [('loss', avg_cost.name)]
com_pass = Compressor(
place,
fluid.global_scope(),
fluid.default_main_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('./filter_pruning/compress.yaml')
eval_graph = com_pass.run()
self.assertTrue(
abs((com_pass.context.eval_results['acc_top1'][-1] - 0.969) / 0.969)
< 0.02)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py 0 → 100644
浏览文件 @ 2e5831f0
# 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.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import six
import numpy as np
from paddle.fluid.contrib.slim.graph import GraphWrapper
from paddle.fluid import core
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 8, 8], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
data.stop_gradinet = False
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return data, label, loss
class TestGraphWrapper(unittest.TestCase):
def build_program(self):
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
image, label, self.loss = residual_block(2)
eval_program = main.clone()
opt = fluid.optimizer.SGD(learning_rate=0.001)
opt.minimize(self.loss)
self.scope = core.Scope()
exe = fluid.Executor(place)
exe.run(startup, scope=self.scope)
self.eval_graph = GraphWrapper(
program=eval_program,
in_nodes={'image': image.name,
'label': label.name},
out_nodes={'loss': self.loss.name})
self.train_graph = GraphWrapper(
program=main,
in_nodes={'image': image.name,
'label': label.name},
out_nodes={'loss': self.loss.name})
def test_all_parameters(self):
self.build_program()
self.assertEquals(len(self.train_graph.all_parameters()), 24)
def test_all_vars(self):
self.build_program()
self.assertEquals(len(self.train_graph.vars()), 90)
def test_numel_params(self):
self.build_program()
self.assertEquals(self.train_graph.numel_params(), 13258)
def test_compile(self):
self.build_program()
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
self.train_graph.compile()
exe.run(self.train_graph.compiled_graph,
scope=self.scope,
feed={
'image':
np.random.randint(0, 40, [16, 1, 8, 8]).astype('float32'),
'label': np.random.randint(0, 10, [16, 1]).astype('int64')
})
def test_pre_and_next_ops(self):
self.build_program()
for op in self.train_graph.ops():
for next_op in self.train_graph.next_ops(op):
self.assertTrue(op in self.train_graph.pre_ops(next_op))
def test_get_optimize_graph(self):
self.build_program()
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
opt = fluid.optimizer.SGD(learning_rate=0.001)
train_graph = self.eval_graph.get_optimize_graph(
opt, place, self.scope, no_grad_var_names=['image'])
self.assertEquals(len(self.train_graph.ops()), len(train_graph.ops()))
exe = fluid.Executor(place)
train_graph.compile()
image = np.random.randint(0, 225, [16, 1, 8, 8]).astype('float32')
label = np.random.randint(0, 10, [16, 1]).astype('int64')
exe.run(train_graph.compiled_graph,
scope=self.scope,
feed={'image': image,
'label': label})
def test_flops(self):
self.build_program()
self.assertEquals(self.train_graph.flops(), 354624)
if __name__ == '__main__':
unittest.main()
python/requirements.txt
浏览文件 @ 2e5831f0
......@@ -12,3 +12,4 @@ six
funcsigs
pyyaml
decorator
prettytable
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