From 846f6d5c60defa1a04cc5798311951ea774c9fcc Mon Sep 17 00:00:00 2001 From: Guanghua Yu <742925032@qq.com> Date: Thu, 14 Oct 2021 14:56:36 +0800 Subject: [PATCH] add post quant (#4255) --- configs/slim/README.md | 16 +++- .../slim/post_quant/ppyolo_mbv3_large_ptq.yml | 10 ++ ppdet/engine/export_utils.py | 23 ++++- ppdet/engine/trainer.py | 81 +++++++++------- ppdet/slim/__init__.py | 8 ++ ppdet/slim/quant.py | 33 +++++++ tools/post_quant.py | 96 +++++++++++++++++++ 7 files changed, 231 insertions(+), 36 deletions(-) create mode 100644 configs/slim/post_quant/ppyolo_mbv3_large_ptq.yml create mode 100644 tools/post_quant.py diff --git a/configs/slim/README.md b/configs/slim/README.md index b175266f8..ac025688b 100755 --- a/configs/slim/README.md +++ b/configs/slim/README.md @@ -4,10 +4,11 @@ - [剪裁](prune) - [量化](quant) +- [离线量化](post_quant) - [蒸馏](distill) - [联合策略](extensions) -推荐您使用剪裁和蒸馏联合训练，或者使用剪裁和量化，进行检测模型压缩。 下面以YOLOv3为例，进行剪裁、蒸馏和量化实验。 +推荐您使用剪裁和蒸馏联合训练，或者使用剪裁、量化训练和离线量化，进行检测模型压缩。 下面以YOLOv3为例，进行剪裁、蒸馏和量化实验。 ## 实验环境 @@ -20,7 +21,8 @@ **PaddleDetection、 PaddlePaddle与PaddleSlim 版本关系:** | PaddleDetection版本 | PaddlePaddle版本 | PaddleSlim版本 | 备注 | | :------------------: | :---------------: | :-------: |:---------------: | -| release/2.1 | >= 2.1.0 | 2.1 | 量化模型导出依赖最新Paddle develop分支，可在[PaddlePaddle每日版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev)中下载安装 | +| release/2.3 | >= 2.1 | 2.1 | 离线量化依赖Paddle 2.2及PaddleSlim 2.2 | +| release/2.1 | 2.2 | >= 2.1.0 | 2.1 | 量化模型导出依赖最新Paddle develop分支，可在[PaddlePaddle每日版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev)中下载安装 | | release/2.0 | >= 2.0.1 | 2.0 | 量化依赖Paddle 2.1及PaddleSlim 2.1 | @@ -145,6 +147,16 @@ python tools/export_model.py -c configs/{MODEL.yml} --slim_config configs/slim/{ - 上述V100预测时延非量化模型均是使用TensorRT-FP32测试，量化模型均使用TensorRT-INT8测试，并且都包含NMS耗时。 - SD855预测时延为使用PaddleLite部署，使用arm8架构并使用4线程(4 Threads)推理时延。 +### 离线量化 +需要准备val集，用来对离线量化模型进行校准，运行方式： +```shell +python tools/post_quant.py -c configs/{MODEL.yml} --slim_config configs/slim/post_quant/{SLIM_CONFIG.yml} +``` +例如： +```shell +python3.7 tools/post_quant.py -c configs/ppyolo/ppyolo_mbv3_large_coco.yml --slim_config=configs/slim/post_quant/ppyolo_mbv3_large_ptq.yml +``` + ### 蒸馏 #### COCO上benchmark diff --git a/configs/slim/post_quant/ppyolo_mbv3_large_ptq.yml b/configs/slim/post_quant/ppyolo_mbv3_large_ptq.yml new file mode 100644 index 000000000..712651fa5 --- /dev/null +++ b/configs/slim/post_quant/ppyolo_mbv3_large_ptq.yml @@ -0,0 +1,10 @@ +weights: https://paddledet.bj.bcebos.com/models/ppyolo_mbv3_large_coco.pdparams +slim: PTQ + +PTQ: + ptq_config: { + 'activation_quantizer': 'HistQuantizer', + 'upsample_bins': 127, + 'hist_percent': 0.999} + quant_batch_num: 10 + fuse: True diff --git a/ppdet/engine/export_utils.py b/ppdet/engine/export_utils.py index 35e22eec7..50cf5b277 100644 --- a/ppdet/engine/export_utils.py +++ b/ppdet/engine/export_utils.py @@ -20,6 +20,7 @@ import os import yaml from collections import OrderedDict +import paddle from ppdet.data.source.category import get_categories from ppdet.utils.logger import setup_logger @@ -50,6 +51,24 @@ KEYPOINT_ARCH = ['HigherHRNet', 'TopDownHRNet'] MOT_ARCH = ['DeepSORT', 'JDE', 'FairMOT'] +def _prune_input_spec(input_spec, program, targets): + # try to prune static program to figure out pruned input spec + # so we perform following operations in static mode + paddle.enable_static() + pruned_input_spec = [{}] + program = program.clone() + program = program._prune(targets=targets) + global_block = program.global_block() + for name, spec in input_spec[0].items(): + try: + v = global_block.var(name) + pruned_input_spec[0][name] = spec + except Exception: + pass + paddle.disable_static() + return pruned_input_spec + + def _parse_reader(reader_cfg, dataset_cfg, metric, arch, image_shape): preprocess_list = [] @@ -97,7 +116,7 @@ def _dump_infer_config(config, path, image_shape, model): arch_state = False from ppdet.core.config.yaml_helpers import setup_orderdict setup_orderdict() - use_dynamic_shape = True if image_shape[1] == -1 else False + use_dynamic_shape = True if image_shape[2] == -1 else False infer_cfg = OrderedDict({ 'mode': 'fluid', 'draw_threshold': 0.5, @@ -141,7 +160,7 @@ def _dump_infer_config(config, path, image_shape, model): dataset_cfg = config['TestDataset'] infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader( - reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape) + reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:]) yaml.dump(infer_cfg, open(path, 'w')) logger.info("Export inference config file to {}".format(os.path.join(path))) diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index f537661b9..f7835f515 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -41,7 +41,7 @@ import ppdet.utils.stats as stats from ppdet.utils import profiler from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval, VisualDLWriter -from .export_utils import _dump_infer_config +from .export_utils import _dump_infer_config, _prune_input_spec from ppdet.utils.logger import setup_logger logger = setup_logger('ppdet.engine') @@ -541,12 +541,7 @@ class Trainer(object): name, ext = os.path.splitext(image_name) return os.path.join(output_dir, "{}".format(name)) + ext - def export(self, output_dir='output_inference'): - self.model.eval() - model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0] - save_dir = os.path.join(output_dir, model_name) - if not os.path.exists(save_dir): - os.makedirs(save_dir) + def _get_infer_cfg_and_input_spec(self, save_dir, prune_input=True): image_shape = None if self.cfg.architecture in MOT_ARCH: test_reader_name = 'TestMOTReader' @@ -555,9 +550,11 @@ class Trainer(object): if 'inputs_def' in self.cfg[test_reader_name]: inputs_def = self.cfg[test_reader_name]['inputs_def'] image_shape = inputs_def.get('image_shape', None) - # set image_shape=[3, -1, -1] as default + # set image_shape=[None, 3, -1, -1] as default if image_shape is None: - image_shape = [3, -1, -1] + image_shape = [None, 3, -1, -1] + if len(image_shape) == 3: + image_shape = [None] + image_shape if hasattr(self.model, 'deploy'): self.model.deploy = True @@ -574,7 +571,7 @@ class Trainer(object): input_spec = [{ "image": InputSpec( - shape=[None] + image_shape, name='image'), + shape=image_shape, name='image'), "im_shape": InputSpec( shape=[None, 2], name='im_shape'), "scale_factor": InputSpec( @@ -585,13 +582,29 @@ class Trainer(object): "crops": InputSpec( shape=[None, 3, 192, 64], name='crops') }) + if prune_input: + static_model = paddle.jit.to_static( + self.model, input_spec=input_spec) + # NOTE: dy2st do not pruned program, but jit.save will prune program + # input spec, prune input spec here and save with pruned input spec + pruned_input_spec = _prune_input_spec( + input_spec, static_model.forward.main_program, + static_model.forward.outputs) + else: + static_model = None + pruned_input_spec = input_spec + + return static_model, pruned_input_spec + + def export(self, output_dir='output_inference'): + self.model.eval() + model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0] + save_dir = os.path.join(output_dir, model_name) + if not os.path.exists(save_dir): + os.makedirs(save_dir) - static_model = paddle.jit.to_static(self.model, input_spec=input_spec) - # NOTE: dy2st do not pruned program, but jit.save will prune program - # input spec, prune input spec here and save with pruned input spec - pruned_input_spec = self._prune_input_spec( - input_spec, static_model.forward.main_program, - static_model.forward.outputs) + static_model, pruned_input_spec = self._get_infer_cfg_and_input_spec( + save_dir) # dy2st and save model if 'slim' not in self.cfg or self.cfg['slim_type'] != 'QAT': @@ -606,22 +619,26 @@ class Trainer(object): input_spec=pruned_input_spec) logger.info("Export model and saved in {}".format(save_dir)) - def _prune_input_spec(self, input_spec, program, targets): - # try to prune static program to figure out pruned input spec - # so we perform following operations in static mode - paddle.enable_static() - pruned_input_spec = [{}] - program = program.clone() - program = program._prune(targets=targets) - global_block = program.global_block() - for name, spec in input_spec[0].items(): - try: - v = global_block.var(name) - pruned_input_spec[0][name] = spec - except Exception: - pass - paddle.disable_static() - return pruned_input_spec + def post_quant(self, output_dir='output_inference'): + model_name = os.path.splitext(os.path.split(self.cfg.filename)[-1])[0] + save_dir = os.path.join(output_dir, model_name) + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + for idx, data in enumerate(self.loader): + self.model(data) + if idx == int(self.cfg.get('quant_batch_num', 10)): + break + + # TODO: support prune input_spec + _, pruned_input_spec = self._get_infer_cfg_and_input_spec( + save_dir, prune_input=False) + + self.cfg.slim.save_quantized_model( + self.model, + os.path.join(save_dir, 'model'), + input_spec=pruned_input_spec) + logger.info("Export Post-Quant model and saved in {}".format(save_dir)) def _flops(self, loader): self.model.eval() diff --git a/ppdet/slim/__init__.py b/ppdet/slim/__init__.py index d929807c3..6d3bb4223 100644 --- a/ppdet/slim/__init__.py +++ b/ppdet/slim/__init__.py @@ -48,6 +48,14 @@ def build_slim_model(cfg, slim_cfg, mode='train'): load_pretrain_weight(model, weights) cfg['model'] = model cfg['slim_type'] = cfg.slim + elif slim_load_cfg['slim'] == 'PTQ': + model = create(cfg.architecture) + load_config(slim_cfg) + load_pretrain_weight(model, cfg.weights) + slim = create(cfg.slim) + cfg['slim_type'] = cfg.slim + cfg['model'] = slim(model) + cfg['slim'] = slim else: load_config(slim_cfg) model = create(cfg.architecture) diff --git a/ppdet/slim/quant.py b/ppdet/slim/quant.py index 837055969..ab81127ae 100644 --- a/ppdet/slim/quant.py +++ b/ppdet/slim/quant.py @@ -49,3 +49,36 @@ class QAT(object): def save_quantized_model(self, layer, path, input_spec=None, **config): self.quanter.save_quantized_model( model=layer, path=path, input_spec=input_spec, **config) + + +@register +@serializable +class PTQ(object): + def __init__(self, + ptq_config, + quant_batch_num=10, + output_dir='output_inference', + fuse=True, + fuse_list=None): + super(PTQ, self).__init__() + self.ptq_config = ptq_config + self.quant_batch_num = quant_batch_num + self.output_dir = output_dir + self.fuse = fuse + self.fuse_list = fuse_list + + def __call__(self, model): + paddleslim = try_import('paddleslim') + self.ptq = paddleslim.PTQ(**self.ptq_config) + model.eval() + quant_model = self.ptq.quantize( + model, fuse=self.fuse, fuse_list=self.fuse_list) + + return quant_model + + def save_quantized_model(self, + quant_model, + quantize_model_path, + input_spec=None): + self.ptq.save_quantized_model(quant_model, quantize_model_path, + input_spec) diff --git a/tools/post_quant.py b/tools/post_quant.py new file mode 100644 index 000000000..fe6f9c79b --- /dev/null +++ b/tools/post_quant.py @@ -0,0 +1,96 @@ +# Copyright (c) 2021 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 os +import sys + +# add python path of PadleDetection to sys.path +parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 2))) +sys.path.insert(0, parent_path) + +# ignore warning log +import warnings +warnings.filterwarnings('ignore') + +import paddle + +from ppdet.core.workspace import load_config, merge_config +from ppdet.utils.check import check_gpu, check_version, check_config +from ppdet.utils.cli import ArgsParser +from ppdet.engine import Trainer +from ppdet.slim import build_slim_model + +from ppdet.utils.logger import setup_logger +logger = setup_logger('post_quant') + + +def parse_args(): + parser = ArgsParser() + parser.add_argument( + "--output_dir", + type=str, + default="output_inference", + help="Directory for storing the output model files.") + parser.add_argument( + "--slim_config", + default=None, + type=str, + help="Configuration file of slim method.") + args = parser.parse_args() + return args + + +def run(FLAGS, cfg): + # build detector + trainer = Trainer(cfg, mode='eval') + + # load weights + if cfg.architecture in ['DeepSORT']: + if cfg.det_weights != 'None': + trainer.load_weights_sde(cfg.det_weights, cfg.reid_weights) + else: + trainer.load_weights_sde(None, cfg.reid_weights) + else: + trainer.load_weights(cfg.weights) + + # post quant model + trainer.post_quant(FLAGS.output_dir) + + +def main(): + FLAGS = parse_args() + cfg = load_config(FLAGS.config) + # TODO: to be refined in the future + if 'norm_type' in cfg and cfg['norm_type'] == 'sync_bn': + FLAGS.opt['norm_type'] = 'bn' + merge_config(FLAGS.opt) + + if FLAGS.slim_config: + cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test') + + # FIXME: Temporarily solve the priority problem of FLAGS.opt + merge_config(FLAGS.opt) + check_config(cfg) + check_gpu(cfg.use_gpu) + check_version() + + run(FLAGS, cfg) + + +if __name__ == '__main__': + main() -- GitLab