From 0ea122f0124332f55250c6027a21ca3f70994297 Mon Sep 17 00:00:00 2001 From: Bai Yifan Date: Fri, 25 Oct 2019 10:21:19 +0800 Subject: [PATCH] Copy slim from release/1.6 to develop (#3758) --- slim/distillation/README.md | 141 ++++++++ slim/distillation/compress.py | 325 ++++++++++++++++++ slim/distillation/run.sh | 47 +++ ...ilenet_v1_yolov3_resnet34_distillation.yml | 18 + slim/distillation/yolov3_resnet34.yml | 34 ++ slim/infer.py | 50 ++- slim/prune/README.md | 52 ++- slim/prune/compress.py | 33 +- slim/quantization/README.md | 106 ++++-- slim/quantization/compress.py | 39 +-- slim/quantization/freeze.py | 86 +++-- slim/quantization/yolov3_mobilenet_v1_voc.yml | 87 ----- 12 files changed, 790 insertions(+), 228 deletions(-) create mode 100755 slim/distillation/README.md create mode 100644 slim/distillation/compress.py create mode 100644 slim/distillation/run.sh create mode 100644 slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml create mode 100644 slim/distillation/yolov3_resnet34.yml delete mode 100644 slim/quantization/yolov3_mobilenet_v1_voc.yml diff --git a/slim/distillation/README.md b/slim/distillation/README.md new file mode 100755 index 000000000..e970cc42b --- /dev/null +++ b/slim/distillation/README.md @@ -0,0 +1,141 @@ +>运行该示例前请安装Paddle1.6或更高版本 + +# 检测模型蒸馏示例 + +## 概述 + +该示例使用PaddleSlim提供的[蒸馏策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#3-蒸馏)对检测库中的模型进行蒸馏训练。 +在阅读该示例前，建议您先了解以下内容： + +- [检测库的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection) +- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md) + + +## 配置文件说明 + +关于配置文件如何编写您可以参考： + +- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8) +- [蒸馏策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#23-蒸馏) + +这里以ResNet34-YoloV3蒸馏MobileNetV1-YoloV3模型为例，首先，为了对`student model`和`teacher model`有个总体的认识，从而进一步确认蒸馏的对象，我们通过以下命令分别观察两个网络变量（Variable）的名称和形状： + +```python +# 观察student model的Variable +for v in fluid.default_main_program().list_vars(): + if "py_reader" not in v.name and "double_buffer" not in v.name and "generated_var" not in v.name: + print(v.name, v.shape) +# 观察teacher model的Variable +for v in teacher_program.list_vars(): + print(v.name, v.shape) +``` + +经过对比可以发现，`student model`和`teacher model`的部分中间结果分别为： + +```bash +# student model +conv2d_15.tmp_0 +# teacher model +teacher_teacher_conv2d_1.tmp_0 +``` + + +所以，我们用`l2_distiller`对这两个特征图做蒸馏。在配置文件中进行如下配置： + +```yaml +distillers: + l2_distiller: + class: 'L2Distiller' + teacher_feature_map: 'teacher_teacher_conv2d_1.tmp_0' + student_feature_map: 'conv2d_15.tmp_0' + distillation_loss_weight: 1 +strategies: + distillation_strategy: + class: 'DistillationStrategy' + distillers: ['l2_distiller'] + start_epoch: 0 + end_epoch: 270 +``` + +我们也可以根据上述操作为蒸馏策略选择其他loss，PaddleSlim支持的有`FSP_loss`, `L2_loss`和`softmax_with_cross_entropy_loss` 。 + +## 训练 + +根据[PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/tools/train.py)编写压缩脚本compress.py。 +在该脚本中定义了Compressor对象，用于执行压缩任务。 + + + + +您可以通过运行脚本`run.sh`运行该示例。 + + +### 保存断点（checkpoint） + +如果在配置文件中设置了`checkpoint_path`, 则在蒸馏任务执行过程中会自动保存断点，当任务异常中断时， +重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复， +需要修改配置文件中的`checkpoint_path`，或者将`checkpoint_path`路径下文件清空。 + +>注意：配置文件中的信息不会保存在断点中，重启前对配置文件的修改将会生效。 + + +## 评估 + +如果在配置文件中设置了`checkpoint_path`，则每个epoch会保存一个压缩后的用于评估的模型， +该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__`和`__params__`两个文件。 +其中，`__model__`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。 + +如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。 + +运行命令为： +``` +python ../eval.py \ + --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \ + --model_name __model__ \ + --params_name __params__ \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" +``` + +## 预测 + +如果在配置文件中设置了`checkpoint_path`，并且在定义Compressor对象时指定了`prune_infer_model`选项，则每个epoch都会 +保存一个`inference model`。该模型是通过删除eval_program中多余的operators而得到的。 + +该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下，包含`__model__.infer`和`__params__`两个文件。 +其中，`__model__.infer`用于保存模型结构信息，`__params__`用于保存参数（parameters）信息。 + +更多关于`prune_infer_model`选项的介绍，请参考：[Compressor介绍](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#121-%E5%A6%82%E4%BD%95%E6%94%B9%E5%86%99%E6%99%AE%E9%80%9A%E8%AE%AD%E7%BB%83%E8%84%9A%E6%9C%AC) + +### python预测 + +在脚本slim/infer.py中展示了如何使用fluid python API加载使用预测模型进行预测。 + +运行命令为： +``` +python ../infer.py \ + --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \ + --model_name __model__.infer \ + --params_name __params__ \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + --infer_dir ../../demo +``` + +### PaddleLite + +该示例中产出的预测（inference）模型可以直接用PaddleLite进行加载使用。 +关于PaddleLite如何使用，请参考：[PaddleLite使用文档](https://github.com/PaddlePaddle/Paddle-Lite/wiki#%E4%BD%BF%E7%94%A8) + +## 示例结果 + +>当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。 + +### MobileNetV1-YOLO-V3 + +| FLOPS |Box AP| +|---|---| +|baseline|76.2 | +|蒸馏后|- | + + +## FAQ diff --git a/slim/distillation/compress.py b/slim/distillation/compress.py new file mode 100644 index 000000000..f1e0189ab --- /dev/null +++ b/slim/distillation/compress.py @@ -0,0 +1,325 @@ +# 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 os +import time +import multiprocessing +import numpy as np +from collections import deque, OrderedDict +from paddle.fluid.contrib.slim.core import Compressor +from paddle.fluid.framework import IrGraph + + +def set_paddle_flags(**kwargs): + for key, value in kwargs.items(): + if os.environ.get(key, None) is None: + os.environ[key] = str(value) + + +# NOTE(paddle-dev): All of these flags should be set before +# `import paddle`. Otherwise, it would not take any effect. +set_paddle_flags( + FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory +) + +from paddle import fluid + +import sys +sys.path.append("../../") +from ppdet.core.workspace import load_config, merge_config, create +from ppdet.data.data_feed import create_reader +from ppdet.utils.eval_utils import parse_fetches, eval_results +from ppdet.utils.stats import TrainingStats +from ppdet.utils.cli import ArgsParser +from ppdet.utils.check import check_gpu +import ppdet.utils.checkpoint as checkpoint +from ppdet.modeling.model_input import create_feed + +import logging +FORMAT = '%(asctime)s-%(levelname)s: %(message)s' +logging.basicConfig(level=logging.INFO, format=FORMAT) +logger = logging.getLogger(__name__) + + +def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): + """ + Run evaluation program, return program outputs. + """ + iter_id = 0 + results = [] + if len(cls) != 0: + values = [] + for i in range(len(cls)): + _, accum_map = cls[i].get_map_var() + cls[i].reset(exe) + values.append(accum_map) + + images_num = 0 + start_time = time.time() + has_bbox = 'bbox' in keys + for data in reader(): + data = test_feed.feed(data) + feed_data = {'image': data['image'], 'im_size': data['im_size']} + outs = exe.run(compile_program, + feed=feed_data, + fetch_list=[values[0]], + return_numpy=False) + outs.append(data['gt_box']) + outs.append(data['gt_label']) + outs.append(data['is_difficult']) + res = { + k: (np.array(v), v.recursive_sequence_lengths()) + for k, v in zip(keys, outs) + } + results.append(res) + if iter_id % 100 == 0: + logger.info('Test iter {}'.format(iter_id)) + iter_id += 1 + images_num += len(res['bbox'][1][0]) if has_bbox else 1 + logger.info('Test finish iter {}'.format(iter_id)) + + end_time = time.time() + fps = images_num / (end_time - start_time) + if has_bbox: + logger.info('Total number of images: {}, inference time: {} fps.'. + format(images_num, fps)) + else: + logger.info('Total iteration: {}, inference time: {} batch/s.'.format( + images_num, fps)) + + return results + + +def main(): + cfg = load_config(FLAGS.config) + if 'architecture' in cfg: + main_arch = cfg.architecture + else: + raise ValueError("'architecture' not specified in config file.") + + merge_config(FLAGS.opt) + if 'log_iter' not in cfg: + cfg.log_iter = 20 + + # check if set use_gpu=True in paddlepaddle cpu version + check_gpu(cfg.use_gpu) + + if cfg.use_gpu: + devices_num = fluid.core.get_cuda_device_count() + else: + devices_num = int( + os.environ.get('CPU_NUM', multiprocessing.cpu_count())) + + if 'train_feed' not in cfg: + train_feed = create(main_arch + 'TrainFeed') + else: + train_feed = create(cfg.train_feed) + + if 'eval_feed' not in cfg: + eval_feed = create(main_arch + 'EvalFeed') + else: + eval_feed = create(cfg.eval_feed) + + place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() + exe = fluid.Executor(place) + + lr_builder = create('LearningRate') + optim_builder = create('OptimizerBuilder') + + # build program + model = create(main_arch) + train_loader, train_feed_vars = create_feed(train_feed, iterable=True) + train_fetches = model.train(train_feed_vars) + loss = train_fetches['loss'] + lr = lr_builder() + opt = optim_builder(lr) + opt.minimize(loss) + #for v in fluid.default_main_program().list_vars(): + # if "py_reader" not in v.name and "double_buffer" not in v.name and "generated_var" not in v.name: + # print(v.name, v.shape) + + cfg.max_iters = 258 + train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir) + train_loader.set_sample_list_generator(train_reader, place) + + exe.run(fluid.default_startup_program()) + + # parse train fetches + train_keys, train_values, _ = parse_fetches(train_fetches) + train_keys.append('lr') + train_values.append(lr.name) + + train_fetch_list = [] + for k, v in zip(train_keys, train_values): + train_fetch_list.append((k, v)) + print("train_fetch_list: {}".format(train_fetch_list)) + + eval_prog = fluid.Program() + startup_prog = fluid.Program() + with fluid.program_guard(eval_prog, startup_prog): + with fluid.unique_name.guard(): + model = create(main_arch) + _, test_feed_vars = create_feed(eval_feed, iterable=True) + fetches = model.eval(test_feed_vars) + eval_prog = eval_prog.clone(True) + + eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir) + test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place) + + # parse eval fetches + extra_keys = [] + if cfg.metric == 'COCO': + extra_keys = ['im_info', 'im_id', 'im_shape'] + if cfg.metric == 'VOC': + extra_keys = ['gt_box', 'gt_label', 'is_difficult'] + eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog, + extra_keys) + + eval_fetch_list = [] + for k, v in zip(eval_keys, eval_values): + eval_fetch_list.append((k, v)) + print("eval_fetch_list: {}".format(eval_fetch_list)) + + exe.run(startup_prog) + checkpoint.load_params(exe, + fluid.default_main_program(), cfg.pretrain_weights) + + best_box_ap_list = [] + + def eval_func(program, scope): + results = eval_run(exe, program, eval_reader, eval_keys, eval_values, + eval_cls, test_data_feed) + + resolution = None + is_bbox_normalized = False + if 'mask' in results[0]: + resolution = model.mask_head.resolution + box_ap_stats = eval_results(results, eval_feed, cfg.metric, + cfg.num_classes, resolution, + is_bbox_normalized, FLAGS.output_eval) + if len(best_box_ap_list) == 0: + best_box_ap_list.append(box_ap_stats[0]) + elif box_ap_stats[0] > best_box_ap_list[0]: + best_box_ap_list[0] = box_ap_stats[0] + logger.info("Best test box ap: {}".format(best_box_ap_list[0])) + return best_box_ap_list[0] + + test_feed = [('image', test_feed_vars['image'].name), + ('im_size', test_feed_vars['im_size'].name)] + + teacher_cfg = load_config(FLAGS.teacher_config) + teacher_arch = teacher_cfg.architecture + teacher_programs = [] + teacher_program = fluid.Program() + teacher_startup_program = fluid.Program() + with fluid.program_guard(teacher_program, teacher_startup_program): + with fluid.unique_name.guard('teacher_'): + teacher_feed_vars = OrderedDict() + for name, var in train_feed_vars.items(): + teacher_feed_vars[name] = teacher_program.global_block( + )._clone_variable( + var, force_persistable=False) + model = create(teacher_arch) + train_fetches = model.train(teacher_feed_vars) + #print("="*50+"teacher_model_params"+"="*50) + #for v in teacher_program.list_vars(): + # print(v.name, v.shape) + #return + + exe.run(teacher_startup_program) + assert FLAGS.teacher_pretrained and os.path.exists( + FLAGS.teacher_pretrained + ), "teacher_pretrained should be set when teacher_model is not None." + + def if_exist(var): + return os.path.exists(os.path.join(FLAGS.teacher_pretrained, var.name)) + + fluid.io.load_vars( + exe, + FLAGS.teacher_pretrained, + main_program=teacher_program, + predicate=if_exist) + + teacher_programs.append(teacher_program.clone(for_test=True)) + + com = Compressor( + place, + fluid.global_scope(), + fluid.default_main_program(), + train_reader=train_reader, + train_feed_list=[(key, value.name) + for key, value in train_feed_vars.items()], + train_fetch_list=train_fetch_list, + eval_program=eval_prog, + eval_reader=eval_reader, + eval_feed_list=test_feed, + eval_func={'map': eval_func}, + eval_fetch_list=eval_fetch_list[0:1], + save_eval_model=True, + prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]], + teacher_programs=teacher_programs, + train_optimizer=None, + distiller_optimizer=opt, + log_period=20) + com.config(FLAGS.slim_file) + com.run() + + +if __name__ == '__main__': + parser = ArgsParser() + parser.add_argument( + "-t", + "--teacher_config", + default=None, + type=str, + help="Config file of teacher architecture.") + parser.add_argument( + "-s", + "--slim_file", + default=None, + type=str, + help="Config file of PaddleSlim.") + parser.add_argument( + "-r", + "--resume_checkpoint", + default=None, + type=str, + help="Checkpoint path for resuming training.") + parser.add_argument( + "--eval", + action='store_true', + default=False, + help="Whether to perform evaluation in train") + parser.add_argument( + "--teacher_pretrained", + default=None, + type=str, + help="Whether to use pretrained model.") + parser.add_argument( + "--output_eval", + default=None, + type=str, + help="Evaluation directory, default is current directory.") + parser.add_argument( + "-d", + "--dataset_dir", + default=None, + type=str, + help="Dataset path, same as DataFeed.dataset.dataset_dir") + FLAGS = parser.parse_args() + main() diff --git a/slim/distillation/run.sh b/slim/distillation/run.sh new file mode 100644 index 000000000..a5497bdce --- /dev/null +++ b/slim/distillation/run.sh @@ -0,0 +1,47 @@ +#!/usr/bin/env bash + +# download pretrain model +root_url="https://paddlemodels.bj.bcebos.com/object_detection" +yolov3_r34_voc="yolov3_r34_voc.tar" +pretrain_dir='./pretrain' + +if [ ! -d ${pretrain_dir} ]; then + mkdir ${pretrain_dir} +fi + +cd ${pretrain_dir} + +if [ ! -f ${yolov3_r34_voc} ]; then + wget ${root_url}/${yolov3_r34_voc} + tar xf ${yolov3_r34_voc} +fi +cd - + +# enable GC strategy +export FLAGS_fast_eager_deletion_mode=1 +export FLAGS_eager_delete_tensor_gb=0.0 + +# for distillation +#----------------- +export CUDA_VISIBLE_DEVICES=0,1,2,3 + + +# Fixing name conflicts in distillation +cd ${pretrain_dir}/yolov3_r34_voc +for files in $(ls teacher_*) + do mv $files ${files#*_} +done +for files in $(ls *) + do mv $files "teacher_"$files +done +cd - + +python -u compress.py \ +-c ../../configs/yolov3_mobilenet_v1_voc.yml \ +-t yolov3_resnet34.yml \ +-s yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml \ +-o YoloTrainFeed.batch_size=64 \ +-d ../../dataset/voc \ +--teacher_pretrained ./pretrain/yolov3_r34_voc \ +> yolov3_distallation.log 2>&1 & +tailf yolov3_distallation.log diff --git a/slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml b/slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml new file mode 100644 index 000000000..6a2a5a257 --- /dev/null +++ b/slim/distillation/yolov3_mobilenet_v1_yolov3_resnet34_distillation.yml @@ -0,0 +1,18 @@ +version: 1.0 +distillers: + l2_distiller: + class: 'L2Distiller' + teacher_feature_map: 'teacher_teacher_conv2d_1.tmp_0' + student_feature_map: 'conv2d_15.tmp_0' + distillation_loss_weight: 1 +strategies: + distillation_strategy: + class: 'DistillationStrategy' + distillers: ['l2_distiller'] + start_epoch: 0 + end_epoch: 270 +compressor: + epoch: 271 + checkpoint_path: './checkpoints/' + strategies: + - distillation_strategy diff --git a/slim/distillation/yolov3_resnet34.yml b/slim/distillation/yolov3_resnet34.yml new file mode 100644 index 000000000..c04bdde9f --- /dev/null +++ b/slim/distillation/yolov3_resnet34.yml @@ -0,0 +1,34 @@ +architecture: YOLOv3 +log_smooth_window: 20 +metric: VOC +map_type: 11point +num_classes: 20 +weight_prefix_name: teacher_ + +YOLOv3: + backbone: ResNet + yolo_head: YOLOv3Head + +ResNet: + norm_type: sync_bn + freeze_at: 0 + freeze_norm: false + norm_decay: 0. + depth: 34 + feature_maps: [3, 4, 5] + +YOLOv3Head: + anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] + anchors: [[10, 13], [16, 30], [33, 23], + [30, 61], [62, 45], [59, 119], + [116, 90], [156, 198], [373, 326]] + norm_decay: 0. + ignore_thresh: 0.7 + label_smooth: false + nms: + background_label: -1 + keep_top_k: 100 + nms_threshold: 0.45 + nms_top_k: 1000 + normalized: false + score_threshold: 0.01 diff --git a/slim/infer.py b/slim/infer.py index 4a212ad6b..a5c00de98 100644 --- a/slim/infer.py +++ b/slim/infer.py @@ -19,11 +19,13 @@ from __future__ import print_function import os import sys import glob +import time import numpy as np from PIL import Image sys.path.append("../../") + def set_paddle_flags(**kwargs): for key, value in kwargs.items(): if os.environ.get(key, None) is None: @@ -117,20 +119,19 @@ def main(): test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img) test_feed.dataset.add_images(test_images) - place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) - infer_prog, feed_var_names, fetch_list = fluid.io.load_inference_model( - dirname=FLAGS.model_path, model_filename=FLAGS.model_name, - params_filename=FLAGS.params_name, - executor=exe) + dirname=FLAGS.model_path, + model_filename=FLAGS.model_name, + params_filename=FLAGS.params_name, + executor=exe) reader = create_reader(test_feed) - feeder = fluid.DataFeeder(place=place, feed_list=feed_var_names, - program=infer_prog) + feeder = fluid.DataFeeder( + place=place, feed_list=feed_var_names, program=infer_prog) # parse infer fetches assert cfg.metric in ['COCO', 'VOC'], \ @@ -140,7 +141,9 @@ def main(): extra_keys = ['im_info', 'im_id', 'im_shape'] if cfg['metric'] == 'VOC': extra_keys = ['im_id', 'im_shape'] - keys, values, _ = parse_fetches({'bbox':fetch_list}, infer_prog, extra_keys) + keys, values, _ = parse_fetches({ + 'bbox': fetch_list + }, infer_prog, extra_keys) # parse dataset category if cfg.metric == 'COCO': @@ -166,9 +169,33 @@ def main(): imid2path = reader.imid2path keys = ['bbox'] + infer_time = True + compile_prog = fluid.compiler.CompiledProgram(infer_prog) + for iter_id, data in enumerate(reader()): feed_data = [[d[0], d[1]] for d in data] - outs = exe.run(infer_prog, + # for infer time + if infer_time: + warmup_times = 10 + repeats_time = 100 + feed_data_dict = feeder.feed(feed_data) + for i in range(warmup_times): + exe.run(compile_prog, + feed=feed_data_dict, + fetch_list=fetch_list, + return_numpy=False) + start_time = time.time() + for i in range(repeats_time): + exe.run(compile_prog, + feed=feed_data_dict, + fetch_list=fetch_list, + return_numpy=False) + + print("infer time: {} ms/sample".format((time.time() - start_time) * + 1000 / repeats_time)) + infer_time = False + + outs = exe.run(compile_prog, feed=feeder.feed(feed_data), fetch_list=fetch_list, return_numpy=False) @@ -258,10 +285,7 @@ if __name__ == '__main__': default="tb_log_dir/image", help='Tensorboard logging directory for image.') parser.add_argument( - '--model_path', - type=str, - default=None, - help="inference model path") + '--model_path', type=str, default=None, help="inference model path") parser.add_argument( '--model_name', type=str, diff --git a/slim/prune/README.md b/slim/prune/README.md index b883cd09e..b06fdd2bd 100644 --- a/slim/prune/README.md +++ b/slim/prune/README.md @@ -7,7 +7,8 @@ 该示例使用PaddleSlim提供的[卷积通道剪裁压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#2-%E5%8D%B7%E7%A7%AF%E6%A0%B8%E5%89%AA%E8%A3%81%E5%8E%9F%E7%90%86)对检测库中的模型进行压缩。 在阅读该示例前，建议您先了解以下内容： -- 检测库的常规训练方法 +- 检测库的常规训练方法 +- [检测模型数据准备](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/INSTALL_cn.md#%E6%95%B0%E6%8D%AE%E9%9B%86) - [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md) @@ -29,7 +30,7 @@ from paddle.fluid.framework import IrGraph from paddle.fluid import core graph = IrGraph(core.Graph(train_prog.desc), for_test=True) -marked_nodes = set() +marked_nodes = set() for op in graph.all_op_nodes(): print(op.name()) if op.name().find('conv') > -1: @@ -39,12 +40,12 @@ graph.draw('.', 'forward', marked_nodes) 该示例中MobileNetV1-YoloV3模型结构的可视化结果：MobileNetV1-YoloV3.pdf -同时通过以下命令观察目标卷积层的参数（parameters）的名称和shape: +同时通过以下命令观察目标卷积层的参数（parameters）的名称和shape: ``` for param in fluid.default_main_program().global_block().all_parameters(): if 'weights' in param.name: - print param.name, param.shape + print(param.name, param.shape) ``` @@ -109,6 +110,7 @@ python compress.py \ -s yolov3_mobilenet_v1_slim.yaml \ -c ../../configs/yolov3_mobilenet_v1_voc.yml \ -o max_iters=258 \ + YoloTrainFeed.batch_size=64 \ -d "../../dataset/voc" ``` @@ -117,9 +119,9 @@ python compress.py \ 如果要调整训练卡数，需要调整配置文件`yolov3_mobilenet_v1_voc.yml`中的以下参数： - **max_iters:** 一个`epoch`中batch的数量，需要设置为`total_num / batch_size`, 其中`total_num`为训练样本总数量，`batch_size`为多卡上总的batch size. -- **YoloTrainFeed.batch_size:** 单张卡上的batch size, 受限于显存大小。 +- **YoloTrainFeed.batch_size:** 当使用DataLoader时，表示单张卡上的batch size; 当使用普通reader时，则表示多卡上的总的`batch_size`。`batch_size`受限于显存大小。 - **LeaningRate.base_lr:** 根据多卡的总`batch_size`调整`base_lr`，两者大小正相关，可以简单的按比例进行调整。 -- **LearningRate.schedulers.PiecewiseDecay.milestones：**请根据batch size的变化对其调整。 +- **LearningRate.schedulers.PiecewiseDecay.milestones：** 请根据batch size的变化对其调整。 - **LearningRate.schedulers.PiecewiseDecay.LinearWarmup.steps：** 请根据batch size的变化对其进行调整。 @@ -130,7 +132,7 @@ python compress.py \ -s yolov3_mobilenet_v1_slim.yaml \ -c ../../configs/yolov3_mobilenet_v1_voc.yml \ -o max_iters=258 \ - -o YoloTrainFeed.batch_size = 16 \ + YoloTrainFeed.batch_size=64 \ -d "../../dataset/voc" ``` @@ -140,9 +142,9 @@ python compress.py \ -s yolov3_mobilenet_v1_slim.yaml \ -c ../../configs/yolov3_mobilenet_v1_voc.yml \ -o max_iters=516 \ - -o LeaningRate.base_lr=0.005 \ # 0.001 /2 - -o YoloTrainFeed.batch_size = 16 \ - -o LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [110000, 124000]}, !LinearWarmup {start_factor: 0., steps: 2000}]' \ + LeaningRate.base_lr=0.005 \ + YoloTrainFeed.batch_size=32 \ + LearningRate.schedulers='[!PiecewiseDecay {gamma: 0.1, milestones: [110000, 124000]}, !LinearWarmup {start_factor: 0., steps: 2000}]' \ -d "../../dataset/voc" ``` @@ -166,6 +168,16 @@ python compress.py \ 如果不需要保存评估模型，可以在定义Compressor对象时，将`save_eval_model`选项设置为False（默认为True）。 +运行命令为： +``` +python ../eval.py \ + --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \ + --model_name __model__ \ + --params_name __params__ \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" +``` + ## 预测 如果在配置文件中设置了`checkpoint_path`，并且在定义Compressor对象时指定了`prune_infer_model`选项，则每个epoch都会 @@ -180,6 +192,16 @@ python compress.py \ 在脚本PaddleDetection/tools/infer.py中展示了如何使用fluid python API加载使用预测模型进行预测。 +运行命令为： +``` +python ../infer.py \ + --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \ + --model_name __model__.infer \ + --params_name __params__ \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + --infer_dir ../../demo +``` + ### PaddleLite 该示例中产出的预测（inference）模型可以直接用PaddleLite进行加载使用。 @@ -187,13 +209,13 @@ python compress.py \ ## 示例结果 +> 当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。 + ### MobileNetV1-YOLO-V3 -| FLOPS |top1_acc/top5_acc| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)| +| FLOPS |Box AP| model_size |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)| |---|---|---|---|---| -|baseline|- |- |- |-| -|-10%|- |- |- |-| -|-30%|- |- |- |-| -|-50%|- |- |- |-| +|baseline|76.2 |93M |- |-| +|-50%|69.48 |51M |- |-| ## FAQ diff --git a/slim/prune/compress.py b/slim/prune/compress.py index 2d012e46f..38dc32d82 100644 --- a/slim/prune/compress.py +++ b/slim/prune/compress.py @@ -24,11 +24,13 @@ import sys sys.path.append("../../") from paddle.fluid.contrib.slim import Compressor + def set_paddle_flags(**kwargs): for key, value in kwargs.items(): if os.environ.get(key, None) is None: os.environ[key] = str(value) + # NOTE(paddle-dev): All of these flags should be set before # `import paddle`. Otherwise, it would not take any effect. set_paddle_flags( @@ -48,6 +50,8 @@ import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) + + def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): """ Run evaluation program, return program outputs. @@ -66,8 +70,7 @@ def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): has_bbox = 'bbox' in keys for data in reader(): data = test_feed.feed(data) - feed_data = {'image': data['image'], - 'im_size': data['im_size']} + feed_data = {'image': data['image'], 'im_size': data['im_size']} outs = exe.run(compile_program, feed=feed_data, fetch_list=[values[0]], @@ -147,9 +150,7 @@ def main(): optimizer = optim_builder(lr) optimizer.minimize(loss) - - train_reader = create_reader(train_feed, cfg.max_iters * devices_num, - FLAGS.dataset_dir) + train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir) train_loader.set_sample_list_generator(train_reader, place) # parse train fetches @@ -157,7 +158,7 @@ def main(): train_keys.append("lr") train_values.append(lr.name) - train_fetch_list=[] + train_fetch_list = [] for k, v in zip(train_keys, train_values): train_fetch_list.append((k, v)) @@ -181,8 +182,8 @@ def main(): if cfg.metric == 'VOC': extra_keys = ['gt_box', 'gt_label', 'is_difficult'] eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog, - extra_keys) - eval_fetch_list=[] + extra_keys) + eval_fetch_list = [] for k, v in zip(eval_keys, eval_values): eval_fetch_list.append((k, v)) @@ -195,21 +196,20 @@ def main(): #place = fluid.CPUPlace() #exe = fluid.Executor(place) - results = eval_run(exe, program, eval_reader, - eval_keys, eval_values, eval_cls, test_data_feed) + results = eval_run(exe, program, eval_reader, eval_keys, eval_values, + eval_cls, test_data_feed) resolution = None if 'mask' in results[0]: resolution = model.mask_head.resolution - box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes, - resolution, False, FLAGS.output_eval) + box_ap_stats = eval_results(results, eval_feed, cfg.metric, + cfg.num_classes, resolution, False, + FLAGS.output_eval) if len(best_box_ap_list) == 0: best_box_ap_list.append(box_ap_stats[0]) elif box_ap_stats[0] > best_box_ap_list[0]: best_box_ap_list[0] = box_ap_stats[0] - checkpoint.save(exe, train_prog, os.path.join(save_dir,"best_model")) - logger.info("Best test box ap: {}".format( - best_box_ap_list[0])) + logger.info("Best test box ap: {}".format(best_box_ap_list[0])) return best_box_ap_list[0] test_feed = [('image', test_feed_vars['image'].name), @@ -228,13 +228,12 @@ def main(): eval_func={'map': eval_func}, eval_fetch_list=[eval_fetch_list[0]], save_eval_model=True, - prune_infer_model=[["image", "im_size"],["multiclass_nms_0.tmp_0"]], + prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]], train_optimizer=None) com.config(FLAGS.slim_file) com.run() - if __name__ == '__main__': parser = ArgsParser() parser.add_argument( diff --git a/slim/quantization/README.md b/slim/quantization/README.md index 83b8667e3..acb4c9efc 100644 --- a/slim/quantization/README.md +++ b/slim/quantization/README.md @@ -37,28 +37,76 @@ - config: 检测库的配置，其中配置了训练超参数、数据集信息等。 - slim_file: PaddleSlim的配置文件，参见[配置文件说明](#配置文件说明)。 -您可以通过运行以下命令运行该示例，请确保已正确下载[pretrained model](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD)。 +您可以通过运行以下命令运行该示例。 -step1: 开启显存优化策略 +step1: 设置gpu卡 ``` -export FLAGS_fast_eager_deletion_mode=1 -export FLAGS_eager_delete_tensor_gb=0.0 +export CUDA_VISIBLE_DEVICES=0 ``` -step2: 设置gpu卡,目前的超参设置适合2卡训练 +step2: 开始训练 +使用PaddleDetection提供的配置文件在用8卡进行训练： + +``` +python compress.py \ + -s yolov3_mobilenet_v1_slim.yaml \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" \ + -o max_iters=258 \ + LearningRate.base_lr=0.0001 \ + LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [258, 516]}]" \ + pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar \ + YoloTrainFeed.batch_size=64 +``` + +>通过命令行覆盖设置max_iters选项，因为PaddleDetection中训练是以`batch`为单位迭代的，并没有涉及`epoch`的概念，但是PaddleSlim需要知道当前训练进行到第几个`epoch`, 所以需要将`max_iters`设置为一个`epoch`内的`batch`的数量。 + +如果要调整训练卡数，需要调整配置文件`yolov3_mobilenet_v1_voc.yml`中的以下参数： + +- **max_iters:** 一个`epoch`中batch的数量，需要设置为`total_num / batch_size`, 其中`total_num`为训练样本总数量，`batch_size`为多卡上总的batch size. +- **YoloTrainFeed.batch_size:** 当使用DataLoader时，表示单张卡上的batch size; 当使用普通reader时，则表示多卡上的总的batch_size。batch_size受限于显存大小。 +- **LeaningRate.base_lr:** 根据多卡的总`batch_size`调整`base_lr`，两者大小正相关，可以简单的按比例进行调整。 +- **LearningRate.schedulers.PiecewiseDecay.milestones：** 请根据batch size的变化对其调整。 +- **LearningRate.schedulers.PiecewiseDecay.LinearWarmup.steps：** 请根据batch size的变化对其进行调整。 + + +以下为4卡训练示例，通过命令行覆盖`yolov3_mobilenet_v1_voc.yml`中的参数： + ``` -export CUDA_VISIBLE_DEVICES=0,1 +python compress.py \ + -s yolov3_mobilenet_v1_slim.yaml \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" \ + -o max_iters=258 \ + LearningRate.base_lr=0.0001 \ + LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [258, 516]}]" \ + pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar \ + YoloTrainFeed.batch_size=64 + ``` -step3: 开始训练 + +以下为2卡训练示例，受显存所制，单卡`batch_size`不变, 总`batch_size`减小，`base_lr`减小，一个epoch内batch数量增加，同时需要调整学习率相关参数，如下： + ``` python compress.py \ -s yolov3_mobilenet_v1_slim.yaml \ - -c yolov3_mobilenet_v1_voc.yml + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" \ + -o max_iters=516 \ + LearningRate.base_lr=0.00005 \ + LearningRate.schedulers="[!PiecewiseDecay {gamma: 0.1, milestones: [516, 1012]}]" \ + pretrain_weights=https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar \ + YoloTrainFeed.batch_size=32 ``` +通过`python compress.py --help`查看可配置参数。 +通过`python ../../tools/configure.py ${option_name} help`查看如何通过命令行覆盖配置文件`yolov3_mobilenet_v1_voc.yml`中的参数。 + + + ### 训练时的模型结构 这部分介绍来源于[量化low-level API介绍](https://github.com/PaddlePaddle/models/tree/develop/PaddleSlim/quant_low_level_api#1-%E9%87%8F%E5%8C%96%E8%AE%AD%E7%BB%83low-level-apis%E4%BB%8B%E7%BB%8D)。 -PaddlePaddle框架中有四个和量化相关的IrPass, 分别是QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass以及TransformForMobilePass。在训练时，对网络应用了QuantizationTransformPass，作用是在网络中的conv2d、depthwise_conv2d、mul等算子的各个输入前插入连续的量化op和反量化op，并改变相应反向算子的某些输入。示例图如下： +PaddlePaddle框架中和量化相关的IrPass, 分别有QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass。在训练时，对网络应用了QuantizationTransformPass，作用是在网络中的conv2d、depthwise_conv2d、mul等算子的各个输入前插入连续的量化op和反量化op，并改变相应反向算子的某些输入。示例图如下：


@@ -76,10 +124,10 @@ PaddlePaddle框架中有四个和量化相关的IrPass, 分别是QuantizationTra ### 保存评估和预测模型 -如果在配置文件的量化策略中设置了`float_model_save_path`, `int8_model_save_path`, `mobile_model_save_path`, 在训练结束后，会保存模型量化压缩之后用于预测的模型。接下来介绍这三种预测模型的区别。 +如果在配置文件的量化策略中设置了`float_model_save_path`, `int8_model_save_path` 在训练结束后，会保存模型量化压缩之后用于预测的模型。接下来介绍这2种预测模型的区别。 #### FP32模型 -在介绍量化训练时的模型结构时介绍了PaddlePaddle框架中有四个和量化相关的IrPass, 分别是QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass以及TransformForMobilePass。FP32模型是在应用QuantizationFreezePass并删除eval_program中多余的operators之后，保存的模型。 +在介绍量化训练时的模型结构时介绍了PaddlePaddle框架中和量化相关的IrPass, 分别是QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass。FP32模型是在应用QuantizationFreezePass并删除eval_program中多余的operators之后，保存的模型。 QuantizationFreezePass主要用于改变IrGraph中量化op和反量化op的顺序，即将类似图1中的量化op和反量化op顺序改变为图2中的布局。除此之外，QuantizationFreezePass还会将`conv2d`、`depthwise_conv2d`、`mul`等算子的权重离线量化为int8_t范围内的值(但数据类型仍为float32)，以减少预测过程中对权重的量化操作，示例如图2： @@ -97,19 +145,13 @@ QuantizationFreezePass主要用于改变IrGraph中量化op和反量化op的顺 图3：应用ConvertToInt8Pass后的结果

-#### mobile模型 -经TransformForMobilePass转换后，用户可得到兼容[paddle-lite](https://github.com/PaddlePaddle/Paddle-Lite)移动端预测库的量化模型。paddle-mobile中的量化op和反量化op的名称分别为`quantize`和`dequantize`。`quantize`算子和PaddlePaddle框架中的`fake_quantize_abs_max`算子簇的功能类似，`dequantize` 算子和PaddlePaddle框架中的`fake_dequantize_max_abs`算子簇的功能相同。若选择paddle-mobile执行量化训练输出的模型，则需要将`fake_quantize_abs_max`等算子改为`quantize`算子以及将`fake_dequantize_max_abs`等算子改为`dequantize`算子，示例如图4： - -

-
-图4：应用TransformForMobilePass后的结果 -

> 综上，可得在量化过程中有以下几种模型结构： + 1. 原始模型 2. 经QuantizationTransformPass之后得到的适用于训练的量化模型结构，在${checkpoint_path}下保存的`eval_model`是这种结构，在训练过程中每个epoch结束时也使用这个网络结构进行评估，虽然这个模型结构不是最终想要的模型结构，但是每个epoch的评估结果可用来挑选模型。 3. 经QuantizationFreezePass之后得到的FP32模型结构，具体结构已在上面进行介绍。本文档中列出的数据集的评估结果是对FP32模型结构进行评估得到的结果。这种模型结构在训练过程中只会保存一次，也就是在量化配置文件中设置的`end_epoch`结束时进行保存，如果想将其他epoch的训练结果转化成FP32模型，可使用脚本 PaddleSlim/classification/quantization/freeze.py进行转化，具体使用方法在[评估](#评估)中介绍。 4. 经ConvertToInt8Pass之后得到的8-bit模型结构，具体结构已在上面进行介绍。这种模型结构在训练过程中只会保存一次，也就是在量化配置文件中设置的`end_epoch`结束时进行保存，如果想将其他epoch的训练结果转化成8-bit模型，可使用脚本 slim/quantization/freeze.py进行转化，具体使用方法在[评估](#评估)中介绍。 -5. 经TransformForMobilePass之后得到的mobile模型结构，具体结构已在上面进行介绍。这种模型结构在训练过程中只会保存一次，也就是在量化配置文件中设置的`end_epoch`结束时进行保存，如果想将其他epoch的训练结果转化成mobile模型，可使用脚本 slim/quantization/freeze.py进行转化，具体使用方法在[评估](#评估)中介绍。 + ## 评估 @@ -128,21 +170,24 @@ python ../eval.py \ --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \ --model_name __model__ \ --params_name __params__ \ - -c yolov3_mobilenet_v1_voc.yml + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" ``` -在评估之后，选取效果最好的epoch的模型，可使用脚本 slim/quantization/freeze.py将该模型转化为以上介绍的三种模型：FP32模型，int8模型，mobile模型，需要配置的参数为： +在评估之后，选取效果最好的epoch的模型，可使用脚本 slim/quantization/freeze.py将该模型转化为以上介绍的2种模型：FP32模型，int8模型，需要配置的参数为： - model_path, 加载的模型路径，`为${checkpoint_path}/${epoch_id}/eval_model/` - weight_quant_type 模型参数的量化方式，和配置文件中的类型保持一致 -- save_path `FP32`, `8-bit`, `mobile`模型的保存路径，分别为 `${save_path}/float/`, `${save_path}/int8/`, `${save_path}/mobile/` +- save_path `FP32`, `8-bit` 模型的保存路径，分别为 `${save_path}/float/`, `${save_path}/int8/` 运行命令示例： ``` python freeze.py \ --model_path ${checkpoint_path}/${epoch_id}/eval_model/ \ --weight_quant_type ${weight_quant_type} \ - --save_path ${any path you want} + --save_path ${any path you want} \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" ``` ### 最终评估模型 @@ -150,10 +195,11 @@ python freeze.py \ 运行命令为： ``` python ../eval.py \ - --model_path ${float_model_path} + --model_path ${float_model_path} --model_name model \ --params_name weights \ - -c yolov3_mobilenet_v1_voc.yml + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ + -d "../../dataset/voc" ``` ## 预测 @@ -169,7 +215,7 @@ python ../infer.py \ --model_path ${save_path}/float \ --model_name model \ --params_name weights \ - -c yolov3_mobilenet_v1_voc.yml \ + -c ../../configs/yolov3_mobilenet_v1_voc.yml \ --infer_dir ../../demo ``` @@ -180,7 +226,9 @@ FP32模型可使用PaddleLite进行加载预测，可参见教程[Paddle-Lite如 ## 示例结果 -### MobileNetV1 +>当前release的结果并非超参调优后的最好结果，仅做示例参考，后续我们会优化当前结果。 + +### MobileNetV1-YOLO-V3 | weight量化方式 | activation量化方式| Box ap |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)| |---|---|---|---|---| @@ -189,9 +237,5 @@ FP32模型可使用PaddleLite进行加载预测，可参见教程[Paddle-Lite如 |abs_max|moving_average_abs_max|- |- |-| |channel_wise_abs_max|abs_max|- |- |-| ->训练超参： - ## FAQ - - diff --git a/slim/quantization/compress.py b/slim/quantization/compress.py index a520fc71d..40aed6d72 100644 --- a/slim/quantization/compress.py +++ b/slim/quantization/compress.py @@ -28,11 +28,13 @@ from paddle.fluid.contrib.slim import Compressor from paddle.fluid.framework import IrGraph from paddle.fluid import core + def set_paddle_flags(**kwargs): for key, value in kwargs.items(): if os.environ.get(key, None) is None: os.environ[key] = str(value) + # NOTE(paddle-dev): All of these flags should be set before # `import paddle`. Otherwise, it would not take any effect. set_paddle_flags( @@ -46,7 +48,7 @@ from ppdet.data.data_feed import create_reader from ppdet.utils.eval_utils import parse_fetches, eval_results from ppdet.utils.stats import TrainingStats -from ppdet.utils.cli import ArgsParser +from ppdet.utils.cli import ArgsParser, print_total_cfg from ppdet.utils.check import check_gpu, check_version import ppdet.utils.checkpoint as checkpoint from ppdet.modeling.model_input import create_feed @@ -55,6 +57,8 @@ import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) + + def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): """ Run evaluation program, return program outputs. @@ -73,11 +77,10 @@ def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): has_bbox = 'bbox' in keys for data in reader(): data = test_feed.feed(data) - feed_data = {'image': data['image'], - 'im_size': data['im_size']} + feed_data = {'image': data['image'], 'im_size': data['im_size']} outs = exe.run(compile_program, feed=feed_data, - fetch_list=values[0], + fetch_list=[values[0]], return_numpy=False) outs.append(data['gt_box']) outs.append(data['gt_label']) @@ -118,8 +121,8 @@ def main(): # check if set use_gpu=True in paddlepaddle cpu version check_gpu(cfg.use_gpu) + # print_total_cfg(cfg) #check_version() - if cfg.use_gpu: devices_num = fluid.core.get_cuda_device_count() else: @@ -155,16 +158,14 @@ def main(): optimizer = optim_builder(lr) optimizer.minimize(loss) - - train_reader = create_reader(train_feed, cfg.max_iters * devices_num, - FLAGS.dataset_dir) + train_reader = create_reader(train_feed, cfg.max_iters, FLAGS.dataset_dir) train_loader.set_sample_list_generator(train_reader, place) # parse train fetches train_keys, train_values, _ = parse_fetches(train_fetches) train_values.append(lr) - train_fetch_list=[] + train_fetch_list = [] for k, v in zip(train_keys, train_values): train_fetch_list.append((k, v)) print("train_fetch_list: {}".format(train_fetch_list)) @@ -188,14 +189,13 @@ def main(): if cfg.metric == 'VOC': extra_keys = ['gt_box', 'gt_label', 'is_difficult'] eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog, - extra_keys) + extra_keys) # print(eval_values) - eval_fetch_list=[] + eval_fetch_list = [] for k, v in zip(eval_keys, eval_values): eval_fetch_list.append((k, v)) - exe.run(startup_prog) start_iter = 0 @@ -208,21 +208,20 @@ def main(): #place = fluid.CPUPlace() #exe = fluid.Executor(place) - results = eval_run(exe, program, eval_reader, - eval_keys, eval_values, eval_cls, test_data_feed) + results = eval_run(exe, program, eval_reader, eval_keys, eval_values, + eval_cls, test_data_feed) resolution = None if 'mask' in results[0]: resolution = model.mask_head.resolution - box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes, - resolution, False, FLAGS.output_eval) + box_ap_stats = eval_results(results, eval_feed, cfg.metric, + cfg.num_classes, resolution, False, + FLAGS.output_eval) if len(best_box_ap_list) == 0: best_box_ap_list.append(box_ap_stats[0]) elif box_ap_stats[0] > best_box_ap_list[0]: best_box_ap_list[0] = box_ap_stats[0] - checkpoint.save(exe, train_prog, os.path.join(save_dir,"best_model")) - logger.info("Best test box ap: {}".format( - best_box_ap_list[0])) + logger.info("Best test box ap: {}".format(best_box_ap_list[0])) return best_box_ap_list[0] test_feed = [('image', test_feed_vars['image'].name), @@ -240,12 +239,12 @@ def main(): eval_feed_list=test_feed, eval_func={'map': eval_func}, eval_fetch_list=[eval_fetch_list[0]], + prune_infer_model=[["image", "im_size"], ["multiclass_nms_0.tmp_0"]], train_optimizer=None) com.config(FLAGS.slim_file) com.run() - if __name__ == '__main__': parser = ArgsParser() parser.add_argument( diff --git a/slim/quantization/freeze.py b/slim/quantization/freeze.py index 04271a4f2..de34d1d5f 100644 --- a/slim/quantization/freeze.py +++ b/slim/quantization/freeze.py @@ -32,11 +32,13 @@ from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass from paddle.fluid.contrib.slim.quantization import TransformForMobilePass + def set_paddle_flags(**kwargs): for key, value in kwargs.items(): if os.environ.get(key, None) is None: os.environ[key] = str(value) + # NOTE(paddle-dev): All of these flags should be set before # `import paddle`. Otherwise, it would not take any effect. set_paddle_flags( @@ -59,6 +61,8 @@ import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) + + def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): """ Run evaluation program, return program outputs. @@ -71,8 +75,7 @@ def eval_run(exe, compile_program, reader, keys, values, cls, test_feed): has_bbox = 'bbox' in keys for data in reader(): data = test_feed.feed(data) - feed_data = {'image': data['image'], - 'im_size': data['im_size']} + feed_data = {'image': data['image'], 'im_size': data['im_size']} outs = exe.run(compile_program, feed=feed_data, fetch_list=values[0], @@ -123,7 +126,6 @@ def main(): devices_num = int( os.environ.get('CPU_NUM', multiprocessing.cpu_count())) - if 'eval_feed' not in cfg: eval_feed = create(main_arch + 'EvalFeed') else: @@ -138,85 +140,79 @@ def main(): #eval_pyreader.decorate_sample_list_generator(eval_reader, place) test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place) - assert os.path.exists(FLAGS.model_path) infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model( - dirname=FLAGS.model_path, executor=exe, - model_filename='__model__', - params_filename='__params__') + dirname=FLAGS.model_path, + executor=exe, + model_filename='__model__.infer', + params_filename='__params__') eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult'] - eval_values = ['multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult'] + eval_values = [ + 'multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult' + ] eval_cls = [] eval_values[0] = fetch_targets[0] - results = eval_run(exe, infer_prog, eval_reader, - eval_keys, eval_values, eval_cls, test_data_feed) + results = eval_run(exe, infer_prog, eval_reader, eval_keys, eval_values, + eval_cls, test_data_feed) resolution = None if 'mask' in results[0]: resolution = model.mask_head.resolution box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes, - resolution, False, FLAGS.output_eval) + resolution, False, FLAGS.output_eval) logger.info("freeze the graph for inference") test_graph = IrGraph(core.Graph(infer_prog.desc), for_test=True) freeze_pass = QuantizationFreezePass( - scope=fluid.global_scope(), - place=place, - weight_quantize_type=FLAGS.weight_quant_type) + scope=fluid.global_scope(), + place=place, + weight_quantize_type=FLAGS.weight_quant_type) freeze_pass.apply(test_graph) server_program = test_graph.to_program() fluid.io.save_inference_model( - dirname=os.path.join(FLAGS.save_path, 'float'), - feeded_var_names=feed_names, - target_vars=fetch_targets, - executor=exe, - main_program=server_program, - model_filename='model', - params_filename='weights') + dirname=os.path.join(FLAGS.save_path, 'float'), + feeded_var_names=feed_names, + target_vars=fetch_targets, + executor=exe, + main_program=server_program, + model_filename='model', + params_filename='weights') logger.info("convert the weights into int8 type") convert_int8_pass = ConvertToInt8Pass( - scope=fluid.global_scope(), - place=place) + scope=fluid.global_scope(), place=place) convert_int8_pass.apply(test_graph) server_int8_program = test_graph.to_program() fluid.io.save_inference_model( - dirname=os.path.join(FLAGS.save_path, 'int8'), - feeded_var_names=feed_names, - target_vars=fetch_targets, - executor=exe, - main_program=server_int8_program, - model_filename='model', - params_filename='weights') + dirname=os.path.join(FLAGS.save_path, 'int8'), + feeded_var_names=feed_names, + target_vars=fetch_targets, + executor=exe, + main_program=server_int8_program, + model_filename='model', + params_filename='weights') logger.info("convert the freezed pass to paddle-lite execution") mobile_pass = TransformForMobilePass() mobile_pass.apply(test_graph) mobile_program = test_graph.to_program() fluid.io.save_inference_model( - dirname=os.path.join(FLAGS.save_path, 'mobile'), - feeded_var_names=feed_names, - target_vars=fetch_targets, - executor=exe, - main_program=mobile_program, - model_filename='model', - params_filename='weights') - - - + dirname=os.path.join(FLAGS.save_path, 'mobile'), + feeded_var_names=feed_names, + target_vars=fetch_targets, + executor=exe, + main_program=mobile_program, + model_filename='model', + params_filename='weights') if __name__ == '__main__': parser = ArgsParser() parser.add_argument( - "-m", - "--model_path", - default=None, - type=str, - help="path of checkpoint") + "-m", "--model_path", default=None, type=str, help="path of checkpoint") parser.add_argument( "--output_eval", default=None, diff --git a/slim/quantization/yolov3_mobilenet_v1_voc.yml b/slim/quantization/yolov3_mobilenet_v1_voc.yml deleted file mode 100644 index 8cc562371..000000000 --- a/slim/quantization/yolov3_mobilenet_v1_voc.yml +++ /dev/null @@ -1,87 +0,0 @@ -architecture: YOLOv3 -train_feed: YoloTrainFeed -eval_feed: YoloEvalFeed -test_feed: YoloTestFeed -use_gpu: true -max_iters: 1000 -log_smooth_window: 20 -save_dir: output -snapshot_iter: 2000 -metric: VOC -map_type: 11point -pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1_voc.tar -weights: output/yolov3_mobilenet_v1_voc/model_final -num_classes: 20 - -YOLOv3: - backbone: MobileNet - yolo_head: YOLOv3Head - -MobileNet: - norm_type: sync_bn - norm_decay: 0. - conv_group_scale: 1 - with_extra_blocks: false - -YOLOv3Head: - anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] - anchors: [[10, 13], [16, 30], [33, 23], - [30, 61], [62, 45], [59, 119], - [116, 90], [156, 198], [373, 326]] - norm_decay: 0. - ignore_thresh: 0.7 - label_smooth: false - nms: - background_label: -1 - keep_top_k: 100 - nms_threshold: 0.45 - nms_top_k: 1000 - normalized: false - score_threshold: 0.01 - -LearningRate: - base_lr: 0.0001 - schedulers: - - !PiecewiseDecay - gamma: 0.1 - milestones: - - 1000 - - 2000 -#- !LinearWarmup -#start_factor: 0. -#steps: 1000 - -OptimizerBuilder: - optimizer: - momentum: 0.9 - type: Momentum - regularizer: - factor: 0.0005 - type: L2 - -YoloTrainFeed: - batch_size: 8 - dataset: - dataset_dir: ../../dataset/voc - annotation: VOCdevkit/VOC_all/ImageSets/Main/train.txt - image_dir: VOCdevkit/VOC_all/JPEGImages - use_default_label: true - num_workers: 8 - bufsize: 128 - use_process: true - mixup_epoch: 250 - -YoloEvalFeed: - batch_size: 8 - image_shape: [3, 608, 608] - dataset: - dataset_dir: ../../dataset/voc - annotation: VOCdevkit/VOC_all/ImageSets/Main/val.txt - image_dir: VOCdevkit/VOC_all/JPEGImages - use_default_label: true - -YoloTestFeed: - batch_size: 1 - image_shape: [3, 608, 608] - dataset: - use_default_label: true -- GitLab