diff --git a/slim/MODEL_ZOO.md b/slim/MODEL_ZOO.md index 116b7de8075135e22de9245ce2a48bc9369f9038..146cf8d0fd651ead84e30fbd8e461f7cd67f5e87 100644 --- a/slim/MODEL_ZOO.md +++ b/slim/MODEL_ZOO.md @@ -37,6 +37,10 @@ | 骨架网络 | 裁剪策略 | 输入尺寸 | Box AP | 下载 | | :----------------| :-------: | :------: |:------: | :-----------------------------------------------------: | | ResNet50-vd-dcn | sensity | 320 | 39.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_r50_dcn_prune1x.tar) | +| ResNet50-vd-dcn | sensity | 320 | 38.3 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_r50_dcn_prune578.tar) | +| MobileNetV1 | sensity | 608 | 30.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune1x.tar) | +| MobileNetV1 | sensity | 416 | 29.7 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune1x.tar) | +| MobileNetV1 | sensity | 320 | 27.2 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune1x.tar) | | MobileNetV1 | r578 | 608 | 27.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune578.tar) | | MobileNetV1 | r578 | 416 | 26.8 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune578.tar) | | MobileNetV1 | r578 | 320 | 24.0 | [下载链接](https://paddlemodels.bj.bcebos.com/PaddleSlim/prune/yolov3_mobilenet_v1_prune578.tar) | diff --git a/slim/prune/README.md b/slim/prune/README.md index aa0a1725493bce1e9a1a9bc3b535688912379718..8ba9b7b43f7706efd2a865dbbbd7ee8c768de15f 100644 --- a/slim/prune/README.md +++ b/slim/prune/README.md @@ -58,7 +58,19 @@ python prune.py \ --pruned_ratios="0.2 0.3 0.4" ``` -## 5. 扩展模型 +## 5. 评估剪裁模型 + +训练剪裁任务完成后，可通过`eval.py`评估剪裁模型精度，通过`--pruned_params`和`--pruned_ratios`指定裁剪的参数名称列表和各参数裁剪比例。 + +``` +python eval.py \ +-c ../../configs/yolov3_mobilenet_v1_voc.yml \ +--pruned_params "yolo_block.0.0.0.conv.weights,yolo_block.0.0.1.conv.weights,yolo_block.0.1.0.conv.weights" \ +--pruned_ratios="0.2 0.3 0.4" \ +-o weights=output/yolov3_mobilenet_v1_voc/model_final +``` + +## 6. 扩展模型 如果需要对自己的模型进行修改，可以参考`prune.py`中对`paddleslim.prune.Pruner`接口的调用方式，基于自己的模型训练脚本进行修改。 本节我们介绍的剪裁示例，需要用户根据先验知识指定每层的剪裁率，除此之外，PaddleSlim还提供了敏感度分析等功能，协助用户选择合适的剪裁率。更多详情请参考：[PaddleSlim使用文档](https://paddlepaddle.github.io/PaddleSlim/) diff --git a/slim/prune/eval.py b/slim/prune/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..7d421a2775d31381305b139f2eff48a4b9bffb6f --- /dev/null +++ b/slim/prune/eval.py @@ -0,0 +1,229 @@ +# 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 + + +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 +) + +import paddle.fluid as fluid +from paddleslim.prune import Pruner +from paddleslim.analysis import flops + +from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results, json_eval_results +import ppdet.utils.checkpoint as checkpoint +from ppdet.utils.check import check_gpu, check_version + +from ppdet.data.reader import create_reader + +from ppdet.core.workspace import load_config, merge_config, create +from ppdet.utils.cli import ArgsParser + +import logging +FORMAT = '%(asctime)s-%(levelname)s: %(message)s' +logging.basicConfig(level=logging.INFO, format=FORMAT) +logger = logging.getLogger(__name__) + + +def main(): + """ + Main evaluate function + """ + 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) + # check if set use_gpu=True in paddlepaddle cpu version + check_gpu(cfg.use_gpu) + # check if paddlepaddle version is satisfied + check_version() + + multi_scale_test = getattr(cfg, 'MultiScaleTEST', None) + + # define executor + place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() + exe = fluid.Executor(place) + + # build program + model = create(main_arch) + startup_prog = fluid.Program() + eval_prog = fluid.Program() + with fluid.program_guard(eval_prog, startup_prog): + with fluid.unique_name.guard(): + inputs_def = cfg['EvalReader']['inputs_def'] + feed_vars, loader = model.build_inputs(**inputs_def) + if multi_scale_test is None: + fetches = model.eval(feed_vars) + else: + fetches = model.eval(feed_vars, multi_scale_test) + eval_prog = eval_prog.clone(True) + + reader = create_reader(cfg.EvalReader) + loader.set_sample_list_generator(reader, place) + + dataset = cfg['EvalReader']['dataset'] + + # eval already exists json file + if FLAGS.json_eval: + logger.info( + "In json_eval mode, PaddleDetection will evaluate json files in " + "output_eval directly. And proposal.json, bbox.json and mask.json " + "will be detected by default.") + json_eval_results( + cfg.metric, json_directory=FLAGS.output_eval, dataset=dataset) + return + + pruned_params = FLAGS.pruned_params + assert ( + FLAGS.pruned_params is not None + ), "FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option." + pruned_params = FLAGS.pruned_params.strip().split(",") + logger.info("pruned params: {}".format(pruned_params)) + pruned_ratios = [float(n) for n in FLAGS.pruned_ratios.strip().split(",")] + logger.info("pruned ratios: {}".format(pruned_ratios)) + assert (len(pruned_params) == len(pruned_ratios) + ), "The length of pruned params and pruned ratios should be equal." + assert (pruned_ratios > [0] * len(pruned_ratios) and + pruned_ratios < [1] * len(pruned_ratios) + ), "The elements of pruned ratios should be in range (0, 1)." + + base_flops = flops(eval_prog) + pruner = Pruner() + eval_prog, _, _ = pruner.prune( + eval_prog, + fluid.global_scope(), + params=pruned_params, + ratios=pruned_ratios, + place=place, + only_graph=True) + pruned_flops = flops(eval_prog) + logger.info("pruned FLOPS: {}".format( + float(base_flops - pruned_flops) / base_flops)) + + compile_program = fluid.compiler.CompiledProgram( + eval_prog).with_data_parallel() + + assert cfg.metric != 'OID', "eval process of OID dataset \ + is not supported." + + if cfg.metric == "WIDERFACE": + raise ValueError("metric type {} does not support in tools/eval.py, " + "please use tools/face_eval.py".format(cfg.metric)) + assert cfg.metric in ['COCO', 'VOC'], \ + "unknown metric type {}".format(cfg.metric) + extra_keys = [] + + if cfg.metric == 'COCO': + extra_keys = ['im_info', 'im_id', 'im_shape'] + if cfg.metric == 'VOC': + extra_keys = ['gt_bbox', 'gt_class', 'is_difficult'] + + keys, values, cls = parse_fetches(fetches, eval_prog, extra_keys) + + # whether output bbox is normalized in model output layer + is_bbox_normalized = False + if hasattr(model, 'is_bbox_normalized') and \ + callable(model.is_bbox_normalized): + is_bbox_normalized = model.is_bbox_normalized() + + sub_eval_prog = None + sub_keys = None + sub_values = None + # build sub-program + if 'Mask' in main_arch and multi_scale_test: + sub_eval_prog = fluid.Program() + with fluid.program_guard(sub_eval_prog, startup_prog): + with fluid.unique_name.guard(): + inputs_def = cfg['EvalReader']['inputs_def'] + inputs_def['mask_branch'] = True + feed_vars, eval_loader = model.build_inputs(**inputs_def) + sub_fetches = model.eval( + feed_vars, multi_scale_test, mask_branch=True) + assert cfg.metric == 'COCO' + extra_keys = ['im_id', 'im_shape'] + sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog, + extra_keys) + sub_eval_prog = sub_eval_prog.clone(True) + + # load model + exe.run(startup_prog) + if 'weights' in cfg: + checkpoint.load_params(exe, eval_prog, cfg.weights) + + results = eval_run(exe, compile_program, loader, keys, values, cls, cfg, + sub_eval_prog, sub_keys, sub_values) + + # evaluation + resolution = None + if 'mask' in results[0]: + resolution = model.mask_head.resolution + # if map_type not set, use default 11point, only use in VOC eval + map_type = cfg.map_type if 'map_type' in cfg else '11point' + eval_results( + results, + cfg.metric, + cfg.num_classes, + resolution, + is_bbox_normalized, + FLAGS.output_eval, + map_type, + dataset=dataset) + + +if __name__ == '__main__': + parser = ArgsParser() + parser.add_argument( + "--json_eval", + action='store_true', + default=False, + help="Whether to re eval with already exists bbox.json or mask.json") + parser.add_argument( + "-f", + "--output_eval", + default=None, + type=str, + help="Evaluation file directory, default is current directory.") + + parser.add_argument( + "-p", + "--pruned_params", + default=None, + type=str, + help="The parameters to be pruned when calculating sensitivities.") + parser.add_argument( + "--pruned_ratios", + default=None, + type=str, + help="The ratios pruned iteratively for each parameter when calculating sensitivities." + ) + + FLAGS = parser.parse_args() + main() diff --git a/slim/prune/prune.py b/slim/prune/prune.py index 90dfc5732b92ace4767dd744e5f42b291d5a8754..4ccdb274af92c508ad7ea70269f748ec56be2934 100644 --- a/slim/prune/prune.py +++ b/slim/prune/prune.py @@ -115,11 +115,13 @@ def main(): train_values.append(lr) if FLAGS.print_params: - print("-------------------------All parameters in current graph----------------------") + param_delimit_str = '-' * 20 + "All parameters in current graph" + '-' * 20 + print(param_delimit_str) for block in train_prog.blocks: for param in block.all_parameters(): - print("parameter name: {}\tshape: {}".format(param.name, param.shape)) - print("------------------------------------------------------------------------------") + print("parameter name: {}\tshape: {}".format(param.name, + param.shape)) + print('-' * len(param_delimit_str)) return if FLAGS.eval: @@ -174,19 +176,20 @@ def main(): checkpoint.load_checkpoint(exe, train_prog, FLAGS.resume_checkpoint) start_iter = checkpoint.global_step() elif cfg.pretrain_weights: - checkpoint.load_params( - exe, train_prog, cfg.pretrain_weights) - + checkpoint.load_params(exe, train_prog, cfg.pretrain_weights) pruned_params = FLAGS.pruned_params - assert (FLAGS.pruned_params is not None), "FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option." + assert FLAGS.pruned_params is not None, \ + "FLAGS.pruned_params is empty!!! Please set it by '--pruned_params' option." pruned_params = FLAGS.pruned_params.strip().split(",") logger.info("pruned params: {}".format(pruned_params)) - pruned_ratios = [float(n) for n in FLAGS.pruned_ratios.strip().split(" ")] + pruned_ratios = [float(n) for n in FLAGS.pruned_ratios.strip().split(",")] logger.info("pruned ratios: {}".format(pruned_ratios)) - assert(len(pruned_params) == len(pruned_ratios)), "The length of pruned params and pruned ratios should be equal." - assert(pruned_ratios > [0] * len(pruned_ratios) and pruned_ratios < [1] * len(pruned_ratios)), "The elements of pruned ratios should be in range (0, 1)." - + assert len(pruned_params) == len(pruned_ratios), \ + "The length of pruned params and pruned ratios should be equal." + assert (pruned_ratios > [0] * len(pruned_ratios) and + pruned_ratios < [1] * len(pruned_ratios) + ), "The elements of pruned ratios should be in range (0, 1)." pruner = Pruner() train_prog = pruner.prune( @@ -213,11 +216,11 @@ def main(): place=place, only_graph=True)[0] pruned_flops = flops(eval_prog) - logger.info("FLOPs -{}; total FLOPs: {}; pruned FLOPs: {}".format(float(base_flops - pruned_flops)/base_flops, base_flops, pruned_flops)) + logger.info("FLOPs -{}; total FLOPs: {}; pruned FLOPs: {}".format( + float(base_flops - pruned_flops) / base_flops, base_flops, + pruned_flops)) compiled_eval_prog = fluid.compiler.CompiledProgram(eval_prog) - - train_reader = create_reader(cfg.TrainReader, (cfg.max_iters - start_iter) * devices_num, cfg) train_loader.set_sample_list_generator(train_reader, place) @@ -248,12 +251,10 @@ def main(): tb_loss_step = 0 tb_mAP_step = 0 - - if FLAGS.eval: # evaluation - results = eval_run(exe, compiled_eval_prog, eval_loader, - eval_keys, eval_values, eval_cls) + results = eval_run(exe, compiled_eval_prog, eval_loader, eval_keys, + eval_values, eval_cls) resolution = None if 'mask' in results[0]: resolution = model.mask_head.resolution @@ -268,8 +269,6 @@ def main(): map_type, dataset=dataset) - - for it in range(start_iter, cfg.max_iters): start_time = end_time end_time = time.time() @@ -373,9 +372,10 @@ if __name__ == '__main__': help="The parameters to be pruned when calculating sensitivities.") parser.add_argument( "--pruned_ratios", - default="0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9", + default=None, type=str, - help="The ratios pruned iteratively for each parameter when calculating sensitivities.") + help="The ratios pruned iteratively for each parameter when calculating sensitivities." + ) parser.add_argument( "-P", "--print_params",