# 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, sys # add python path of PadleDetection to sys.path parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 3))) if parent_path not in sys.path: sys.path.append(parent_path) import time import numpy as np import datetime from collections import deque import paddle from paddle import fluid from ppdet.experimental import mixed_precision_context from ppdet.core.workspace import load_config, merge_config, create from ppdet.data.reader import create_reader from ppdet.utils import dist_utils from ppdet.utils.eval_utils import parse_fetches, eval_run, eval_results from ppdet.utils.stats import TrainingStats from ppdet.utils.cli import ArgsParser from ppdet.utils.check import check_gpu, check_version, check_config, enable_static_mode import ppdet.utils.checkpoint as checkpoint from paddleslim.prune import sensitivity import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) def main(): env = os.environ print("FLAGS.config: {}".format(FLAGS.config)) cfg = load_config(FLAGS.config) merge_config(FLAGS.opt) check_config(cfg) check_version() main_arch = cfg.architecture place = fluid.CUDAPlace(0) exe = fluid.Executor(place) # build program startup_prog = fluid.Program() eval_prog = fluid.Program() with fluid.program_guard(eval_prog, startup_prog): with fluid.unique_name.guard(): model = create(main_arch) inputs_def = cfg['EvalReader']['inputs_def'] feed_vars, eval_loader = model.build_inputs(**inputs_def) fetches = model.eval(feed_vars) eval_prog = eval_prog.clone(True) if FLAGS.print_params: print( "-------------------------All parameters in current graph----------------------" ) for block in eval_prog.blocks: for param in block.all_parameters(): print("parameter name: {}\tshape: {}".format(param.name, param.shape)) print( "------------------------------------------------------------------------------" ) return eval_reader = create_reader(cfg.EvalReader) # When iterable mode, set set_sample_list_generator(eval_reader, place) eval_loader.set_sample_list_generator(eval_reader) # parse eval fetches 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'] if cfg.metric == 'WIDERFACE': extra_keys = ['im_id', 'im_shape', 'gt_box'] eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog, extra_keys) exe.run(startup_prog) fuse_bn = getattr(model.backbone, 'norm_type', None) == 'affine_channel' ignore_params = cfg.finetune_exclude_pretrained_params \ if 'finetune_exclude_pretrained_params' in cfg else [] start_iter = 0 if cfg.weights: checkpoint.load_params(exe, eval_prog, cfg.weights) else: logger.warning("Please set cfg.weights to load trained model.") # 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() # 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' def test(program): compiled_eval_prog = fluid.CompiledProgram(program) results = eval_run( exe, compiled_eval_prog, eval_loader, eval_keys, eval_values, eval_cls, cfg=cfg) resolution = None if 'mask' in results[0]: resolution = model.mask_head.resolution dataset = cfg['EvalReader']['dataset'] box_ap_stats = eval_results( results, cfg.metric, cfg.num_classes, resolution, is_bbox_normalized, FLAGS.output_eval, map_type, dataset=dataset) return box_ap_stats[0] 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)) sensitivity( eval_prog, place, pruned_params, test, sensitivities_file=FLAGS.sensitivities_file, pruned_ratios=pruned_ratios) if __name__ == '__main__': enable_static_mode() parser = ArgsParser() 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") parser.add_argument( "-s", "--sensitivities_file", default="sensitivities.data", type=str, help="The file used to save sensitivities.") parser.add_argument( "-p", "--pruned_params", default=None, type=str, help="The parameters to be pruned when calculating sensitivities.") parser.add_argument( "-r", "--pruned_ratios", default="0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9", type=str, help="The ratios pruned iteratively for each parameter when calculating sensitivities." ) parser.add_argument( "-P", "--print_params", default=False, action='store_true', help="Whether to only print the parameters' names and shapes.") FLAGS = parser.parse_args() main()