# 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 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 paddle.fluid as fluid from paddleslim.prune import Pruner from paddleslim.analysis import flops import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) try: 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, check_config, enable_static_mode from ppdet.data.reader import create_reader from ppdet.core.workspace import load_config, merge_config, create from ppdet.utils.cli import ArgsParser except ImportError as e: if sys.argv[0].find('static') >= 0: logger.error("Importing ppdet failed when running static model " "with error: {}\n" "please try:\n" "\t1. run static model under PaddleDetection/static " "directory\n" "\t2. run 'pip uninstall ppdet' to uninstall ppdet " "dynamic version firstly.".format(e)) sys.exit(-1) else: raise e def main(): """ Main evaluate function """ cfg = load_config(FLAGS.config) merge_config(FLAGS.opt) check_config(cfg) # check if set use_gpu=True in paddlepaddle cpu version check_gpu(cfg.use_gpu) # check if paddlepaddle version is satisfied check_version() main_arch = cfg.architecture 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) exe.run(startup_prog) reader = create_reader(cfg.EvalReader) # When iterable mode, set set_sample_list_generator(reader, place) loader.set_sample_list_generator(reader) 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=False) pruned_flops = flops(eval_prog) logger.info("pruned FLOPS: {}".format( float(base_flops - pruned_flops) / base_flops)) compile_program = fluid.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 if 'weights' in cfg: checkpoint.load_checkpoint(exe, eval_prog, cfg.weights) resolution = None if 'Mask' in cfg.architecture: resolution = model.mask_head.resolution results = eval_run( exe, compile_program, loader, keys, values, cls, cfg, sub_eval_prog, sub_keys, sub_values, resolution=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__': enable_static_mode() 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()