# 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 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.modeling.model_input import create_feed from ppdet.data.data_feed 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() if 'eval_feed' not in cfg: eval_feed = create(main_arch + 'EvalFeed') else: eval_feed = create(cfg.eval_feed) 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(): loader, feed_vars = create_feed(eval_feed) 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(eval_feed, args_path=FLAGS.dataset_dir) loader.set_sample_list_generator(reader, place) # 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( eval_feed, cfg.metric, json_directory=FLAGS.output_eval) return compile_program = fluid.compiler.CompiledProgram( eval_prog).with_data_parallel() # load model exe.run(startup_prog) if 'weights' in cfg: checkpoint.load_params(exe, eval_prog, cfg.weights) 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_box', 'gt_label', '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(): _, feed_vars = create_feed(eval_feed, False, sub_prog_feed=True) sub_fetches = model.eval( feed_vars, multi_scale_test, mask_branch=True) extra_keys = [] if cfg.metric == 'COCO': extra_keys = ['im_id', 'im_shape'] if cfg.metric == 'VOC': extra_keys = ['gt_box', 'gt_label', 'is_difficult'] sub_keys, sub_values, _ = parse_fetches(sub_fetches, sub_eval_prog, extra_keys) sub_eval_prog = sub_eval_prog.clone(True) if 'weights' in cfg: checkpoint.load_params(exe, sub_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, eval_feed, cfg.metric, cfg.num_classes, resolution, is_bbox_normalized, FLAGS.output_eval, map_type) 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( "-d", "--dataset_dir", default=None, type=str, help="Dataset path, same as DataFeed.dataset.dataset_dir") parser.add_argument( "-f", "--output_eval", default=None, type=str, help="Evaluation file directory, default is current directory.") FLAGS = parser.parse_args() main()