# 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 numpy as np from PIL import Image from paddle import fluid import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) try: from ppdet.core.workspace import load_config, merge_config, create from ppdet.utils.eval_utils import parse_fetches from ppdet.utils.cli import ArgsParser from ppdet.utils.check import check_gpu, check_version, check_config, enable_static_mode from ppdet.utils.visualizer import visualize_results import ppdet.utils.checkpoint as checkpoint from ppdet.data.reader import create_reader from tools.infer import get_test_images, get_save_image_name 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 from paddleslim.quant import quant_aware, convert def main(): 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 dataset = cfg.TestReader['dataset'] test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img) dataset.set_images(test_images) place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) model = create(main_arch) startup_prog = fluid.Program() infer_prog = fluid.Program() with fluid.program_guard(infer_prog, startup_prog): with fluid.unique_name.guard(): inputs_def = cfg['TestReader']['inputs_def'] feed_vars, loader = model.build_inputs(**inputs_def) test_fetches = model.test(feed_vars) infer_prog = infer_prog.clone(True) reader = create_reader(cfg.TestReader) # When iterable mode, set set_sample_list_generator(reader, place) loader.set_sample_list_generator(reader) not_quant_pattern = [] if FLAGS.not_quant_pattern: not_quant_pattern = FLAGS.not_quant_pattern config = { 'weight_quantize_type': 'channel_wise_abs_max', 'activation_quantize_type': 'moving_average_abs_max', 'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'], 'not_quant_pattern': not_quant_pattern } infer_prog = quant_aware(infer_prog, place, config, for_test=True) exe.run(startup_prog) if cfg.weights: checkpoint.load_params(exe, infer_prog, cfg.weights) infer_prog = convert(infer_prog, place, config, save_int8=False) # parse infer fetches assert cfg.metric in ['COCO', 'VOC', 'OID', 'WIDERFACE'], \ "unknown metric type {}".format(cfg.metric) extra_keys = [] if cfg['metric'] in ['COCO', 'OID']: extra_keys = ['im_info', 'im_id', 'im_shape'] if cfg['metric'] == 'VOC' or cfg['metric'] == 'WIDERFACE': extra_keys = ['im_id', 'im_shape'] keys, values, _ = parse_fetches(test_fetches, infer_prog, extra_keys) # parse dataset category if cfg.metric == 'COCO': from ppdet.utils.coco_eval import bbox2out, mask2out, get_category_info if cfg.metric == 'OID': from ppdet.utils.oid_eval import bbox2out, get_category_info if cfg.metric == "VOC": from ppdet.utils.voc_eval import bbox2out, get_category_info if cfg.metric == "WIDERFACE": from ppdet.utils.widerface_eval_utils import bbox2out, get_category_info anno_file = dataset.get_anno() with_background = dataset.with_background use_default_label = dataset.use_default_label clsid2catid, catid2name = get_category_info(anno_file, with_background, use_default_label) # 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() imid2path = dataset.get_imid2path() iter_id = 0 try: loader.start() while True: outs = exe.run(infer_prog, fetch_list=values, return_numpy=False) res = { k: (np.array(v), v.recursive_sequence_lengths()) for k, v in zip(keys, outs) } logger.info('Infer iter {}'.format(iter_id)) iter_id += 1 bbox_results = None mask_results = None if 'bbox' in res: bbox_results = bbox2out([res], clsid2catid, is_bbox_normalized) if 'mask' in res: mask_results = mask2out([res], clsid2catid, model.mask_head.resolution) # visualize result im_ids = res['im_id'][0] for im_id in im_ids: image_path = imid2path[int(im_id)] image = Image.open(image_path).convert('RGB') image = visualize_results(image, int(im_id), catid2name, FLAGS.draw_threshold, bbox_results, mask_results) save_name = get_save_image_name(FLAGS.output_dir, image_path) logger.info("Detection bbox results save in {}".format( save_name)) image.save(save_name, quality=95) except (StopIteration, fluid.core.EOFException): loader.reset() if __name__ == '__main__': enable_static_mode() parser = ArgsParser() parser.add_argument( "--infer_dir", type=str, default=None, help="Directory for images to perform inference on.") parser.add_argument( "--infer_img", type=str, default=None, help="Image path, has higher priority over --infer_dir") parser.add_argument( "--output_dir", type=str, default="output", help="Directory for storing the output visualization files.") parser.add_argument( "--draw_threshold", type=float, default=0.5, help="Threshold to reserve the result for visualization.") parser.add_argument( "--not_quant_pattern", nargs='+', type=str, help="Layers which name_scope contains string in not_quant_pattern will not be quantized" ) FLAGS = parser.parse_args() main()