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'] inputs_def['iterable'] = True 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, devices_num=1) loader.set_sample_list_generator(reader, place) exe.run(startup_prog) if cfg.weights: checkpoint.load_params(exe, infer_prog, cfg.weights) # 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() # use tb-paddle to log image if FLAGS.use_tb: from tb_paddle import SummaryWriter tb_writer = SummaryWriter(FLAGS.tb_log_dir) tb_image_step = 0 tb_image_frame = 0 # each frame can display ten pictures at most. imid2path = dataset.get_imid2path() for iter_id, data in enumerate(loader()): outs = exe.run(infer_prog, feed=data, 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)) 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') # use tb-paddle to log original image if FLAGS.use_tb: original_image_np = np.array(image) tb_writer.add_image("original/frame_{}".format(tb_image_frame), original_image_np, tb_image_step, dataformats='HWC') image = visualize_results(image, int(im_id), catid2name, FLAGS.draw_threshold, bbox_results, mask_results) # use tb-paddle to log image with bbox if FLAGS.use_tb: infer_image_np = np.array(image) tb_writer.add_image("bbox/frame_{}".format(tb_image_frame), infer_image_np, tb_image_step, dataformats='HWC') tb_image_step += 1 if tb_image_step % 10 == 0: tb_image_step = 0 tb_image_frame += 1 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)
def main(): 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) # print_total_cfg(cfg) if 'test_feed' not in cfg: test_feed = create(main_arch + 'TestFeed') else: test_feed = create(cfg.test_feed) test_images = get_test_images(FLAGS.infer_dir, FLAGS.infer_img) test_feed.dataset.add_images(test_images) place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace() exe = fluid.Executor(place) infer_prog, feed_var_names, fetch_list = fluid.io.load_inference_model( dirname=FLAGS.model_path, model_filename=FLAGS.model_name, params_filename=FLAGS.params_name, executor=exe) reader = create_reader(test_feed) feeder = fluid.DataFeeder( place=place, feed_list=feed_var_names, program=infer_prog) # parse infer fetches 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 = ['im_id', 'im_shape'] keys, values, _ = parse_fetches({ 'bbox': fetch_list }, 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 == "VOC": from ppdet.utils.voc_eval import bbox2out, get_category_info anno_file = getattr(test_feed.dataset, 'annotation', None) with_background = getattr(test_feed, 'with_background', True) use_default_label = getattr(test_feed, 'use_default_label', False) 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 # use tb-paddle to log image if FLAGS.use_tb: from tb_paddle import SummaryWriter tb_writer = SummaryWriter(FLAGS.tb_log_dir) tb_image_step = 0 tb_image_frame = 0 # each frame can display ten pictures at most. imid2path = reader.imid2path keys = ['bbox'] infer_time = True compile_prog = fluid.compiler.CompiledProgram(infer_prog) for iter_id, data in enumerate(reader()): feed_data = [[d[0], d[1]] for d in data] # for infer time if infer_time: warmup_times = 10 repeats_time = 100 feed_data_dict = feeder.feed(feed_data) for i in range(warmup_times): exe.run(compile_prog, feed=feed_data_dict, fetch_list=fetch_list, return_numpy=False) start_time = time.time() for i in range(repeats_time): exe.run(compile_prog, feed=feed_data_dict, fetch_list=fetch_list, return_numpy=False) print("infer time: {} ms/sample".format((time.time() - start_time) * 1000 / repeats_time)) infer_time = False outs = exe.run(compile_prog, feed=feeder.feed(feed_data), fetch_list=fetch_list, return_numpy=False) res = { k: (np.array(v), v.recursive_sequence_lengths()) for k, v in zip(keys, outs) } res['im_id'] = [[d[2] for d in data]] logger.info('Infer iter {}'.format(iter_id)) 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') # use tb-paddle to log original image if FLAGS.use_tb: original_image_np = np.array(image) tb_writer.add_image( "original/frame_{}".format(tb_image_frame), original_image_np, tb_image_step, dataformats='HWC') image = visualize_results(image, int(im_id), catid2name, FLAGS.draw_threshold, bbox_results, mask_results) # use tb-paddle to log image with bbox if FLAGS.use_tb: infer_image_np = np.array(image) tb_writer.add_image( "bbox/frame_{}".format(tb_image_frame), infer_image_np, tb_image_step, dataformats='HWC') tb_image_step += 1 if tb_image_step % 10 == 0: tb_image_step = 0 tb_image_frame += 1 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)