# 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 glob import numpy as np from PIL import Image 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 ) from paddle import fluid from ppdet.utils.cli import print_total_cfg from ppdet.core.workspace import load_config, merge_config, create from ppdet.modeling.model_input import create_feed from ppdet.data.data_feed import create_reader from ppdet.utils.eval_utils import parse_fetches from ppdet.utils.cli import ArgsParser from ppdet.utils.check import check_gpu, check_version from ppdet.utils.visualizer import visualize_results import ppdet.utils.checkpoint as checkpoint import logging FORMAT = '%(asctime)s-%(levelname)s: %(message)s' logging.basicConfig(level=logging.INFO, format=FORMAT) logger = logging.getLogger(__name__) def get_save_image_name(output_dir, image_path): """ Get save image name from source image path. """ if not os.path.exists(output_dir): os.makedirs(output_dir) image_name = os.path.split(image_path)[-1] name, ext = os.path.splitext(image_name) return os.path.join(output_dir, "{}".format(name)) + ext def get_test_images(infer_dir, infer_img): """ Get image path list in TEST mode """ assert infer_img is not None or infer_dir is not None, \ "--infer_img or --infer_dir should be set" assert infer_img is None or os.path.isfile(infer_img), \ "{} is not a file".format(infer_img) assert infer_dir is None or os.path.isdir(infer_dir), \ "{} is not a directory".format(infer_dir) images = [] # infer_img has a higher priority if infer_img and os.path.isfile(infer_img): images.append(infer_img) return images infer_dir = os.path.abspath(infer_dir) assert os.path.isdir(infer_dir), \ "infer_dir {} is not a directory".format(infer_dir) exts = ['jpg', 'jpeg', 'png', 'bmp'] exts += [ext.upper() for ext in exts] for ext in exts: images.extend(glob.glob('{}/*.{}'.format(infer_dir, ext))) assert len(images) > 0, "no image found in {}".format(infer_dir) logger.info("Found {} inference images in total.".format(len(images))) return images def prune_feed_vars(feeded_var_names, target_vars, prog): """ Filter out feed variables which are not in program, pruned feed variables are only used in post processing on model output, which are not used in program, such as im_id to identify image order, im_shape to clip bbox in image. """ exist_var_names = [] prog = prog.clone() prog = prog._prune(targets=target_vars) global_block = prog.global_block() for name in feeded_var_names: try: v = global_block.var(name) exist_var_names.append(str(v.name)) except Exception: logger.info('save_inference_model pruned unused feed ' 'variables {}'.format(name)) pass return exist_var_names def save_infer_model(FLAGS, exe, feed_vars, test_fetches, infer_prog): cfg_name = os.path.basename(FLAGS.config).split('.')[0] save_dir = os.path.join(FLAGS.output_dir, cfg_name) feeded_var_names = [var.name for var in feed_vars.values()] target_vars = list(test_fetches.values()) feeded_var_names = prune_feed_vars(feeded_var_names, target_vars, infer_prog) logger.info("Save inference model to {}, input: {}, output: " "{}...".format(save_dir, feeded_var_names, [str(var.name) for var in target_vars])) fluid.io.save_inference_model( save_dir, feeded_var_names=feeded_var_names, target_vars=target_vars, executor=exe, main_program=infer_prog, params_filename="__params__") 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) # check if paddlepaddle version is satisfied check_version() 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) 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(): loader, feed_vars = create_feed(test_feed, iterable=True) test_fetches = model.test(feed_vars) infer_prog = infer_prog.clone(True) reader = create_reader(test_feed) loader.set_sample_list_generator(reader, place) exe.run(startup_prog) if cfg.weights: checkpoint.load_params(exe, infer_prog, cfg.weights) if FLAGS.save_inference_model: save_infer_model(FLAGS, exe, feed_vars, test_fetches, 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(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 == "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 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 = reader.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) if __name__ == '__main__': 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( "--save_inference_model", action='store_true', default=False, help="Save inference model in output_dir if True.") parser.add_argument( "--use_tb", type=bool, default=False, help="whether to record the data to Tensorboard.") parser.add_argument( '--tb_log_dir', type=str, default="tb_log_dir/image", help='Tensorboard logging directory for image.') FLAGS = parser.parse_args() main()