def get_all_checkpoint_files(self): """ Returns: list: All available checkpoint files (.pth files) in target directory. """ all_model_checkpoints = [ os.path.join(self.save_dir, file) for file in PathManager.ls(self.save_dir) if PathManager.isfile(os.path.join(self.save_dir, file)) and file.endswith(".pth") ] return all_model_checkpoints
def read_image(file_name, format=None): """ Read an image into the given format. Will apply rotation and flipping if the image has such exif information. Args: file_name (str): image file path format (str): one of the supported image modes in PIL, or "BGR" Returns: image (np.ndarray): an HWC image """ with PathManager.open(file_name, "rb") as f: image = Image.open(f) # capture and ignore this bug: https://github.com/python-pillow/Pillow/issues/3973 try: image = ImageOps.exif_transpose(image) except Exception: pass if format is not None: # PIL only supports RGB, so convert to RGB and flip channels over below conversion_format = format if format == "BGR": conversion_format = "RGB" image = image.convert(conversion_format) image = np.asarray(image) if format == "BGR": # flip channels if needed image = image[:, :, ::-1] # PIL squeezes out the channel dimension for "L", so make it HWC if format == "L": image = np.expand_dims(image, -1) image = Image.fromarray(image) return image
def load(self, path: str): """ Load from the given checkpoint. When path points to network file, this function has to be called on all ranks. Args: path (str): path or url to the checkpoint. If empty, will not load anything. Returns: dict: extra data loaded from the checkpoint that has not been processed. For example, those saved with :meth:`.save(**extra_data)`. """ if not path: # no checkpoint provided self.logger.info( "No checkpoint found. Training model from scratch") return {} self.logger.info("Loading checkpoint from {}".format(path)) if not os.path.isfile(path): path = PathManager.get_local_path(path) assert os.path.isfile(path), "Checkpoint {} not found!".format( path) checkpoint = self._load_file(path) self._load_model(checkpoint) # for key, obj in self.checkpointables.items(): # if key in checkpoint: # self.logger.info("Loading {} from {}".format(key, path)) # obj.load_state_dict(checkpoint.pop(key)) # return any further checkpoint data return checkpoint
def has_checkpoint(self): """ Returns: bool: whether a checkpoint exists in the target directory. """ save_file = os.path.join(self.save_dir, "last_checkpoint") return PathManager.exists(save_file)
def tag_last_checkpoint(self, last_filename_basename: str): """ Tag the last checkpoint. Args: last_filename_basename (str): the basename of the last filename. """ save_file = os.path.join(self.save_dir, "last_checkpoint") with PathManager.open(save_file, "w") as f: f.write(last_filename_basename)
def default_setup(cfg, args): """ Perform some basic common setups at the beginning of a job, including: 1. Set up the detectron2 logger 2. Log basic information about environment, cmdline arguments, and config 3. Backup the config to the output directory Args: cfg (CfgNode): the full config to be used args (argparse.NameSpace): the command line arguments to be logged """ output_dir = cfg.OUTPUT_DIR if comm.is_main_process() and output_dir: PathManager.mkdirs(output_dir) rank = comm.get_rank() setup_logger(output_dir, distributed_rank=rank, name="fvcore") logger = setup_logger(output_dir, distributed_rank=rank) logger.info("Rank of current process: {}. World size: {}".format( rank, comm.get_world_size())) logger.info("Environment info:\n" + collect_env_info()) logger.info("Command line arguments: " + str(args)) if hasattr(args, "config_file") and args.config_file != "": logger.info("Contents of args.config_file={}:\n{}".format( args.config_file, PathManager.open(args.config_file, "r").read())) logger.info("Running with full config:\n{}".format(cfg)) if comm.is_main_process() and output_dir: # Note: some of our scripts may expect the existence of # config.yaml in output directory path = os.path.join(output_dir, "config.yaml") with PathManager.open(path, "w") as f: f.write(cfg.dump()) logger.info("Full config saved to {}".format(os.path.abspath(path))) # make sure each worker has a different, yet deterministic seed if specified seed_all_rng() # cudnn benchmark has large overhead. It shouldn't be used considering the small size of # typical validation set. if not (hasattr(args, "eval_only") and args.eval_only): torch.backends.cudnn.benchmark = cfg.CUDNN_BENCHMARK
def get_checkpoint_file(self): """ Returns: str: The latest checkpoint file in target directory. """ save_file = os.path.join(self.save_dir, "last_checkpoint") try: with PathManager.open(save_file, "r") as f: last_saved = f.read().strip() except IOError: # if file doesn't exist, maybe because it has just been # deleted by a separate process return "" return os.path.join(self.save_dir, last_saved)
def after_step(self): if self._profiler is None: return self._profiler.__exit__(None, None, None) out_file = os.path.join( self._output_dir, "profiler-trace-iter{}.json".format(self.trainer.iter)) if "://" not in out_file: self._profiler.export_chrome_trace(out_file) else: # Support non-posix filesystems with tempfile.TemporaryDirectory( prefix="detectron2_profiler") as d: tmp_file = os.path.join(d, "tmp.json") self._profiler.export_chrome_trace(tmp_file) with open(tmp_file) as f: content = f.read() with PathManager.open(out_file, "w") as f: f.write(content)
def save(self, name: str, **kwargs: dict): """ Dump model and checkpointables to a file. Args: name (str): name of the file. kwargs (dict): extra arbitrary data to save. """ if not self.save_dir or not self.save_to_disk: return data = {} data["model"] = self.model.state_dict() for key, obj in self.checkpointables.items(): data[key] = obj.state_dict() data.update(kwargs) basename = "{}.pth".format(name) save_file = os.path.join(self.save_dir, basename) assert os.path.basename(save_file) == basename, basename self.logger.info("Saving checkpoint to {}".format(save_file)) with PathManager.open(save_file, "wb") as f: torch.save(data, f) self.tag_last_checkpoint(basename)
def preprocess_split(self): # This function is a bit complex and ugly, what it does is # 1. extract data from cuhk-03.mat and save as png images # 2. create 20 classic splits (Li et al. CVPR'14) # 3. create new split (Zhong et al. CVPR'17) if osp.exists(self.imgs_labeled_dir) \ and osp.exists(self.imgs_detected_dir) \ and osp.exists(self.split_classic_det_json_path) \ and osp.exists(self.split_classic_lab_json_path) \ and osp.exists(self.split_new_det_json_path) \ and osp.exists(self.split_new_lab_json_path): return import h5py from imageio import imwrite from scipy.io import loadmat PathManager.mkdirs(self.imgs_detected_dir) PathManager.mkdirs(self.imgs_labeled_dir) print('Extract image data from "{}" and save as png'.format( self.raw_mat_path)) mat = h5py.File(self.raw_mat_path, 'r') def _deref(ref): return mat[ref][:].T def _process_images(img_refs, campid, pid, save_dir): img_paths = [] # Note: some persons only have images for one view for imgid, img_ref in enumerate(img_refs): img = _deref(img_ref) if img.size == 0 or img.ndim < 3: continue # skip empty cell # images are saved with the following format, index-1 (ensure uniqueness) # campid: index of camera pair (1-5) # pid: index of person in 'campid'-th camera pair # viewid: index of view, {1, 2} # imgid: index of image, (1-10) viewid = 1 if imgid < 5 else 2 img_name = '{:01d}_{:03d}_{:01d}_{:02d}.png'.format( campid + 1, pid + 1, viewid, imgid + 1) img_path = osp.join(save_dir, img_name) if not osp.isfile(img_path): imwrite(img_path, img) img_paths.append(img_path) return img_paths def _extract_img(image_type): print('Processing {} images ...'.format(image_type)) meta_data = [] imgs_dir = self.imgs_detected_dir if image_type == 'detected' else self.imgs_labeled_dir for campid, camp_ref in enumerate(mat[image_type][0]): camp = _deref(camp_ref) num_pids = camp.shape[0] for pid in range(num_pids): img_paths = _process_images(camp[pid, :], campid, pid, imgs_dir) assert len( img_paths) > 0, 'campid{}-pid{} has no images'.format( campid, pid) meta_data.append((campid + 1, pid + 1, img_paths)) print('- done camera pair {} with {} identities'.format( campid + 1, num_pids)) return meta_data meta_detected = _extract_img('detected') meta_labeled = _extract_img('labeled') def _extract_classic_split(meta_data, test_split): train, test = [], [] num_train_pids, num_test_pids = 0, 0 num_train_imgs, num_test_imgs = 0, 0 for i, (campid, pid, img_paths) in enumerate(meta_data): if [campid, pid] in test_split: for img_path in img_paths: camid = int(osp.basename(img_path).split('_') [2]) - 1 # make it 0-based test.append((img_path, num_test_pids, camid)) num_test_pids += 1 num_test_imgs += len(img_paths) else: for img_path in img_paths: camid = int(osp.basename(img_path).split('_') [2]) - 1 # make it 0-based train.append((img_path, num_train_pids, camid)) num_train_pids += 1 num_train_imgs += len(img_paths) return train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs print('Creating classic splits (# = 20) ...') splits_classic_det, splits_classic_lab = [], [] for split_ref in mat['testsets'][0]: test_split = _deref(split_ref).tolist() # create split for detected images train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \ _extract_classic_split(meta_detected, test_split) splits_classic_det.append({ 'train': train, 'query': test, 'gallery': test, 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs, 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs, 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs }) # create split for labeled images train, num_train_pids, num_train_imgs, test, num_test_pids, num_test_imgs = \ _extract_classic_split(meta_labeled, test_split) splits_classic_lab.append({ 'train': train, 'query': test, 'gallery': test, 'num_train_pids': num_train_pids, 'num_train_imgs': num_train_imgs, 'num_query_pids': num_test_pids, 'num_query_imgs': num_test_imgs, 'num_gallery_pids': num_test_pids, 'num_gallery_imgs': num_test_imgs }) with PathManager.open(self.split_classic_det_json_path, 'w') as f: json.dump(splits_classic_det, f, indent=4, separators=(',', ': ')) with PathManager.open(self.split_classic_lab_json_path, 'w') as f: json.dump(splits_classic_lab, f, indent=4, separators=(',', ': ')) def _extract_set(filelist, pids, pid2label, idxs, img_dir, relabel): tmp_set = [] unique_pids = set() for idx in idxs: img_name = filelist[idx][0] camid = int(img_name.split('_')[2]) - 1 # make it 0-based pid = pids[idx] if relabel: pid = pid2label[pid] img_path = osp.join(img_dir, img_name) tmp_set.append((img_path, int(pid), camid)) unique_pids.add(pid) return tmp_set, len(unique_pids), len(idxs) def _extract_new_split(split_dict, img_dir): train_idxs = split_dict['train_idx'].flatten() - 1 # index-0 pids = split_dict['labels'].flatten() train_pids = set(pids[train_idxs]) pid2label = {pid: label for label, pid in enumerate(train_pids)} query_idxs = split_dict['query_idx'].flatten() - 1 gallery_idxs = split_dict['gallery_idx'].flatten() - 1 filelist = split_dict['filelist'].flatten() train_info = _extract_set(filelist, pids, pid2label, train_idxs, img_dir, relabel=True) query_info = _extract_set(filelist, pids, pid2label, query_idxs, img_dir, relabel=False) gallery_info = _extract_set(filelist, pids, pid2label, gallery_idxs, img_dir, relabel=False) return train_info, query_info, gallery_info print('Creating new split for detected images (767/700) ...') train_info, query_info, gallery_info = _extract_new_split( loadmat(self.split_new_det_mat_path), self.imgs_detected_dir) split = [{ 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0], 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2], 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2], 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2] }] with PathManager.open(self.split_new_det_json_path, 'w') as f: json.dump(split, f, indent=4, separators=(',', ': ')) print('Creating new split for labeled images (767/700) ...') train_info, query_info, gallery_info = _extract_new_split( loadmat(self.split_new_lab_mat_path), self.imgs_labeled_dir) split = [{ 'train': train_info[0], 'query': query_info[0], 'gallery': gallery_info[0], 'num_train_pids': train_info[1], 'num_train_imgs': train_info[2], 'num_query_pids': query_info[1], 'num_query_imgs': query_info[2], 'num_gallery_pids': gallery_info[1], 'num_gallery_imgs': gallery_info[2] }] with PathManager.open(self.split_new_lab_json_pat, 'w') as f: json.dump(split, f, indent=4, separators=(',', ': '))
def __init__(self, root='datasets', split_id=0, cuhk03_labeled=False, cuhk03_classic_split=False, **kwargs): # self.root = osp.abspath(osp.expanduser(root)) self.root = root self.dataset_dir = osp.join(self.root, self.dataset_dir) self.data_dir = osp.join(self.dataset_dir, 'cuhk03_release') self.raw_mat_path = osp.join(self.data_dir, 'cuhk-03.mat') self.imgs_detected_dir = osp.join(self.dataset_dir, 'images_detected') self.imgs_labeled_dir = osp.join(self.dataset_dir, 'images_labeled') self.split_classic_det_json_path = osp.join( self.dataset_dir, 'splits_classic_detected.json') self.split_classic_lab_json_path = osp.join( self.dataset_dir, 'splits_classic_labeled.json') self.split_new_det_json_path = osp.join(self.dataset_dir, 'splits_new_detected.json') self.split_new_lab_json_path = osp.join(self.dataset_dir, 'splits_new_labeled.json') self.split_new_det_mat_path = osp.join( self.dataset_dir, 'cuhk03_new_protocol_config_detected.mat') self.split_new_lab_mat_path = osp.join( self.dataset_dir, 'cuhk03_new_protocol_config_labeled.mat') required_files = [ self.dataset_dir, self.data_dir, self.raw_mat_path, self.split_new_det_mat_path, self.split_new_lab_mat_path ] self.check_before_run(required_files) self.preprocess_split() if cuhk03_labeled: split_path = self.split_classic_lab_json_path if cuhk03_classic_split else self.split_new_lab_json_path else: split_path = self.split_classic_det_json_path if cuhk03_classic_split else self.split_new_det_json_path with PathManager.open(split_path) as f: splits = json.load(f) assert split_id < len( splits ), 'Condition split_id ({}) < len(splits) ({}) is false'.format( split_id, len(splits)) split = splits[split_id] train = split['train'] tmp_train = [] for img_path, pid, camid in train: new_pid = self.dataset_name + "_" + str(pid) tmp_train.append((img_path, new_pid, camid)) train = tmp_train del tmp_train query = split['query'] gallery = split['gallery'] from ipdb import set_trace set_trace() super(CUHK03, self).__init__(train, query, gallery, **kwargs)
parser.add_argument("--output", default='demo_output', help='path to save features') parser.add_argument( "--opts", help="Modify config options using the command-line 'KEY VALUE' pairs", default=[], nargs=argparse.REMAINDER, ) return parser if __name__ == '__main__': args = get_parser().parse_args() cfg = setup_cfg(args) demo = FeatureExtractionDemo(cfg, parallel=args.parallel) PathManager.mkdirs(args.output) if args.input: if PathManager.isdir(args.input[0]): args.input = glob.glob(os.path.expanduser(args.input[0])) assert args.input, "The input path(s) was not found" for path in tqdm.tqdm(args.input): img = cv2.imread(path) feat = demo.run_on_image(img) feat = feat.numpy() np.save( os.path.join(args.output, path.replace('.jpg', '.npy').split('/')[-1]), feat)
help='path to save converted caffe model') parser.add_argument( "--opts", help="Modify config options using the command-line 'KEY VALUE' pairs", default=[], nargs=argparse.REMAINDER, ) return parser if __name__ == '__main__': args = get_parser().parse_args() cfg = setup_cfg(args) cfg.defrost() cfg.MODEL.BACKBONE.PRETRAIN = False cfg.MODEL.HEADS.POOL_LAYER = "identity" cfg.MODEL.BACKBONE.WITH_NL = False model = build_model(cfg) Checkpointer(model).load(cfg.MODEL.WEIGHTS) model.eval() print(model) inputs = torch.randn(1, 3, cfg.INPUT.SIZE_TEST[0], cfg.INPUT.SIZE_TEST[1]).cuda() PathManager.mkdirs(args.output) pytorch_to_caffe.trans_net(model, inputs, args.name) pytorch_to_caffe.save_prototxt(f"{args.output}/{args.name}.prototxt") pytorch_to_caffe.save_caffemodel(f"{args.output}/{args.name}.caffemodel")