| import os |
| import json |
| import zipfile |
| import numpy as np |
| import pickle |
| import torch |
|
|
|
|
| def load_pickle(filename): |
| with open(filename, "rb") as f: |
| return pickle.load(f) |
|
|
|
|
| def save_pickle(data, filename): |
| with open(filename, "wb") as f: |
| pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL) |
|
|
|
|
| def is_jsonable(x): |
| try: |
| json.dumps(x) |
| return True |
| except: |
| return False |
|
|
|
|
| def load_json(filename): |
| with open(filename, "r") as f: |
| return json.load(f) |
|
|
|
|
| def save_json(data, filename, save_pretty=False, sort_keys=False): |
| with open(filename, "w") as f: |
| if save_pretty: |
| f.write(json.dumps(data, indent=4, sort_keys=sort_keys)) |
| else: |
| json.dump(data, f) |
|
|
|
|
| def load_jsonl(filename): |
| with open(filename, "r") as f: |
| return [json.loads(l.strip("\n")) for l in f.readlines()] |
|
|
|
|
| def save_jsonl(data, filename): |
| """data is a list""" |
| with open(filename, "w") as f: |
| f.write("\n".join([json.dumps(e) for e in data])) |
|
|
|
|
| def concat_json_list(filepaths, save_path): |
| json_lists = [] |
| for p in filepaths: |
| json_lists += load_json(p) |
| save_json(json_lists, save_path) |
|
|
|
|
| def save_lines(list_of_str, filepath): |
| with open(filepath, "w") as f: |
| f.write("\n".join(list_of_str)) |
|
|
|
|
| def read_lines(filepath): |
| with open(filepath, "r") as f: |
| return [e.strip("\n") for e in f.readlines()] |
|
|
|
|
| def mkdirp(p): |
| if not os.path.exists(p): |
| os.makedirs(p) |
|
|
|
|
| def flat_list_of_lists(l): |
| """flatten a list of lists [[1,2], [3,4]] to [1,2,3,4]""" |
| return [item for sublist in l for item in sublist] |
|
|
|
|
| def convert_to_seconds(hms_time): |
| """ convert '00:01:12' to 72 seconds. |
| :hms_time (str): time in comma separated string, e.g. '00:01:12' |
| :return (int): time in seconds, e.g. 72 |
| """ |
| times = [float(t) for t in hms_time.split(":")] |
| return times[0] * 3600 + times[1] * 60 + times[2] |
|
|
|
|
| def get_video_name_from_url(url): |
| return url.split("/")[-1][:-4] |
|
|
|
|
| def merge_dicts(list_dicts): |
| merged_dict = list_dicts[0].copy() |
| for i in range(1, len(list_dicts)): |
| merged_dict.update(list_dicts[i]) |
| return merged_dict |
|
|
|
|
| def l2_normalize_np_array(np_array, eps=1e-5): |
| """np_array: np.ndarray, (*, D), where the last dim will be normalized""" |
| return np_array / (np.linalg.norm(np_array, axis=-1, keepdims=True) + eps) |
|
|
|
|
| def make_zipfile(src_dir, save_path, enclosing_dir="", exclude_dirs=None, exclude_extensions=None, |
| exclude_dirs_substring=None): |
| """make a zip file of root_dir, save it to save_path. |
| exclude_paths will be excluded if it is a subdir of root_dir. |
| An enclosing_dir is added is specified. |
| """ |
| abs_src = os.path.abspath(src_dir) |
| with zipfile.ZipFile(save_path, "w") as zf: |
| for dirname, subdirs, files in os.walk(src_dir): |
| if exclude_dirs is not None: |
| for e_p in exclude_dirs: |
| if e_p in subdirs: |
| subdirs.remove(e_p) |
| if exclude_dirs_substring is not None: |
| to_rm = [] |
| for d in subdirs: |
| if exclude_dirs_substring in d: |
| to_rm.append(d) |
| for e in to_rm: |
| subdirs.remove(e) |
| arcname = os.path.join(enclosing_dir, dirname[len(abs_src) + 1:]) |
| zf.write(dirname, arcname) |
| for filename in files: |
| if exclude_extensions is not None: |
| if os.path.splitext(filename)[1] in exclude_extensions: |
| continue |
| absname = os.path.join(dirname, filename) |
| arcname = os.path.join(enclosing_dir, absname[len(abs_src) + 1:]) |
| zf.write(absname, arcname) |
|
|
|
|
| class AverageMeter(object): |
| """Computes and stores the average and current/max/min value""" |
| def __init__(self): |
| self.val = 0 |
| self.avg = 0 |
| self.sum = 0 |
| self.count = 0 |
| self.max = -1e10 |
| self.min = 1e10 |
| self.reset() |
|
|
| def reset(self): |
| self.val = 0 |
| self.avg = 0 |
| self.sum = 0 |
| self.count = 0 |
| self.max = -1e10 |
| self.min = 1e10 |
|
|
| def update(self, val, n=1): |
| self.max = max(val, self.max) |
| self.min = min(val, self.min) |
| self.val = val |
| self.sum += val * n |
| self.count += n |
| self.avg = self.sum / self.count |
|
|
|
|
| def dissect_by_lengths(np_array, lengths, dim=0, assert_equal=True): |
| """Dissect an array (N, D) into a list a sub-array, |
| np_array.shape[0] == sum(lengths), Output is a list of nd arrays, singlton dimention is kept""" |
| if assert_equal: |
| assert len(np_array) == sum(lengths) |
| length_indices = [0, ] |
| for i in range(len(lengths)): |
| length_indices.append(length_indices[i] + lengths[i]) |
| if dim == 0: |
| array_list = [np_array[length_indices[i]:length_indices[i+1]] for i in range(len(lengths))] |
| elif dim == 1: |
| array_list = [np_array[:, length_indices[i]:length_indices[i + 1]] for i in range(len(lengths))] |
| elif dim == 2: |
| array_list = [np_array[:, :, length_indices[i]:length_indices[i + 1]] for i in range(len(lengths))] |
| else: |
| raise NotImplementedError |
| return array_list |
|
|
|
|
| def get_ratio_from_counter(counter_obj, threshold=200): |
| keys = counter_obj.keys() |
| values = counter_obj.values() |
| filtered_values = [counter_obj[k] for k in keys if k > threshold] |
| return float(sum(filtered_values)) / sum(values) |
|
|
|
|
| def get_rounded_percentage(float_number, n_floats=2): |
| return round(float_number * 100, n_floats) |
|
|
|
|
| def move_to_cuda(batch): |
| if isinstance(batch, torch.Tensor): |
| return batch.cuda(non_blocking=True) |
| elif isinstance(batch, list): |
| new_batch = [move_to_cuda(t) for t in batch] |
| elif isinstance(batch, tuple): |
| new_batch = tuple(move_to_cuda(t) for t in batch) |
| elif isinstance(batch, dict): |
| new_batch = {n: move_to_cuda(t) for n, t in batch.items()} |
| else: |
| return batch |
| return new_batch |
|
|
|
|
