text stringlengths 1 93.6k |
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return vae
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# <FILESEP>
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"""
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coding=utf-8
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Copyright 2018, Antonio Mendoza Hao Tan, Mohit Bansal, Huggingface team :)
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Adapted From Facebook Inc, Detectron2
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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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,
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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.import copy
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"""
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import copy
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import fnmatch
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import json
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import os
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import pickle as pkl
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import shutil
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import sys
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import tarfile
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import tempfile
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from collections import OrderedDict
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from contextlib import contextmanager
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from functools import partial
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from hashlib import sha256
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from io import BytesIO
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from pathlib import Path
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from urllib.parse import urlparse
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from zipfile import ZipFile, is_zipfile
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import numpy as np
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from PIL import Image
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from tqdm.auto import tqdm
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import cv2
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import requests
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import wget
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from filelock import FileLock
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from yaml import Loader, dump, load
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try:
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import torch
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_torch_available = True
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except ImportError:
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_torch_available = False
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try:
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from torch.hub import _get_torch_home
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torch_cache_home = _get_torch_home()
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except ImportError:
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torch_cache_home = os.path.expanduser(
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os.getenv("TORCH_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "torch"))
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)
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default_cache_path = os.path.join(torch_cache_home, "transformers")
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CLOUDFRONT_DISTRIB_PREFIX = "https://cdn.huggingface.co"
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S3_BUCKET_PREFIX = "https://s3.amazonaws.com/models.huggingface.co/bert"
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PATH = "/".join(str(Path(__file__).resolve()).split("/")[:-1])
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CONFIG = os.path.join(PATH, "config.yaml")
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ATTRIBUTES = os.path.join(PATH, "attributes.txt")
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OBJECTS = os.path.join(PATH, "objects.txt")
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PYTORCH_PRETRAINED_BERT_CACHE = os.getenv("PYTORCH_PRETRAINED_BERT_CACHE", default_cache_path)
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PYTORCH_TRANSFORMERS_CACHE = os.getenv("PYTORCH_TRANSFORMERS_CACHE", PYTORCH_PRETRAINED_BERT_CACHE)
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TRANSFORMERS_CACHE = os.getenv("TRANSFORMERS_CACHE", PYTORCH_TRANSFORMERS_CACHE)
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WEIGHTS_NAME = "pytorch_model.bin"
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CONFIG_NAME = "config.yaml"
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def load_labels(objs=OBJECTS, attrs=ATTRIBUTES):
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vg_classes = []
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with open(objs) as f:
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for object in f.readlines():
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vg_classes.append(object.split(",")[0].lower().strip())
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vg_attrs = []
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with open(attrs) as f:
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for object in f.readlines():
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vg_attrs.append(object.split(",")[0].lower().strip())
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return vg_classes, vg_attrs
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def load_checkpoint(ckp):
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r = OrderedDict()
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with open(ckp, "rb") as f:
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ckp = pkl.load(f)["model"]
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for k in copy.deepcopy(list(ckp.keys())):
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