inputs stringlengths 312 52k | targets stringlengths 1 3.1k ⌀ | block_type stringclasses 11
values | scenario stringclasses 7
values |
|---|---|---|---|
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except (ValueError, TypeError):
pass | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except Exception as e:
print(e)
return False | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except (ValueError, TypeError):
pass | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except AttributeError as e:
# backwards compatibility with pydantic < 2
return target_type.parse_obj(data) | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except json.JSONDecodeError:
return False | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/language_models/openai_api.py<fim_prefix>from typing import List
import logging
import time
# import abstract base class
from openai import OpenAI
from openai.types import CreateEmbeddingResponse
from openai.types.fine_tuning import FineTuningJob
from tanuki.language_models.llm_finetune... | except Exception as e:
if ("error" in response and
"code" in response["error"] and
response["error"]["code"] == 'invalid_api_key'):
raise Exception(f"The supplied OpenAI API key {self.api_key} is invalid")
if counter == ... | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except ValueError:
raise TypeError(
f"Item of type {type(item).__name__} does not match expected type {item_type[0].__name__}.") | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | except TypeError:
raise TypeError(f"Failed to instantiate {target_type.__name__} from dictionary.") | CATCH | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | for arg in get_args(target_type):
try:
return self.instantiate(data, arg)
except:
continue | FOR | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/bloom_filter.py<fim_prefix>import hashlib
import logging
import math
import numpy as np
from bitarray import bitarray
from tanuki.persistence.filter.bloom_interface import IBloomFilterPersistence
class BloomFilter:
def __init__(self,
persistence: IBloomFilterPers... | for seed in range(self.hash_count):
index = (hash1 + seed * hash2) % self.size
self.bit_array[index] = 1
#print(f"Add: Seed={seed}, Digest={index}, BitValue={self.bit_array[index]}") | FOR | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | for item in data:
# For each item, validate and instantiate it
instantiated_item = self.instantiate(item, item_type[0])
instantiated_items.add(instantiated_item)
# If the instantiated item does not match the expected type, ... | FOR | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | for i, item in enumerate(data):
# For each item, validate and instantiate it
instantiated_item = self.instantiate(item, item_types[i])
instantiated_items.append(instantiated_item)
# If the instantiated item does not match t... | FOR | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | for item in data:
# For each item, validate and instantiate it
try:
instantiated_item = self.instantiate(item, item_type)
except ValueError:
raise TypeError(
f"... | FOR | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/validator.py<fim_prefix>import abc
from collections import defaultdict
import collections
import typing
from collections import deque
import dataclasses
import inspect
import json
from dataclasses import is_dataclass
from typing import get_origin, get_args, Any, Mapping, MutableMapping, O... | for base in target_type.__orig_bases__:
if get_args(base):
return base, get_args(base) | FOR | prefix_suffix_full_complete_current_block_no_evidence |
<filename>tanuki_py/src/tanuki/language_models/openai_api.py<fim_prefix>from typing import List
import logging
import time
# import abstract base class
from openai import OpenAI
from openai.types import CreateEmbeddingResponse
from openai.types.fine_tuning import FineTuningJob
from tanuki.language_models.llm_finetune... | while counter <= 5:
try:
openai_headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
}
response = requests.post(
OPENAI_URL, headers=openai_headers, json=params, ... | WHILE | prefix_suffix_full_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/dataset/truthfulqa.py<fim_prefix># @Author : YeZhaohui Wang
# @Email : wyzh0912@126.com
import csv
import json
import os
import random
from uhgeval.dataset.base import BaseDataset
class TruthfunQAGeneration(BaseDataset):
def __init__(self, path: str, shuffle: bool = False, seed: int =... | = [] | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | accuracy, precision, recall, f1 | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | = 2 * (precision * recall) / (precision + recall) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | null | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | = sum(1 for a, b in zip(references, predictions) if a == 0 and b == 1) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | = sum(1 for a, b in zip(references, predictions) if a == b) / len(predictions) if len(predictions) > 0 else 0 | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | = sum(1 for a, b in zip(references, predictions) if a == 1 and b == 1) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | = true_positive / (true_positive + false_positive) if (true_positive + false_positive) > 0 else 0 | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/dataset/truthfulqa.py<fim_prefix># @Author : YeZhaohui Wang
# @Email : wyzh0912@126.com
import csv
import json
import os
import random
from uhgeval.dataset.base import BaseDataset
class TruthfunQAGeneration(BaseDataset):
def __init__(self, path: str, shuffle: bool = False, seed: int =... | self.data[:] | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | result | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | precision + recall == 0:
f1 = 0
else:
f1 = 2 * (precision * recall) / (precision + recall) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:<fim_s... |
result = func(*args, **kwargs)
return result | TRY | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... | Exception as e:
logger.warning(repr(e)) | CATCH | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UHGEval/uhgeval/metric/common.py<fim_prefix># @Author : Shichao Song
# @Email : song.shichao@outlook.com
from typing import Callable
import evaluate
import jieba
from loguru import logger
from text2vec import Similarity
def catch_all_exceptions(func):
def wrapper(*args, **kwargs):
try:
... |
Calculate accuracy, precision, recall, and F1 in a binary classification problem.
Args:
predictions (list[bool]): List of predicted values (0 or 1).
references (list[bool]): List of true values (0 or 1).
Returns:
tuple: Accuracy, precision, recall, and F1 scores.
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/image_list.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import division
from typing import Any, List, Tuple
import torch
from torch import device
from torch.nn import functional as F
from detectron2.layers.wrappers import shapes_to_tensor
cla... |
Access the individual image in its original size.
Args:
idx: int or slice
Returns:
Tensor: an image of shape (H, W) or (C_1, ..., C_K, H, W) where K >= 1
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/solver/build.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import logging
from collections import defaultdict
from enum import Enum
from typing import Any, Callable, Dict, Iterable, List, Optional, Set, Type, Union
import torch
from fvcore.comm... |
Build a LR scheduler from config.
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... |
For each untracked instance, assign a new id
Args:
instances: D2 Instances, for predictions of the current frame
Return:
D2 Instances with new ID assigned
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... |
Returns:
Boxes: tight bounding boxes around bitmasks.
If a mask is empty, it's bounding box will be all zero.
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... |
Before each uodate call, reset fields first
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/boxes.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.... |
Args:
item: int, slice, or a BoolTensor
Returns:
Boxes: Create a new :class:`Boxes` by indexing.
The following usage are allowed:
1. `new_boxes = boxes[3]`: return a `Boxes` which contains only one box.
2. `new_boxes = boxes[2:10]`: return a slice of b... | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/instances.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import itertools
from typing import Any, Dict, List, Tuple, Union
import torch
class Instances:
"""
This class represents a list of instances in an image.
It stores the attributes of instances... |
Args:
image_size (height, width): the spatial size of the image.
kwargs: fields to add to this `Instances`.
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... |
Arguments:
polygons (list[list[np.ndarray]]): The first
level of the list correspond to individual instances,
the second level to all the polygons that compose the
instance, and the third level to the polygon coordinates.
The third lev... | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... |
Recursively instantiate objects defined in dictionaries by
"_target_" and arguments.
Args:
cfg: a dict-like object with "_target_" that defines the caller, and
other keys that define the arguments
Returns:
object instantiated by cfg
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... |
If input instances don't have ID, ID_period, lost_frame_count fields,
this method is used to initialize these fields.
Args:
instances: D2 Instances, for predictions of the current frame
Return:
D2 Instances with extra fields added
""" | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | += len(instances) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/external/davis2017-evaluation/davis2017/metrics.py<fim_prefix>import math
import numpy as np
import cv2
def db_eval_iou(annotation, segmentation, void_pixels=None):
""" Compute region similarity as the Jaccard Index.
Arguments:
annotation (ndarray): binary annotation map.
... | = np.sum((segmentation & annotation) & np.logical_not(void_pixels), axis=(-2, -1)) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/layers/losses.py<fim_prefix>import math
import torch
def diou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Distance Intersection over Union Loss (Zhaohui Zheng et. al)
https://arxiv.org/abs/... | = (xkis2[mask] - xkis1[mask]) * (ykis2[mask] - ykis1[mask]) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/layers/losses.py<fim_prefix>import math
import torch
def diou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Distance Intersection over Union Loss (Zhaohui Zheng et. al)
https://arxiv.org/abs/... | = torch.min(x1, x1g) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/hungarian_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
import torch
from detectron2.structures import Boxes, Instances
from .base_tracker import BaseTracker
from scipy.optimize import linear_sum... | = torch.IntTensor(untracked_instances.pred_classes) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | m2 = min(names), max(names) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... | mask_util.decode(rle).astype(np.bool) | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... | 2:] = maxxy | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/layers/losses.py<fim_prefix>import math
import torch
def diou_loss(
boxes1: torch.Tensor,
boxes2: torch.Tensor,
reduction: str = "none",
eps: float = 1e-7,
) -> torch.Tensor:
"""
Distance Intersection over Union Loss (Zhaohui Zheng et. al)
https://arxiv.org/abs/... | = (x1g + x2g) / 2 | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/hungarian_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
import torch
from detectron2.structures import Boxes, Instances
from .base_tracker import BaseTracker
from scipy.optimize import linear_sum... | null | STATEMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/hungarian_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
import torch
from detectron2.structures import Boxes, Instances
from .base_tracker import BaseTracker
from scipy.optimize import linear_sum... | not instances.has("lost_frame_count"):
instances.set("lost_frame_count", [None] * len(instances)) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/external/davis2017-evaluation/davis2017/metrics.py<fim_prefix>import math
import numpy as np
import cv2
def db_eval_iou(annotation, segmentation, void_pixels=None):
""" Compute region similarity as the Jaccard Index.
Arguments:
annotation (ndarray): binary annotation map.
... | precision + recall == 0:
F = 0
else:
F = 2 * precision * recall / (precision + recall) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/config.py<fim_prefix># -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
... | support_var_arg: # forward all arguments to from_config, if from_config accepts them
ret = from_config_func(*args, **kwargs)
else:
# forward supported arguments to from_config
supported_arg_names = set(signature.parameters.keys())
extra_kwargs = {}
for name in list(kwargs.k... | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | not instances.has("ID_period"):
instances.set("ID_period", [None] * len(instances)) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... | isinstance(cls, str):
cls_name = cls
cls = locate(cls_name)
assert cls is not None, cls_name
else:
try:
cls_name = cls.__module__ + "." + cls.__qualname__
except Exception:
# target could be anything, so the above could... | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | not instances.has("ID"):
instances.set("ID", [None] * len(instances)) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/instances.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import itertools
from typing import Any, Dict, List, Tuple, Union
import torch
class Instances:
"""
This class represents a list of instances in an image.
It stores the attributes of instances... | name == "_fields" or name not in self._fields:
raise AttributeError("Cannot find field '{}' in the given Instances!".format(name)) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | not instances.has("lost_frame_count"):
instances.set("lost_frame_count", [None] * len(instances)) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | self._prev_instances is None:
instances.ID = list(range(len(instances)))
self._id_count += len(instances)
instances.ID_period = [1] * len(instances)
instances.lost_frame_count = [0] * len(instances) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/hungarian_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
import torch
from detectron2.structures import Boxes, Instances
from .base_tracker import BaseTracker
from scipy.optimize import linear_sum... | instances.has("pred_masks"):
untracked_instances.pred_masks.append(prev_masks[idx].numpy().astype(np.uint8)) | IF | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/boxes.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.... | [M] | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/boxes.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import math
import numpy as np
from enum import IntEnum, unique
from typing import List, Tuple, Union
import torch
from torch import device
_RawBoxType = Union[List[float], Tuple[float, ...], torch.Tensor, np.... | the inputs (and consequently confuses jit) | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | RPN hidden representation conv | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/external/davis2017-evaluation/davis2017/metrics.py<fim_prefix>import math
import numpy as np
import cv2
def db_eval_iou(annotation, segmentation, void_pixels=None):
""" Compute region similarity as the Jaccard Index.
Arguments:
annotation (ndarray): binary annotation map.
... | Get the pixel boundaries of both masks | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | ckpt_key string, if it matches | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/iou_weighted_hungarian_bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
from typing import List
import numpy as np
from .base_tracker import TRACKER_HEADS_REGISTRY
from .vanilla_hungarian_bbox_iou_tracker import Vanilla... | assign (-1 * IoU) for above threshold pairs, algorithms will minimize cost | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/external/davis2017-evaluation/davis2017/metrics.py<fim_prefix>import math
import numpy as np
import cv2
def db_eval_iou(annotation, segmentation, void_pixels=None):
""" Compute region similarity as the Jaccard Index.
Arguments:
annotation (ndarray): binary annotation map.
... | Intersection between all sets | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | remove the meaningless prediction weight for background class | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... | return as-is if don't know what to do | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | -------------------------------------------------------------------------- | LINE_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... | idx, polygons_per_instance in enumerate(self.polygons):
minxy = torch.as_tensor([float("inf"), float("inf")], dtype=torch.float32)
maxxy = torch.zeros(2, dtype=torch.float32)
for polygon in polygons_per_instance:
coords = torch.from_numpy(polygon).view(-1, 2).to(dtyp... | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | idx_model, idx_ckpt in enumerate(idxs.tolist()):
if idx_ckpt == -1:
continue
key_model = model_keys[idx_model]
key_ckpt = ckpt_keys[idx_ckpt]
value_ckpt = ckpt_state_dict[key_ckpt]
shape_in_model = model_state_dict[key_model].shape
if shape_in_model != value... | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... | idx in range(self.tensor.shape[0]):
x = torch.where(x_any[idx, :])[0]
y = torch.where(y_any[idx, :])[0]
if len(x) > 0 and len(y) > 0:
boxes[idx, :] = torch.as_tensor(
[x[0], y[0], x[-1] + 1, y[-1] + 1], dtype=torch.float32
) | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/utils.py<fim_prefix>#!/usr/bin/env python3
from detectron2.structures import Instances
import numpy as np
from typing import List
def create_prediction_pairs(
instances: Instances,
prev_instances: Instances,
iou_all: np.ndarray,
threshold: float = 0.5,
) -> List:
... | j in range(len(prev_instances)):
if iou_all[i, j] < threshold:
continue
bbox_pairs.append(
{
"idx": i,
"prev_idx": j,
"prev_id": prev_instances.ID[j],
"IoU": iou_all[i, j],
... | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | j in range(len(self._prev_instances)):
bbox_pairs.append(
{
"idx": i,
"prev_idx": j,
"prev_id": self._prev_instances.ID[j],
"IoU": iou_all[i, j],
"prev_period": se... | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | bbox_pair in bbox_pairs:
idx = bbox_pair["idx"]
prev_id = bbox_pair["prev_id"]
if idx in self._matched_idx \
or prev_id in self._matched_ID \
or bbox_pair["IoU"] < self._track_iou_threshold:
continue
... | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/hungarian_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
import torch
from detectron2.structures import Boxes, Instances
from .base_tracker import BaseTracker
from scipy.optimize import linear_sum... | i in range(matched_idx.size):
instances.ID[matched_idx[i]] = self._prev_instances.ID[matched_prev_idx[i]]
instances.ID_period[matched_idx[i]] = \
self._prev_instances.ID_period[matched_prev_idx[i]] + 1
instances.lost_frame_count[matched_idx[i]] = 0 | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/bbox_iou_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
from typing import List
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.structures import Boxes, Instances
from detec... | i in range(len(instances)):
for j in range(len(self._prev_instances)):
bbox_pairs.append(
{
"idx": i,
"prev_idx": j,
"prev_id": self._prev_instances.ID[j],
"IoU": iou_all[i, j... | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/tracking/hungarian_tracker.py<fim_prefix>#!/usr/bin/env python3
# Copyright 2004-present Facebook. All Rights Reserved.
import copy
import numpy as np
import torch
from detectron2.structures import Boxes, Instances
from .base_tracker import BaseTracker
from scipy.optimize import linear_sum... | idx in untracked_idx:
instances.ID[idx] = self._id_count
self._id_count += 1
instances.ID_period[idx] = 1
instances.lost_frame_count[idx] = 0 | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/external/davis2017-evaluation/davis2017/metrics.py<fim_prefix>import math
import numpy as np
import cv2
def db_eval_iou(annotation, segmentation, void_pixels=None):
""" Compute region similarity as the Jaccard Index.
Arguments:
annotation (ndarray): binary annotation map.
... | y in range(h):
if b[y, x]:
j = 1 + math.floor((y - 1) + height / h)
i = 1 + math.floor((x - 1) + width / h)
bmap[j, i] = 1 | FOR | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... | omegaconf import ListConfig | IMPORT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/layers/roi_align.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
from torch import nn
from torchvision.ops import roi_align
# NOTE: torchvision's RoIAlign has a different default aligned=False
class ROIAlign(nn.Module):
def __init__(self, output_size, spatial_scale, sa... | torchvision import __version__ | IMPORT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/external/davis2017-evaluation/davis2017/metrics.py<fim_prefix>import math
import numpy as np
import cv2
def db_eval_iou(annotation, segmentation, void_pixels=None):
""" Compute region similarity as the Jaccard Index.
Arguments:
annotation (ndarray): binary annotation map.
... | skimage.morphology import disk | IMPORT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/utils/registry.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
from typing import Any
import pydoc
from fvcore.common.registry import Registry # for backward compatibility.
"""
``Registry`` and `locate` provide ways to map a string (typically found
in config files) to cal... | hydra.utils import _locate | IMPORT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/config.py<fim_prefix># -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
... | .defaults import _C | IMPORT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | match(a, b):
# Matched ckpt_key should be a complete (starts with '.') suffix.
# For example, roi_heads.mesh_head.whatever_conv1 does not match conv1,
# but matches whatever_conv1 or mesh_head.whatever_conv1.
return a == b or a.endswith("." + b) | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... | process_polygons(
polygons_per_instance: List[Union[torch.Tensor, np.ndarray]]
) -> List[np.ndarray]:
if not isinstance(polygons_per_instance, list):
raise ValueError(
"Cannot create polygons: Expect a list of polygons per instance. "
... | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | fpn_map(name):
"""
Look for keys with the following patterns:
1) Starts with "fpn.inner."
Example: "fpn.inner.res2.2.sum.lateral.weight"
Meaning: These are lateral pathway convolutions
2) Starts with "fpn.res"
Example: "fpn.res2.2.sum.weight"
... | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/checkpoint/c2_model_loading.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import re
from typing import Dict, List
import torch
from tabulate import tabulate
def convert_basic_c2_names(original_keys):
"""
Apply some basic name conversion... | _submodule_name(key):
pos = key.rfind(".")
if pos < 0:
return None
prefix = key[: pos + 1]
return prefix | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/config.py<fim_prefix># -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
... | wrapped(self, *args, **kwargs):
try:
from_config_func = type(self).from_config
except AttributeError as e:
raise AttributeError(
"Class with @configurable must have a 'from_config' classmethod."
) from e
if not insp... | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/structures/masks.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import copy
import itertools
import numpy as np
from typing import Any, Iterator, List, Union
import pycocotools.mask as mask_util
import torch
from torch import device
from detectron2.layers.roi_align import ... | _make_array(t: Union[torch.Tensor, np.ndarray]) -> np.ndarray:
# Use float64 for higher precision, because why not?
# Always put polygons on CPU (self.to is a no-op) since they
# are supposed to be small tensors.
# May need to change this assumption if GPU placement beco... | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/config.py<fim_prefix># -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
... | wrapped(*args, **kwargs):
if _called_with_cfg(*args, **kwargs):
explicit_args = _get_args_from_config(from_config, *args, **kwargs)
return orig_func(**explicit_args)
else:
return orig_func(*args, **kwargs) | METHOD | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/utils/registry.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
from typing import Any
import pydoc
from fvcore.common.registry import Registry # for backward compatibility.
"""
``Registry`` and `locate` provide ways to map a string (typically found
in config files) to cal... |
# from hydra.utils import get_method - will print many errors
from hydra.utils import _locate | TRY | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... |
return cls(**cfg) | TRY | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... |
cls_name = cls.__module__ + "." + cls.__qualname__ | TRY | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/config.py<fim_prefix># -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
... |
from_config_func = type(self).from_config | TRY | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... | Exception:
# target could be anything, so the above could fail
cls_name = str(cls) | CATCH | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/utils/registry.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
from typing import Any
import pydoc
from fvcore.common.registry import Registry # for backward compatibility.
"""
``Registry`` and `locate` provide ways to map a string (typically found
in config files) to cal... | ImportError as e:
raise ImportError(f"Cannot dynamically locate object {name}!") from e | CATCH | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/config.py<fim_prefix># -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
import functools
import inspect
import logging
from fvcore.common.config import CfgNode as _CfgNode
from detectron2.utils.file_io import PathManager
class CfgNode(_CfgNode):
"""
... | AttributeError as e:
raise AttributeError(
"Class with @configurable must have a 'from_config' classmethod."
) from e | CATCH | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>UniRef/detectron2/config/instantiate.py<fim_prefix># Copyright (c) Facebook, Inc. and its affiliates.
import dataclasses
import logging
from collections import abc
from typing import Any
from detectron2.utils.registry import _convert_target_to_string, locate
__all__ = ["dump_dataclass", "instantiate"]
def... | TypeError:
logger = logging.getLogger(__name__)
logger.error(f"Error when instantiating {cls_name}!")
raise | CATCH | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
<filename>camp_zipnerf/internal/spin_math.py<fim_prefix># coding=utf-8
# Copyright 2023 The Google Research Authors.
#
# 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.... | the input according to the generalized bias and gain function.
References:
https://arxiv.org/abs/2010.09714
Args:
x: The inputs array with values in [0, 1] to map.
slope: The slope parameter of the curve which controls the slope of the
curve at the threshold.
threshold: The value at which `... | BLOCK_COMMENT | prefix_full_suffix_func_empty_complete_current_block_no_evidence |
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