python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
"""Type symbols class."""
import copy
import os
from typing import Dict, List, Optional, Set, Union
from tqdm.auto import tqdm
from bootleg.symbols.constants import edit_op
from bootleg.utils import utils
from bootleg.utils.classes.nested_vocab_tries import TwoLayerVocabularyScoreTrie
def _convert_to_trie(qid2type... | bootleg-master | bootleg/symbols/type_symbols.py |
"""Constants."""
import logging
import os
from distutils.util import strtobool
from functools import wraps
from bootleg import log_rank_0_info
logger = logging.getLogger(__name__)
USE_STRIP = strtobool(os.environ.get("BOOTLEG_STRIP", "true"))
USE_LOWER = strtobool(os.environ.get("BOOTLEG_LOWER", "true"))
LANG_CODE ... | bootleg-master | bootleg/symbols/constants.py |
"""Symbols init."""
| bootleg-master | bootleg/symbols/__init__.py |
"""Entity symbols."""
import copy
import logging
import os
from typing import Callable, Dict, Optional, Union
from tqdm.auto import tqdm
import bootleg.utils.utils as utils
from bootleg.symbols.constants import edit_op
from bootleg.utils.classes.nested_vocab_tries import (
TwoLayerVocabularyScoreTrie,
Vocabul... | bootleg-master | bootleg/symbols/entity_symbols.py |
"""Bootleg slice dataset."""
import hashlib
import logging
import multiprocessing
import os
import shutil
import time
import traceback
from collections import defaultdict
import numpy as np
import ujson
from tqdm.auto import tqdm
from bootleg import log_rank_0_debug, log_rank_0_info
from bootleg.symbols.constants imp... | bootleg-master | bootleg/slicing/slice_dataset.py |
"""Slicing initializer."""
| bootleg-master | bootleg/slicing/__init__.py |
import re
import tempfile
from pathlib import Path
from subprocess import call
import argh
from rich.console import Console
from bootleg.utils.utils import load_yaml_file
console = Console(soft_wrap=True)
bert_dir = tempfile.TemporaryDirectory().name
checkpoint_regex = re.compile(r"checkpoint_(\d+\.{0,1}\d*).model.... | bootleg-master | configs/gcp/launch_gcp.py |
import os
import jsonlines
import numpy as np
import pandas as pd
import requests
import tagme
import ujson
from tqdm.auto import tqdm
from bootleg.symbols.entity_profile import EntityProfile
pd.options.display.max_colwidth = 500
def load_train_data(train_file, title_map, entity_profile=None):
"""Loads a jsonl... | bootleg-master | tutorials/utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
from monodepth.depth_model_registry import get_depth_model, get_depth_model_list
from depth_fine_tuning import DepthFineTuningParams
from scale_calibration import ScaleCalibrationParams
from utils import frame_sampling, frame_r... | consistent_depth-main | params.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import cv2
import numpy as np
import os
from os.path import join as pjoin
import logging
from typing import Optional, Set
import torch
from utils.helpers import SuppressedStdout
from loaders.video_dataset import _dtype, load_color
from tools.c... | consistent_depth-main | scale_calibration.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import copy
import cv2
import numpy as np
import os
import torch
from third_party.flownet2.models import FlowNet2
from third_party.OpticalFlowToolkit.lib.flowlib import flow_to_image
from utils.image_io import save_raw_float32_i... | consistent_depth-main | optical_flow_flownet2_homography.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import cv2
import itertools
import json
import math
import os
from os.path import join as pjoin
import time
import torch
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
import torchvision.utils as vutils... | consistent_depth-main | depth_fine_tuning.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import cv2
import json
import numpy as np
import os
from os.path import join as pjoin
import torch
from third_party.OpticalFlowToolkit.lib import flowlib
from utils.url_helpers import get_model_from_url
import optical_flow_flownet2_homography... | consistent_depth-main | flow.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import logging
import os
from os.path import join as pjoin
import shutil
from depth_fine_tuning import DepthFineTuner
from flow import Flow
from scale_calibration import calibrate_scale
from tools import make_video as mkvid
from utils.frame_ran... | consistent_depth-main | process.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from params import Video3dParamsParser
from process import DatasetProcessor
if __name__ == "__main__":
parser = Video3dParamsParser()
params = parser.parse()
dp = DatasetProcessor()
dp.process(params)
| consistent_depth-main | main.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import cv2
import logging
import os
from os.path import join as pjoin
import sys
import tempfile
from utils import (frame_sampling, image_io)
from utils.helpers import mkdir_ifnotexists
ffmpeg = "ffmpeg"
ffprobe = "ffprobe"
def sample_pairs(... | consistent_depth-main | video.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import logging
import sys
import os
from os.path import join as pjoin
import shutil
import subprocess
from typing import Tuple, Optional, List
import cv2
LOG = logging.getLogger()
LOG.setLevel("INFO")
formatter = logging.Forma... | consistent_depth-main | tools/make_video.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import logging
import os
from os.path import join as pjoin
import subprocess
import sys
import numpy as np
class COLMAPParams:
def __init__(self):
self.parser = argparse.ArgumentParser()
self.parser.add_arg... | consistent_depth-main | tools/colmap_processor.py |
#!/usr/bin/env python3
from torch.optim.optimizer import Optimizer
from torch.optim import Adam
OPTIMIZER_MAP = {
"Adam": Adam,
}
OPTIMIZER_NAMES = OPTIMIZER_MAP.keys()
OPTIMIZER_CLASSES = OPTIMIZER_MAP.values()
def create(optimizer_name: str, *args, **kwargs) -> Optimizer:
return OPTIMIZER_MAP[optimizer... | consistent_depth-main | optimizer/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
import torch.nn as nn
from utils.torch_helpers import _device
from utils.geometry import (
pixel_grid,
focal_length,
project,
pixels_to_points,
reproject_points,
sample,
)
def select_tensors(x):
"""
... | consistent_depth-main | loss/consistency_loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
class LossParams:
"""
Loss related parameters
"""
@staticmethod
def add_arguments(parser):
parser.add_argument(
"--lambda_view_baseline",
type=float,
default=-1,
help=... | consistent_depth-main | loss/loss_params.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
class ParameterLoss(torch.nn.Module):
def __init__(self, parameters_init, opt):
self.parameters_init = parameters_init
self.opt = opt
assert opt.lambda_parameter > 0
def __call__(self, parameters):... | consistent_depth-main | loss/parameter_loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from typing import List, Optional
import torch
from torch.nn import Parameter
from .parameter_loss import ParameterLoss
from .consistency_loss import ConsistencyLoss
from utils.torch_helpers import _device
from loaders.video_dataset import _dt... | consistent_depth-main | loss/joint_loss.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from collections import namedtuple
from enum import Enum, unique, auto
from typing import Iterable, NamedTuple, Dict, Any, Set
import numpy as np
from .frame_range import FrameRange
@unique
class SamplePairsMode(Enum):
EXHAUSTED = 0
C... | consistent_depth-main | utils/frame_sampling.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from typing import Set, Optional
from collections import namedtuple
# set is an OptionalSet as below
NamedOptionalSet = namedtuple("NamedOptionalSet", ["name", "set"])
class OptionalSet:
def __init__(self, set: Optional[Set] = None):
... | consistent_depth-main | utils/frame_range.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import numpy as np
import os
from PIL import Image
import cv2
import struct
from subprocess import call
import warnings
import six
if six.PY2:
class ResourceWarning(RuntimeWarning):
pass
# Needed to suppress Resou... | consistent_depth-main | utils/image_io.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from __future__ import absolute_import, division, print_function, unicode_literals
import numpy as np
from third_party.colmap.scripts.python.read_write_model import (
CAMERA_MODELS,
rotmat2qvec,
Camera,
BaseImage,
write_mode... | consistent_depth-main | utils/load_colmap.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
import torch.nn
def sample(data, uv):
"""Sample data (H, W, <C>) by uv (H, W, 2) (in pixels). """
shape = data.shape
# data from (H, W, <C>) to (1, C, H, W)
data = data.reshape(data.shape[:2] + (-1,))
dat... | consistent_depth-main | utils/consistency.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import os
from os.path import join as pjoin
import wget
from zipfile import ZipFile
def get_model_from_url(
url: str, local_path: str, is_zip: bool = False, path_root: str = "checkpoints"
) -> str:
local_path = pjoin(path_root, local_p... | consistent_depth-main | utils/url_helpers.py |
consistent_depth-main | utils/__init__.py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import cv2
import numpy
import os
import subprocess
import sys
import logging
from matplotlib.cm import get_cmap
from . import image_io
CM_MAGMA = (numpy.array([get_cmap('magma').colors]).
transpose([1, 0, 2]) * 255)[..., ::-1].a... | consistent_depth-main | utils/visualization.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from typing import Tuple
def reproject(pts3d: np.ndarray, extr: np.ndarray) -> np.ndarray:
assert pts3d.shape[0] == extr.shape[0] and pts3d.shape[0] == 3
p_dim, _ = pts3d.shape
R, t = extr[:, :p_dim], extr[:, -1:... | consistent_depth-main | utils/geometry_np.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
_device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
def to_device(data):
if isinstance(data, torch.Tensor):
data = data.to(_device, non_blocking=True)
return data
if isin... | consistent_depth-main | utils/torch_helpers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
from .torch_helpers import _device
from typing import List
def pixel_grid(batch_size, shape):
"""Returns pixel grid of size (batch_size, 2, H, W).
pixel positions (x, y) are in range [0, W-1] x [0, H-1]
top left is (0,... | consistent_depth-main | utils/geometry.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import numpy as np
from numpy.linalg import inv
import cv2
from sklearn import linear_model
def resize_small(gt, x, interp=cv2.INTER_NEAREST):
"""
Resize to match the smaller image.
"""
def size(x):
return x.shape[:2][:... | consistent_depth-main | utils/calibrate.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import os
import sys
class dotdict(dict):
"""dot.notation access to dictionary attributes"""
__getattr__ = dict.get
__setattr__ = dict.__setitem__
__delattr__ = dict.__delitem__
def mkdir_ifnotexists(dir):
if os.path.exis... | consistent_depth-main | utils/helpers.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import os
from os.path import join as pjoin
from typing import Dict, Tuple
import numpy as np
from . import load_colmap, image_io as tr
from .geometry_np import reproject, project, sample
def store_visible_points_per_image(
points3D: Dict[... | consistent_depth-main | utils/calibration.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import os
import cv2
from os.path import join as pjoin
import json
import math
import numpy as np
import torch.utils.data as data
import torch
from typing import Optional
from utils import image_io, frame_sampling as sampling
_dtype = torch.f... | consistent_depth-main | loaders/video_dataset.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
from utils.url_helpers import get_model_from_url
from .midas_v2.midas_net import MidasNet
from .depth_model import DepthModel
class MidasV2Model(DepthModel):
# Requirements and default settings
align = 32
learning_ra... | consistent_depth-main | monodepth/midas_v2_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
import torch
import torch.autograd as autograd
from utils.helpers import SuppressedStdout
from utils.url_helpers import get_model_from_url
from .mannequin_challenge.models import pix2pix_model
from .mannequin_challenge.options.train_options im... | consistent_depth-main | monodepth/mannequin_challenge_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from os.path import join as pjoin
import torch
from utils.url_helpers import get_model_from_url
from .depth_model import DepthModel
from .monodepth2.networks.resnet_encoder import ResnetEncoder
from .monodepth2.networks.depth_decoder import D... | consistent_depth-main | monodepth/monodepth2_model.py |
consistent_depth-main | monodepth/__init__.py | |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from abc import abstractmethod
import torch
class DepthModel(torch.nn.Module):
def __init__(self):
super().__init__()
def forward(self, images, metadata=None):
"""
Images should be feed in the format (N, C, H, ... | consistent_depth-main | monodepth/depth_model.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
from .depth_model import DepthModel
from .mannequin_challenge_model import MannequinChallengeModel
from .midas_v2_model import MidasV2Model
from .monodepth2_model import Monodepth2Model
from typing import List
def get_depth_model_list() -> Li... | consistent_depth-main | monodepth/depth_model_registry.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os.path as osp
import setuptools
cur_dir = osp.dirname(osp.realpath(__file__))
requirementPath = osp.join(cur_di... | bc-irl-main | setup.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import os.path as osp
from collections import defaultdict
from typing import Dict, Optional
import gym.spaces ... | bc-irl-main | imitation_learning/run.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| bc-irl-main | imitation_learning/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import hydra
from omegaconf import OmegaConf
from imitation_learning.run import main
@hydra.main(config_path="config",... | bc-irl-main | imitation_learning/eval.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
from collections import defaultdict
from functools import partial
import numpy as np
import torch
impor... | bc-irl-main | imitation_learning/gail/updater.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from enum import Enum, auto
from typing import Tuple
import torch
import torch.nn as nn
from rl_utils.common import make... | bc-irl-main | imitation_learning/gail/discriminator.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from functools import partial
import numpy as np
import torch
import torch.nn as nn
from rl_utils.models import (FixedCa... | bc-irl-main | imitation_learning/policy_opt/policy.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| bc-irl-main | imitation_learning/policy_opt/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
from typing import Dict, Optional
import torch
def _flatten_helper(T, N, _tensor):... | bc-irl-main | imitation_learning/policy_opt/storage.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Any, Dict
import torch
import torch.nn as nn
from hydra.utils import instantiate as hydra_instantiate... | bc-irl-main | imitation_learning/policy_opt/ppo.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from higher.optim import DifferentiableOptimizer
from hydra.utils import instantiate
f... | bc-irl-main | imitation_learning/bc_irl/differentiable_ppo.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| bc-irl-main | imitation_learning/bc_irl/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Callable
import higher
import torch
import torch.nn as nn
from hydra.utils import call, instantiate
f... | bc-irl-main | imitation_learning/bc_irl/updater.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from enum import Enum, auto
import torch
import torch.nn as nn
from hydra.utils import instantiate
from rl_utils.common ... | bc-irl-main | imitation_learning/bc_irl/rewards.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import itertools
from collections import defaultdict
import numpy as np
import torch
import torch.nn as nn
import torch.... | bc-irl-main | imitation_learning/f_irl/updater.py |
bc-irl-main | imitation_learning/config/logger/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import numpy as np
import torch
import torch.nn as nn
from hydra.utils import call, instantiate
from omegaconf import Dic... | bc-irl-main | imitation_learning/maxent/updater.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Tuple
import torch
import torch.nn as nn
from rl_utils.common import make_mlp_layers
class AirlDisc... | bc-irl-main | imitation_learning/airl/discriminator.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from hydra.utils import call, instantiate
from omegaconf import DictConfig
from rl_uti... | bc-irl-main | imitation_learning/gcl/updater.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os.path as osp
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import seabo... | bc-irl-main | imitation_learning/common/pointmass_utils.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os.path as osp
import matplotlib.pyplot as plt
import numpy as np
def plot_actions(pred_actions, gt_actions, n_... | bc-irl-main | imitation_learning/common/plotting.py |
from enum import Enum, auto
import torch
import torch.nn as nn
from rl_utils.common import make_mlp_layers
class RewardInputType(Enum):
ACTION = auto()
NEXT_STATE = auto()
CUR_NEXT_STATE = auto()
class NeuralReward(nn.Module):
def __init__(
self,
obs_shape,
action_dim,
... | bc-irl-main | imitation_learning/common/net.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
| bc-irl-main | imitation_learning/common/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Dict, List, Tuple
import torch
from rl_utils.common import DictDataset
def log_finished_rewards(
... | bc-irl-main | imitation_learning/common/utils.py |
from setuptools import setup, find_packages
setup(
name='treedlib',
version='0.1.3',
description='Library of tree features.',
packages=find_packages(),
install_requires=[
'lxml',
],
classifiers=[
"License :: OSI Approved :: MIT License",
],
url='https://github.com/Ha... | treedlib-master | setup.py |
from feature_template import *
from itertools import chain
def get_mention_templates(cid, d):
"""
Generate the DDLib mention features as per
http://deepdive.stanford.edu/doc/basics/gen_feats.html
"""
return [
# The set of POS tags comprising the mention
Indicator(Mention(cid), 'pos'),
# The... | treedlib-master | archive/basic_features.py |
from itertools import chain
import re
# op_0 : X -> p(T)
# op_1 : p(T) -> p(T)
# ind : p(T) -> {0,1}^F
class FeatureTemplate:
"""Base feature template class"""
def __init__(self):
self.label = None
self.xpaths = set(['//node'])
self.subsets = None # subtrees / p(T)
def apply(self, root):
"""
... | treedlib-master | archive/feature_template.py |
from collections import namedtuple
import re
import sys
def print_gen(gen):
"""Print the results of a generator one-per-line"""
for e in gen:
print(e)
def print_error(err_string):
"""Function to write to stderr"""
sys.stderr.write("ERROR[UDF]: " + str(err_string) + "\n")
BOOL_PARSER = {
't' : True,
... | treedlib-master | treedlib/util.py |
import os
# Set TREEDLIB_APP env var, for use in libs we load
os.environ["TREEDLIB_LIB"] = os.path.dirname(os.path.realpath(__file__))
# Load treedlib libs
from treedlib.util import *
from treedlib.structs import *
from treedlib.templates import *
from treedlib.features import *
| treedlib-master | treedlib/__init__.py |
from treedlib.templates import *
import lxml.etree as et
def compile_relation_feature_generator(dictionaries=None, opts={}, is_multary=False):
"""
Given optional arguments, returns a generator function which accepts an xml root
and two lists of mention indexes, and will generate relation features for this relati... | treedlib-master | treedlib/features.py |
from itertools import chain
import re
import lxml.etree as et
from collections import defaultdict
# NODESET:
# ===========
class NodeSet:
"""
NodeSet objects are functions f : 2^T -> 2^T
---------------
They are applied compositionally and lazily, by constructing an xpath query
We use these to get the *sub... | treedlib-master | treedlib/templates.py |
import json
import os
import re
import lxml.etree as et
import sys
# This should be set by the lib wrapper __init__.py file
APP_HOME = os.environ["TREEDLIB_LIB"]
# Load IPython display functionality libs if possible i.e. if in IPython
try:
from IPython.core.display import display_html, HTML, display_javascript, Jav... | treedlib-master | treedlib/structs.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from distutils.core import setup
setup(
name="bela",
version="0.1",
packages=["bela"],
) | BELA-main | setup.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# HACK: Need to import protobuf before pytorch_lightning to prevent Segmentation Fault: https://github.com/protocolbuffers/protobuf/issues/11... | BELA-main | bela/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import hydra
from bela.conf.config import MainConfig
from omegaconf import OmegaConf
from pytorch_lightning.trainer import Trainer... | BELA-main | bela/main.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
import logging
import mmap
from typing import List, Optional
import torch
from pytorch_lightning import LightningDataModule
fro... | BELA-main | bela/datamodule/joint_el_datamodule.py |
BELA-main | bela/tests/__init__.py | |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import os
import torch
import torch
from bela.transforms.joint_el_transform import JointELTransform
from bela.datamodule.joi... | BELA-main | bela/tests/test_datamodules.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from bela.models.hf_encoder import HFEncoder
from bela.transforms.joint_el_transform import JointELTransform
c... | BELA-main | bela/tests/test_models.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import unittest
import torch
from bela.transforms.joint_el_transform import JointELTransform, JointELXlmrRawTextTransform
class TestJointE... | BELA-main | bela/tests/test_transforms.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
from functools import lru_cache
from bela.evaluation.model_eval import ModelEval
from bela.transforms.sp... | BELA-main | bela/utils/prediction_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
class DummyPathManager:
def get_local_path(self, path, *args, **kwargs):
return path
def open(self, path, *args, **kwargs):
... | BELA-main | bela/utils/utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass
from typing import Any, Dict, Optional, List
@dataclass
class Entity:
entity_id: str # E.g. "Q331212... | BELA-main | bela/utils/analysis_utils.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import torch.nn as nn
from transformers import AutoModel, AutoConfig
class HFEncoder(nn.Module):
def __ini... | BELA-main | bela/models/hf_encoder.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from transformers import AutoTokenizer
class HFTransform(nn.Module):
def __init__(
self,
model_pa... | BELA-main | bela/transforms/hf_transform.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: sentencepiece.proto
"""Generated protocol buffer... | BELA-main | bela/transforms/sentencepiece_pb2.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from typing import Optional
import os
import torch.nn as nn
import sentencepiece as spm
from .sentencepiece_pb2 import SentencePieceText
cl... | BELA-main | bela/transforms/spm_transform.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from enum import Enum
from typing import Any, Dict, List, Optional, Tuple
import torch
import torch.nn as nn
from torch.nn.utils.rnn import ... | BELA-main | bela/transforms/joint_el_transform.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
from collections import OrderedDict
from typing import Any, Dict, NamedTuple, Optional, Tuple, Union
import faiss
import fais... | BELA-main | bela/task/joint_el_task.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
import yaml
from hydra.experimental import compose, initialize_config_module
import hydra
import torch
from tqdm imp... | BELA-main | bela/evaluation/model_eval.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from typing import List, Any
# @manual "//github/facebookresearch/hydra:hydra"
from hydra.core.conf... | BELA-main | bela/conf/config.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from dataclasses import dataclass, field
from . import config
@dataclass
class TransformConf:
pass
@dataclass
class DataModuleConf:
... | BELA-main | bela/conf/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
from itertools import product
from tqdm import tqdm
import numpy as np
from bela.evaluation.model_eval import Mode... | BELA-main | scripts/grid_search_thresholds.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import warnings
warnings.filterwarnings('ignore')
import yaml
from hydra.experimental import compose, initialize_config_module
import hydra
... | BELA-main | scripts/evaluate.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import logging
import os
import pickle
import re
import pandas
import jsonlines
from mgenre.utils import chunk_it, get_wiki... | BELA-main | preprocessing_scripts/preprocess_TR2016.py |
BELA-main | mblink/__init__.py |
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