python_code stringlengths 0 4.04M | repo_name stringlengths 7 58 | file_path stringlengths 5 147 |
|---|---|---|
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import atexit
import contextlib
import json
import logging
import os
import re
import shutil
import tempfile
from collections import defaultdict
from unittest import mock
import numpy as np
import torch.utils.data as data
f... | d2go-main | d2go/data/utils.py |
#!/usr/bin/env python3
from typing import List, NamedTuple, Tuple
from detectron2.utils.registry import Registry
KEYPOINT_METADATA_REGISTRY = Registry("KEYPOINT_METADATA")
KEYPOINT_METADATA_REGISTRY.__doc__ = "Registry keypoint metadata definitions"
class KeypointMetadata(NamedTuple):
names: List[str]
fli... | d2go-main | d2go/data/keypoint_metadata_registry.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from detectron2.utils.registry import Registry
D2GO_DATA_MAPPER_REGISTRY = Registry("D2GO_DATA_MAPPER")
def build_dataset_mapper(cfg, is_train, *args, **kwargs):
name = cfg.D2GO_DATA.MAPPER.NAME
return D2GO_DATA_M... | d2go-main | d2go/data/dataset_mappers/build.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
import torch
from d2go.data.dataset_mappers.build import D2GO_DATA_MAPPER_REGISTRY
from d2go.data.dataset_mappers.data_reading import (
read_image_with_prefetch,
read_se... | d2go-main | d2go/data/dataset_mappers/d2go_dataset_mapper.py |
from io import BytesIO
import numpy as np
from detectron2.data import detection_utils as utils
from detectron2.utils.file_io import PathManager
from PIL import Image
def read_image_with_prefetch(file_name, format=None, prefetched=None):
if prefetched is None:
return utils.read_image(file_name, format)
... | d2go-main | d2go/data/dataset_mappers/data_reading.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from d2go.data.dataset_mappers.build import (
build_dataset_mapper,
D2GO_DATA_MAPPER_REGISTRY,
)
from d2go.data.dataset_mappers.d2go_dataset_mapper import D2GoDatasetMapper
from d2go.data.dataset_mappers.rotated_datas... | d2go-main | d2go/data/dataset_mappers/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import logging
import numpy as np
import torch
from d2go.data.dataset_mappers.build import D2GO_DATA_MAPPER_REGISTRY
from d2go.data.dataset_mappers.d2go_dataset_mapper import D2GoDatasetMapper
from detectron2.da... | d2go-main | d2go/data/dataset_mappers/rotated_dataset_mapper.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import json
import logging
from typing import Dict, List, Optional, Tuple
from detectron2.config import CfgNode
from detectron2.data import transforms as d2T
from detectron2.utils.registry import Registry
logger = logging... | d2go-main | d2go/data/transforms/build.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Callable, List, Union
import detectron2.data.transforms.augmentation as aug
import numpy as np
from d2go.data.transforms.build import _json_load, TRANSFORM_OP_REGISTRY
from detectron2.config import CfgNod... | d2go-main | d2go/data/transforms/color_yuv.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import List, Optional, Union
import detectron2.data.transforms.augmentation as aug
import numpy as np
import torchvision.transforms as tvtf
from d2go.data.transforms.build import _json_load, TRANSFORM_OP_REGIST... | d2go-main | d2go/data/transforms/auto_aug.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import copy
import json
import random
from typing import List, Optional, Tuple
import cv2
import numpy as np
import torchvision.transforms as T
from d2go.data.transforms.build import TRANSFORM_OP_REGISTRY
from detectron2.con... | d2go-main | d2go/data/transforms/affine.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# Populating registreis
from d2go.data.transforms import ( # noqa
affine as _affine,
auto_aug,
blur as _blur,
box_utils as _box_utils,
color_yuv as _color_yuv,
crop as _crop,
d2_native as _d2_nat... | d2go-main | d2go/data/transforms/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import functools
from typing import Any, List, Tuple, Union
import detectron2.data.transforms.augmentation as aug
import numpy as np
import torch
from d2go.data.transforms.build import _json_load, TRANSFORM_OP_REGISTRY
from ... | d2go-main | d2go/data/transforms/box_utils.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Any, List, Optional, Union
import numpy as np
import torch
from detectron2.data.transforms.augmentation import Augmentation, AugmentationList
from detectron2.structures import Boxes
from fvcore.transforms... | d2go-main | d2go/data/transforms/tensor.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import math
from typing import Any, List, Optional, Tuple, Union
import d2go.data.transforms.box_utils as bu
import detectron2.data.transforms.augmentation as aug
import numpy as np
from d2go.data.transforms.build import _j... | d2go-main | d2go/data/transforms/crop.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import random
from typing import Dict, List, Tuple
import cv2
import detectron2.data.transforms.augmentation as aug
import numpy as np
from d2go.data.transforms.build import _json_load, TRANSFORM_OP_REGISTRY
from detectron2... | d2go-main | d2go/data/transforms/blur.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import logging
from typing import List, Optional, Union
import detectron2.data.transforms.augmentation as aug
from d2go.data.transforms.build import _json_load, TRANSFORM_OP_REGISTRY
from detectron2.config import CfgNode
fr... | d2go-main | d2go/data/transforms/d2_native.py |
#!/usr/bin/env python3
# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
import logging
from functools import partial
import torch.nn as nn
from d2go.config import CfgNode as CN
from d2go.modeling import modeling_hook as mh
from d2go.registry.builtin import MODELING_HOOK_REGISTRY
from d2go.train... | d2go-main | d2go/trainer/activation_checkpointing.py |
#!/usr/bin/env python3
# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
import contextlib
import logging
from enum import Enum
from typing import Generator, Optional
import torch
import torch.nn as nn
from d2go.config import CfgNode as CN
from d2go.modeling.modeling_hook import ModelingHook
fro... | d2go-main | d2go/trainer/fsdp.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
| d2go-main | d2go/trainer/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Trainer APIs on which D2Go's binary can build on top.
"""
from dataclasses import dataclass
from typing import Dict, Optional
from d2go.evaluation.api import AccuracyDict, MetricsDict
@dataclass
class TrainNetOutput:... | d2go-main | d2go/trainer/api.py |
from functools import partial
from typing import Any, Callable, Iterable, List, Optional, Union
import torch
from detectron2.utils.registry import Registry
from torch.distributed.fsdp.wrap import (
always_wrap_policy as _always_wrap_policy,
size_based_auto_wrap_policy as _size_based_auto_wrap_policy,
tran... | d2go-main | d2go/trainer/helper.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
| d2go-main | d2go/trainer/lightning/__init__.py |
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
from typing import Dict
import pytorch_lightning as pl
from d2go.config import CfgNode, temp_defrost
from d2go.runner.lightning_task import GeneralizedRCNNTask
from d2go.utils.misc import dump_trained_model_configs... | d2go-main | d2go/trainer/lightning/training_loop.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import networkx as nx
import numpy as np
import os
import tempfile
import torch
import torch.nn as nn
from networkx.algorithms.bipartite.matrix import from_biadjacency_matrix
from scipy.sparse import csr_matrix
from sklearn.metrics.pairwise import cosine_similarity
fr... | bitext-lexind-main | align/models.py |
# Copyright (c) Facebook, Inc. and its affiliates.
from train import *
from data import AlignDataset
import collections
import copy
import numpy as np
from models import Aligner
def eval_align(gold, silver, adjust=0):
assert len(gold) == len(silver)
a_size = s_size = p_size = ap_inter = as_inter = 0
for ... | bitext-lexind-main | align/test.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import regex
from data import AlignDataset
from evaluate import evaluate
from models import Aligner
import collections
resdict = collections.defaultdict(None)
aligner = Aligner(
'criss-align', distortion=0,
path='criss/criss-3rd.pt',
args_path='criss/ar... | bitext-lexind-main | align/eval_simalign_criss.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import collections
import copy
import dotdict
import json
import numpy as np
import os
import random
import regex
import tempfile
import torch
import torch.nn as nn
from glob import glob
from chinese_converter import to_traditional, to_simplified
from ... | bitext-lexind-main | align/train.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import regex
def evaluate(gold, silver, offset=0):
assert len(gold) == len(silver)
a_size = s_size = p_size = ap_inter = as_inter = 0
for i, g in enumerate(gold):
s = set([
tuple(map(lambda x: int(x), item.split('-')))
for... | bitext-lexind-main | align/evaluate.py |
# Copyright (c) Facebook, Inc. and its affiliates.
from torch.utils.data import DataLoader, Dataset
import regex
import json
import numpy as np
import os
class BitextAlignmentDataset(Dataset):
def __init__(self, bitext_path, alignment_path):
super(BitextAlignmentDataset, self).__init__()
self.bit... | bitext-lexind-main | align/data.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import collections
import copy
import dotdict
import json
import numpy as np
import os
import random
import regex
import tempfile
import torch
import torch.nn as nn
from chinese_converter import to_traditional, to_simplified
from tqdm import tqdm
from... | bitext-lexind-main | src/weakly_sup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
from transformers import AutoTokenizer
import numpy as np
import torch
import torch.nn as nn
class CRISSWrapper(object):
def __init__(self, path='criss/criss-3rd.pt',
args_path='criss/args.pt',
tokenizer='facebook/mbart-large-cc... | bitext-lexind-main | src/models.py |
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import collections
import copy
import dotdict
import json
import numpy as np
import os
import random
import regex
import tempfile
import torch
import torch.nn as nn
from chinese_converter import to_traditional, to_simplified
from tqdm import tqdm
from... | bitext-lexind-main | src/fully_unsup.py |
# Copyright (c) Facebook, Inc. and its affiliates.
def evaluate(pr_pairs, gt_pairs):
gt_set = set([tuple(x) for x in gt_pairs])
pr_set = set([tuple(x) for x in pr_pairs])
prec = sum([1 if x in gt_set else 0 for x in pr_set]) \
/ float(len(pr_set)) if len(pr_set) > 0 else 0
rec = sum([1 if x in... | bitext-lexind-main | src/evaluate.py |
"""For pip."""
from setuptools import setup, find_packages
import os
exec(open('embedding/__version__.py').read())
setup(
name="embedding",
version=__version__,
description="compute word embeddings",
packages=["embedding"],
install_requires=[
"torch",
"numba",
"scipy",
... | embedding-master | setup.py |
#!/usr/bin/env python
import torch
import numpy as np
import pandas as pd
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import seaborn as sns
import embedding
sns.set(style="whitegrid", color_codes=True)
ref = embedding.Embedding(gpu=False)
ref.load_vectors("output/pi.1000.txt")
ref.embed... | embedding-master | plot_convergence.py |
import torch
import numpy
import unittest
import embedding.tensor_type as tensor_type
torch_types = {"CPU Dense": [torch.FloatTensor,
torch.DoubleTensor,
# torch.HalfTensor,
torch.ByteTensor,
torch... | embedding-master | test/test_tensor_type_conversion.py |
import torch
import unittest
import embedding.util as util
ind = torch.LongTensor([[0, 0], [0, 1], [0, 2],
[1, 0], [1, 1], [1, 2],
[2, 0], [2, 1], [2, 2]]).t()
v = torch.FloatTensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
mat = torch.sparse.FloatTensor(ind, v, torch.Size([3, 3]))
... | embedding-master | test/test_sampling.py |
from __future__ import print_function, absolute_import
import logging
import logging.config
def init_logging(level=logging.INFO):
cfg = dict(
version=1,
formatters={
"f": {"format":
"%(levelname)-8s [%(asctime)s] %(message)s",
... | embedding-master | embedding/logging_config.py |
from __future__ import print_function, absolute_import
import torch
import numpy as np
import time
import os
import struct
import sys
import sparsesvd
import scipy.sparse
import logging
import embedding.util as util
# TODO: automatically match defaults from cmd line?
def power_iteration(mat, x, x0=None, iterations... | embedding-master | embedding/solver.py |
from __future__ import print_function, absolute_import
import torch
import numba
import numpy as np
import time
import sys
import argparse
import logging
import scipy
import scipy.sparse
import embedding.tensor_type as tensor_type
def synthetic(n, nnz):
"""This function generates a synthetic matrix."""
begi... | embedding-master | embedding/util.py |
import torch
def to_cpu(tt):
tt = string2tt(tt)
assert(tt[0])
tt[0] = False
return eval(tt2string(tt))
def to_gpu(tt):
tt = string2tt(tt)
assert(not tt[0])
tt[0] = True
return eval(tt2string(tt))
def to_dense(tt):
tt = string2tt(tt)
assert(tt[1])
tt[1] = False
retur... | embedding-master | embedding/tensor_type.py |
from .main import Embedding
from .main import main
from .evaluate import evaluate
from .__version__ import __version__
__all__ = ('solver')
from .logging_config import init_logging
init_logging()
| embedding-master | embedding/__init__.py |
__version__ = "0.0"
| embedding-master | embedding/__version__.py |
from __future__ import print_function, absolute_import
import argparse
import embedding.util as util
from embedding.__version__ import __version__
def get_parser():
parser = argparse.ArgumentParser(description="Tools for embeddings.")
# Add version to parser
parser.add_argument("-v", "--version",
... | embedding-master | embedding/parser.py |
from __future__ import print_function, absolute_import
import os
import argparse
import numpy as np
import scipy.stats
import logging
def evaluate(words, vectors):
# TODO: give option to just pass in vocab and vectors (not filename)
if type(words) == str:
with open(words, 'r') as f:
words... | embedding-master | embedding/evaluate.py |
from __future__ import print_function, absolute_import
import torch
import numpy as np
import time
import os
import struct
import argparse
import sys
import subprocess
import math
import logging
import pandas
import collections
import scipy
import embedding.solver as solver
import embedding.util as util
import embedd... | embedding-master | embedding/main.py |
"""Script to allow code to run via command line."""
import sys
from .main import main
main(sys.argv[1:])
| embedding-master | embedding/__main__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import os
from pathlib import Path
SRC_ROOT = Path(os.path.dirname(os.path.realpath(__file__)))
PRO_ROOT = SRC_ROOT.pare... | anli-main | src/config.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import argparse
from pathlib import Path
from torch.optim import Adam
from transformers import RobertaTokenizer, RobertaF... | anli-main | src/nli/training_extra.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import torch
import logging
from captum.attr import LayerIntegratedGradients
logger = logging.getLogger(__name__)
def ... | anli-main | src/nli/inspection_tools.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import argparse
from pathlib import Path
import config
from flint.data_utils.fields import RawFlintField, LabelFlintField... | anli-main | src/nli/evaluation.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree. | anli-main | src/nli/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import argparse
from pathlib import Path
import uuid
import numpy as np
import config
from flint.data_utils.batchbuilder ... | anli-main | src/nli/inference_debug.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import argparse
from pathlib import Path
from transformers import RobertaTokenizer, RobertaForSequenceClassification
from... | anli-main | src/nli/training.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import os
from pathlib import Path
import config
from datetime import datetime
from utils import common
class ScoreLog... | anli-main | src/utils/save_tool.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree. | anli-main | src/utils/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import uuid
def list_to_dict(d_list, key_fields): # '_id' or 'pid'
d_dict = dict()
for item in d_list:
... | anli-main | src/utils/list_dict_data_tool.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import json
from json import JSONEncoder
from tqdm import tqdm
import config
registered_jsonabl_classes = {}
# Some Jso... | anli-main | src/utils/common.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree. | anli-main | src/modeling/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
""" PyTorch Dummy XLNet model.
"""
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLos... | anli-main | src/modeling/dummy_modeling_xlnet.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
from torch import optim
from torch.autograd import Variable
from torch.nn import MSELoss... | anli-main | src/modeling/res_encoder.py |
anli-main | src/hg_api/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
def evaluate(tokenizer, model, pr... | anli-main | src/hg_api/interactive.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import json
def get_prediction(t... | anli-main | src/hg_api/interactive_eval.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree. | anli-main | src/dataset_tools/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
import config
from dataset_tools.format_convert import sm_nli2std_format, fever_nli2std_format, ... | anli-main | src/dataset_tools/build_data.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
from utils import common
from typing import List, Dict
from tqdm import tqdm
from collections import defaultdict
import co... | anli-main | src/dataset_tools/format_convert.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
i... | anli-main | src/flint/torch_util.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree. | anli-main | src/flint/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import torch
class FlintField(object):
@classmethod
def batching(cls, batched_data):
raise NotImplemente... | anli-main | src/flint/data_utils/fields.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree.
import torch
from typing import Dict, Type
from flint.data_utils.fields import FlintField, RawFlintField
class BaseBatc... | anli-main | src/flint/data_utils/batchbuilder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under Creative Commons-Non Commercial 4.0 found in the
# LICENSE file in the root directory of this source tree. | anli-main | src/flint/data_utils/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import dreamerv2.api as dv2
from dreamerv2.train import run
def str2bool(v):
if isinstance(v, bool):
... | cascade-main | main.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import tensorflow as tf
from tensorflow_probability import distributions as tfd
import agent
import common
class Random... | cascade-main | dreamerv2/expl.py |
import logging
import os
import pathlib
import sys
import warnings
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
logging.getLogger().setLevel('ERROR')
warnings.filterwarnings('ignore', '.*box bound precision lowered.*')
sys.path.append(str(pathlib.Path(__file__).parent))
sys.path.append(str(pathlib.Path(__file__).parent.p... | cascade-main | dreamerv2/api.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow.keras import mixed_precision as prec
from dr... | cascade-main | dreamerv2/agent.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
import pathlib
import re
import sys
import warnings
import pickle
try:
import rich.traceback
... | cascade-main | dreamerv2/train.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import atexit
import os
import sys
import threading
import traceback
import cloudpickle
import gym
import numpy as np
fr... | cascade-main | dreamerv2/common/envs.py |
import argparse
import collections
import functools
import itertools
import json
import multiprocessing as mp
import os
import pathlib
import re
import subprocess
import warnings
os.environ['NO_AT_BRIDGE'] = '1' # Hide X org false warning.
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
impor... | cascade-main | dreamerv2/common/plot.py |
import json
import pathlib
import re
class Config(dict):
SEP = '.'
IS_PATTERN = re.compile(r'.*[^A-Za-z0-9_.-].*')
def __init__(self, *args, **kwargs):
mapping = dict(*args, **kwargs)
mapping = self._flatten(mapping)
mapping = self._ensure_keys(mapping)
mapping = self._ensure_values(mapping)
... | cascade-main | dreamerv2/common/config.py |
import pathlib
import pickle
import re
import numpy as np
import tensorflow as tf
from tensorflow.keras import mixed_precision as prec
try:
from tensorflow.python.distribute import values
except Exception:
from google3.third_party.tensorflow.python.distribute import values
tf.tensor = tf.convert_to_tensor
for ba... | cascade-main | dreamerv2/common/tfutils.py |
import tensorflow as tf
import tensorflow_probability as tfp
from tensorflow_probability import distributions as tfd
# Patch to ignore seed to avoid synchronization across GPUs.
_orig_random_categorical = tf.random.categorical
def random_categorical(*args, **kwargs):
kwargs['seed'] = None
return _orig_random_cate... | cascade-main | dreamerv2/common/dists.py |
import re
import sys
class Flags:
def __init__(self, *args, **kwargs):
from .config import Config
self._config = Config(*args, **kwargs)
def parse(self, argv=None, known_only=False, help_exists=None):
if help_exists is None:
help_exists = not known_only
if argv is None:
argv = sys.ar... | cascade-main | dreamerv2/common/flags.py |
import datetime
import json
import pathlib
import imageio
import numpy as np
class Recorder:
def __init__(
self, env, directory, save_stats=True, save_video=True,
save_episode=True, video_size=(512, 512)):
if directory and save_stats:
env = StatsRecorder(env, directory)
if directory and ... | cascade-main | dreamerv2/common/recorder.py |
# General tools.
from .config import *
from .counter import *
from .flags import *
from .logger import *
from .when import *
from .eval import *
from .cdmc import *
# RL tools.
from .other import *
from .driver import *
from .envs import *
from .replay import *
# TensorFlow tools.
from .tfutils import *
from .dists i... | cascade-main | dreamerv2/common/__init__.py |
import collections
import contextlib
import re
import time
import numpy as np
import tensorflow as tf
from tensorflow_probability import distributions as tfd
from . import dists
from . import tfutils
class RandomAgent:
def __init__(self, act_space, logprob=False):
self.act_space = act_space['action']
sel... | cascade-main | dreamerv2/common/other.py |
import json
import os
import pathlib
import time
import numpy as np
class Logger:
def __init__(self, step, outputs, multiplier=1):
self._step = step
self._outputs = outputs
self._multiplier = multiplier
self._last_step = None
self._last_time = None
self._metrics = []
def add(self, mappi... | cascade-main | dreamerv2/common/logger.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
import datetime
import io
import pathlib
import uuid
import numpy as np
import tensorflow as tf
clas... | cascade-main | dreamerv2/common/replay.py |
"""In gym, the RAM is represented as an 128-element array, where each element in the array can range from 0 to 255
The atari_dict below is organized as so:
key: the name of the game
value: the game dictionary
Game dictionary is organized as:
key: state variable name
value: the element in the RAM array w... | cascade-main | dreamerv2/common/ram_annotations.py |
class Every:
def __init__(self, every):
self._every = every
self._last = None
def __call__(self, step):
step = int(step)
if not self._every:
return False
if self._last is None:
self._last = step
return True
if step >= self._last + self._every:
self._last += self._ev... | cascade-main | dreamerv2/common/when.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from collections import defaultdict
from .cdmc import DMC_TASK_IDS
import numpy as np
from scipy.stats import gmean
d... | cascade-main | dreamerv2/common/eval.py |
import numpy as np
class Driver:
def __init__(self, envs, **kwargs):
self._envs = envs
self._kwargs = kwargs
self._on_steps = []
self._on_resets = []
self._on_episodes = []
self._act_spaces = [env.act_space for env in envs]
self.reset()
def on_step(self, callback):
self._on_steps... | cascade-main | dreamerv2/common/driver.py |
import functools
@functools.total_ordering
class Counter:
def __init__(self, initial=0):
self.value = initial
def __int__(self):
return int(self.value)
def __eq__(self, other):
return int(self) == other
def __ne__(self, other):
return int(self) != other
def __lt__(self, other):
retu... | cascade-main | dreamerv2/common/counter.py |
import re
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers as tfkl
from tensorflow_probability import distributions as tfd
from tensorflow.keras.mixed_precision import experimental as prec
import common
class EnsembleRSSM(common.Module):
def __init__(
self, ensemble=5, stoch=3... | cascade-main | dreamerv2/common/nets.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
import os
from dm_control import mujoco
from dm_control.rl import control
from dm_control.suite import... | cascade-main | dreamerv2/common/cdmc/walker.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
from .walker import make_walker
from .cheetah import make_cheetah
def make_dmc_all(domain, task,
task_kwargs=Non... | cascade-main | dreamerv2/common/cdmc/__init__.py |
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import collections
from dm_control import mujoco
from dm_control.rl import control
from dm_control.suite import base
from... | cascade-main | dreamerv2/common/cdmc/cheetah.py |
#!/usr/bin/env python
#
# Copyright 2008, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... | dimmwitted-master | lib/gtest-1.7.0/xcode/Scripts/versiongenerate.py |
#!/usr/bin/env python
#
# Copyright 2006, Google Inc.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list... | dimmwitted-master | lib/gtest-1.7.0/test/gtest_xml_output_unittest.py |
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