python_code stringlengths 0 4.04M | repo_name stringlengths 8 58 | file_path stringlengths 5 147 |
|---|---|---|
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 |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torchvision
from transformers import BertForSequenceClassification, AdamW, get_scheduler
class ToyNet(torch.nn.Module):
def __init__(self, dim, gammas):
super(ToyNet, self).__init__()
# gammas is a list of ... | BalancingGroups-main | models.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import os
import re
import tarfile
from zipfile import ZipFile
import logging
logging.basicConfig(level=logging.INFO)
import gdown
import pandas as pd
from six import remove_move
def download_and_extract(url, dst, remove=True):
... | BalancingGroups-main | setup_datasets.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import torch
import submitit
from itertools import product
from train import run_experiment, parse_args
def product_dict(**kwargs):
keys = kwargs.keys()
vals = kwargs.values()
for instance in product(*vals):
yield di... | BalancingGroups-main | train_toy.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import torch
import pandas as pd
import numpy as np
from PIL import Image
from torchvision import transforms
from transformers import BertTokenizer
from torch.utils.data import DataLoader
from sklearn.datasets import make_blobs
import pa... | BalancingGroups-main | datasets.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import matplotlib
from matplotlib.colors import ListedColormap
import numpy as np
import torch
import torch.utils.data
from models import ToyNet
from parse import parse_json_to_df
from datasets import Toy
import matplotlib.pyplot as plt
from torch ... | BalancingGroups-main | plot_toy_scatter.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#!/usr/bin/env python
import os
import sys
import json
import time
import torch
import submitit
import argparse
import numpy as np
import models
from datasets import get_loaders
class Tee:
def __init__(self, fname, stream, mode="a+"):
... | BalancingGroups-main | train.py |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#!/usr/bin/env python
import os
import glob
import json
import argparse
from typing import ContextManager
import pandas as pd
from pandas.core.indexes import multi
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from pathl... | BalancingGroups-main | parse.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
from utils.masks import generate_masks, evaluate_masks
import torch
def train(*args, **kwargs):
return {}
params =... | calibration_membership-main | api.py |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import json
import os
import sys
import inspect
currentdir = os.path.dirname(os... | calibration_membership-main | training/image_classification.py |
# -*- coding: utf-8 -*-
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import argparse
import json
import os
from models import build_model
from datasets import get_... | calibration_membership-main | training/language_modeling.py |
calibration_membership-main | training/__init__.py | |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import Dataset, Tenso... | calibration_membership-main | datasets/__init__.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
class TextIterator:
def __init__(self, sequence, batch_size, seq_len):
assert sequence.ndim =... | calibration_membership-main | datasets/text_data.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
# Taken from https://github.com/facebookresearch/XLM
import argparse
FALSY_STRINGS = {'off', 'false', '0'}
TRUTHY_STRI... | calibration_membership-main | utils/misc.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
from datetime import timedelta
import logging
import re
import sys
import time
class LogFormatter():
def __init_... | calibration_membership-main | utils/logger.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import numpy as np
import torch
import operator
def to_mask(n_data, indices):
mask = torch.zeros(n_data, dtype=bo... | calibration_membership-main | utils/masks.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import re
import inspect
import json
import itertools
from torch import optim
import numpy as np
from logging import ge... | calibration_membership-main | utils/optimizer.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
from collections import OrderedDict
import functools
import os
import time
import numpy as np
import torch
from torch.... | calibration_membership-main | utils/trainer.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
from logging import getLogger
from collections import OrderedDict
import numpy as np
import torch
from torch.nn import ... | calibration_membership-main | utils/evaluator.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
import torch.nn.functional as F
class KLLeNet(nn.Module):
def __init__(self, params):
... | calibration_membership-main | models/KLlenet.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
import torch.nn.functional as F
class LinearNet(nn.Module):
def __init__(self, params):
... | calibration_membership-main | models/linear.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
import torch.nn.functional as F
from .lenet import LeNet
from .KLlenet import KLLeNet
from .lstml... | calibration_membership-main | models/__init__.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
import torch.nn.functional as F
class MLP(nn.Module):
def __init__(self, params):
su... | calibration_membership-main | models/mlp.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch.nn as nn
import torch.nn.functional as F
class LeNet(nn.Module):
def __init__(self, params):
... | calibration_membership-main | models/lenet.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
from opacus.layers import DPLSTM
import torch
import torch.nn as nn
class LSTMLM(nn.Module):
def __init__(self, p... | calibration_membership-main | models/lstmlm.py |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import torch
import torch.nn as nn
class AlexNet(nn.Module):
def __init__(self, params):
super(AlexNet, s... | calibration_membership-main | models/alexnet.py |
calibration_membership-main | attacks/__init__.py | |
# Copyright (c) 2015-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the CC-by-NC license found in the
# LICENSE file in the root directory of this source tree.
#
import os
from posixpath import join
import sys
import inspect
import math
from random import randrange
import pickle ... | calibration_membership-main | attacks/privacy_attacks.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/closest_augs.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/test_imagenet.py |
# Copyright (c) Facebook, Inc. and its 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 argparse
import overlap.utils.logging as lu
import decimal
import simplejson
import numpy as np
import omegaconf
from itertoo... | augmentation-corruption-fbr_main | experiments/sample_datasets.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net_jsd import train_net
from overlap.test_net import test_... | augmentation-corruption-fbr_main | experiments/train_imagenet_jsd.py |
# Copyright (c) Facebook, Inc. and its 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 numpy as np
import argparse
parser = argparse.ArgumentParser(description="Calculate corruptions distance "\
"to the ... | augmentation-corruption-fbr_main | experiments/calc_distance_shifts.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/severity_scan_imagenet.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net_jsd import train_net
from overlap.test_net import test_... | augmentation-corruption-fbr_main | experiments/train_cifar10_jsd.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/feature_corrupt_error.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/severity_scan.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/test_cifar10.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/train_imagenet.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 hydra.utils import instantiate
import logging
from overlap.train_net import train_net
from overlap.test_net import test_net
... | augmentation-corruption-fbr_main | experiments/train_cifar10.py |
# Copyright (c) Facebook, Inc. and its 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 argparse
import overlap.utils.logging as lu
import decimal
import simplejson
import numpy as np
import omegaconf
from itertoo... | augmentation-corruption-fbr_main | experiments/tools/get_target_error.py |
# Copyright (c) Facebook, Inc. and its 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 argparse
import overlap.utils.logging as lu
import decimal
import simplejson
import numpy as np
import omegaconf
parser = ar... | augmentation-corruption-fbr_main | experiments/tools/summarize.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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 argparse
parser = argparse.ArgumentParser(description="Generate random indicies '\
'for sampling from the C... | augmentation-corruption-fbr_main | experiments/tools/sample_image_indices.py |
# Copyright (c) Facebook, Inc. and its 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
import logging
from .utils import logging as lu
from omegaconf import open_dict
from .augmentations.utils import aug_finder
from... | augmentation-corruption-fbr_main | experiments/overlap/test_corrupt_net.py |
# Copyright (c) Facebook, Inc. and its 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
import logging
from .utils import logging as lu
log = logging.getLogger(__name__)
def test_net(model, test_dataset, batch_size,... | augmentation-corruption-fbr_main | experiments/overlap/test_net.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResHead(nn.Module):
"""ResNet head."""
def __in... | augmentation-corruption-fbr_main | experiments/overlap/models.py |
# Copyright (c) Facebook, Inc. and its 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
import logging
from .utils import logging as lu
import numpy as np
import os
log = logging.getLogger(__name__)
def distributed_... | augmentation-corruption-fbr_main | experiments/overlap/extract_features.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import augmentations as aug
from .augmentations.utils.converters import NumpyToTensor, PilToNumpy
from .augmentations.utils.aug_finder ... | augmentation-corruption-fbr_main | experiments/overlap/datasets.py |
augmentation-corruption-fbr_main | experiments/overlap/__init__.py | |
# Copyright (c) Facebook, Inc. and its 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
import logging
import os
import time
import datetime
import torch.nn as nn
import torch.nn.functional as F
log = logging.getLogg... | augmentation-corruption-fbr_main | experiments/overlap/train_net_jsd.py |
# Copyright (c) Facebook, Inc. and its 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
import numpy as np
from hydra.utils import instantiate
from .train_net import train_net
class Network(object):
def __in... | augmentation-corruption-fbr_main | experiments/overlap/feature_extractor.py |
# This source code is adapted from code licensed under the MIT license
# found in third_party/wideresnet_license from the root directory of
# this source tree.
"""WideResNet implementation (https://arxiv.org/abs/1605.07146)."""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class Bas... | augmentation-corruption-fbr_main | experiments/overlap/wideresnet.py |
# Copyright (c) Facebook, Inc. and its 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
import logging
import os
import time
import datetime
log = logging.getLogger(__name__)
def eta_str(eta_td):
"""Converts an ... | augmentation-corruption-fbr_main | experiments/overlap/train_net.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import simplejson
import decimal
import logging
log = logging.getLogger(__name__)
_TAG = 'json_stats: '
def log_json_stats(stats):
"""Lo... | augmentation-corruption-fbr_main | experiments/overlap/utils/logging.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
class Cosine(object):
def __init__(self, base_lr, max_epoch):
self.base_lr = base_lr
self.max_epoch = ... | augmentation-corruption-fbr_main | experiments/overlap/utils/lr_policy.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
from scipy.ndimage import gaussian_filter
from .utils.severity import float_parameter, int_parameter, sample_le... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/distortion.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
from .utils.image import bilinear_interpolation
from .utils.severity import float_parameter, int_parameter, sam... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/blurs.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
from math import floor, ceil
import numpy as np
class Gaussian(Augmentation):
name = 'pg_gaussian'
tag... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/patch_gaussian.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
import numpy as np
from .utils.severity import int_parameter, float_parameter, sample_level
from .utils.image i... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/color.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from . import identity
from . import base
from . import pil
from . import obscure
from . import additive_noise
from . import color
from . impo... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/__init__.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
from .utils.severity import float_parameter, int_parameter, sample_level
from .utils.image import smoothstep
fr... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/additive_noise.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
import numpy as np
class Cifar10CropAndFlip(Augmentation):
def sample_parameters(self):
crop_pos ... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/standard_augmentations.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
from .utils.severity import float_parameter, int_parameter, sample_level
from PIL import Image, ImageOps, Imag... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/pil.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from math import floor, ceil
import numpy as np
from .base import Augmentation
from .utils.severity import int_parameter, sample_level, float_... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/obscure.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
class Identity(Augmentation):
tags = ["identity"]
name = ['identity']
def __init__(self, severit... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/identity.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from .base import Augmentation
from collections import namedtuple
import numpy as np
class Augmix(Augmentation):
tags = ['compositor', '... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/compositions.py |
# This source code is adapted from code licensed under the license at
# third_party/imagenetc_license from the root directory of the repository
# Originally available: github.com/hendrycks/robustness
# Modifications Copyright (c) Facebook, Inc. and its affiliates,
# licensed under the MIT license found in the LICENSE... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/imagenetc.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import abc
import numpy as np
def is_iterable(obj):
try:
iter(obj)
except:
return False
else:
return Tru... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/base.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
def int_parameter(level, maxval):
return int(level * maxval / 10)
def float_parameter(level, maxval):
return float(l... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/utils/severity.py |
augmentation-corruption-fbr_main | experiments/overlap/augmentations/utils/__init__.py | |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from ... import augmentations as aug
master_aug_list = [
aug.pil.AutoContrast,
aug.pil.Equalize,
aug.pil.Posterize,
aug.pil.S... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/utils/aug_finder.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
class PerlinNoiseGenerator(object):
def __init__(self, random_state=None):
self.rand = np.random if random_sta... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/utils/noise.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
from PIL import Image
import torch
class PilToNumpy(object):
def __init__(self, as_float=False, scaled_to_one=False):
... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/utils/converters.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
def smoothstep(low, high, x):
x = np.clip(x, low, high)
x = (x - low) / (high - low)
return np.clip(3 * (x ** ... | augmentation-corruption-fbr_main | experiments/overlap/augmentations/utils/image.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from transform_finder import build_transform
import torch
import torchvision as tv
from utils.converters import PilToNumpy, NumpyToTensor
CIF... | augmentation-corruption-fbr_main | imagenet_c_bar/test_c_bar.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import corrupt as corr
transform_list = [
corr.ColorBalance,
corr.QuadrilateralNoBars,
corr.PerspectiveNoBars,
corr.SingleFre... | augmentation-corruption-fbr_main | imagenet_c_bar/transform_finder.py |
# Copyright (c) Facebook, Inc. and its 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 torchvision as tv
from transform_finder import build_transform
from utils.converters import PilToNumpy, NumpyToPil
impo... | augmentation-corruption-fbr_main | imagenet_c_bar/make_imagenet_c_bar.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
from math import floor, ceil
from PIL import Image
from scipy.fftpack import ifft2
from scipy.ndimage import gaussian_filt... | augmentation-corruption-fbr_main | imagenet_c_bar/corrupt.py |
# Copyright (c) Facebook, Inc. and its 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 torchvision as tv
from transform_finder import build_transform
from utils.converters import PilToNumpy, NumpyToPil
impo... | augmentation-corruption-fbr_main | imagenet_c_bar/make_cifar10_c_bar.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import abc
import numpy as np
def is_iterable(obj):
try:
iter(obj)
except:
return False
else:
return Tru... | augmentation-corruption-fbr_main | imagenet_c_bar/base.py |
# Copyright (c) Facebook, Inc. and its affiliates.
#
# 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
from PIL import Image
import torch
class PilToNumpy(object):
def __init__(self, as_float=False, scaled_to_one=False):
... | augmentation-corruption-fbr_main | imagenet_c_bar/utils/converters.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.