repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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ReAgent | ReAgent-master/reagent/optimizer/utils.py | #!/usr/bin/env python3
import inspect
import torch
def is_strict_subclass(a, b):
if not inspect.isclass(a) or not inspect.isclass(b):
return False
return issubclass(a, b) and a != b
def is_torch_optimizer(cls):
return is_strict_subclass(cls, torch.optim.Optimizer)
def is_torch_lr_scheduler(c... | 399 | 19 | 73 | py |
ReAgent | ReAgent-master/reagent/optimizer/scheduler.py | #!/usr/bin/env python3
import inspect
from typing import Any, Dict
import torch
from reagent.core.dataclasses import dataclass
from reagent.core.registry_meta import RegistryMeta
from .utils import is_torch_lr_scheduler
@dataclass(frozen=True)
class LearningRateSchedulerConfig(metaclass=RegistryMeta):
def make... | 1,054 | 27.513514 | 77 | py |
ReAgent | ReAgent-master/reagent/optimizer/optimizer.py | #!/usr/bin/env python3
"""
For each Torch optimizer, we create a wrapper pydantic dataclass around it.
We also add this class to our Optimizer registry.
Usage:
Whenever you want to use this Optimizer__Union, specify it as the type.
E.g.
class Parameters:
rl: RLParameters = field(default_factory=RLParameters)
... | 2,673 | 31.609756 | 101 | py |
ReAgent | ReAgent-master/reagent/workflow/gym_batch_rl.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import json
import logging
import random
from typing import Optional
import gym
import numpy as np
import pandas as pd
import pytorch_lightning as pl
import torch
from reagent.data.spark_utils import call_spark_class, get_s... | 5,597 | 31.358382 | 87 | py |
ReAgent | ReAgent-master/reagent/workflow/training.py | #!/usr/bin/env python3
import dataclasses
import logging
import time
from typing import Dict, Optional
import torch
from reagent.core.parameters import NormalizationData
from reagent.core.tensorboardX import summary_writer_context
from reagent.data.manual_data_module import get_sample_range
from reagent.data.oss_data... | 10,454 | 32.944805 | 118 | py |
ReAgent | ReAgent-master/reagent/workflow/utils.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
from typing import Dict, List, Optional
import pytorch_lightning as pl
import torch
# pyre-fixme[21]: Could not find `petastorm`.
from petastorm import make_batch_reader
# pyre-fixme[21]: Could not find mod... | 4,998 | 32.326667 | 88 | py |
ReAgent | ReAgent-master/reagent/reporting/discrete_crr_reporter.py | #!/usr/bin/env python3
import itertools
import logging
from typing import List, Optional
import torch
from reagent.core import aggregators as agg
from reagent.core.observers import IntervalAggregatingObserver
from reagent.reporting.reporter_base import (
ReporterBase,
)
from reagent.workflow.training_reports impo... | 4,053 | 37.245283 | 86 | py |
ReAgent | ReAgent-master/reagent/reporting/seq2reward_reporter.py | #!/usr/bin/env python3
import itertools
import logging
from typing import List
import torch
from reagent.core import aggregators as agg
from reagent.core.observers import IntervalAggregatingObserver
from reagent.reporting.reporter_base import ReporterBase
from reagent.workflow.training_reports import Seq2RewardTraini... | 5,987 | 38.137255 | 85 | py |
ReAgent | ReAgent-master/reagent/reporting/reporter_base.py | #!/usr/bin/env python3
import abc
import logging
from typing import Dict
import torch
from reagent.core.observers import (
CompositeObserver,
EpochEndObserver,
IntervalAggregatingObserver,
ValueListObserver,
)
from reagent.core.result_registries import TrainingReport
from reagent.core.tracker import O... | 2,631 | 32.316456 | 81 | py |
ReAgent | ReAgent-master/reagent/reporting/discrete_dqn_reporter.py | #!/usr/bin/env python3
import itertools
import logging
from collections import OrderedDict
from typing import List, Optional
import torch
from reagent.core import aggregators as agg
from reagent.core.observers import IntervalAggregatingObserver, ValueListObserver
from reagent.reporting.reporter_base import (
Repo... | 4,086 | 37.196262 | 86 | py |
ReAgent | ReAgent-master/reagent/replay_memory/prioritized_replay_buffer.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
# Copyright 2018 The Dopamine Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ... | 7,773 | 41.480874 | 88 | py |
ReAgent | ReAgent-master/reagent/replay_memory/circular_replay_buffer.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
# We removed Tensorflow dependencies.
# OutOfGraphReplayBuffer is renamed ReplayBuffer
# Copyright 2018 The Dopamine Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file e... | 37,435 | 41.015713 | 141 | py |
ReAgent | ReAgent-master/reagent/net_builder/discrete_dqn_net_builder.py | #!/usr/bin/env python3
import abc
from typing import List
import reagent.core.types as rlt
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.models.base import ModelBase
from reagent.prediction.predictor_wrapper import (
DiscreteD... | 3,123 | 32.234043 | 91 | py |
ReAgent | ReAgent-master/reagent/net_builder/continuous_actor_net_builder.py | #!/usr/bin/env python3
import abc
import reagent.core.types as rlt
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.models.base import ModelBase
from reagent.prediction.predictor_wrapper import (
ActorWithPreprocessor,
Rankin... | 3,653 | 33.471698 | 86 | py |
ReAgent | ReAgent-master/reagent/net_builder/slate_ranking_net_builder.py | #!/usr/bin/env python3
import abc
import torch
from reagent.core.registry_meta import RegistryMeta
class SlateRankingNetBuilder:
"""
Base class for slate ranking network builder.
"""
@abc.abstractmethod
def build_slate_ranking_network(
self, state_dim, candidate_dim, candidate_size, sla... | 367 | 18.368421 | 66 | py |
ReAgent | ReAgent-master/reagent/net_builder/synthetic_reward_net_builder.py | #!/usr/bin/env python3
import abc
from typing import List, Optional
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.models.base import ModelBase
from reagent.preprocessing.preprocessor import Preprocessor
if IS_FB_ENVIRONMENT:
... | 2,259 | 33.242424 | 91 | py |
ReAgent | ReAgent-master/reagent/net_builder/parametric_dqn_net_builder.py | #!/usr/bin/env python3
import abc
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.core.registry_meta import RegistryMeta
from reagent.models.base import ModelBase
from reagent.prediction.predictor_wrapper import ParametricDqnWithPre... | 1,777 | 30.192982 | 82 | py |
ReAgent | ReAgent-master/reagent/net_builder/slate_reward_net_builder.py | #!/usr/bin/env python3
import abc
import torch
class SlateRewardNetBuilder:
"""
Base class for slate reward network builder.
"""
@abc.abstractmethod
def build_slate_reward_network(
self, state_dim, candidate_dim, candidate_size, slate_size
) -> torch.nn.Module:
pass
@ab... | 400 | 17.227273 | 66 | py |
ReAgent | ReAgent-master/reagent/net_builder/discrete_actor_net_builder.py | #!/usr/bin/env python3
import abc
from typing import List
import reagent.core.types as rlt
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.models.base import ModelBase
from reagent.prediction.predictor_wrapper import ActorWithPrepro... | 1,608 | 27.732143 | 82 | py |
ReAgent | ReAgent-master/reagent/net_builder/value_net_builder.py | #!/usr/bin/env python3
import abc
import torch
from reagent.core.parameters import NormalizationData
from reagent.core.registry_meta import RegistryMeta
class ValueNetBuilder:
"""
Base class for value-network builder.
"""
@abc.abstractmethod
def build_value_network(
self, state_normaliz... | 389 | 18.5 | 57 | py |
ReAgent | ReAgent-master/reagent/net_builder/categorical_dqn_net_builder.py | #!/usr/bin/env python3
import abc
from typing import List
import reagent.core.types as rlt
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.core.registry_meta import RegistryMeta
from reagent.models.base import ModelBase
from reagent... | 2,019 | 30.076923 | 82 | py |
ReAgent | ReAgent-master/reagent/net_builder/quantile_dqn_net_builder.py | #!/usr/bin/env python3
import abc
from typing import List
import reagent.core.types as rlt
import torch
from reagent.core.fb_checker import IS_FB_ENVIRONMENT
from reagent.core.parameters import NormalizationData
from reagent.core.registry_meta import RegistryMeta
from reagent.models import ModelBase, Sequential
from ... | 2,210 | 30.140845 | 81 | py |
ReAgent | ReAgent-master/reagent/net_builder/synthetic_reward/ngram_synthetic_reward.py | #!/usr/bin/env python3
from typing import List, Optional
import torch
from reagent.core.dataclasses import dataclass, field
from reagent.core.parameters import NormalizationData, param_hash, ConvNetParameters
from reagent.models.base import ModelBase
from reagent.models.synthetic_reward import (
NGramConvolutiona... | 3,678 | 34.375 | 86 | py |
ReAgent | ReAgent-master/reagent/net_builder/slate_ranking/slate_ranking_scorer.py | #!/usr/bin/env python3
from dataclasses import asdict
from typing import List
from typing import Optional
import torch
import torch.nn as nn
from reagent.core.dataclasses import dataclass, field
from reagent.core.parameters import param_hash
from reagent.models.base import ModelBase
from reagent.models.fully_connect... | 2,915 | 27.871287 | 80 | py |
ReAgent | ReAgent-master/reagent/net_builder/value/fully_connected.py | #!/usr/bin/env python3
from typing import List
import torch
from reagent.core.dataclasses import dataclass, field
from reagent.core.parameters import NormalizationData, param_hash
from reagent.models.fully_connected_network import FloatFeatureFullyConnected
from reagent.net_builder.value_net_builder import ValueNetBu... | 1,410 | 33.414634 | 78 | py |
ReAgent | ReAgent-master/reagent/net_builder/value/seq2reward_rnn.py | #!/usr/bin/env python3
import torch
from reagent.core.dataclasses import dataclass
from reagent.core.parameters import NormalizationData, param_hash
from reagent.models.seq2reward_model import Seq2RewardNetwork
from reagent.net_builder.value_net_builder import ValueNetBuilder
from reagent.preprocessing.normalization i... | 966 | 30.193548 | 71 | py |
ReAgent | ReAgent-master/reagent/model_utils/seq2slate_utils.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import copy
import logging
import math
from enum import Enum
import torch
import torch.nn as nn
import torch.nn.functional as F
logger = logging.getLogger(__name__)
PADDING_SYMBOL = 0
DECODER_START_SYMBOL = 1
class Seq2S... | 6,842 | 34.455959 | 105 | py |
ReAgent | ReAgent-master/reagent/preprocessing/sparse_preprocessor.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import abc
import logging
from typing import Dict, Tuple
import reagent.core.types as rlt
import torch
logger = logging.getLogger(__name__)
@torch.jit.script
def map_id_list(raw_values: torch.Tensor, id2index: Dict[int,... | 7,650 | 34.752336 | 88 | py |
ReAgent | ReAgent-master/reagent/preprocessing/postprocessor.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Dict, Tuple
import torch
import torch.nn as nn
from reagent.core.parameters import NormalizationParameters
from reagent.preprocessing.identify_types import (
CONTINUOUS_ACTION,
DISCRETE_ACTION,
... | 2,812 | 36.013158 | 96 | py |
ReAgent | ReAgent-master/reagent/preprocessing/sparse_to_dense.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Dict, List, Tuple
import torch
from reagent.preprocessing import normalization
class SparseToDenseProcessor:
def __init__(
self, sorted_features: List[int], set_missing_value_to_zero: bool ... | 2,628 | 31.45679 | 86 | py |
ReAgent | ReAgent-master/reagent/preprocessing/batch_preprocessor.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Dict
import torch
import torch.nn as nn
import torch.nn.functional as F
from reagent.core import types as rlt
from reagent.preprocessing.preprocessor import Preprocessor
class BatchPreprocessor(nn.Modul... | 6,551 | 41 | 87 | py |
ReAgent | ReAgent-master/reagent/preprocessing/normalization.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import json
import logging
from dataclasses import asdict
from typing import Dict, List, Optional, Tuple
import numpy as np
import reagent.core.types as rlt
import six
import torch
from reagent.core.parameters import Normal... | 11,199 | 34.897436 | 88 | py |
ReAgent | ReAgent-master/reagent/preprocessing/preprocessor.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
from typing import Dict, List, Optional, Tuple, cast
import torch
from reagent.core.parameters import NormalizationParameters
from reagent.preprocessing.identify_types import DO_NOT_PREPROCESS, ENUM, FEATURE_... | 23,384 | 38.040067 | 88 | py |
ReAgent | ReAgent-master/reagent/preprocessing/transforms.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
from typing import Callable, List, Optional
import numpy as np
import reagent.core.types as rlt
import torch
import torch.nn.functional as F
from reagent.core.parameters import NormalizationData
from reagent.... | 12,655 | 32.217848 | 111 | py |
ReAgent | ReAgent-master/reagent/prediction/predictor_wrapper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
import logging
from typing import Dict, List, Optional, Tuple
import reagent.core.types as rlt
import torch
import torch.nn.functional as F
from reagent.core.torch_utils import gather
from reagent.model_utils.seq2slate_util... | 31,722 | 35.75898 | 99 | py |
ReAgent | ReAgent-master/reagent/prediction/synthetic_reward/synthetic_reward_predictor_wrapper.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
from typing import Tuple
import torch
import torch.nn as nn
from reagent.models.base import ModelBase
from reagent.preprocessing.preprocessor import Preprocessor
def split_features(
state_and_action_with_presence: Tupl... | 2,372 | 35.507692 | 83 | py |
ReAgent | ReAgent-master/reagent/prediction/ranking/predictor_wrapper.py | from enum import Enum
from typing import Tuple, List, Optional
import torch
import torch.nn.functional as F
class Kernel(Enum):
# <x, y> = dot_product(x, y)
Linear = "linear"
# <x, y> = exp(-||x-y||^2 / (2 * sigma^2))
RBF = "rbf"
class DeterminantalPointProcessPredictorWrapper(torch.jit.ScriptModu... | 3,643 | 29.881356 | 85 | py |
ReAgent | ReAgent-master/docs/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup --------------------------------------------------------------
from __future__ import abso... | 2,292 | 29.171053 | 82 | py |
EFT | EFT-main/incremental_dataloader.py | '''
TaICML incremental learning
Copyright (c) Jathushan Rajasegaran, 2019
'''
import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import Sampler
from torchvision import datasets, transf... | 18,859 | 36.871486 | 210 | py |
EFT | EFT-main/idatasets/CUB200.py | import os
import pandas as pd
from torchvision.datasets.folder import default_loader
from torchvision.datasets.utils import download_url
from torch.utils.data import Dataset
class Cub2011(Dataset):
base_folder = 'CUB_200_2011/images'
url = 'http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_201... | 3,104 | 35.104651 | 107 | py |
EFT | EFT-main/idatasets/tinyimagenet.py | from __future__ import print_function
import os
import shutil
import tempfile
import torch
from .folder import ImageFolder
from .utils import check_integrity, download_and_extract_archive, extract_archive, \
verify_str_arg
ARCHIVE_DICT = {
'train': {
'url': 'http://www.image-net.org/challenges/LSVRC/20... | 6,657 | 38.39645 | 100 | py |
EFT | EFT-main/idatasets/omniglot.py | import os
import pandas as pd
from torchvision.datasets.folder import default_loader
from torchvision.datasets.utils import download_url
from torch.utils.data import Dataset
class Omniglot(Dataset):
"""`Omniglot <https://github.com/brendenlake/omniglot>`_ Dataset.
Args:
root (string): Root directory ... | 5,662 | 39.45 | 117 | py |
EFT | EFT-main/idatasets/celeb_1m.py | import os
import pandas as pd
from torchvision.datasets.folder import default_loader
from torchvision.datasets.utils import download_url
from torch.utils.data import Dataset
import numpy as np
import random
from collections import Counter
class MS1M(Dataset):
def __init__(self, root, train=True, transform=None, l... | 2,478 | 28.511905 | 95 | py |
EFT | EFT-main/idatasets/imagenet.py | from __future__ import print_function
import os
import shutil
import tempfile
import torch
from .folder import ImageFolder
from .utils import check_integrity, download_and_extract_archive, extract_archive, \
verify_str_arg
ARCHIVE_DICT = {
'train': {
'url': 'http://www.image-net.org/challenges/LSVRC/20... | 6,657 | 38.39645 | 100 | py |
EFT | EFT-main/idatasets/data_celeb.py | import random
import numpy as np
import torch
from PIL import Image
from torch.utils.data import DataLoader
from torch.utils.data.sampler import SubsetRandomSampler
from torch.utils.data import Sampler
from torchvision import datasets, transforms
# from imagenet import ImageNet
from CUB200 import Cub2011
import collec... | 14,326 | 34.72818 | 188 | py |
IAN | IAN-master/test.py | import os
import torch
import logging
from train import parse_options
from network import create_model
from options.yaml_opt import dict2str
from dataset import create_dataloader, create_dataset
from base_utils.utils import get_time_str, make_exp_dirs
from base_utils.logger import get_root_logger, get_env_info
def m... | 1,890 | 31.603448 | 79 | py |
IAN | IAN-master/train.py | import os
import math
import time
import torch
import random
import logging
import argparse
import datetime
from network import create_model
from options.yaml_opt import parse, dict2str
from dataset.data_sampler import EnlargedSampler
from dataset import create_dataset, create_dataloader
from base_utils.logger import g... | 8,133 | 36.483871 | 111 | py |
IAN | IAN-master/dataset/AdobeMI_dataset.py | import ast
import torch
import numpy as np
from torch.utils import data as data
from dataset.data_utils import img2tensor, imread, parse_adobe_dataset, select_one2one_data
from dataset.transforms import augment, multi_random_crop
def showimg(img, name):
import numpy as np
import cv2
img = (img + 1) / 2 * ... | 9,494 | 34.830189 | 121 | py |
IAN | IAN-master/dataset/anytoany_dataset.py | import os
import torch
import numpy as np
from torch.utils import data as data
from base_utils.utils import load_depth
from dataset.data_utils import img2tensor, imread
from dataset.transforms import multi_random_crop, dir_augment
'''Image{idx}_{color}_{angle}.png'''
class Any2anyTrainingDataset(data.Dataset):
de... | 14,109 | 40.378299 | 182 | py |
IAN | IAN-master/dataset/paired_dataset.py | from dataset.data_utils import paired_paths_from_folder, img2tensor, imread
from dataset.transforms import augment, paired_random_crop
from torch.utils import data as data
# from data_utils import paired_paths_from_folder, img2tensor, imread
# from transforms import augment, paired_random_crop
# from torch.utils import... | 3,237 | 36.651163 | 100 | py |
IAN | IAN-master/dataset/paired_with_auxiliary_dataset.py | import torch
import numpy as np
from torch.utils import data as data
from base_utils.utils import load_depth
from dataset.transforms import augment, multi_random_crop
from dataset.data_utils import paired_paths_from_folder, img2tensor, imread
class PairedImageWithAuxiliaryDataset(data.Dataset):
def __init__(self... | 4,611 | 39.45614 | 160 | py |
IAN | IAN-master/dataset/data_utils.py | import cv2
import numpy as np
import os
import math
from torch.utils import data
from torchvision.utils import make_grid
import torch
import json
def imread(path, float32=True):
'''
params:
path: str
float32: bool
output:
img: ndarray(uint8/float32) [-1, 1]
description:
... | 10,844 | 34.097087 | 109 | py |
IAN | IAN-master/dataset/data_sampler.py | import math
import torch
from torch.utils.data.sampler import Sampler
class EnlargedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
Modified from torch.utils.data.distributed.DistributedSampler
Support enlarging the dataset for iteration-based training, for saving
... | 1,649 | 34.106383 | 75 | py |
IAN | IAN-master/dataset/videodemo_dataset.py | import math
import torch
import numpy as np
from torch.utils import data as data
from dataset.data_utils import img2tensor, imread
def load_light(path):
with open(path, 'r') as fr:
lines = fr.readlines()
light_params = np.array([float(l[:-1]) for l in lines])
return light_params
def get_pos(l):
... | 3,804 | 32.086957 | 82 | py |
IAN | IAN-master/dataset/__init__.py | import importlib
import numpy as np
import random
import torch
import torch.utils.data
from functools import partial
import os
from base_utils.logger import get_root_logger
# import .anytoany_Dataset
__all__ = ['create_dataset', 'create_dataloader']
# automatically scan and import dataset modules
# scan all the file... | 3,932 | 34.116071 | 77 | py |
IAN | IAN-master/dataset/DPR_dataset.py | from dataset.data_utils import img2tensor, imread
from dataset.transforms import multi_random_crop
from torch.utils import data as data
import os
import os.path as osp
import torch
import numpy as np
def load_light(path):
with open(path, 'r') as fr:
lines = fr.readlines()
light_params = np.array([floa... | 2,987 | 34.571429 | 132 | py |
IAN | IAN-master/dataset/transforms.py | import cv2
import torch
import random
import torchvision.transforms as transforms
def mod_crop(img, scale):
"""Mod crop images, used during testing.
Args:
img (ndarray): Input image.
scale (int): Scale factor.
Returns:
ndarray: Result image.
"""
img = img.copy()
if img.n... | 7,111 | 31.623853 | 90 | py |
IAN | IAN-master/lpips-pytorch/setup.py | from setuptools import setup
setup(
name='lpips_pytorch',
version='latest',
description='LPIPS as a Package.',
packages=['lpips_pytorch', 'lpips_pytorch.modules'],
author='So Uchida',
author_email='s.aiueo32@gmail.com',
install_requires=["torch", "torchvision"],
url='https://github.com/... | 348 | 25.846154 | 56 | py |
IAN | IAN-master/lpips-pytorch/tests/test_allclose.py | from pathlib import Path
import torchvision.transforms.functional as TF
from PIL import Image
from torch.testing import assert_allclose
from lpips_pytorch import LPIPS
from lpips_pytorch import lpips
from PerceptualSimilarity.models import PerceptualLoss
img = Image.open(Path(__file__).parents[1].joinpath('data/lenn... | 1,889 | 27.636364 | 70 | py |
IAN | IAN-master/lpips-pytorch/lpips_pytorch/__init__.py | import torch
from .modules.lpips import LPIPS
def lpips(x: torch.Tensor,
y: torch.Tensor,
net_type: str = 'alex',
version: str = '0.1'):
r"""Function that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
x, y (torch.Tensor): the input tensors t... | 635 | 27.909091 | 68 | py |
IAN | IAN-master/lpips-pytorch/lpips_pytorch/modules/lpips.py | import torch
import torch.nn as nn
from .networks import get_network, LinLayers
from .utils import get_state_dict
class LPIPS(nn.Module):
r"""Creates a criterion that measures
Learned Perceptual Image Patch Similarity (LPIPS).
Arguments:
net_type (str): the network type to compare the features: ... | 1,151 | 30.135135 | 71 | py |
IAN | IAN-master/lpips-pytorch/lpips_pytorch/modules/utils.py | from collections import OrderedDict
import torch
def normalize_activation(x, eps=1e-10):
norm_factor = torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))
return x / (norm_factor + eps)
def get_state_dict(net_type: str = 'alex', version: str = '0.1'):
# build url
url = 'https://raw.githubusercontent... | 885 | 27.580645 | 79 | py |
IAN | IAN-master/lpips-pytorch/lpips_pytorch/modules/networks.py | from typing import Sequence
from itertools import chain
import torch
import torch.nn as nn
from torchvision import models
from .utils import normalize_activation
def get_network(net_type: str):
if net_type == 'alex':
return AlexNet()
elif net_type == 'squeeze':
return SqueezeNet()
elif ... | 2,654 | 26.371134 | 79 | py |
IAN | IAN-master/network/lr_scheduler.py | import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
class MultiStepRestartLR(_LRScheduler):
""" MultiStep with restarts learning rate scheme.
Args:
optimizer (torch.nn.optimizer): Torch optimizer.
milestones (list): Iterations that will decrease learni... | 4,312 | 37.508929 | 79 | py |
IAN | IAN-master/network/base_model.py | import logging
import os
import torch
from collections import OrderedDict
from copy import deepcopy
from torch.nn.parallel import DataParallel, DistributedDataParallel
from network import lr_scheduler as lr_scheduler
logger = logging.getLogger('relighting')
class BaseModel():
"""Base model."""
def __init__... | 11,448 | 37.163333 | 80 | py |
IAN | IAN-master/network/relight_model.py | import os
import cv2
import torch
import importlib
import numpy as np
from tqdm import tqdm
from copy import deepcopy
from network.loss import MSELoss
from collections import OrderedDict
from base_utils.utils import col_stitch
from network.arch import define_network
from network.base_model import BaseModel
from torch.n... | 32,717 | 39.69403 | 121 | py |
IAN | IAN-master/network/arch/full_model_one2one_arch.py | import torch
import torch.nn as nn
import torch.nn.functional as F
conv_s2 = 4
pad0 = 1
align_corners=True
def mean_channels(F):
assert(F.dim() == 4)
spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
return spatial_sum / (F.size(2) * F.size(3))
def std_channels(F):
assert(F.dim() == 4)
F... | 14,082 | 40.178363 | 120 | py |
IAN | IAN-master/network/arch/full_model_one2one_woaux_arch.py | import torch
import torch.nn as nn
import torch.nn.functional as F
conv_s2 = 4
pad0 = 1
align_corners=True
def mean_channels(F):
assert(F.dim() == 4)
spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
return spatial_sum / (F.size(2) * F.size(3))
def std_channels(F):
assert(F.dim() == 4)
... | 13,759 | 39.351906 | 115 | py |
IAN | IAN-master/network/arch/vgg_arch.py | import os
import torch
from collections import OrderedDict
from torch import nn as nn
from torchvision.models import vgg as vgg
VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth'
NAMES = {
'vgg11': [
'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2',
'conv3_1', 'relu3... | 6,223 | 36.721212 | 79 | py |
IAN | IAN-master/network/arch/any2any_arch.py | import torch
import torch.nn as nn
import torch.nn.functional as F
conv_s2 = 4
pad0 = 1
align_corners=True
def mean_channels(F):
assert(F.dim() == 4)
spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
return spatial_sum / (F.size(2) * F.size(3))
def std_channels(F):
assert(F.dim() == 4)
F_... | 16,008 | 41.128947 | 120 | py |
IAN | IAN-master/network/arch/any2any_woaux_arch.py | import torch
import torch.nn as nn
import torch.nn.functional as F
conv_s2 = 4
pad0 = 1
align_corners=True
def mean_channels(F):
assert(F.dim() == 4)
spatial_sum = F.sum(3, keepdim=True).sum(2, keepdim=True)
return spatial_sum / (F.size(2) * F.size(3))
def std_channels(F):
assert(F.dim() == 4)
F... | 15,796 | 40.680739 | 122 | py |
IAN | IAN-master/network/arch/DPR_arch.py |
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import sys
import numpy as np
import time
# we define Hour Glass network based on the paper
# Stacked Hourglass Networks for Human Pose Estimation
# Alejandro Newell, Kaiyu Yang, and Jia Deng
# the code is ada... | 10,302 | 39.72332 | 104 | py |
IAN | IAN-master/network/loss/losses.py | import math
import torch
from torch import autograd as autograd
from torch import nn as nn
from torch.nn import functional as F
from network.arch.vgg_arch import VGGFeatureExtractor
from network.loss.loss_utils import weighted_loss, ssim, create_window, rgb2gray
_reduction_modes = ['none', 'mean', 'sum']
@weighted_l... | 21,903 | 36.635739 | 131 | py |
IAN | IAN-master/network/loss/loss_utils.py | import functools
from torch.nn import functional as F
import torch
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size, channel=1):
_1D_window = ga... | 5,179 | 30.975309 | 103 | py |
IAN | IAN-master/base_utils/utils.py | import numpy as np
import cv2
import random
import torch
import time
import os
from PIL import Image
from tqdm import tqdm
def get_time_str():
return time.strftime('%Y%m%d_%H%M%S', time.localtime())
def set_random_seed(seed):
"""Set random seeds."""
random.seed(seed)
np.random.seed(seed)
torch.ma... | 7,695 | 32.754386 | 152 | py |
IAN | IAN-master/base_utils/logger.py | import datetime
import logging
import time
import os
def get_env_info():
import torch
import torchvision
msg = ('\nVersion Information: '
f'\n\tPyTorch: {torch.__version__}'
f'\n\tTorchVision: {torchvision.__version__}')
return msg
def init_tb_logger(log_dir):
from torch.... | 4,431 | 35.933333 | 79 | py |
IAN | IAN-master/base_utils/matlab_utils.py | import math
import numpy as np
import torch
def cubic(x):
"""cubic function used for calculate_weights_indices."""
absx = torch.abs(x)
absx2 = absx**2
absx3 = absx**3
return (1.5 * absx3 - 2.5 * absx2 + 1) * (
(absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx +
... | 13,720 | 39.958209 | 79 | py |
IAN | IAN-master/base_utils/gen_surface_normals.py | import numpy as np
import cv2
import random
import torch
import os
from tqdm import tqdm
import argparse
# from base_utils.utils import load_depth
# args = argparse.ArgumentParser(description='Gen_Surface_Normal')
# args.add_argument('--save_dir', type=str)
# args.add_argument('--input_dir', type=str)
# args = args.pa... | 2,297 | 28.844156 | 114 | py |
SiPRNet | SiPRNet-main/Save_result_SiPRNet.py | import h5py
import torch
import os
from pathlib import Path
import numpy as np
from Utils.Util import save_tensor_img
from Model.model_SiPRNet import MyNet
import random
# Seed everything
seed = 123
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
# Set the device
os.env... | 1,633 | 29.259259 | 88 | py |
SiPRNet | SiPRNet-main/Model/model_SiPRNet.py | import torch # to load PyTorch library
import torch.nn as nn # to load PyTorch library
import torch.nn.functional as F
class MyNet(nn.Module):
def __init__(self):
super(MyNet, self).__init__()
in_dim = 1
out_dim = 2
in_size = 128
dim = 32
num_layer_res = 1
... | 7,166 | 39.954286 | 115 | py |
Molformer | Molformer-master/model/tr_msa.py | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from model.tr_spe import Embeddings, FeedForward, clones, Generator3D, Feat_Embedding
from model.tr_cpe import Encoder, EncoderLayer
def build_model(vocab, tgt, dist_bar, N=6, embed_dim=512, ffn_dim=2048, head=8, dropout=0.1,... | 4,202 | 35.868421 | 110 | py |
Molformer | Molformer-master/model/tr_all.py | import copy
import torch.nn as nn
from model.tr_spe import Embeddings, FeedForward, Generator3D
from model.tr_afps import MultiRelationEncoder, EncoderLayer
from model.tr_msa import MultiScaleMultiHeadedAttention
def build_model(vocab, tgt, dist_bar, k, N=6, embed_dim=512, ffn_dim=2048, head=8, dropout=0.1):
ass... | 1,179 | 34.757576 | 107 | py |
Molformer | Molformer-master/model/tr_lsa.py | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def build_model(vocab, tgt, dist_bar, N=6, embed_dim=512, ffn_dim=2048, head=8, dropout=0.1, out_both=False):
c = copy.deepcopy
attn = MultiHeadedAttention(head, embed_dim)
ff = ... | 7,868 | 31.25 | 116 | py |
Molformer | Molformer-master/model/tr_cpe.py | import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
sys.path.append("..")
from model.tr_spe import Embeddings, FeedForward
from model.tr_spe import LayerNorm, SublayerConnection, clones, Generator3D
def build_model(vocab, tgt, N=6, embed_dim=512, ffn_dim=2048, head... | 4,071 | 33.803419 | 112 | py |
Molformer | Molformer-master/model/tr_spe.py | """ reference from Harvard NLP: https://nlp.seas.harvard.edu/2018/04/03/attention.html"""
import copy
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from mendeleev import element
import sys
sys.path.append("..")
####################################... | 9,470 | 33.819853 | 117 | py |
Molformer | Molformer-master/model/tr_afps.py | import copy
import torch
import torch.nn as nn
import sys
sys.path.append("..")
from model.tr_spe import Embeddings, FeedForward, LayerNorm, SublayerConnection, clones, Generator3D
from model.tr_cpe import MultiHeadedAttention
def build_model(vocab, tgt, k, N=6, embed_dim=512, ffn_dim=2048, head=8, dropout=0.1):
... | 3,849 | 32.478261 | 116 | py |
SeqOT | SeqOT-main/tools/read_samples.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: read samp... | 7,579 | 49.198675 | 158 | py |
SeqOT | SeqOT-main/tools/loss.py | import torch
import torch.nn as nn
import os
import numpy as np
import sys
def best_pos_distance(query, pos_vecs):
num_pos = pos_vecs.shape[0]
query_copies = query.repeat(int(num_pos), 1)
diff = ((pos_vecs - query_copies) ** 2).sum(1)
min_pos, _ = diff.min(0)
max_pos, _ = diff.max(0)
return mi... | 3,410 | 33.11 | 111 | py |
SeqOT | SeqOT-main/modules/gem.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-6):
super(GeM, self).__init__()
self.p = nn.Parameter(torch.ones(1) * p)
self.eps = eps
def forward(self, x):
return self.gem(x, p=self.p, eps=self.eps)
... | 701 | 29.521739 | 117 | py |
SeqOT | SeqOT-main/modules/netvlad.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class NetVLADLoupe(nn.Module):
def __init__(self, feature_size, max_samples, cluster_size, output_dim,
gating=True, add_batch_norm=True, is_training=True):
super(NetVLADLoupe, self).__init__()
self.feat... | 3,612 | 33.740385 | 83 | py |
SeqOT | SeqOT-main/modules/seqTransformerCat.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: architect... | 3,992 | 41.031579 | 144 | py |
SeqOT | SeqOT-main/test/test_gem_prepare.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: generate ... | 4,373 | 41.057692 | 129 | py |
SeqOT | SeqOT-main/visualize/viz.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: Visualiza... | 6,135 | 42.828571 | 129 | py |
SeqOT | SeqOT-main/train/gen_sub_descriptors.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: generate ... | 4,032 | 47.011905 | 150 | py |
SeqOT | SeqOT-main/train/training_seqot.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: train Seq... | 8,499 | 41.288557 | 141 | py |
SeqOT | SeqOT-main/train/training_gem.py | #!/usr/bin/env python3
# Developed by Junyi Ma, Xieyuanli Chen
# This file is covered by the LICENSE file in the root of the project SeqOT: https://github.com/BIT-MJY/SeqOT
# SeqOT is the sequence enhanced version of our previous work OverlapTransformer: https://github.com/haomo-ai/OverlapTransformer
# Brief: train GeM... | 8,258 | 39.886139 | 144 | py |
mlcube | mlcube-master/mlcube/mlcube/shell.py | """Various utils to work with shell (mostly - running external processes).
- `Shell`: This class provides a collection of methods to work with shell to run external processes.
"""
import copy
import logging
import os
import shutil
import sys
import typing as t
from distutils import dir_util
from pathlib import Path
f... | 19,348 | 48.997416 | 120 | py |
Relation-CZSL | Relation-CZSL-master/eval.py | import os, shutil
import json
import os.path as osp
import re
import logging
import time
import random
from functools import reduce
import resource
# rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
# resource.setrlimit(resource.RLIMIT_NOFILE, (2048, rlimit[1]))
import numpy as np
import scipy as sp
from scipy.spat... | 13,469 | 46.263158 | 183 | py |
Relation-CZSL | Relation-CZSL-master/train.py | import os, shutil
import json
import os.path as osp
import re
import logging
import time
import random
from functools import reduce
import resource
rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
resource.setrlimit(resource.RLIMIT_NOFILE, (2048, rlimit[1]))
import numpy as np
import scipy as sp
from scipy.spatial.... | 29,461 | 46.983713 | 238 | py |
Relation-CZSL | Relation-CZSL-master/model/SepMask.py | import itertools
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision.models import resnet18
from scipy.sparse import diags
VIS_BACKBONE_FEAT_DIM = 512
class Discriminator(nn.Module):
def __init__(self, input_dims=512, hidden_dims=512, output_dims=2)... | 12,973 | 41.260586 | 161 | py |
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