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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sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/models/GroundingDINO/backbone/backbone.py | # ------------------------------------------------------------------------
# Grounding DINO
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------... | 7,972 | 34.914414 | 112 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/util/inference.py | from typing import Tuple, List
import re
import cv2
import numpy as np
import supervision as sv
import torch
from PIL import Image
from torchvision.ops import box_convert
import groundingdino.datasets.transforms as T
from groundingdino.models import build_model
from groundingdino.util.misc import clean_state_dict
fro... | 8,705 | 32.875486 | 194 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/util/vl_utils.py | import os
import random
from typing import List
import torch
def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
"""construct a map such that positive_map[i,j] = True iff box i is associated to token j
Input:
- tokenized:
- input_ids: Tensor[1, ntokens]
... | 3,489 | 33.554455 | 92 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/util/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import colorsys
import datetime
import functools
import io
import json
import os
import pickle
import subprocess
import time
from collections impo... | 23,348 | 31.519499 | 141 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/util/utils.py | import argparse
import json
import warnings
from collections import OrderedDict
from copy import deepcopy
from typing import Any, Dict, List
import numpy as np
import torch
from transformers import AutoTokenizer
from groundingdino.util.slconfig import SLConfig
def slprint(x, name="x"):
if isinstance(x, (torch.T... | 17,712 | 28.085386 | 119 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/util/visualizer.py | # -*- coding: utf-8 -*-
"""
@File : visualizer.py
@Time : 2022/04/05 11:39:33
@Author : Shilong Liu
@Contact : slongliu86@gmail.com
"""
import datetime
import os
import cv2
import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib import transforms
from matplotlib.collections imp... | 12,047 | 36.768025 | 119 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/util/box_ops.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Utilities for bounding box manipulation and GIoU.
"""
import torch
from torchvision.ops.boxes import box_area
def box_cxcywh_to_xyxy(x):
x_c, y_c, w, h = x.unbind(-1)
b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 *... | 3,905 | 26.702128 | 110 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/datasets/cocogrounding_eval.py | # ------------------------------------------------------------------------
# Grounding DINO. Midified by Shilong Liu.
# url: https://github.com/IDEA-Research/GroundingDINO
# Copyright (c) 2023 IDEA. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -----------------------... | 9,422 | 33.643382 | 109 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/groundingdino/datasets/transforms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Transforms and data augmentation for both image + bbox.
"""
import os
import random
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as F
from groundingdino.util.box_ops import box_xyxy_to_cxc... | 9,711 | 30.128205 | 98 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/demo/gradio_app.py | import argparse
from functools import partial
import cv2
import requests
import os
from io import BytesIO
from PIL import Image
import numpy as np
from pathlib import Path
import warnings
import torch
# prepare the environment
os.system("python setup.py build develop --user")
os.system("pip install packaging==21.3"... | 4,463 | 34.428571 | 121 | py |
sam-hq | sam-hq-main/seginw/GroundingDINO/demo/inference_on_a_image.py | import argparse
import os
import sys
import numpy as np
import torch
from PIL import Image, ImageDraw, ImageFont
import groundingdino.datasets.transforms as T
from groundingdino.models import build_model
from groundingdino.util import box_ops
from groundingdino.util.slconfig import SLConfig
from groundingdino.util.ut... | 6,001 | 33.693642 | 113 | py |
FASeg | FASeg-main/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
MaskFormer Training Script.
This script is a simplified version of the training script in detectron2/tools.
"""
# Modifications copyright (c) 2022 ZIP Group Group, Haoyu He
import copy
import itertools
import logging
import os
from collection... | 13,101 | 38.823708 | 108 | py |
FASeg | FASeg-main/tools/evaluate_pq_for_semantic_segmentation.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import argparse
import json
import os
from collections import defaultdict
from tqdm import tqdm
import numpy as np
import torch
from detectron2.data import MetadataCatalog
from detectron2.data.detection_utils import read_image
from detectron2.u... | 9,706 | 38.45935 | 166 | py |
FASeg | FASeg-main/tools/analyze_model.py | # -*- coding: utf-8 -*-
# Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detectron2/blob/main/tools/analyze_model.py
import logging
import numpy as np
from collections import Counter
import tqdm
from fvcore.nn import flop_count_table # can also try ... | 5,717 | 31.123596 | 110 | py |
FASeg | FASeg-main/tools/convert-torchvision-to-d2.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates.
import pickle as pkl
import sys
import torch
"""
Usage:
# download one of the ResNet{18,34,50,101,152} models from torchvision:
wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
# run the conversion
./convert-tor... | 1,434 | 26.596154 | 87 | py |
FASeg | FASeg-main/tools/convert-pretrained-swin-model-to-d2.py | #!/usr/bin/env python
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import pickle as pkl
import sys
import torch
"""
Usage:
# download pretrained swin model:
wget https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth
# run the conversion
... | 856 | 26.645161 | 107 | py |
FASeg | FASeg-main/mask2former/test_time_augmentation.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
from itertools import count
import numpy as np
import torch
from fvcore.transforms import HFlipTransform
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from detectron2.data.detection_utils import read_image
from ... | 7,158 | 36.481675 | 97 | py |
FASeg | FASeg-main/mask2former/maskformer_model.py | # Copyright (c) Facebook, Inc. and its affiliates.
from typing import Tuple
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_he... | 16,903 | 43.367454 | 149 | py |
FASeg | FASeg-main/mask2former/evaluation/instance_evaluation.py | # Copyright (c) Facebook, Inc. and its affiliates.
import contextlib
import copy
import io
import itertools
import json
import logging
import numpy as np
import os
import pickle
from collections import OrderedDict
import pycocotools.mask as mask_util
import torch
from pycocotools.coco import COCO
from pycocotools.cocoe... | 4,625 | 41.833333 | 96 | py |
FASeg | FASeg-main/mask2former/utils/misc.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
from typing import List, Optional
import torch
import torch.distribu... | 3,897 | 33.803571 | 96 | py |
FASeg | FASeg-main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
import copy
import logging
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
fr... | 5,810 | 34.006024 | 109 | py |
FASeg | FASeg-main/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
import torch
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import MetadataCatalog
from detectron2.data import detection_utils as utils
from detectron2.data import tra... | 6,873 | 36.156757 | 98 | py |
FASeg | FASeg-main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
import torch
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.structures impo... | 6,230 | 36.536145 | 98 | py |
FASeg | FASeg-main/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
import copy
import logging
import numpy as np
import pycocotools.mask as mask_util
import torch
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
from detectron2.data import transforms... | 6,595 | 35.441989 | 97 | py |
FASeg | FASeg-main/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/d2/detr/dataset_mapper.py
import copy
import logging
import numpy as np
import torch
from detectron2.config import configurable
from detectron2.data import detection_utils as utils
fr... | 7,203 | 36.915789 | 119 | py |
FASeg | FASeg-main/mask2former/modeling/matcher.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/matcher.py
"""
Modules to compute the matching cost and solve the corresponding LSAP.
"""
import torch
import torch.nn.functional as F
from scipy.optimize import linear_sum_assig... | 7,564 | 38.196891 | 115 | py |
FASeg | FASeg-main/mask2former/modeling/criterion.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/models/detr.py
"""
MaskFormer criterion.
"""
import logging
import torch
import torch.nn.functional as F
from torch import nn
from detectron2.utils.comm import get_world_size
from det... | 18,407 | 40.553047 | 108 | py |
FASeg | FASeg-main/mask2former/modeling/backbone/swin.py | # --------------------------------------------------------
# Swin Transformer
# Copyright (c) 2021 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Ze Liu, Yutong Lin, Yixuan Wei
# --------------------------------------------------------
# Copyright (c) Facebook, Inc. and its affiliate... | 27,476 | 34.638132 | 157 | py |
FASeg | FASeg-main/mask2former/modeling/meta_arch/mask_former_head.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modifications copyright (c) 2022 ZIP Group, Haoyu He
import logging
from typing import Dict
from torch import nn
from detectron2.config import configurable
from detectron2.layers import ShapeSpec
from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
from ..tran... | 10,822 | 42.119522 | 134 | py |
FASeg | FASeg-main/mask2former/modeling/meta_arch/per_pixel_baseline.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
from typing import Callable, Dict, List, Optional, Tuple, Union
import fvcore.nn.weight_init as weight_init
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detectron2.layers import Conv2d, Shape... | 9,433 | 37.663934 | 102 | py |
FASeg | FASeg-main/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
# Modifications copyright (c) 2022 ZIP Group Group, Haoyu He from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
import logging
import fvcore.nn.w... | 26,031 | 40.059937 | 150 | py |
FASeg | FASeg-main/mask2former/modeling/transformer_decoder/position_encoding.py | # Copyright (c) Facebook, Inc. and its affiliates.
# # Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/position_encoding.py
"""
Various positional encodings for the transformer.
"""
import math
import torch
from torch import nn
class PositionEmbeddingSine(nn.Module):
"""... | 2,517 | 38.34375 | 114 | py |
FASeg | FASeg-main/mask2former/modeling/transformer_decoder/transformer.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/transformer.py
"""
Transformer class.
Copy-paste from torch.nn.Transformer with modifications:
* positional encodings are passed in MHattention
* extra LN at the end of... | 11,943 | 31.281081 | 106 | py |
FASeg | FASeg-main/mask2former/modeling/transformer_decoder/maskformer_transformer_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modified by Bowen Cheng from: https://github.com/facebookresearch/detr/blob/master/models/detr.py
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from detectron2.config import configurable
from detec... | 7,065 | 36.386243 | 99 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/msdeformattn.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Modifications copyright (c) 2022 ZIP Group, Haoyu He
import numpy as np
from typing import Callable, Dict, List, Optional, Tuple, Union
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.... | 24,875 | 41.668954 | 132 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/fpn.py | # Copyright (c) Facebook, Inc. and its affiliates.
import logging
import numpy as np
from typing import Callable, Dict, List, Optional, Tuple, Union
import fvcore.nn.weight_init as weight_init
import torch
from torch import nn
from torch.nn import functional as F
from torch.nn.init import xavier_uniform_, constant_, u... | 12,411 | 38.654952 | 122 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/ops/test.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 4,223 | 44.419355 | 172 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/ops/setup.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 3,038 | 37.468354 | 139 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 3,728 | 50.082192 | 138 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/ops/functions/__init__.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 734 | 51.5 | 98 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/ops/modules/__init__.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 720 | 54.461538 | 98 | py |
FASeg | FASeg-main/mask2former/modeling/pixel_decoder/ops/modules/ms_deform_attn.py | # ------------------------------------------------------------------------------------------------
# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# -------------------------------------------------------------------------... | 6,633 | 53.377049 | 153 | py |
FASeg | FASeg-main/demo/predictor.py | # Copyright (c) Facebook, Inc. and its affiliates.
# Copied from: https://github.com/facebookresearch/detectron2/blob/master/demo/predictor.py
import atexit
import bisect
import multiprocessing as mp
from collections import deque
import cv2
import torch
from detectron2.data import MetadataCatalog
from detectron2.engi... | 16,801 | 36.927765 | 96 | py |
semisup-adv | semisup-adv-master/tinyimages_prediction.py | """
Select unlabeled data from TinyImages using the trained data sourcing model
"""
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
import logging
import os
import pickle
from utils import get_model, load_cifar10_keywords
import argparse
import numpy as np
from torchvision import transforms
import t... | 7,189 | 33.238095 | 107 | py |
semisup-adv | semisup-adv-master/losses.py | """
Robust training losses. Based on code from
https://github.com/yaodongyu/TRADES
"""
import contextlib
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
import pdb
def entropy_loss(unlabeled_logits):
unlabeled_probs = F.softmax(unlabeled... | 3,853 | 32.513043 | 78 | py |
semisup-adv | semisup-adv-master/tinyimages_preliminaries.py | """
Preliminary computations for sourcing unlabeled data:
1) Compute the l2 distance between every image in TinyImages and its nearest
neighbor in the CIFAR-10 test set. We will use this later to make sure
that none of our unlabeled data appears in the test set.
2) Select 1.01M TinyImages with keywords that don't appea... | 8,906 | 36.902128 | 97 | py |
semisup-adv | semisup-adv-master/generate_pseudolabels.py | """
Code for running a generating pseudolabels for unlabeled TinyImages data
"""
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
import logging
import os
import pickle
import argparse
import numpy as np
from torchvision import transforms
import torch
from torch import nn
from torch.nn import DataParal... | 3,627 | 32.906542 | 97 | py |
semisup-adv | semisup-adv-master/attack_pgd.py | ### PGD implementation
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable
import torch.optim as optim
import logging
def pgd(model,
X,
y,
epsilon=8 / 255,
num_steps=20,
step_size=0.01,
... | 1,582 | 28.314815 | 80 | py |
semisup-adv | semisup-adv-master/dataloader.py | """
Based on code from https://github.com/hysts/pytorch_shake_shake
"""
import numpy as np
import torch
import torchvision
import os
import pickle
from torch.utils import data
import pdb
def get_loader(batch_size, num_workers, use_gpu):
mean = np.array([0.4914, 0.4822, 0.4465])
std = np.array([0.2470, 0.2435,... | 6,434 | 35.5625 | 80 | py |
semisup-adv | semisup-adv-master/utils.py | """
Utilities. Partially based on code from
https://github.com/modestyachts/CIFAR-10.1
"""
import io
import json
import os
import pickle
import numpy as np
import pathlib
from models.wideresnet import WideResNet
from models.shake_shake import ShakeNet
from models.cifar_resnet import ResNet
import torch
from torch.nn... | 6,705 | 36.674157 | 98 | py |
semisup-adv | semisup-adv-master/robust_self_training.py | """
Main robust self-training script. Based loosely on code from
https://github.com/yaodongyu/TRADES
"""
import os
import sys
import argparse
import torch
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
import torch.backends.cudnn as cudnn
from torch.utils.data import D... | 20,304 | 41.214137 | 81 | py |
semisup-adv | semisup-adv-master/train_cifar10_vs_ti.py | """
Train data sourcing model. Based on code from
https://github.com/hysts/pytorch_shake_shake
"""
import argparse
from collections import OrderedDict
import importlib
import json
import logging
import pathlib
import random
import time
import numpy as np
import torch
import torch.nn as nn
import torchvision
from util... | 11,652 | 28.879487 | 78 | py |
semisup-adv | semisup-adv-master/smoothing_evaluation.py | """
Run randomized certification on the test set. Loosely based on code from
https://github.com/locuslab/smoothing
"""
import argparse
import os
from time import time
import datetime
from utils import get_model
import logging
import pandas as pd
import torch
import torch.nn
import torch.nn.functional as F
from data... | 6,566 | 38.8 | 79 | py |
semisup-adv | semisup-adv-master/cutout.py | """
Cutout augmentation implementation. Code taken from
https://github.com/uoguelph-mlrg/Cutout/blob/master/util/cutout.py
"""
import torch
import numpy as np
# TODO: add credit
class Cutout(object):
"""Randomly mask out one or more patches from an image.
Args:
n_holes (int): Number of patches to c... | 1,320 | 24.901961 | 82 | py |
semisup-adv | semisup-adv-master/datasets.py | """
Datasets with unlabeled (or pseudo-labeled) data
"""
from torchvision.datasets import CIFAR10, SVHN
from torch.utils.data import Sampler, Dataset
import torch
import numpy as np
import os
import pickle
import logging
DATASETS = ['cifar10', 'svhn']
class SemiSupervisedDataset(Dataset):
def __init__(self,
... | 8,369 | 38.481132 | 118 | py |
semisup-adv | semisup-adv-master/attack_evaluation.py | """
Evaluate robustness against specific attack.
Loosely based on code from https://github.com/yaodongyu/TRADES
"""
import os
import json
import numpy as np
import re
import argparse
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torch.autograd import Variable... | 10,798 | 42.898374 | 80 | py |
semisup-adv | semisup-adv-master/smoothing.py | """
Randomized smooothing certification. Based on code from
https://github.com/locuslab/smoothing
"""
import torch
import torch.nn
import torch.nn.functional as F
import numpy as np
from scipy.stats import norm, binom_test
from statsmodels.stats.proportion import proportion_confint
from math import ceil
def quick_... | 6,745 | 40.641975 | 119 | py |
semisup-adv | semisup-adv-master/models/shake_shake.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .shake_shake_function import get_alpha_beta, shake_function
"""
Based on code from https://github.com/hysts/pytorch_shake_shake
"""
def initialize_weights(module):
if isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weig... | 6,108 | 29.393035 | 79 | py |
semisup-adv | semisup-adv-master/models/cifar_resnet.py | from __future__ import absolute_import
'''
This file is from: https://raw.githubusercontent.com/bearpaw/pytorch-classification/master/models/cifar/resnet.py
by Wei Yang
'''
import torch.nn as nn
import math
# __all__ = ['resnet']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
r... | 5,054 | 30.01227 | 116 | py |
semisup-adv | semisup-adv-master/models/shake_shake_function.py | """
Based on code from https://github.com/hysts/pytorch_shake_shake
"""
import torch
from torch.autograd import Function
class ShakeFunction(Function):
@staticmethod
def forward(ctx, x1, x2, alpha, beta):
ctx.save_for_backward(x1, x2, alpha, beta)
y = x1 * alpha + x2 * (1 - alpha)
re... | 1,402 | 24.981481 | 64 | py |
semisup-adv | semisup-adv-master/models/wideresnet.py | """Based on code from https://github.com/yaodongyu/TRADES"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in... | 4,067 | 40.090909 | 116 | py |
muzero-general | muzero-general-master/self_play.py | import math
import time
import numpy
import ray
import torch
import models
@ray.remote
class SelfPlay:
"""
Class which run in a dedicated thread to play games and save them to the replay-buffer.
"""
def __init__(self, initial_checkpoint, Game, config, seed):
self.config = config
sel... | 21,137 | 36.019264 | 180 | py |
muzero-general | muzero-general-master/diagnose_model.py | import matplotlib.pyplot as plt
import numpy
import seaborn
import torch
import models
from self_play import MCTS, Node, SelfPlay
class DiagnoseModel:
"""
Tools to understand the learned model.
Args:
weights: weights for the model to diagnose.
config: configuration class instance relate... | 13,660 | 35.822102 | 154 | py |
muzero-general | muzero-general-master/muzero.py | import copy
import importlib
import json
import math
import pathlib
import pickle
import sys
import time
import nevergrad
import numpy
import ray
import torch
from torch.utils.tensorboard import SummaryWriter
import diagnose_model
import models
import replay_buffer
import self_play
import shared_storage
import traine... | 26,493 | 36.158485 | 211 | py |
muzero-general | muzero-general-master/models.py | import math
from abc import ABC, abstractmethod
import torch
class MuZeroNetwork:
def __new__(cls, config):
if config.network == "fullyconnected":
return MuZeroFullyConnectedNetwork(
config.observation_shape,
config.stacked_observations,
len(con... | 22,899 | 32.188406 | 115 | py |
muzero-general | muzero-general-master/replay_buffer.py | import copy
import time
import numpy
import ray
import torch
import models
@ray.remote
class ReplayBuffer:
"""
Class which run in a dedicated thread to store played games and generate batch.
"""
def __init__(self, initial_checkpoint, initial_buffer, config):
self.config = config
sel... | 14,754 | 38.451872 | 113 | py |
muzero-general | muzero-general-master/shared_storage.py | import copy
import ray
import torch
@ray.remote
class SharedStorage:
"""
Class which run in a dedicated thread to store the network weights and some information.
"""
def __init__(self, checkpoint, config):
self.config = config
self.current_checkpoint = copy.deepcopy(checkpoint)
... | 1,124 | 26.439024 | 92 | py |
muzero-general | muzero-general-master/trainer.py | import copy
import time
import numpy
import ray
import torch
import models
@ray.remote
class Trainer:
"""
Class which run in a dedicated thread to train a neural network and save it
in the shared storage.
"""
def __init__(self, initial_checkpoint, config):
self.config = config
... | 11,315 | 36.594684 | 119 | py |
muzero-general | muzero-general-master/games/cartpole.py | import datetime
import pathlib
import gym
import numpy
import torch
from .abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information is available here: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization
self.seed... | 9,175 | 43.980392 | 226 | py |
muzero-general | muzero-general-master/games/spiel.py | import datetime
import pathlib
import numpy
import torch
from .abstract_game import AbstractGame
# This is a Game wrapper for open_spiel games. It allows you to run any game in the open_spiel library.
try:
import pyspiel
except ImportError:
import sys
sys.exit(
"You need to install open_spiel... | 11,728 | 38.35906 | 226 | py |
muzero-general | muzero-general-master/games/lunarlander.py | import datetime
import pathlib
import gym
import numpy
import torch
from .abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information is available here: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization
self.seed... | 25,665 | 38.365031 | 259 | py |
muzero-general | muzero-general-master/games/gomoku.py | import datetime
import math
import pathlib
import numpy
import torch
from .abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information is available here: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization
self.see... | 13,433 | 39.709091 | 226 | py |
muzero-general | muzero-general-master/games/connect4.py | import datetime
import pathlib
import numpy
import torch
from .abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information is available here: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization
self.seed = 0 # See... | 14,244 | 40.051873 | 226 | py |
muzero-general | muzero-general-master/games/tictactoe.py | import datetime
import pathlib
import numpy
import torch
from .abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information is available here: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization
self.seed = 0 # See... | 13,847 | 38.340909 | 226 | py |
muzero-general | muzero-general-master/games/gridworld.py | import datetime
import pathlib
import gym
import numpy
import torch
from .abstract_game import AbstractGame
try:
import gym_minigrid
except ModuleNotFoundError:
raise ModuleNotFoundError('Please run "pip install gym_minigrid"')
class MuZeroConfig:
def __init__(self):
# fmt: off
# More i... | 9,532 | 43.546729 | 226 | py |
muzero-general | muzero-general-master/games/simple_grid.py | import datetime
import pathlib
import numpy
import torch
from .abstract_game import AbstractGame
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information is available here: https://github.com/werner-duvaud/muzero-general/wiki/Hyperparameter-Optimization
self.seed = 0 # See... | 9,933 | 42.191304 | 226 | py |
muzero-general | muzero-general-master/games/atari.py | import datetime
import pathlib
import gym
import numpy
import torch
from .abstract_game import AbstractGame
try:
import cv2
except ModuleNotFoundError:
raise ModuleNotFoundError('\nPlease run "pip install gym[atari]"')
class MuZeroConfig:
def __init__(self):
# fmt: off
# More informatio... | 9,281 | 45.41 | 226 | py |
muzero-general | muzero-general-master/games/twentyone.py | """
This is a very simple form of twenty one. Ace only counts as value 1 not 1 or
11 for simplicity. This means that there is no such thing as a natural or two
card 21. This is a good example of showing how it can provide a good solution
to even luck based games.
"""
import datetime
import pathlib
import numpy
import... | 12,031 | 37.938511 | 226 | py |
muzero-general | muzero-general-master/games/breakout.py | import datetime
import pathlib
import gym
import numpy
import torch
from .abstract_game import AbstractGame
try:
import cv2
except ModuleNotFoundError:
raise ModuleNotFoundError('Please run "pip install gym[atari]"')
class MuZeroConfig:
def __init__(self):
# fmt: off
# More information ... | 9,242 | 45.215 | 226 | py |
Probe-Ably | Probe-Ably-master/probe_ably/core/models/abstract_model.py | from abc import ABC, abstractmethod
from typing import Dict
from torch import Tensor
from torch.nn import Module
class AbstractModel(Module, ABC):
subclasses = {}
def __init_subclass__(cls, **kwargs):
super().__init_subclass__(**kwargs)
name = cls.__module__ + "." + cls.__qualname__
... | 1,831 | 28.079365 | 113 | py |
Probe-Ably | Probe-Ably-master/probe_ably/core/models/mlp.py | ## ADAPTED FROM https://github.com/rycolab/pareto-probing/blob/master/src/h02_learn/model/mlp.py
from typing import Dict
import numpy as np
import torch
from probe_ably.core.models import AbstractModel
from torch import Tensor, nn
class MLPModel(AbstractModel):
def __init__(
self, params: Dict
): #... | 3,240 | 33.115789 | 97 | py |
Probe-Ably | Probe-Ably-master/probe_ably/core/models/linear.py | ### ADAPTED FROM https://github.com/rycolab/pareto-probing/blob/master/src/h02_learn/model/linear.py
from typing import Dict
import math
import numpy as np
import torch
from probe_ably.core.models import AbstractModel
from torch import Tensor, nn
class LinearModel(AbstractModel):
def __init__(self, params: Dict):... | 2,718 | 32.567901 | 100 | py |
Probe-Ably | Probe-Ably-master/probe_ably/core/tasks/probing/prepare_data_for_probing_task.py | from overrides import overrides
from prefect import Task
from loguru import logger
import sklearn
from sklearn.model_selection import train_test_split
from typing import Dict
import torch
from torch.utils.data import Dataset
import numpy as np
class PrepareDataForProbingTask(Task):
@staticmethod
def prepare_en... | 5,938 | 36.588608 | 113 | py |
Probe-Ably | Probe-Ably-master/probe_ably/core/tasks/probing/deprobe_task.py | import random
from typing import Dict, List
from copy import copy, deepcopy
import numpy as np
import torch
from loguru import logger
from overrides import overrides
from prefect import Task
from probe_ably.core.metrics import AbstractIntraModelMetric
from probe_ably.core.models import LinearModel
from probe_ably.core.... | 12,158 | 33.347458 | 88 | py |
Probe-Ably | Probe-Ably-master/probe_ably/core/tasks/probing/train_probing_task.py | import random
from typing import Dict
from copy import copy, deepcopy
import numpy as np
import torch
from loguru import logger
from overrides import overrides
from prefect import Task
from probe_ably.core.metrics import AbstractIntraModelMetric
from probe_ably.core.models import LinearModel
from probe_ably.core.utils ... | 12,280 | 33.594366 | 96 | py |
trac-trunk | trac-trunk/trac/web/chrome.py | # -*- coding: utf-8 -*-
#
# Copyright (C) 2005-2023 Edgewall Software
# Copyright (C) 2005-2006 Christopher Lenz <cmlenz@gmx.de>
# All rights reserved.
#
# This software is licensed as described in the file COPYING, which
# you should have received as part of this distribution. The terms
# are also available at https:/... | 63,552 | 39.274398 | 93 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/train_multi.py | """
training code
"""
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import os
import torch
from apex import amp
from config import cfg, assert_and_infer_cfg
from utils.misc import AverageMeter, prep_experiment, evaluate_eval_multi, fast_hist
import datasets
impor... | 16,378 | 42.794118 | 123 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/demo_folder.py | import os
import sys
import time
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
... | 3,199 | 36.209302 | 162 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/loss.py | """
Loss.py
"""
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from config import cfg
def get_loss(args, tasks=None):
"""
Get the criterion based on the loss function
args: commandline arguments
return: criterion, criterion_val
"""
criter... | 7,375 | 35.88 | 99 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/config.py | """
# Code adapted from:
# https://github.com/facebookresearch/Detectron/blob/master/detectron/core/config.py
Source License
# Copyright (c) 2017-present, Facebook, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obta... | 4,586 | 33.75 | 115 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/demo.py | import os
import sys
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
parser = arg... | 2,091 | 32.741935 | 140 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/eval.py | """
Evaluation Script
Support Two Modes: Pooling based inference and sliding based inference
Pooling based inference is simply whole image inference.
"""
import os
import logging
import sys
import argparse
import re
import queue
import threading
from math import ceil
from datetime import datetime
from tqdm import tqdm
... | 22,353 | 35.171521 | 94 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/eval_multi.py | """
Evaluation Script
Support Two Modes: Pooling based inference and sliding based inference
Pooling based inference is simply whole image inference.
"""
import os
import logging
import sys
import argparse
import re
import queue
import threading
from math import ceil
from datetime import datetime
from tqdm import tqdm
... | 22,816 | 35.448882 | 94 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/train_multi_gradnorm.py | """
training code
"""
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import os
import torch
from apex import amp
from config import cfg, assert_and_infer_cfg
from utils.misc import AverageMeter, prep_experiment, evaluate_eval_multi, fast_hist
import datasets
impor... | 18,734 | 43.395735 | 151 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/train.py | """
training code
"""
from __future__ import absolute_import
from __future__ import division
import argparse
import logging
import os
import torch
from apex import amp
from config import cfg, assert_and_infer_cfg
from utils.misc import AverageMeter, prep_experiment, evaluate_eval, fast_hist
import datasets
import loss... | 13,258 | 39.547401 | 100 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/optimizer.py | """
Pytorch Optimizer and Scheduler Related Task
"""
import math
import logging
import torch
from torch import optim
from config import cfg
def get_optimizer(args, net):
"""
Decide Optimizer (Adam or SGD)
"""
param_groups = net.parameters()
if args.sgd:
optimizer = optim.SGD(param_groups,... | 4,514 | 38.26087 | 92 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/datasets/kitti.py | """
KITTI Dataset Loader
http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015
"""
import os
import sys
import numpy as np
from PIL import Image
from torch.utils import data
import logging
import datasets.uniform as uniform
import datasets.cityscapes_labels as cityscapes_labels
import json
from ... | 9,775 | 34.809524 | 133 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/datasets/sampler.py | """
# Code adapted from:
# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
#
# BSD 3-Clause License
#
# Copyright (c) 2017,
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions ar... | 4,347 | 38.527273 | 118 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/datasets/tartanair_multi.py | """
TartanAir Multi Dataset Loader
http://www.cvlibs.net/datasets/kitti/eval_semseg.php?benchmark=semantics2015
"""
import os
import sys
import numpy as np
from PIL import Image
from torch.utils import data
import logging
import datasets.uniform as uniform
import datasets.tartanair_labels as tartanair_labels
import js... | 13,150 | 38.139881 | 145 | py |
mtl-segmentation-mtl | mtl-segmentation-mtl/datasets/cityscapes.py | """
Cityscapes Dataset Loader
"""
import logging
import json
import os
import numpy as np
from PIL import Image
from torch.utils import data
import torchvision.transforms as transforms
import datasets.uniform as uniform
import datasets.cityscapes_labels as cityscapes_labels
from config import cfg
trainid_to_name = c... | 19,309 | 38.488753 | 100 | py |
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