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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Stark | Stark-main/lib/test/evaluation/running.py | import numpy as np
import multiprocessing
import os
import sys
from itertools import product
from collections import OrderedDict
from lib.test.evaluation import Sequence, Tracker
import torch
def _save_tracker_output(seq: Sequence, tracker: Tracker, output: dict):
"""Saves the output of the tracker."""
if no... | 6,676 | 35.288043 | 117 | py |
Stark | Stark-main/lib/test/analysis/plot_results.py | import tikzplotlib
import matplotlib
import matplotlib.pyplot as plt
import os
import torch
import pickle
import json
from lib.test.evaluation.environment import env_settings
from lib.test.analysis.extract_results import extract_results
def get_plot_draw_styles():
plot_draw_style = [{'color': (1.0, 0.0, 0.0), 'li... | 23,043 | 45.459677 | 131 | py |
Stark | Stark-main/lib/test/analysis/extract_results.py | import os
import sys
import numpy as np
from lib.test.utils.load_text import load_text
import torch
import pickle
from tqdm import tqdm
env_path = os.path.join(os.path.dirname(__file__), '../../..')
if env_path not in sys.path:
sys.path.append(env_path)
from lib.test.evaluation.environment import env_settings
d... | 7,733 | 41.262295 | 171 | py |
Stark | Stark-main/lib/test/vot20/stark_vot20lt.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import cv2
import torch
import vot
import sys
import time
import os
from lib.test.evaluation import Tracker
from lib.test.vot20.vot20_utils import *
'''stark_vot20_lt c... | 3,152 | 33.648352 | 92 | py |
Stark | Stark-main/lib/test/vot20/stark_vot20.py | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import cv2
import torch
import vot
import sys
import time
import os
from lib.test.evaluation import Tracker
from lib.test.vot20.vot20_utils import *
'''stark_vot20 clas... | 3,809 | 36.352941 | 99 | py |
Stark | Stark-main/lib/train/base_functions.py | import torch
from torch.utils.data.distributed import DistributedSampler
# datasets related
from lib.train.dataset import Lasot, Got10k, MSCOCOSeq, ImagenetVID, TrackingNet
from lib.train.dataset import Lasot_lmdb, Got10k_lmdb, MSCOCOSeq_lmdb, ImagenetVID_lmdb, TrackingNet_lmdb
from lib.train.data import sampler, openc... | 10,551 | 55.427807 | 123 | py |
Stark | Stark-main/lib/train/run_training.py | import os
import sys
import argparse
import importlib
import cv2 as cv
import torch.backends.cudnn
import torch.distributed as dist
import random
import numpy as np
torch.backends.cudnn.benchmark = False
import _init_paths
import lib.train.admin.settings as ws_settings
def init_seeds(seed):
random.seed(seed)
... | 4,893 | 44.738318 | 132 | py |
Stark | Stark-main/lib/train/train_script_distill.py | import os
# loss function related
from lib.utils.box_ops import giou_loss
from torch.nn.functional import l1_loss
from torch.nn import BCEWithLogitsLoss
# train pipeline related
from lib.train.trainers import LTRTrainer
# distributed training related
from torch.nn.parallel import DistributedDataParallel as DDP
# some m... | 4,675 | 40.75 | 120 | py |
Stark | Stark-main/lib/train/train_script.py | import os
# loss function related
from lib.utils.box_ops import giou_loss
from torch.nn.functional import l1_loss
from torch.nn import BCEWithLogitsLoss
# train pipeline related
from lib.train.trainers import LTRTrainer
# distributed training related
from torch.nn.parallel import DistributedDataParallel as DDP
# some m... | 4,474 | 42.872549 | 123 | py |
Stark | Stark-main/lib/train/dataset/got10k_lmdb.py | import os
import os.path
import numpy as np
import torch
import csv
import pandas
import random
from collections import OrderedDict
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
from lib.train.admin import env_settings
'''2021.1.16 Gok10k for loading lmdb dataset'''
from li... | 7,966 | 42.298913 | 125 | py |
Stark | Stark-main/lib/train/dataset/base_image_dataset.py | import torch.utils.data
from lib.train.data.image_loader import jpeg4py_loader
class BaseImageDataset(torch.utils.data.Dataset):
""" Base class for image datasets """
def __init__(self, name, root, image_loader=jpeg4py_loader):
"""
args:
root - The root path to the dataset
... | 2,427 | 25.107527 | 121 | py |
Stark | Stark-main/lib/train/dataset/tracking_net_lmdb.py | import torch
import os
import os.path
import numpy as np
import random
from collections import OrderedDict
from lib.train.data import jpeg4py_loader
from .base_video_dataset import BaseVideoDataset
from lib.train.admin import env_settings
import json
from lib.utils.lmdb_utils import decode_img, decode_str
def list_s... | 5,796 | 38.168919 | 169 | py |
Stark | Stark-main/lib/train/dataset/lasot_lmdb.py | import os
import os.path
import torch
import numpy as np
import pandas
import csv
import random
from collections import OrderedDict
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
from lib.train.admin import env_settings
'''2021.1.16 Lasot for loading lmdb dataset'''
from lib.... | 6,510 | 38.222892 | 121 | py |
Stark | Stark-main/lib/train/dataset/tracking_net.py | import torch
import os
import os.path
import numpy as np
import pandas
import random
from collections import OrderedDict
from lib.train.data import jpeg4py_loader
from .base_video_dataset import BaseVideoDataset
from lib.train.admin import env_settings
def list_sequences(root, set_ids):
""" Lists all the videos ... | 5,880 | 37.690789 | 169 | py |
Stark | Stark-main/lib/train/dataset/imagenetvid.py | import os
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
import xml.etree.ElementTree as ET
import json
import torch
from collections import OrderedDict
from lib.train.admin import env_settings
def get_target_to_image_ratio(seq):
anno = torch.Tensor(seq['anno'])
img... | 7,149 | 43.6875 | 120 | py |
Stark | Stark-main/lib/train/dataset/lasot.py | import os
import os.path
import torch
import numpy as np
import pandas
import csv
import random
from collections import OrderedDict
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
from lib.train.admin import env_settings
class Lasot(BaseVideoDataset):
""" LaSOT dataset.
... | 6,524 | 37.609467 | 130 | py |
Stark | Stark-main/lib/train/dataset/got10k.py | import os
import os.path
import numpy as np
import torch
import csv
import pandas
import random
from collections import OrderedDict
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
from lib.train.admin import env_settings
class Got10k(BaseVideoDataset):
""" GOT-10k datase... | 7,870 | 41.317204 | 130 | py |
Stark | Stark-main/lib/train/dataset/coco_seq_lmdb.py | import os
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
import torch
import random
from collections import OrderedDict
from lib.train.admin import env_settings
from lib.train.dataset.COCO_tool import COCO
from lib.utils.lmdb_utils import decode_img, decode_json
import time
... | 6,727 | 36.797753 | 120 | py |
Stark | Stark-main/lib/train/dataset/coco.py | import os
from .base_image_dataset import BaseImageDataset
import torch
import random
from collections import OrderedDict
from lib.train.data import jpeg4py_loader
from lib.train.admin import env_settings
from pycocotools.coco import COCO
class MSCOCO(BaseImageDataset):
""" The COCO object detection dataset.
... | 5,752 | 35.643312 | 120 | py |
Stark | Stark-main/lib/train/dataset/coco_seq.py | import os
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
import torch
import random
from pycocotools.coco import COCO
from collections import OrderedDict
from lib.train.admin import env_settings
class MSCOCOSeq(BaseVideoDataset):
""" The COCO dataset. COCO is an image d... | 6,388 | 36.362573 | 120 | py |
Stark | Stark-main/lib/train/dataset/imagenetvid_lmdb.py | import os
from .base_video_dataset import BaseVideoDataset
from lib.train.data import jpeg4py_loader
import torch
from collections import OrderedDict
from lib.train.admin import env_settings
from lib.utils.lmdb_utils import decode_img, decode_json
def get_target_to_image_ratio(seq):
anno = torch.Tensor(seq['anno'... | 3,866 | 41.494505 | 120 | py |
Stark | Stark-main/lib/train/dataset/base_video_dataset.py | import torch.utils.data
# 2021.1.5 use jpeg4py_loader_w_failsafe as default
from lib.train.data.image_loader import jpeg4py_loader_w_failsafe
class BaseVideoDataset(torch.utils.data.Dataset):
""" Base class for video datasets """
def __init__(self, name, root, image_loader=jpeg4py_loader_w_failsafe):
... | 3,104 | 26.972973 | 121 | py |
Stark | Stark-main/lib/train/actors/stark_lightningXtrt_distill.py | from . import BaseActor
from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy
import torch
from lib.utils.merge import get_qkv, merge_template_search
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.functional import l1_loss
class STARKLightningXtrtdistillActor(BaseActor):
""" Acto... | 7,427 | 50.227586 | 120 | py |
Stark | Stark-main/lib/train/actors/stark_lightningXtrt.py | from . import BaseActor
from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy
import torch
from lib.utils.merge import get_qkv
import torch.nn as nn
import torch.nn.functional as F
class STARKLightningXtrtActor(BaseActor):
""" Actor for training the STARK-S and STARK-ST(Stage1)"""
def __init__(s... | 3,804 | 43.764706 | 120 | py |
Stark | Stark-main/lib/train/actors/stark_s.py | from . import BaseActor
from lib.utils.misc import NestedTensor
from lib.utils.box_ops import box_cxcywh_to_xyxy, box_xywh_to_xyxy
import torch
from lib.utils.merge import merge_template_search
class STARKSActor(BaseActor):
""" Actor for training the STARK-S and STARK-ST(Stage1)"""
def __init__(self, net, obj... | 3,838 | 45.253012 | 157 | py |
Stark | Stark-main/lib/train/admin/tensorboard.py | import os
from collections import OrderedDict
try:
from torch.utils.tensorboard import SummaryWriter
except:
print('WARNING: You are using tensorboardX instead sis you have a too old pytorch version.')
from tensorboardX import SummaryWriter
class TensorboardWriter:
def __init__(self, directory, loader... | 1,177 | 42.62963 | 117 | py |
Stark | Stark-main/lib/train/admin/multigpu.py | import torch.nn as nn
# Here we use DistributedDataParallel(DDP) rather than DataParallel(DP) for multiple GPUs training
def is_multi_gpu(net):
return isinstance(net, (MultiGPU, nn.parallel.distributed.DistributedDataParallel))
class MultiGPU(nn.parallel.distributed.DistributedDataParallel):
def __getattr__... | 467 | 28.25 | 98 | py |
Stark | Stark-main/lib/train/data/processing_utils.py | import torch
import math
import cv2 as cv
import torch.nn.functional as F
import numpy as np
'''modified from the original test implementation
Replace cv.BORDER_REPLICATE with cv.BORDER_CONSTANT
Add a variable called att_mask for computing attention and positional encoding later'''
def sample_target(im, target_bb, s... | 6,745 | 38.91716 | 125 | py |
Stark | Stark-main/lib/train/data/sampler.py | import random
import torch.utils.data
from lib.utils import TensorDict
import numpy as np
def no_processing(data):
return data
class TrackingSampler(torch.utils.data.Dataset):
""" Class responsible for sampling frames from training sequences to form batches.
The sampling is done in the following ways.... | 17,631 | 49.52149 | 135 | py |
Stark | Stark-main/lib/train/data/processing.py | import torch
import torchvision.transforms as transforms
from lib.utils import TensorDict
import lib.train.data.processing_utils as prutils
import torch.nn.functional as F
def stack_tensors(x):
if isinstance(x, (list, tuple)) and isinstance(x[0], torch.Tensor):
return torch.stack(x)
return x
class B... | 8,541 | 54.109677 | 126 | py |
Stark | Stark-main/lib/train/data/bounding_box_utils.py | import torch
def rect_to_rel(bb, sz_norm=None):
"""Convert standard rectangular parametrization of the bounding box [x, y, w, h]
to relative parametrization [cx/sw, cy/sh, log(w), log(h)], where [cx, cy] is the center coordinate.
args:
bb - N x 4 tensor of boxes.
sz_norm - [N] x 2 tens... | 2,979 | 30.368421 | 104 | py |
Stark | Stark-main/lib/train/data/loader.py | import torch
import torch.utils.data.dataloader
import importlib
import collections
from torch._six import string_classes, int_classes
from lib.utils import TensorDict, TensorList
def _check_use_shared_memory():
if hasattr(torch.utils.data.dataloader, '_use_shared_memory'):
return getattr(torch.utils.data... | 9,378 | 48.104712 | 116 | py |
Stark | Stark-main/lib/train/data/transforms.py | import random
import numpy as np
import math
import cv2 as cv
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as tvisf
class Transform:
"""A set of transformations, used for e.g. data augmentation.
Args of constructor:
transforms: An arbitrary number of transforma... | 12,387 | 35.869048 | 162 | py |
Stark | Stark-main/lib/train/trainers/base_trainer.py | import os
import glob
import torch
import traceback
from lib.train.admin import multigpu
from torch.utils.data.distributed import DistributedSampler
class BaseTrainer:
"""Base trainer class. Contains functions for training and saving/loading checkpoints.
Trainer classes should inherit from this one and overlo... | 11,633 | 41.459854 | 118 | py |
Stark | Stark-main/lib/train/trainers/ltr_trainer.py | import os
from collections import OrderedDict
from lib.train.trainers import BaseTrainer
from lib.train.admin import AverageMeter, StatValue
from lib.train.admin import TensorboardWriter
import torch
import time
from torch.utils.data.distributed import DistributedSampler
from torch.cuda.amp import autocast
from torch.c... | 6,955 | 39.678363 | 115 | py |
Stark | Stark-main/lib/utils/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Misc functions, including distributed helpers.
Mostly copy-paste from torchvision references.
"""
import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from typing import Optional, List... | 15,537 | 32.130064 | 141 | py |
Stark | Stark-main/lib/utils/merge.py | import torch
def merge_template_search(inp_list, return_search=False, return_template=False):
"""NOTICE: search region related features must be in the last place"""
seq_dict = {"feat": torch.cat([x["feat"] for x in inp_list], dim=0),
"mask": torch.cat([x["mask"] for x in inp_list], dim=1),
... | 1,259 | 41 | 89 | py |
Stark | Stark-main/lib/utils/box_ops.py | import torch
from torchvision.ops.boxes import box_area
import numpy as np
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 * h)]
return torch.stack(b, dim=-1)
def box_xywh_to_xyxy(x):
x1, y1, w, h = x.unbind(-1)
... | 2,547 | 22.813084 | 60 | py |
Stark | Stark-main/lib/utils/tensor.py | import functools
import torch
import copy
from collections import OrderedDict
class TensorDict(OrderedDict):
"""Container mainly used for dicts of torch tensors. Extends OrderedDict with pytorch functionality."""
def concat(self, other):
"""Concatenates two dicts without copying internal data."""
... | 8,259 | 32.714286 | 121 | py |
Stark | Stark-main/tracking/profile_model_lightning_X_trt.py | import argparse
import torch
import _init_paths
from lib.utils.merge import get_qkv
from thop import profile
from thop.utils import clever_format
import time
from lib.models.stark.repvgg import repvgg_model_convert
from lib.models.stark import build_stark_lightning_x_trt
from lib.config.stark_lightning_X_trt.config imp... | 4,892 | 39.775 | 115 | py |
Stark | Stark-main/tracking/ORT_lightning_X_trt_complete.py | import argparse
import torch
import _init_paths
from lib.models.stark.repvgg import repvgg_model_convert
from lib.models.stark import build_stark_lightning_x_trt
from lib.config.stark_lightning_X_trt.config import cfg, update_config_from_file
from lib.utils.box_ops import box_xyxy_to_cxcywh
import torch.nn as nn
import... | 9,203 | 45.720812 | 121 | py |
Stark | Stark-main/tracking/ORT_lightning_X_trt_backbone_bottleneck_pe.py | import argparse
import torch
import _init_paths
from lib.models.stark.repvgg import repvgg_model_convert
from lib.models.stark import build_stark_lightning_x_trt
from lib.config.stark_lightning_X_trt.config import cfg, update_config_from_file
import torch.nn as nn
import torch.nn.functional as F
import torch.onnx
impor... | 5,399 | 40.538462 | 121 | py |
Stark | Stark-main/tracking/profile_model.py | import argparse
import torch
import _init_paths
from lib.utils.merge import merge_template_search
# from lib.config.stark_s.config import cfg, update_config_from_file
# from lib.models.stark.stark_s import build_starks
from lib.utils.misc import NestedTensor
from thop import profile
from thop.utils import clever_format... | 5,767 | 37.453333 | 103 | py |
Stark | Stark-main/tracking/train.py | import os
import argparse
def parse_args():
"""
args for training.
"""
parser = argparse.ArgumentParser(description='Parse args for training')
# for train
parser.add_argument('--script', type=str, help='training script name')
parser.add_argument('--config', type=str, default='baseline', he... | 2,580 | 50.62 | 118 | py |
GOT | GOT-main/weighting_module.py | import torch
from util.ClassifierDataset import ClassifierDataset
import torch.nn as nn
from util.influence_function import InF
import os
from util.utils import load_args
import os
class WeightingModule():
def __init__(self, config='configs/weighting.yaml'):
self.args = load_args(config)
self.args... | 4,408 | 38.720721 | 134 | py |
GOT | GOT-main/generating_module.py | from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
import pandas as pd
from tqdm import tqdm
from collections import Counter
from model.ConditionalLM import ConditionalLM
import numpy as np
from util.utils import load_args
import pandas as pd
from collections import Counter, defaultdict
import ra... | 7,963 | 40.479167 | 142 | py |
GOT | GOT-main/locating_module.py | import pandas as pd
from tqdm import tqdm
import json
import torch
from nltk.corpus import stopwords
from transformers import GPT2LMHeadModel
from model.ConditionalLM import ConditionalLM
from util.utils import load_args, compute_logProb, init_tokenizer
from util.CLMDataset import CLMDataset
from torch.utils.data impor... | 6,922 | 42.540881 | 132 | py |
GOT | GOT-main/train.py | import torch
import time
import numpy as np
import copy
import os
import torch.optim as optim
import torch.nn as nn
from util.ClassifierDataSet import ClassifierDataSet, OODClassifierDataSet
from model.Classifier import Classifier
from sklearn import metrics
import torch.nn.functional as F
from util.utils import load_a... | 9,964 | 38.86 | 146 | py |
GOT | GOT-main/train_clm.py | from util.utils import load_args, set_seed, init_tokenizer
from transformers import GPT2LMHeadModel
from util.CLMDataset import CLMDataset
from torch.utils.data import DataLoader
import torch
import torch.nn as nn
from model.ConditionalLM import ConditionalLM
import os
import copy
class Train():
def __init__(sel... | 3,333 | 42.298701 | 111 | py |
GOT | GOT-main/util/ClassifierDataSet.py | from torch.utils.data import Dataset
import pandas as pd
class ClassifierDataSet(Dataset):
def __init__(self, file_name):
data = pd.read_csv(file_name)
self.x = data['utt']
self.y = data['index']
def __getitem__(self, index):
return self.x[index], self.y[index]
def __len_... | 837 | 22.277778 | 63 | py |
GOT | GOT-main/util/influence_function.py | import torch
from torch.autograd import grad
from tqdm import tqdm
import os
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
from util.ClassifierDataset import ClassifierDataset
from torch.utils.data import ConcatDataset
import numpy as np
import math
class InF():
def __init__(self, arg... | 8,875 | 41.4689 | 143 | py |
GOT | GOT-main/util/CLMDataset.py | from torch.utils.data import Dataset
import pandas as pd
import torch
class CLMDataset(Dataset):
def __init__(self, fname, tokenizer, device):
super().__init__()
data = pd.read_csv(fname)
text_out = list(data['utt'])
text_in = [tokenizer.bos_token + txt for txt in text_out]
... | 1,009 | 39.4 | 108 | py |
GOT | GOT-main/util/utils.py | import os
import torch
import yaml
import time
import argparse
import numpy as np
import random
import torch.nn.functional as F
def load_args(config):
with open(config) as f:
cfg = yaml.load(f, Loader=yaml.FullLoader)
cfg['timestamp'] = str(time.strftime("%Y-%m_%d-%H_%M_%S", time.localtime()))
# ... | 1,677 | 29.509091 | 80 | py |
GOT | GOT-main/model/Classifier.py | import torch.nn as nn
from model.BERT import BERT
class Classifier(nn.Module):
def __init__(self, args):
super(Classifier, self).__init__()
self.args = args
self.encoder = BERT(args)
self.mlp = nn.Linear(768, args.intent_num)
def forward(self, sens):
pooled_output ... | 418 | 26.933333 | 50 | py |
GOT | GOT-main/model/BERT.py | from transformers import BertModel, BertTokenizer
import torch.nn as nn
class BERT(nn.Module):
def __init__(self, args):
super(BERT, self).__init__()
self.args = args
self.tokenizer = BertTokenizer.from_pretrained(self.args.bert_path)
self.encoder = BertModel.from_pretrained(s... | 720 | 36.947368 | 87 | py |
GOT | GOT-main/model/ConditionalLM.py | import torch.nn as nn
import torch
class ConditionalLM(nn.Module):
def __init__(self, gpu, dataset, label_num, fix_word_embedding=False):
super().__init__()
pre_embedding = torch.load('output/params/{}/embedding'.format(dataset),\
map_location='cuda:{}'.format(gpu) if gpu != -1 else 'c... | 1,191 | 40.103448 | 99 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/batch_size/prune.py | from __future__ import print_function
import argparse
import os
import random
import time
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
from three_regime_taxonomy.utils import get_dataloader, get_model, Bar, Logger, AverageMeter, accura... | 8,911 | 37.747826 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/batch_size/retrain.py | from __future__ import print_function
import sys
import argparse
import os
import random
import shutil
import time
import torch
from os.path import join
import torch.nn as nn
import numpy as np
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.nn.utils.prune as to... | 12,012 | 35.963077 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/batch_size/train.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
from torch.nn.modules.batchnorm import _BatchNorm
from pathlib import Path
from three_regime_taxonomy.... | 14,751 | 35.334975 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/training_epochs/prune.py | from __future__ import print_function
import argparse
import os
import random
import time
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
from three_regime_taxonomy.utils import get_dataloader, get_model, Bar, Logger, AverageMeter, accura... | 8,911 | 37.747826 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/training_epochs/retrain.py | from __future__ import print_function
import argparse
import os
import random
import shutil
import time
import logging
import torch
from pathlib import Path
from os.path import join
import torch.nn as nn
import numpy as np
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import... | 12,655 | 37.468085 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/training_epochs/train.py | from __future__ import print_function
import argparse
import logging
import os
import random
import shutil
import time
import sys
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torch.optim as optim
from pathlib import Path
from torch.nn.modules.batchnorm import _BatchNorm
from three_re... | 14,775 | 36.219144 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/densenet_curves.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from . import curves
__all__ = ['densenet_curve']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, ... | 5,439 | 35.02649 | 122 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/preresnet.py | from __future__ import absolute_import
import math
import torch.nn as nn
from . import curves
__all__ = ['preresnet', 'preresnet_curve']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
de... | 9,758 | 31.969595 | 123 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/vgg_curves.py | '''VGG for CIFAR10. FC layers are removed.
(c) YANG, Wei
'''
from . import curves
import torch.nn as nn
import math
__all__ = [
'vgg11_curve', 'vgg11_bn_curve',
'vgg13_curve', 'vgg13_bn_curve',
'vgg16_curve', 'vgg16_bn_curve',
'vgg19_curve', 'vgg19_bn_curve'
]
model_urls = {
'vgg11': 'https:/... | 5,134 | 30.121212 | 129 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/vgg.py | '''VGG for CIFAR10. FC layers are removed.
(c) YANG, Wei
'''
import torch.nn as nn
import math
__all__ = [
'VGG', 'vgg11', 'vgg11_bn', 'vgg13', 'vgg13_bn', 'vgg16', 'vgg16_bn',
'vgg19_bn', 'vgg19',
]
model_urls = {
'vgg11': 'https://download.pytorch.org/models/vgg11-bbd30ac9.pth',
'vgg13': 'https:/... | 4,181 | 28.871429 | 129 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/densenet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
__all__ = ['densenet']
class Bottleneck(nn.Module):
def __init__(self, inplanes, expansion=4, growthRate=12, dropRate=0):
super(Bottleneck, self).__init__()
planes = expansion * grow... | 5,496 | 31.720238 | 114 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/preresnet_width.py | from __future__ import absolute_import
import math
import torch.nn as nn
from . import curves
__all__ = ['preresnet_width', 'preresnet_width_curve']
def conv3x3(in_planes, out_planes, stride=1):
"3x3 convolution with padding"
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
... | 9,909 | 32.47973 | 100 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/models/cifar/curves.py | import numpy as np
import math
import torch
import torch.nn.functional as F
from torch.nn import Module, Parameter
from torch.nn.modules.utils import _pair
from scipy.special import binom
class Bezier(Module):
def __init__(self, num_bends):
super(Bezier, self).__init__()
self.register_buffer(
... | 13,635 | 36.155313 | 100 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/loss_landscape_measure/lmc.py | """Test mode connectivity between two models
"""
from __future__ import print_function
import argparse
import os
import random
import numpy as np
import tabulate
import torch
import torch.nn.functional as F
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.nn.utils.prune a... | 11,778 | 37.619672 | 135 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/loss_landscape_measure/mc_utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
from three_regime_taxonomy.utils import Bar
import three_regime_taxonomy.models.cifar.curves as curves
def l2_regularizer(weight_decay):
def regularizer(model):
l2 = 0.0
for p in model.parameters():
l2 += torch.... | 5,863 | 29.38342 | 114 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/loss_landscape_measure/cka_simi.py | """Test Jensen-Shannon Divergence or CKA and JSD similarity between two models
"""
from __future__ import print_function
import argparse
import os
import random
import time
import numpy as np
from os.path import join
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
impo... | 8,800 | 39.187215 | 176 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/loss_landscape_measure/cka_utils.py | """The following code is adapted from
Similarity of Neural Network Representations Revisited
Simon Kornblith, Mohammad Norouzi, Honglak Lee and Geoffrey Hinton
https://colab.research.google.com/github/google-research/google-research/blob/master/representation_similarity/Demo.ipynb
"""
import numpy as np
import torch
... | 10,864 | 34.275974 | 121 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/layer_adaptive_sparsity/pruners.py | import numpy as np
from torch.nn.utils import prune
from three_regime_taxonomy.layer_adaptive_sparsity.utils import get_modules
def weight_pruner_loader(config):
"""
Gives you the pruning methods:
"""
if config['prune_layer_sparsity'] == 'glob':
print("--------------------> use unstructured gl... | 2,495 | 31 | 104 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/layer_adaptive_sparsity/utils.py | import torch
import torch.nn as nn
from copy import deepcopy
# Preliminaries. Not to be exported.
def _is_prunable_module(m):
return (isinstance(m,nn.Linear) or isinstance(m,nn.Conv2d))
def _get_sparsity(tsr):
total = tsr.numel()
nnz = tsr.nonzero().size(0)
return nnz/total
def _get_nnz(tsr):
... | 2,044 | 24.5625 | 92 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/utils/misc.py | import errno
import os
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.utils.data as data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from three_regime_taxonomy.models import cifar as models
__all__ = ['get_model', 'get_dataloader', 'mkdir_p', 'Ave... | 5,175 | 34.210884 | 144 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/utils/logger.py | from __future__ import absolute_import
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def plot_overlap(logger, names=None):
names = lo... | 4,349 | 33.52381 | 100 | py |
ThreeRegimePruning | ThreeRegimePruning-main/src/three_regime_taxonomy/utils/sam.py | import torch
class SAM(torch.optim.Optimizer):
def __init__(self, params, base_optimizer, rho=0.05, adaptive=False, **kwargs):
assert rho >= 0.0, f"Invalid rho, should be non-negative: {rho}"
defaults = dict(rho=rho, adaptive=adaptive, **kwargs)
super(SAM, self).__init__(params, defaults)... | 2,484 | 37.828125 | 131 | py |
shapley_algorithms | shapley_algorithms-main/shapley_algorithms/benchmark.py | import os
import shap
import time
import pickle
import xgboost
import numpy as np
import pandas as pd
from shapley_algorithms.explain import Exact
from shapley_algorithms.explain import MultilinearFeature
from shapley_algorithms.explain import Multilinear
from shapley_algorithms.explain import RandomOrderFeature
from ... | 7,506 | 31.925439 | 77 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/utils.py | # -*- coding: utf-8 -*-
'''Some helper functions for PyTorch, including:
- get_mean_and_std: calculate the mean and std value of dataset.
- msr_init: net parameter initialization.
- progress_bar: progress bar mimic xlua.progress.
'''
import os
import sys
import time
import math
import torch.nn as nn
impor... | 3,501 | 26.147287 | 96 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/vitprune.py | import torch
from torch import nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models.vit import ViT, channel_selection
from models.vit_slim import ViT_slim
device = 'cuda' if tor... | 5,289 | 28.887006 | 129 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/train_cifar10.py | # -*- coding: utf-8 -*-
'''
Train CIFAR10 with PyTorch and Vision Transformers!
written by @kentaroy47, @arutema47
'''
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import numpy as np
import ... | 7,464 | 30.765957 | 139 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/models/resnet.py | # -*- coding: utf-8 -*-
'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(... | 4,027 | 32.289256 | 102 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/models/vgg.py | # -*- coding: utf-8 -*-
'''VGG11/13/16/19 in Pytorch.'''
import torch
import torch.nn as nn
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256... | 1,466 | 28.938776 | 117 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/models/vit.py | # https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_pytorch.py
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
MIN_NUM_PATCHES = 16
class channel_selection(nn.Module):
def __init__(self, num_channels):
"""
Initialize the `indexes` w... | 7,067 | 32.028037 | 166 | py |
ViT-cifar10-pruning | ViT-cifar10-pruning-main/models/vit_slim.py | # https://github.com/lucidrains/vit-pytorch/blob/main/vit_pytorch/vit_pytorch.py
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
MIN_NUM_PATCHES = 16
defaultcfg = {
# 6 : [512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, 512, ... | 7,690 | 33.644144 | 166 | py |
umberto | umberto-master/hubconf.py | # Copyright (c) Musixmatch, spa. 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.
dependencies = [
'fairseq',
'sentencepiece',
'torch',
]
def umberto_commoncrawl_cased(**kwargs):
from fairseq import hub_ut... | 1,285 | 32.842105 | 108 | py |
GIMZoomer | GIMZoomer-master/zoom_interface_v3_3.py | import traceback
import sys
import os
import time
import math
from PyQt5.QtWidgets import QWidget, QToolTip, QPushButton, QMainWindow, QApplication, QMessageBox, QDesktopWidget, \
QFileDialog, QSlider, QAbstractSlider, QLineEdit, QGridLayout, QLabel, QTreeView, QFileSystemModel, QTreeWidget, \
QTreeWidgetItem, ... | 13,484 | 42.640777 | 128 | py |
GIMZoomer | GIMZoomer-master/archive/zoom_interface_v3.py | import sys
import os
from PyQt5.QtWidgets import QWidget, QToolTip, QPushButton, QMainWindow, QApplication, QDesktopWidget, \
QFileDialog, QSlider, QLineEdit, QGridLayout, QLabel, QTreeView, QAbstractItemView
from PyQt5.QtCore import Qt
from PyQt5.QtGui import QFont, QIcon, QStandardItemModel, QStandardItem
from co... | 8,932 | 40.742991 | 113 | py |
GIMZoomer | GIMZoomer-master/archive/zoom_interface_v3_2.py | import sys
import os
import time
import math
from PyQt5.QtWidgets import QWidget, QToolTip, QPushButton, QMainWindow, QApplication, QMessageBox, QDesktopWidget, \
QFileDialog, QSlider, QAbstractSlider, QLineEdit, QGridLayout, QLabel, QTreeView, QFileSystemModel, QTreeWidget, \
QTreeWidgetItem, QAbstractItemView... | 11,129 | 41.319392 | 120 | py |
GIMZoomer | GIMZoomer-master/archive/zoom_interface_v3_1.py | import sys
import os
import time
import math
from PyQt5.QtWidgets import QWidget, QToolTip, QPushButton, QMainWindow, QApplication, QMessageBox, QDesktopWidget, \
QFileDialog, QSlider, QAbstractSlider, QLineEdit, QGridLayout, QLabel, QTreeView, QFileSystemModel, QTreeWidget, \
QTreeWidgetItem, QAbstractItemView... | 11,651 | 41.370909 | 119 | py |
QCRNet | QCRNet-main/CRNet_NA/main.py | import torch
import torch.nn as nn
from utils.parser import args
from utils import logger, Tester
from utils import init_device, init_model
from dataset import Cost2100DataLoader
import math
class Loss(nn.Module):
def __init__(self, mode='const', lamda_start=1e-2, lamda_end=0, lamda_max=5e-2, T_max=1000, alpha=0)... | 2,592 | 30.621951 | 114 | py |
QCRNet | QCRNet-main/CRNet_NA/dataset/cost2100.py | import os
import numpy as np
import scipy.io as sio
import torch
from torch.utils.data import DataLoader, TensorDataset
__all__ = ['Cost2100DataLoader']
class Cost2100DataLoader(object):
r""" PyTorch DataLoader for COST2100 dataset.
"""
def __init__(self, root, batch_size, num_workers, pin_memory, scena... | 2,574 | 39.873016 | 84 | py |
QCRNet | QCRNet-main/CRNet_NA/models/quantization.py | import torch
from torch import nn
import math
NORM = 1
class quant(torch.autograd.Function):
@staticmethod
def forward(ctx, x, num_levels, thrs, levels):
y = torch.zeros_like(x)
y_zeros = torch.zeros_like(x)
for i in range(num_levels - 1):
g = torch.gt(x,thrs[i])
... | 2,270 | 28.881579 | 102 | py |
QCRNet | QCRNet-main/CRNet_NA/models/crnet.py | r""" The proposed CRNet
"""
import torch
import torch.nn as nn
import sys
from collections import OrderedDict
from .quantization import Quantization
sys.path.append("..")
from utils import logger
__all__ = ["crnet"]
class ConvBN(nn.Sequential):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, gr... | 5,998 | 35.357576 | 97 | py |
QCRNet | QCRNet-main/CRNet_NA/utils/statics.py | import torch
__all__ = ['AverageMeter', 'evaluator']
class AverageMeter(object):
r"""Computes and stores the average and current value
Imported from https://github.com/pytorch/examples/blob/master/imagenet/main.py#L247-L262
"""
def __init__(self, name):
self.reset()
self.val = 0
... | 1,392 | 26.86 | 95 | py |
QCRNet | QCRNet-main/CRNet_NA/utils/scheduler.py | import math
from torch.optim.lr_scheduler import _LRScheduler
__all__ = ['WarmUpCosineAnnealingLR', 'FakeLR']
class WarmUpCosineAnnealingLR(_LRScheduler):
def __init__(self, optimizer, T_max, T_warmup, eta_min=0, last_epoch=-1):
self.T_max = T_max
self.T_warmup = T_warmup
self.eta_min = e... | 955 | 33.142857 | 104 | py |
QCRNet | QCRNet-main/CRNet_NA/utils/init.py | import os
import random
import thop
import torch
from models import crnet
from utils import logger, line_seg
__all__ = ["init_device", "init_model"]
def init_device(seed=None, cpu=None, gpu=None, affinity=None):
# set the CPU affinity
if affinity is not None:
os.system(f'taskset -p {affinity} {os.ge... | 2,356 | 29.61039 | 79 | py |
QCRNet | QCRNet-main/CRNet_NA/utils/solver.py | import time
import torch
import math
from collections import namedtuple
from utils import logger
from utils.statics import AverageMeter, evaluator
__all__ = ['Tester']
field = ('nmse', 'rho', 'epoch','SNR')
Result = namedtuple('Result', field, defaults=(None,) * len(field))
class Tester:
r""" The testing inter... | 2,423 | 32.666667 | 127 | py |
QCRNet | QCRNet-main/CRNet_LA/main.py | import torch
import torch.nn as nn
from utils.parser import args
from utils import logger, Tester
from utils import init_device, init_model
from dataset import Cost2100DataLoader
def main():
logger.set_file('./log_LA_{}_{}_{}bit'.format(args.scenario, args.cr, args.nbit))
logger.info('=> PyTorch Version... | 1,209 | 24.744681 | 91 | py |
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