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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SimpleDG | SimpleDG-main/ddp_training/augment.py | import math
import random
import torch
import torch.nn.functional as F
import numpy as np
from scipy.stats import beta
from timm.data import mixup
def fftfreqnd(h, w=None, z=None):
"""Get bin values for discrete fourier transform of size (h, w, z)
:param h: Required, first dimension size
:param w: Option... | 10,037 | 30.077399 | 128 | py |
SimpleDG | SimpleDG-main/ddp_training/utils.py | import os
import time
import logging
import torch
import torch.distributed as dist
from functools import lru_cache
from importlib.util import spec_from_file_location, module_from_spec
def load_config_from_file(config_file, class_name="Config"):
spec = spec_from_file_location("config", config_file)
m = module_... | 4,341 | 25.638037 | 81 | py |
SimpleDG | SimpleDG-main/ddp_training/model.py | import torchvision
import timm
def build_model(model_name, num_classes):
if model_name in (
"resnet18",
"resnet34",
"resnet50",
"resnet101",
"resnet152",
):
model = getattr(torchvision.models, model_name)(
zero_init_residual=True, num_classes=num_cla... | 667 | 23.740741 | 78 | py |
SimpleDG | SimpleDG-main/ddp_training/dataset.py | import os
import json
import numpy as np
from PIL import Image
from glob import glob
from torch.utils import data
class NICODataset(data.Dataset):
def __init__(self, image_path_list, label_map_json, transform):
super().__init__()
self.image_path_list = image_path_list
self.transform = tran... | 2,348 | 33.544118 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/ensemble.py | import os
import glob
import torch
import argparse
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--track", type=int, choices=[1, 2])
args = parser.parse_args()
model_list = glob.glob(f'outputs/track_{args.track}/resnet101/seed*/finetune/models/best')
if len(model_list) < 5:
... | 1,103 | 31.470588 | 108 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track1_seed4.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 4
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track1_seed5.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 5
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track2_seed3.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 3
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track2_seed2.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 2
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track1_seed3.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 3
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track2_seed3.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 3
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track1_seed2.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 2
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track1_seed3.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 3
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track2_seed1.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 1
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track2_seed1.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 1
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track2_seed5.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 5
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track2_seed2.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 2
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track1_seed4.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 4
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track2_seed4.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 4
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/scratch_track1_seed1.py | import os
import torch
from config.base_scratch import BaseScratch
class Config(BaseScratch):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 1
# model ... | 779 | 21.285714 | 85 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track2_seed5.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 5
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track1_seed2.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 2
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track1_seed1.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 1
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track1_seed5.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 1
seed = 5
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/ddp_training/config/finetune_track2_seed4.py | import os
import torch
from config.base_finetune import BaseFinetune
class Config(BaseFinetune):
# parallel config
ngpus_per_node = torch.cuda.device_count()
dist_url = "tcp://localhost:12345"
world_size = ngpus_per_node
backend = "nccl"
# dataset config
track = 2
seed = 4
# mod... | 990 | 21.022222 | 86 | py |
SimpleDG | SimpleDG-main/domainbed/command_launchers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
A command launcher launches a list of commands on a cluster; implement your own
launcher to add support for your cluster. We've provided an example launcher
which runs all commands serially on the local machine.
"""
import subprocess
import ti... | 1,788 | 27.854839 | 79 | py |
SimpleDG | SimpleDG-main/domainbed/augment.py | import torch.nn.functional as F
import torch
import math
import random
import numpy as np
from scipy.stats import beta
from timm.data import mixup
def fftfreqnd(h, w=None, z=None):
""" Get bin values for discrete fourier transform of size (h, w, z)
:param h: Required, first dimension size
:param w: Opt... | 9,872 | 32.696246 | 128 | py |
SimpleDG | SimpleDG-main/domainbed/networks.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models
import pytorch_pretrained_vit as vits
import efficientnet_pytorch as eff
from domainbed.lib import wide_resnet
import domainbed.lib.basic_resnet as custo... | 13,160 | 31.984962 | 122 | py |
SimpleDG | SimpleDG-main/domainbed/datasets.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import json
import torch
from PIL import Image, ImageFile
from torchvision import transforms
import torchvision.datasets.folder
from torch.utils.data import TensorDataset, Subset
from torchvision.datasets.folder import default_loader
from... | 18,904 | 35.637597 | 122 | py |
SimpleDG | SimpleDG-main/domainbed/algorithms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import sys
from turtle import update
sys.path.append('../')
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
from torch.autograd import Variable
from timm.data import mixup
import copy
import num... | 103,064 | 36.877619 | 175 | py |
SimpleDG | SimpleDG-main/domainbed/optimizer.py | import torch
class SAM(torch.optim.Optimizer):
"""An implementation of paper `SHARPNESS-AWARE MINIMIZATION FOR EFFICIENTLY IMPROVING GENERALIZATION`
code borrowed from https://github.com/davda54/sam
"""
def __init__(self, params, base_optimizer, rho=0.03, adaptive=False, defaults=None, **kwargs):
... | 6,714 | 36.513966 | 131 | py |
SimpleDG | SimpleDG-main/domainbed/test/test_datasets.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Unit tests."""
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed impor... | 1,454 | 28.1 | 80 | py |
SimpleDG | SimpleDG-main/domainbed/test/test_networks.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed import algorithms
from d... | 1,181 | 30.105263 | 79 | py |
SimpleDG | SimpleDG-main/domainbed/test/test_models.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Unit tests."""
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed impor... | 1,427 | 31.454545 | 91 | py |
SimpleDG | SimpleDG-main/domainbed/test/helpers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
DEBUG_DATASETS = ['Debug28', 'Debug224']
def make_minibatches(dataset, batch_size):
"""Test helper to make a minibatches array like train.py"""
minibatches = []
for env in dataset:
X = torch.stack([env[i][0] for i... | 509 | 30.875 | 70 | py |
SimpleDG | SimpleDG-main/domainbed/test/test_model_selection.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Unit tests."""
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import model_selection
from domainbed.lib.query import Q
from parameterize... | 4,334 | 29.744681 | 77 | py |
SimpleDG | SimpleDG-main/domainbed/test/scripts/test_train.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
# import argparse
# import itertools
import json
import os
import subprocess
# import sys
# import time
import unittest
import uuid
import torch
# import datasets
# import hparams_registry
# import algorithms
# import networks
# from parameterize... | 1,654 | 32.1 | 83 | py |
SimpleDG | SimpleDG-main/domainbed/test/scripts/test_sweep.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed import algorithms
from d... | 5,273 | 39.569231 | 89 | py |
SimpleDG | SimpleDG-main/domainbed/test/scripts/test_collect_results.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import itertools
import json
import os
import subprocess
import sys
import time
import unittest
import uuid
import torch
from domainbed import datasets
from domainbed import hparams_registry
from domainbed import algorithms
from d... | 3,718 | 31.622807 | 96 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/save_images.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Save some representative images from each dataset to disk.
"""
import random
import torch
import argparse
from domainbed import hparams_registry
from domainbed import datasets
import imageio
import os
from tqdm import tqdm
if __name__ == '__ma... | 2,029 | 38.803922 | 113 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/sweep.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Run sweeps
"""
import argparse
import copy
import getpass
import hashlib
import json
import os
import random
import shutil
import time
import uuid
import numpy as np
import torch
from domainbed import datasets
from domainbed import hparams_r... | 7,391 | 34.710145 | 104 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/download.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from torchvision.datasets import MNIST
import xml.etree.ElementTree as ET
from zipfile import ZipFile
import argparse
import tarfile
import shutil
import gdown
import uuid
import json
import os
from wilds.datasets.camelyon17_dataset import Camelyo... | 9,095 | 32.076364 | 108 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/weight_average.py | import argparse
from copy import deepcopy
import torch
import json
import os
from domainbed import algorithms, hparams_registry, datasets
from domainbed.lib.fast_data_loader import FastDataLoader
from domainbed.lib import misc
from tqdm import tqdm
from glob import glob
from refile import smart_glob, smart_open
def... | 7,498 | 37.854922 | 144 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/eval.py | import argparse
import torch
import json
import os
from domainbed import datasets, algorithms
from domainbed import hparams_registry
from domainbed.lib import misc
from domainbed.lib.fast_data_loader import FastDataLoader
from tqdm import tqdm
import numpy as np
import torch.nn.functional as F
from domainbed import ... | 8,613 | 39.824645 | 117 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/ensemble.py | import argparse
import torch
import json
import os
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
def filter_state_dict(state_dict):
filtered_state_dict = {k.replace('module.', ''): v for k, v in state_dict['model_dict'].items() if k.startswith('network')}
return filtered_state_dict... | 2,652 | 36.9 | 129 | py |
SimpleDG | SimpleDG-main/domainbed/scripts/train.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import argparse
import collections
from glob import glob
import json
import os
import random
import sys
import time
from math import ceil
import numpy as np
import PIL
import torch
import torchvision
import torch.utils.data
import torch.nn as nn
... | 12,279 | 38.7411 | 103 | py |
SimpleDG | SimpleDG-main/domainbed/lib/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
Things that don't belong anywhere else
"""
import hashlib
import json
import os
import sys
from shutil import copyfile
from collections import OrderedDict, defaultdict
from numbers import Number
import operator
import numpy as np
import torch... | 6,900 | 29.004348 | 99 | py |
SimpleDG | SimpleDG-main/domainbed/lib/wide_resnet.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""
From https://github.com/meliketoy/wide-resnet.pytorch
"""
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
def conv3x3(in_plane... | 3,242 | 29.885714 | 79 | py |
SimpleDG | SimpleDG-main/domainbed/lib/basic_resnet.py | from torch import nn
from torch.utils import model_zoo
from torchvision.models.resnet import BasicBlock, model_urls, Bottleneck
import torch
from domainbed.lib.mixstyle import MixStyle
from timm.models.layers import DropPath
class ResNet(nn.Module):
def __init__(self, block, layers, classes=1000, hparams=None):
... | 5,894 | 34.727273 | 129 | py |
SimpleDG | SimpleDG-main/domainbed/lib/mixstyle.py | import random
from contextlib import contextmanager
import torch
import torch.nn as nn
def deactivate_mixstyle(m):
if type(m) == MixStyle:
m.set_activation_status(False)
def activate_mixstyle(m):
if type(m) == MixStyle:
m.set_activation_status(True)
def random_mixstyle(m):
if type(m) =... | 3,170 | 24.368 | 87 | py |
SimpleDG | SimpleDG-main/domainbed/lib/fast_data_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
class _InfiniteSampler(torch.utils.data.Sampler):
"""Wraps another Sampler to yield an infinite stream."""
def __init__(self, sampler):
self.sampler = sampler
def __iter__(self):
while True:
fo... | 2,156 | 28.148649 | 79 | py |
SABR | SABR-main/src/main.py | import os
import random
import sys
import numpy as np
import torch
from datetime import datetime
import json
from torch.utils.tensorboard import SummaryWriter
from bunch import Bunch
from src.AIDomains.abstract_layers import Sequential
from src.parse_args import parse_args
from src.datasets import get_data_loader
fro... | 8,183 | 42.531915 | 174 | py |
SABR | SABR-main/src/regularization.py | import torch
import torch.nn.functional as F
from src.AIDomains.abstract_layers import ReLU, Normalization
def compute_bound_reg(model, eps, max_eps, reference = 0.5, reg_lambda=0.5):
reg = torch.zeros((), device=model[0][1].weight.device)
layers = model.get_layers()
relu_layers = [layer for layer in laye... | 2,466 | 37.546875 | 96 | py |
SABR | SABR-main/src/networks.py | import numpy as np
import torch.nn as nn
from src.AIDomains.concrete_layers import Normalization
class myNet(nn.Module):
def __init__(self, device, dataset, n_class=10, input_size=32, input_channel=3, conv_widths=None,
kernel_sizes=None, linear_sizes=None, depth_conv=None, paddings=None, strides=... | 6,449 | 36.283237 | 126 | py |
SABR | SABR-main/src/adv_attack.py | import numpy as np
import torch
from torch.autograd import Variable
import torch.optim as optim
import torch.nn.functional as F
from src.AIDomains.zonotope import HybridZonotope
def margin_loss(logits, y, y_target=None):
logit_org = logits.gather(1, y.view(-1, 1))
if y_target is None:
y_target = (log... | 6,044 | 39.57047 | 143 | py |
SABR | SABR-main/src/datasets.py | import os
import torch
from torchvision import datasets, transforms
class IndexDataset(torch.utils.data.Dataset):
def __init__(self, data):
self.dataset = data
def __getitem__(self, index):
data, target = self.dataset.__getitem__(index)
return data, target, index
def __len__(self... | 4,351 | 42.089109 | 123 | py |
SABR | SABR-main/src/util.py | import numpy as np
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset
from torchvision import datasets, transforms
import math
import random
import os
import sys
from typing import Optional
try:
from pip._internal.operations im... | 27,114 | 42.804523 | 141 | py |
SABR | SABR-main/src/convert_to_dict_mnbab.py | import argparse
import random
import numpy as np
import torch
import re
RANDOM_SEED = 3
random.seed(RANDOM_SEED)
np.random.seed(RANDOM_SEED)
torch.manual_seed(RANDOM_SEED)
torch.cuda.manual_seed(RANDOM_SEED)
def parse_conversion_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model_path',... | 4,558 | 38.643478 | 123 | py |
SABR | SABR-main/src/train.py | import socket
import sys
import torch
import torch.nn as nn
import tqdm
from time import time
import torch.nn.functional as F
# hostname = socket.gethostname()
# if hostname == "dlsrlclarge.inf.ethz.ch" or hostname == "dlsrlplarge" or hostname == "dlsrlzlarge" or \
# hostname == "dlsrltitan":
# sys.path.ap... | 12,162 | 43.229091 | 311 | py |
SABR | SABR-main/src/AIDomains/abstract_layers.py | import torch
import torch.nn as nn
from torch import Tensor
import torch.nn.functional as F
import numpy as np
from functools import reduce
from typing import Optional, List, Tuple, Union
from src.AIDomains.zonotope import HybridZonotope
from src.AIDomains.ai_util import AbstractElement
import src.AIDomains.concrete_l... | 46,080 | 39.42193 | 200 | py |
SABR | SABR-main/src/AIDomains/concrete_layers.py | import torch
import torch.nn as nn
class Bias(nn.Module):
def __init__(self, in_dim=None, bias=None):
super().__init__()
assert in_dim is not None or bias is not None
in_dim = list(bias.shape) if in_dim is None else in_dim
self.out_dim = in_dim if isinstance(in_dim, list) else [in_d... | 6,731 | 37.25 | 136 | py |
SABR | SABR-main/src/AIDomains/zonotope.py | """
Based on HybridZonotope from DiffAI (https://github.com/eth-sri/diffai/blob/master/ai.py)
"""
import numpy as np
import random
import torch
import torch.nn.functional as F
from typing import Optional, List, Tuple, Union
from torch import Tensor
from src.AIDomains.ai_util import clamp_image, clamp_image_const_error... | 77,986 | 52.269809 | 185 | py |
SABR | SABR-main/src/AIDomains/ai_util.py | import numpy as np
import torch
from torch import Tensor
from typing import Optional, List, Tuple, Union
def clamp_image(x, eps, clamp_min=0, clamp_max=1):
min_x = torch.clamp(x-eps, min=clamp_min)
max_x = torch.clamp(x+eps, max=clamp_max)
x_center = 0.5 * (max_x + min_x)
x_beta = 0.5 * (max_x - min_x... | 3,190 | 26.747826 | 113 | py |
SABR | SABR-main/src/AIDomains/deeppoly.py | import torch
import torch.nn.functional as F
import numpy as np
from typing import Optional, List, Tuple, Union
from torch import Tensor
from src.AIDomains.abstract_layers import Normalization, Linear, ReLU, Conv2d, Flatten, GlobalAvgPool2d, AvgPool2d, Upsample, _BatchNorm, Bias, Scale, ResBlock, Sequential
from src.A... | 18,372 | 48.522911 | 171 | py |
SABR | SABR-main/src/AIDomains/wrappers.py | import torch
import numpy as np
from src.AIDomains.deeppoly import DeepPoly, backward_deeppoly, forward_deeppoly
from src.AIDomains.ai_util import construct_C
def propagate_abs(net_abs, domain, data_abs, y, get_bounds_only=False):
net_abs.reset_bounds()
if get_bounds_only:
# construct a querry matrix ... | 1,874 | 39.76087 | 112 | py |
CausalRepID | CausalRepID-main/test.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#Common imports
import sys
import os
import argparse
import random
import copy
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from to... | 9,964 | 37.774319 | 214 | py |
CausalRepID | CausalRepID-main/train.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
#Common imports
import sys
import os
import argparse
import random
import copy
import torch
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from... | 4,986 | 34.877698 | 199 | py |
CausalRepID | CausalRepID-main/models/image_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | 1,514 | 31.934783 | 82 | py |
CausalRepID | CausalRepID-main/models/image_slot_attention_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | 2,873 | 37.837838 | 115 | py |
CausalRepID | CausalRepID-main/models/image_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
from torchvision import models as vision_models
from torchvision.models import resnet18, resnet50
from torchvision import transforms
class ImageEncoder(torch.nn.Module):
def __init__(self, latent_dim):
... | 1,101 | 30.485714 | 70 | py |
CausalRepID | CausalRepID-main/models/poly_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | 1,980 | 29.953125 | 70 | py |
CausalRepID | CausalRepID-main/models/image_resnet_decoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | 3,700 | 34.932039 | 114 | py |
CausalRepID | CausalRepID-main/models/encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
class Encoder(torch.nn.Module):
def __init__(self, data_dim, latent_dim):
super(Encoder, self).__init__()
self.data_dim = data_dim
self.latent_dim = latent_dim
... | 797 | 28.555556 | 76 | py |
CausalRepID | CausalRepID-main/models/decoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from torchvision.models.... | 1,045 | 29.764706 | 70 | py |
CausalRepID | CausalRepID-main/models/linear_auto_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from torch import nn
class LinearAutoEncoder(torch.nn.Module):
def __init__(self, data_dim, latent_dim, batch_norm= False):
super(LinearAutoEncoder, self).__init__()
self.data_dim = data_dim
... | 827 | 30.846154 | 74 | py |
CausalRepID | CausalRepID-main/algorithms/poly_auto_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | 1,842 | 33.12963 | 143 | py |
CausalRepID | CausalRepID-main/algorithms/ioss_auto_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | 2,931 | 36.113924 | 134 | py |
CausalRepID | CausalRepID-main/algorithms/base_auto_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | 9,450 | 36.208661 | 238 | py |
CausalRepID | CausalRepID-main/algorithms/image_auto_encoder.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import math
import torch
import torch.utils.data as data_utils
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.a... | 3,211 | 41.263158 | 235 | py |
CausalRepID | CausalRepID-main/scripts/ioss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import math
import argparse
import numpy as np
import time
## Imports for plotting
import matplotlib.pyplot as plt
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('svg', 'pdf') # For export
from matplotlib.colors... | 7,660 | 30.397541 | 125 | py |
CausalRepID | CausalRepID-main/utils/helper.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import numpy as np
import torch
import torch.utils.data as data_utils
path= os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(path)
from data.data_loader import BaseDataLoader
from data.fine_tune... | 3,709 | 45.375 | 158 | py |
CausalRepID | CausalRepID-main/utils/metrics.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import sys
import copy
import torch
import torchvision
import numpy as np
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression, Lasso, Ridge, LassoCV, RidgeCV
from sklearn.linear_model import LogisticRegression
fro... | 13,128 | 35.267956 | 156 | py |
CausalRepID | CausalRepID-main/data/causal_mechanisms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
"""Defining a set of classes that represent causal functions/ mechanisms.
Author: Diviyan Kalainathan
Modified by Philippe Brouillard, July 24th 2019
Modified by Divyat Mahajan, December 30th 2022
.. MIT License
..
.. Copyright (c) 2018 Diviyan K... | 32,521 | 32.771547 | 98 | py |
CausalRepID | CausalRepID-main/data/balls_dataset.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import random
import argparse
import torch
import numpy as np
import pygame
from pygame import gfxdraw, init
from typing import Callable, Optional
from matplotlib import pyplot as plt
if "SDL_VIDEODRIVER" not in os.environ:
... | 9,082 | 28.683007 | 129 | py |
CausalRepID | CausalRepID-main/data/fine_tune_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import copy
import numpy as np
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
from sklearn.preprocessing import StandardScaler
#Base Class
from data.data_loader import BaseDataLoader
clas... | 1,165 | 28.897436 | 87 | py |
CausalRepID | CausalRepID-main/data/data_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import sys
import copy
import numpy as np
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
class BaseDataLoader(data_utils.Dataset):
def __init__(self, data_dir='', data_case='train', se... | 2,228 | 35.540984 | 113 | py |
CausalRepID | CausalRepID-main/data/balls_dataset_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import os
import copy
import numpy as np
import torch
import torch.utils.data as data_utils
from torchvision import datasets, transforms
from sklearn.preprocessing import StandardScaler
# Base Class
from data.data_loader import BaseDataLoader
cl... | 2,752 | 35.223684 | 154 | py |
BMN-Boundary-Matching-Network | BMN-Boundary-Matching-Network-master/main.py | import sys
from dataset import VideoDataSet
from loss_function import bmn_loss_func, get_mask
import os
import json
import torch
import torch.nn.parallel
import torch.optim as optim
import numpy as np
import opts
from models import BMN
import pandas as pd
from post_processing import BMN_post_processing
from eval import... | 7,357 | 39.20765 | 121 | py |
BMN-Boundary-Matching-Network | BMN-Boundary-Matching-Network-master/dataset.py | # -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import json
import torch.utils.data as data
import torch
from utils import ioa_with_anchors, iou_with_anchors
def load_json(file):
with open(file) as json_file:
json_data = json.load(json_file)
return json_data
class VideoDataSet(dat... | 5,891 | 45.03125 | 118 | py |
BMN-Boundary-Matching-Network | BMN-Boundary-Matching-Network-master/loss_function.py | # -*- coding: utf-8 -*-
import torch
import numpy as np
import torch.nn.functional as F
def get_mask(tscale):
mask = np.zeros([tscale, tscale], np.float32)
for i in range(tscale):
for j in range(i, tscale):
mask[i, j] = 1
return torch.Tensor(mask)
def bmn_loss_func(pred_bm, pred_star... | 3,120 | 32.202128 | 89 | py |
BMN-Boundary-Matching-Network | BMN-Boundary-Matching-Network-master/models.py | # -*- coding: utf-8 -*-
import math
import numpy as np
import torch
import torch.nn as nn
class BMN(nn.Module):
def __init__(self, opt):
super(BMN, self).__init__()
self.tscale = opt["temporal_scale"]
self.prop_boundary_ratio = opt["prop_boundary_ratio"]
self.num_sample = opt["num_... | 5,706 | 40.656934 | 130 | py |
BMN-Boundary-Matching-Network | BMN-Boundary-Matching-Network-master/data/activitynet_feature_cuhk/ldb_process.py | # -*- coding: utf-8 -*-
"""
Created on Mon May 15 22:31:31 2017
@author: wzmsltw
"""
import caffe
import leveldb
import numpy as np
from caffe.proto import caffe_pb2
import pandas as pd
col_names=[]
for i in range(200):
col_names.append("f"+str(i))
df=pd.read_table("./input_spatial_list.txt",names=['image','fram... | 983 | 21.883721 | 84 | py |
anesthetic | anesthetic-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/master/config
# -- Path setup ------------------------------------------------------------... | 7,420 | 29.539095 | 132 | py |
GATS | GATS-main/src/calibloss.py | from typing import NamedTuple
import abc
import numpy as np
import torch
import torch.nn.functional as nnf
from torch import nn, Tensor, LongTensor, BoolTensor
from KDEpy import FFTKDE
# ref: https://stackoverflow.com/a/71801795
# do partial sums along dim 0 of tensor t
def partial_sums(t: Tensor, lens: LongTensor) -... | 9,565 | 38.366255 | 129 | py |
GATS | GATS-main/src/utils.py | import os
import math
import random
import argparse
import torch
import yaml
import numpy as np
import matplotlib.pyplot as plt
from pathlib import Path
from collections import defaultdict
from typing import Sequence
from src.calibloss import ECE, Reliability
def set_global_seeds(seed):
"""
Set global seed for... | 7,506 | 38.930851 | 132 | py |
GATS | GATS-main/src/calibration.py | import abc
import torch
from torch import Tensor, LongTensor
import torch.nn.functional as F
import os
import gc
from pathlib import Path
from src.data.data_utils import load_data, load_node_to_nearest_training
from src.model.model import create_model
from src.calibrator.calibrator import \
TS, VS, ETS, CaGCN, GATS... | 9,465 | 38.940928 | 131 | py |
GATS | GATS-main/src/train.py | import os
import math
import random
import abc
import gc
import copy
import numpy as np
from pathlib import Path
from collections import defaultdict
import torch
import torch.nn.functional as F
from torch import Tensor, LongTensor
from src.model.model import create_model
from src.utils import set_global_seeds, arg_par... | 6,632 | 36.055866 | 136 | py |
GATS | GATS-main/src/calibrator/calibrator.py | from typing import Sequence
import numpy as np
import scipy
from scipy.interpolate import interp1d
from sklearn.isotonic import IsotonicRegression
import copy
import torch
from torch import nn, optim
from torch.nn import functional as F
from src.calibrator.attention_ts import CalibAttentionLayer
from src.model.model i... | 23,584 | 35.007634 | 181 | py |
GATS | GATS-main/src/calibrator/attention_ts.py | from typing import Union, Optional
from torch_geometric.typing import OptPairTensor, Adj, OptTensor
import torch
from torch import Tensor
import torch.nn.functional as F
from torch.nn import Parameter
from torch_geometric.nn.dense.linear import Linear
from torch_geometric.nn.conv import MessagePassing
from torch_geome... | 4,795 | 35.610687 | 127 | py |
GATS | GATS-main/src/data/split.py | from typing import Union, List, Tuple
from torch import Tensor
from torch_geometric.data import Dataset
import torch
import numpy as np
from torch_geometric.io.planetoid import index_to_mask
def get_idx_split(
dataset: Dataset,
samples_per_class_in_one_fold: Union[int, float] = None,
k_fold:... | 5,090 | 43.657895 | 120 | py |
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