repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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fork--wilds-public | fork--wilds-public-main/examples/utils.py | import sys
import os
import csv
import argparse
import random
from pathlib import Path
import numpy as np
import torch
import pandas as pd
try:
import wandb
except Exception as e:
pass
def update_average(prev_avg, prev_counts, curr_avg, curr_counts):
denom = prev_counts + curr_counts
if isinstance(cur... | 12,745 | 32.020725 | 104 | py |
fork--wilds-public | fork--wilds-public-main/examples/scheduler.py | from transformers import (get_linear_schedule_with_warmup,
get_cosine_schedule_with_warmup)
from torch.optim.lr_scheduler import ReduceLROnPlateau, StepLR, MultiStepLR
def initialize_scheduler(config, optimizer, n_train_steps):
# construct schedulers
if config.scheduler is None:
... | 1,947 | 37.196078 | 105 | py |
fork--wilds-public | fork--wilds-public-main/examples/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/examples/train.py | import os
import sys
import time
import math
from datetime import datetime
from tqdm import tqdm
import torch
from utils import save_model, save_pred, get_pred_prefix, get_model_prefix, detach_and_clone, collate_list
from configs.supported import process_outputs_functions
def run_epoch(algorithm, dataset, general_lo... | 17,057 | 38.034325 | 106 | py |
fork--wilds-public | fork--wilds-public-main/examples/run_expt.py | import os, csv
import time
import argparse
import torch
import torch.nn as nn
import torchvision
import sys
from collections import defaultdict
import wilds
from wilds.common.data_loaders import get_train_loader, get_eval_loader
from wilds.common.grouper import CombinatorialGrouper
from utils import (
set_seed, L... | 16,183 | 38.186441 | 84 | py |
fork--wilds-public | fork--wilds-public-main/examples/optimizer.py | from torch.optim import SGD, Adam
from transformers import AdamW
def initialize_optimizer(config, model):
# initialize optimizers
if config.optimizer=='SGD':
params = filter(lambda p: p.requires_grad, model.parameters())
optimizer = SGD(
params,
lr=config.lr,
... | 1,364 | 30.022727 | 141 | py |
fork--wilds-public | fork--wilds-public-main/examples/transforms.py | import random
import torchvision.transforms as transforms
import torchvision.transforms.functional as TF
from transformers import BertTokenizerFast, DistilBertTokenizerFast
import torch
def initialize_transform(transform_name, config, dataset, is_training):
"""
Transforms should take in a single (x, y)
an... | 5,609 | 35.907895 | 118 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/code_gpt.py | from transformers import GPT2LMHeadModel, GPT2Model
import torch
class GPT2LMHeadLogit(GPT2LMHeadModel):
def __init__(self, config):
super().__init__(config)
self.d_out = config.vocab_size
def __call__(self, x):
outputs = super().__call__(x)
logits = outputs[0] # [batch_size, ... | 1,058 | 28.416667 | 75 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/layers.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Identity(nn.Module):
"""An identity layer"""
def __init__(self, d):
super().__init__()
self.in_features = d
self.out_features = d
def forward(self, x):
return x
| 280 | 19.071429 | 31 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/gnn.py | import torch
from torch_geometric.nn import MessagePassing
from torch_geometric.nn import global_mean_pool, global_add_pool
import torch.nn.functional as F
from ogb.graphproppred.mol_encoder import AtomEncoder,BondEncoder
class GINVirtual(torch.nn.Module):
"""
Graph Isomorphism Network augmented with virtual ... | 6,856 | 37.094444 | 162 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/examples/models/resnet_multispectral.py | #####
# Adapted from torchvision.models.resnet
import torch
import torch.nn as nn
import torch.nn.functional as F
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
pa... | 9,067 | 35.12749 | 106 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/initializer.py | import torch
import torch.nn as nn
from models.layers import Identity
def initialize_model(config, d_out, is_featurizer=False):
"""
Initializes models according to the config
Args:
- config (dictionary): config dictionary
- d_out (int): the dimensionality of the model output
... | 7,275 | 37.909091 | 127 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/CNN_genome.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
def single_conv(in_channels, out_channels, kernel_size=7):
padding_size = int((kernel_size-1)/2)
return nn.Sequential(
nn.Conv1d(in_channels, out_channels, kernel_size, padding=padding_size),
nn.B... | 4,645 | 38.372881 | 90 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/bert/bert.py | from transformers import BertForSequenceClassification, BertModel
import torch
class BertClassifier(BertForSequenceClassification):
def __init__(self, config):
super().__init__(config)
self.d_out = config.num_labels
def __call__(self, x):
input_ids = x[:, :, 0]
attentio... | 1,047 | 28.942857 | 65 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/bert/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/examples/models/bert/distilbert.py | from transformers import DistilBertForSequenceClassification, DistilBertModel
class DistilBertClassifier(DistilBertForSequenceClassification):
def __init__(self, config):
super().__init__(config)
def __call__(self, x):
input_ids = x[:, :, 0]
attention_mask = x[:, :, 1]
outputs... | 902 | 27.21875 | 77 | py |
fork--wilds-public | fork--wilds-public-main/examples/models/detection/fasterrcnn.py | """
This module adapts Faster-RCNN from the torchvision library to compute per-image losses,
instead of the default per-batch losses.
It is based on the version from torchvision==0.8.2,
and has not been tested on other versions.
The torchvision library is distributed under the BSD 3-Clause License:
https://github.com/... | 21,680 | 43.067073 | 219 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/deepCORAL.py | import torch
from models.initializer import initialize_model
from algorithms.single_model_algorithm import SingleModelAlgorithm
from wilds.common.utils import split_into_groups
class DeepCORAL(SingleModelAlgorithm):
"""
Deep CORAL.
This algorithm was originally proposed as an unsupervised domain adaptation... | 4,345 | 35.216667 | 124 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/algorithm.py | import torch
import torch.nn as nn
from utils import move_to, detach_and_clone
class Algorithm(nn.Module):
def __init__(self, device):
super().__init__()
self.device = device
self.out_device = 'cpu'
self._has_log = False
self.reset_log()
def update(self, batch):
... | 3,178 | 28.990566 | 101 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/ERM.py | import torch
from algorithms.single_model_algorithm import SingleModelAlgorithm
from models.initializer import initialize_model
import sys
class ERM(SingleModelAlgorithm):
def __init__(self, config, d_out, grouper, loss, metric, n_train_steps):
model = initialize_model(config, d_out).to(config.device)
... | 859 | 30.851852 | 76 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/IRM.py | import torch
from models.initializer import initialize_model
from algorithms.single_model_algorithm import SingleModelAlgorithm
from wilds.common.utils import split_into_groups
import torch.autograd as autograd
from wilds.common.metrics.metric import ElementwiseMetric, MultiTaskMetric
from optimizer import initialize_o... | 4,125 | 38.295238 | 100 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/group_algorithm.py | import torch, time
import numpy as np
from algorithms.algorithm import Algorithm
from utils import update_average
from scheduler import step_scheduler
from wilds.common.utils import get_counts, numel
class GroupAlgorithm(Algorithm):
"""
Parent class for algorithms with group-wise logging.
Also handles sch... | 9,677 | 40.536481 | 152 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/groupDRO.py | import torch
from algorithms.single_model_algorithm import SingleModelAlgorithm
from models.initializer import initialize_model
class GroupDRO(SingleModelAlgorithm):
"""
Group distributionally robust optimization.
Original paper:
@inproceedings{sagawa2019distributionally,
title={Distrib... | 4,131 | 37.981132 | 142 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/single_model_algorithm.py | import torch
import math
from algorithms.group_algorithm import GroupAlgorithm
from scheduler import initialize_scheduler
from optimizer import initialize_optimizer
from torch.nn.utils import clip_grad_norm_
from utils import move_to
class SingleModelAlgorithm(GroupAlgorithm):
"""
An abstract class for algor... | 5,485 | 34.623377 | 87 | py |
fork--wilds-public | fork--wilds-public-main/examples/algorithms/initializer.py | from wilds.common.utils import get_counts
from algorithms.ERM import ERM
from algorithms.groupDRO import GroupDRO
from algorithms.deepCORAL import DeepCORAL
from algorithms.IRM import IRM
from configs.supported import algo_log_metrics
from losses import initialize_loss
def initialize_algorithm(config, datasets, train... | 3,034 | 35.130952 | 99 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/supported.py | # metrics
from wilds.common.metrics.all_metrics import Accuracy, MultiTaskAccuracy, MSE, multiclass_logits_to_pred, binary_logits_to_pred, MultiTaskAveragePrecision
algo_log_metrics = {
'accuracy': Accuracy(prediction_fn=multiclass_logits_to_pred),
'mse': MSE(),
'multitask_accuracy': MultiTaskAccuracy(pred... | 1,565 | 38.15 | 154 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/data_loader.py | loader_defaults = {
'loader_kwargs': {
'num_workers': 4,
'pin_memory': True,
},
'n_groups_per_batch': 4,
}
| 135 | 16 | 28 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/algorithm.py | algorithm_defaults = {
'ERM': {
'train_loader': 'standard',
'uniform_over_groups': False,
'eval_loader': 'standard',
},
'groupDRO': {
'train_loader': 'standard',
'uniform_over_groups': True,
'distinct_groups': True,
'eval_loader': 'standard',
'... | 783 | 25.133333 | 40 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/utils.py | from configs.algorithm import algorithm_defaults
from configs.model import model_defaults
from configs.scheduler import scheduler_defaults
from configs.data_loader import loader_defaults
from configs.datasets import dataset_defaults, split_defaults
def populate_defaults(config):
"""Populates hyperparameters with ... | 3,358 | 36.322222 | 118 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/model.py | model_defaults = {
'bert-base-uncased': {
'optimizer': 'AdamW',
'max_grad_norm': 1.0,
'scheduler': 'linear_schedule_with_warmup',
},
'distilbert-base-uncased': {
'optimizer': 'AdamW',
'max_grad_norm': 1.0,
'scheduler': 'linear_schedule_with_warmup',
},
... | 1,510 | 22.609375 | 51 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/scheduler.py | scheduler_defaults = {
'linear_schedule_with_warmup': {
'scheduler_kwargs':{
'num_warmup_steps': 0,
},
},
'cosine_schedule_with_warmup': {
'scheduler_kwargs':{
'num_warmup_steps': 0,
},
},
'ReduceLROnPlateau': {
'scheduler_kwargs':{},
... | 511 | 18.692308 | 36 | py |
fork--wilds-public | fork--wilds-public-main/examples/configs/datasets.py | dataset_defaults = {
'amazon': {
'split_scheme': 'official',
'model': 'distilbert-base-uncased',
'transform': 'bert',
'max_token_length': 512,
'loss_function': 'cross_entropy',
'algo_log_metric': 'accuracy',
'batch_size': 8,
'lr': 1e-5,
'weight... | 15,142 | 32.354626 | 128 | py |
fork--wilds-public | fork--wilds-public-main/scripts/gather_py150_finer.py | #!/usr/bin/python
import gzip, sys
import numpy as np
# get printable mean and std
def get_mean(x, pt=2):
return round(np.mean(x), pt)
def get_std(x, pt=2):
return round(np.std(x), pt)
assert len(sys.argv) == 2
if sys.argv[1].endswith(".gz"):
input_text = gzip.open(sys.argv[1])
else:
input_text =... | 3,927 | 24.341935 | 110 | py |
fork--wilds-public | fork--wilds-public-main/scripts/gather_rxrx1.py | #!/usr/bin/python
import gzip, sys
import numpy as np
# get printable mean and std
def get_mean(x, pt=1):
return round(np.mean(x) * 100, pt)
def get_std(x, pt=1):
return round(np.std(x) * 100, pt)
assert len(sys.argv) == 2
if sys.argv[1].endswith(".gz"):
input_text = gzip.open(sys.argv[1])
else:
... | 2,651 | 21.474576 | 91 | py |
fork--wilds-public | fork--wilds-public-main/scripts/gather_py150.py | #!/usr/bin/python
import gzip, sys
import numpy as np
# get printable mean and std
def get_mean(x, pt=1):
return round(np.mean(x), pt)
def get_std(x, pt=1):
return round(np.std(x), pt)
assert len(sys.argv) == 2
if sys.argv[1].endswith(".gz"):
input_text = gzip.open(sys.argv[1])
else:
input_text =... | 3,927 | 24.341935 | 110 | py |
fork--wilds-public | fork--wilds-public-main/scripts/gather_fmow.py | #!/usr/bin/python
import gzip, sys
import numpy as np
# get printable mean and std
def get_mean(x, pt=1):
return round(np.mean(x) * 100, pt)
def get_std(x, pt=1):
return round(np.std(x) * 100, pt)
assert len(sys.argv) == 2
if sys.argv[1].endswith(".gz"):
input_text = gzip.open(sys.argv[1])
else:
... | 5,205 | 26.114583 | 105 | py |
fork--wilds-public | fork--wilds-public-main/scripts/wilds_iwildcamera.py | #!/usr/bin/python
import gzip, sys
import numpy as np
# get printable mean and std
def get_mean(x, pt=1):
return round(np.mean(x) * 100, pt)
def get_std(x, pt=1):
return round(np.std(x) * 100, pt)
assert len(sys.argv) == 2
if sys.argv[1].endswith(".gz"):
input_text = gzip.open(sys.argv[1])
else:
... | 3,827 | 23.696774 | 91 | py |
fork--wilds-public | fork--wilds-public-main/wilds/download_datasets.py | import os, sys
import argparse
import wilds
def main():
"""
Downloads the latest versions of all specified datasets,
if they do not already exist.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', required=True,
help='The directory where [dataset]/... | 1,301 | 36.2 | 229 | py |
fork--wilds-public | fork--wilds-public-main/wilds/get_dataset.py | import wilds
def get_dataset(dataset, version=None, **dataset_kwargs):
"""
Returns the appropriate WILDS dataset class.
Input:
dataset (str): Name of the dataset
version (str): Dataset version number, e.g., '1.0'.
Defaults to the latest version.
dataset_kwargs... | 3,674 | 38.945652 | 112 | py |
fork--wilds-public | fork--wilds-public-main/wilds/version.py | # Adapted from https://github.com/snap-stanford/ogb/blob/master/ogb/version.py
import os
import logging
from threading import Thread
__version__ = '1.2.1'
try:
os.environ['OUTDATED_IGNORE'] = '1'
from outdated import check_outdated # noqa
except ImportError:
check_outdated = None
def check():
try:
... | 703 | 24.142857 | 78 | py |
fork--wilds-public | fork--wilds-public-main/wilds/__init__.py | from .version import __version__
from .get_dataset import get_dataset
benchmark_datasets = [
'amazon',
'camelyon17',
'civilcomments',
'iwildcam',
'ogb-molpcba',
'poverty',
'fmow',
'py150',
'rxrx1',
'globalwheat',
]
additional_datasets = [
'celebA',
'waterbirds',
'ye... | 429 | 14.925926 | 61 | py |
fork--wilds-public | fork--wilds-public-main/wilds/common/grouper.py | import numpy as np
import torch
from wilds.common.utils import get_counts
from wilds.datasets.wilds_dataset import WILDSSubset
import warnings
class Grouper:
"""
Groupers group data points together based on their metadata.
They are used for training and evaluation,
e.g., to measure the accuracies of di... | 6,466 | 40.722581 | 151 | py |
fork--wilds-public | fork--wilds-public-main/wilds/common/data_loaders.py | import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.data.sampler import WeightedRandomSampler, SubsetRandomSampler
from wilds.common.utils import get_counts, split_into_groups
def get_train_loader(loader, dataset, batch_size,
uniform_over_groups=None, grouper=None, distinct... | 6,923 | 41.740741 | 139 | py |
fork--wilds-public | fork--wilds-public-main/wilds/common/utils.py | import torch
import numpy as np
from torch.utils.data import Subset
from pandas.api.types import CategoricalDtype
def minimum(numbers, empty_val=0.):
if isinstance(numbers, torch.Tensor):
if numbers.numel()==0:
return torch.tensor(empty_val, device=numbers.device)
else:
retu... | 4,719 | 31.108844 | 99 | py |
fork--wilds-public | fork--wilds-public-main/wilds/common/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/wilds/common/metrics/all_metrics.py | import torch
import torch.nn as nn
from torchvision.ops.boxes import box_iou
from torchvision.models.detection._utils import Matcher
from torchvision.ops import nms, box_convert
import numpy as np
import torch.nn.functional as F
from wilds.common.metrics.metric import Metric, ElementwiseMetric, MultiTaskMetric
from wil... | 9,896 | 35.791822 | 148 | py |
fork--wilds-public | fork--wilds-public-main/wilds/common/metrics/loss.py | import torch
from wilds.common.utils import avg_over_groups, maximum
from wilds.common.metrics.metric import ElementwiseMetric, Metric, MultiTaskMetric
class Loss(Metric):
def __init__(self, loss_fn, name=None):
self.loss_fn = loss_fn
if name is None:
name = 'loss'
super().__in... | 3,004 | 32.764045 | 82 | py |
fork--wilds-public | fork--wilds-public-main/wilds/common/metrics/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/wilds/common/metrics/metric.py | import numpy as np
from wilds.common.utils import avg_over_groups, get_counts, numel
import torch
class Metric:
"""
Parent class for metrics.
"""
def __init__(self, name):
self._name = name
def _compute(self, y_pred, y_true):
"""
Helper function for computing the metric.
... | 9,802 | 38.212 | 111 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/waterbirds_dataset.py | import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy
class WaterbirdsDataset(WILDSDataset):
"""
The Waterbirds dataset... | 6,088 | 38.797386 | 144 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/fmow_dataset.py | from pathlib import Path
import shutil
import pandas as pd
import torch
from torch.utils.data import Dataset
import pickle
import numpy as np
import torchvision.transforms.functional as F
from torchvision import transforms
import tarfile
import datetime
import pytz
from PIL import Image
from tqdm import tqdm
from wilds... | 11,827 | 49.763948 | 1,070 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/civilcomments_dataset.py | import os
import torch
import pandas as pd
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy
class CivilCommentsDataset(WILDSDataset):
"""
The CivilComments-wilds toxicity classifi... | 7,530 | 38.223958 | 140 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/camelyon17_dataset.py | import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy
class Camelyon17Dataset(WILDSDataset):
"""
The CAMELYON17-WILDS h... | 6,188 | 38.170886 | 236 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/yelp_dataset.py | import os, csv
import torch
import pandas as pd
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import map_to_id_array
from wilds.common.metrics.all_metrics import Accuracy
from wilds.common.grouper import CombinatorialGrouper
NOT_IN_DATASET = -1
class YelpDataset(WILD... | 7,651 | 43.748538 | 151 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/sqf_dataset.py | import os
import torch
import pandas as pd
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.metrics.all_metrics import Accuracy, PrecisionAtRecall, binary_logits_to_score, multiclass_logits_to_pred
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.utils im... | 13,817 | 44.304918 | 158 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/iwildcam_dataset.py | from datetime import datetime
from pathlib import Path
import os
from PIL import Image
import pandas as pd
import numpy as np
import torch
import json
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy, Reca... | 6,275 | 38.225 | 124 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/py150_dataset.py | from pathlib import Path
import os
import pandas as pd
import numpy as np
import torch
import json
import gc
from wilds.common.metrics.all_metrics import Accuracy
from wilds.datasets.wilds_dataset import WILDSDataset
from transformers import GPT2Tokenizer
class Py150Dataset(WILDSDataset):
"""
The Py150 d... | 8,245 | 39.029126 | 124 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/globalwheat_dataset.py | import numpy as np
import pandas as pd
import torch
from pathlib import Path
from PIL import Image
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import DetectionAccuracy
SESSIONS = [
'Arvalis_1',
'Arvalis_2',
... | 12,057 | 34.154519 | 694 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/encode_dataset.py | import os, time
import torch
import pandas as pd
import numpy as np
import pyBigWig
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import subsample_idxs
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import MultiTaskAveragePrecision
# Human ch... | 18,102 | 40.808314 | 457 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/wilds_dataset.py | import os
import time
import torch
import numpy as np
class WILDSDataset:
"""
Shared dataset class for all WILDS datasets.
Each data point in the dataset is an (x, y, metadata) tuple, where:
- x is the input features
- y is the target
- metadata is a vector of relevant information, e.g., domai... | 19,146 | 39.22479 | 280 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/rxrx1_dataset.py | import os
from pathlib import Path
from collections import defaultdict
from PIL import Image
import pandas as pd
import numpy as np
import torch
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy
class RxR... | 8,976 | 39.804545 | 124 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/bdd100k_dataset.py | import numpy as np
import pandas as pd
import torch
from pathlib import Path
from PIL import Image
from wilds.common.metrics.all_metrics import MultiTaskAccuracy
from wilds.datasets.wilds_dataset import WILDSDataset
class BDD100KDataset(WILDSDataset):
"""
The BDD100K-wilds driving dataset.
This is a modif... | 6,918 | 50.634328 | 129 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/amazon_dataset.py | import os, csv
import torch
import pandas as pd
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.utils import map_to_id_array
from wilds.common.metrics.all_metrics import Accuracy
from wilds.common.grouper import CombinatorialGrouper
NOT_IN_DATASET = -1
class AmazonDataset(W... | 9,158 | 44.341584 | 151 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/ogbmolpcba_dataset.py | import os
import torch
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from ogb.graphproppred import PygGraphPropPredDataset, Evaluator
from ogb.utils.url import download_url
from torch_geometric.data.dataloader import Collater as PyGCollater
import torch_geometric
class OGBPCBADataset(WILDSDa... | 4,931 | 39.42623 | 143 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/download_utils.py | """
This file contains utility functions for downloading datasets.
The code in this file is taken from the torchvision package,
specifically, https://github.com/pytorch/vision/blob/master/torchvision/datasets/utils.py.
We package it here to avoid users having to install the rest of torchvision.
It is licensed under the... | 11,909 | 34.658683 | 133 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/poverty_dataset.py | from pathlib import Path
import pandas as pd
import torch
from torch.utils.data import Dataset
import pickle
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.metrics.all_metrics import MSE, PearsonCorrelation
from wilds.common.grouper import CombinatorialGrouper
from wilds.comm... | 11,412 | 41.114391 | 194 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/celebA_dataset.py | import os
import torch
import pandas as pd
from PIL import Image
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy
class CelebADataset(WILDSDataset):
"""
A variant of the CelebA da... | 5,669 | 38.103448 | 144 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/archive/poverty_v1_0_dataset.py | from pathlib import Path
import pandas as pd
import torch
from torch.utils.data import Dataset
import pickle
import numpy as np
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.metrics.all_metrics import MSE, PearsonCorrelation
from wilds.common.grouper import CombinatorialGrouper
from wilds.comm... | 12,047 | 41.875445 | 194 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/archive/iwildcam_v1_0_dataset.py | from datetime import datetime
from pathlib import Path
import os
from PIL import Image
import pandas as pd
import numpy as np
import torch
import json
from wilds.datasets.wilds_dataset import WILDSDataset
from wilds.common.grouper import CombinatorialGrouper
from wilds.common.metrics.all_metrics import Accuracy, Reca... | 6,922 | 39.964497 | 124 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/archive/fmow_v1_0_dataset.py | from pathlib import Path
import shutil
import pandas as pd
import torch
from torch.utils.data import Dataset
import pickle
import numpy as np
import torchvision.transforms.functional as F
from torchvision import transforms
import tarfile
import datetime
import pytz
from PIL import Image
from tqdm import tqdm
from wilds... | 11,840 | 50.25974 | 1,070 | py |
fork--wilds-public | fork--wilds-public-main/wilds/datasets/archive/__init__.py | 0 | 0 | 0 | py | |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/poverty/batcher.py | ########
# ADAPTED from github.com/sustainlab-group/africa_poverty
########
from dataset_constants import SIZES, SURVEY_NAMES, MEANS_DICT, STD_DEVS_DICT
from glob import glob
import os
import tensorflow as tf
ROOT_DIR = '/atlas/u/chrisyeh/africa_poverty/'
DHS_TFRECORDS_PATH_ROOT = os.path.join(ROOT_DIR, 'data/dhs_t... | 20,304 | 39.773092 | 100 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/poverty/convert_poverty_to_npy.py | '''
Adapted from github.com/sustainlab-group/africa_poverty/data_analysis/dhs.ipynb
'''
import tensorflow as tf
import numpy as np
import batcher
import dataset_constants
from tqdm import tqdm
FOLDS = ['A', 'B', 'C', 'D', 'E']
SPLITS = ['train', 'val', 'test']
BAND_ORDER = ['BLUE', 'GREEN', 'RED', 'SWIR1', 'SWIR2', 'T... | 3,358 | 31.298077 | 151 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/poverty/split_npys.py | import os, sys
import argparse
import numpy as np
from PIL import Image
from pathlib import Path
from tqdm import tqdm
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', required=True,
help='The directory where [dataset]/data can be found (or should be dow... | 821 | 30.615385 | 131 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/poverty/dataset_constants.py | DHS_COUNTRIES = [
'angola', 'benin', 'burkina_faso', 'cameroon', 'cote_d_ivoire',
'democratic_republic_of_congo', 'ethiopia', 'ghana', 'guinea', 'kenya',
'lesotho', 'malawi', 'mali', 'mozambique', 'nigeria', 'rwanda', 'senegal',
'sierra_leone', 'tanzania', 'togo', 'uganda', 'zambia', 'zimbabwe']
LSMS_C... | 7,920 | 38.605 | 106 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/poverty/process_metadata_poverty.py | ########
# ADAPTED from github.com/sustainlab-group/africa_poverty
########
import tensorflow as tf
import numpy as np
import batcher
import dataset_constants
from tqdm import tqdm
from utils.general import load_npz
import pickle
import pandas as pd
from pathlib import Path
FOLDS = ['A', 'B', 'C', 'D', 'E']
SPLITS =... | 1,709 | 28.482759 | 109 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/fmow/process_metadata_fmow.py | from pathlib import Path
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
from torchvision import transforms
from wilds.datasets.fmow_dataset import categories
from PIL import Image
import shutil
import time
root = Path('/u/scr/nlp/dro/fMoW/')
dstroot = Path('/u/scr/nlp/dro/fMoW/data')
# build... | 5,293 | 37.926471 | 133 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/fmow/convert_npy_to_jpg.py | import os, sys
import argparse
import numpy as np
from PIL import Image
from pathlib import Path
from tqdm import tqdm
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--root_dir', required=True,
help='The directory where [dataset]/data can be found (or should be dow... | 948 | 31.724138 | 131 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/camelyon17/generate_all_patch_coords.py | # Code adapted from https://github.com/liucong3/camelyon17
# and https://github.com/cv-lee/Camelyon17
import openslide
import cv2
import numpy as np
# import pandas as pd
import os
import csv
import argparse
from tqdm import tqdm
from xml.etree.ElementTree import parse
from PIL import Image
PATCH_LEVEL = 2
MASK_LEVE... | 8,972 | 34.466403 | 120 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/camelyon17/generate_final_metadata.py | # import pandas as pd
from matplotlib import pyplot as plt
import argparse
import os,sys
import numpy as np
from tqdm import tqdm
from collections import defaultdict
def generate_final_metadata(output_root):
import pandas as pd
df = pd.read_csv(os.path.join(output_root, 'all_patch_coords.csv'),
... | 4,428 | 36.218487 | 150 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/camelyon17/extract_final_patches_to_disk.py | import openslide
import argparse
import numpy as np
# import pandas as pd
import os
import random
from tqdm import tqdm
from generate_all_patch_coords import PATCH_LEVEL, MASK_LEVEL, CENTER_SIZE
def write_patch_images_from_df(slide_root, output_root):
import pandas as pd
read_df = pd.read_csv(
os.path.... | 2,164 | 30.376812 | 97 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/civilcomments/augment_identities_and_split.py | # import pandas as pd
from matplotlib import pyplot as plt
import os,sys
import numpy as np
from tqdm import tqdm
import argparse
from attr_definitions import GROUP_ATTRS, AGGREGATE_ATTRS, ORIG_ATTRS
def load_df(root):
"""
Loads the data and removes all examples where we don't have identity annotations.
"... | 5,352 | 34.926174 | 100 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/civilcomments/attr_definitions.py | ORIG_ATTRS = [
'male',
'female',
'transgender',
'other_gender',
'heterosexual',
'homosexual_gay_or_lesbian',
'bisexual',
'other_sexual_orientation',
'christian',
'jewish',
'muslim',
'hindu',
'buddhist',
'athe... | 1,959 | 20.304348 | 46 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/iwildcam/create_split.py | from datetime import datetime
from pathlib import Path
import argparse
import json
from PIL import Image
# import pandas as pd
import numpy as np
def create_split(data_dir, seed):
import pandas as pd
np_rng = np.random.default_rng(seed)
# Loading json was adapted from
# https://www.kaggle.com/ateplyu... | 8,617 | 43.42268 | 149 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/amazon_yelp/process_yelp.py | import os, sys, torch, json, csv, argparse
import numpy as np
# import pandas as pd
from transformers import BertTokenizerFast
from utils import *
#############
### PATHS ###
#############
def data_dir(root_dir):
return os.path.join(root_dir, 'yelp', 'data')
def token_length_path(data_dir):
return os.path.j... | 5,726 | 38.770833 | 144 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/amazon_yelp/subsample_amazon.py | import argparse
import csv
import os
# import pandas as pd
import numpy as np
# Fix the seed for reproducibility
np.random.seed(0)
"""
Subsample the Amazon dataset.
Usage:
python dataset_preprocessing/amazon_yelp/subsample_amazon.py <path> <frac>
"""
NOT_IN_DATASET = -1
# Split: {'train': 0, 'val': 1, 'id_val'... | 5,144 | 31.358491 | 100 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/amazon_yelp/generate_splits_yelp.py | import os, json, gzip, argparse, time, csv
import numpy as np
# import pandas as pd
from utils import *
def data_dir(root_dir):
return os.path.join(root_dir, 'yelp', 'data')
def load_reviews(data_dir):
import pandas as pd
reviews_df = pd.read_csv(reviews_path(data_dir),
dtype={'review... | 1,370 | 28.804348 | 94 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/amazon_yelp/utils.py | import os, json, gzip, argparse, time, csv
import numpy as np
# import pandas as pd
TRAIN, VAL, TEST = range(3)
_, OOD_VAL, ID_VAL, OOD_TEST, ID_TEST = range(5)
#############
### PATHS ###
#############
def raw_data_dir(data_dir):
return os.path.join(data_dir, 'raw')
def preprocessing_dir(data_dir):
return... | 7,910 | 41.532258 | 161 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/amazon_yelp/process_amazon.py | import os, json, gzip, argparse, time, csv, urllib
import numpy as np
# import pandas as pd
import networkx as nx
from networkx.algorithms.core import k_core
from transformers import AutoTokenizer, BertTokenizerFast, BertTokenizer
from utils import *
CATEGORIES = ["AMAZON_FASHION", "All_Beauty","Appliances", "Arts_Cra... | 8,425 | 43.582011 | 581 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/amazon_yelp/generate_splits_amazon.py | import os, json, gzip, argparse, time, csv
import numpy as np
# import pandas as pd
from utils import *
CATEGORIES = ["AMAZON_FASHION", "All_Beauty","Appliances", "Arts_Crafts_and_Sewing", "Automotive", "Books", "CDs_and_Vinyl", "Cell_Phones_and_Accessories", "Clothing_Shoes_and_Jewelry", "Digital_Music", "Electronic... | 5,628 | 42.3 | 581 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/encode/prep_metadata_labels.py | import os, csv
import scipy, numpy as np, time
from scipy import sparse
import pyBigWig
# Human chromosome names
chr_IDs = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10',
'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'chr18', 'chr19',
'chr20', 'c... | 6,693 | 44.537415 | 457 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/encode/prep_accessibility.py | # Adapted from https://github.com/GuanLab/Leopard/blob/master/data/quantile_normalize_bigwig.py
import argparse, time
import numpy as np
import pyBigWig
# Human chromosomes in hg19, and their sizes in bp
chrom_sizes = {'chr1': 249250621, 'chr10': 135534747, 'chr11': 135006516, 'chr12': 133851895, 'chr13': 115169878, ... | 2,456 | 43.672727 | 457 | py |
fork--wilds-public | fork--wilds-public-main/dataset_preprocessing/encode/prep_sequence.py | import argparse, time
import numpy as np
from tqdm import tqdm
# Sequence preprocessing. Code adapted from Jacob Schreiber.
# Human chromosome names
chr_IDs = ['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8', 'chr9', 'chr10',
'chr11', 'chr12', 'chr13', 'chr14', 'chr15', 'chr16', 'chr17', 'c... | 6,347 | 47.090909 | 288 | py |
adcgan | adcgan-main/BigGAN-PyTorch/make_hdf5.py | """ Convert dataset to HDF5
This script preprocesses a dataset and saves it (images and labels) to
an HDF5 file for improved I/O. """
import os
import sys
from argparse import ArgumentParser
from tqdm import tqdm, trange
import h5py as h5
import numpy as np
import torch
import torchvision.datasets as dset
imp... | 4,971 | 44.2 | 178 | py |
adcgan | adcgan-main/BigGAN-PyTorch/losses.py | import torch
import torch.nn.functional as F
# DCGAN loss
def loss_dcgan_dis(dis_fake, dis_real):
L1 = torch.mean(F.softplus(-dis_real))
L2 = torch.mean(F.softplus(dis_fake))
return L1, L2
def loss_dcgan_gen(dis_fake):
loss = torch.mean(F.softplus(-dis_fake))
return loss
# Hinge Loss
def loss_hinge_dis(d... | 1,526 | 24.881356 | 103 | py |
adcgan | adcgan-main/BigGAN-PyTorch/sample.py | ''' Sample
This script loads a pretrained net and a weightsfile and sample '''
import functools
import math
import numpy as np
from tqdm import tqdm, trange
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
i... | 8,346 | 44.612022 | 157 | py |
adcgan | adcgan-main/BigGAN-PyTorch/test.py | ''' Test
This script loads a pretrained net and a weightsfile and test '''
import functools
import math
import numpy as np
from tqdm import tqdm, trange
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
impor... | 7,928 | 34.084071 | 151 | py |
adcgan | adcgan-main/BigGAN-PyTorch/BigGANdeep.py | import numpy as np
import math
import functools
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
import torch.nn.functional as F
from torch.nn import Parameter as P
import layers
from sync_batchnorm import SynchronizedBatchNorm2d as SyncBatchNorm2d
# BigGAN-deep: uses a differ... | 22,982 | 41.958879 | 126 | py |
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