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
value |
|---|---|---|---|---|---|---|
fast-dpsgd | fast-dpsgd-main/pyvacydp.py | '''
Pyvacy implementations
'''
import time
import torch
import torch.nn.functional as F
from pyvacy import analysis, optim
from torch import nn
import data
import utils
from pytorch import get_data, model_dict
def main(args):
print(args)
assert args.dpsgd
torch.backends.cudnn.benchmark = True
trai... | 1,971 | 29.8125 | 90 | py |
fast-dpsgd | fast-dpsgd-main/owkindp.py | '''
Code for Grad-CNN implementations
'''
import time
import torch
import torch.nn.functional as F
from gradcnn import crb, make_optimizer
from torch import nn, optim
import data
import utils
from pytorch import get_data
class MNISTNet(crb.Module):
def __init__(self, **_):
super().__init__()
se... | 4,604 | 28.519231 | 94 | py |
fast-dpsgd | fast-dpsgd-main/memory_experiment.py | import argparse
import pickle
import subprocess
from utils import pr_green, pr_red
# yapf: disable
CMDS = dict((
('jax', 'python jaxdp.py {} --no_dpsgd --no_save --dummy_data'),
('tf2', 'python tf2dp.py {} --no_dpsgd --no_xla --no_save --dummy_data'),
('tf1', 'python tf1dp.py {} --no_... | 5,000 | 37.767442 | 114 | py |
fast-dpsgd | fast-dpsgd-main/backpackdp.py | '''
BackPACK experiments in this file
'''
import time
import torch
import torch.nn.functional as F
from backpack import backpack, extend
from backpack.extensions import BatchGrad, BatchL2Grad
from torch import nn
from torch.optim import Optimizer
import data
import utils
from pytorch import get_data, model_dict
def... | 4,755 | 31.8 | 98 | py |
nocturne | nocturne-main/examples/imitation_learning/waymo_data_loader.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Dataloader for imitation learning in Nocturne."""
from collections import defaultdict
import random
import torch
from ... | 8,395 | 40.564356 | 97 | py |
nocturne | nocturne-main/examples/imitation_learning/model.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Model for an imitation learning agent."""
import torch
from torch import nn
from torch.distributions.multivariate_norma... | 6,354 | 39.221519 | 103 | py |
nocturne | nocturne-main/examples/imitation_learning/filters.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""A streaming mean-std filter used to whiten inputs."""
import torch
from torch import nn
class MeanStdFilter(nn.Module... | 2,385 | 28.825 | 77 | py |
nocturne | nocturne-main/examples/imitation_learning/train.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Imitation learning training script (behavioral cloning)."""
from datetime import datetime
from pathlib import Path
impo... | 9,424 | 35.111111 | 79 | py |
nocturne | nocturne-main/examples/imitation_learning/replay_video.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Replay a video of a trained controller."""
from collections import defaultdict
import json
from pathlib import Path
imp... | 8,334 | 43.572193 | 86 | py |
nocturne | nocturne-main/examples/sample_factory_files/visualize_sample_factory.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Use to create movies of trained policies."""
import argparse
from collections import deque
import json
import sys
impor... | 11,171 | 39.923077 | 116 | py |
nocturne | nocturne-main/examples/sample_factory_files/run_sample_factory.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Runner script for sample factory.
To run in single agent mode on one file for testing.
python -m run_sample_factory a... | 14,308 | 39.535411 | 120 | py |
nocturne | nocturne-main/examples/rllib_files/run_rllib.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Example run script for RLlib."""
import os
import hydra
from omegaconf import OmegaConf
from cfgs.config import set_di... | 5,607 | 31.229885 | 84 | py |
nocturne | nocturne-main/examples/on_policy_files/nocturne_runner.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
"""Runner for PPO from https://github.com/marlbenchmark/on... | 21,461 | 37.120782 | 117 | py |
nocturne | nocturne-main/algos/ppo/env_wrappers.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
"""
Modified from OpenAI Baselines code to work with multi... | 29,079 | 32.502304 | 99 | py |
nocturne | nocturne-main/algos/ppo/base_runner.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import wandb
import os
import numpy as np
import torch
fro... | 7,111 | 38.292818 | 84 | py |
nocturne | nocturne-main/algos/ppo/r_mappo/r_mappo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import numpy as np
import torch
import torch.nn as nn
from... | 10,421 | 41.538776 | 116 | py |
nocturne | nocturne-main/algos/ppo/r_mappo/algorithm/r_actor_critic.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch
import torch.nn as nn
from algos.ppo.ppo_util... | 8,798 | 43.439394 | 121 | py |
nocturne | nocturne-main/algos/ppo/r_mappo/algorithm/rMAPPOPolicy.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch
from algos.ppo.r_mappo.algorithm.r_actor_crit... | 7,556 | 47.133758 | 120 | py |
nocturne | nocturne-main/algos/ppo/utils/valuenorm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import numpy as np
import torch
import torch.nn as nn
c... | 3,604 | 35.785714 | 85 | py |
nocturne | nocturne-main/algos/ppo/utils/shared_buffer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch
import numpy as np
from algos.ppo.utils.util ... | 29,299 | 49.08547 | 120 | py |
nocturne | nocturne-main/algos/ppo/utils/util.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import numpy as np
import math
import torch
def check(in... | 2,524 | 28.360465 | 75 | py |
nocturne | nocturne-main/algos/ppo/utils/separated_buffer.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch
import numpy as np
from collections import de... | 24,402 | 47.227273 | 231 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/distributions.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch
import torch.nn as nn
from .util import init
... | 4,168 | 26.427632 | 85 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/cnn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
from torchvision import transforms
import torch.nn as nn
f... | 2,471 | 29.518519 | 78 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/mlp.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch.nn as nn
from .util import init, get_clones
"... | 2,308 | 32.463768 | 77 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/popart.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import math
import numpy as np
import torch
import torch.n... | 4,510 | 36.280992 | 79 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/util.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import copy
import numpy as np
import torch
import torch.... | 690 | 25.576923 | 76 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/act.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
from .distributions import Bernoulli, Categorical, DiagGau... | 8,915 | 43.58 | 99 | py |
nocturne | nocturne-main/algos/ppo/ppo_utils/rnn.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
# Code modified from https://github.com/marlbenchmark/on-policy
import torch
import torch.nn as nn
"""RNN modules."""
cl... | 3,188 | 34.043956 | 88 | py |
nocturne | nocturne-main/nocturne/envs/base_env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Default environment for Nocturne."""
from typing import Any, Dict, Sequence, Union
from collections import defaultdict... | 26,180 | 46.088129 | 113 | py |
nocturne | nocturne-main/nocturne/utils/eval/average_displacement.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Average displacement error computation."""
from collections import defaultdict
from itertools import repeat
import json... | 9,552 | 41.0837 | 93 | py |
nocturne | nocturne-main/nocturne/utils/eval/goal_reaching_rate.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Goal reaching rate computation."""
from pathlib import Path
import numpy as np
import torch
from nocturne import Simul... | 4,169 | 37.611111 | 96 | py |
nocturne | nocturne-main/nocturne/utils/eval/collision_rate.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Collision rate computation."""
from pathlib import Path
import numpy as np
import torch
from nocturne import Simulatio... | 4,539 | 40.651376 | 113 | py |
nocturne | nocturne-main/nocturne/utils/eval/goal_by_intersection.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Goal reaching rate and collision rate computation as a function of number of intersections in expert trajectory."""
fro... | 10,583 | 39.707692 | 118 | py |
nocturne | nocturne-main/scripts/paper_plots/eval_sample_factory.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Run a policy over the entire train set.
TODO(ev) refactor, this is wildly similar to visualize_sample_factory
"""
fro... | 61,047 | 45.318665 | 118 | py |
nocturne | nocturne-main/scripts/paper_plots/eval_il_agents.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""Run script that generates summary statistics for a folder of IL agents."""
import json
import os
import numpy as np
im... | 2,665 | 40.65625 | 114 | py |
fork--wilds-public | fork--wilds-public-main/setup.py | import setuptools
import os
import sys
here = os.path.abspath(os.path.dirname(__file__))
sys.path.insert(0, os.path.join(here, 'wilds'))
from version import __version__
print(f'Version {__version__}')
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="w... | 1,281 | 28.136364 | 128 | py |
fork--wilds-public | fork--wilds-public-main/examples/losses.py | import torch.nn as nn
from wilds.common.metrics.loss import ElementwiseLoss, Loss, MultiTaskLoss
from wilds.common.metrics.all_metrics import MSE
def initialize_loss(config, d_out):
if config.loss_function == 'cross_entropy':
return ElementwiseLoss(loss_fn=nn.CrossEntropyLoss(reduction='none'))
elif c... | 939 | 38.166667 | 87 | py |
fork--wilds-public | fork--wilds-public-main/examples/evaluate.py | import argparse
import json
import os
import urllib.request
from ast import literal_eval
from typing import Dict, List
from urllib.parse import urlparse
import numpy as np
import torch
from wilds import benchmark_datasets
from wilds import get_dataset
from wilds.datasets.wilds_dataset import WILDSDataset, WILDSSubset... | 9,843 | 33.784452 | 124 | py |
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/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/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/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/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/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/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/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/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/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 |
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 |
adcgan | adcgan-main/BigGAN-PyTorch/train_fns.py | ''' train_fns.py
Functions for the main loop of training different conditional image models
'''
import torch
import torch.nn as nn
import torchvision
import os
import utils
import losses
# Dummy training function for debugging
def dummy_training_function():
def train(x, y):
return {}
return train
def GAN_t... | 11,139 | 47.017241 | 181 | py |
adcgan | adcgan-main/BigGAN-PyTorch/BigGAN.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
# Architectures for G
# Att... | 20,469 | 43.307359 | 267 | py |
adcgan | adcgan-main/BigGAN-PyTorch/utils.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
''' Utilities file
This file contains utility functions for bookkeeping, logging, and data loading.
Methods which directly affect training should either go in layers, the model,
or train_fns.py.
'''
from __future__ import print_function
import sys
import os
import numpy a... | 49,789 | 39.878489 | 109 | py |
adcgan | adcgan-main/BigGAN-PyTorch/layers.py | ''' Layers
This file contains various layers for the BigGAN models.
'''
import numpy as np
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
from sync_batchnorm import SynchronizedBatchNorm2d as SyncBN2d
# ... | 17,130 | 36.32244 | 101 | py |
adcgan | adcgan-main/BigGAN-PyTorch/datasets.py | ''' Datasets
This file contains definitions for our CIFAR, ImageFolder, and HDF5 datasets
'''
import os
import os.path
import sys
from PIL import Image
import numpy as np
from tqdm import tqdm, trange
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torchvision.datasets.utils im... | 11,416 | 30.451791 | 139 | py |
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