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 |
|---|---|---|---|---|---|---|
genesis | genesis-master/datasets/sketchy_config.py | # =========================== A2I Copyright Header ===========================
#
# Copyright (c) 2003-2021 University of Oxford. All rights reserved.
# Authors: Applied AI Lab, Oxford Robotics Institute, University of Oxford
# https://ori.ox.ac.uk/labs/a2i/
#
# This file is the property of the University of Ox... | 3,099 | 32.333333 | 80 | py |
genesis | genesis-master/datasets/multid_config.py | # =========================== A2I Copyright Header ===========================
#
# Copyright (c) 2003-2021 University of Oxford. All rights reserved.
# Authors: Applied AI Lab, Oxford Robotics Institute, University of Oxford
# https://ori.ox.ac.uk/labs/a2i/
#
# This file is the property of the University of Ox... | 5,294 | 35.517241 | 80 | py |
genesis | genesis-master/utils/plotting.py | # =========================== A2I Copyright Header ===========================
#
# Copyright (c) 2003-2021 University of Oxford. All rights reserved.
# Authors: Applied AI Lab, Oxford Robotics Institute, University of Oxford
# https://ori.ox.ac.uk/labs/a2i/
#
# This file is the property of the University of Ox... | 1,419 | 36.368421 | 78 | py |
genesis | genesis-master/utils/misc.py | # =========================== A2I Copyright Header ===========================
#
# Copyright (c) 2003-2021 University of Oxford. All rights reserved.
# Authors: Applied AI Lab, Oxford Robotics Institute, University of Oxford
# https://ori.ox.ac.uk/labs/a2i/
#
# This file is the property of the University of Ox... | 9,348 | 33.498155 | 79 | py |
genesis | genesis-master/utils/geco.py | # =========================== A2I Copyright Header ===========================
#
# Copyright (c) 2003-2021 University of Oxford. All rights reserved.
# Authors: Applied AI Lab, Oxford Robotics Institute, University of Oxford
# https://ori.ox.ac.uk/labs/a2i/
#
# This file is the property of the University of Ox... | 1,968 | 36.865385 | 80 | py |
GPim | GPim-master/setup.py | __author__ = "Maxim Ziatdinov"
__copyright__ = "Copyright Maxim Ziatdinov (2020)"
__version__ = "0.3.9"
__maintainer__ = "Maxim Ziatdinov"
__email__ = "maxim.ziatdinov@ai4microcopy.com"
__date__ = "11/25/2020"
from setuptools import setup, find_packages
import os
module_dir = os.path.dirname(os.path.abspath(__file__)... | 1,390 | 33.775 | 76 | py |
GPim | GPim-master/gpim/gprutils.py | """
gprutils.py
===========
Utility functions for the analysis of sparse image and hyperspectral data
with Gaussian processes.
Author: Maxim Ziatdinov (email: maxim.ziatdinov@ai4microcopy.com)
"""
import copy
import os
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import numpy as np
import pyro
import ... | 34,554 | 35.799787 | 116 | py |
GPim | GPim-master/gpim/gpreg/skgpr.py | '''
skgpr.py
======
Gaussian process regression model with a structured kernel interpolation
or a spectral mixture kernel. Serves as a high-level wrapper
for GPyTorch's (https://gpytorch.ai) Gaussian process modules with
structred kernel interpolation and spectral mixture kernel methods.
Author: Maxim Ziatdinov (email... | 19,370 | 42.142539 | 97 | py |
GPim | GPim-master/gpim/gpreg/gpr.py | '''
gpr.py
======
Gaussian process regression:
model training, prediction and uncertainty exploration
This module serves as a high-level wrapper for sparse Gaussian processes module
from Pyro probabilistic programming library (https://pyro.ai/)
for easy work with scientific image (2D) and hyperspectral (3D) data.
Autho... | 14,144 | 41.863636 | 103 | py |
GPim | GPim-master/gpim/gpreg/vgpr.py | '''
vgpr.py
======
Gaussian process regression model for vector-valued functions.
Serves as a high-level wrapper for GPyTorch's (https://gpytorch.ai)
Gaussian processes with correlated and independent output dimensions.
Author: Maxim Ziatdinov (email: maxim.ziatdinov@ai4microcopy.com)
'''
import time
import numpy as n... | 14,802 | 40.698592 | 97 | py |
GPim | GPim-master/gpim/gpbayes/boptim.py | """
boptim.py
===========
Utility functions for the Gaussian process-based
Bayesian optimization for selecting the next query points in
images and image-like data.
Author: Maxim Ziatdinov (email: maxim.ziatdinov@ai4microcopy.com)
"""
import types
import copy
import torch
import numpy as np
from scipy import spatial
... | 22,004 | 44.277778 | 103 | py |
GPim | GPim-master/gpim/kernels/pyro_kernels.py | '''
pyro_kernels.py
======
Pyro kernels
(some customized kernels TBA)
'''
import pyro.contrib.gp as gp
import pyro.distributions as dist
import torch
import warnings
def get_kernel(kernel_type, input_dim, lengthscale, use_gpu=False, **kwargs):
"""
Initalizes one of the following kernels:
RBF, Rational Qu... | 2,930 | 29.216495 | 77 | py |
GPim | GPim-master/gpim/kernels/gpytorch_kernels.py | '''
gpytorch_kernels.py
======
Gpytorch kernels
(some customized kernels TBA)
'''
import gpytorch
import torch
def get_kernel(kernel_type, input_dim, on_gpu=True, **kwargs):
"""
Initializes one of the following gpytorch kernels: RBF, Matern
Args:
kernel_type (str):
Kernel type ('RBF'... | 2,659 | 31.839506 | 79 | py |
GPim | GPim-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 5,119 | 29.47619 | 79 | py |
SAT | SAT-master/test.py |
import argparse
import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
import torchvision.utils as vutils
from networks.wideresnet import WideResNet
from ... | 4,582 | 36.876033 | 158 | py |
SAT | SAT-master/test_roa.py |
import argparse
import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
import torchvision.utils as vutils
from networks.wideresnet import WideResNet
from ... | 3,568 | 36.568421 | 158 | py |
SAT | SAT-master/attack.py | import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torch.autograd import Variable
from torch.autograd.gradcheck import zero_gradients
import copy
import numpy as np
... | 1,371 | 25.384615 | 94 | py |
SAT | SAT-master/test_common_corruptions.py | import argparse
import numpy as np
import torch
import torchvision
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from networks.resnet_skip_att import *
parser = argparse.ArgumentParser(description... | 2,317 | 35.793651 | 139 | py |
SAT | SAT-master/networks/wideresnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.ReLU(inplace=True)
se... | 4,503 | 35.032 | 78 | py |
SAT | SAT-master/ROA/ROA.py | # Coutsy of https://github.com/tongwu2020/phattacks
# This file basically runs Rectangular Occlusion Attacks (ROA) see paper
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import numpy as np
import os
class ROA(object):
'''
Make sticker
'''
def __init... | 8,083 | 40.035533 | 139 | py |
saliency | saliency-master/saliency/tf1/grad_cam_test.py | # Copyright 2021 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... | 4,483 | 37.324786 | 89 | py |
DCAN | DCAN-master/main.py |
from options import args_parser
import random
import numpy as np
import torch
import csv
import sys
from dataloader import load_lookups, prepare_instance, MyDataset, my_collate
from utils import early_stop, save_everything
from models import pick_model
import torch.optim as optim
from collections import defaultdict
fr... | 5,163 | 39.031008 | 149 | py |
DCAN | DCAN-master/dataloader.py | import csv
import torch
import numpy as np
from collections import defaultdict
from transformers import AutoTokenizer
from torch.utils.data import Dataset
from elmo import elmo
def load_vocab_dict(args, vocab_file):
"""
Load vocabulary dictionary from file: vocab_file
"""
vocab = set()
with open(v... | 6,914 | 34.829016 | 109 | py |
DCAN | DCAN-master/utils.py | import gensim.models
import numpy as np
from tqdm import tqdm
import csv
from scipy.sparse import csr_matrix
import gensim.models.word2vec as w2v
import gensim.models.fasttext as fasttext
import codecs
import re
def reformat(code, is_diag):
"""
Put a period in the right place because the MIMIC-3 data file... | 14,357 | 35.910026 | 158 | py |
DCAN | DCAN-master/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from torch.nn.utils import weight_norm
from math import floor
import numpy as np
import os
from dataloader import build_pretrain_embedding, load_embeddings
class WordRep(nn.Module):
def __init__(self, args... | 13,242 | 37.164265 | 134 | py |
DCAN | DCAN-master/train_test.py | import torch
import numpy as np
from utils import all_metrics, print_metrics
import json
import pickle
def train(args, model, optimizer, epoch, gpu, data_loader):
print("EPOCH %d" % epoch)
device = torch.device('cuda:{}'.format(args.gpu)) if args.gpu != -1 else torch.device('cpu')
losses = []
model.tr... | 2,281 | 35.806452 | 141 | py |
diofant | diofant-master/docs/conf.py | """
Diofant documentation build configuration file.
This file is execfile()d with the current directory set to its
containing dir.
The contents of this file are pickled, so don't put values in the
namespace that aren't pickleable (module imports are okay, they're
removed automatically).
"""
import inspect
import os
... | 6,973 | 31.741784 | 89 | py |
diofant | diofant-master/diofant/printing/latex.py | """A Printer which converts an expression into its LaTeX equivalent."""
import itertools
import re
import mpmath.libmp as mlib
from mpmath.libmp import prec_to_dps
from ..core import Add, Integer, Mod, oo
from ..core.alphabets import greeks
from ..core.function import _coeff_isneg
from ..core.operations import Assoc... | 63,123 | 34.562817 | 155 | py |
private-transformers | private-transformers-main/setup.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 1,958 | 32.20339 | 89 | py |
private-transformers | private-transformers-main/private_transformers/privacy_engine.py | # Copyright (c) Xuechen Li. All Rights Reserved.
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 24,722 | 41.045918 | 121 | py |
private-transformers | private-transformers-main/private_transformers/transformers_support.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 32,588 | 43.76511 | 120 | py |
private-transformers | private-transformers-main/private_transformers/supported_layers_grad_samplers.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 15,231 | 43.932153 | 143 | py |
private-transformers | private-transformers-main/private_transformers/lora_utils.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 3,727 | 31.701754 | 116 | py |
private-transformers | private-transformers-main/private_transformers/autograd_grad_sample.py | # Copyright (c) Xuechen Li. All Rights Reserved.
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 7,602 | 36.638614 | 120 | py |
private-transformers | private-transformers-main/examples/classification/run_classification.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 35,067 | 39.077714 | 120 | py |
private-transformers | private-transformers-main/examples/classification/src/dataset.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 33,819 | 43.093872 | 120 | py |
private-transformers | private-transformers-main/examples/classification/src/common.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 814 | 39.75 | 93 | py |
private-transformers | private-transformers-main/examples/classification/src/models.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 15,400 | 37.989873 | 115 | py |
private-transformers | private-transformers-main/examples/classification/src/trainer.py | # coding=utf-8
# Copyright (c) Xuechen Li. All Rights Reserved.
# Copyright 2020-present the HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.o... | 28,437 | 40.334302 | 120 | py |
private-transformers | private-transformers-main/examples/classification/spectral_analysis/rebuttal_plots_neurips_2022.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 5,233 | 31.918239 | 234 | py |
private-transformers | private-transformers-main/examples/classification/spectral_analysis/geometric_median.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 10,185 | 33.528814 | 120 | py |
private-transformers | private-transformers-main/examples/classification/spectral_analysis/rebuttal_neurips_2022.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 4,950 | 34.618705 | 110 | py |
private-transformers | private-transformers-main/examples/image_classification/main.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 4,296 | 34.512397 | 108 | py |
private-transformers | private-transformers-main/examples/table2text/run_language_modeling.py | # coding=utf-8
# Copyright (c) Xuechen Li. All Rights Reserved.
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in co... | 12,516 | 38.863057 | 114 | py |
private-transformers | private-transformers-main/examples/table2text/models.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 5,644 | 37.931034 | 119 | py |
private-transformers | private-transformers-main/examples/table2text/trainer.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 50,541 | 46.103448 | 119 | py |
private-transformers | private-transformers-main/examples/table2text/data_utils/language_modeling.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 15,291 | 36.116505 | 113 | py |
private-transformers | private-transformers-main/examples/table2text/data_utils/data_collator.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 10,548 | 43.889362 | 165 | py |
private-transformers | private-transformers-main/tests/test_privacy_engine.py | # Copyright (c) Xuechen Li. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or ag... | 24,432 | 41.053356 | 120 | py |
HAPPIER | HAPPIER-main/happier/evaluate.py | import os
import logging
import argparse
import torch
import numpy as np
from omegaconf import open_dict
import happier.lib as lib
import happier.engine as eng
from happier.getter import Getter
def print_metrics(metrics):
for split, mtrc in metrics.items():
for k, v in mtrc.items():
if k == ... | 6,029 | 34.680473 | 157 | py |
HAPPIER | HAPPIER-main/happier/getter.py | import torch
from torch import optim
import torchvision.transforms as T
import happier.lib as lib
from happier import losses
from happier import datasets
from happier import models
from happier import engine
from happier.models import schedulers
class Getter:
"""
This class allows to create differents object... | 6,762 | 41.534591 | 115 | py |
HAPPIER | HAPPIER-main/happier/run.py | import os
from os.path import join
import random
import numpy as np
from omegaconf import OmegaConf
import hydra
import torch
import torch.nn as nn
from torch.utils.tensorboard import SummaryWriter
import happier.lib as lib
import happier.engine as eng
from happier.getter import Getter
def if_func(cond, x, y):
... | 5,964 | 33.479769 | 113 | py |
HAPPIER | HAPPIER-main/happier/models/get_backbone.py | import torch.nn as nn
import torchvision.models as models
import happier.lib as lib
def get_backbone(name, pretrained=True, **kwargs):
if name == 'resnet34':
lib.LOGGER.info("using ResNet-34")
out_dim = 512
backbone = models.resnet34(pretrained=pretrained)
backbone = nn.Sequential... | 732 | 32.318182 | 65 | py |
HAPPIER | HAPPIER-main/happier/models/retrieval_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import happier.lib as lib
from happier.models.get_pooling import get_pooling
from happier.models.get_backbone import get_backbone
def flatten(tens):
if tens.ndim == 2:
return tens.squeeze(1)
if tens.ndim == 3:
return tens.squ... | 2,469 | 29.493827 | 115 | py |
HAPPIER | HAPPIER-main/happier/models/get_pooling.py | import torch.nn as nn
def get_pooling(pool, cfg):
name = cfg if isinstance(cfg, str) else cfg.name
kwargs = {} if isinstance(cfg, str) else cfg.kwargs
if name == 'default':
return pool
elif name == 'none':
return nn.Identity()
elif name == 'max':
return nn.AdaptiveMaxPool2... | 475 | 25.444444 | 55 | py |
HAPPIER | HAPPIER-main/happier/models/schedulers/cosine_annealing_lr.py | from torch.optim import lr_scheduler
class CosineAnnealingLR(lr_scheduler.CosineAnnealingLR):
def __repr__(self):
repr = f"{self.__class__.__name__}(\n"
repr = repr + f" T_max={self.T_max},\n"
repr = repr + f" eta_min={self.eta_min},\n"
repr = repr + ')'
return repr
| 320 | 25.75 | 56 | py |
HAPPIER | HAPPIER-main/happier/datasets/base_dataset.py | import os
from os.path import join, isfile
import torch
from torch.utils.data import Dataset
from PIL import Image
from tqdm import tqdm
import happier.lib as lib
import happier.engine as eng
class BaseDataset(Dataset):
RELEVANCE_BS = 256
CACHE_FILE = "HAPPIER_relevances_{mode}_{relevance_type}_{alpha}.trc... | 4,398 | 31.109489 | 137 | py |
HAPPIER | HAPPIER-main/happier/datasets/samplers/m_per_class_sampler.py | """
adapted from :
https://github.com/Andrew-Brown1/Smooth_AP/blob/master/src/datasets.py
"""
import copy
import numpy as np
from pytorch_metric_learning import samplers
import happier.lib as lib
def flatten(list_):
return [item for sublist in list_ for item in sublist]
class MPerClassSampler:
def __init_... | 3,721 | 26.776119 | 132 | py |
HAPPIER | HAPPIER-main/happier/datasets/samplers/hierarchical_sampler.py | from pytorch_metric_learning.samplers import HierarchicalSampler as PMLHierarchicalSampler
import happier.lib as lib
class HierarchicalSampler(PMLHierarchicalSampler):
def __init__(
self,
dataset,
batch_size,
samples_per_class,
batches_per_super_tuple=4,
super_cla... | 1,582 | 31.979167 | 90 | py |
HAPPIER | HAPPIER-main/happier/engine/checkpoint.py | import sys
from os.path import join
import torch
import happier.lib as lib
def checkpoint(
log_dir,
save_checkpoint,
net,
optimizer,
scheduler,
criterion,
scaler,
epoch,
config,
metrics,
):
state_dict = {}
if torch.cuda.device_count() > 1:
state_dict["net_stat... | 1,441 | 27.84 | 96 | py |
HAPPIER | HAPPIER-main/happier/engine/base_training_loop.py | import os
import torch
from tqdm import tqdm
import happier.lib as lib
def _calculate_loss_and_backward(
config,
net,
batch,
relevance_fn,
criterion,
optimizer,
scaler,
epoch,
):
with torch.cuda.amp.autocast(enabled=(scaler is not None)):
di = net(batch["image"].cuda())
... | 3,588 | 25.585185 | 102 | py |
HAPPIER | HAPPIER-main/happier/engine/accuracy_calculator.py | import os
import torch
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
import happier.lib as lib
from happier.engine.get_knn import get_knn
from happier.engine.metrics import get_metrics_dict
from happier.engine.compute_embeddings import compute_embeddings
from happier.engine.overall_accuracy_... | 8,360 | 36.493274 | 125 | py |
HAPPIER | HAPPIER-main/happier/engine/compute_relevance_on_the_fly.py | import torch
import happier.lib as lib
def relevance_for_batch(
batch,
alpha=1.0,
check_for=None,
type='decay',
):
if type == 'map':
relevances = get_relevances_map(
batch,
weights=alpha,
)
elif type == 'pop':
relevances = get_relevances_pop(
... | 1,859 | 24.479452 | 91 | py |
HAPPIER | HAPPIER-main/happier/engine/get_knn.py | import os
import numpy as np
import torch
import faiss
import pytorch_metric_learning.utils.common_functions as c_f
import happier.lib as lib
def get_knn(references, queries, num_k, embeddings_come_from_same_source, with_faiss=False):
with_faiss = lib.str_to_bool(os.getenv("WITH_FAISS", with_faiss))
num_k ... | 1,836 | 28.629032 | 92 | py |
HAPPIER | HAPPIER-main/happier/engine/metrics.py | from functools import partial
import torch
# import numpy as np
import happier.lib as lib
def ap(sorted_rel, at_R=False, reduce=True, **kwargs):
normalizing_factor = sorted_rel.sum(-1)
relevance_mask = torch.ones(sorted_rel.shape, device=sorted_rel.device)
if at_R:
for i, R in enumerate(normali... | 6,501 | 27.643172 | 108 | py |
HAPPIER | HAPPIER-main/happier/engine/compute_embeddings.py | import os
import torch
from tqdm import tqdm
import happier.lib as lib
def compute_embeddings(
net,
loader,
convert_to_cuda=False,
with_paths=False,
):
features = []
mode = net.training
net.eval()
lib.LOGGER.info("Computing embeddings")
for i, batch in enumerate(tqdm(loader, dis... | 915 | 23.105263 | 93 | py |
HAPPIER | HAPPIER-main/happier/engine/train.py | from time import time
import numpy as np
import torch
from torch.utils.data import DataLoader
import happier.lib as lib
from happier.engine.checkpoint import checkpoint
from happier.engine.accuracy_calculator import evaluate
from happier.engine.base_training_loop import base_training_loop
def train(
config,
... | 4,537 | 30.734266 | 140 | py |
HAPPIER | HAPPIER-main/happier/lib/load_state.py | import torch
from happier.lib.expand_path import expand_path
def load_state(path, key=None):
state = torch.load(expand_path(path), map_location='cpu')
if key is not None:
return state[key]
return state
def load_config(path):
return load_state(path, 'config')
| 290 | 16.117647 | 61 | py |
HAPPIER | HAPPIER-main/happier/lib/freeze_batch_norm.py | import torch.nn as nn
def freeze_batch_norm(model):
for module in filter(lambda m: type(m) == nn.BatchNorm2d, model.modules()):
module.eval()
module.train = lambda _: None
return model
| 211 | 22.555556 | 79 | py |
HAPPIER | HAPPIER-main/happier/lib/mask_logsumexp.py | import torch
def mask_logsumexp(tens, mask):
tens[~mask] = -float('inf')
return torch.logsumexp(tens, dim=1)
| 119 | 16.142857 | 39 | py |
HAPPIER | HAPPIER-main/happier/lib/get_set_random_state.py | import random
from functools import wraps
import numpy as np
import torch
from .logger import LOGGER
def random_seed(seed, backend=True):
LOGGER.info(f"Training with seed {seed}")
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if backend:
... | 1,317 | 27.042553 | 69 | py |
HAPPIER | HAPPIER-main/happier/lib/create_label_matrix.py | from typing import Optional, Union
import torch
def create_label_matrix(
labels: torch.Tensor,
other_labels: Optional[torch.Tensor] = None,
hierarchy_level: Optional[Union[int, str]] = None,
dtype: torch.dtype = torch.float,
):
if other_labels is None:
other_labels = labels
if (hiera... | 609 | 29.5 | 89 | py |
HAPPIER | HAPPIER-main/happier/lib/create_relevance_matrix.py | import torch
def create_relevance_matrix(label_matrix, relevance):
return torch.gather(relevance, 1, label_matrix)
| 121 | 19.333333 | 53 | py |
HAPPIER | HAPPIER-main/happier/lib/get_gradient_norm.py | import torch
def get_gradient_norm(net, norm_type=2):
with torch.no_grad():
parameters = [p for p in net.parameters() if p.grad is not None and p.requires_grad]
if len(parameters) == 0:
return 0.0
else:
device = parameters[0].grad.device
return torch.nor... | 426 | 34.583333 | 133 | py |
HAPPIER | HAPPIER-main/happier/lib/groupby_mean.py | import torch
# from : https://discuss.pytorch.org/t/groupby-aggregate-mean-in-pytorch/45335/8
def groupby_mean(value: torch.Tensor, labels: torch.LongTensor) -> torch.Tensor:
"""Group-wise average for (sparse) grouped tensors
Args:
value (torch.Tensor): values to average (# samples, latent dimension)... | 1,962 | 39.061224 | 115 | py |
HAPPIER | HAPPIER-main/happier/losses/cluster_loss.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import happier.lib as lib
# adapted from :
# https://github.com/azgo14/classification_metric_learning/blob/master/metric_learning/modules/losses.py
class ClusterLoss(nn.Module):
"""
L2 normalize weights and apply temperature scal... | 3,518 | 35.278351 | 120 | py |
HAPPIER | HAPPIER-main/happier/losses/csl_loss.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import happier.lib as lib
class CSLLoss(nn.Module):
def __init__(
self,
num_proxies,
margins=[0.25, 0.35, 0.45],
scale=32.0,
embedding_size=512,
reduce_type='sum',
proxies_seed... | 4,520 | 35.168 | 120 | py |
HAPPIER | HAPPIER-main/happier/losses/hap_loss.py | from functools import partial
import torch
import torch.nn as nn
import happier.lib as lib
from happier.losses.tools import reduce
def heaviside(tens, val=1., target=None, general=None):
return torch.heaviside(tens, values=torch.tensor(val, device=tens.device, dtype=tens.dtype))
def tau_sigmoid(tensor, tau, t... | 9,961 | 36.878327 | 153 | py |
HAPPIER | HAPPIER-main/happier/losses/tools/avg_non_zero_reducer.py | import torch
def avg_non_zero_reducer(losses):
threshold_condition = losses > 0.
num_past_filter = torch.sum(threshold_condition)
if num_past_filter >= 1:
loss = torch.mean(losses[threshold_condition])
else:
loss = torch.sum(losses * 0)
return loss
| 287 | 23 | 54 | py |
HAPPIER | HAPPIER-main/happier/losses/tools/__init__.py | import torch
from .avg_non_zero_reducer import avg_non_zero_reducer
__all__ = [
'avg_non_zero_reducer',
]
REDUCE_DICT = {
'none': torch.nn.Identity(),
'mean': torch.mean,
'sum': torch.sum,
'avg_non_zero': avg_non_zero_reducer,
}
def reduce(tens, reduce_type='mean'):
return REDUCE_DICT[red... | 336 | 15.047619 | 54 | py |
orconvqa-release | orconvqa-release-master/modeling.py | import os
import logging
import collections
import torch
from transformers import BertModel, BertPreTrainedModel, AlbertModel
from transformers.modeling_bert import (BertEncoder, BertOutput, BertAttention,
BertIntermediate, BertLayer, BertEmbeddings,
... | 54,396 | 52.018519 | 139 | py |
orconvqa-release | orconvqa-release-master/utils.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a cop... | 65,026 | 41.668635 | 123 | py |
orconvqa-release | orconvqa-release-master/train_retriever.py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
from __future__ import absolute_import, division, print_function
# import os
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"]="1"
import argparse
import logging
import os
import random
import glob
import timeit
import json
import fai... | 37,293 | 43.663473 | 144 | py |
orconvqa-release | orconvqa-release-master/train_pipeline.py | #!/usr/bin/env python
# coding: utf-8
# In[1]:
from __future__ import absolute_import, division, print_function
# import os
# os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"]="1,2"
import argparse
import logging
import os
import random
import glob
import timeit
import json
import l... | 53,201 | 48.352505 | 151 | py |
orconvqa-release | orconvqa-release-master/retriever_utils.py | from __future__ import absolute_import, division, print_function
import json
import logging
import math
import collections
import linecache
import numpy as np
from io import open
from tqdm import tqdm
from torch.utils.data import Dataset
from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
... | 15,465 | 46.152439 | 130 | py |
pyLDLE | pyLDLE-main/ldle.py | import pdb
import numpy as np
import copy
from scipy.spatial.distance import pdist, squareform
from scipy.sparse.csgraph import laplacian, minimum_spanning_tree, breadth_first_order
from scipy.sparse import coo_matrix, csr_matrix, diags
from scipy.linalg import eigh, svd, qr
from scipy.sparse.linalg import eigs, eigs... | 60,817 | 39.599466 | 186 | py |
CUFAR | CUFAR-main/train_single_task.py | import os
device = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = device
import time
import torch
import torch.nn.functional as F
from model.modules.ODE import *
from src.metrics import get_MSE, get_MAE, get_MAPE
from src.utils import get_dataloader, print_model_parm_nums
from src.args import get_args
from src.urbanpy_train_... | 7,800 | 41.396739 | 131 | py |
CUFAR | CUFAR-main/train_finetune.py | import os
device = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = device
import time
import torch
import torch.nn.functional as F
from src.metrics import get_MSE, get_MAE, get_MAPE
from src.utils import get_dataloader, print_model_parm_nums
from src.args import get_args
from src.urbanpy_train_finetune import UrbanPy_finetune... | 10,179 | 43.845815 | 139 | py |
CUFAR | CUFAR-main/train_joint.py | import os
device = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = device
import time
import torch
import torch.nn.functional as F
from src.metrics import get_MSE, get_MAE, get_MAPE
from src.utils import get_dataloader_joint, print_model_parm_nums
from src.args import get_args
from model.UrbanFM import UrbanFM
from model.Urba... | 7,078 | 38.547486 | 127 | py |
CUFAR | CUFAR-main/train_continual.py | import os
device = '0'
os.environ["CUDA_VISIBLE_DEVICES"] = device
import time
import torch
from model.modules.AKR import continual
from model.modules.memory_buffer import Buffer
from src.metrics import get_MSE, get_MAE, get_MAPE
from src.utils import get_dataloader, print_model_parm_nums
from src.args import get_args
... | 11,136 | 47.633188 | 139 | py |
CUFAR | CUFAR-main/src/urbanpy_train_finetune.py | import os
import sys
import warnings
import numpy as np
import random
import warnings
import time
import torch
import torch.nn as nn
from src.metrics import get_MAE, get_MSE, get_MAPE
from src.utils import print_model_parm_nums, get_lapprob_dataloader, get_gt_densities
from model.UrbanPy import UrbanPy, weights_init_no... | 10,817 | 45.034043 | 144 | py |
CUFAR | CUFAR-main/src/urbanpy_train_continual.py | import os
import sys
import warnings
import numpy as np
import random
import warnings
import time
import torch
import torch.nn as nn
from src.metrics import get_MAE, get_MSE, get_MAPE
from src.utils import print_model_parm_nums, get_lapprob_dataloader
from model.UrbanPy import UrbanPy, weights_init_normal
from model.mo... | 11,592 | 48.122881 | 133 | py |
CUFAR | CUFAR-main/src/utils.py | import numpy as np
import os
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
def get_dataloader(args, datapath, dataset= "TaxiBJ", batch_size= 16, mode='train', task_id=0):
cuda = True if torch.cuda.is_available() else False
Tensor = torch.cuda.FloatTensor if cuda else tor... | 10,058 | 46.672986 | 125 | py |
CUFAR | CUFAR-main/src/urbanpy_train_single_task.py | import os
import warnings
import numpy as np
import warnings
import time
import torch
import torch.nn as nn
from src.metrics import get_MAE, get_MSE, get_MAPE
from src.utils import print_model_parm_nums, get_lapprob_dataloader, get_gt_densities
from model.UrbanPy import UrbanPy, weights_init_normal
from model.modules.u... | 9,075 | 45.54359 | 162 | py |
CUFAR | CUFAR-main/model/CUFAR.py | import torch
import torch.nn as nn
from einops import rearrange
class mini_model(nn.Module):
def __init__(self, n_channel, scale_factor, in_channel, kernel_size, padding, groups):
super(mini_model, self).__init__()
self.n_channels = n_channel
self.scale_factor = scale_factor
self.in... | 7,109 | 43.716981 | 114 | py |
CUFAR | CUFAR-main/model/UrbanFM.py | import torch.nn as nn
import torch.nn.functional as F
import torch
class N2_Normalization(nn.Module):
def __init__(self, upscale_factor):
super(N2_Normalization, self).__init__()
self.upscale_factor = upscale_factor
self.avgpool = nn.AvgPool2d(upscale_factor)
self.upsample = nn.Upsa... | 5,736 | 34.63354 | 110 | py |
CUFAR | CUFAR-main/model/UrbanODE.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from .modules.ODE import *
from torchvision.models import vgg19
import math
class N2_Normalization(nn.Module):
def __init__(self, upscale_factor): # 4
super(N2_Normalization, self).__init__()
self.upscale_factor = upscale_factor
... | 16,947 | 37.343891 | 120 | py |
CUFAR | CUFAR-main/model/DeepLGR.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from torchvision.models import vgg19
import math
class N2_Normalization(nn.Module):
def __init__(self, upscale_factor):
super(N2_Normalization, self).__init__()
self.upscale_factor = upscale_factor
self.avgpool = nn.AvgPool... | 11,658 | 38.522034 | 124 | py |
CUFAR | CUFAR-main/model/UrbanPy.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from model.modules.urbanpy_layers import LocalConv
import math
def n2_normalization_func(x, scale_factor):
out = F.avg_pool2d(x, scale_factor) * scale_factor ** 2
out = F.upsample(out, scale_factor=scale_factor)
return torch.div(x, out + 1e... | 8,888 | 38.331858 | 151 | py |
CUFAR | CUFAR-main/model/FODE.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from .modules.ODE import *
class N2_Normalization(nn.Module):
def __init__(self, upscale_factor): # 4
super(N2_Normalization, self).__init__()
self.upscale_factor = upscale_factor
self.avgpool = nn.AvgPool2d(upscale_factor)... | 8,965 | 36.514644 | 135 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.