repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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negev | negev-main/dlib/losses/dice.py | from typing import Optional, List
import sys
from os.path import dirname, abspath
root_dir = dirname(dirname(dirname(abspath(__file__))))
sys.path.append(root_dir)
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from dlib.functional._functional import soft_dice_score, to_tensor
fr... | 4,239 | 34.630252 | 82 | py |
negev | negev-main/dlib/losses/soft_bce.py | from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn, Tensor
__all__ = ["SoftBCEWithLogitsLoss"]
class SoftBCEWithLogitsLoss(nn.Module):
__constants__ = ["weight", "pos_weight", "reduction", "ignore_index",
"smooth_factor"]
def __init__(
... | 2,472 | 29.9125 | 79 | py |
negev | negev-main/dlib/losses/lovasz.py | """
Lovasz-Softmax and Jaccard hinge loss in PyTorch
Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
"""
from __future__ import print_function, division
import sys
from os.path import dirname, abspath
from typing import Optional
root_dir = dirname(dirname(dirname(abspath(__file__))))
sys.path.append(root_dir)
imp... | 8,265 | 31.543307 | 82 | py |
negev | negev-main/dlib/losses/soft_ce.py | import sys
from os.path import dirname, abspath
from typing import Optional
root_dir = dirname(dirname(dirname(abspath(__file__))))
sys.path.append(root_dir)
from torch import nn
import torch
import torch.nn.functional as F
from dlib.functional._functional import label_smoothed_nll_loss
__all__ = ["SoftCrossEntropyL... | 1,666 | 28.245614 | 81 | py |
negev | negev-main/dlib/losses/jaccard.py | import sys
from os.path import dirname, abspath
from typing import Optional, List
root_dir = dirname(dirname(dirname(abspath(__file__))))
sys.path.append(root_dir)
import torch
import torch.nn.functional as F
from torch.nn.modules.loss import _Loss
from dlib.functional._functional import soft_jaccard_score, to_tenso... | 4,213 | 34.116667 | 82 | py |
negev | negev-main/dlib/losses/entropy.py | import sys
from os.path import dirname, abspath
import torch
import torch.nn as nn
root_dir = dirname(dirname(dirname(abspath(__file__))))
sys.path.append(root_dir)
from dlib.utils.reproducibility import set_seed
__all__ = ['Entropy']
class _CrossEntropy(nn.Module):
"""
Compute Entropy between two distrib... | 3,197 | 27.052632 | 79 | py |
negev | negev-main/dlib/fpn/model.py | import sys
from os.path import dirname, abspath
from typing import Optional, Union
root_dir = dirname(dirname(dirname(abspath(__file__))))
sys.path.append(root_dir)
from dlib.fpn.decoder import FPNDecoder
from dlib.base import SegmentationModel, SegmentationHead, ClassificationHead
from dlib.encoders import get_encod... | 4,689 | 37.760331 | 79 | py |
negev | negev-main/dlib/fpn/decoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class Conv3x3GNReLU(nn.Module):
def __init__(self, in_channels, out_channels, upsample=False):
super().__init__()
self.upsample = upsample
self.block = nn.Sequential(
nn.Conv2d(
in_channels, out_... | 4,043 | 29.870229 | 105 | py |
negev | negev-main/dlib/functional/core.py | import torch
def _take_channels(*xs, ignore_channels=None):
if ignore_channels is None:
return xs
else:
channels = [channel for channel in range(xs[0].shape[1]) if channel not in ignore_channels]
xs = [torch.index_select(x, dim=1, index=torch.tensor(channels).to(x.device)) for x in xs]... | 3,765 | 28.888889 | 99 | py |
negev | negev-main/dlib/functional/_functional.py | import math
import numpy as np
from typing import Optional
import torch
import torch.nn.functional as F
__all__ = [
"focal_loss_with_logits",
"softmax_focal_loss_with_logits",
"soft_jaccard_score",
"soft_dice_score",
"wing_loss",
]
def to_tensor(x, dtype=None) -> torch.Tensor:
if isinstanc... | 8,837 | 31.255474 | 109 | py |
pyratbay | pyratbay-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# Pyrat-Bay documentation build configuration file, created by
# sphinx-quickstart on Fri Jan 8 16:23:24 2016.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
#... | 11,649 | 30.486486 | 80 | py |
ssm | ssm-master/ssm/stats.py | import autograd.numpy as np
from autograd.scipy.special import gammaln, logsumexp
from autograd.scipy.linalg import solve_triangular
from ssm.util import one_hot
def flatten_to_dim(X, d):
"""
Flatten an array of dimension k + d into an array of dimension 1 + d.
Example:
X = npr.rand(10, 5, 2, 2)... | 23,265 | 33.519288 | 114 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/test2adversarial.py | from __future__ import print_function
import os
import argparse
import torch
from torch.nn import functional as F
from torchvision import datasets, transforms
from data.cifar10_c import CIFAR10C
from models.resnet import resnet18
from utils import logger
parser = argparse.ArgumentParser(description='PyTorch CIFAR TRA... | 3,429 | 35.489362 | 147 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/utils.py | import torch
import torch.nn as nn
import os
import time
import numpy as np
import random
import copy
from pdb import set_trace
from collections import OrderedDict
def normalize_fn(tensor, mean, std):
"""Differentiable version of torchvision.functional.normalize"""
# here we assume the color channel is in at ... | 10,411 | 32.371795 | 156 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/train_trades_cifar10_semisupervised.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from torch.utils.data.dataset import Dataset
from torch.optim.lr_scheduler import MultiStepLR
import torchvision
import torchvision.transforms as tr... | 20,520 | 40.456566 | 178 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/train_simCLR.py | import argparse
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.datasets as datasets
from torch.utils.data.sampler import SubsetRandomSampler
from models.resnet_multi_bn import resnet18, proj_head
from utils import *
import torchvision.transforms as transforms
import ... | 14,494 | 35.977041 | 153 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/train_trades.py | from __future__ import print_function
import os
import argparse
import torch
import torch.nn.functional as F
import torchvision
import torch.optim as optim
from torchvision import datasets, transforms
from utils import NormalizeByChannelMeanStd, setup_seed
from models.resnet import resnet18
from trades import trades_l... | 16,708 | 38.223005 | 173 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/trades.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import torch.optim as optim
from utils import pgd_attack, fix_bn
import numpy as np
def squared_l2_norm(x):
flattened = x.view(x.unsqueeze(0).shape[0], -1)
return (flattened ** 2).sum(1)
def l2_norm(x):
... | 3,564 | 31.117117 | 93 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/models/resnet.py | import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
from utils import NormalizeByChannelMeanStd
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_resnet50_2', 'wide_resnet10... | 13,879 | 37.77095 | 107 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/models/utils.py | import torch
import torch.nn as nn
def normalize_fn(tensor, mean, std):
"""Differentiable version of torchvision.functional.normalize"""
# here we assume the color channel is in at dim=1
mean = mean[None, :, None, None]
std = std[None, :, None, None]
return tensor.sub(mean).div(std)
class Normal... | 873 | 30.214286 | 68 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/models/resnet_multi_bn.py | import torch
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
from utils import NormalizeByChannelMeanStd
from pdb import set_trace
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
'wide_r... | 17,335 | 36.201717 | 123 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/data/cifar10_c.py | import torch
from torch.utils.data import Dataset
from PIL import Image
from os.path import join
import numpy as np
from torchvision import transforms
class CIFAR10C(Dataset):
def __init__(self, root, transform=None, severity=5, attack_type=''):
dataPath = join(root, '{}.npy'.format(attack_type))
... | 1,006 | 28.617647 | 99 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/data/cifar10.py | import torch
from torchvision.datasets import CIFAR10, CIFAR100
from PIL import Image
class CustomCIFAR10(CIFAR10):
def __init__(self, withLabel=False, labelSubSet=None, labelTrans=None, **kwds):
super().__init__(**kwds)
self.withLabel = withLabel
self.labelTrans = labelTrans
if la... | 1,544 | 30.530612 | 83 | py |
Adversarial-Contrastive-Learning | Adversarial-Contrastive-Learning-master/optimizer/lars.py | """ Layer-wise adaptive rate scaling for SGD in PyTorch! """
import torch
from torch.optim.optimizer import Optimizer, required
class LARS(Optimizer):
r"""Implements layer-wise adaptive rate scaling for SGD.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
par... | 4,123 | 37.185185 | 113 | py |
vse_infty | vse_infty-master/train.py | """Training script"""
import os
import time
import numpy as np
import torch
from transformers import BertTokenizer
from lib.datasets import image_caption
from lib.vse import VSEModel
from lib.evaluation import i2t, t2i, AverageMeter, LogCollector, encode_data, compute_sim
import logging
import tensorboard_logger as t... | 10,188 | 37.161049 | 125 | py |
vse_infty | vse_infty-master/lib/loss.py | import torch
import torch.nn as nn
from torch.autograd import Variable
class ContrastiveLoss(nn.Module):
"""
Compute contrastive loss (max-margin based)
"""
def __init__(self, opt, margin=0, max_violation=False):
super(ContrastiveLoss, self).__init__()
self.opt = opt
self.marg... | 1,695 | 27.745763 | 62 | py |
vse_infty | vse_infty-master/lib/vse.py | """VSE model"""
import numpy as np
import torch
import torch.nn as nn
import torch.nn.init
import torch.backends.cudnn as cudnn
from torch.nn.utils import clip_grad_norm_
from lib.encoders import get_image_encoder, get_text_encoder
from lib.loss import ContrastiveLoss
import logging
logger = logging.getLogger(__nam... | 8,054 | 39.074627 | 116 | py |
vse_infty | vse_infty-master/lib/evaluation.py | """Evaluation"""
from __future__ import print_function
import logging
import time
import torch
import numpy as np
from collections import OrderedDict
from transformers import BertTokenizer
from lib.datasets import image_caption
from lib.vse import VSEModel
logger = logging.getLogger(__name__)
class AverageMeter(ob... | 17,350 | 35.223382 | 104 | py |
vse_infty | vse_infty-master/lib/encoders.py | """VSE modules"""
import torch
import torch.nn as nn
import numpy as np
from collections import OrderedDict
from transformers import BertModel
from lib.modules.resnet import ResnetFeatureExtractor
from lib.modules.aggr.gpo import GPO
from lib.modules.mlp import MLP
import logging
logger = logging.getLogger(__name_... | 6,810 | 34.473958 | 110 | py |
vse_infty | vse_infty-master/lib/modules/resnet.py | import os
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import logging
logger = logging.getLogger(__name__)
__all__ = ['ResNet', 'resnet50', 'resnet101', 'resnet152']
model_urls = {
'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
'resne... | 11,451 | 35.823151 | 122 | py |
vse_infty | vse_infty-master/lib/modules/mlp.py | import torch.nn as nn
import torch.nn.functional as F
class TwoLayerMLP(nn.Module):
def __init__(self, num_features, hid_dim, out_dim, return_hidden=False):
super().__init__()
self.return_hidden = return_hidden
self.model = nn.Sequential(
nn.Linear(num_features, hid_dim),
... | 1,567 | 31.666667 | 103 | py |
vse_infty | vse_infty-master/lib/modules/aggr/gpo.py | # coding=utf-8
import torch
import torch.nn as nn
import math
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
def positional_encoding_1d(d_model, length):
"""
:param d_model: dimension of the model
:param length: length of positions
:return: length*d_model position matrix
... | 3,239 | 36.674419 | 99 | py |
vse_infty | vse_infty-master/lib/datasets/image_caption.py | """COCO dataset loader"""
import torch
import torch.utils.data as data
import os
import os.path as osp
import numpy as np
from imageio import imread
import random
import json
import cv2
import logging
logger = logging.getLogger(__name__)
class RawImageDataset(data.Dataset):
"""
Load precomputed captions and... | 14,003 | 37.054348 | 116 | py |
cfqp | cfqp-main/condgen/utils.py | import os.path
import torch
DATA_DIR = os.path.join(os.path.dirname(__file__), '..','data')
import numpy as np
import pandas as pd
import pickle
import sys
from torch import Tensor
class LinearScheduler(object):
def __init__(self, iters, maxval=1.0, start = 0):
self._iters = max(1, iters)
... | 7,674 | 39.824468 | 108 | py |
cfqp | cfqp-main/condgen/counterfactuals/train_deepscm.py | from distutils.util import strtobool
from argparse import ArgumentParser
from condgen.data_utils.data_utils_MNIST import MNISTDataModule
from condgen.data_utils.data_utils import PendulumDataModule
from condgen.models.deepscm import DeepSCM
from condgen.data_utils.data_utils_cf_traj import SimpleTrajDataModule
import p... | 3,163 | 34.550562 | 129 | py |
cfqp | cfqp-main/condgen/counterfactuals/train_cf.py | from distutils.util import strtobool
from argparse import ArgumentParser
from data_utils.data_utils_MNIST import MNISTDataModule
from models.CFGAN import CFGAN
import pytorch_lightning as pl
from pytorch_lightning.loggers import WandbLogger
from pytorch_lightning.callbacks.model_checkpoint import ModelCheckpoint
from ... | 4,218 | 36.669643 | 204 | py |
cfqp | cfqp-main/condgen/counterfactuals/train_cf_cluster.py | from distutils.util import strtobool
from argparse import ArgumentParser
from condgen.data_utils.data_utils_MNIST import MNISTDataModule
from condgen.data_utils.data_utils_cf_traj import SimpleTrajDataModule
from condgen.models.CFGAN import CFGAN
import pytorch_lightning as pl
from pytorch_lightning.loggers import Wan... | 7,113 | 34.929293 | 90 | py |
cfqp | cfqp-main/condgen/counterfactuals/train_cf_baselines.py | from distutils.util import strtobool
from argparse import ArgumentParser
from condgen.data_utils.data_utils_MNIST import MNISTDataModule
from condgen.data_utils.data_utils_cf_traj import SimpleTrajDataModule
from condgen.models.baselines_cf import CFBaseline
import pytorch_lightning as pl
from pytorch_lightning.logger... | 3,721 | 34.447619 | 169 | py |
cfqp | cfqp-main/condgen/models/scorers.py | import torch
import numpy as np
import torch.nn as nn
from condgen.models.CFGAN import ImageEmbedder
class Dense1D(nn.Module):
"""A fully connected layer that reshapes outputs to feature maps."""
def __init__(self, input_dim, output_dim):
super().__init__()
self.dense = nn.Linear(input_dim, output_dim)
d... | 16,976 | 39.810096 | 185 | py |
cfqp | cfqp-main/condgen/models/CFGAN.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from argparse import ArgumentParser
from condgen.utils import str2bool
import math
import numpy as np
from torchvision.utils import make_grid
import wandb
import pandas as pd
import copy
import plotly.express as px
import ... | 25,540 | 43.887522 | 351 | py |
cfqp | cfqp-main/condgen/models/neuralode.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
from argparse import ArgumentParser
from condgen.utils import str2bool
from torchdiffeq import odeint
class ContinuousTreatmentODE(nn.Module):
def __init__(self,h_dim,u_dim,shared_u_dim, continuous_treatment, fun_treatment, dropout_p, planned_treatm... | 8,666 | 44.857143 | 201 | py |
cfqp | cfqp-main/condgen/models/score_matching.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
import functools
import numpy as np
from distutils.util import strtobool
from condgen.models.scorers import ScoreNet, TemporalScoreNet, ConditionalScoreNet
from torch.optim.lr_scheduler import ReduceLROnPlateau
import condgen.models.samplers as samplers
... | 10,811 | 38.316364 | 247 | py |
cfqp | cfqp-main/condgen/models/baselines_cf.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from argparse import ArgumentParser
from condgen.utils import str2bool
import math
import numpy as np
from torchvision.utils import make_grid
import wandb
import pandas as pd
import copy
import plotly.express as px
import ... | 5,464 | 33.15625 | 152 | py |
cfqp | cfqp-main/condgen/models/RNN.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
from argparse import ArgumentParser
from condgen.utils import str2bool
class RNN_seq2seq(pl.LightningModule):
def __init__(self, input_long_size, hidden_dim, baseline_size, lr, rnn_layers, weight_decay, T_cond, T_horizon, reconstruction_size, plann... | 7,350 | 46.121795 | 188 | py |
cfqp | cfqp-main/condgen/models/deepscm.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
import functools
import numpy as np
from distutils.util import strtobool
from torch.optim.lr_scheduler import ReduceLROnPlateau
import condgen.models.samplers as samplers
from condgen.models.CFGAN import ImageEmbedder, GaussianFourierProjection, Dense
fr... | 23,197 | 38.587031 | 247 | py |
cfqp | cfqp-main/condgen/models/cfact.py |
class ScoreMatcher(pl.LightningModule):
def __init__(self,weight_decay, lr, sigma, conditional_score, conditional_dim, **kwargs):
super().__init__()
self.save_hyperparameters()
self.weight_decay = weight_decay
self.lr = lr
self.sigma = sigma
self.conditional = cond... | 4,503 | 36.533333 | 154 | py |
cfqp | cfqp-main/condgen/models/transformer.py | import pytorch_lightning as pl
import torch
import torch.nn as nn
import torch.nn.functional as F
from argparse import ArgumentParser
from condgen.utils import str2bool
import math
import numpy as np
class GaussianFourierProjection(nn.Module):
"""Gaussian random features for encoding time steps."""
def __init__(... | 39,556 | 44.889791 | 354 | py |
cfqp | cfqp-main/condgen/models/samplers.py |
from scipy import integrate
import torch
import numpy as np
import tqdm
## The error tolerance for the black-box ODE solver
def ode_sampler(score_model,
marginal_prob_std,
diffusion_coeff,
batch_size=64,
atol=1e-5,
rtol=1e-5,
... | 5,200 | 41.284553 | 129 | py |
cfqp | cfqp-main/condgen/score_matching/train_score_matching.py | from distutils.util import strtobool
from argparse import ArgumentParser
from condgen.data_utils.data_utils_MNIST import MNISTDataModule
from condgen.data_utils.data_utils import PendulumDataModule
from condgen.models.score_matching import ConditionalScoreMatcher
from condgen.data_utils.data_utils_cf_traj import Simp... | 3,256 | 34.791209 | 129 | py |
cfqp | cfqp-main/condgen/score_matching/train_temporal_score_matching.py | from distutils.util import strtobool
from argparse import ArgumentParser
from data_utils import PendulumDataModule, SyntheticMMDataModule
from cv_data_utils import CVDataModule
from models.score_matching import TemporalScoreMatcher
import pytorch_lightning as pl
from pytorch_lightning.loggers import WandbLogger
from py... | 3,153 | 34.044444 | 155 | py |
cfqp | cfqp-main/condgen/data_utils/data_utils_MNIST.py | import pytorch_lightning as pl
import torchvision.transforms as transforms
from torchvision.datasets import MNIST, VisionDataset
import numpy as np
from torch.utils.data import Dataset, DataLoader, Subset
import torch
from PIL import Image
import os
from condgen.utils import str2bool
from condgen.utils import DATA_DIR
... | 10,916 | 41.980315 | 351 | py |
cfqp | cfqp-main/condgen/data_utils/physionet_data_utils.py | import pytorch_lightning as pl
import sys
from condgen.utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import condgen.utils as utils
from condgen.utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset
import os
import argparse
import numpy as np
from scipy.int... | 8,510 | 35.527897 | 198 | py |
cfqp | cfqp-main/condgen/data_utils/data_utils.py |
import pytorch_lightning as pl
import sys
#sys.path.insert(0,"../")
from condgen.utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import condgen.utils as utils
from condgen.utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset, TensorDataset
import os
import ... | 21,998 | 37.867491 | 278 | py |
cfqp | cfqp-main/condgen/data_utils/synthetic_data_utils.py | import pytorch_lightning as pl
import sys
sys.path.insert(0,"../")
from utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import utils
from utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset, TensorDataset
import os
import argparse
import numpy as np
from sc... | 7,357 | 36.733333 | 152 | py |
cfqp | cfqp-main/condgen/data_utils/cv_data_utils.py |
import pytorch_lightning as pl
from condgen.utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import condgen.utils as utils
from condgen.utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset
import os
import argparse
import numpy as np
from scipy.integrate imp... | 16,955 | 32.377953 | 311 | py |
cfqp | cfqp-main/condgen/data_utils/mm_data_utils.py | import pytorch_lightning as pl
import sys
from condgen.utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import condgen.utils as utils
from condgen.utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset, TensorDataset
import os
import argparse
import numpy as np... | 10,221 | 39.086275 | 336 | py |
cfqp | cfqp-main/condgen/data_utils/mmsynthetic_data_utils.py |
import pytorch_lightning as pl
import sys
from condgen.utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import condgen.utils as utils
from condgen.utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset, TensorDataset
import os
import argparse
import numpy as n... | 21,568 | 35.557627 | 230 | py |
cfqp | cfqp-main/condgen/data_utils/data_utils_cf_traj.py | import pytorch_lightning as pl
import torchvision.transforms as transforms
from torchvision.datasets import MNIST, VisionDataset
import numpy as np
from torch.utils.data import Dataset, DataLoader, Subset
import torch
from PIL import Image
from condgen.utils import str2bool
from condgen.data_utils.cv_data_utils import... | 15,348 | 36.620098 | 285 | py |
cfqp | cfqp-main/condgen/data_utils/semi_synthetic_data.py | import pytorch_lightning as pl
import sys
sys.path.insert(0,"../")
from utils import DATA_DIR
import utils
from utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset, TensorDataset
import os
import argparse
import numpy as np
from scipy.integrate import odeint
import pandas as pd
... | 6,445 | 34.224044 | 152 | py |
cfqp | cfqp-main/condgen/data_utils/cancer_data_utils.py | import pytorch_lightning as pl
import sys
from condgen.utils import DATA_DIR
#from causalode.datagen import cancer_simulation
import condgen.utils
from condgen.utils import str2bool
import torch
from torch.utils.data import Dataset, DataLoader, Subset, TensorDataset
import os
import argparse
import numpy as np
from sci... | 48,306 | 41.411765 | 241 | py |
ugali | ugali-master/ugali/scratch/PlotAllSkyHealpix.py | #!/usr/bin/env python
import healpy
import pylab as plt
import numpy
import ugali.utils.skymap
from ugali.utils.projector import celToGal
from ugali.utils.logger import logger
default_kwargs = dict( xytext=(5,5),textcoords='offset points',
ha="left",va="center",
color='w'... | 4,838 | 48.377551 | 83 | py |
TimeAwareRNN | TimeAwareRNN-master/winding/main.py | import os
import sys
import numpy as np
from time import time
import torch
GPU = torch.cuda.is_available()
parent = os.path.dirname(sys.path[0])#os.getcwd())
sys.path.append(parent)
from taho.model import MIMO, GRUCell, HOGRUCell, IncrHOGRUCell, HOARNNCell, IncrHOARNNCell
from taho.train import EpochTrainer
from taho.... | 13,425 | 36.713483 | 164 | py |
TimeAwareRNN | TimeAwareRNN-master/CSTR/main.py | import os
import sys
import numpy as np
from time import time
import torch
GPU = torch.cuda.is_available()
parent = os.path.dirname(sys.path[0])#os.getcwd())
sys.path.append(parent)
from taho.model import MIMO, GRUCell, HOGRUCell, IncrHOGRUCell, HOARNNCell, IncrHOARNNCell
from taho.train import EpochTrainer
from taho.... | 15,063 | 36.379653 | 134 | py |
TimeAwareRNN | TimeAwareRNN-master/taho/model.py | import torch
import torch.nn as nn
import math
def RK(x0, y, f, dt, scheme, x_half=None, x_full=None):
# explicit Runge Kutta methods
# scheme in ['Euler', 'Midpoint', 'Kutta3', 'RK4']
# x0 = x(t_n); optional x_half = x(t + 0.5 * dt), x_full = x(t + dt);
# if not present, x0 is used (e.g. for piecewi... | 11,438 | 36.382353 | 144 | py |
TimeAwareRNN | TimeAwareRNN-master/taho/util.py | import numpy as np
import os
import itertools
import torch
import torch.nn as nn
import matplotlib
import matplotlib.pyplot as plt
plt.switch_backend('agg')
class SimpleLogger(object):
def __init__(self, f, header='#logger output'):
dir = os.path.dirname(f)
#print('test dir', dir, 'from', f)
... | 1,377 | 24.518519 | 62 | py |
TimeAwareRNN | TimeAwareRNN-master/taho/train.py | import numpy as np
import torch
"""
EpochTrainer for training recurrent models on single sequence of inputs and outputs,
by chunking into bbtt-long segments.
"""
class EpochTrainer(object):
def __init__(self, model, optimizer, epochs, X, Y, dt, batch_size=1, gpu=False, bptt=50):
self.model = model
... | 4,540 | 38.833333 | 124 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/jtnn_enc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import deque
from mol_tree import Vocab, MolTree
from nnutils import create_var, index_select_ND
class JTNNEncoder(nn.Module):
def __init__(self, hidden_size, depth, embedding):
super(JTNNEncoder, self).__init__()
... | 4,521 | 33 | 80 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/datautils.py | import torch
from torch.utils.data import Dataset, DataLoader
from mol_tree import MolTree
import numpy as np
from jtnn_enc import JTNNEncoder
from mpn import MPN
from jtmpn import JTMPN
import cPickle as pickle
import os, random
class PairTreeFolder(object):
def __init__(self, data_folder, vocab, batch_size, num... | 4,642 | 32.644928 | 129 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/nnutils.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
def create_var(tensor, requires_grad=None):
if requires_grad is None:
return Variable(tensor).cuda()
else:
return Variable(tensor, requires_grad=requires_grad).cuda()
def index_select_ND(sour... | 2,042 | 29.492537 | 67 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/mpn.py | import torch
import torch.nn as nn
import rdkit.Chem as Chem
import torch.nn.functional as F
from nnutils import *
from chemutils import get_mol
ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
ATOM_FDIM = len(ELEM_LIST) ... | 4,524 | 34.629921 | 171 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/diff_vae.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mol_tree import Vocab, MolTree
from nnutils import create_var, flatten_tensor, avg_pool
from jtnn_enc import JTNNEncoder
from jtnn_dec import JTNNDecoder
from mpn import MPN
from jtmpn import JTMPN
from chemutils import enum_assemble, set_atommap,... | 10,228 | 44.462222 | 144 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/scaff_gan.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
from mol_tree import Vocab, MolTree
from nnutils import create_var, avg_pool, index_select_ND, GRU
from jtnn_enc import JTNNEncoder
class ScaffoldGAN(nn.Module):
def __init__(self, jtnn, hidden_size, beta, gumbel=... | 4,955 | 35.441176 | 90 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/jtmpn.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from nnutils import create_var, index_select_ND
from chemutils import get_mol
import rdkit.Chem as Chem
ELEM_LIST = ['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'Al', 'I', 'B', 'K', 'Se', 'Zn', 'H', 'Cu', 'Mn', 'unknown']
A... | 5,393 | 37.805755 | 184 | py |
iclr19-graph2graph | iclr19-graph2graph-master/fast_jtnn/jtnn_dec.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mol_tree import Vocab, MolTree, MolTreeNode
from nnutils import create_var, GRU
from chemutils import enum_assemble
import copy
MAX_NB = 15
MAX_DECODE_LEN = 100
MAX_SOFT_DECODE_LEN = 60
class JTNNDecoder(nn.Module):
def __init__(self, vocab,... | 18,688 | 39.54013 | 440 | py |
iclr19-graph2graph | iclr19-graph2graph-master/scripts/preprocess.py | import torch
import torch.nn as nn
from multiprocessing import Pool
import math, random, sys
import cPickle as pickle
import argparse
from fast_jtnn import *
import rdkit
def tensorize(smiles, assm=False):
mol_tree = MolTree(smiles)
mol_tree.recover()
if assm:
mol_tree.assemble()
for node... | 2,244 | 27.417722 | 66 | py |
iclr19-graph2graph | iclr19-graph2graph-master/diff_vae_gan/decode.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
import numpy as np
import argparse
from collections import deque
import cPickle as pickle
from fast_jtnn i... | 2,010 | 31.435484 | 98 | py |
iclr19-graph2graph | iclr19-graph2graph-master/diff_vae_gan/arae_train.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
import numpy as np
import argparse
from collections import deque
import cPickle as pickle
from fast_jtnn i... | 5,713 | 38.136986 | 295 | py |
iclr19-graph2graph | iclr19-graph2graph-master/diff_vae/decode.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
import numpy as np
import argparse
from collections import deque
import cPickle as pickle
from fast_jtnn i... | 2,010 | 31.435484 | 98 | py |
iclr19-graph2graph | iclr19-graph2graph-master/diff_vae/vae_train.py | import torch
import torch.nn as nn
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
from torch.autograd import Variable
import math, random, sys
import numpy as np
import argparse
from collections import deque
import cPickle as pickle
from fast_jtnn i... | 3,333 | 33.020408 | 174 | py |
DTGRM | DTGRM-main/main.py | import torch
from model import Trainer
from batch_gen import BatchGenerator
import os
import argparse
import random
import pickle
import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
seed = 1538574472
random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.b... | 3,024 | 34.174419 | 110 | py |
DTGRM | DTGRM-main/model.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import optim
import copy
import numpy as np
from layers import SingleStageModel, GCNStageModel, exchange_time
class MultiStageModel(nn.Module):
def __init__(self, num_stages, num_layers, num_f_maps, df_size, dim, num_classes, actions_di... | 5,969 | 50.465517 | 176 | py |
DTGRM | DTGRM-main/layers.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from scipy.special import softmax
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
import copy
def exchange_time(x, exchange_rate=0.2):
exchange_label = torch.zeros(x.shape[0], x... | 6,878 | 40.690909 | 142 | py |
DTGRM | DTGRM-main/batch_gen.py | import torch
import numpy as np
import random
class BatchGenerator(object):
def __init__(self, num_classes, actions_dict, gt_path, features_path, sample_rate):
self.list_of_examples = list()
self.index = 0
self.num_classes = num_classes
self.actions_dict = actions_dict
self... | 2,400 | 41.875 | 132 | py |
ImmClassifier | ImmClassifier-master/bin/dnn.py | import pandas as pd
from keras.models import Sequential
from keras.layers import Dense
from sklearn.model_selection import train_test_split
import numpy as np
import random
from sklearn import metrics
from keras.layers import Dropout
import tensorflow
from tensorflow.keras.models import load_model
def logit(x, norm)... | 5,208 | 40.015748 | 139 | py |
AAAI-CML | AAAI-CML-master/run_simplequestion.py | import math
import pickle
import os
import sys
import time
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import scipy.stats as st
from utils import *
from data_loader import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import numpy as np
import ran... | 31,176 | 45.742129 | 140 | py |
AAAI-CML | AAAI-CML-master/run_fewrel.py | import math
import pickle
import os
import sys
import time
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import scipy.stats as st
from utils import *
from data_loader import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import numpy as np
import ran... | 31,173 | 45.807808 | 140 | py |
AAAI-CML | AAAI-CML-master/utils.py | import pickle as pkl
import json
import random
import numpy as np
import torch
from sklearn import preprocessing
from sklearn.cluster import KMeans
def read_pickle(file_path):
with open(file_path, 'rb') as f:
vec = pkl.load(f)
return vec
def dump_pickle(file_path, obj):
with open(file_path, ... | 7,182 | 41.755952 | 150 | py |
AAAI-CML | AAAI-CML-master/model.py | '''
This code is based on the Pytorch Orientaion:
https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html#sphx-glr-beginner-nlp-sequence-models-tutorial-py
Original Author: Robert Guthrie
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
torch.manual_s... | 9,685 | 44.905213 | 122 | py |
AAAI-CML | AAAI-CML-master/run_tacred.py | import math
import pickle
import os
import sys
import time
from sklearn.metrics.pairwise import cosine_similarity
from tqdm import tqdm
import scipy.stats as st
from utils import *
from data_loader import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import numpy as np
import ran... | 31,136 | 45.752252 | 140 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/tests/test_loss_functions.py | # Copyright (c) Microsoft Corporation
# Licensed under the MIT License.
import os
import json
import copy
import shutil
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import accuracy_score
import ran... | 16,596 | 47.814706 | 199 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/sweep_management.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import os
import json
import threading
import io
import numpy as np
import mlflow
from flask import send_file
from PIL import Image
from queue import Queue
from backwardcompatibilityml.helpers import training
from backwardcompatibilityml.metrics ... | 9,214 | 46.5 | 179 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/metrics.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
from sklearn.metrics import accuracy_score
def model_accuracy(model, dataset, device="cpu"):
model_performance = 0
number_of_batches = len(dataset)
with torch.no_grad():
for batch_ids, data, target in dataset:
... | 2,339 | 38 | 90 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/widgets/model_comparison/model_comparison.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import pkg_resources
from jinja2 import Template
from IPython.display import (
display,
HTML
)
import torch.optim as optim
from flask import Response
from backwardcompatibilityml import loss
from backwardcompatibilityml.compar... | 7,999 | 39.40404 | 179 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/widgets/compatibility_analysis/compatibility_analysis.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import pkg_resources
from jinja2 import Template
from IPython.display import (
display,
HTML
)
import torch.optim as optim
from flask import Response
from backwardcompatibilityml import loss
from backwardcompatibilityml.sweep_... | 11,543 | 39.93617 | 179 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/helpers/training.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import copy
import json
import mlflow
import numpy as np
import torch
import backwardcompatibilityml.scores as scores
from backwardcompatibilityml.metrics import (
model_accuracy,
model_accuracy_by_class)
def train_epoch(epoch, network,... | 51,366 | 50.990891 | 172 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/helpers/utils.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import gc
def clean_from_gpu(tensors):
"""
Utility function to clean tensors from the GPU.
This is only intended to be used when investigating
why memory usage is high.
An in production solution should instead r... | 4,215 | 29.550725 | 92 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/helpers/models.py | import torch.nn as nn
import torch.nn.functional as F
class MLPClassifier(nn.Module):
def __init__(self, input_size, num_classes, hidden_sizes=[50, 10]):
super(MLPClassifier, self).__init__()
layer_sizes = [input_size] + hidden_sizes + [num_classes]
self.layers = [nn.Linear(layer_sizes[i]... | 1,292 | 28.386364 | 106 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/loss/new_error.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from backwardcompatibilityml.helpers import utils
class BCNLLLoss(nn.Module):
"""
Backward Compatibility Negative Log Likelihood Loss
This class implements the back... | 10,550 | 36.548043 | 105 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/loss/strict_imitation.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import torch
import torch.nn as nn
import torch.nn.functional as F
from backwardcompatibilityml.helpers import utils
class StrictImitationNLLLoss(nn.Module):
"""
Strict Imitation Negative Log Likelihood Loss
This class implements t... | 11,427 | 38.406897 | 111 | py |
BackwardCompatibilityML-dev | BackwardCompatibilityML-dev/backwardcompatibilityml/tensorflow/models.py | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import tensorflow.compat.v2 as tf
class BCNewErrorCompatibilityModel(tf.keras.models.Sequential):
"""
BackwardCompatibility base model for Tensorflow
You may create a new Tensorflow model by subclassing
your new model h2 from t... | 4,750 | 36.706349 | 103 | py |
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