repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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risk-slim | risk-slim-master/riskslim/__init__.py | from .coefficient_set import CoefficientSet
from .lattice_cpa import run_lattice_cpa, setup_lattice_cpa, finish_lattice_cpa
from .utils import load_data_from_csv, print_model | 174 | 57.333333 | 79 | py |
risk-slim | risk-slim-master/riskslim/mip.py | from math import ceil, floor
import numpy as np
from cplex import Cplex, SparsePair, infinity as CPX_INFINITY
from .coefficient_set import CoefficientSet
from .utils import print_log
#todo: add loss cut
#todo: add constraint function
#todo: default cplex parameters
#todo: check cores
#todo: pass compute_loss to conver... | 17,079 | 32.754941 | 134 | py |
risk-slim | risk-slim-master/riskslim/tests/test_risk_slim.py | import os
import pprint
import numpy as np
import riskslim
# Dataset Strategy
#
# variables: binary, real,
# N+: 0, 1, >1
# N-: 0, 1, >1
# Testing Strategy
#
# loss_computation normal, fast, lookup
# max_coefficient 0, 1, >1
# max_L0_value 0, 1, >1
# max_offset 0, 1, Inf
# c0_v... | 6,690 | 50.076336 | 170 | py |
risk-slim | risk-slim-master/riskslim/tests/test_loss_functions.py | #noinspection
import numpy as np
import riskslim.loss_functions.fast_log_loss as fast
import riskslim.loss_functions.log_loss as normal
import riskslim.loss_functions.log_loss_weighted as weighted
import riskslim.loss_functions.lookup_log_loss as lookup
from riskslim.setup_functions import _setup_training_weights
np.... | 7,112 | 37.657609 | 128 | py |
risk-slim | risk-slim-master/riskslim/tests/__init__.py | 0 | 0 | 0 | py | |
risk-slim | risk-slim-master/riskslim/loss_functions/log_loss_weighted.py | import numpy as np
def log_loss_value(Z, weights, total_weights, rho):
"""
computes the value and slope of the logistic loss in a numerically stable way
supports sample non-negative weights for each example in the training data
see http://stackoverflow.com/questions/20085768/
Parameters
------... | 3,880 | 37.425743 | 88 | py |
risk-slim | risk-slim-master/riskslim/loss_functions/build_cython_loss_functions.py | #!/usr/bin/env python
"""
This script builds loss functions using Cython on a local machine.
To run this script
1. Change to the directory
$REPO_DIR/riskslim/loss_functions
2. Run the following commands in Bash:
python2 build_cython_loss_functions.py build_ext --inplace
python3 build_cython_loss_functions.py build... | 1,404 | 23.224138 | 81 | py |
risk-slim | risk-slim-master/riskslim/loss_functions/log_loss.py | import numpy as np
def log_loss_value(Z, rho):
"""
computes the value and slope of the logistic loss in a numerically stable way
see also: http://stackoverflow.com/questions/20085768/
Parameters
----------
Z numpy.array containing training data with shape = (n_rows, n_cols)
rho ... | 3,795 | 32.298246 | 95 | py |
risk-slim | risk-slim-master/riskslim/loss_functions/__init__.py | from .log_loss import *
from .log_loss_weighted import *
try:
from .fast_log_loss import *
except ImportError:
print("warning: could not import fast log loss")
print("warning: returning handle to standard loss functions")
# todo replace with warning object
import log_loss as fast_log_loss
try:
... | 572 | 27.65 | 65 | py |
risk-slim | risk-slim-master/batch/train_risk_slim.py | #!/usr/bin/python
"""
This file is to train a RiskSLIM model in a batch computing environment
It parses command line arguments, and can be called as:
python train_risk_slim.py --data="${data_file}" --results="${results_file}"
where:
data_file csv file containing the training data
results_file file name for... | 9,707 | 36.338462 | 122 | py |
ShiftCNN | ShiftCNN-master/shiftcnn_quantization.py | import sys
import os
import numpy as np
import pickle
import matplotlib.pyplot as plt
#
N = 2
B = 4
#
#model = "squeezenet_v1.1"
model = "ResNet-50"
SOURCE_PATH = os.environ["HOME"]+"/github/caffe/models/"+model+"/"
prototxt = SOURCE_PATH+"train_val.prototxt"
source = SOURCE_PATH+model+".caffemodel"
qtarget = SOURCE... | 1,514 | 28.134615 | 120 | py |
agd | agd-main/main.py | import sys
import os
import math
import argparse
import pickle
import torch
import importlib
from tqdm import tqdm
from agd import AGD
from architecture.fcn import *
from architecture.vgg import *
from architecture.resnet import *
###############################################################################... | 8,893 | 41.966184 | 122 | py |
agd | agd-main/agd.py | import math
import torch
from torch.optim.optimizer import Optimizer
from torch.nn.init import orthogonal_
def singular_value(p):
sv = math.sqrt(p.shape[0] / p.shape[1])
if p.dim() == 4:
sv /= math.sqrt(p.shape[2] * p.shape[3])
return sv
class AGD(Optimizer):
def __init__(self, net, ... | 1,412 | 26.173077 | 81 | py |
agd | agd-main/architecture/fcn.py | import math
import torch.nn as nn
import torch.nn.functional as F
class FCN(nn.Module):
def __init__(self, depth, width, input_dim, output_dim, bias=False):
super(FCN, self).__init__()
self.initial = nn.Linear(input_dim, width, bias=bias)
self.layers = nn.ModuleList([nn.Linear(widt... | 718 | 28.958333 | 97 | py |
agd | agd-main/architecture/resnet.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from functools import partial
from typing import Any, Callable, List, Optional, Type, Union
import torch
import torch.nn as nn
from torch import Tensor
### For CIFAR-10
def PreActResNet18(output_dim): return PreActResNet(PreActBlock, ... | 14,531 | 35.512563 | 118 | py |
agd | agd-main/architecture/vgg.py | import torch.nn as nn
def VGG11(output_dim): return VGG_CIFAR([64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], output_dim)
def VGG13(output_dim): return VGG_CIFAR([64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'], output_dim)
def VGG16(output_dim): return VGG_CIFAR([64, 64, 'M'... | 1,587 | 44.371429 | 156 | py |
agd | agd-main/architecture/__init__.py | 0 | 0 | 0 | py | |
agd | agd-main/latex/algorithm/agd.py | import math
import torch
from torch.nn.init import orthogonal_
def singular_value(p):
sv = math.sqrt(p.shape[0] / p.shape[1])
if p.dim() == 4:
sv /= math.sqrt(p.shape[2] * p.shape[3])
return sv
class AGD:
@torch.no_grad()
def __init__(self, net, gain=1.0):
self.net = net
... | 1,174 | 26.97619 | 77 | py |
agd | agd-main/data/cifar100.py | from torchvision import datasets, transforms
def getData():
mean = (0.5071, 0.4867, 0.4408)
std = (0.2675, 0.2565, 0.2761)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Norm... | 764 | 27.333333 | 96 | py |
agd | agd-main/data/cifar10.py | from torchvision import datasets, transforms
def getData():
mean = (0.4914, 0.4822, 0.4465)
std = (0.2023, 0.1994, 0.2010)
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Norm... | 761 | 27.222222 | 95 | py |
agd | agd-main/data/__init__.py | 0 | 0 | 0 | py | |
agd | agd-main/data/imagenet.py | import os
from torchvision import datasets, transforms
def getData():
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
traindir = os.path.join(os.getenv('IMAGENET_PATH'), "train")
valdir = os.path.join(os.getenv('IMAGENET_PATH'), "val")
trainset = datasets.ImageFolder(
traindir,
... | 887 | 25.117647 | 64 | py |
agd | agd-main/data/mnist.py | from torchvision import datasets, transforms
def getData():
mean = (0.1307,)
std = (0.3081,)
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
trainset = datasets.MNIST('./data', train=True, download=True, transform=transform)
testset =... | 493 | 25 | 87 | py |
aldiplusplus | aldiplusplus-main/forecasting_results.py | import os
import argparse
import glob
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from functools import partial
from collections import defaultdict
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_log_error, mean_squared_error
from utils ... | 3,154 | 35.264368 | 96 | py |
aldiplusplus | aldiplusplus-main/train_lgb_meter.py | import os
import argparse
import yaml
from datetime import datetime
import lightgbm as lgb
import numpy as np
from utils import (
timer,
Logger,
make_dir,
rmsle,
load_data,
get_validation_months,
)
parser = argparse.ArgumentParser(description="")
parser.add_argument(
"--overwrite", action... | 8,108 | 32.097959 | 129 | py |
aldiplusplus | aldiplusplus-main/predict_lgb_meter.py | import argparse
import glob
import yaml
import numpy as np
import pandas as pd
import lightgbm as lgb
from utils import (
Logger,
timer,
rmsle,
load_data,
make_dir,
)
parser = argparse.ArgumentParser(description="")
parser.add_argument(
"--normalize_target",
action="store_true",
help="... | 4,845 | 25.480874 | 97 | py |
aldiplusplus | aldiplusplus-main/aldi_gmm_dyn_none_both.py | from scipy import stats
import math
import torch
#import stumpy
import pyscamp
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.mplot3d import Axes3D
import seaborn as sns
import calmap # not working with latest pandas
import calplot
import joypy
import sys
impo... | 43,203 | 36.865031 | 135 | py |
aldiplusplus | aldiplusplus-main/utils.py | import os
import time
import pickle
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib
import datetime
from datetime import datetime
from contextlib import contextmanager, redirect_stdout
from functools import partial
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing... | 20,883 | 34.336717 | 124 | py |
aldiplusplus | aldiplusplus-main/vae.py | import torch
from torch import nn
from torch.utils.data import DataLoader
class VAE(nn.Module):
def __init__(self, num_input, latent_dim, hidden_size=[300, 200, 100]):
super().__init__()
self.latent_dim = latent_dim
self.num_input = num_input
self.encoder = nn.Sequential(
... | 1,984 | 30.015625 | 75 | py |
aldiplusplus | aldiplusplus-main/aldi_evaluation_metrics.py | from functools import reduce
from sklearn.metrics import accuracy_score
from sklearn.metrics import roc_auc_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
class AldiEvalua... | 9,212 | 33.897727 | 112 | py |
aldiplusplus | aldiplusplus-main/prepare_predictions.py | import os
import argparse
import glob
import numpy as np
import pandas as pd
from functools import partial
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
from utils import (
load_data,
rmsle,
timer,
GeneralizedMeanBlender
)
parser = argparse.ArgumentParser(desc... | 1,835 | 28.142857 | 92 | py |
aldiplusplus | aldiplusplus-main/data_import_ashrae.py | import numpy as np
import pandas as pd
class DataImportAshrae():
"""
class provides different methods to import BDG2 data
for experiments with Discord Detectors
"""
def __init__(self):
"""
method initializes df_all_data
"""
self.df_all_data = None
def get_met... | 14,877 | 39.210811 | 146 | py |
aldiplusplus | aldiplusplus-main/anomaly_detection.py | import warnings
import os
import sys
import logging
import yaml
import wandb
import torch
import pandas as pd
from sklearn.cluster import SpectralClustering, KMeans
from sklearn.metrics import silhouette_score
from datetime import timedelta
from collections import Counter
from matplotlib import pyplot as plt
from util... | 7,508 | 37.116751 | 210 | py |
aldiplusplus | aldiplusplus-main/preprocess_modeling.py | import gc
import sys
import logging
import yaml
import numpy as np
import pandas as pd
from pandas.tseries.holiday import USFederalHolidayCalendar as calendar
from utils import timer, load_data, reduce_mem_usage
from encoders import GaussianTargetEncoder
# define groupings and corresponding priors
groups_and_priors = ... | 10,908 | 38.241007 | 94 | py |
aldiplusplus | aldiplusplus-main/encoders.py | import numpy as np
class FastLabelEncoder():
"""Map categorical variable into {0, 1, ..., n_categories}.
Note: https://stackoverflow.com/questions/45321999/how-can-i-optimize-label-encoding-for-large-data-sets-sci-kit-learn?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
... | 2,816 | 33.353659 | 199 | py |
aldiplusplus | aldiplusplus-main/GMM_training.py | import pandas as pd
import numpy as np
from sklearn.mixture import GaussianMixture
class GMMTraining():
def __init__(self, values):
self.values = np.array([[val] for val in values])
self.x_values = np.linspace(0, 1, 1000)
'''
p_values = np.array([ [val] for val in df_pD_values.p.... | 1,803 | 33.037736 | 89 | py |
aldiplusplus | aldiplusplus-main/aldi.py | from scipy import stats
import stumpy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import calmap # not working with latest pandas
import calplot
import joypy
import sys
import time
import datetime as dt
class ALDI():
def __init__(self, df_meters, df_metadata, m=24, c... | 19,418 | 37.993976 | 119 | py |
pytorch_RVAE | pytorch_RVAE-master/sample.py | import argparse
import os
import numpy as np
import torch as t
from utils.batch_loader import BatchLoader
from utils.parameters import Parameters
from model.rvae import RVAE
if __name__ == '__main__':
assert os.path.exists('trained_RVAE'), \
'trained model not found'
parser = argparse.ArgumentParse... | 1,265 | 31.461538 | 78 | py |
pytorch_RVAE | pytorch_RVAE-master/__init__.py | from . import nn_layers
from . import utility
| 46 | 14.666667 | 23 | py |
pytorch_RVAE | pytorch_RVAE-master/train_word_embeddings.py | import argparse
import numpy as np
import torch as t
from torch.autograd import Variable
from torch.optim import SGD
from utils.batch_loader import BatchLoader
from utils.parameters import Parameters
from selfModules.neg import NEG_loss
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='wo... | 2,183 | 36.016949 | 116 | py |
pytorch_RVAE | pytorch_RVAE-master/train.py | import argparse
import os
import numpy as np
import torch as t
from torch.optim import Adam
from utils.batch_loader import BatchLoader
from utils.parameters import Parameters
from model.rvae import RVAE
if __name__ == "__main__":
if not os.path.exists('data/word_embeddings.npy'):
raise FileNotFoundError... | 4,032 | 37.04717 | 102 | py |
pytorch_RVAE | pytorch_RVAE-master/selfModules/embedding.py | import numpy as np
import torch as t
import torch.nn as nn
from torch.nn import Parameter
from .tdnn import TDNN
class Embedding(nn.Module):
def __init__(self, params, path='../../../'):
super(Embedding, self).__init__()
self.params = params
word_embed = np.load(path + 'data/word_embedd... | 2,001 | 37.5 | 98 | py |
pytorch_RVAE | pytorch_RVAE-master/selfModules/highway.py | import torch.nn as nn
import torch.nn.functional as F
class Highway(nn.Module):
def __init__(self, size, num_layers, f):
super(Highway, self).__init__()
self.num_layers = num_layers
self.nonlinear = [nn.Linear(size, size) for _ in range(num_layers)]
for i, module in enumerate(se... | 1,743 | 33.88 | 105 | py |
pytorch_RVAE | pytorch_RVAE-master/selfModules/neg.py | import torch as t
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import Parameter
from utils.functional import *
class NEG_loss(nn.Module):
def __init__(self, num_classes, embed_size):
"""
:param num_classes: An int. The number of possible classes.
:param embed_si... | 2,619 | 37.529412 | 118 | py |
pytorch_RVAE | pytorch_RVAE-master/selfModules/tdnn.py | import torch as t
from torch.nn import Parameter
import torch.nn as nn
import torch.nn.functional as F
class TDNN(nn.Module):
def __init__(self, params):
super(TDNN, self).__init__()
self.params = params
self.kernels = [Parameter(t.Tensor(out_dim, self.params.char_embed_size, kW).uniform... | 1,769 | 33.038462 | 117 | py |
pytorch_RVAE | pytorch_RVAE-master/selfModules/__init__.py | 0 | 0 | 0 | py | |
pytorch_RVAE | pytorch_RVAE-master/utils/visualize_word_embeddings.py | import os
import matplotlib.pyplot as plt
import numpy as np
from sklearn.decomposition import PCA
from utils.batch_loader import BatchLoader
if __name__ == "__main__":
if not os.path.exists('../../data/word_embeddings.npy'):
raise FileNotFoundError("word embeddings file was't found")
pca = PCA(n_co... | 807 | 25.933333 | 67 | py |
pytorch_RVAE | pytorch_RVAE-master/utils/functional.py | def fold(f, l, a):
return a if (len(l) == 0) else fold(f, l[1:], f(a, l[0]))
def f_and(x, y):
return x and y
def f_or(x, y):
return x or y
def parameters_allocation_check(module):
parameters = list(module.parameters())
return fold(f_and, parameters, True) or not fold(f_or, parameters, False)
... | 648 | 19.28125 | 77 | py |
pytorch_RVAE | pytorch_RVAE-master/utils/batch_loader.py | import collections
import os
import re
import numpy as np
from six.moves import cPickle
from .functional import *
class BatchLoader:
def __init__(self, path='../../'):
'''
:properties
data_files - array containing paths to data sources
idx_files - array of ... | 14,202 | 42.434251 | 119 | py |
pytorch_RVAE | pytorch_RVAE-master/utils/parameters.py | from .functional import *
class Parameters:
def __init__(self, max_word_len, max_seq_len, word_vocab_size, char_vocab_size):
self.max_word_len = int(max_word_len)
self.max_seq_len = int(max_seq_len) + 1 # go or eos token
self.word_vocab_size = int(word_vocab_size)
self.char_vocab... | 780 | 30.24 | 90 | py |
pytorch_RVAE | pytorch_RVAE-master/utils/__init__.py | 0 | 0 | 0 | py | |
pytorch_RVAE | pytorch_RVAE-master/model/rvae.py | import numpy as np
import torch as t
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from .decoder import Decoder
from .encoder import Encoder
from selfModules.embedding import Embedding
from utils.functional import kld_coef, parameters_allocation_check, fold
class RVAE(nn... | 7,319 | 38.567568 | 119 | py |
pytorch_RVAE | pytorch_RVAE-master/model/encoder.py | import torch as t
import torch.nn as nn
import torch.nn.functional as F
from selfModules.highway import Highway
from utils.functional import parameters_allocation_check
class Encoder(nn.Module):
def __init__(self, params):
super(Encoder, self).__init__()
self.params = params
self.hw1 = ... | 1,685 | 34.125 | 115 | py |
pytorch_RVAE | pytorch_RVAE-master/model/decoder.py | import torch as t
import torch.nn as nn
import torch.nn.functional as F
from utils.functional import parameters_allocation_check
class Decoder(nn.Module):
def __init__(self, params):
super(Decoder, self).__init__()
self.params = params
self.rnn = nn.LSTM(input_size=self.params.latent_va... | 2,142 | 39.433962 | 103 | py |
pytorch_RVAE | pytorch_RVAE-master/model/__init__.py | 0 | 0 | 0 | py | |
semantic-abstraction | semantic-abstraction-main/plot_utils.py | import numpy as np
from matplotlib.patches import Patch
import matplotlib.pyplot as plt
import io
from PIL import Image
import open3d as o3d
from skimage.measure import block_reduce
import matplotlib.cm as cm
import matplotlib as mpl
def plot_to_png(fig):
buf = io.BytesIO()
plt.savefig(buf, format="png")
... | 6,575 | 33.429319 | 84 | py |
semantic-abstraction | semantic-abstraction-main/generate_relevancy.py | from typing import List
from pathlib import Path
import h5py
import torch
from tqdm import tqdm
import ray
from utils import write_to_hdf5
from filelock import FileLock
import numpy as np
from CLIP.clip import ClipWrapper, saliency_configs, imagenet_templates
from dataset import synonyms, deref_h5py
import typer
import... | 18,591 | 39.77193 | 88 | py |
semantic-abstraction | semantic-abstraction-main/fusion.py | # Copyright (c) 2018 Andy Zeng
# Source: https://github.com/andyzeng/tsdf-fusion-python/blob/master/fusion.py
# BSD 2-Clause License
# Copyright (c) 2019, Princeton University
# All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the fo... | 14,231 | 39.31728 | 88 | py |
semantic-abstraction | semantic-abstraction-main/point_cloud.py | import pybullet_data
import numpy as np
from numba import njit, prange
import pybullet as p
import matplotlib.pyplot as plt
def transform_pointcloud(xyz_pts, rigid_transform):
"""Apply rigid transformation to 3D pointcloud.
Args:
xyz_pts: Nx3 float array of 3D points
rigid_transform: 3x4 or 4x... | 9,990 | 33.451724 | 113 | py |
semantic-abstraction | semantic-abstraction-main/summarize.py | import pandas as pd
import rich
import pickle
from dataset import synonyms
import numpy as np
from rich.console import Console
from rich.table import Table
test_objs = set(
map(lambda l: l.rstrip().lstrip(), open("test_semantic_classes.txt", "r"))
)
def summarize_ovssc(metric="voxel32x32x32_iou"):
ssc_approa... | 10,203 | 36.105455 | 87 | py |
semantic-abstraction | semantic-abstraction-main/train_vool.py | from typing import Dict, Tuple, Union
import numpy as np
from dataset import ObjectLocalizationDataset
from net import (
SemAbsVOOL,
ClipSpatialVOOL,
SemanticAwareVOOL,
)
import utils
from torch.nn.functional import binary_cross_entropy_with_logits
import torch
import pandas as pd
def get_detailed_stats(
... | 8,243 | 34.230769 | 88 | py |
semantic-abstraction | semantic-abstraction-main/utils.py | from __future__ import annotations
import os
import pickle
import signal
from typing import Optional, Tuple, Type
import numpy as np
import pandas as pd
import torch
from torch.backends import cudnn
from tqdm import tqdm
from transformers import get_scheduler
from argparse import ArgumentParser
import random
from CLIP.... | 27,394 | 35.526667 | 88 | py |
semantic-abstraction | semantic-abstraction-main/dataset.py | import numpy as np
import torch
from torch.utils.data import Dataset
from fusion import TSDFVolume
from point_cloud import (
check_pts_in_frustum,
filter_pts_bounds,
get_pointcloud,
)
from typing import List, Optional, Tuple
import h5py
from transforms3d import affines, euler
from torchtyping import TensorT... | 52,891 | 41.689266 | 138 | py |
semantic-abstraction | semantic-abstraction-main/net.py | from typing import List, Tuple
import torch
from torch.nn import (
Sequential,
LeakyReLU,
Linear,
Module,
Dropout,
ParameterDict,
)
from torch.nn.parameter import Parameter
from torch.nn.functional import grid_sample
from torch_scatter import scatter
import numpy as np
from unet3d import Residua... | 25,154 | 36.047128 | 88 | py |
semantic-abstraction | semantic-abstraction-main/eval.py | import pandas as pd
import numpy as np
from tqdm import tqdm
import torch
import os
import pickle
from dataset import ObjectLocalizationDataset, SceneCompletionDataset
from train_vool import get_losses as vool_get_losses, approach as vool_approaches
from train_ovssc import get_losses as ovssc_get_losses, approach as ov... | 3,625 | 37.574468 | 86 | py |
semantic-abstraction | semantic-abstraction-main/unet3d.py | """
Code from the 3D UNet implementation:
https://github.com/wolny/pytorch-3dunet/
"""
import importlib
import torch
import torch.nn as nn
from torch.nn import functional as F
from functools import partial
def number_of_features_per_level(init_channel_number, num_levels):
return [init_channel_number * 2**k for k ... | 25,729 | 36.289855 | 144 | py |
semantic-abstraction | semantic-abstraction-main/visualize.py | import io
import logging
from pathlib import Path
import textwrap
from typing import Any, Dict, List, Tuple
from skimage.measure import marching_cubes
import numpy as np
import torch
import os
import pickle
from net import SemAbs3D, SemAbsVOOL
from point_cloud import (
check_pts_in_frustum,
filter_pts_bounds,
... | 23,057 | 35.084507 | 110 | py |
semantic-abstraction | semantic-abstraction-main/train_ovssc.py | import numpy as np
import torch
from torch.nn.functional import binary_cross_entropy_with_logits
from net import SemAbs3D, SemanticAwareOVSSC
import utils
import pandas as pd
from dataset import SceneCompletionDataset
from typing import Dict, Tuple, Union
def get_detailed_stats(
prediction,
gt_label,
xyz_... | 6,693 | 32.808081 | 87 | py |
semantic-abstraction | semantic-abstraction-main/generate_thor_data.py | import logging
import re
from copy import deepcopy
import shutil
from argparse import ArgumentParser
from typing import List
import ray
from ai2thor.controller import Controller
from ai2thor.platform import CloudRendering
from matplotlib import pyplot as plt
import numpy as np
import torch
from transforms3d import aff... | 46,662 | 37.405761 | 91 | py |
semantic-abstraction | semantic-abstraction-main/arm/utils.py | # Adapted from: https://github.com/stepjam/ARM/blob/main/arm/utils.py
import torch
import numpy as np
from scipy.spatial.transform import Rotation
import pyrender
import trimesh
from pyrender.trackball import Trackball
def normalize_quaternion(quat):
return np.array(quat) / np.linalg.norm(quat, axis=-1, keepdim... | 7,072 | 32.842105 | 88 | py |
semantic-abstraction | semantic-abstraction-main/arm/network_utils.py | # Adapted from https://github.com/stepjam/ARM/blob/main/arm/network_utils.py
import copy
from typing import List, Union
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
LRELU_SLOPE = 0.02
def act_layer(act):
if act == "relu":
return nn.ReLU()
elif act == "lrelu"... | 23,208 | 30.363514 | 88 | py |
semantic-abstraction | semantic-abstraction-main/arm/__init__.py | 0 | 0 | 0 | py | |
semantic-abstraction | semantic-abstraction-main/arm/optim/__init__.py | 0 | 0 | 0 | py | |
semantic-abstraction | semantic-abstraction-main/arm/optim/lamb.py | # From https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py
"""Lamb optimizer."""
import collections
import math
import torch
from torch.optim import Optimizer
# def log_lamb_rs(optimizer: Optimizer, event_writer: SummaryWriter, token_count: int):
# """Log a histogram of trust ratio sca... | 5,163 | 39.34375 | 103 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/setup.py | import os
import pkg_resources
from setuptools import setup, find_packages
setup(
name="clip",
py_modules=["clip"],
version="1.0",
description="",
author="OpenAI",
packages=find_packages(exclude=["tests*"]),
install_requires=[
str(r)
for r in pkg_resources.parse_requirement... | 491 | 21.363636 | 77 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/clip_explainability.py | # modified from: https://github.com/hila-chefer/Transformer-MM-Explainability/blob/main/CLIP/clip/clip.py
import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
from pkg_resources import packaging
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize... | 9,663 | 34.270073 | 154 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/simple_tokenizer.py | import gzip
import html
import os
from functools import lru_cache
import ftfy
import regex as re
@lru_cache()
def default_bpe():
return os.path.join(
os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz"
)
@lru_cache()
def bytes_to_unicode():
"""
Returns list of utf-8 b... | 4,851 | 31.13245 | 111 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/auxiliary.py | # adding hooks, copied from: https://github.com/hila-chefer/Transformer-MM-Explainability/blob/e63b4ab0d0722faa11ff2f7549c4f88074e7edd7/CLIP/clip/auxilary.py
import torch
import warnings
from typing import Tuple, Optional
import torch
from torch import Tensor
from torch.nn.init import xavier_uniform_
from torch.nn.ini... | 21,829 | 38.981685 | 157 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/clip.py | import hashlib
import os
import urllib
import warnings
from typing import Any, Union, List
import torch
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
from tqdm import tqdm
from .model import build_model
from .simple_tokenizer import SimpleTokenizer as _Token... | 9,497 | 33.791209 | 154 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/model.py | from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from .auxiliary import interpolate_positional_emb
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1):
super(... | 20,260 | 33.457483 | 112 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/clip_gradcam.py | from typing import List
import torch
import torch.nn as nn
from .clip_explainability import load
from .clip import tokenize
from torch import device
import numpy as np
import torch.nn.functional as nnf
import itertools
def zeroshot_classifier(clip_model, classnames, templates, device):
with torch.no_grad():
... | 5,420 | 36.909091 | 165 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/__init__.py | from .clip import *
from .clip_gradcam import ClipGradcam
import torch
import numpy as np
from PIL import Image
import torchvision
from functools import reduce
def factors(n):
return set(
reduce(
list.__add__,
([i, n // i] for i in range(1, int(n**0.5) + 1) if n % i == 0),
... | 12,890 | 33.934959 | 88 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/clip/model_explainability.py | # modified from: https://github.com/hila-chefer/Transformer-MM-Explainability/blob/main/CLIP/clip/model.py
from collections import OrderedDict
from typing import Tuple, Union
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from .auxiliary import (
multi_head_attention_forward,
... | 20,409 | 32.84743 | 112 | py |
semantic-abstraction | semantic-abstraction-main/CLIP/tests/test_consistency.py | import numpy as np
import pytest
import torch
from PIL import Image
import clip
@pytest.mark.parametrize("model_name", clip.available_models())
def test_consistency(model_name):
device = "cpu"
jit_model, transform = clip.load(model_name, device=device, jit=True)
py_model, _ = clip.load(model_name, device... | 812 | 30.269231 | 73 | py |
UniVL | UniVL-main/main_task_retrieval.py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch
from torch.utils.data import (SequentialSampler)
import numpy as np
import random
import os
from metrics import compute_metrics
import time
import argparse
f... | 24,353 | 46.28932 | 144 | py |
UniVL | UniVL-main/main_pretrain.py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch
from torch.utils.data import (SequentialSampler)
import numpy as np
import random
import os
from collections import OrderedDict
import pickle
import time
imp... | 19,914 | 47.691932 | 140 | py |
UniVL | UniVL-main/main_task_caption.py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import torch
from torch.utils.data import (SequentialSampler)
import numpy as np
import random
import os
from collections import OrderedDict
from nlgeval import NLGEval
i... | 33,617 | 47.792453 | 151 | py |
UniVL | UniVL-main/util.py | import torch
import torch.nn as nn
import threading
from torch._utils import ExceptionWrapper
import logging
def get_a_var(obj):
if isinstance(obj, torch.Tensor):
return obj
if isinstance(obj, list) or isinstance(obj, tuple):
for result in map(get_a_var, obj):
if isinstance(result,... | 2,495 | 33.191781 | 99 | py |
UniVL | UniVL-main/metrics.py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from __future__ import print_function
import numpy as np
def compute_metrics(x):
sx = np.sort(-x, axis=1)
d = np.diag(-x)
d = d[:, np.newaxis]
ind = sx - d
ind = np.where(ind == 0)
in... | 796 | 27.464286 | 92 | py |
UniVL | UniVL-main/modules/module_visual.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | 19,708 | 45.374118 | 139 | py |
UniVL | UniVL-main/modules/optimization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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.org/licenses/LICENS... | 7,260 | 42.220238 | 141 | py |
UniVL | UniVL-main/modules/module_decoder.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | 18,283 | 43.923833 | 138 | py |
UniVL | UniVL-main/modules/tokenization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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.org/licenses/LICENS... | 16,424 | 39.158924 | 219 | py |
UniVL | UniVL-main/modules/modeling.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | 22,558 | 51.707944 | 153 | py |
UniVL | UniVL-main/modules/until_module.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | 10,299 | 39.873016 | 114 | py |
UniVL | UniVL-main/modules/beam.py | """
Manage beam search info structure.
Heavily borrowed from OpenNMT-py.
For code in OpenNMT-py, please check the following link (maybe in oldest version):
https://github.com/OpenNMT/OpenNMT-py/blob/master/onmt/Beam.py
"""
import torch
class Constants():
def __init__(self):
self.PAD = 0
self.UNK =... | 3,840 | 31.82906 | 97 | py |
UniVL | UniVL-main/modules/module_bert.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | 21,157 | 46.333333 | 139 | py |
UniVL | UniVL-main/modules/module_cross.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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 copy... | 17,516 | 43.346835 | 108 | py |
UniVL | UniVL-main/modules/file_utils.py | """
Utilities for working with the local dataset cache.
This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp
Copyright by the AllenNLP authors.
"""
import os
import logging
import shutil
import tempfile
import json
from urllib.parse import urlparse
from pathlib import Path
from typing ... | 8,021 | 32.425 | 98 | py |
UniVL | UniVL-main/modules/__init__.py | 0 | 0 | 0 | py |
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