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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pylops | pylops-master/pylops/signalprocessing/fredholm1.py | __all__ = ["Fredholm1"]
import numpy as np
from pylops import LinearOperator
from pylops.utils.backend import get_array_module
from pylops.utils.decorators import reshaped
from pylops.utils.typing import DTypeLike, NDArray
class Fredholm1(LinearOperator):
r"""Fredholm integral of first kind.
Implement a mu... | 5,263 | 34.093333 | 86 | py |
pylops | pylops-master/pylops/signalprocessing/sliding3d.py | __all__ = [
"sliding3d_design",
"Sliding3D",
]
import logging
from typing import Tuple
from pylops import LinearOperator
from pylops.basicoperators import BlockDiag, Diagonal, HStack, Restriction
from pylops.signalprocessing.sliding2d import _slidingsteps
from pylops.utils.tapers import taper3d
from pylops.ut... | 7,683 | 32.701754 | 113 | py |
pylops | pylops-master/pylops/signalprocessing/_radon2d_numba.py | import os
import numpy as np
from numba import jit
# detect whether to use parallel or not
numba_threads = int(os.getenv("NUMBA_NUM_THREADS", "1"))
parallel = True if numba_threads != 1 else False
@jit(nopython=True)
def _linear_numba(x, t, px):
return t + px * x
@jit(nopython=True)
def _parabolic_numba(x, t,... | 2,416 | 28.839506 | 86 | py |
pylops | pylops-master/pylops/signalprocessing/convolvend.py | __all__ = ["ConvolveND"]
from typing import Optional, Union
import numpy as np
from numpy.core.multiarray import normalize_axis_index
from pylops import LinearOperator
from pylops.utils._internal import _value_or_sized_to_tuple
from pylops.utils.backend import (
get_array_module,
get_convolve,
get_correl... | 4,683 | 33.189781 | 84 | py |
pylops | pylops-master/pylops/signalprocessing/__init__.py | """
Signal processing
=================
The subpackage signalprocessing provides linear operators for several signal
processing algorithms with forward and adjoint functionalities.
A list of operators present in pylops.signalprocessing:
Convolve1D 1D convolution operator.
Convolve2D ... | 3,069 | 32.010753 | 80 | py |
pylops | pylops-master/pylops/signalprocessing/_chirpradon3d.py | import numpy as np
from pylops.utils.backend import get_array_module
from pylops.utils.typing import NDArray
try:
import pyfftw
except ImportError:
pyfftw = None
def _chirp_radon_3d(
data: NDArray, dt: float, dy: float, dx: float, pmax: NDArray, mode: str = "f"
) -> NDArray:
r"""3D Chirp Radon trans... | 7,719 | 32.419913 | 88 | py |
pylops | pylops-master/pylops/signalprocessing/chirpradon3d.py | __all__ = ["ChirpRadon3D"]
import logging
import numpy as np
from pylops import LinearOperator
from pylops.utils import deps
from pylops.utils.decorators import reshaped
from pylops.utils.typing import DTypeLike, NDArray
from ._chirpradon3d import _chirp_radon_3d
pyfftw_message = deps.pyfftw_import("the chirpradon... | 4,439 | 33.96063 | 119 | py |
pylops | pylops-master/pylops/signalprocessing/convolve2d.py | __all__ = ["Convolve2D"]
from typing import Union
from pylops.signalprocessing import ConvolveND
from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray
class Convolve2D(ConvolveND):
r"""2D convolution operator.
Apply two-dimensional convolution with a compact filter to model
(and data) along... | 2,753 | 30.655172 | 105 | py |
pylops | pylops-master/pylops/signalprocessing/radon3d.py | __all__ = ["Radon3D"]
import logging
from typing import Callable, Optional, Tuple
import numpy as np
from pylops.basicoperators import Spread
from pylops.utils import deps
from pylops.utils.typing import DTypeLike, NDArray
jit_message = deps.numba_import("the radon3d module")
if jit_message is None:
from numba... | 11,959 | 29.35533 | 94 | py |
pylops | pylops-master/pylops/signalprocessing/fftnd.py | __all__ = ["FFTND"]
import logging
import warnings
from typing import Optional, Sequence, Union
import numpy as np
import numpy.typing as npt
from pylops.signalprocessing._baseffts import _BaseFFTND, _FFTNorms
from pylops.utils.backend import get_sp_fft
from pylops.utils.decorators import reshaped
from pylops.utils.... | 17,149 | 40.225962 | 130 | py |
pylops | pylops-master/pylops/signalprocessing/dct.py | __all__ = ["DCT"]
from typing import List, Optional, Union
import numpy as np
from scipy import fft
from pylops import LinearOperator
from pylops.utils._internal import _value_or_sized_to_tuple
from pylops.utils.decorators import reshaped
from pylops.utils.typing import DTypeLike, InputDimsLike, NDArray
class DCT(... | 3,296 | 32.989691 | 116 | py |
pylops | pylops-master/pylops/signalprocessing/sliding2d.py | __all__ = [
"sliding2d_design",
"Sliding2D",
]
import logging
from typing import Tuple
import numpy as np
from pylops import LinearOperator
from pylops.basicoperators import BlockDiag, Diagonal, HStack, Restriction
from pylops.utils.tapers import taper2d
from pylops.utils.typing import InputDimsLike, NDArray... | 6,953 | 30.466063 | 113 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/sotabench.py | import os
import numpy as np
import PIL
import torch
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from torchvision.datasets import ImageNet
from efficientnet_pytorch import EfficientNet
from sotabencheval.image_classification import ImageNetEvaluator
from sotabencheval.utils imp... | 2,094 | 28.097222 | 131 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/setup.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# Note: To use the 'upload' functionality of this file, you must:
# $ pipenv install twine --dev
import io
import os
import sys
from shutil import rmtree
from setuptools import find_packages, setup, Command
# Package meta-data.
NAME = 'efficientnet_pytorch'
DESCRIPTIO... | 3,543 | 27.580645 | 96 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/hubconf.py | from efficientnet_pytorch import EfficientNet as _EfficientNet
dependencies = ['torch']
def _create_model_fn(model_name):
def _model_fn(num_classes=1000, in_channels=3, pretrained='imagenet'):
"""Create Efficient Net.
Described in detail here: https://arxiv.org/abs/1905.11946
Args:
... | 1,709 | 37.863636 | 78 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/efficientnet_pytorch/utils.py | """utils.py - Helper functions for building the model and for loading model parameters.
These helper functions are built to mirror those in the official TensorFlow implementation.
"""
# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added ... | 24,957 | 39.450567 | 130 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/efficientnet_pytorch/model.py | """model.py - Model and module class for EfficientNet.
They are built to mirror those in the official TensorFlow implementation.
"""
# Author: lukemelas (github username)
# Github repo: https://github.com/lukemelas/EfficientNet-PyTorch
# With adjustments and added comments by workingcoder (github username).
import... | 17,388 | 40.402381 | 107 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/efficientnet_pytorch/__init__.py | __version__ = "0.7.1"
from .model import EfficientNet, VALID_MODELS
from .utils import (
GlobalParams,
BlockArgs,
BlockDecoder,
efficientnet,
get_model_params,
)
| 182 | 17.3 | 45 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/examples/imagenet/main.py | """
Evaluate on ImageNet. Note that at the moment, training is not implemented (I am working on it).
that being said, evaluation is working.
"""
import argparse
import os
import random
import shutil
import time
import warnings
import PIL
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backend... | 17,107 | 37.531532 | 96 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tests/test_model.py | from collections import OrderedDict
import pytest
import torch
import torch.nn as nn
from efficientnet_pytorch import EfficientNet
# -- fixtures -------------------------------------------------------------------------------------
@pytest.fixture(scope='module', params=[x for x in range(4)])
def model(request):
... | 4,122 | 31.984 | 99 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/load_tf_weights_tf1.py | import numpy as np
import tensorflow as tf
import torch
def load_param(checkpoint_file, conversion_table, model_name):
"""
Load parameters according to conversion_table.
Args:
checkpoint_file (string): pretrained checkpoint model file in tensorflow
conversion_table (dict): { pytorch tensor... | 10,344 | 58.797688 | 126 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/load_tf_weights.py | import numpy as np
import tensorflow as tf
import torch
tf.compat.v1.disable_v2_behavior()
def load_param(checkpoint_file, conversion_table, model_name):
"""
Load parameters according to conversion_table.
Args:
checkpoint_file (string): pretrained checkpoint model file in tensorflow
conve... | 10,410 | 58.491429 | 126 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/eval_ckpt_main.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 8,644 | 37.252212 | 94 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_builder.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 11,804 | 34.772727 | 80 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/efficientnet_model.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 26,027 | 35.453782 | 80 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/preprocessing.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 9,508 | 38.293388 | 80 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/utils.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 15,742 | 37.775862 | 91 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/eval_ckpt_main_tf1.py | # Copyright 2019 The TensorFlow Authors. 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 applica... | 8,524 | 37.400901 | 80 | py |
EfficientNet-PyTorch | EfficientNet-PyTorch-master/tf_to_pytorch/convert_tf_to_pt/original_tf/__init__.py | 0 | 0 | 0 | py | |
neuron-importance-zsl | neuron-importance-zsl-master/mod2alpha.py | # Code to map from any modality to alphas.
# Train using class_info and alphas from a trained network
import argparse
import numpy as np
import random
random.seed(1234)
from random import shuffle
import pickle
from pprint import pprint
from dotmap import DotMap
import pdb
import csv
import os
import json
import tensorf... | 18,735 | 41.103371 | 377 | py |
neuron-importance-zsl | neuron-importance-zsl-master/alpha2w.py | # Finetune a network in tensorflow on the CUB dataset
import argparse
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import ntpath
import json
import pdb
import random
import torchfile
import importlib
from scipy.stats import spearmanr
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as p... | 49,803 | 47.589268 | 419 | py |
neuron-importance-zsl | neuron-importance-zsl-master/seen_pretraining/alpha_extraction.py | """
Code to extract and save alphas from a trained network
1. Note that we're gonna have to coincide the use of previous and new finetuning dataset JSONs
2. Take bypassing into account
3. Data loader can be the same as the CNN finetuning scheme
"""
import os
import sys
import json
import codecs
import random
import imp... | 15,568 | 46.036254 | 239 | py |
neuron-importance-zsl | neuron-importance-zsl-master/seen_pretraining/cnn_finetune.py | """
Code to finetune a given CNN on the seen-images for the seen-classes concerned datasets
(Have to add special checks to handle images with multiple frames)
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import sys
import pdb
import json
import random
import importlib
import itertools
# Add slim folder path ... | 40,175 | 54.877608 | 173 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/movielens/code/model.py | '''
Tensorflow implementation of AutoInt described in:
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks.
author: Chence Shi
email: chenceshi@pku.edu.cn
'''
import os
import numpy as np
import tensorflow as tf
from time import time
from sklearn.base import BaseEstimator, TransformerMix... | 22,320 | 45.502083 | 190 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/movielens/code/__init__.py | 0 | 0 | 0 | py | |
RecommenderSystems | RecommenderSystems-master/featureRec/movielens/code/train.py | import math
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics import make_scorer
from sklearn.model_selection import StratifiedKFold
from time import time
from .model import AutoInt
import argparse
import os
def str2list(v):
v=v.split(',')
v=[int(_.strip('[]')) for _ in v]
... | 5,236 | 37.792593 | 107 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/movielens/data/preprocess.py | dict = {}
user_count = 6040
gender = {}
gender['M'] = 1
gender['F'] = 2
dict[1] = "Gender-male"
dict[2] = "Gender-female"
age = {}
age['1'] = 3
age['18'] = 4
age['25'] = 5
age['35'] = 6
age['45'] = 7
age['50'] = 8
age['56'] = 9
dict[3] = "Age-under 18"
dict[4] = "Age-18-24"
dict[5] = "Age-25-34"
dict[6] = "Age-35-... | 7,665 | 20.840456 | 67 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/autoint/model.py | '''
Tensorflow implementation of AutoInt described in:
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks.
author: Chence Shi
email: chenceshi@pku.edu.cn
'''
import os
import numpy as np
import tensorflow as tf
from time import time
from sklearn.base import BaseEstimator, TransformerMix... | 20,281 | 43.772627 | 205 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/autoint/train.py | import math
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics import make_scorer
from sklearn.model_selection import StratifiedKFold
from time import time
from .model import AutoInt
import argparse
import os
def str2list(v):
v=v.split(',')
v=[int(_.strip('[]')) for _ in v]
... | 5,264 | 37.713235 | 107 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/Kfold_split/config.py | DATA_PATH = './Criteo/'
TRAIN_I = DATA_PATH + 'train_i.txt'
TRAIN_X = DATA_PATH + 'train_x.txt'
TRAIN_Y = DATA_PATH + 'train_y.txt'
NUM_SPLITS = 10
RANDOM_SEED = 2018
| 169 | 17.888889 | 35 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/Kfold_split/stratifiedKfold.py | #Email of the author: zjduan@pku.edu.cn
import numpy as np
import config
import os
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn import preprocessing
scale = ""
train_x_name = "train_x.npy"
train_y_name = "train_y.npy"
Column = 13
def _load_data(_nrows=None, debug = False):
... | 2,976 | 28.186275 | 90 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/KDD2012/scale.py | import math
import config
import numpy as np
def scale(x):
if x > 2:
x = int(math.log(float(x))**2)
return x
def scale_each_fold():
for i in range(1,11):
print('now part %d' % i)
data = np.load(config.DATA_PATH + 'part'+str(i)+'/train_x.npy')
part = data[:,0:13]
fo... | 582 | 23.291667 | 78 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/KDD2012/preprocess.py | #coding=utf-8
#Email of the author: zjduan@pku.edu.cn
'''
0. Click：
1. Impression（numerical）
2. DisplayURL: (categorical)
3. AdID:(categorical)
4. AdvertiserID:(categorical)
5. Depth:(numerical)
6. Position:(numerical)
7. QueryID: (categorical) the key of the data file 'queryid_tokensid.txt'.
8. KeywordID: (categori... | 4,042 | 29.398496 | 86 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/Criteo/scale.py | import math
import config
import numpy as np
def scale(x):
if x > 2:
x = int(math.log(float(x))**2)
return x
def scale_each_fold():
for i in range(1,11):
print('now part %d' % i)
data = np.load(config.DATA_PATH + 'part'+str(i)+'/train_x.npy')
part = data[:,0:13]
fo... | 582 | 23.291667 | 78 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/Criteo/config.py | DATA_PATH = './Criteo/'
SOURCE_DATA = './train_examples.txt' | 60 | 29.5 | 36 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/Criteo/preprocess.py | import config
train_path = config.SOURCE_DATA
f1 = open(train_path,'r')
dic= {}
# generate three fold.
# train_x: value
# train_i: index
# train_y: label
f_train_value = open(config.DATA_PATH + 'train_x.txt','w')
f_train_index = open(config.DATA_PATH + 'train_i.txt','w')
f_train_label = open(config.DATA_PATH + 'train_... | 2,286 | 24.696629 | 97 | py |
RecommenderSystems | RecommenderSystems-master/featureRec/data/Dataprocess/Avazu/preprocess.py | #coding=utf-8
#Email of the author: zjduan@pku.edu.cn
'''
0.id: ad identifier
1.click: 0/1 for non-click/click
2.hour: format is YYMMDDHH, so 14091123 means 23:00 on Sept. 11, 2014 UTC.
3.C1 -- anonymized categorical variable
4.banner_pos
5.site_id
6.site_domain
7.site_category
8.app_id
9.app_domain
10.app_category
11.... | 2,863 | 22.669421 | 92 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/data/preprocess_DoubanMovie.py | import pandas as pd
import numpy as np
import math
import argparse
import random
from collections import Counter
'''
The original DoubanMovie data can be found at:
https://www.dropbox.com/s/tmwuitsffn40vrz/Douban.tar.gz?dl=0
'''
PATH_TO_DATA = './Douban/'
SOCIAL_NETWORK_FILE = PATH_TO_DATA + 'socialnet/socialnet.ts... | 7,559 | 46.54717 | 206 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/inits.py | import tensorflow as tf
import numpy as np
# DISCLAIMER:
# This file is derived from
# https://github.com/tkipf/gcn
# which is also under the MIT license
def uniform(shape, scale=0.05, name=None):
"""Uniform init."""
initial = tf.random_uniform(shape, minval=-scale, maxval=scale, dtype=tf.float32)
return... | 903 | 28.16129 | 95 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/aggregators.py | import tensorflow as tf
from .layers import Layer, Dense
from .inits import glorot, zeros
# Mean, MaxPool, GCN aggregators are collected from
# https://github.com/williamleif/GraphSAGE
# which is also under the MIT license
class MeanAggregator(Layer):
"""
Aggregates via mean followed by matmul and non-linea... | 11,210 | 32.168639 | 92 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/test.py | #coding=utf-8
from __future__ import division
from __future__ import print_function
import os, sys
import argparse
import tensorflow as tf
import numpy as np
import time
from .utils import *
from .minibatch import MinibatchIterator
from .model import DGRec
seed = 123
np.random.seed(seed)
tf.set_random_seed(seed)
... | 8,420 | 39.485577 | 183 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/utils.py | #coding=utf-8
from __future__ import print_function
import numpy as np
import pandas as pd
import random
def load_adj(data_path):
df_adj = pd.read_csv(data_path + '/adj.tsv', sep='\t', dtype={0:np.int32, 1:np.int32})
return df_adj
def load_latest_session(data_path):
ret = []
for line in open(data... | 1,519 | 31.340426 | 105 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/model.py | import tensorflow as tf
import numpy as np
from .aggregators import *
from .layers import Dense
class DGRec(object):
def __init__(self, args, support_sizes, placeholders):
self.support_sizes = support_sizes
if args.aggregator_type == "mean":
self.aggregator_cls = MeanAggregator
... | 14,311 | 50.855072 | 155 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/minibatch.py | #coding=utf-8
from __future__ import division
from __future__ import print_function
import numpy as np
import pandas as pd
import sys
from .neigh_samplers import UniformNeighborSampler
from .utils import *
np.random.seed(123)
class MinibatchIterator(object):
def __init__(self,
adj_info, # i... | 13,192 | 41.15016 | 120 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/layers.py | from __future__ import division
from __future__ import print_function
import tensorflow as tf
from .inits import zeros
# DISCLAIMER:
# This file is forked from
# https://github.com/tkipf/gcn
# which is also under the MIT license
# global unique layer ID dictionary for layer name assignment
_LAYER_UIDS = {}
def g... | 3,731 | 31.172414 | 105 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/neigh_samplers.py | from __future__ import division
from __future__ import print_function
import numpy as np
"""
Classes that are used to sample node neighborhoods
"""
class UniformNeighborSampler(object):
"""
Uniformly samples neighbors.
Assumes that adj lists are padded with random re-sampling
"""
def __init__(sel... | 1,740 | 37.688889 | 88 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/__init__.py | from __future__ import print_function
from __future__ import division
| 70 | 22.666667 | 37 | py |
RecommenderSystems | RecommenderSystems-master/socialRec/dgrec/train.py | #coding=utf-8
from __future__ import division
from __future__ import print_function
import os, sys
import argparse
import tensorflow as tf
import numpy as np
import time
from .utils import *
from .minibatch import MinibatchIterator
from .model import DGRec
seed = 123
np.random.seed(seed)
tf.set_random_seed(seed)
de... | 10,966 | 40.541667 | 141 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/markovChains/sampler.py | #coding=utf-8
'''
Author: Weiping Song
Contact: songweiping@pku.edu.cn
Reference: https://github.com/kang205/SASRec/blob/master/sampler.py
'''
# Disclaimer:
# Part of this file is derived from
# https://github.com/kang205/SASRec/
import numpy as np
from multiprocessing import Process, Queue
def random_neg(pos, n, s... | 3,798 | 34.504673 | 127 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/markovChains/utils.py | #coding=utf-8
'''
Author: Weiping Song
Contact: Weiping Song
'''
import pandas as pd
import numpy as np
import random
import os
import json
import datetime as dt
from collections import Counter
# path of folder that contains all the datas.
data_path = 'data/'
class Dictionary(object):
def __init__(self):
... | 10,819 | 41.101167 | 130 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/markovChains/model.py | #coding=utf-8
'''
Author: Chence Shi
Contact: chenceshi@pku.edu.cn
'''
import tensorflow as tf
import sys
import os
import numpy as np
def log2(x):
numerator = tf.log(x)
denominator = tf.log(tf.constant(2, dtype=numerator.dtype))
return numerator / denominator
class FOSSIL(object):
def __init__(... | 11,107 | 49.262443 | 151 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/markovChains/__init__.py | 0 | 0 | 0 | py | |
RecommenderSystems | RecommenderSystems-master/sequentialRec/markovChains/train.py | #coding: utf-8
'''
Author: Chence Shi
Contact: chenceshi@pku.edu.cn
'''
import tensorflow as tf
import argparse
import numpy as np
import sys
import time
import math
from .utils import *
from .model import *
from .sampler import *
parser = argparse.ArgumentParser(description='Sequential or session-based recommendati... | 6,793 | 38.5 | 116 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/base.py | # coding: utf-8
'''
Author: Weiping Song, Chence Shi, Zheye Deng
Contact: songweiping@pku.edu.cn, chenceshi@pku.edu.cn, dzy97@pku.edu.cn
'''
import tensorflow as tf
import numpy as np
from tensorflow.contrib import rnn
class LSTMNet(object):
def __init__(self, layers=1, hidden_units=100, hidden_activation="tanh", ... | 15,214 | 43.75 | 182 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/test.py | #coding: utf-8
'''
Author: Weiping Song
Contact: songweiping@pku.edu.cn
'''
import tensorflow as tf
import argparse
import numpy as np
import sys
import time
import math
from .utils import *
from .model import *
from .eval import Evaluation
parser = argparse.ArgumentParser(description='Sequential or session-based re... | 5,225 | 41.836066 | 127 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/sampler.py | #coding=utf-8
'''
Author: Weiping Song
Contact: songweiping@pku.edu.cn
Reference: https://github.com/kang205/SASRec/blob/master/sampler.py
'''
# Disclaimer:
# Part of this file is derived from
# https://github.com/kang205/SASRec/
import numpy as np
from multiprocessing import Process, Queue
def random_neg(pos, n, s... | 3,473 | 34.814433 | 121 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/utils.py | #coding=utf-8
'''
Author: Weiping Song
Contact: Weiping Song
'''
import pandas as pd
import numpy as np
import random
import os
import json
import datetime as dt
from collections import Counter
data_path = 'data/'
class Dictionary(object):
def __init__(self):
self.item2idx = {}
self.idx2item = []... | 9,599 | 40.921397 | 145 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/model.py | #coding=utf-8
'''
Author: Weiping Song
Contact: songweiping@pku.edu.cn
'''
import tensorflow as tf
import sys
from .base import LSTMNet
from .base import TemporalConvNet
from .base import TransformerNet
def log2(x):
numerator = tf.log(x)
denominator = tf.log(tf.constant(2, dtype=numerator.dtype))
return n... | 6,096 | 45.9 | 171 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/eval.py | import numpy as np
class Evaluation:
'''
In progress...
Eventually, we aim to include popular evaluation metrics as many as possible.
'''
def __init__(self, ks = [1, 5, 10, 20], ndcg_cutoff = 20):
self.k = ks
self.ndcg_cutoff = ndcg_cutoff
self.clear()
def clear(self):... | 2,624 | 32.653846 | 89 | py |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/__init__.py | 0 | 0 | 0 | py | |
RecommenderSystems | RecommenderSystems-master/sequentialRec/neural/train.py | #coding: utf-8
'''
Author: Weiping Song
Contact: songweiping@pku.edu.cn
'''
import tensorflow as tf
import argparse
import numpy as np
import sys
import time
import math
from .utils import *
from .model import *
from .sampler import *
parser = argparse.ArgumentParser(description='Sequential or session-based recommen... | 6,636 | 41.006329 | 127 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/test.py | import argparse
import os
import random
import shutil
import time
import warnings
import sys
import cv2
import numpy as np
import scipy.misc
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing... | 13,359 | 39.731707 | 124 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/train_sr.py | import argparse
import os
import copy
import torch
from torch import nn
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
from sr_models.model import RDN, VGGLoss
from sr_models.datasets import TrainDataset, EvalDataset
from sr_mo... | 4,950 | 41.316239 | 173 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/train.py | import argparse
import os
import random
import shutil
import time
import warnings
import sys
import cv2
import numpy as np
import scipy.misc
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import torch.optim
import torch.multiprocessing... | 17,834 | 40.866197 | 195 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/models/customize.py | ##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Hang Zhang
## ECE Department, Rutgers University
## Email: zhang.hang@rutgers.edu
## Copyright (c) 2017
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this sou... | 10,973 | 36.71134 | 99 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/models/resnet.py | """Dilated ResNet"""
import math
import torch
import torch.utils.model_zoo as model_zoo
import torch.nn as nn
from .customize import GlobalAvgPool2d
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152', 'BasicBlock', 'Bottleneck', 'get_resnet']
model_urls = {
'resnet18': '... | 11,165 | 35.135922 | 162 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/models/resnet_cifar.py | """Dilated ResNet"""
import torch.nn as nn
from .customize import FrozenBatchNorm2d
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
}
def conv_1_3x3(input_channel):
return nn.Sequential(nn.Co... | 11,254 | 37.412969 | 140 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/models/__init__.py | from .resnet import get_resnet
from .resnet_cifar import get_cifar_resnet
def get_classification_model(arch, pretrained, **kwargs):
return get_resnet(arch, pretrained, **kwargs)
def get_cifar_classification_model(arch, pretrained, **kwargs):
return get_cifar_resnet(arch, pretrained, **kwargs)
| 304 | 32.888889 | 63 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/sr_models/utils.py | import torch
import numpy as np
def convert_rgb_to_y(img, dim_order='hwc'):
if dim_order == 'hwc':
return 16. + (64.738 * img[..., 0] + 129.057 * img[..., 1] + 25.064 * img[..., 2]) / 256.
else:
return 16. + (64.738 * img[0] + 129.057 * img[1] + 25.064 * img[2]) / 256.
def denormalize(img):
... | 1,061 | 22.086957 | 97 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/sr_models/model.py | import torch
from torch import nn
class DenseLayer(nn.Module):
def __init__(self, in_channels, out_channels):
super(DenseLayer, self).__init__()
self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=3 // 2)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
... | 4,943 | 36.172932 | 122 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/sr_models/datasets.py | import random
import h5py
import numpy as np
from PIL import Image
from torch.utils.data import Dataset
from scipy.ndimage import gaussian_filter
from scipy.ndimage.filters import convolve
from io import BytesIO
import copy
class TrainDataset(Dataset):
def __init__(self, file_path, patch_size, scale, aug=False, c... | 10,057 | 32.415282 | 102 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/datasets/base.py | ###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2017
###########################################################################
import random
import numpy as np
from PIL import Image, ImageOps, ImageFilter
import to... | 835 | 22.222222 | 75 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/datasets/image_dataset.py | ###########################################################################
# Created by: Hang Zhang
# Email: zhang.hang@rutgers.edu
# Copyright (c) 2018
###########################################################################
import os
import sys
import random
import numpy as np
from tqdm import tqdm, trange
from ... | 2,300 | 31.871429 | 144 | py |
BeyondtheSpectrum | BeyondtheSpectrum-main/datasets/__init__.py | import warnings
from torchvision.datasets import *
from .base import *
from .image_dataset import BinaryImageDataset
datasets = {
'image': BinaryImageDataset,
}
def get_dataset(name, **kwargs):
return datasets[name.lower()](**kwargs)
| 246 | 16.642857 | 45 | py |
ps-lite | ps-lite-master/tracker/dmlc_local.py | #!/usr/bin/env python
"""
DMLC submission script, local machine version
"""
import argparse
import sys
import os
import subprocess
from threading import Thread
import tracker
import signal
import logging
keepalive = """
nrep=0
rc=254
while [ $rc -eq 254 ];
do
export DMLC_NUM_ATTEMPT=$nrep
%s
rc=$?;
nr... | 3,051 | 29.217822 | 88 | py |
ps-lite | ps-lite-master/tracker/tracker.py | """
Tracker script for DMLC
Implements the tracker control protocol
- start dmlc jobs
- start ps scheduler and rabit tracker
- help nodes to establish links with each other
Tianqi Chen
"""
import sys
import os
import socket
import struct
import subprocess
import time
import logging
import random
from threading imp... | 14,308 | 31.970046 | 113 | py |
ps-lite | ps-lite-master/tracker/dmlc_ssh.py | #!/usr/bin/env python
"""
DMLC submission script by ssh
One need to make sure all slaves machines are ssh-able.
"""
import argparse
import sys
import os
import subprocess
import tracker
import logging
from threading import Thread
class SSHLauncher(object):
def __init__(self, args, unknown):
self.args = a... | 3,896 | 33.184211 | 92 | py |
ps-lite | ps-lite-master/tracker/dmlc_mpi.py | #!/usr/bin/env python
"""
DMLC submission script, MPI version
"""
import argparse
import sys
import os
import subprocess
import tracker
from threading import Thread
parser = argparse.ArgumentParser(description='DMLC script to submit dmlc job using MPI')
parser.add_argument('-n', '--nworker', required=True, type=int,
... | 3,173 | 33.5 | 92 | py |
ps-lite | ps-lite-master/tests/lint.py | #!/usr/bin/env python
# pylint: disable=protected-access, unused-variable, locally-disabled, redefined-variable-type
"""Lint helper to generate lint summary of source.
Copyright by Contributors
"""
import codecs
import sys
import re
import os
import cpplint
from cpplint import _cpplint_state
from pylint import epylint... | 6,474 | 36.212644 | 98 | py |
ps-lite | ps-lite-master/docs/sphinx_util.py | import sys, os, subprocess
if not os.path.exists('../recommonmark'):
subprocess.call('cd ..; git clone https://github.com/tqchen/recommonmark', shell = True)
else:
subprocess.call('cd ../recommonmark; git pull', shell=True)
sys.path.insert(0, os.path.abspath('../recommonmark/'))
from recommonmark import par... | 571 | 27.6 | 92 | py |
ps-lite | ps-lite-master/docs/conf.py | # -*- coding: utf-8 -*-
#
# ps-lite documentation build configuration file, created by
# sphinx-quickstart on Sun Mar 20 20:12:23 2016.
#
# Mu: additional changes
# - add breathe into extensions
# - change html theme into sphinx_rtd_theme
# - add sphnix_util.py
# - add .md into source_suffix
# - add setup() a... | 10,101 | 30.968354 | 80 | py |
SLIT | SLIT-master/setup.py | from setuptools import setup
setup(name='SLIT',
version='0.1',
description='Code for colour lens/source separation and lensed source reconstruction',
author='Remy Joseph, Frederic Courbin, Jean-Luc Starck',
author_email='remy.joseph@epfl.ch',
packages=['SLIT'],
zip_safe=False)
| 315 | 30.6 | 92 | py |
SLIT | SLIT-master/Tests/Result_slope.py | import numpy as np
import matplotlib.pyplot as plt
import SLIT
import pyfits as pf
import matplotlib.cm as cm
import os
import glob
nsim = 11
ranges = np.array([1.9,1.95,2.0,2.025,2.05])#np.linspace(0,1,11)
Truth = pf.open('IMG2.fits')[0].data
sigma = 0.00119
Sources = 0
FSs = 0
#thetas = np.zeros((nsim, ranges.siz... | 3,313 | 30.865385 | 156 | py |
SLIT | SLIT-master/Tests/Launch_Test.py | import Test_center as tc
import sys
import numpy as np
variable = sys.argv[1]
shift = sys.argv[2]
if variable == 'center':
tc.test_center(np.float(shift))
if variable == 'slope':
tc.test_slope(np.float(shift))
| 224 | 13.0625 | 35 | py |
SLIT | SLIT-master/Tests/gaussian.py | import numpy as np
import scipy.misc as spm
import matplotlib.pyplot as plt
import matplotlib.cm as cm
def gaussian(n1,n2,x0,y0,A,e1,e2,alpha):
#img = gaussian(n1,n2,x0,y0,A,e1,e2,alpha)
#produces a gaussian profile image
#INPUTS:
# n1,n2: size of the output image
# x0,y0: centroid of the gaus... | 4,915 | 26.463687 | 78 | py |
SLIT | SLIT-master/Tests/Test_center.py | import pyfits as pf
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.cm as cm
import SLIT
import gaussian as gs
import time
from scipy import signal as scp
import warnings
warnings.simplefilter("ignore")
#Example of a run of the SLIT algorithm on simulated images.
#Here the first part of the file... | 5,537 | 32.97546 | 116 | py |
SLIT | SLIT-master/Tests/Results_center.py | import numpy as np
import matplotlib.pyplot as plt
import SLIT
import pyfits as pf
import matplotlib.cm as cm
import os
import glob
nsim =49
ranges = np.array([0.0,0.1,0.2,0.3,0.4,0.5])#np.linspace(0,1,11)
Truth = pf.open('IMG2.fits')[0].data
sigma = 0.00119
Sources = 0
FSs = 0
thetas = np.zeros((nsim, ranges.size)... | 3,528 | 30.508929 | 155 | py |
SLIT | SLIT-master/SLIT/Solve.py | #from __future__ import division
import wave_transform as mw
import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
import matplotlib.cm as cm
from scipy import signal as scp
import scipy.ndimage.filters as med
import MuSCADeT as wine
from numpy import linalg as LA
import multiprocess as mtp
from patho... | 24,278 | 33.004202 | 186 | py |
SLIT | SLIT-master/SLIT/wave_transform.py | import numpy as np
import scipy.signal as cp
import matplotlib.pyplot as plt
import scipy.ndimage.filters as sc
def symmetrise(img, size):
n3, n4 = np.shape(img)
n1,n2 = size
img[:(n3-n1)/2, :] = np.flipud(img[(n3-n1)/2:(n3-n1),:])
img[:,:(n4-n2)/2] = np.fliplr(img[:,(n4-n2)/2:(n4-n2)])
img[(n3+n... | 3,715 | 25.169014 | 81 | py |
SLIT | SLIT-master/SLIT/tools.py | import numpy as np
import matplotlib.pyplot as plt
import pyfits as pf
from scipy import signal as scp
import gaussian as gs
import scipy.ndimage.filters as sc
import scipy.ndimage.filters as med
import scipy.signal as cp
def MOM(A, B, levelA, levelB):
A = A[:-1,:,:]
B = B[:-1,:,:]
levelA = levelA[:-1,:,:]... | 10,239 | 27.444444 | 131 | py |
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