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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LiteFlowNet2 | LiteFlowNet2-master/models/testing/runtime.py | #!/usr/bin/env python
import os, sys
import subprocess
caffe_bin = 'bin/caffe.bin'
# =========================================================
my_dir = os.path.dirname(os.path.realpath(__file__))
os.chdir(my_dir)
if not os.path.isfile(caffe_bin):
print('Caffe tool binaries not found. Did you compile caffe with t... | 674 | 29.681818 | 204 | py |
LogicLLaMA | LogicLLaMA-main/run_eval.py | from tqdm import tqdm
import torch
from functools import partial
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
from peft import PeftModel, prepare_model_for_int8_training
from utils import TranslationDataPreparer, ContinuousCorrectionDataPreparer, make_parent_dirs
from generate import llam... | 6,146 | 32.227027 | 111 | py |
LogicLLaMA | LogicLLaMA-main/generate.py | from transformers import GenerationConfig, LlamaForCausalLM
import torch
from utils import DataPreparer, all_exists
from typing import Dict, Optional, Callable
from functools import partial
def llama_generate(
llama_model: LlamaForCausalLM,
data_preparer: DataPreparer,
input_str: str,
max_new_tokens: ... | 2,327 | 37.8 | 113 | py |
LogicLLaMA | LogicLLaMA-main/sft.py | import json
import os
from typing import List, Optional
import torch
import transformers
from datasets import load_dataset
from utils import all_exists
from functools import partial
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_int8_training,
get_peft_model_state_dict
)
from transform... | 7,533 | 32.04386 | 108 | py |
LogicLLaMA | LogicLLaMA-main/utils/gpt_requests.py | import json
import fire
import openai
from utils import Prompter, wrap_function_with_timeout, all_exists, make_parent_dirs
from typing import Optional, Dict, List, Union, Callable
from utils import all_exists
import os
from functools import partial
from tqdm import tqdm
folio_5_shot = \
"""
Here are some examples y... | 8,957 | 26.906542 | 167 | py |
big-sleep | big-sleep-main/setup.py | import sys
from setuptools import setup, find_packages
sys.path[0:0] = ['big_sleep']
from version import __version__
setup(
name = 'big-sleep',
packages = find_packages(),
include_package_data = True,
entry_points={
'console_scripts': [
'dream = big_sleep.cli:main',
],
},
version = __version... | 1,080 | 22 | 65 | py |
big-sleep | big-sleep-main/big_sleep/resample.py | """Good differentiable image resampling for PyTorch."""
from functools import update_wrapper
import math
import torch
from torch.nn import functional as F
def sinc(x):
return torch.where(x != 0, torch.sin(math.pi * x) / (math.pi * x), x.new_ones([]))
def lanczos(x, a):
cond = torch.logical_and(-a < x, x ... | 2,062 | 24.7875 | 87 | py |
big-sleep | big-sleep-main/big_sleep/clip.py | from collections import OrderedDict
from typing import Tuple, Union
import torch
import torch.nn.functional as F
from torch import nn
from pathlib import Path
import hashlib
import os
import urllib
import warnings
from typing import Union, List
import torch
from PIL import Image
from torchvision.transforms import Co... | 28,958 | 37.509309 | 178 | py |
big-sleep | big-sleep-main/big_sleep/biggan.py | # this code is a copy from huggingface
# with some minor modifications
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import json
import copy
import logging
import os
import shutil
import tempfile
from functools import wraps
from hashlib import sha256
import sys
from io import open
imp... | 22,399 | 37.62069 | 119 | py |
big-sleep | big-sleep-main/big_sleep/cli.py | import fire
import random as rnd
from big_sleep import Imagine, version
from pathlib import Path
from .version import __version__;
def train(
text=None,
img=None,
text_min="",
lr = .07,
image_size = 512,
gradient_accumulate_every = 1,
epochs = 20,
iterations = 1050,
save_every = 50... | 1,950 | 24.337662 | 97 | py |
big-sleep | big-sleep-main/big_sleep/ema.py | # Exponential Moving Average (from https://gist.github.com/crowsonkb/76b94d5238272722290734bf4725d204)
"""Exponential moving average for PyTorch. Adapted from
https://www.zijianhu.com/post/pytorch/ema/ by crowsonkb
"""
from copy import deepcopy
import torch
from torch import nn
class EMA(nn.Module):
def __init__... | 2,098 | 37.163636 | 152 | py |
big-sleep | big-sleep-main/big_sleep/big_sleep.py | import os
import sys
import subprocess
import signal
import string
import re
from datetime import datetime
from pathlib import Path
import random
import torch
import torch.nn.functional as F
from torch import nn
from torch.optim import Adam
from torchvision.utils import save_image
import torchvision.transforms as T
f... | 17,794 | 34.167984 | 150 | py |
big-sleep | big-sleep-main/test/multi_prompt_minmax.py | import time
import shutil
import torch
from big_sleep import Imagine
terminate = False
def signal_handling(signum,frame):
global terminate
terminate = True
num_attempts = 4
for attempt in range(num_attempts):
dream = Imagine(
text = "an armchair in the form of pikachu\\an armchair imitating pikac... | 1,085 | 24.255814 | 93 | py |
Quantum | Quantum-master/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,355 | 31.273973 | 79 | py |
Quantum | Quantum-master/docs_zh_CN/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | 2,329 | 31.816901 | 79 | py |
SWTD3 | SWTD3-main/main.py | import argparse
import os
import socket
import gym
import numpy as np
import torch
import TD3
import SWTD3
import utils
# Runs policy for X episodes and returns average reward
# A fixed seed is used for the eval environment
def evaluate_policy(agent, env_name, seed, eval_episodes=10):
eval_env = gym.make(env_na... | 6,850 | 41.552795 | 118 | py |
SWTD3 | SWTD3-main/utils.py | import numpy as np
import torch
class ExperienceReplayBuffer(object):
def __init__(self, state_dim, action_dim, max_size=int(1e6)):
self.max_size = max_size
self.ptr = 0
self.size = 0
self.state = np.zeros((max_size, state_dim))
self.action = np.zeros((max_size, action_dim... | 1,398 | 34.871795 | 82 | py |
SWTD3 | SWTD3-main/TD3.py | import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Implementation of the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3)
# Paper: https://arxiv.org/abs/1802.09477
# Note: This implementation heavily r... | 5,894 | 34.512048 | 107 | py |
SWTD3 | SWTD3-main/SWTD3.py | import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Implementation of the Stochastic Weighted Twin Delayed Deep Deterministic Policy Gradient algorithm (SWTD3)
# Note: This implementation heavily re... | 6,521 | 35.033149 | 118 | py |
multigen | multigen-master/scripts/main.py | from __future__ import absolute_import, division, print_function
import json
import argparse
import glob
import logging
import os
import pickle
import random
import re
import shutil
import subprocess
from typing import List, Dict
import csv
import logging
import sys
import collections
import math
import spacy
import n... | 26,926 | 42.360709 | 239 | py |
multigen | multigen-master/scripts/optimization.py | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICEN... | 8,634 | 44.687831 | 130 | py |
multigen | multigen-master/scripts/dictionary.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from collections import Counter
from multiprocessing import Pool
import os
import torch
from fairseq.tokenizer import tokenize_line
from fai... | 10,932 | 33.05919 | 109 | py |
multigen | multigen-master/scripts/modeling_gpt2.py | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License... | 50,225 | 46.788773 | 148 | py |
multigen | multigen-master/scripts/data.py | import torch
import os
import json
import logging
import csv
import itertools
from torch.utils.data import Dataset
import random
from transformers import BertTokenizer
logger = logging.getLogger()
def normalize_case(text):
if len(text) > 1:
try:
normalized = text[0].upper() + text[1:].lower()... | 9,321 | 38.004184 | 151 | py |
multigen | multigen-master/scripts/seq_generator.py | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
from fairseq import search, utils
from fairseq.data import data_utils
from fairseq.models import FairseqIncremental... | 33,724 | 41.36809 | 136 | py |
multigen | multigen-master/preprocess/find_neighbours.py | import configparser
import networkx as nx
import itertools
import math
import random
import json
from tqdm import tqdm
import sys
import time
import timeit
import numpy as np
import torch
from collections import Counter
import spacy
from scipy import spatial
import sys
config = configparser.ConfigParser()
config.read(... | 6,759 | 29.86758 | 117 | py |
DeepModel | DeepModel-master/testing/demo.py | import sys
paths = {}
with open('../path.config', 'r') as f:
for line in f:
name, path = line.split(': ')
print name, path
paths[name] = path
sys.path.insert(0, paths['pycaffe_root'])
import caffe
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d
from mpl_toolkits.mplot3d impo... | 2,400 | 33.797101 | 100 | py |
machine-learning-applied-to-cfd | machine-learning-applied-to-cfd-master/notebooks/helper_module.py | '''Module containing function that are too large to be included in the notebooks.'''
import torch
import numpy as np
class SimpleMLP(torch.nn.Module):
def __init__(self, n_inputs=1, n_outputs=1, n_layers=1, n_neurons=10, activation=torch.sigmoid, batch_norm=False):
super().__init__()
self.n_inputs... | 6,317 | 44.128571 | 118 | py |
adapt | adapt-master/tests/test_ccsa.py | import numpy as np
import tensorflow as tf
from adapt.utils import make_classification_da
from adapt.feature_based import CCSA
from tensorflow.keras.initializers import GlorotUniform
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
np.random.seed(0)... | 1,311 | 36.485714 | 98 | py |
adapt | adapt-master/tests/test_tradaboost.py | """
Test functions for tradaboost module.
"""
import copy
import numpy as np
import scipy
from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge, RidgeClassifier
from sklearn.metrics import r2_score, accuracy_score
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Ad... | 7,644 | 34.55814 | 93 | py |
adapt | adapt-master/tests/test_iwc.py | """
Test functions for iwn module.
"""
import numpy as np
from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.instance_based import IWC
from adapt.utils import get_default_discriminator
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from ... | 1,428 | 27.58 | 83 | py |
adapt | adapt-master/tests/test_adda.py | """
Test functions for adda module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.initializers import GlorotUniform
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.k... | 3,128 | 31.59375 | 83 | py |
adapt | adapt-master/tests/test_dann.py | """
Test functions for dann module.
"""
import pytest
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from adapt.fea... | 4,469 | 33.651163 | 91 | py |
adapt | adapt-master/tests/test_mdd.py | """
Test functions for dann module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initial... | 3,369 | 30.495327 | 87 | py |
adapt | adapt-master/tests/test_iwn.py | """
Test functions for iwn module.
"""
from sklearn.linear_model import RidgeClassifier
from adapt.utils import make_classification_da
from adapt.instance_based import IWN
from adapt.utils import get_default_task
from sklearn.neighbors import KNeighborsClassifier
try:
from tensorflow.keras.optimizers.legacy import... | 1,349 | 31.142857 | 78 | py |
adapt | adapt-master/tests/test_coral.py | """
Test functions for coral module.
"""
import numpy as np
from sklearn.linear_model import LogisticRegression
from scipy import linalg
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.initializers import GlorotUniform
from adapt.f... | 2,957 | 30.806452 | 73 | py |
adapt | adapt-master/tests/test_mcd.py | """
Test functions for dann module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initial... | 2,758 | 30 | 66 | py |
adapt | adapt-master/tests/test_regular.py | """
Test functions for regular module.
"""
import pytest
import numpy as np
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.gaussian_process import GaussianProcessRegressor, GaussianProcessClassifier
from sklearn.gaussian_process.kernels import Matern, WhiteKernel
from sklearn.base i... | 8,066 | 32.473029 | 88 | py |
adapt | adapt-master/tests/test_wann.py | """
Test functions for wann module.
"""
import numpy as np
from sklearn.linear_model import LinearRegression
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
import tensorflow as tf
from adapt.instance_based import WANN
np.random.seed(0)
Xs = np.con... | 871 | 27.129032 | 59 | py |
adapt | adapt-master/tests/test_cdan.py | """
Test functions for cdan module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initial... | 3,932 | 32.330508 | 89 | py |
adapt | adapt-master/tests/test_base.py | """
Test base
"""
import copy
import shutil
import numpy as np
import pytest
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from sklear... | 9,849 | 31.943144 | 99 | py |
adapt | adapt-master/tests/test_finetuning.py | import numpy as np
import tensorflow as tf
from sklearn.base import clone
from adapt.utils import make_classification_da
from adapt.parameter_based import FineTuning
from tensorflow.keras.initializers import GlorotUniform
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.op... | 3,453 | 41.121951 | 102 | py |
adapt | adapt-master/tests/test_wdgrl.py | """
Test functions for wdgrl module.
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, Model
from tensorflow.keras.layers import Dense
try:
from tensorflow.keras.optimizers.legacy import Adam
except:
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.initia... | 2,835 | 31.227273 | 78 | py |
adapt | adapt-master/tests/test_utils.py | """
Test functions for utils module.
"""
import copy
import numpy as np
import pytest
import tensorflow as tf
import tensorflow.keras.backend as K
from sklearn.linear_model import LinearRegression, LogisticRegression, Ridge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
fro... | 16,182 | 34.567033 | 88 | py |
adapt | adapt-master/adapt/base.py | """
Base for adapt
"""
import warnings
import inspect
from copy import deepcopy
import numpy as np
import tensorflow as tf
from sklearn.base import BaseEstimator
from sklearn.utils import check_array
from sklearn.metrics.pairwise import KERNEL_PARAMS
from sklearn.exceptions import NotFittedError
from tensorflow.keras... | 60,667 | 35.111905 | 106 | py |
adapt | adapt-master/adapt/utils.py | """
Utility functions for adapt package.
"""
import warnings
import inspect
from copy import deepcopy
import numpy as np
from sklearn.datasets import make_classification
from sklearn.utils import check_array
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.base import BaseEstimator, ... | 20,777 | 30.52959 | 95 | py |
adapt | adapt-master/adapt/metrics.py | import inspect
import copy
import numpy as np
import tensorflow as tf
from tensorflow.keras.optimizers import Adam
from scipy import linalg
from sklearn.metrics import pairwise
from sklearn.base import clone
from sklearn.model_selection import train_test_split
from sklearn.utils import check_array
from adapt.utils imp... | 17,730 | 28.01964 | 95 | py |
adapt | adapt-master/adapt/instance_based/_wann.py | """
Weighting Adversarial Neural Network (WANN)
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network, get_default_task
EPS = np.finfo(np.float32).eps
@make_insert_doc(["task", "weighter"], supervised=True)
class WANN(BaseAdaptDeep... | 9,168 | 33.996183 | 114 | py |
adapt | adapt-master/adapt/instance_based/_iwn.py | """
Importance Weighting Network (IWN)
"""
import warnings
import inspect
from copy import deepcopy
import numpy as np
from sklearn.utils import check_array
import tensorflow as tf
from tensorflow.keras import Model
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import (check_arrays, check_net... | 13,414 | 31.639903 | 89 | py |
adapt | adapt-master/adapt/feature_based/_cdan.py | """
CDAN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from tensorflow.keras.initializers import GlorotUniform
from adapt.utils import (check_network,
get_default_encoder,
get_default_discriminator)
EPS = np.finf... | 15,371 | 40.433962 | 118 | py |
adapt | adapt-master/adapt/feature_based/_adda.py | """
DANN
"""
import numpy as np
import tensorflow as tf
from adapt.base import BaseAdaptDeep, make_insert_doc
from adapt.utils import check_network
EPS = np.finfo(np.float32).eps
# class SetEncoder(tf.keras.callbacks.Callback):
# def __init__(self):
# self.pretrain = True
# def on_epoch_e... | 11,893 | 32.694051 | 102 | py |
adapt | adapt-master/adapt/parameter_based/_regular.py | """
Regular Transfer
"""
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from scipy.sparse.linalg import lsqr
from sklearn.gaussian_process import GaussianProcessRegressor, GaussianProcessClassifier
from sklearn.linear_model import LinearRegression
import tensorflow as tf
from tensorflow.keras impo... | 22,633 | 33.821538 | 105 | py |
SCKD | SCKD-main/main.py | import argparse
import random
from sampler import data_sampler
from config import Config
import torch
from model.bert_encoder import Bert_Encoder
from model.dropout_layer import Dropout_Layer
from model.classifier import Softmax_Layer, Proto_Softmax_Layer
from data_loader import get_data_loader
import torch.nn.function... | 30,103 | 42.377522 | 153 | py |
SCKD | SCKD-main/data_loader.py | import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
class data_set(Dataset):
def __init__(self, data,config=None):
self.data = data
self.config = config
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
return self.data[i... | 1,197 | 25.622222 | 89 | py |
SCKD | SCKD-main/model/base_model.py | import torch
import torch.nn as nn
import os
import json
class base_model(nn.Module):
def __init__(self):
super(base_model, self).__init__()
self.zero_const = nn.Parameter(torch.Tensor([0]))
self.zero_const.requires_grad = False
self.pi_const = nn.Parameter(torch.Tensor([3.14159265... | 1,737 | 31.185185 | 76 | py |
SCKD | SCKD-main/model/classifier.py | from torch import nn, optim
from model.base_model import base_model
import torch
class Softmax_Layer(base_model):
"""
Softmax classifier for sentence-level relation extraction.
"""
def __init__(self, input_size, num_class):
"""
Args:
num_class: number of classes
""... | 1,751 | 25.545455 | 72 | py |
SCKD | SCKD-main/model/dropout_layer.py | from torch import nn
import torch
from torch.nn.parameter import Parameter
import torch.nn.functional as F
from model.base_model import base_model
class LayerNorm(nn.Module):
def __init__(self, input_dim, cond_dim=0, center=True, scale=True, epsilon=None, conditional=False,
hidden_units=None, hid... | 3,674 | 36.886598 | 116 | py |
SCKD | SCKD-main/model/bert_encoder.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from model.base_model import base_model
from transformers import BertModel, BertConfig
class Bert_Encoder(base_model):
def __init__(self, config):
super(Bert_Encoder, self).__init__()
# load model
self.... | 3,473 | 41.888889 | 111 | py |
TaNP | TaNP-main/TaNP/TaNP_training.py | import os
import torch
import pickle
import random
from eval import testing
def training(trainer, opt, train_dataset, test_dataset, batch_size, num_epoch, model_save=True, model_filename=None, logger=None):
training_set_size = len(train_dataset)
for epoch in range(num_epoch):
random.shuffle(train_datas... | 1,561 | 41.216216 | 130 | py |
TaNP | TaNP-main/TaNP/embeddings_TaNP.py | import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
class Item(torch.nn.Module):
def __init__(self, config):
super(Item, self).__init__()
self.feature_dim = config['if_dim']
self.first_embedding_dim = config['first_embedding_dim']
self.sec... | 14,501 | 38.194595 | 117 | py |
TaNP | TaNP-main/TaNP/eval.py | """
Run evaluation with saved models.
"""
import random
import argparse
from tqdm import tqdm
import torch
from utils.scorer import *
def testing(trainer, opt, test_dataset):
test_dataset_len = len(test_dataset)
#batch_size = opt["batch_size"]
minibatch_size = 1
a, b, c, d = zip(*test_dataset)
tra... | 1,551 | 32.021277 | 100 | py |
TaNP | TaNP-main/TaNP/train_TaNP.py | import os
from datetime import datetime
import time
import numpy as np
import random
import argparse
import pickle
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import json
from utils.loader import Preprocess
from TaNP import Trainer
from TaNP_training import trainin... | 7,850 | 47.462963 | 121 | py |
TaNP | TaNP-main/TaNP/TaNP.py | import torch
import numpy as np
from random import randint
from copy import deepcopy
from torch.autograd import Variable
from torch.nn import functional as F
from collections import OrderedDict
from embeddings_TaNP import Item, User, Encoder, MuSigmaEncoder, Decoder, Gating_Decoder, TaskEncoder, MemoryUnit
import torch... | 11,208 | 49.040179 | 168 | py |
TaNP | TaNP-main/TaNP/utils/torch_utils.py | """
Utility functions for torch.
"""
import torch
from torch import nn, optim
from torch.optim.optimizer import Optimizer
### class
class MyAdagrad(Optimizer):
"""My modification of the Adagrad optimizer that allows to specify an initial
accumulater value. This mimics the behavior of the default Adagrad imple... | 5,696 | 32.710059 | 106 | py |
TaNP | TaNP-main/TaNP/utils/loader.py | import json
import random
import torch
import numpy as np
import pickle
import codecs
import re
import os
import datetime
import tqdm
import pandas as pd
#convert userids to userdict key-id(int), val:onehot_vector(tensor)
#element in list is str type.
def to_onehot_dict(list):
dict={}
length = len(list)
fo... | 10,229 | 45.712329 | 128 | py |
crfasrnn | crfasrnn-master/python-scripts/crfasrnn_demo.py | # -*- coding: utf-8 -*-
"""
This package contains code for the "CRF-RNN" semantic image segmentation method, published in the
ICCV 2015 paper Conditional Random Fields as Recurrent Neural Networks. Our software is built on
top of the Caffe deep learning library.
Contact:
Shuai Zheng (szheng@robots.ox.ac.uk), Sadeep Ja... | 6,612 | 31.101942 | 133 | py |
Nested-UNet | Nested-UNet-master/model_logic.py | '''
'''
import keras
import tensorflow as tf
from keras.models import Model
from keras import backend as K
from keras.layers import Input, merge, Conv2D, ZeroPadding2D, UpSampling2D, Dense, concatenate, Conv2DTranspose
from keras.layers.pooling import MaxPooling2D, GlobalAveragePooling2D, MaxPooling2D
from keras.lay... | 12,054 | 44.149813 | 186 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/model.py | from .builder import build_xnet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2',
'block5_pool', 'block4_pool', 'block3_pool', 'block2_pool... | 5,117 | 46.388889 | 115 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
import copy
def build_xnet(backbone, classes, skip_connection_layers,
decoder_fi... | 7,554 | 41.926136 | 112 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/xnet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage, cols):
conv_name = 'decoder_stage{}-{}_conv'.format(stag... | 3,112 | 38.910256 | 106 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/functions.py | import numpy as np
import tensorflow as tf
def transpose_shape(shape, target_format, spatial_axes):
"""Converts a tuple or a list to the correct `data_format`.
It does so by switching the positions of its elements.
# Arguments
shape: Tuple or list, often representing shape,
correspondi... | 3,882 | 32.188034 | 88 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/layers.py | from keras.engine import Layer
from keras.engine import InputSpec
from keras.utils import conv_utils
from keras.legacy import interfaces
from keras.utils.generic_utils import get_custom_objects
from .functions import resize_images
class ResizeImage(Layer):
"""ResizeImage layer for 2D inputs.
Repeats the rows... | 3,623 | 42.662651 | 102 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/common/blocks.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.layers import BatchNormalization
def Conv2DBlock(n_filters, kernel_size,
activation='relu',
use_batchnorm=True,
name='conv_block',
**kwargs):
"""Extension of Conv2D layer ... | 682 | 31.52381 | 71 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/model.py | from .builder import build_psp
from ..utils import freeze_model
from ..backbones import get_backbone
PSP_BASE_LAYERS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4'),
'resnet18': ('stage4_unit... | 4,891 | 39.429752 | 95 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/builder.py | """
Code is constructed based on following repositories:
https://github.com/ykamikawa/PSPNet/
https://github.com/hujh14/PSPNet-Keras/
https://github.com/Vladkryvoruchko/PSPNet-Keras-tensorflow/
And original paper of PSPNet:
https://arxiv.org/pdf/1612.01105.pdf
"""
from keras.layers import Conv2D
from ... | 1,860 | 28.078125 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/pspnet/blocks.py | import numpy as np
from keras.layers import MaxPool2D
from keras.layers import AveragePooling2D
from keras.layers import Concatenate
from keras.layers import Permute
from keras.layers import Reshape
from keras.backend import int_shape
from ..common import Conv2DBlock
from ..common import ResizeImage
def InterpBlock(... | 3,539 | 32.396226 | 104 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/model.py | from .builder import build_nestnet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2',
'block5_pool', 'block4_pool', 'block3_pool', 'block2_p... | 5,129 | 46.5 | 115 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
import copy
def build_nestnet(backbone, classes, skip_connection_layers,
decoder... | 7,484 | 41.771429 | 112 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/nestnet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage, cols):
conv_name = 'decoder_stage{}-{}_conv'.format(stag... | 2,735 | 38.085714 | 106 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/model.py | from .builder import build_linknet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4', 'block2_conv2... | 4,257 | 46.311111 | 115 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import DecoderBlock
from ..utils import get_layer_number, to_tuple
def build_linknet(backbone,
classes,
skip_connection_layers,
decoder_filters=(None, ... | 1,663 | 32.28 | 86 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/linknet/blocks.py | import keras.backend as K
from keras.layers import Conv2DTranspose as Transpose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
def handle_block_names(stage):
conv_name = 'decoder_sta... | 4,938 | 28.753012 | 86 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/model.py | from .builder import build_unet
from ..utils import freeze_model
from ..backbones import get_backbone
DEFAULT_SKIP_CONNECTIONS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3', 'block2_conv2', 'block1_conv2'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4', 'block2_... | 3,928 | 42.655556 | 118 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/builder.py | from keras.layers import Conv2D
from keras.layers import Activation
from keras.models import Model
from .blocks import Transpose2D_block
from .blocks import Upsample2D_block
from ..utils import get_layer_number, to_tuple
def build_unet(backbone, classes, skip_connection_layers,
decoder_filters=(256,12... | 1,491 | 30.083333 | 86 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/unet/blocks.py | from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Concatenate
def handle_block_names(stage):
conv_name = 'decoder_stage{}_conv'.format(stage)
bn... | 2,552 | 36 | 106 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/preprocessing.py | """
Image pre-processing functions.
Images are assumed to be read in uint8 format (range 0-255).
"""
from keras.applications import vgg16
from keras.applications import vgg19
from keras.applications import densenet
from keras.applications import inception_v3
from keras.applications import inception_resnet_v2
identica... | 1,019 | 28.142857 | 62 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/inception_v3.py | # -*- coding: utf-8 -*-
"""Inception V3 model for Keras.
Note that the input image format for this model is different than for
the VGG16 and ResNet models (299x299 instead of 224x224),
and that the input preprocessing function is also different (same as Xception).
# Reference
- [Rethinking the Inception Architecture fo... | 15,272 | 36.898263 | 152 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/inception_resnet_v2.py | # -*- coding: utf-8 -*-
"""Inception-ResNet V2 model for Keras.
Model naming and structure follows TF-slim implementation (which has some additional
layers and different number of filters from the original arXiv paper):
https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_resnet_v2.py
Pre-train... | 16,002 | 42.134771 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/backbones.py |
from .classification_models.classification_models import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from .classification_models.classification_models import ResNeXt50, ResNeXt101
from .inception_resnet_v2 import InceptionResNetV2
from .inception_v3 import InceptionV3
from keras.applications import DenseNet12... | 930 | 28.09375 | 107 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/utils.py | from keras.utils import get_file
def find_weights(weights_collection, model_name, dataset, include_top):
w = list(filter(lambda x: x['model'] == model_name, weights_collection))
w = list(filter(lambda x: x['dataset'] == dataset, w))
w = list(filter(lambda x: x['include_top'] == include_top, w))
return... | 1,263 | 38.5 | 91 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/builder.py | import keras.backend as K
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import GlobalAveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import D... | 3,364 | 31.355769 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnext/blocks.py | from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import Lambda
from keras.layers import Concatenate
from keras.layers import ZeroPadding2D
from .params import get_conv_params
from .params import get_bn_params
... | 4,292 | 36.657895 | 107 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/builder.py | import keras.backend as K
from keras.layers import Input
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import GlobalAveragePooling2D
from keras.layers import ZeroPadding2D
from keras.layers import D... | 3,750 | 32.491071 | 92 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/classification_models/resnet/blocks.py | from keras.layers import Conv2D
from keras.layers import BatchNormalization
from keras.layers import Activation
from keras.layers import Add
from keras.layers import ZeroPadding2D
from .params import get_conv_params
from .params import get_bn_params
def handle_block_names(stage, block):
name_base = 'stage{}_unit... | 6,363 | 37.569697 | 100 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/backbones/classification_models/tests/test_imagenet.py | import numpy as np
from skimage.io import imread
from keras.applications.imagenet_utils import decode_predictions
import sys
sys.path.insert(0, '..')
from classification_models import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from classification_models import ResNeXt50, ResNeXt101
from classification_models ... | 4,964 | 29.838509 | 135 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/model.py | from .builder import build_fpn
from ..backbones import get_backbone
from ..utils import freeze_model
DEFAULT_FEATURE_PYRAMID_LAYERS = {
'vgg16': ('block5_conv3', 'block4_conv3', 'block3_conv3'),
'vgg19': ('block5_conv4', 'block4_conv4', 'block3_conv4'),
'resnet18': ('stage4_u... | 3,960 | 42.054348 | 107 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/builder.py | import numpy as np
from keras.layers import Conv2D
from keras.layers import Concatenate
from keras.layers import Activation
from keras.layers import SpatialDropout2D
from keras.models import Model
from .blocks import pyramid_block
from ..common import ResizeImage
from ..common import Conv2DBlock
from ..utils import ex... | 3,418 | 34.614583 | 95 | py |
Nested-UNet | Nested-UNet-master/segmentation_models/fpn/blocks.py | from keras.layers import Add
from ..common import Conv2DBlock
from ..common import ResizeImage
from ..utils import to_tuple
def pyramid_block(pyramid_filters=256, segmentation_filters=128, upsample_rate=2,
use_batchnorm=False, stage=0):
"""
Pyramid block according to:
http://present... | 1,750 | 33.333333 | 83 | py |
hackathon-ci-2020 | hackathon-ci-2020-master/solutions/unaccountable_penguis/pix2pix_cloudtop.py | import numpy as np
import tensorflow as tf
import os
import time
#_____________________
#Loading and preprocessing the data
#_____________________
my_path = '/home/harder/imagery'
CloudTop = np.load(my_path + '/X_train_CI20.npy')
TrueColor = np.load(my_path + '/Y_train_CI20.npy')
#sort out dark pictures (just quick ... | 8,978 | 39.813636 | 127 | py |
BayesianRelevance | BayesianRelevance-master/src/attack_networks.py | import argparse
import numpy as np
import os
import torch
from attacks.gradient_based import evaluate_attack
from attacks.run_attacks import *
from networks.advNN import *
from networks.baseNN import *
from networks.fullBNN import *
from utils import savedir
from utils.data import *
from utils.seeding import *
parser... | 6,374 | 45.195652 | 127 | py |
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