repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
value |
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WPAL-network | WPAL-network-master/lib/wpal_net/recog.py | import math
import cv2
import numpy as np
from utils.blob import img_list_to_blob
from config import cfg
class ResizedImageTooLargeException(Exception):
pass
class ResizedSideTooShortException(Exception):
pass
def _get_image_blob(img, neglect):
"""Converts an image into a network input.
Argument... | 5,787 | 32.651163 | 93 | py |
WPAL-network | WPAL-network-master/lib/wpal_net/config.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 6,652 | 28.30837 | 82 | py |
WPAL-network | WPAL-network-master/lib/wpal_net/train.py | #!/usr/bin/env python
# --------------------------------------------------------------------
# This file is part of
# Weakly-supervised Pedestrian Attribute Localization Network.
#
# Weakly-supervised Pedestrian Attribute Localization Network
# is free software: you can redistribute it and/or modify
# it under the ter... | 4,383 | 36.152542 | 93 | py |
DiffVAE | DiffVAE-master/neural_network_models/neural_network.py | from keras.layers import Input, Dense, BatchNormalization, Dropout
from keras.models import Model
from keras import optimizers
from keras.utils import np_utils
import numpy as np
from neural_network_models.base_NeuralNetwork import BaseNeuralNetwork
class NeuralNetwork(BaseNeuralNetwork):
def __init__(self,
... | 2,744 | 37.125 | 117 | py |
DiffVAE | DiffVAE-master/neural_network_models/base_NeuralNetwork.py | from keras.models import load_model
def NN_predictions(input_data, neural_network_filename):
model = load_model(neural_network_filename)
return model.predict(input_data)
class BaseNeuralNetwork():
def __init__(self, input_size, num_classes, batch_size, epochs, learning_rate, dropout_probability):
... | 963 | 33.428571 | 104 | py |
DiffVAE | DiffVAE-master/autoencoder_models/VAE_models.py | from keras.layers import Input, Dense, BatchNormalization, Lambda, Dropout
from keras.models import Model, Sequential
from keras import metrics, optimizers
import numpy as np
from keras import metrics
from keras import backend as K
import tensorflow as tf
from autoencoder_models.base.base_VAE import BaseVAE
class D... | 5,870 | 41.23741 | 117 | py |
DiffVAE | DiffVAE-master/autoencoder_models/GraphDiffVAE.py | from keras.layers import Input, Lambda, Average, Concatenate
from keras.models import Model, Sequential
from keras import metrics, optimizers
from keras import backend as K
from keras.regularizers import l2
import tensorflow as tf
import numpy as np
from autoencoder_models.base.base_VAE import BaseVAE
from autoencoder... | 4,042 | 38.252427 | 114 | py |
DiffVAE | DiffVAE-master/autoencoder_models/SimpleAutoEncoder.py | from keras.layers import Input, Dense, BatchNormalization
from keras.models import Model, Sequential
from keras import optimizers
import numpy as np
from autoencoder_models.base.base_AutoEncoder import BaseAutoEncoder
class SimpleAutoEncoder(BaseAutoEncoder):
def __init__(self, original_dim, latent_dim, hidden_l... | 3,634 | 39.842697 | 117 | py |
DiffVAE | DiffVAE-master/autoencoder_models/base/gcn_layers.py | from keras import activations, initializers, constraints
from keras import regularizers
from keras.layers import Dropout
from keras.engine import Layer
import keras.backend as K
####################################################################
#### Code adapted from: https://github.com/tkipf/keras-gcn #########
###... | 4,042 | 39.43 | 74 | py |
DiffVAE | DiffVAE-master/autoencoder_models/base/base_VAE.py | from keras import backend as K
import tensorflow as tf
from autoencoder_models.base.base_AutoEncoder import BaseAutoEncoder
class BaseVAE(BaseAutoEncoder):
def __init__(self, original_dim, latent_dim, batch_size, epochs, learning_rate):
BaseAutoEncoder.__init__(self, original_dim, latent_dim, batch_size,... | 610 | 37.1875 | 99 | py |
DiffVAE | DiffVAE-master/autoencoder_models/base/base_AutoEncoder.py | from keras.models import load_model
import numpy as np
def get_weights_latent_genes(decoder_filename):
decoder = load_model(decoder_filename)
decoder_weights = []
sequential_layer = decoder.layers[1]
indices = np.arange(0, len(sequential_layer.get_weights()), step=2)
for index in indices:
... | 1,547 | 31.25 | 84 | py |
DrQA | DrQA-main/drqa/pipeline/drqa.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Full DrQA pipeline."""
import torch
import regex
import heapq
import math
import time
import logging
from ... | 12,090 | 37.629393 | 80 | py |
DrQA | DrQA-main/drqa/reader/model.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""DrQA Document Reader model"""
import torch
import torch.optim as optim
import torch.nn.functional as F
impo... | 18,486 | 37.275362 | 80 | py |
DrQA | DrQA-main/drqa/reader/data.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Data processing/loading helpers."""
import numpy as np
import logging
import unicodedata
from torch.utils.... | 4,043 | 29.636364 | 80 | py |
DrQA | DrQA-main/drqa/reader/layers.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Definitions of model layers/NN modules"""
import torch
import torch.nn as nn
import torch.nn.functional as ... | 10,175 | 31.615385 | 80 | py |
DrQA | DrQA-main/drqa/reader/rnn_reader.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Implementation of the RNN based DrQA reader."""
import torch
import torch.nn as nn
from . import layers
#... | 5,168 | 37.007353 | 80 | py |
DrQA | DrQA-main/drqa/reader/vector.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Functions for putting examples into torch format."""
from collections import Counter
import torch
def vec... | 4,422 | 33.554688 | 74 | py |
DrQA | DrQA-main/scripts/pipeline/interactive.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Interactive interface to full DrQA pipeline."""
import torch
import argparse
import code
import prettytable... | 3,782 | 31.612069 | 80 | py |
DrQA | DrQA-main/scripts/pipeline/predict.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Run predictions using the full DrQA retriever-reader pipeline."""
import torch
import os
import time
import... | 5,081 | 37.5 | 80 | py |
DrQA | DrQA-main/scripts/reader/interactive.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""A script to run the DrQA reader model interactively."""
import torch
import code
import argparse
import log... | 2,622 | 29.858824 | 80 | py |
DrQA | DrQA-main/scripts/reader/predict.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""A script to make and save model predictions on an input dataset."""
import os
import time
import torch
impo... | 4,007 | 35.108108 | 80 | py |
DrQA | DrQA-main/scripts/reader/train.py | #!/usr/bin/env python3
# Copyright 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
"""Main DrQA reader training script."""
import argparse
import torch
import numpy as np
import json
import os
... | 21,645 | 38.572212 | 80 | py |
MSPRL | MSPRL-main/main.py | import json
import os
import random
import time
from datetime import timedelta
import torch
from config import get_config
from test import test
from train import train
from utils.init_util import init_dir, create_valid_data_loader
from valid import valid
from model import build_net
def main(args):
print('--------... | 2,234 | 31.391304 | 83 | py |
MSPRL | MSPRL-main/test.py | from __future__ import print_function
import json
import os
import time
from skimage.metrics import structural_similarity as compare_ssim
from skimage.metrics import peak_signal_noise_ratio as compare_psnr
import cv2
import torch
from PIL import Image
from torchvision.transforms import transforms
from torchvision.uti... | 2,771 | 30.146067 | 85 | py |
MSPRL | MSPRL-main/demo.py | from __future__ import print_function
import os
import torch
from PIL import Image
from torchvision.transforms import transforms
from torchvision.utils import save_image
from model import build_net
def test(demo_path, demo_output, net, device):
net.eval()
with torch.no_grad():
for filename in os.lis... | 1,453 | 31.311111 | 73 | py |
MSPRL | MSPRL-main/train.py | from __future__ import division
import datetime
import json
import time
import numpy as np
import torch
from torch.utils.tensorboard import SummaryWriter
from torchvision.transforms import transforms
from skimage.metrics import peak_signal_noise_ratio as compare_psnr
from utils.init_util import *
import torch.distrib... | 6,079 | 32.96648 | 110 | py |
MSPRL | MSPRL-main/valid.py | from __future__ import print_function
import json
import os
import time
from skimage.metrics import structural_similarity as compare_ssim
from skimage.metrics import peak_signal_noise_ratio as compare_psnr
import cv2
import torch
from PIL import Image
from torchvision.transforms import transforms
import numpy as np
f... | 1,771 | 27.126984 | 80 | py |
MSPRL | MSPRL-main/utils/init_util.py | import json
import math
import os
import random
import time
import numpy as np
import torch
from torch import nn
from torch.backends import cudnn
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DistributedSampler
from dataloader import HalftoneDataSet
from model import build_net
... | 5,046 | 38.740157 | 120 | py |
MSPRL | MSPRL-main/model/MSPRL.py | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
class MSPRL(nn.Module):
def __init__(self, image_channel, num_blocks=[8, 8, 8, 8]):
super(MSPRL, self).__init__()
dim = 48
# ------------------------------ Content Aggregation ---------... | 4,892 | 30.365385 | 113 | py |
MSPRL | MSPRL-main/dataloader/HalftoneDataSet.py | import os
import torch
from torch.utils import data
from torchvision import transforms
from PIL import Image
import glob
from dataloader.transforms import *
def get_data(halftone_path, target_path):
halftone_path = glob.glob(halftone_path)
target_path = glob.glob(target_path)
return halftone_path, target... | 1,884 | 29.403226 | 85 | py |
MSPRL | MSPRL-main/dataloader/transforms.py | import random
import torchvision.transforms as transforms
import torchvision.transforms.functional as F
class PairRandomCrop(transforms.RandomCrop):
def __call__(self, image, label):
i, j, h, w = self.get_params(image, self.size)
return F.crop(image, i, j, h, w), F.crop(label, i, j, h, w)
clas... | 1,531 | 24.966102 | 78 | py |
gameanalysis | gameanalysis-master/gameanalysis/learning.py | """Package for learning complete games from data
The API of this individual module is still unstable and may change as
improvements or refinements are made.
There are two general game types in this module: learned games and deviation
games. Learned games vary by the method, but generally expose methods for
computing ... | 34,156 | 33.50202 | 88 | py |
FGRL4KG | FGRL4KG-main/evaluate.py | #from nltk.stem.porter import *
import torch
#from utils import Progbar
#from pykp.metric.bleu import bleu
from pykp.masked_loss import masked_cross_entropy
from utils.statistics import LossStatistics, RewardStatistics
import time
from utils.time_log import time_since
#from nltk.stem.porter import *
import pykp
import ... | 37,462 | 54.011747 | 218 | py |
FGRL4KG | FGRL4KG-main/train_rl.py | import torch
import numpy as np
import pykp.io
import torch.nn as nn
from utils.statistics import RewardStatistics
from utils.time_log import time_since
import time
from sequence_generator import SequenceGenerator
from utils.report import export_train_and_valid_loss, export_train_and_valid_reward
import sys
import logg... | 19,187 | 52.747899 | 220 | py |
FGRL4KG | FGRL4KG-main/train_predicted_bert.py | import torch
import torch.nn as nn
import transformers as trf
import fastNLP as fnlp
import os
class BertPredictModel(nn.Module):
def __init__(self, from_pretrained: str, vocab=None):
super().__init__()
self.vocab = vocab
# self.bert = fnlp.embeddings.BertEmbedding(vocab, from_pretrai... | 13,618 | 41.827044 | 135 | py |
FGRL4KG | FGRL4KG-main/train_ml.py | import torch.nn as nn
from pykp.masked_loss import masked_cross_entropy
from utils.statistics import LossStatistics
from utils.time_log import time_since
from evaluate import evaluate_loss
import time
import math
import logging
import torch
import sys
import os
from utils.report import export_train_and_valid_loss
from ... | 17,623 | 51.924925 | 439 | py |
FGRL4KG | FGRL4KG-main/interactive_predict.py | import torch
from sequence_generator import SequenceGenerator
import config
import argparse
from preprocess import read_tokenized_src_file
from utils.data_loader import load_vocab
from pykp.io import build_interactive_predict_dataset, KeyphraseDataset
from torch.utils.data import DataLoader
import predict
import os
d... | 4,327 | 35.369748 | 180 | py |
FGRL4KG | FGRL4KG-main/beam.py | import torch
import penalties
import logging
class Beam:
def __init__(self, size, pad, bos, eos,
n_best=1, cuda=False,
global_scorer=None,
min_length=0,
stepwise_penalty=False,
block_ngram_repeat=0,
exclusion_toke... | 11,217 | 43.515873 | 160 | py |
FGRL4KG | FGRL4KG-main/sequence_generator.py | """
Adapted from
OpenNMT-py: https://github.com/OpenNMT/OpenNMT-py
and seq2seq-keyphrase-pytorch: https://github.com/memray/seq2seq-keyphrase-pytorch
"""
import sys
import torch
import pykp
import logging
from beam import Beam
from beam import GNMTGlobalScorer
EPS = 1e-8
class SequenceGenerator(object):
"""Class... | 25,883 | 53.492632 | 314 | py |
FGRL4KG | FGRL4KG-main/evaluate_prediction.py | import numpy as np
import argparse
import config
from utils.string_helper import *
from collections import defaultdict
import os
import logging
import pykp.io
import pickle
import torch
def check_valid_keyphrases(str_list):
num_pred_seq = len(str_list)
is_valid = np.zeros(num_pred_seq, dtype=bool)
for i, ... | 107,983 | 42.107385 | 255 | py |
FGRL4KG | FGRL4KG-main/predict.py | import torch
from sequence_generator import SequenceGenerator
import logging
import config
from pykp.io import KeyphraseDataset
from torch.utils.data import DataLoader
import time
from utils.time_log import time_since
from evaluate import evaluate_beam_search
import pykp.io
import sys
import argparse
from utils.data_lo... | 6,177 | 33.322222 | 116 | py |
FGRL4KG | FGRL4KG-main/train.py | import torch
import argparse
import config
import logging
import os
import json
from pykp.io import KeyphraseDataset
from pykp.model import Seq2SeqModel
from torch.optim import Adam
import pykp
import train_ml
import train_rl
from utils.time_log import time_since
from utils.data_loader import load_data_and_vocab
impo... | 7,724 | 33.181416 | 156 | py |
FGRL4KG | FGRL4KG-main/preprocess.py | import argparse
from collections import Counter
import torch
import pickle
import pykp.io
import config
def read_tokenized_src_file(path, remove_eos=True):
"""
read tokenized source text file and convert them to list of list of words
:param path:
:param remove_eos: concatenate the words in title and c... | 15,444 | 45.104478 | 169 | py |
FGRL4KG | FGRL4KG-main/penalties.py | from __future__ import division
import torch
class PenaltyBuilder(object):
"""
Returns the Length and Coverage Penalty function for Beam Search.
Args:
length_pen (str): option name of length pen
cov_pen (str): option name of cov pen
"""
def __init__(self, cov_pen, length_pen):
... | 2,327 | 27.048193 | 74 | py |
FGRL4KG | FGRL4KG-main/pykp/masked_loss.py | import torch
import math
import logging
EPS = 1e-8
def masked_cross_entropy(class_dist, target, trg_mask, trg_lens=None,
coverage_attn=False, coverage=None, attn_dist=None, lambda_coverage=0, coverage_loss=False,
delimiter_hidden_states=None, orthogonal_loss=False, la... | 9,961 | 43.275556 | 168 | py |
FGRL4KG | FGRL4KG-main/pykp/rnn_encoder.py | import logging
import torch
import torch.nn as nn
import math
import logging
from pykp.masked_softmax import MaskedSoftmax
class RNNEncoder(nn.Module):
"""
Base class for rnn encoder
"""
def forward(self, src, src_lens, src_mask=None, title=None, title_lens=None, title_mask=None):
raise NotImp... | 8,270 | 49.742331 | 142 | py |
FGRL4KG | FGRL4KG-main/pykp/rnn_decoder.py | import logging
import torch
import torch.nn as nn
from pykp.attention import Attention
import numpy as np
from pykp.masked_softmax import MaskedSoftmax
import math
import logging
from pykp.target_encoder import TargetEncoder
class RNNDecoder(nn.Module):
def __init__(self, vocab_size, embed_size, hidden_size, num_l... | 17,127 | 47.384181 | 185 | py |
FGRL4KG | FGRL4KG-main/pykp/dataloader.py | # -*- coding: utf-8 -*-
"""
Large chunk borrowed from PyTorch DataLoader
"""
import os
__author__ = "Rui Meng"
__email__ = "rui.meng@pitt.edu"
import torch
import torch.multiprocessing as multiprocessing
from torch.utils.data.sampler import SequentialSampler, RandomSampler, BatchSampler
import collections
import re
... | 14,457 | 36.071795 | 157 | py |
FGRL4KG | FGRL4KG-main/pykp/model.py | import logging
import torch
import torch.nn as nn
import numpy as np
import random
import pykp
from pykp.mask import GetMask, masked_softmax, TimeDistributedDense
from pykp.rnn_encoder import *
from pykp.rnn_decoder import RNNDecoder
from pykp.target_encoder import TargetEncoder
from pykp.attention import Attention
fro... | 26,114 | 51.864372 | 218 | py |
FGRL4KG | FGRL4KG-main/pykp/reward.py | import numpy as np
from utils.string_helper import *
from evaluate_prediction import *
import torch
def sample_list_to_str_2dlist(sample_list, oov_lists, idx2word, vocab_size, eos_idx, delimiter_word, unk_idx=None, replace_unk=False, src_str_list=None, separate_present_absent=False, present_absent_delimiter_word=None... | 20,173 | 56.475783 | 220 | py |
FGRL4KG | FGRL4KG-main/pykp/target_encoder.py | import torch
import torch.nn as nn
from pykp.attention import Attention
from pykp.masked_softmax import MaskedSoftmax
class TargetEncoder(nn.Module):
def __init__(self, embed_size, hidden_size, vocab_size, pad_idx):
super(TargetEncoder, self).__init__()
self.embed_size = embed_size
self.hid... | 1,019 | 31.903226 | 87 | py |
FGRL4KG | FGRL4KG-main/pykp/manager.py | import torch
import torch.nn as nn
class ManagerBasic(nn.Module):
def __init__(self, goal_vector_size):
super(ManagerBasic, self).__init__()
self.goal_vector_size = goal_vector_size
present_goal_vector = torch.zeros(self.goal_vector_size)
absent_goal_vector = torch.zeros(self.goal_... | 1,194 | 32.194444 | 87 | py |
FGRL4KG | FGRL4KG-main/pykp/masked_softmax.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class MaskedSoftmax(nn.Module):
def __init__(self, dim):
super(MaskedSoftmax, self).__init__()
self.dim = dim
def forward(self, logit, mask=None):
if mask is None:
dist = F.softmax(logit - torch.max(logit, d... | 1,105 | 35.866667 | 107 | py |
FGRL4KG | FGRL4KG-main/pykp/attention.py | import logging
import torch
import torch.nn as nn
import numpy as np
from pykp.masked_softmax import MaskedSoftmax
class Attention(nn.Module):
def __init__(self, decoder_size, memory_bank_size, coverage_attn, attn_mode):
super(Attention, self).__init__()
# attention
if attn_mode == "concat"... | 9,362 | 51.307263 | 166 | py |
FGRL4KG | FGRL4KG-main/pykp/io.py | # -*- coding: utf-8 -*-
"""
Python File Template
Built on the source code of seq2seq-keyphrase-pytorch: https://github.com/memray/seq2seq-keyphrase-pytorch
"""
import codecs
import inspect
import itertools
import json
import re
import traceback
from collections import Counter
from collections import defaultdict
import ... | 34,911 | 44.696335 | 223 | py |
FGRL4KG | FGRL4KG-main/pykp/mask.py | import torch
import numpy as np
import torch.nn.functional as F
class GetMask(torch.nn.Module):
'''
inputs: x: any size
outputs:mask: same size as input x
'''
def __init__(self, pad_idx=0):
super(GetMask, self).__init__()
self.pad_idx = pad_idx
def forward(self,... | 2,287 | 27.246914 | 119 | py |
FGRL4KG | FGRL4KG-main/utils/data_loader.py | import torch
import logging
from pykp.io import KeyphraseDataset
from torch.utils.data import DataLoader
def load_vocab(opt):
# load vocab
logging.info("Loading vocab from disk: %s" % (opt.vocab))
if not opt.custom_vocab_filename_suffix:
word2idx, idx2word, vocab = torch.load(opt.vocab + '/vocab.p... | 5,914 | 60.614583 | 248 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/Train_Custom_Features.py | # This script trains the BiLSTM-CNN-CRF architecture with customly defined features.
# You can specify which features the network should use by changing the featureNames-parameter.
# Per default, the networks uses tokens and casing as features.
# The input data contains a column with POS data, which we can use for trai... | 2,893 | 36.102564 | 200 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/Train_MultiTask.py | # This file contain an example how to perform multi-task learning using the
# BiLSTM-CNN-CRF implementation.
# In the datasets variable, we specify two datasets: POS-tagging (unidep_pos) and conll2000_chunking.
# The network will then train jointly on both datasets.
# The network can on more datasets by adding more ent... | 2,327 | 28.1 | 157 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/Train_NER_German.py | # This script trains the BiLSTM-CNN-CRF architecture for NER in German using
# the GermEval 2014 dataset (https://sites.google.com/site/germeval2014ner/).
# The code use the embeddings by Reimers et al. (https://www.ukp.tu-darmstadt.de/research/ukp-in-challenges/germeval-2014/)
from __future__ import print_function
imp... | 2,582 | 35.380282 | 162 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/Train_MultiTask_Different_Levels.py | # This file contain an example how to perform multi-task learning on different levels.
# In the datasets variable, we specify two datasets: POS-tagging (unidep_pos) and conll2000_chunking.
# We pass a special parameter to the network (customClassifier), that allows that task are supervised at different levels.
# For th... | 2,613 | 31.675 | 157 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/Train_Chunking.py | # This script trains the BiLSTM-CNN-CRF architecture for Chunking in English using
# the CoNLL 2000 dataset (https://www.clips.uantwerpen.be/conll2000/chunking/).
# The code use the embeddings by Komninos et al. (https://www.cs.york.ac.uk/nlp/extvec/)
from __future__ import print_function
import os
import logging
impor... | 2,695 | 35.931507 | 200 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/neuralnets/BiLSTM.py | """
A bidirectional LSTM with optional CRF and character-based presentation for NLP sequence tagging used for multi-task learning.
Author: Nils Reimers
License: Apache-2.0
"""
from __future__ import print_function
from util import BIOF1Validation
import keras
from keras.optimizers import *
from keras.models import M... | 28,359 | 43.944532 | 206 | py |
emnlp2017-bilstm-cnn-crf | emnlp2017-bilstm-cnn-crf-master/neuralnets/keraslayers/ChainCRF.py | # -*- coding: utf-8 -*-
'''
Author: Philipp Gross @ https://github.com/phipleg/keras/blob/crf/keras/layers/crf.py
'''
from __future__ import absolute_import
import keras
from keras import backend as K
from keras import regularizers
from keras import constraints
from keras import initializers
from keras.engine import... | 14,712 | 36.822622 | 133 | py |
PatchSearch | PatchSearch-main/main_moco_files_dataset_strong_aug.py | import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
from functools import partial
import logging
from pathlib import Path
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.distributed as dist
import to... | 20,311 | 39.462151 | 121 | py |
PatchSearch | PatchSearch-main/patch_search_iterative_search.py | import re
import argparse
import os
import copy
from collections import Counter
import time
import shutil
import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.utils.data import DataLoader, Dataset
import torchvision.transforms as transforms
import torchvision.datasets as datasets
import torch... | 25,191 | 38.4241 | 183 | py |
PatchSearch | PatchSearch-main/main_lincls.py | import argparse
import builtins
import math
import os
import random
import shutil
import time
import warnings
import re
from pathlib import Path
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.multiprocess... | 29,537 | 41.01707 | 227 | py |
PatchSearch | PatchSearch-main/patch_search_poison_classifier.py | import math
import argparse
import os
import random
import shutil
import time
import warnings
import glob
from functools import partial
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
from torch.utils.data import DataLoader, Da... | 20,262 | 36.316759 | 204 | py |
PatchSearch | PatchSearch-main/tools.py | import shutil
import logging
import os
import torch
from torch import nn
from torchvision import models
def get_logger(logpath, filepath, package_files=[], displaying=True, saving=True, debug=False):
logger = logging.getLogger()
if debug:
level = logging.DEBUG
else:
level = logging.INFO
... | 4,053 | 27.152778 | 95 | py |
PatchSearch | PatchSearch-main/vits.py | import math
import torch
import torch.nn as nn
from functools import partial, reduce
from operator import mul
from timm.models.vision_transformer import VisionTransformer, _cfg
from timm.models.layers.helpers import to_2tuple
from timm.models.layers import PatchEmbed
__all__ = [
'vit_small',
'vit_base',
... | 5,652 | 39.963768 | 120 | py |
PatchSearch | PatchSearch-main/eval_utils.py | import shutil
import logging
import os
import torch
from torch import nn
from torchvision import models
def get_logger(logpath, filepath, package_files=[], displaying=True, saving=True, debug=False):
logger = logging.getLogger()
if debug:
level = logging.DEBUG
else:
level = logging.INFO
... | 3,349 | 27.87931 | 95 | py |
PatchSearch | PatchSearch-main/moco/builder.py | import torch
import torch.nn as nn
def rand_bbox(size, lam):
W, H = size
cut_rat = (1. - lam).sqrt()
cut_w = (W * cut_rat).to(torch.long)
cut_h = (H * cut_rat).to(torch.long)
cx = torch.zeros_like(cut_w, dtype=cut_w.dtype).random_(0, W)
cy = torch.zeros_like(cut_h, dtype=cut_h.dtype).random_(... | 7,335 | 35.316832 | 114 | py |
PatchSearch | PatchSearch-main/moco/loader.py | from PIL import Image, ImageFilter, ImageOps
import math
import random
import torchvision.transforms.functional as tf
class TwoCropsTransform:
"""Take two random crops of one image"""
def __init__(self, base_transform1, base_transform2):
self.base_transform1 = base_transform1
self.base_transf... | 987 | 25.702703 | 82 | py |
PatchSearch | PatchSearch-main/moco/optimizer.py | import torch
class LARS(torch.optim.Optimizer):
"""
LARS optimizer, no rate scaling or weight decay for parameters <= 1D.
"""
def __init__(self, params, lr=0, weight_decay=0, momentum=0.9, trust_coefficient=0.001):
defaults = dict(lr=lr, weight_decay=weight_decay, momentum=momentum, trust_coef... | 1,443 | 37 | 113 | py |
RATFM | RATFM-master/RATFM/test.py | import os
import warnings
import argparse
import numpy as np
import torch
from .utils.metrics import *
from .utils.misc import *
from .utils.data_sr_road import get_dataloader_sr
from .models.RATFM import RATFM
from .modules.transformer import build_transformer
from .modules.position_encoding import build_position_en... | 5,013 | 42.6 | 109 | py |
RATFM | RATFM-master/RATFM/train.py | import os
import warnings
import numpy as np
import argparse
import warnings
from datetime import datetime
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from .utils.metrics import *
from .utils.misc import *
from .utils.data_sr_road import get_dataloader_sr... | 8,163 | 41.300518 | 125 | py |
RATFM | RATFM-master/RATFM/modules/road_1dconv.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class OneD_Block(nn.Module):
def __init__(self, in_channels, n_filters):
super(OneD_Block, self).__init__()
self.deconv1 = nn.Conv2d(
in_channels, in_channels // 2, (1, 9), padding=(0, 4)
)
self.deconv2 =... | 2,219 | 33.6875 | 68 | py |
RATFM | RATFM-master/RATFM/modules/position_encoding.py | import math
import torch
from torch import nn
from ..utils.misc import NestedTensor
class PositionEmbeddingSine(nn.Module):
"""
This is a more standard version of the position embedding, very similar to the one
used by the Attention is all you need paper, generalized to work on images.
"""
def __in... | 3,453 | 38.25 | 103 | py |
RATFM | RATFM-master/RATFM/modules/transformer.py | import copy
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn, Tensor
class Transformer(nn.Module):
def __init__(self, d_model=512, nhead=8, num_encoder_layers=6,
num_decoder_layers=6, dim_feedforward=2048, dropout=0.1,
activation="relu"... | 12,017 | 41.020979 | 109 | py |
RATFM | RATFM-master/RATFM/models/RATFM.py | import torch.nn as nn
import torch.nn.functional as F
import torch
from torchvision.models import vgg19
from torch.nn.parameter import Parameter
from torch.utils.data import Dataset
from ..utils.misc import get_nested_tensor, NestedTensor
from ..modules.road_1dconv import OneD_Block
class ResidualBlock(nn.Module):
... | 8,159 | 37.130841 | 139 | py |
RATFM | RATFM-master/RATFM/utils/misc.py | import os
import subprocess
import time
from collections import defaultdict, deque
import datetime
import pickle
from typing import Optional, List
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import torch
import torch.distributed as dist
from torch import Tensor
# needed due to empty tenso... | 9,387 | 35.529183 | 166 | py |
RATFM | RATFM-master/RATFM/utils/data_sr_road.py | import numpy as np
import os
import math
import torch
from torch.utils.data import DataLoader
from torch.utils import data
import random
import cv2
from PIL import Image
import PIL.ImageOps
# import seaborn as sns
uppath = os.path.abspath('.')
# print(uppath)
s_road_path = uppath + '/RATFM/road_map/xian1.png'
t_road_p... | 4,653 | 40.185841 | 182 | py |
cellpose | cellpose-master/setup.py | import setuptools
from setuptools import setup
install_deps = ['numpy>=1.20.0', 'scipy', 'natsort',
'tifffile', 'tqdm', 'numba',
'torch>=1.6',
'opencv-python-headless',
'fastremap'
]
gui_deps = [
'pyqtgraph>=0.11.0rc0',
... | 1,850 | 22.43038 | 52 | py |
cellpose | cellpose-master/paper/cp_unets.py | import sys, os, time, string, shutil
from natsort import natsorted
from glob import glob
from pathlib import Path
import numpy as np
import matplotlib.pyplot as plt
import mxnet as mx
import matplotlib.pyplot as plt
from matplotlib import rc
import cv2
from scipy import stats
from cellpose import models, datasets, util... | 19,670 | 46.745146 | 270 | py |
cellpose | cellpose-master/cellpose/resnet_torch.py |
import os, sys, time, shutil, tempfile, datetime, pathlib, subprocess
import numpy as np
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import datetime
from . import transforms, io, dynamics, utils
sz = 3
def convbatchrelu(in_channels, out_channels, sz):
return nn.Se... | 8,856 | 37.012876 | 131 | py |
cellpose | cellpose-master/cellpose/__main__.py | import sys, os, argparse, glob, pathlib, time
import subprocess
import numpy as np
from natsort import natsorted
from tqdm import tqdm
from cellpose import utils, models, io, core
try:
from cellpose.gui import gui
GUI_ENABLED = True
except ImportError as err:
GUI_ERROR = err
GUI_ENABLED = False
... | 19,925 | 56.423631 | 176 | py |
cellpose | cellpose-master/cellpose/core.py | import os, sys, time, shutil, tempfile, datetime, pathlib, subprocess
import logging
import numpy as np
from tqdm import trange, tqdm
from urllib.parse import urlparse
import tempfile
import cv2
from scipy.stats import mode
import fastremap
from . import transforms, dynamics, utils, plot, metrics
import torch
# fr... | 42,019 | 44.085837 | 166 | py |
cellpose | cellpose-master/cellpose/test_mkl.py | import os, sys
os.environ["MKLDNN_VERBOSE"]="1"
import numpy as np
import time
try:
import mxnet as mx
x = mx.sym.Variable('x')
MXNET_ENABLED = True
except:
MXNET_ENABLED = False
def test_mkl():
if MXNET_ENABLED:
num_filter = 32
kernel = (3, 3)
pad = (1, 1)
shape =... | 879 | 23.444444 | 109 | py |
cellpose | cellpose-master/cellpose/utils.py | import os, warnings, time, tempfile, datetime, pathlib, shutil, subprocess
from tqdm import tqdm
from urllib.request import urlopen
from urllib.parse import urlparse
import cv2
from scipy.ndimage import find_objects, gaussian_filter, generate_binary_structure, label, maximum_filter1d, binary_fill_holes
from scipy.spati... | 16,017 | 33.670996 | 126 | py |
cellpose | cellpose-master/cellpose/models.py | import os, sys, time, shutil, tempfile, datetime, pathlib, subprocess
from pathlib import Path
import numpy as np
from tqdm import trange, tqdm
from urllib.parse import urlparse
import torch
import logging
models_logger = logging.getLogger(__name__)
from . import transforms, dynamics, utils, plot
from .core import Un... | 51,328 | 48.11866 | 152 | py |
cellpose | cellpose-master/cellpose/dynamics.py | import time, os
from scipy.ndimage.filters import maximum_filter1d
import torch
import scipy.ndimage
import numpy as np
import tifffile
from tqdm import trange
from numba import njit, float32, int32, vectorize
import cv2
import fastremap
import logging
dynamics_logger = logging.getLogger(__name__)
from . import utils... | 26,300 | 33.561104 | 134 | py |
cellpose | cellpose-master/cellpose/gui/menus.py | from PyQt5.QtWidgets import QAction
from . import io
from .. import models
from ..io import save_server
def mainmenu(parent):
main_menu = parent.menuBar()
file_menu = main_menu.addMenu("&File")
# load processed data
loadImg = QAction("&Load image (*.tif, *.png, *.jpg)", parent)
loadImg.setShortcut(... | 4,885 | 39.04918 | 97 | py |
cellpose | cellpose-master/cellpose/gui/gui.py | import sys, os, pathlib, warnings, datetime, tempfile, glob, time
import gc
from natsort import natsorted
from tqdm import tqdm, trange
from PyQt5 import QtGui, QtCore, Qt, QtWidgets
from PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QScrollBar, QSlider, QComboBox, QGridLayout, QPushButton, QFrame, QCheck... | 77,102 | 42.833428 | 923 | py |
cellpose | cellpose-master/cellpose/gui/guiparts.py | from PyQt5 import QtGui, QtCore, QtWidgets
from PyQt5.QtGui import QPainter, QPixmap
from PyQt5.QtWidgets import QApplication, QRadioButton, QWidget, QDialog, QButtonGroup, QSlider, QStyle, QStyleOptionSlider, QGridLayout, QPushButton, QLabel, QLineEdit, QDialogButtonBox, QComboBox, QCheckBox
import pyqtgraph as pg
fro... | 35,576 | 40.272622 | 538 | py |
cellpose | cellpose-master/docs/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... | 3,019 | 31.473118 | 79 | py |
UAL-CVPR2020 | UAL-CVPR2020-master/SSIM.py | import torch
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from math import exp
def gaussian(window_size, sigma):
gauss = torch.Tensor([exp(-(x - window_size//2)**2/float(2*sigma**2)) for x in range(window_size)])
return gauss/gauss.sum()
def create_window(window_size,... | 2,639 | 34.675676 | 104 | py |
UAL-CVPR2020 | UAL-CVPR2020-master/Adaptor.py | import torch
import numpy as np
import scipy.io as sio
from torch.autograd import Variable
import os
from function import *
import torch.nn as nn
from torch.nn.functional import upsample
from Spa_downs import *
import time
from SSIM import *
import matplotlib.pyplot as plt
names_CAVE_test = [
'real_and_fake_apple... | 6,660 | 38.64881 | 191 | py |
UAL-CVPR2020 | UAL-CVPR2020-master/ThreeBranch_3.py | import torch
import torch.nn as nn
#changed the G generated by itself and add the mask to guide the Infor part
class ThreeBranch_Net(nn.Module):
def __init__(self, Dim=[3,34,31], Depth=3, KS_1=3, KS_2=3, KS_3=3):
super(ThreeBranch_Net, self).__init__()
block1_1 = []
block1_2 = []
... | 5,961 | 37.714286 | 110 | py |
UAL-CVPR2020 | UAL-CVPR2020-master/Spa_downs.py | import numpy as np
import torch
import torch.nn as nn
class Spa_Downs(nn.Module):
'''
http://www.realitypixels.com/turk/computergraphics/ResamplingFilters.pdf
'''
def __init__(self, n_planes, factor, kernel_type, phase=0, kernel_width=None, support=None, sigma=None, preserve_size=False):
#... | 5,468 | 30.796512 | 129 | py |
UAL-CVPR2020 | UAL-CVPR2020-master/function.py | import torch
import torch.nn as nn
import numpy as np
class ReshapeTo2D(nn.Module):
def __init__(self):
super(ReshapeTo2D, self).__init__()
def forward(self,x):
return torch.reshape(x, (x.shape[0], x.shape[1], x.shape[2]*x.shape[3]))
class ReshapeTo3D(nn.Module):
def __init__(self):
... | 3,331 | 27.478632 | 110 | py |
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