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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DeepSatModels | DeepSatModels-master/models/CropTypeMapping/modelling/cgru.py | import torch
import torch.nn as nn
from models.CropTypeMapping.modelling.recurrent_norm import RecurrentNorm2d
from models.CropTypeMapping.modelling.cgru_cell import ConvGRUCell
from models.CropTypeMapping.modelling.util import initialize_weights
class CGRU(nn.Module):
def __init__(self, input_size, hidden_dims,... | 3,928 | 40.357895 | 132 | py |
DeepSatModels | DeepSatModels-master/models/BiConvRNN/conv_gru.py | # code from https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/convlstm/convlstm.py
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
class ConvGRUCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias, device):
... | 7,380 | 35.359606 | 108 | py |
DeepSatModels | DeepSatModels-master/models/BiConvRNN/biconv_rnn.py | # code from https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/sequenceencoder.py
import torch
import torch.nn
from models.BiConvRNN.conv_lstm import ConvLSTMCell, ConvLSTM
# from models.MTLCC.ConvLSTMx import ConvLSTM as ConvLSTMx
# from models.DoubleAttentionNet.ConvLSTM import ConvLSTM as ConvLSTMd
from... | 18,140 | 47.24734 | 125 | py |
DeepSatModels | DeepSatModels-master/models/BiConvRNN/conv_lstm.py | # code from https://github.com/TUM-LMF/MTLCC-pytorch/blob/master/src/models/convlstm/convlstm.py
import torch.nn as nn
from torch.autograd import Variable
import torch
class ConvLSTMCell(nn.Module):
def __init__(self, input_size, input_dim, hidden_dim, kernel_size, bias, device):
"""
Initialize C... | 7,115 | 33.543689 | 108 | py |
DeepSatModels | DeepSatModels-master/models/UNet3D/unet3d.py | import torch
import torch.nn as nn
from utils.config_files_utils import get_params_values
def conv_block(in_dim, middle_dim, out_dim):
model = nn.Sequential(
nn.Conv3d(in_dim, middle_dim, kernel_size=3, stride=1, padding=1),
nn.BatchNorm3d(middle_dim),
nn.LeakyReLU(inplace=True),
n... | 4,602 | 35.531746 | 108 | py |
DeepSatModels | DeepSatModels-master/models/UNet3D/unet3df.py | import torch
import torch.nn as nn
from utils.config_files_utils import get_params_values
from models.LocalSelfAttention.cscl import ContextSelfSimilarity, AttentionAggregate
def conv_block(in_dim, middle_dim, out_dim):
model = nn.Sequential(
nn.Conv3d(in_dim, middle_dim, kernel_size=3, stride=1, padding=... | 11,970 | 42.216606 | 125 | py |
DeepSatModels | DeepSatModels-master/train_and_eval/segmentation_cscl_training.py | import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.tensorboard import SummaryWriter
from utils.torch_utils import load_from_checkpoint
import os
from models import get_model
from utils.config_files_utils import read_yaml, copy_yaml, get_params_v... | 9,432 | 44.350962 | 131 | py |
DeepSatModels | DeepSatModels-master/train_and_eval/segmentation_training.py | import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.tensorboard import SummaryWriter
import numpy as np
from models import get_model
from utils.config_files_utils import read_yaml, copy_yaml, get_params_values
from utils.torch_utils imp... | 11,756 | 47.184426 | 134 | py |
DeepSatModels | DeepSatModels-master/metrics/loss_functions.py | import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn as nn
from utils.config_files_utils import get_params_values
from copy import deepcopy
def get_loss(config, device, reduction='mean'):
model_config = config['MODEL']
loss_config = config['SOLVER... | 14,491 | 39.367688 | 120 | py |
DeepSatModels | DeepSatModels-master/metrics/torch_metrics.py | import torch
from metrics.numpy_metrics import get_classification_metrics
import numpy as np
def get_binary_metrics(logits, labels, return_all=False, thresh=0.5, name=""):
logits = logits.reshape(-1, 1)#.cpu()
#labels = labels.cpu()
probs = torch.nn.functional.sigmoid(logits)
pred = (probs > thresh).t... | 5,798 | 48.144068 | 115 | py |
DeepSatModels | DeepSatModels-master/utils/tensor_utils.py | import torch
import torch.nn.functional as F
def resize_match2d(target_size, source, dim=[2, 3], mode='bilinear'):
"""
source must have shape [..., H, W]
:param mode: 'nearest'
"""
target_h, target_w = target_size
source_h, source_w = source.shape[dim[0]], source.shape[dim[1]]
if (source_h... | 671 | 34.368421 | 94 | py |
DeepSatModels | DeepSatModels-master/utils/torch_utils.py | import torch
import os
import glob
import sys
def load_from_checkpoint(net, checkpoint, partial_restore=False, device=None):
assert checkpoint is not None, "no path provided for checkpoint, value is None"
if os.path.isdir(checkpoint):
checkpoint = max(glob.iglob(checkpoint + '/*.pth'), key=os.pat... | 2,338 | 34.984615 | 131 | py |
DeepSatModels | DeepSatModels-master/data/__init__.py | import torch
from data.MTLCC.dataloader import get_dataloader as get_mtlcc_dataloader
from data.MTLCC.data_transforms import MTLCC_transform
from data.France.dataloader import get_dataloader as get_france_dataloader
from data.France.data_transforms import France_segmentation_transform
from utils.tensor_utils import res... | 5,242 | 44.198276 | 120 | py |
DeepSatModels | DeepSatModels-master/data/France/dataloader.py | # MTLCC_prev dataset
# eval: [[2, 2], [3, 197], [4, 21], [5, 23], [6, 3], [7, 11], [8, 30],
# [9, 74], [10, 19], [11, 4704], [12, 18], [13, 17], [14, 30], [15, 11510]]
from __future__ import print_function, division
import os
import torch
import pandas as pd
# import matplotlib.pyplot as plt
from torch.utils... | 29,610 | 39.014865 | 195 | py |
DeepSatModels | DeepSatModels-master/data/France/data_transforms.py | from __future__ import print_function, division
# from skimage import io, transform
import numpy as np
import torch
# import matplotlib.pyplot as plt
# from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F
from torchvision import transforms, utils
from copy import deepcopy
import random
from ... | 45,861 | 41.941948 | 184 | py |
DeepSatModels | DeepSatModels-master/data/MTLCC/dataloader.py | # MTLCC_prev dataset
# eval: [[2, 2], [3, 197], [4, 21], [5, 23], [6, 3], [7, 11], [8, 30],
# [9, 74], [10, 19], [11, 4704], [12, 18], [13, 17], [14, 30], [15, 11510]]
from __future__ import print_function, division
import os
import torch
import pandas as pd
# import matplotlib.pyplot as plt
from torch.utils... | 6,354 | 34.502793 | 155 | py |
DeepSatModels | DeepSatModels-master/data/MTLCC/data_transforms.py | from __future__ import print_function, division
from skimage import io, transform
import numpy as np
import torch
import matplotlib.pyplot as plt
from torch.utils.data import Dataset, DataLoader
import torch.nn.functional as F
from torchvision import transforms, utils
from copy import deepcopy
import random
from utils.... | 23,825 | 39.728205 | 171 | py |
gpt-2-output-dataset | gpt-2-output-dataset-master/detector/download.py | import os
import requests
import torch.distributed as dist
from tqdm import tqdm
from .utils import distributed
ALL_DATASETS = [
'webtext',
'small-117M', 'small-117M-k40', 'small-117M-nucleus',
'medium-345M', 'medium-345M-k40', 'medium-345M-nucleus',
'large-762M', 'large-762M-k40', 'large-762M-nu... | 1,600 | 31.02 | 115 | py |
gpt-2-output-dataset | gpt-2-output-dataset-master/detector/server.py | import os
import sys
from http.server import HTTPServer, SimpleHTTPRequestHandler
from multiprocessing import Process
import subprocess
from transformers import RobertaForSequenceClassification, RobertaTokenizer
import json
import fire
import torch
from urllib.parse import urlparse, unquote
model: RobertaForSequenceC... | 3,964 | 31.768595 | 120 | py |
gpt-2-output-dataset | gpt-2-output-dataset-master/detector/utils.py | import sys
from functools import reduce
from torch import nn
import torch.distributed as dist
def summary(model: nn.Module, file=sys.stdout):
def repr(model):
# We treat the extra repr like the sub-module, one item per line
extra_lines = []
extra_repr = model.extra_repr()
# empty ... | 1,975 | 30.365079 | 75 | py |
gpt-2-output-dataset | gpt-2-output-dataset-master/detector/dataset.py | import json
import numpy as np
from typing import List
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from transformers import PreTrainedTokenizer
from .download import download
def load_texts(data_file, expected_size=None):
texts = []
for line in tqdm(open(data_file), total=expect... | 3,549 | 39.804598 | 120 | py |
gpt-2-output-dataset | gpt-2-output-dataset-master/detector/train.py | """Training code for the detector model"""
import argparse
import os
import subprocess
import sys
from itertools import count
from multiprocessing import Process
import torch
import torch.distributed as dist
from torch import nn
from torch.nn.parallel import DistributedDataParallel
from torch.optim import Adam
from t... | 12,124 | 38.624183 | 116 | py |
MARN | MARN-main/main.py |
from options import args_parser
import random
import numpy as np
import torch
import csv
import sys
from dataloader import load_lookups, prepare_instance, prepare_instance_bert, MyDataset, my_collate, my_collate_bert, load_lookups_MTL, prepare_instance_MTL, prepare_instance_bert_MTL, MyDataset, my_collate_MTL, my_co... | 9,811 | 43.6 | 233 | py |
MARN | MARN-main/dataloader.py | import gensim.models
import numpy as np
from tqdm import tqdm
import csv
from scipy.sparse import csr_matrix
import gensim.models.word2vec as w2v
import gensim.models.fasttext as fasttext
import codecs
import struct
import re
import operator
from collections import defaultdict
from transformers import AutoTokenizer, Au... | 31,033 | 35.467685 | 157 | py |
MARN | MARN-main/utils.py | import gensim.models
import numpy as np
from tqdm import tqdm
import csv
from scipy.sparse import csr_matrix
import gensim.models.word2vec as w2v
import gensim.models.fasttext as fasttext
import codecs
import re
def reformat(code, is_diag):
"""
Put a period in the right place because the MIMIC-3 data file... | 15,856 | 36.935407 | 158 | py |
MARN | MARN-main/lr_layerwise.py | from torch.optim.lr_scheduler import LambdaLR
# Bert layerwise learning rates for Bio_ClinicalBERT, NOT USED
def Bio_ClinicalBERT_layer_lr(layer_name: str, args):
name_list = layer_name.split(sep='.')
if name_list[0] == 'embeddings':
return args.lr * args.lr_layer_decay ** 14
elif name_list[0] == ... | 2,372 | 31.958333 | 121 | py |
MARN | MARN-main/models.py |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.init import xavier_uniform_
from gensim.models import KeyedVectors
from math import floor
import numpy as np
from transformers import AutoModel
from torch.autograd import Variable
import os
from elmo.elmo import Elmo
import json
from emb... | 25,133 | 38.456829 | 134 | py |
MARN | MARN-main/train_test.py | import torch
import numpy as np
from utils import all_metrics, print_metrics
max_grad_norm = 1.0
def train(args, model, optimizer, epoch, gpu, data_loader, lr_scheduler = None):
print("EPOCH %d" % epoch)
device = torch.device('cuda:{}'.format(args.gpu)) if args.gpu != -1 else torch.device('cpu')
losses =... | 9,750 | 48 | 152 | py |
MARN | MARN-main/elmo/elmo_lstm.py | from typing import Optional, Tuple, List
import warnings
import torch
from torch.nn.utils.rnn import PackedSequence, pad_packed_sequence
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
import h5py
import numpy
from .encoder_base import _EncoderBase
from .lstm_cell_wit... | 15,873 | 52.810169 | 110 | py |
MARN | MARN-main/elmo/lstm_cell_with_projection.py | from typing import Optional, Tuple, List
import torch
from allennlp.nn.util import get_dropout_mask
from allennlp.nn.initializers import block_orthogonal
class LstmCellWithProjection(torch.nn.Module):
"""
An LSTM with Recurrent Dropout and a projected and clipped hidden state and
memory. Note: this impl... | 12,282 | 53.349558 | 103 | py |
MARN | MARN-main/elmo/encoder_base.py | from typing import Tuple, Union, Optional, Callable
import torch
from torch.nn.utils.rnn import pack_padded_sequence, PackedSequence
from allennlp.nn.util import get_lengths_from_binary_sequence_mask, sort_batch_by_length
# We have two types here for the state, because storing the state in something
# which is Iterab... | 17,470 | 54.996795 | 109 | py |
MARN | MARN-main/elmo/elmo.py | import json
import logging
from typing import Union, List, Dict, Any
import warnings
import torch
from torch.nn.modules import Dropout
import numpy
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=FutureWarning)
import h5py
from overrides import overrides
from allennlp.common import... | 28,037 | 44.07717 | 113 | py |
MARN | MARN-main/elmo/scalar_mix.py | from typing import List
import torch
from torch.nn import ParameterList, Parameter
from allennlp.common.checks import ConfigurationError
class ScalarMix(torch.nn.Module):
"""
Computes a parameterised scalar mixture of N tensors, ``mixture = gamma * sum(s_k * tensor_k)``
where ``s = softmax(w)``, with ``w... | 4,010 | 46.188235 | 104 | py |
DualHGCN | DualHGCN-main/DualHGCN.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import copy
import math
import numpy as np
import torch
import torch.nn as nn
from torch import Tensor,optim
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from time import time
device = torch.device("cuda:1" if torch.cuda.is_... | 8,030 | 32.60251 | 148 | py |
DualHGCN | DualHGCN-main/data_helper.py | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import math
import random
import numpy as np
import networkx as nx
from tqdm import tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import multiprocessing
from gensim.models import Word2Vec
from concurrent.futures import as_completed, Proc... | 2,310 | 32.492754 | 90 | py |
NGSP | NGSP-main/code/train_partnet.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils, eval_utils
from models import PointNetPPSeg
from utils import device
import ast
from copy import deepcopy, copy
import random
from tqdm import t... | 12,938 | 25.78882 | 118 | py |
NGSP | NGSP-main/code/train_lik_models.py | from lik_mods.sem_label_lik import SemLik
from lik_mods.reg_group_lik import RegLik
import utils
import os
from grammar import Grammar
import data_utils
import numpy as np
from random import choices, sample
from tqdm import tqdm
import eval_utils
import torch
from utils import device
def sampleShape(meshes, seg_label... | 4,194 | 27.154362 | 83 | py |
NGSP | NGSP-main/code/train_bae_net.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils, eval_utils
from utils import device
import ast
from copy import deepcopy, copy
import random
from tqdm import tqdm
import pickle
from bae_net.b... | 15,756 | 26.167241 | 100 | py |
NGSP | NGSP-main/code/infer_lhss.py | from grammar import Grammar
import sys
sys.path.append('lhss')
sys.path.append('lhss/pygco')
from mrf import eval_mrf
import torch
import data_utils
from tqdm import tqdm
from models import LHSSNet
import train_utils, eval_utils
import utils
from utils import device
import numpy as np
from eval_utils import calc_mIoU
f... | 9,053 | 27.742857 | 127 | py |
NGSP | NGSP-main/code/train_lhss.py | from grammar import Grammar
import sys
sys.path.append('lhss')
sys.path.append('lhss/pygco')
from mrf import eval_mrf
import torch
import data_utils
from tqdm import tqdm
from models import LHSSNet
import train_utils, eval_utils
import utils
from utils import device
import numpy as np
from eval_utils import calc_mIoU
f... | 13,743 | 27.87395 | 127 | py |
NGSP | NGSP-main/code/make_arti_data.py | import utils
import os
from grammar import Grammar
import data_utils
import numpy as np
from random import choices, sample
from tqdm import tqdm
import random
import torch
MAX_ITERS = 10000
def make_arti_props(ind, labels, grammar, args):
os.system(f'mkdir {args.search_data_path}/{ind} > /dev/null 2>&1')
pro... | 3,044 | 25.478261 | 82 | py |
NGSP | NGSP-main/code/sem_label_data_utils.py | import numpy as np
import torch
import os
import utils
from tqdm import tqdm
from utils import device
import json
from random import sample, randint
from copy import deepcopy
def check_valid_neg(
area_data,
pos_sig,
neg_sig,
args,
name
):
area_sim = calc_area_s... | 4,160 | 23.767857 | 83 | py |
NGSP | NGSP-main/code/utils.py | import torch
import numpy as np
import random
import os
import matplotlib.pyplot as plt
import argparse
import sys
import ast
NUM_SAMP_PTS = 1000000
NUM_PROP_INPUT_PTS = 10000
NUM_SEARCH_INPUT_PTS = 4096
NUM_EVAL_POINTS = 10000
device = torch.device('cuda')
DEF_ARGS = [
# Set per run
('-en', '--exp_nam... | 14,865 | 30.764957 | 90 | py |
NGSP | NGSP-main/code/data_utils.py | import torch
import numpy as np
import json
import utils
import os
from make_areas import get_area
from tqdm import tqdm
colors = [
(31, 119, 180),
(174, 199, 232),
(255,127,14),
(255, 187, 120),
(44,160,44),
(152,223,138),
(214,39,40),
(255,152,150),
(148, 103, 189),
(192,176,... | 6,685 | 21.436242 | 82 | py |
NGSP | NGSP-main/code/train_lel_net.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils, eval_utils
from utils import device
import ast
from copy import deepcopy, copy
import random
from tqdm import tqdm
import pickle
from models imp... | 15,366 | 26.441071 | 118 | py |
NGSP | NGSP-main/code/make_splits.py | import ast
import numpy as np
import os
import sys
import numpy as np
from copy import deepcopy
from tqdm import tqdm
from grammar import Grammar
import utils, torch
from random import shuffle
import json
from make_dataset import DATA_DIR
PARSED_DIR = '../data'
OUT_DIR = 'data_splits'
DO_SMART_SPLIT = True
# Each se... | 5,745 | 25 | 77 | py |
NGSP | NGSP-main/code/make_areas.py | import ast
import numpy as np
import os
import sys
import numpy as np
from copy import deepcopy
from tqdm import tqdm
from grammar import Grammar
import json
import torch
import utils
DATA_DIR = "TODO_PATH_TO_PARTNET"
def get_area(_v, _f):
vs = torch.tensor(_v).float().unsqueeze(0)
faces = torch.tensor(_f).lo... | 1,319 | 21 | 58 | py |
NGSP | NGSP-main/code/ngsp_eval.py | from lik_mods.sem_label_lik import SemLik
from lik_mods.reg_group_lik import RegLik
from models import PointNetPPCls
import train_guide_net as tfs
from math import exp, log
import random
import utils
import os
from grammar import Grammar
import data_utils
import numpy as np
from random import choices, sample
from tqdm... | 7,656 | 27.047619 | 120 | py |
NGSP | NGSP-main/code/train_utils.py | import json
import utils
import torch
import time
import matplotlib.pyplot as plt
def model_train(loader, net, opt, batch_train_fn):
if isinstance(net, tuple):
if opt is None:
net[0].eval()
net[1].eval()
else:
net[0].train()
net[1].train()
... | 3,649 | 23.013158 | 79 | py |
NGSP | NGSP-main/code/eval_sem_label_models.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils, utils, eval_utils
from torch.utils.data import DataLoader
from models import PointNetPPCls
from utils import device
import ast
import random
from copy import deepcopy
from tqdm import tqdm
import train_guide_net as t... | 3,761 | 31.713043 | 106 | py |
NGSP | NGSP-main/code/focal_loss.py | from typing import Optional
import torch
import torch.nn as nn
import torch.nn.functional as F
# based on:
# https://github.com/zhezh/focalloss/blob/master/focalloss.py
class FocalLoss(nn.Module):
r"""Criterion that computes Focal loss.
According to [1], the Focal loss is computed as follows:
.. math:... | 2,886 | 32.569767 | 80 | py |
NGSP | NGSP-main/code/make_bae_data.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils, eval_utils
from utils import device
import ast
from copy import deepcopy, copy
import random
from tqdm import tqdm
import pickle
import json
im... | 2,244 | 22.385417 | 76 | py |
NGSP | NGSP-main/code/make_lhss_data.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils, eval_utils
from utils import device
import ast
from copy import deepcopy, copy
import random
from tqdm import tqdm
import pickle
import json
imp... | 1,350 | 22.701754 | 85 | py |
NGSP | NGSP-main/code/train_sem_label_models.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import sem_label_data_utils as search_data_utils
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils
from models import PointNetPPCls
from utils import device
import ast
import random
from copy imp... | 26,361 | 28.03304 | 117 | py |
NGSP | NGSP-main/code/models.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import pointnet2.pointnet2_utils as pointnet2_utils
import numpy as np
import sys
class _BNBase(nn.Sequential):
def __init__(self, in_size, batch_norm=None, name=""):
super(_BNBase, self).__init__()
self.add_module(name + "bn", batc... | 24,942 | 28.979567 | 155 | py |
NGSP | NGSP-main/code/train_guide_net.py | from grammar import Grammar
import sys, os, torch
import numpy as np
import argparse
import data_utils
import utils
from torch.utils.data import DataLoader
import train_utils, eval_utils
from models import PointNetPPCls
from utils import device
import ast
from copy import deepcopy, copy
import random
from tqdm import t... | 13,270 | 26.880252 | 118 | py |
NGSP | NGSP-main/code/eval_utils.py | import torch
import utils
import time
import data_utils
from copy import deepcopy, copy
import numpy as np
import heapq
from tqdm import tqdm
import os
def search_beam(probs, num, keep_ll=False):
with torch.no_grad():
return _search_beam(probs, num, keep_ll)
def _search_beam(probs, num, keep_ll):
LL =... | 12,791 | 23.790698 | 117 | py |
NGSP | NGSP-main/code/pc_enc/pc_ae.py | import sys
sys.path.append('../')
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from models import PointNetPPEnc
import data_utils
import utils
from tqdm import tqdm
import json
from utils import device
import numpy as np
import faiss
import time
import matplotlib.pyplot as plt
from mul... | 10,470 | 29.002865 | 134 | py |
NGSP | NGSP-main/code/pc_enc/pairpc_ae.py | import sys
sys.path.append('../')
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from models import PointNetPPEnc
import data_utils
import utils
from tqdm import tqdm
import json
from utils import device
import numpy as np
import faiss
import time
import matplotlib.pyplot as plt
import ra... | 12,118 | 29.758883 | 134 | py |
NGSP | NGSP-main/code/lel_net/ss_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# Code from
# https://github.com/matheusgadelha/PointCloudLearningACD/blob/ba3348bf3b2aedcf6ee31a1053fb53302cab5a2c/models/pointnet_part_seg.py#L128
class get_selfsup_loss(nn.Module):
def __init__(self, margin=0.5):
super(get_selfsup_loss... | 2,244 | 25.72619 | 136 | py |
NGSP | NGSP-main/code/nets/gated_gcn_net.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import numpy as np
"""
ResGatedGCN: Residual Gated Graph ConvNets
An Experimental Study of Neural Networks for Variable Graphs (Xavier Bresson and Thomas Laurent, ICLR 2018)
https://arxiv.org/pdf/1711.07553v2.pdf
"""
from layers... | 1,866 | 29.112903 | 111 | py |
NGSP | NGSP-main/code/lhss/mrf.py | import sys
sys.path.append('..')
sys.path.append('lhss')
from lhss.pygco.mrf_solve import mrf_solve
import utils
import numpy as np
import torch
import math
NUM_SAMPS_PER_REGION = 100
def sampleShape(v, f):
verts = []
faces = []
verts = torch.from_numpy(v).float()
faces = torch.from_numpy(f).long()... | 2,786 | 22.618644 | 129 | py |
NGSP | NGSP-main/code/lhss/feat_code/calc_feat.py | import os
import torch
import numpy as np
from scipy.io import loadmat
import json
import utils
CACHE_DIR = 'cache_lhss/'
os.system(f'mkdir {CACHE_DIR} > /dev/null 2>&1')
MAX_PTS = 10000
def calcNorms(nv, nf):
vs = torch.from_numpy(nv).float().unsqueeze(0)
faces = torch.from_numpy(nf).long()
face_no... | 2,930 | 24.486957 | 91 | py |
NGSP | NGSP-main/code/layers/mlp_readout_layer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
"""
MLP Layer used after graph vector representation
"""
class MLPReadout(nn.Module):
def __init__(self, input_dim, output_dim, L=2): #L=nb_hidden_layers
super().__init__()
list_FC_layers = [ nn.Linea... | 1,849 | 28.365079 | 109 | py |
NGSP | NGSP-main/code/layers/gated_gcn_layer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
"""
ResGatedGCN: Residual Gated Graph ConvNets
An Experimental Study of Neural Networks for Variable Graphs (Xavier Bresson and Thomas Laurent, ICLR 2018)
https://arxiv.org/pdf/1711.07553v2.pdf
"""
class GatedGCNL... | 6,847 | 34.117949 | 111 | py |
NGSP | NGSP-main/code/bae_net/gen_data.py | # Code from https://github.com/czq142857/BAE-NET
import torch
import sys
import os
import numpy as np
import random
from tqdm import tqdm
V_DIM = 128
BATCH_SIZE = 8192
CACHE_DIR = 'cache_baenet'
os.system(f'mkdir {CACHE_DIR}')
def loadObj(infile):
tverts = []
ttris = []
with open(infile) as f:
f... | 7,549 | 35.298077 | 128 | py |
NGSP | NGSP-main/code/bae_net/bae_net.py | # Architecture from https://github.com/czq142857/BAE-NET
import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
class MLP(nn.Module):
def __init__(self, ind, hdim1, hdim2, odim):
super(MLP, self).__init__()
self.l1 = nn.Linear(ind, hdim1)
self.l2 = nn.Linear(... | 2,625 | 31.419753 | 93 | py |
NGSP | NGSP-main/code/pointnet2/pointnet2_utils.py | from __future__ import (
division,
absolute_import,
with_statement,
print_function,
unicode_literals,
)
import torch
from torch.autograd import Function
import torch.nn as nn
import sys
try:
import builtins
except:
import __builtin__ as builtins
try:
import pointnet2._ext as _ext
excep... | 9,922 | 25.891599 | 103 | py |
NGSP | NGSP-main/code/pointnet2/rebuild/setup.py | from __future__ import division, absolute_import, with_statement, print_function
from setuptools import setup, find_packages
from torch.utils.cpp_extension import BuildExtension, CUDAExtension
import glob
try:
import builtins
except:
import __builtin__ as builtins
builtins.__POINTNET2_SETUP__ = True
import po... | 1,175 | 28.4 | 83 | py |
NGSP | NGSP-main/code/lik_mods/reg_group_lik.py | import os
import torch, utils
from utils import device
from tqdm import tqdm
from copy import deepcopy
import data_utils, train_utils
from grammar import Grammar
import numpy as np
from models import GatedGCN, PointNetPPEnc
import random
import scipy.stats
from focal_loss import FocalLoss
import pc_enc.pc_ae as pc_ae
i... | 26,656 | 29.395667 | 125 | py |
NGSP | NGSP-main/code/lik_mods/sem_label_lik.py | import os
import eval_sem_label_models as esm
import train_sem_label_models as ts
import torch, utils
from utils import device
from tqdm import tqdm
from copy import deepcopy
import numpy as np
VERBOSE = False
def make_shapes(num_segments, samps, segments, seg_map, _all_info, grammar, name):
all_info = []
f... | 6,685 | 26.514403 | 100 | py |
MeLU | MeLU-master/main.py | import os
import torch
import pickle
from MeLU import MeLU
from options import config
from model_training import training
from data_generation import generate
from evidence_candidate import selection
if __name__ == "__main__":
master_path= "./ml"
if not os.path.exists("{}/".format(master_path)):
os.m... | 1,911 | 41.488889 | 149 | py |
MeLU | MeLU-master/data_generation.py | import re
import os
import json
import torch
import numpy as np
import random
import pickle
from tqdm import tqdm
from options import states
from dataset import movielens_1m
def item_converting(row, rate_list, genre_list, director_list, actor_list):
rate_idx = torch.tensor([[rate_list.index(str(row['rate']))]]).... | 6,130 | 43.751825 | 105 | py |
MeLU | MeLU-master/embeddings.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class item(torch.nn.Module):
def __init__(self, config):
super(item, self).__init__()
self.num_rate = config['num_rate']
self.num_genre = config['num_genre']
self.num_director = config['num_director']
self.n... | 2,944 | 35.358025 | 117 | py |
MeLU | MeLU-master/model_training.py | import os
import torch
import pickle
import random
from MeLU import MeLU
from options import config, states
def training(melu, total_dataset, batch_size, num_epoch, model_save=True, model_filename=None):
if config['use_cuda']:
melu.cuda()
training_set_size = len(total_dataset)
melu.train()
f... | 1,008 | 30.53125 | 95 | py |
MeLU | MeLU-master/MeLU.py | import torch
import numpy as np
from copy import deepcopy
from torch.autograd import Variable
from torch.nn import functional as F
from collections import OrderedDict
from embeddings import item, user
class user_preference_estimator(torch.nn.Module):
def __init__(self, config):
super(user_preference_est... | 5,795 | 45 | 139 | py |
MeLU | MeLU-master/evidence_candidate.py | import os
import torch
import pickle
from MeLU import MeLU
from options import config
def selection(melu, master_path, topk):
if not os.path.exists("{}/scores/".format(master_path)):
os.mkdir("{}/scores/".format(master_path))
if config['use_cuda']:
melu.cuda()
melu.eval()
target_stat... | 2,798 | 43.428571 | 130 | py |
GraphemeBERT | GraphemeBERT-master/monolingual_GBERT_pretrain/GBERT_pretrain.py | # -*- coding: utf-8 -*-
#! / usr/bin/env python3
# python2的print bug
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
import torch.nn.functional as F
from torchtext.data import Field, BucketIterator
from torchtext import data, datasets
import ... | 34,994 | 29.483449 | 258 | py |
GraphemeBERT | GraphemeBERT-master/monolingual_G2P_model/Transformer.py | import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
import torch.nn.functional as F
from torchtext.data import Field, BucketIterator
from torchtext import data, datasets
import numpy as np
import random
import math
import time
import copy
from argparse import ArgumentParser
from ... | 55,584 | 31.697059 | 237 | py |
GraphemeBERT | GraphemeBERT-master/monolingual_G2P_model/GBERT_finetuning.py | import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
import torch.nn.functional as F
from torchtext.datasets import TranslationDataset, Multi30k
from torchtext.data import Field, BucketIterator
from torchtext import data, datasets
# import spacy
import numpy as np
import random
import ma... | 71,792 | 32.737312 | 244 | py |
GraphemeBERT | GraphemeBERT-master/monolingual_G2P_model/GBERT_attention.py | # -*- coding: utf-8 -*-
#! / usr/bin/env python3
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torchtext
import torch.nn.functional as F
from torchtext.data import Field, BucketIterator
from torchtext import data, datasets
# import spacy
import numpy ... | 90,208 | 32.900413 | 331 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/predict.py | import os
import numpy as np
import torch
import torch.utils.data as dat
import pickle
from paramiko import SSHClient
from scp import SCPClient
import paramiko
from dotenv import load_dotenv
from scripts.data_loader import load_data_from_file, load_data_from_db
from options.test_options import parse_args
from utils.m... | 3,965 | 36.065421 | 119 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/train.py | import os
import logging
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as dat
from torch.utils.tensorboard import SummaryWriter
from scripts.data_loader import load_data_from_file, load_data_from_db
from options.train_options import parse_args, verbose
from u... | 8,264 | 40.532663 | 119 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/dap_main.py | import datetime
import os
from importlib import reload
from models.deep_ap import DeepAP
from scripts.data_loader import *
from scripts.train_dap import train
from utils.metrics import normalize_mat
def main(args, **kwargs):
""" extract information from target time period """
data_file = os.path.join(kwargs... | 7,660 | 36.370732 | 119 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/ae_main.py | import datetime
import os
from models.auto_encoder import AutoEncoder
from scripts.data_loader import *
from scripts.pretrain_ae import train
from utils.metrics import normalize_mat
def main(args, **kwargs):
""" load data object """
tar_date = args.dates[-1]
data_file = os.path.join(data_dir, '{}_{}m_{}... | 3,411 | 30.302752 | 115 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/predict.py | import os
import numpy as np
import pandas as pd
import argparse
import torch
import torch.utils.data as dat
from scripts.data_loader import DataObj, load_train_val_test
from scripts.result_viz import spatial_viz, temporal_viz, output_prediction
from utils.metrics import normalize_mat, compute_error
def predict(dap,... | 7,363 | 39.021739 | 118 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/models/fc.py | from torch import nn
import torch.nn.functional as F
# a simple Regression model
class FC(nn.Module):
def __init__(self, in_dim, h_dims, out_dim, **kwargs):
super(FC, self).__init__()
# define parameters
self.input_dim = in_dim
self.hidden_dims = h_dims
self.output_dim... | 1,066 | 24.404762 | 94 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/models/mask_net.py | from torch import nn
import torch
class MaskNet(nn.Module):
def __init__(self, in_dim, out_dim, indices_mask, **kwargs):
"""
Params:
in_dim: int
Number of channels of input tensor
out_dim: int
Number of channels of output tensor
... | 1,307 | 30.902439 | 95 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/models/deep_ap.py | import torch
import torch.nn as nn
from models.conv_lstm import ConvLSTM
from models.fc import FC
from models.auto_encoder import AutoEncoder
from models.mask_net import MaskNet
class DeepAP(nn.Module):
def __init__(self, in_dim, ae_en_h_dims, ae_de_h_dims,
conv_lstm_in_size, conv_lstm_in_dim, ... | 4,037 | 33.512821 | 114 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/models/conv_lstm.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
"""
https://github.com/spacejake/convLSTM.pytorch/blob/master/convlstm.py
"""
class ConvLSTMCell(nn.Module):
def __init__(self, in_size, in_dim, h_dim, kernel_size, bias):
"""
Params:
... | 5,914 | 34.419162 | 112 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/models/auto_encoder.py | from torch import nn
import torch.nn.functional as F
# a simple Auto-Encoder model
class AutoEncoder(nn.Module):
def __init__(self, in_dim, en_h_dims, de_h_dims):
super(AutoEncoder, self).__init__()
self.input_dim = in_dim
self.encoder_hidden_dims = en_h_dims
self.decoder_hidde... | 1,359 | 27.93617 | 104 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/backup/utils/early_stopping.py | import logging
import numpy as np
import torch
class EarlyStopping:
"""
Early stops the training if validation loss doesn't improve after a given patience.
"""
def __init__(self, patience=7, verbose=False, delta=0):
"""
Params:
patience (int): How long to wait after la... | 1,845 | 33.830189 | 110 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/models/loss.py | from torch import nn
import torch
class OneStepSpatialLoss(nn.Module):
def __init__(self):
super(OneStepSpatialLoss, self).__init__()
self.mse_loss_func = nn.MSELoss()
def forward(self, input_data):
loss = 0.
t, _, h, w = input_data.shape
loss += self.mse_loss_func(i... | 3,066 | 37.3375 | 115 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/models/deeplatte.py | import torch
import torch.nn as nn
from models.convlstm import ConvLSTM
from models.autoencoder import AutoEncoder
from models.linear import DiagPruneLinear, Stack2Linear
class DeepLatte(nn.Module):
def __init__(self, in_features, en_features, de_features,
in_size, h_channels, kernel_sizes, num... | 4,080 | 41.510417 | 110 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/models/linear.py | from torch import nn
import torch
from torch.nn.utils.prune import l1_unstructured
class DiagPruneLinear(nn.Module):
""" a diagonal linear layer with weight pruning """
def __init__(self, in_features, **kwargs):
"""
params:
in_features (int): size of input sample
"""
... | 2,100 | 31.323077 | 97 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/models/autoencoder.py | from torch import nn
class AutoEncoder(nn.Module):
""" a vallina auto-encoder """
def __init__(self, in_features, en_features, de_features, **kwargs):
"""
at least one encoded hidden layers and at least one decoded hidden layers
params:
in_features: the number... | 1,468 | 29.604167 | 81 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/models/convlstm.py | import torch
import torch.nn as nn
"""
Reference: https://github.com/spacejake/convLSTM.pytorch/blob/master/convlstm.py
"""
class ConvLSTMCell(nn.Module):
def __init__(self, in_size, in_channels, h_channels, kernel_size, bias=True):
"""
params:
in_size (int, int) - height and width ... | 4,724 | 35.627907 | 112 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/scripts/pretrain_ae.py | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.utils.data as dat
from tensorboardX import SummaryWriter
def train(ae, data_obj, args, **kwargs):
print('>>> {}: Training process has started.'.format(kwargs['model_name']))
""" construct index-based data loader "... | 1,859 | 34.769231 | 125 | py |
deeplatte-fine-scale-prediction | deeplatte-fine-scale-prediction-master/utils/early_stopping.py | import logging
import numpy as np
import torch
class EarlyStopping:
def __init__(self, patience=7, verbose=False, delta=0):
"""
params:
patience (int): how long to wait after last time validation loss improved
verbose (bool): if True, prints a message for each validation l... | 1,663 | 32.959184 | 110 | py |
Diagnose_VLN | Diagnose_VLN-master/touchdown/model/VLN-Transformer/setup.py | import sys
import setuptools
long_description = """
Texar-PyTorch is an open-source toolkit based on PyTorch,
aiming to support a broad set of machine learning especially text generation
tasks, such as machine translation, dialog, summarization, content manipulation,
language modeling, and so on.
Texar is designed fo... | 1,908 | 28.369231 | 80 | py |
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