repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
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mff | mff-master/mff/kernels/base.py | import os
from abc import ABCMeta, abstractmethod
from pathlib import Path
path = Path(os.path.abspath(__file__))
Mffpath = path.parent.parent / "cache/"
class Kernel(metaclass=ABCMeta):
@abstractmethod
def __init__(self, kernel_name, *args, **kwargs):
super().__init__(*args, **kwargs)
self.k... | 345 | 23.714286 | 53 | py |
mff | mff-master/mff/kernels/twobodykernel.py | # -*- coding: utf-8 -*-
import logging
import os.path
import pickle
from abc import ABCMeta, abstractmethod
import numpy as np
from mff.kernels.base import Kernel, Mffpath
logger = logging.getLogger(__name__)
def dummy_calc_ff(data):
""" Function used when multiprocessing.
Args:
data (list of obje... | 36,239 | 39.995475 | 121 | py |
mff | mff-master/mff/kernels/threebodykernel.py | # -*- coding: utf-8 -*-
import logging
import os.path
import pickle
from abc import ABCMeta, abstractmethod
import numpy as np
from mff.kernels.base import Kernel, Mffpath
logger = logging.getLogger(__name__)
def dummy_calc_ff(data):
""" Function used when multiprocessing.
Args:
data (list of obje... | 40,850 | 39.728814 | 117 | py |
mff | mff-master/mff/kernels/eamkernel.py | # -*- coding: utf-8 -*-
import logging
import os.path
import pickle
from abc import ABCMeta, abstractmethod
import numpy as np
from mff.kernels.base import Kernel, Mffpath
logger = logging.getLogger(__name__)
def dummy_calc_ff(data):
""" Function used when multiprocessing.
Args:
data (list of obje... | 42,667 | 40.7087 | 134 | py |
mff | mff-master/mff/kernels/__init__.py | from .eamkernel import EamManySpeciesKernel, EamSingleSpeciesKernel
from .manybodykernel import (ManyBodyManySpeciesKernel,
ManyBodySingleSpeciesKernel)
from .threebodykernel import (ThreeBodyManySpeciesKernel,
ThreeBodySingleSpeciesKernel)
from .twobodykernel ... | 684 | 41.8125 | 79 | py |
mff | mff-master/mff/kernels/manybodykernel.py | # -*- coding: utf-8 -*-
import logging
import os.path
import pickle
from abc import ABCMeta, abstractmethod
import numpy as np
from mff.kernels.base import Kernel, Mffpath
logger = logging.getLogger(__name__)
def dummy_calc_ff(data):
""" Function used when multiprocessing.
Args:
data (list of obje... | 40,153 | 38.994024 | 114 | py |
T-Concord3D | T-Concord3D-master/test.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import os
import argparse
import sys
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
import math
from utils.metric_util import per_class_iu, fast_hist_crop, fast_ups_crop... | 16,515 | 44.750693 | 137 | py |
T-Concord3D | T-Concord3D-master/concordance_kitti.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import os
import time
import argparse
import sys
import numpy as np
import glob
import os
import shutil
import random
import math
# https://github.com/ctu-vras/T-Concord3D.git
def main(args):
teacher_1 = args.teacher1
teache... | 4,467 | 44.131313 | 114 | py |
T-Concord3D | T-Concord3D-master/train_tconcord3d.py | # -*- coding:utf-8 -*-
# author: Awet
import argparse
import os
import sys
import time
import warnings
import numpy as np
import torch
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel
from tqdm import tqdm
from builder import data_builder, model_builder, loss_builder
from config.conf... | 6,366 | 33.79235 | 112 | py |
T-Concord3D | T-Concord3D-master/train.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from tqdm import tqdm
from utils.metric_util import per_class_iu, fast_hist_crop
from dataloader.pc_datas... | 13,060 | 42.105611 | 120 | py |
T-Concord3D | T-Concord3D-master/tools/rename_kitti_train_pseudo.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import os
import time
import argparse
import sys
import numpy as np
import numpy as np
import glob
from multiprocessing import Pool
import os
import shutil
import random
import math
def main():
#sequence = ["04"]
#des_seq = ["3... | 1,739 | 33.8 | 140 | py |
T-Concord3D | T-Concord3D-master/config/config.py | # -*- coding:utf-8 -*-
from pathlib import Path
from strictyaml import Bool, Float, Int, Map, Seq, Str, as_document, load
model_params = Map(
{
"model_architecture": Str(),
"output_shape": Seq(Int()),
"fea_dim": Int(),
"out_fea_dim": Int(),
"num_class": Int(),
"num_... | 3,134 | 22.931298 | 90 | py |
T-Concord3D | T-Concord3D-master/config/__init__.py | # -*- coding:utf-8 -*-
| 23 | 11 | 22 | py |
T-Concord3D | T-Concord3D-master/builder/loss_builder.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import torch
from utils.lovasz_losses import lovasz_softmax, lovasz_softmax_lcw, cross_entropy_lcw
from utils.loss_func import FocalLoss
def build(wce=True, lovasz=True, num_class=20, ignore_label=None, weights=None, ssl=False, fl=False... | 1,319 | 30.428571 | 101 | py |
T-Concord3D | T-Concord3D-master/builder/model_builder.py | # -*- coding:utf-8 -*-
from model.cylinder_3d import get_model_class
from model.segment_3d import Asymm_3d_spconv
from model.cylinder_feature import cylinder_fea
def build(model_config):
output_shape = model_config['output_shape']
num_class = model_config['num_class']
num_input_features = model_config['n... | 1,147 | 30.888889 | 64 | py |
T-Concord3D | T-Concord3D-master/builder/data_builder.py | # -*- coding:utf-8 -*-
import torch
from dataloader.dataset_semantickitti import get_model_class, collate_fn_BEV, collate_fn_BEV_tta
from dataloader.pc_dataset import get_pc_model_class
def build(dataset_config,
train_dataloader_config,
val_dataloader_config,
test_dataloader_config=None... | 8,112 | 48.469512 | 111 | py |
T-Concord3D | T-Concord3D-master/builder/__init__.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
| 82 | 19.75 | 29 | py |
T-Concord3D | T-Concord3D-master/utils/trainer_function.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# at 8/10/22
# --------------------------|
import argparse
import os
import sys
import time
import warnings
import numpy as np
import torch
import torch.optim as optim
from torch.nn.parallel import DistributedDataParallel
from tqdm import tqdm
from builder import da... | 8,482 | 42.953368 | 116 | py |
T-Concord3D | T-Concord3D-master/utils/load_save_util.py | # -*- coding:utf-8 -*-
import torch
def load_checkpoint(model_load_path, model, map_location=None):
my_model_dict = model.state_dict()
if map_location is not None:
pre_weight = torch.load(model_load_path, map_location=f'cuda:{map_location}')
else:
pre_weight = torch.load(model_load_path)
... | 1,772 | 26.703125 | 92 | py |
T-Concord3D | T-Concord3D-master/utils/metric_util.py | # -*- coding:utf-8 -*-
import numpy as np
def fast_hist(pred, label, n):
k = (label >= 0) & (label < n)
bin_count = np.bincount(
n * label[k].astype(int) + pred[k], minlength=n ** 2)
return bin_count[:n ** 2].reshape(n, n)
def per_class_iu(hist):
return np.diag(hist) / (hist.sum(1) + hist.su... | 868 | 27.966667 | 82 | py |
T-Concord3D | T-Concord3D-master/utils/__init__.py | # -*- coding:utf-8 -*-
# author: Xinge
# @file: __init__.py.py
| 64 | 15.25 | 24 | py |
T-Concord3D | T-Concord3D-master/utils/ups.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
def enable_dropout(model):
for m in model.modules():
if m.__class__.__name__.startswith('Dropout'):
m.train()
| 218 | 20.9 | 54 | py |
T-Concord3D | T-Concord3D-master/utils/lovasz_losses.py | # -*- coding:utf-8 -*-
# author: Xinge
"""
Lovasz-Softmax and Jaccard hinge loss in PyTorch
Maxim Berman 2018 ESAT-PSI KU Leuven (MIT License)
"""
from __future__ import print_function, division
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import numpy as np
try:
from itertoo... | 15,675 | 36.864734 | 131 | py |
T-Concord3D | T-Concord3D-master/utils/loss_func.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import torch
import torch.nn as nn
import torch.nn.functional as F
class FocalLoss(nn.Module):
def __init__(self, weight=None, ignore_index=None,
gamma=2., reduction='none', ssl=False):
nn.Module.__init__(... | 1,766 | 28.949153 | 69 | py |
T-Concord3D | T-Concord3D-master/utils/log_util.py | # -*- coding:utf-8 -*-
def save_to_log(logdir, logfile, message):
f = open(logdir + '/' + logfile, "a")
f.write(message + '\n')
f.close()
return | 160 | 25.833333 | 42 | py |
T-Concord3D | T-Concord3D-master/model/cylinder_3d.py | # -*- coding:utf-8 -*-
import torch
from torch import nn
REGISTERED_MODELS_CLASSES = {}
def register_model(cls, name=None):
global REGISTERED_MODELS_CLASSES
if name is None:
name = cls.__name__
assert name not in REGISTERED_MODELS_CLASSES, f"exist class: {REGISTERED_MODELS_CLASSES}"
REGISTERE... | 1,901 | 31.793103 | 119 | py |
T-Concord3D | T-Concord3D-master/model/segment_3d.py | # -*- coding:utf-8 -*-
import numpy as np
#import spconv
import spconv.pytorch as spconv
import torch
from torch import nn
def conv3x3(in_planes, out_planes, stride=1, indice_key=None):
return spconv.SubMConv3d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False... | 13,253 | 41.07619 | 121 | py |
T-Concord3D | T-Concord3D-master/model/__init__.py | # -*- coding:utf-8 -*-
| 23 | 11 | 22 | py |
T-Concord3D | T-Concord3D-master/model/cylinder_feature.py | # -*- coding:utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import numba as nb
import multiprocessing
import torch_scatter
class cylinder_fea(nn.Module):
def __init__(self, grid_size, fea_dim=3,
out_pt_fea_dim=64, max_pt_per_encode=64, fea_compr... | 2,812 | 30.965909 | 115 | py |
T-Concord3D | T-Concord3D-master/dataloader/dataset_semantickitti.py | # -*- coding:utf-8 -*-
"""
SemKITTI dataloader
"""
import os
import numpy as np
import torch
import random
import time
import numba as nb
import yaml
from torch.utils import data
import pickle
REGISTERED_DATASET_CLASSES = {}
def register_dataset(cls, name=None):
global REGISTERED_DATASET_CLASSES
if name is N... | 29,622 | 40.372905 | 150 | py |
T-Concord3D | T-Concord3D-master/dataloader/augmentations.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
# from __future__ import (
# division,
# absolute_import,
# with_statement,
# print_function,
# unicode_literals,
# )
import random
import numpy as np
import torch
#from pointnet2.data.data_utils import angle_axis
#... | 11,680 | 27.079327 | 106 | py |
T-Concord3D | T-Concord3D-master/dataloader/pc_dataset.py | # -*- coding:utf-8 -*-
# author: Xinge
# @file: pc_dataset.py
import glob
import os
import pickle
from os.path import exists
import numpy as np
import yaml
from torch.utils import data
REGISTERED_PC_DATASET_CLASSES = {}
# past and future frames global place holders
past = 0
future = 0
T_past = 0
T_future = 0
ssl = ... | 34,323 | 40.354217 | 130 | py |
T-Concord3D | T-Concord3D-master/dataloader/dataset_nuscenes.py | # -*- coding:utf-8 -*-
# author: Xinge
# @file: dataset_nuscenes.py
import numpy as np
import torch
import numba as nb
from torch.utils import data
from dataloader.dataset_semantickitti import register_dataset
def cart2polar(input_xyz):
rho = np.sqrt(input_xyz[:, 0] ** 2 + input_xyz[:, 1] ** 2)
phi = np.arct... | 9,266 | 35.920319 | 114 | py |
T-Concord3D | T-Concord3D-master/dataloader/__init__.py | # -*- coding:utf-8 -*-
# author: Xinge
# @file: __init__.py.py
from . import dataset_nuscenes
| 95 | 15 | 30 | py |
T-Concord3D | T-Concord3D-master/dataloader/preprocess.py | # -*- coding:utf-8 -*-
# author: Awet H. Gebrehiwot
# --------------------------|
import torch
import torchvision.transforms as transforms
from augmentations import RandAugment3D
def preprocessing(point_set, cls):
pts_transform = transforms.Compose(
[]
)
pts_transform.transforms.insert(0, RandAugm... | 397 | 23.875 | 59 | py |
darts | darts-master/cnn/test.py | import os
import sys
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkCIFAR as Network
... | 3,593 | 33.228571 | 102 | py |
darts | darts-master/cnn/architect.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
class Architect(object):
def __init__(self, model, args):
self.network_momentum = args.momentum
self.network_weight_decay = args.weight_decay
self.mo... | 3,429 | 35.88172 | 130 | py |
darts | darts-master/cnn/train_imagenet.py | import os
import sys
import numpy as np
import time
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from torc... | 7,992 | 33.601732 | 106 | py |
darts | darts-master/cnn/utils.py | import os
import numpy as np
import torch
import shutil
import torchvision.transforms as transforms
from torch.autograd import Variable
class AvgrageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.avg = 0
self.sum = 0
self.cnt = 0
def update(self, val, n=1):
self.sum... | 3,080 | 24.254098 | 105 | py |
darts | darts-master/cnn/model.py | import torch
import torch.nn as nn
from operations import *
from torch.autograd import Variable
from utils import drop_path
class Cell(nn.Module):
def __init__(self, genotype, C_prev_prev, C_prev, C, reduction, reduction_prev):
super(Cell, self).__init__()
print(C_prev_prev, C_prev, C)
if reduction_pr... | 6,640 | 29.888372 | 89 | py |
darts | darts-master/cnn/model_search.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from operations import *
from torch.autograd import Variable
from genotypes import PRIMITIVES
from genotypes import Genotype
class MixedOp(nn.Module):
def __init__(self, C, stride):
super(MixedOp, self).__init__()
self._ops = nn.ModuleList(... | 5,009 | 29.54878 | 128 | py |
darts | darts-master/cnn/train_search.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import torch.utils
import torch.nn.functional as F
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model_searc... | 7,212 | 35.80102 | 115 | py |
darts | darts-master/cnn/test_imagenet.py | import os
import sys
import numpy as np
import torch
import utils
import glob
import random
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torchvision.transforms as transforms
import torch.backends.cudnn as cudnn
from torch.autograd i... | 3,785 | 32.504425 | 104 | py |
darts | darts-master/cnn/train.py | import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from model import NetworkCIFAR ... | 6,251 | 35.561404 | 100 | py |
darts | darts-master/cnn/visualize.py | import sys
import genotypes
from graphviz import Digraph
def plot(genotype, filename):
g = Digraph(
format='pdf',
edge_attr=dict(fontsize='20', fontname="times"),
node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),... | 1,419 | 24.357143 | 141 | py |
darts | darts-master/cnn/genotypes.py | from collections import namedtuple
Genotype = namedtuple('Genotype', 'normal normal_concat reduce reduce_concat')
PRIMITIVES = [
'none',
'max_pool_3x3',
'avg_pool_3x3',
'skip_connect',
'sep_conv_3x3',
'sep_conv_5x5',
'dil_conv_3x3',
'dil_conv_5x5'
]
NASNet = Genotype(
normal = [
... | 2,410 | 29.518987 | 429 | py |
darts | darts-master/cnn/operations.py | import torch
import torch.nn as nn
OPS = {
'none' : lambda C, stride, affine: Zero(stride),
'avg_pool_3x3' : lambda C, stride, affine: nn.AvgPool2d(3, stride=stride, padding=1, count_include_pad=False),
'max_pool_3x3' : lambda C, stride, affine: nn.MaxPool2d(3, stride=stride, padding=1),
'skip_connect' : lambd... | 3,717 | 34.075472 | 129 | py |
darts | darts-master/rnn/test.py | import argparse
import os, sys
import time
import math
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import data
import model
from utils import batchify, get_batch, repackage_hidden, create_exp_dir, save_checkpoint
parser = argparse.ArgumentParser(description='PyTorch Pen... | 5,048 | 40.385246 | 118 | py |
darts | darts-master/rnn/architect.py | import torch
import numpy as np
import torch.nn as nn
from torch.autograd import Variable
def _concat(xs):
return torch.cat([x.view(-1) for x in xs])
def _clip(grads, max_norm):
total_norm = 0
for g in grads:
param_norm = g.data.norm(2)
total_norm += param_norm ** 2
total_norm = total_... | 4,003 | 34.122807 | 132 | py |
darts | darts-master/rnn/utils.py | import torch
import torch.nn as nn
import os, shutil
import numpy as np
from torch.autograd import Variable
def repackage_hidden(h):
if type(h) == Variable:
return Variable(h.data)
else:
return tuple(repackage_hidden(v) for v in h)
def batchify(data, bsz, args):
nbatch = data.size(0) // ... | 2,955 | 30.446809 | 137 | py |
darts | darts-master/rnn/model.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from genotypes import STEPS
from utils import mask2d
from utils import LockedDropout
from utils import embedded_dropout
from torch.autograd import Variable
INITRANGE = 0.04
class DARTSCell(nn.Module):
def __init__(self, ninp, nhid, dro... | 5,148 | 30.981366 | 102 | py |
darts | darts-master/rnn/data.py | import os
import torch
from collections import Counter
class Dictionary(object):
def __init__(self):
self.word2idx = {}
self.idx2word = []
self.counter = Counter()
self.total = 0
def add_word(self, word):
if word not in self.word2idx:
self.idx2word.append(... | 4,005 | 30.054264 | 80 | py |
darts | darts-master/rnn/model_search.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from genotypes import PRIMITIVES, STEPS, CONCAT, Genotype
from torch.autograd import Variable
from collections import namedtuple
from model import DARTSCell, RNNModel
class DARTSCellSearch(DARTSCell):
def __init__(self, ninp, nhid, dropouth, dropou... | 3,278 | 32.804124 | 125 | py |
darts | darts-master/rnn/train_search.py | import argparse
import os, sys, glob
import time
import math
import numpy as np
import torch
import logging
import torch.nn as nn
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
from architect import Architect
import gc
import data
import model_search as model
from utils import batchify, get_bat... | 12,639 | 43.041812 | 132 | py |
darts | darts-master/rnn/train.py | import os
import gc
import sys
import glob
import time
import math
import numpy as np
import torch
import torch.nn as nn
import logging
import argparse
import genotypes
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import data
import model
from torch.autograd import Variable
from utils import ba... | 13,900 | 42.037152 | 141 | py |
darts | darts-master/rnn/visualize.py | import sys
import genotypes
from graphviz import Digraph
def plot(genotype, filename):
g = Digraph(
format='pdf',
edge_attr=dict(fontsize='20', fontname="times"),
node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname="times"),... | 1,327 | 26.666667 | 141 | py |
darts | darts-master/rnn/genotypes.py | from collections import namedtuple
Genotype = namedtuple('Genotype', 'recurrent concat')
PRIMITIVES = [
'none',
'tanh',
'relu',
'sigmoid',
'identity'
]
STEPS = 8
CONCAT = 8
ENAS = Genotype(
recurrent = [
('tanh', 0),
('tanh', 1),
('relu', 1),
('tanh', 3),
... | 851 | 22.027027 | 165 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/setup.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#!/usr/bin/env python
import glob
import os
import torch
from setuptools import find_packages
from setuptools import setup
from torch.utils.cpp_extension import CUDA_HOME
from torch.utils.cpp_extension import CppExtension
from torch.utils.cpp_ext... | 2,068 | 28.140845 | 73 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/tools/convert_dataset.py | import os
import numpy as np
import cv2
from shapely.geometry import box, Polygon
from shapely import affinity
import math
def _rect2quad(boxes):
x_min, y_min, x_max, y_max = boxes[:, 0].reshape((-1, 1)), boxes[:, 1].reshape((-1, 1)), boxes[:, 2].reshape((-1, 1)), boxes[:, 3].reshape((-1, 1))
return np.hstac... | 6,623 | 36.213483 | 200 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/tools/test_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:skip
import argparse
import os
import torch... | 3,686 | 34.451923 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/tools/demo.py | import os
import cv2
import torch
from torchvision import transforms as T
from maskrcnn_benchmark.modeling.detector import build_detection_model
from maskrcnn_benchmark.utils.checkpoint import DetectronCheckpointer
from maskrcnn_benchmark.structures.image_list import to_image_list
from maskrcnn_benchmark.config import... | 9,628 | 39.288703 | 123 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/tools/train_net.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
r"""
Basic training script for PyTorch
"""
# Set up custom environment before nearly anything else is imported
# NOTE: this should be the first import (no not reorder)
from maskrcnn_benchmark.utils.env import setup_environment # noqa F401 isort:s... | 4,818 | 30.292208 | 89 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/solver/lr_scheduler.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from bisect import bisect_right
import torch
# FIXME ideally this would be achieved with a CombinedLRScheduler,
# separating MultiStepLR with WarmupLR
# but the current LRScheduler design doesn't allow it
class WarmupMultiStepLR(torch.optim.lr_s... | 2,292 | 33.742424 | 93 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/solver/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .build import make_optimizer
from .build import make_lr_scheduler
from .lr_scheduler import WarmupMultiStepLR
| 187 | 36.6 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/solver/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .lr_scheduler import WarmupMultiStepLR
def make_optimizer(cfg, model):
params = []
for key, value in model.named_parameters():
if not value.requires_grad:
continue
lr = cfg.SOLVER.BASE_LR
... | 1,176 | 29.973684 | 79 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/config/defaults.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
from yacs.config import CfgNode as CN
# -----------------------------------------------------------------------------
# Convention about Training / Test specific parameters
# -------------------------------------... | 14,283 | 37.294906 | 83 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/config/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .defaults import _C as cfg
| 104 | 34 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/config/paths_catalog.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""Centralized catalog of paths."""
import os
class DatasetCatalog(object):
DATA_DIR = "datasets"
# DATA_DIR = "/share/mhliao/MaskTextSpotterV3/datasets/"
DATASETS = {
"coco_2014_train": (
"coco/train2014",
... | 11,140 | 45.810924 | 121 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/nms.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# from ._utils import _C
from maskrcnn_benchmark import _C
# nms = _C.nms
from apex import amp
# Only valid with fp32 inputs - give AMP the hint
nms = amp.float_function(_C.nms)
# nms.__doc__ = """
# This function performs Non-maximum suppresion"... | 323 | 26 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/batch_norm.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
class FrozenBatchNorm2d(nn.Module):
"""
BatchNorm2d where the batch statistics and the affine parameters
are fixed
"""
def __init__(self, n):
super(FrozenBatchNorm2d, self).__init__()... | 1,093 | 34.290323 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/roi_pool.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
from apex import amp
class _ROIPool(Function)... | 1,899 | 28.230769 | 74 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/roi_align.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from torch import nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
from apex import amp
class _ROIAlign(Function... | 2,154 | 29.785714 | 85 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/smooth_l1_loss.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
# TODO maybe push this to nn?
def smooth_l1_loss(input, target, beta=1. / 9, size_average=True):
"""
very similar to the smooth_l1_loss from pytorch, but with
the extra beta parameter
"""
n = torch.abs(input - tar... | 481 | 27.352941 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/_utils.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import glob
import os.path
import torch
try:
from torch.utils.cpp_extension import load as load_ext
from torch.utils.cpp_extension import CUDA_HOME
except ImportError:
raise ImportError("The cpp layer extensions requires PyTorch 0.4 o... | 1,165 | 28.15 | 80 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/misc.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
"""
helper class that supports empty tensors on some nn functions.
Ideally, add support directly in PyTorch to empty tensors in
those functions.
This can be removed once https://github.com/pytorch/pytorch/issues/12013
is implemented
"""
import m... | 6,035 | 31.451613 | 88 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import torch
from .batch_norm import FrozenBatchNorm2d
from .misc import Conv2d
from .misc import DFConv2d
from .misc import ConvTranspose2d
from .misc import interpolate
from .nms import nms
from .roi_align import ROIAlign
from .roi_align import ... | 1,372 | 30.930233 | 150 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/dcn/deform_conv_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn.modules.utils import _pair
from maskrcnn_benchmark import _C
class DeformConvFunction(Function):
@staticmethod
def forward(
ctx,
input,
offset,
weight,
... | 8,309 | 30.596958 | 83 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/dcn/deform_pool_func.py | import torch
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from maskrcnn_benchmark import _C
class DeformRoIPoolingFunction(Function):
@staticmethod
def forward(
ctx,
data,
rois,
offset,
spatial_scale,
out_size,
... | 2,595 | 26.041667 | 99 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/dcn/deform_pool_module.py | from torch import nn
from .deform_pool_func import deform_roi_pooling
class DeformRoIPooling(nn.Module):
def __init__(self,
spatial_scale,
out_size,
out_channels,
no_trans,
group_size=1,
part_size=None,
... | 6,307 | 40.774834 | 79 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/dcn/__init__.py | #
# Copied From [mmdetection](https://github.com/open-mmlab/mmdetection/tree/master/mmdet/ops/dcn)
#
| 101 | 24.5 | 96 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/layers/dcn/deform_conv_module.py | import math
import torch
import torch.nn as nn
from torch.nn.modules.utils import _pair
from .deform_conv_func import deform_conv, modulated_deform_conv
class DeformConv(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
padding=0,
... | 5,802 | 31.601124 | 78 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/engine/text_inference.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import os
import pickle
import subprocess
import time
import cv2
import numpy as np
import torch
from maskrcnn_benchmark.utils.chars import char2num, get_tight_rect, getstr_grid
from PIL import Image, ImageDraw
from ... | 23,471 | 40.839572 | 164 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/engine/trainer.py | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import torch
from maskrcnn_benchmark.utils.comm import get_world_size, is_main_process
from maskrcnn_benchmark.utils.metric_logger import MetricLogger
import torch.distributed as di... | 4,397 | 34.184 | 105 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/c2_model_loading.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import pickle
from collections import OrderedDict
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
def _rename_basic_resnet_weights(layer_keys):
layer_keys = [k.replace("_", ".") for k in ... | 6,944 | 39.144509 | 129 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/metric_logger.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import defaultdict
from collections import deque
import torch
class SmoothedValue(object):
"""Track a series of values and provide access to smoothed values over a
window or the global series average.
"""
def __... | 1,714 | 25.796875 | 82 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/checkpoint.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import torch
from maskrcnn_benchmark.utils.model_serialization import load_state_dict
from maskrcnn_benchmark.utils.c2_model_loading import load_c2_format
from maskrcnn_benchmark.utils.imports import import_file
from mask... | 4,813 | 33.385714 | 87 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/comm.py | # # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# """
# This file contains primitives for multi-gpu communication.
# This is useful when doing distributed training.
# """
# import os
# import pickle
# import tempfile
# import time
# import torch
# import torch.distributed as dist
# # def ... | 11,687 | 29.83905 | 86 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/registry.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
def _register_generic(module_dict, module_name, module):
assert module_name not in module_dict
module_dict[module_name] = module
class Registry(dict):
'''
A helper class for managing registering modules, it extends a dictionary
... | 1,384 | 29.777778 | 76 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/model_zoo.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
import sys
try:
from torch.hub import _download_url_to_file
from torch.hub import urlparse
from torch.hub import HASH_REGEX
except ImportError:
from torch.utils.model_zoo import _download_url_to_file
from torch.utils.... | 3,044 | 48.918033 | 135 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/logging.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import logging
import os
import sys
from tensorboardX import SummaryWriter
def setup_logger(name, save_dir, distributed_rank=0):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
# don't log results for the non-master p... | 1,252 | 28.833333 | 84 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/collect_env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import PIL
from torch.utils.collect_env import get_pretty_env_info
def get_pil_version():
return "\n Pillow ({})".format(PIL.__version__)
def collect_env_info():
env_str = get_pretty_env_info()
env_str += get_pil_version()
... | 338 | 21.6 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/model_serialization.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from collections import OrderedDict
import logging
import torch
from maskrcnn_benchmark.utils.imports import import_file
def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
"""
Strategy: suppose that the models that w... | 3,464 | 41.777778 | 91 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/chars.py | import os
import cv2
import numpy as np
def char2num(char):
if char in "0123456789":
num = ord(char) - ord("0") + 1
elif char in "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ":
num = ord(char.lower()) - ord("a") + 11
else:
num = 0
return num
def num2char(num):
ch... | 6,419 | 31.1 | 89 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/__init__.py | 0 | 0 | 0 | py | |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/miscellaneous.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import errno
import os
def mkdir(path):
try:
os.makedirs(path)
except OSError as e:
if e.errno != errno.EEXIST:
raise
| 228 | 18.083333 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/env.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import os
from maskrcnn_benchmark.utils.imports import import_file
def setup_environment():
"""Perform environment setup work. The default setup is a no-op, but this
function allows the user to specify a Python source file that performs
... | 1,249 | 31.894737 | 90 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/utils/imports.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import importlib
import importlib.util
import sys
# from https://stackoverflow.com/questions/67631/how-to-import-a-module-given-the-full-path?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
def import_file(module_name, fi... | 598 | 38.933333 | 164 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/__init__.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from .build import make_data_loader
| 108 | 35.333333 | 71 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/collate_batch.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
from maskrcnn_benchmark.structures.image_list import to_image_list, to_image_target_list
class BatchCollator(object):
"""
From a list of samples from the dataset,
returns the batched images and targets.
This should be passed to th... | 1,080 | 37.607143 | 115 | py |
MaskTextSpotterV3 | MaskTextSpotterV3-master/maskrcnn_benchmark/data/build.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import bisect
import logging
import torch.utils.data
from maskrcnn_benchmark.utils.comm import get_world_size
from maskrcnn_benchmark.utils.imports import import_file
from . import datasets as D
from . import samplers
from .collate_batch import ... | 6,740 | 37.301136 | 143 | py |
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