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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UnSilence_VOC | UnSilence_VOC-main/src/services/mask_service.py | import torch
from typing import Tuple
from services.tokenize.base_tokenize_service import BaseTokenizeService
from services.arguments.pretrained_arguments_service import PretrainedArgumentsService
class MaskService:
def __init__(
self,
tokenize_service: BaseTokenizeService,
a... | 3,175 | 39.202532 | 145 | py |
UnSilence_VOC | UnSilence_VOC-main/src/services/train_service.py | from logging import error
import os
import sys
import time
import math
from datetime import datetime
from typing import Dict, List, Tuple
import numpy as np
import json
import torch
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from losses.loss_base import LossBase
from models.mo... | 15,591 | 38.877238 | 216 | py |
UnSilence_VOC | UnSilence_VOC-main/src/services/log_service.py | from datetime import datetime, timedelta
import os
from termcolor import colored
import wandb
import torch
import numpy as np
import json
from copy import deepcopy
from entities.metric import Metric
from entities.data_output_log import DataOutputLog
from services.arguments.arguments_service_base import ArgumentsServi... | 11,265 | 35.108974 | 156 | py |
UnSilence_VOC | UnSilence_VOC-main/src/services/dataloader_service.py | from services.log_service import LogService
import numpy as np
from typing import Tuple
import torch
from torch.utils.data import DataLoader
from enums.run_type import RunType
from services.arguments.arguments_service_base import ArgumentsServiceBase
from services.dataset_service import DatasetService
from services... | 2,713 | 32.506173 | 169 | py |
UnSilence_VOC | UnSilence_VOC-main/src/services/evaluation/ner_evaluation_service.py | import csv
import os
from typing import List, Dict
from overrides import overrides
import torch
from entities.batch_representation import BatchRepresentation
from enums.evaluation_type import EvaluationType
from enums.entity_tag_type import EntityTagType
from enums.language import Language
from services.arguments.... | 4,761 | 40.77193 | 142 | py |
UnSilence_VOC | UnSilence_VOC-main/src/services/evaluation/base_evaluation_service.py | from typing import List, Dict
import torch
from entities.batch_representation import BatchRepresentation
from enums.evaluation_type import EvaluationType
class BaseEvaluationService:
def evaluate_batch(
self,
output: torch.Tensor,
batch_input: BatchRepresentation,
... | 1,002 | 33.586207 | 96 | py |
UnSilence_VOC | UnSilence_VOC-main/src/datasets/dataset_base.py | from torch.utils.data import Dataset
from overrides import overrides
class DatasetBase(Dataset):
def __init__(self, **kwargs):
super().__init__()
def __len__(self) -> int:
return len(super())
@overrides
def __getitem__(self, idx):
return super().__getitem__(idx)
def use_... | 430 | 19.52381 | 43 | py |
UnSilence_VOC | UnSilence_VOC-main/src/datasets/ner_dataset.py | import os
from services.log_service import LogService
import numpy as np
import random
import torch
from typing import List, Tuple
from overrides import overrides
from entities.ner.ne_line import NELine
from entities.ner.ne_collection import NECollection
from entities.batch_representation import BatchRepresentation... | 3,799 | 30.147541 | 122 | py |
UnSilence_VOC | UnSilence_VOC-main/src/entities/batch_representation.py | import numpy as np
import torch
from typing import Tuple, List, Dict
class BatchRepresentation:
def __init__(
self,
device: str,
batch_size: int,
character_sequences: list = [],
subword_sequences: list = [],
subword_characters_count: List[Lis... | 8,664 | 35.561181 | 125 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/joint_adamw_transformer_optimizer.py | from torch import optim
from torch.optim.optimizer import Optimizer
from overrides import overrides
from models.model_base import ModelBase
from optimizers.optimizer_base import OptimizerBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
from transformers import AdamW
class JointAdamWT... | 1,182 | 32.8 | 102 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/adamw_transformer_optimizer.py | from torch import optim
from torch.optim.optimizer import Optimizer
from overrides import overrides
from models.model_base import ModelBase
from optimizers.optimizer_base import OptimizerBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
from transformers import AdamW
class AdamWTransf... | 915 | 27.625 | 118 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/optimizer_base.py | from torch.optim.optimizer import Optimizer
from models.model_base import ModelBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
from transformers import AdamW
class OptimizerBase():
def __init__(
self,
arguments_service: ArgumentsServiceBase,
mo... | 753 | 22.5625 | 74 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/adam_optimizer.py | from torch import optim
from torch.optim.optimizer import Optimizer
from overrides import overrides
from models.model_base import ModelBase
from optimizers.optimizer_base import OptimizerBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
class AdamOptimizer(OptimizerBase):
def __ini... | 914 | 25.911765 | 74 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/sgd_optimizer.py | from torch import optim
from torch.optim.optimizer import Optimizer
from overrides import overrides
from models.model_base import ModelBase
from optimizers.optimizer_base import OptimizerBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
class SGDOptimizer(OptimizerBase):
def __init... | 1,002 | 26.108108 | 74 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/adamw_optimizer.py | from torch import optim
from torch.optim.optimizer import Optimizer
from overrides import overrides
from models.model_base import ModelBase
from optimizers.optimizer_base import OptimizerBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
class AdamWOptimizer(OptimizerBase):
def __in... | 916 | 25.970588 | 74 | py |
UnSilence_VOC | UnSilence_VOC-main/src/optimizers/sparse_adam_optimizer.py | from torch import optim
from torch.optim.optimizer import Optimizer
from overrides import overrides
from models.model_base import ModelBase
from optimizers.optimizer_base import OptimizerBase
from services.arguments.arguments_service_base import ArgumentsServiceBase
class SparseAdamOptimizer(OptimizerBase):
def... | 881 | 25.727273 | 74 | py |
UnSilence_VOC | UnSilence_VOC-main/src/losses/joint_loss.py | import torch.nn as nn
from overrides import overrides
from losses.loss_base import LossBase
class JointLoss(LossBase):
def __init__(self):
super(JointLoss, self).__init__()
@overrides
def backward(self, models_outputs):
for model_output in models_outputs:
model_output.backward... | 578 | 25.318182 | 73 | py |
UnSilence_VOC | UnSilence_VOC-main/src/losses/loss_base.py | import torch.nn as nn
class LossBase(nn.Module):
def __init__(self):
super(LossBase, self).__init__()
def backward(self, model_output):
return 0.0
def calculate_loss(self, model_output):
return 0.0
@property
def criterion(self) -> nn.Module:
return None | 309 | 19.666667 | 43 | py |
UnSilence_VOC | UnSilence_VOC-main/src/losses/ner_loss.py | import torch
import torch.nn as nn
from overrides import overrides
from losses.loss_base import LossBase
class NERLoss(LossBase):
def __init__(self):
super().__init__()
@overrides
def backward(self, model_output):
loss = self._calculate_inner_loss(model_output)
loss.backward()
... | 632 | 23.346154 | 55 | py |
StyleFlow | StyleFlow-master/main.py | import sys
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
import qdarkstyle
import qdarkgraystyle
from time import time
from options.test_options import TestOptions
from ui.ui import Ui_Form
import numpy as np
from sklearn.neighbors import NearestNeighbors
from glob import glob
imp... | 13,719 | 36.282609 | 120 | py |
StyleFlow | StyleFlow-master/train_flow.py |
import dnnlib
from torch import nn, optim
import torch
import numpy as np
from torch.utils import data
from module.flow import cnf
from math import log, pi
import os
from tqdm import tqdm
import random
import torchvision.transforms as transforms
from torch.utils.data import Dataset
import torchvision.datasets as dset... | 3,321 | 29.759259 | 131 | py |
StyleFlow | StyleFlow-master/main_attribute.py | import sys
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import *
import qdarkstyle
import qdarkgraystyle
from options.test_options import TestOptions
from ui.ui2 import Ui_Form
import numpy as np
from sklearn.neighbors import NearestNeighbors
from glob import glob
import cv2
from ui.mous... | 15,003 | 35.595122 | 120 | py |
StyleFlow | StyleFlow-master/module/cnf.py | import torch
import torch.nn as nn
from torchdiffeq import odeint_adjoint
from torchdiffeq import odeint as odeint_normal
__all__ = ["CNF", "SequentialFlow"]
class SequentialFlow(nn.Module):
"""A generalized nn.Sequential container for normalizing flows."""
def __init__(self, layer_list):
super(Sequ... | 4,109 | 30.860465 | 96 | py |
StyleFlow | StyleFlow-master/module/odefunc.py | import copy
import torch
import torch.nn as nn
from . import diffeq_layers
__all__ = ["ODEnet", "ODEfunc"]
def divergence_approx(f, y, e=None):
e_dzdx = torch.autograd.grad(f, y, e, create_graph=True)[0]
e_dzdx_e = e_dzdx.mul(e)
cnt = 0
while not e_dzdx_e.requires_grad and cnt < 10:
# print(... | 4,566 | 31.161972 | 119 | py |
StyleFlow | StyleFlow-master/module/utils.py | from pprint import pprint
from sklearn.svm import LinearSVC
from math import log, pi
import os
import torch
import torch.distributed as dist
import random
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
class AverageValueMeter(object):... | 12,034 | 30.754617 | 105 | py |
StyleFlow | StyleFlow-master/module/diffeq_layers.py |
import torch
import torch.nn as nn
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1 or classname.find('Conv') != -1:
nn.init.constant_(m.weight, 0)
nn.init.normal_(m.bias, 0, 0.01)
class IgnoreLinear(nn.Module):
def __init__(self, dim_in, dim_out, ... | 3,329 | 30.714286 | 70 | py |
StyleFlow | StyleFlow-master/module/normalization.py | import torch
import torch.nn as nn
from torch.nn import Parameter
from .utils import reduce_tensor
__all__ = ['MovingBatchNorm1d']
class MovingBatchNormNd(nn.Module):
def __init__(self, num_features, eps=1e-4, decay=0.1, bn_lag=0., affine=True, sync=False):
super(MovingBatchNormNd, self).__init__()
... | 5,099 | 33.693878 | 94 | py |
ADVENT | ADVENT-master/advent/dataset/base_dataset.py | from pathlib import Path
import numpy as np
from PIL import Image
from torch.utils import data
class BaseDataset(data.Dataset):
def __init__(self, root, list_path, set_,
max_iters, image_size, labels_size, mean):
self.root = Path(root)
self.set = set_
self.list_path = lis... | 1,635 | 29.867925 | 92 | py |
ADVENT | ADVENT-master/advent/scripts/test.py | # --------------------------------------------------------
# AdvEnt training
# Copyright (c) 2019 valeo.ai
#
# Written by Tuan-Hung Vu
# --------------------------------------------------------
import argparse
import os
import os.path as osp
import pprint
import warnings
from torch.utils import data
from advent.model... | 3,247 | 35.088889 | 91 | py |
ADVENT | ADVENT-master/advent/scripts/train.py | # --------------------------------------------------------
# AdvEnt training
# Copyright (c) 2019 valeo.ai
#
# Written by Tuan-Hung Vu
# --------------------------------------------------------
import argparse
import os
import os.path as osp
import pprint
import random
import warnings
import numpy as np
import yaml
im... | 5,891 | 39.356164 | 99 | py |
ADVENT | ADVENT-master/advent/domain_adaptation/eval_UDA.py | # --------------------------------------------------------
# Domain adpatation evaluation
# Copyright (c) 2019 valeo.ai
#
# Written by Tuan-Hung Vu
# --------------------------------------------------------
import os.path as osp
import time
import numpy as np
import torch
from torch import nn
from tqdm import tqdm
f... | 6,145 | 41.680556 | 136 | py |
ADVENT | ADVENT-master/advent/domain_adaptation/train_UDA.py | # --------------------------------------------------------
# Domain adpatation training
# Copyright (c) 2019 valeo.ai
#
# Written by Tuan-Hung Vu
# --------------------------------------------------------
import os
import sys
from pathlib import Path
import os.path as osp
import numpy as np
import torch
import torch.b... | 13,897 | 39.51895 | 99 | py |
ADVENT | ADVENT-master/advent/utils/loss.py | import numpy as np
import torch
import torch.nn.functional as F
from torch.autograd import Variable
def cross_entropy_2d(predict, target):
"""
Args:
predict:(n, c, h, w)
target:(n, h, w)
"""
assert not target.requires_grad
assert predict.dim() == 4
assert target.dim() == 3
... | 1,308 | 32.564103 | 86 | py |
ADVENT | ADVENT-master/advent/utils/func.py | import numpy as np
import torch
import torch.nn as nn
from advent.utils.loss import cross_entropy_2d
def bce_loss(y_pred, y_label):
y_truth_tensor = torch.FloatTensor(y_pred.size())
y_truth_tensor.fill_(y_label)
y_truth_tensor = y_truth_tensor.to(y_pred.get_device())
return nn.BCEWithLogitsLoss()(y_p... | 1,878 | 29.306452 | 83 | py |
ADVENT | ADVENT-master/advent/model/discriminator.py | from torch import nn
def get_fc_discriminator(num_classes, ndf=64):
return nn.Sequential(
nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(negative_slope=0.2, inplace=True),
nn.Conv2d(ndf, ndf * 2, kernel_size=4, stride=2, padding=1),
nn.LeakyReLU(negat... | 681 | 41.625 | 72 | py |
ADVENT | ADVENT-master/advent/model/deeplabv2.py | import torch.nn as nn
affine_par = True
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
super(Bottleneck, self).__init__()
# change
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, stride=stride, bias=Fals... | 6,508 | 35.774011 | 99 | py |
fides | fides-master/docs/conf.py | # flake8: noqa
# -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
import os
import sys
# -- Path setup -----------------------... | 5,892 | 29.533679 | 79 | py |
NAS_DIP | NAS_DIP-master/evolution.py | import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--img', type=str, help='path to image for DIP',
required=True)
parser.add_argument('--scale', type=int, help='scale for SR image',
default=4)
parser.add_argument('--depth', type=int, help='depth of encoder-de... | 9,391 | 32.423488 | 77 | py |
NAS_DIP | NAS_DIP-master/model_generation.py | from tensorflow.keras.layers import Conv2D, Add, Concatenate, Input, \
MaxPool2D, UpSampling2D, LeakyReLU, \
BatchNormalization, Layer
from tensorflow.compat.v2.nn import depth_to_space, space_to_depth
from tensorflow.keras import Model
from tensor... | 5,529 | 35.622517 | 73 | py |
NAS_DIP | NAS_DIP-master/train.py | import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--model', type=str, help='path to model',
required=True)
parser.add_argument('--img', type=str, help='path to image for DIP',
required=True)
parser.add_argument('--scale', type=int, help='scale for SR image',... | 4,876 | 34.860294 | 75 | py |
DQBC | DQBC-master/val.py |
from validate import make_validate_func
from models import make_model, model_profile
from utils.config import children, make_config
from functools import partial
import torch
import os.path as osp
import argparse
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--config',h... | 992 | 24.461538 | 72 | py |
DQBC | DQBC-master/test.py | from models import make_model, model_profile
from utils.config import make_config
import torch
import argparse
from datas.utils import imread_rgb
import torchvision.transforms.functional as TF
from PIL import Image
import numpy as np
import os
if __name__=='__main__':
parser = argparse.ArgumentParser()
p... | 1,257 | 24.16 | 88 | py |
DQBC | DQBC-master/train.py | from datas.data_feeder import make_feeder
from utils.config import make_config
from models import make_model, model_profile
from datas import make_dataloader
from utils.count import check_step
from validate import Validator, make_validate_func
from losses import make_loss
import torch
import torch.optim as optim
from u... | 6,329 | 27.513514 | 146 | py |
DQBC | DQBC-master/datas/MiddleBury_Other.py | import torch
import torch.utils.data as data
import torchvision.transforms.functional as TF
import os
from .utils import imread
class MiddelBuryOther(data.Dataset):
def __init__(self, args):
self.triplets = []
self.name_list = ['Beanbags', 'Dimetrodon', 'DogDance', 'Grove2', 'Grove3', 'Hydrangea',... | 1,343 | 31.780488 | 165 | py |
DQBC | DQBC-master/datas/ucf101.py | import torch
import torch.utils.data as data
import torchvision.transforms.functional as TF
import os
from .utils import imread
class UCF101_test(data.Dataset):
def __init__(self, args):
self.sequence_list = []
temp_list = os.listdir(args.dataset_root) # ex) '/hdd/ucf101_interp_ours'
self.... | 1,211 | 31.756757 | 84 | py |
DQBC | DQBC-master/datas/__init__.py | from .Vimeo90K import Vimeo_train
import torch.utils.data as data
from torch.utils.data.distributed import DistributedSampler
def make_dataloader(cfg):
if cfg.data.name=='Vimeo90K':
train_dataset = Vimeo_train(cfg.data)
else:
raise NotImplementedError
sampler = DistributedSampler(tra... | 506 | 35.214286 | 141 | py |
DQBC | DQBC-master/datas/SNU_FILM.py | import torch
import torch.utils.data as data
import torchvision.transforms.functional as TF
import os
from hashlib import sha256
from .utils import imread
class SNU_FILM(data.Dataset):
def __init__(self, args):
self.triplets = []
if args.split in ['easy','medium','hard','extreme']:
spl... | 1,744 | 30.727273 | 78 | py |
DQBC | DQBC-master/datas/Vimeo90K.py | import torch
import torch.utils.data as data
import torchvision.transforms.functional as TF
import cv2
import random
import numpy as np
import os
from .utils import imread
class Vimeo_train(data.Dataset):
def __init__(self, args):
self.crop_size = args.crop_size
self.sequence_list = []
wit... | 6,353 | 35.94186 | 101 | py |
DQBC | DQBC-master/validate/validate_std.py | import torch
from validate.metrics import calculate_psnr, calculate_ssim, calculate_ie
from validate.bucket import Bucket
from .utils import make_validation_set
from tqdm import tqdm
from skimage.metrics import peak_signal_noise_ratio, structural_similarity
import os.path as osp
import os
from PIL import Image
import n... | 5,939 | 29 | 132 | py |
DQBC | DQBC-master/validate/bucket.py | from utils.flow_viz import flow_to_image
import torch
import numpy as np
from PIL import Image
import os
def plain_up(x,scale_factor):
'''
Args:
x: B,C,H,W
scale_factor: int
'''
x = torch.repeat_interleave(x,int(scale_factor),dim=-2)
x = torch.repeat_interleave(x,int(scale_factor),d... | 3,848 | 28.837209 | 83 | py |
DQBC | DQBC-master/validate/utils.py | import datas
import numpy as np
from numpy.random import RandomState
import torch
import torch.nn.functional as F
from PIL import Image
import os.path as osp
def log_print(log_file_path, msg):
print(msg)
if log_file_path is not None:
with open(log_file_path,'a') as f:
f.write(msg+'\n')
def... | 1,849 | 27.90625 | 85 | py |
DQBC | DQBC-master/validate/metrics.py | import numpy as np
import math
from .pytorch_msssim import ssim_matlab
def calculate_psnr(gt, pred, quant=True):
'''
Args:
gt, pred: [0,255] any dtype
'''
if quant:
pred = np.round(pred).astype('uint8')/255.0
else:
pred = pred/255.0
gt = gt/255.0
psnr = -10 * math.l... | 552 | 21.12 | 63 | py |
DQBC | DQBC-master/validate/pytorch_msssim/__init__.py | import torch
import torch.nn.functional as F
from math import exp
import numpy as np
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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.su... | 6,999 | 33.825871 | 118 | py |
DQBC | DQBC-master/models/__init__.py | import torch
from collections import OrderedDict
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel
from utils.model_profile import make_profile
from functools import partial
def load_weights(model, cfg, begin_with_module=False):
if hasattr(cfg,'pretrained') and cfg.pretrained != None:
... | 1,484 | 27.557692 | 76 | py |
DQBC | DQBC-master/models/dqbc/context.py | import torch.nn as nn
from .common import Conv2
import torch
def make_cnet(cfg, in_chans):
return ContextNet([in_chans]+cfg.dims)
class ContextNet(nn.Module):
def __init__(self,c) -> None:
super().__init__()
# self.d = d
self.down0 = Conv2(c[0],c[1])
# self.reduce0 = conv(c[0],... | 1,146 | 28.410256 | 63 | py |
DQBC | DQBC-master/models/dqbc/vfi_model.py | import torch.nn as nn
import torch
from .extractor import BasicEncoder
from .flow_up import make_flowup
from validate.bucket import Bucket
from .flow_gen import make_flowgen
from .synth import make_synth
from .context import make_cnet
from .flow_tea import make_flowtea
from .corr import make_corr_fn
import torch.nn.fu... | 3,538 | 34.39 | 102 | py |
DQBC | DQBC-master/models/dqbc/warplayer.py | import torch
import torch.nn as nn
import torch.nn.functional as F
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
backwarp_tenGrid = {}
def get_grid(flow):
'''
Args:
flow: B,*,H,W
Returns:
grid: B,2,H,W
'''
device = flow.device
k = (str(flow.device), st... | 1,223 | 33 | 108 | py |
DQBC | DQBC-master/models/dqbc/synth.py | import torch
import torch.nn as nn
from .warplayer import warp
import torch.nn.functional as F
from validate.bucket import Bucket
from .context import make_cnet
from .common import deconv, conv, make_conv_block, mlp
def make_synth(cfg):
return SynthNet(cfg)
class UpBlock(nn.Module):
def __init__(self, cfg, s... | 2,694 | 28.944444 | 102 | py |
DQBC | DQBC-master/models/dqbc/extractor.py | from numpy import pad
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
def __init__(self, in_planes, planes, norm_fn='group', stride=1):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=2+stride, ... | 4,873 | 32.613793 | 97 | py |
DQBC | DQBC-master/models/dqbc/common.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def conv373(c):
return nn.Sequential(
conv(c,c,3,1,1),
conv(c,c,7,1,3,groups=4),
conv(c,c,3,1,1)
)
def conv333(c):
return nn.Sequential(
conv(c,c,3,1,1),
conv(c,c,3,1,1),
conv(c,c,3,1,1)
... | 2,672 | 26.556701 | 128 | py |
DQBC | DQBC-master/models/dqbc/interpolator.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .warplayer import warp
from .vfi_model import VFIModel
from validate.bucket import Bucket
class Interpolator(nn.Module):
def __init__(self,cfg) -> None:
super().__init__()
self.cfg = cfg
self.img_size = (0,0)
se... | 1,514 | 24.677966 | 69 | py |
DQBC | DQBC-master/models/dqbc/flow_tea/IFNet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
return nn.Sequential(
torch.nn.ConvTranspose2d(in_channels=in_planes, out_channels=out_planes, kernel_size=4, stride=2, padding=1),
nn.PReLU(out_planes)
)
d... | 1,860 | 34.113208 | 117 | py |
DQBC | DQBC-master/models/dqbc/flow_tea/flow_tea.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .IFNet import IFBlock
from ..warplayer import warp
from validate.bucket import Bucket
class FlowTeacher(nn.Module):
def __init__(self, cfg) -> None:
super().__init__()
self.block_tea = IFBlock(17+4, c=cfg.c, mask=cfg.recalc_mas... | 2,467 | 34.257143 | 134 | py |
DQBC | DQBC-master/models/dqbc/flow_gen/flow_gen.py | import torch.nn as nn
import torch
from ..common import conv
from validate.bucket import Bucket
from ..corr import get_corr_dim
from ..common import Conv2
class IniNet(nn.Module):
def __init__(self, cfg) -> None:
super().__init__()
c = cfg.dims
self.down0 = Conv2(6,c[0])
self.down1 ... | 2,757 | 24.072727 | 67 | py |
DQBC | DQBC-master/models/dqbc/corr/correlation.py |
import torch.nn as nn
import torch
import torch.nn.functional as F
def bilinear_pyramid(x, n):
p = [x]
for _ in range(n-1):
p.append(F.interpolate(p[-1],scale_factor=0.5,mode='bilinear',align_corners=False))
return p
def make_grids(H,W,dev):
'''
don't align corners
Returns:
... | 779 | 23.375 | 91 | py |
DQBC | DQBC-master/models/dqbc/corr/corr_x.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from .correlation import make_grids, to_relative
from validate.bucket import Bucket
from ..common import conv
class CorrLookupX(nn.Module):
def __init__(self,cfg,dev='cuda') -> None:
super().__init__()
self.cfg = cfg
r_list... | 5,287 | 30.47619 | 83 | py |
DQBC | DQBC-master/models/dqbc/corr/__init__.py | import torch.nn as nn
from .corr_x import CorrLookupX
def make_corr_fn(cfg):
if cfg.arch == 'x':
return CorrLookupX(cfg)
else:
raise NotImplementedError
def get_corr_dim(cfg):
if cfg.arch == 'x':
d = CorrLookupX.corr_dim(cfg)
return d, d
else:
raise NotImplement... | 327 | 20.866667 | 37 | py |
DQBC | DQBC-master/models/dqbc/flow_up/flow_up.py | import torch
import torch.nn as nn
from ..warplayer import warp
from ..common import convex_upsample,conv,deconv,make_conv_block,mlp
from validate.bucket import Bucket, plain_up
class FlowDecoder(nn.Module):
def __init__(self, in_c, up=True, flow_in=False, delta=True) -> None:
super().__init__()
se... | 5,177 | 27.766667 | 123 | py |
DQBC | DQBC-master/utils/model_profile.py | import torch
from thop import profile, clever_format
# from thop.vision.calc_func import *
import torch.nn as nn
def M_params(model:nn.Module):
if model == None:
return 0
return sum([p.numel() for p in model.parameters()])/1e6
def make_profile(model_maker, inputs, inputs_description:str)->str:
m... | 778 | 26.821429 | 98 | py |
DQBC | DQBC-master/utils/logger.py | from torch.utils.tensorboard import SummaryWriter
import time
import os.path as osp
class Logger:
def __init__(self,cfg, model, lr_fn, init_step=0):
self.model = model
self.total_steps = init_step
self.lr_fn = lr_fn
self.running_loss = {}
self.info_str = None
self.wr... | 2,261 | 29.16 | 100 | py |
DQBC | DQBC-master/losses/losses.py | import torch.nn.functional as F
from utils.config import children
from functools import partial
def _make_loss(losses):
def f(pred,gt):
metrics = dict()
loss = 0
for tag, L, w in losses:
tag = 'loss/'+tag
metrics[tag] = L(pred,gt)
loss += metrics[tag]*w
... | 761 | 29.48 | 72 | py |
detnet | detnet-master/src/main.py | # -*- coding: utf-8 -*-
from os.path import dirname, abspath
import sys
import torch
sys.path.insert(0, dirname(dirname(abspath(__file__))))
from argparse import ArgumentParser
from utils.res_handler import ResLoader
import utils.config_loader as config
from utils.config_loader import mode, meta_model_name, logger, co... | 4,987 | 30.371069 | 108 | py |
detnet | detnet-master/src/frame/model.py | # -*- coding: utf-8 -*-
from utils.config_loader import device, placement
from os.path import dirname, abspath
import sys
sys.path.insert(0, dirname(dirname(abspath(__file__))))
import data.data_pipe as pipe
from utils.config_loader import config_model, n_ways
from frame.encoder import *
from frame.detector import *
fr... | 18,730 | 33.945896 | 125 | py |
detnet | detnet-master/src/frame/encoder.py | # -*- coding: utf-8 -*-
import math
import torch
import torch.nn as nn
from frame import transformer as trans
class Encoder(nn.Module):
"""
Core encoder is a stack of N layers
"""
def __init__(self, layer, n_layers, ins_attn, ins_attn_layer=None, d_embed=None):
super(Encoder, self).__init_... | 13,303 | 39.193353 | 136 | py |
detnet | detnet-master/src/frame/transformer.py | # -*- coding: utf-8 -*-
import math
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import copy
class Embeddings(nn.Module):
def __init__(self, d_model, vocab_size):
super(Embeddings, self).__init__()
self.lut = nn.Embedding(vocab_size, d_mode... | 7,069 | 33.656863 | 113 | py |
detnet | detnet-master/src/frame/detector.py | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from utils.config_loader import logger
class Detector(nn.Module):
def __init__(self, d_embed, n_doms, score_func, dropout, activate_func=None, gate=None, embed_layer=None):
super(... | 18,991 | 38.89916 | 144 | py |
detnet | detnet-master/src/frame/checkpoint_op.py | # -*- coding: utf-8 -*-
import os
from os import listdir
from os.path import exists, join, dirname, abspath, isfile
import sys
sys.path.insert(0, dirname(dirname(abspath(__file__))))
from frame.model import *
import utils.config_loader as config_loader
import shutil
def save_checkpoint(state, checkpoint, n_iter, is_be... | 4,447 | 34.301587 | 112 | py |
detnet | detnet-master/src/data/data_pipe.py | # -*- coding: utf-8 -*-
import io
from os import listdir
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from os.path import join, isfile, dirname, abspath
import sys
sys.path.insert(0, dirname(dirname(abspath(__file__))))
from data.dataset_parser import DatasetParser
from utils.config... | 13,084 | 34.557065 | 120 | py |
BrnoLM | BrnoLM-master/test/test_runtime_util.py | import torch
from .common import TestCase
from brnolm.runtime.runtime_utils import repackage_hidden
class TensorReorganizerTests(TestCase):
def setUp(self):
tensor = torch.tensor(
[[[0.1, 0.1], [0.2, 0.2], [0.3, 0.3]]],
requires_grad=True
)
self.computed_tensor = t... | 841 | 30.185185 | 62 | py |
BrnoLM | BrnoLM-master/test/common.py | import sys
import os
import argparse
import unittest
import warnings
import contextlib
from functools import wraps
from itertools import product
from copy import deepcopy
import torch
import torch.cuda
from torch.autograd import Variable
torch.set_default_tensor_type('torch.DoubleTensor')
SEED = 0
SEED_SET = 0
de... | 8,988 | 29.784247 | 85 | py |
BrnoLM | BrnoLM-master/test/test_analysis.py | import unittest
import torch
from brnolm.analysis import categorical_entropy, categorical_cross_entropy, categorical_kld
class CategoricalEntropyTests(unittest.TestCase):
def setUp(self):
pass
def test_uniform_2d(self):
p_x = torch.FloatTensor([0.5, 0.5])
H_x = torch.FloatTensor([1... | 4,107 | 24.358025 | 91 | py |
BrnoLM | BrnoLM-master/test/test_smm_ivec_extractor.py | import unittest
from .common import TestCase
import os
import sys
try:
import brnolm.smm_itf.smm_ivec_extractor as smm_ivec_extractor
except ImportError:
sys.stderr.write('Failed to import SMM implementation\n')
from brnolm.language_models.vocab import Vocabulary
import torch
import torch.nn as nn
from torch.... | 3,809 | 41.808989 | 105 | py |
BrnoLM | BrnoLM-master/test/test_tensor_reorganization.py | from brnolm.runtime.tensor_reorganization import TensorReorganizer
import torch
from torch.autograd import Variable
from .common import TestCase
class Dummy_lstm():
def __init__(self, nb_hidden):
self._nb_hidden = nb_hidden
def init_hidden(self, batch_size):
return (
torch.FloatT... | 5,460 | 29.50838 | 86 | py |
BrnoLM | BrnoLM-master/test/test_ivec_appenders.py | from .common import TestCase
import os
import sys
import unittest
import numpy as np
import torch
from sklearn.feature_extraction.text import CountVectorizer
import brnolm.data_pipeline.split_corpus_dataset as split_corpus_dataset
import brnolm.smm_itf.ivec_appenders as ivec_appenders
from brnolm.language_models.voca... | 8,792 | 35.334711 | 105 | py |
BrnoLM | BrnoLM-master/test/test_runtime/test_evaluation.py | from test.common import TestCase
import math
import torch
from brnolm.runtime.evaluation import get_oov_additional_cost
from brnolm.runtime.evaluation import OovCostApplicator
class OovCostTests(TestCase):
def test_simple(self):
oov_cost = get_oov_additional_cost(100, 1000)
expected = -math.log(1... | 1,808 | 35.918367 | 78 | py |
BrnoLM | BrnoLM-master/test/test_data_pipeline/test_masked.py | import unittest
import os
from test.common import TestCase
from torch import tensor
from brnolm.data_pipeline.masked import masked_tensor_from_sentences
class MaskedDataCreationTests(TestCase):
def test_requires_sequence(self):
self.assertRaises(ValueError, masked_tensor_from_sentences, 0)
def tes... | 4,805 | 22.674877 | 96 | py |
BrnoLM | BrnoLM-master/test/test_data_pipeline/test_multistream.py | from brnolm.data_pipeline.multistream import BatchBuilder
from brnolm.data_pipeline.multistream import Batcher
from brnolm.data_pipeline.multistream import LineTooLongError
import brnolm.data_pipeline.split_corpus_dataset as split_corpus_dataset
import brnolm.smm_itf.ivec_appenders as ivec_appenders
import numpy as np... | 15,769 | 30.166008 | 107 | py |
BrnoLM | BrnoLM-master/test/test_data_pipeline/test_reading.py | from test.common import TestCase
import io
import torch
from brnolm.language_models.vocab import Vocabulary
from brnolm.data_pipeline.reading import get_independent_lines
def get_stream(string):
data_source = io.StringIO()
data_source.write(string)
data_source.seek(0)
return data_source
class In... | 1,508 | 25.946429 | 62 | py |
BrnoLM | BrnoLM-master/test/test_data_pipeline/test_split_corpus_dataset.py | import brnolm.data_pipeline.split_corpus_dataset as split_corpus_dataset
import torch
from test.common import TestCase
from test.utils import getStream
class TokenizedSplitTests(TestCase):
def setUp(self):
self.test_words_short = "a b c a".split()
self.test_words_long = "a b c a a".split()
... | 11,846 | 42.237226 | 166 | py |
BrnoLM | BrnoLM-master/test/test_language_models/test_lstm.py | from test.common import TestCase
import torch
from brnolm.language_models.lstm_model import LSTMLanguageModel
from brnolm.language_models.encoders import FlatEmbedding
class OutputExtractionTests(TestCase):
def test_multilayer(self):
encoder = FlatEmbedding(4, 10)
model = LSTMLanguageModel(token... | 557 | 31.823529 | 109 | py |
BrnoLM | BrnoLM-master/test/test_language_models/test_language_model.py | import unittest
import os
import torch
import test.common
from brnolm.language_models.lstm_model import LSTMLanguageModel
from brnolm.language_models.decoders import FullSoftmaxDecoder
from brnolm.language_models.language_model import LanguageModel
from brnolm.language_models.vocab import Vocabulary
from brnolm.lang... | 6,179 | 34.517241 | 97 | py |
BrnoLM | BrnoLM-master/test/test_language_models/test_decoders.py | from test.common import TestCase
import torch
from brnolm.language_models.decoders import FullSoftmaxDecoder
class FullSoftmaxDecoderTests(TestCase):
def test_raw_log_prob_shape(self):
decoder = FullSoftmaxDecoder(4, 3)
o = torch.zeros((2, 3, 4), dtype=torch.float64)
t = torch.tensor([
... | 461 | 24.666667 | 62 | py |
BrnoLM | BrnoLM-master/scripts/display-augmented-data.py | #!/usr/bin/env python3
import argparse
import logging
import pickle
import sys
import torch
from brnolm.data_pipeline.reading import tokens_from_fn
from brnolm.data_pipeline.aug_paper_pipeline import form_input_targets
from brnolm.data_pipeline.aug_paper_pipeline import Corruptor
from brnolm.data_pipeline.aug_paper_p... | 3,192 | 38.9125 | 144 | py |
BrnoLM | BrnoLM-master/scripts/migrator.py | #!/usr/bin/env python3
'''Migrates old LM from before proper brnolm package was introduced.
Build around the proposition of this SO answer:
https://stackoverflow.com/a/53327348/9703830
Uses a separate, monkey-patched pickle (`my_pickle`) for de-serialization
in order to ensure that the pure system pickle is ready to ... | 1,247 | 25 | 79 | py |
BrnoLM | BrnoLM-master/scripts/model-info.py | #!/usr/bin/env python3
import argparse
import torch
from brnolm.runtime.model_statistics import ModelStatistics
def main():
parser = argparse.ArgumentParser()
parser.add_argument('model_path')
args = parser.parse_args()
lm = torch.load(args.model_path, map_location='cpu')
print(ModelStatistics(l... | 364 | 18.210526 | 59 | py |
BrnoLM | BrnoLM-master/scripts/migrator-batch-first.py | #!/usr/bin/env python3
'''Migrates old LM from before proper brnolm package was introduced.
Build around the proposition of this SO answer:
https://stackoverflow.com/a/53327348/9703830
Uses a separate, monkey-patched pickle (`my_pickle`) for de-serialization
in order to ensure that the pure system pickle is ready to ... | 721 | 23.896552 | 79 | py |
BrnoLM | BrnoLM-master/scripts/export-torchscript.py | import argparse
import torch
from brnolm.language_models import language_model
def main(args):
if args.force_cpu:
lm = torch.load(args.lm, map_location='cpu')
else:
lm = torch.load(args.lm)
language_model.torchscript_export(lm, args.frozen_lm)
if __name__ == '__main__':
parser = argp... | 516 | 22.5 | 59 | py |
BrnoLM | BrnoLM-master/scripts/corpus-stats.py | #!/usr/bin/env python
import argparse
import torch
import sys
import collections
from brnolm.data_pipeline.reading import tokens_from_fn, word_splitter, char_splitter
def get_oovs(fn, regime, vocab):
with open(args.train) as f:
lines = f.read().split('\n')
oov_counts = collections.defaultdict(int)
... | 2,223 | 32.19403 | 97 | py |
BrnoLM | BrnoLM-master/scripts/sample-from-lm.py | #!/usr/bin/env python3
import argparse
import torch
from torch.distributions import Categorical
import sys
class Unbuffered:
def __init__(self, stream):
self.stream = stream
def write(self, data):
self.stream.write(data)
self.stream.flush()
def writelines(self, datas):
... | 2,718 | 23.495495 | 90 | py |
BrnoLM | BrnoLM-master/scripts/eval/eval-ivecs-partial.py | #!/usr/bin/env python
import argparse
import math
import torch
from brnolm.smm_itf import ivec_appenders
from brnolm.smm_itf import smm_ivec_extractor
from brnolm.data_pipeline.multistream import BatchBuilder
from brnolm.data_pipeline.temporal_splitting import TemporalSplits
from brnolm.data_pipeline.split_corpus_dat... | 2,888 | 37.52 | 90 | py |
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