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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DisPFL | DisPFL-master/fedml_experiments/standalone/ditto/main_ditto.py | import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
sys.path.insert(0, os.path.abspath("/gdata/dairong/DisPFL/"))
from fedml_api.model.cv.vgg import vgg11, vgg16
from fedml_api.standalone.ditto.ditto_api import DittoAPI
from fedml_api.data_preprocessing.cifar10.data_loader... | 8,806 | 44.164103 | 141 | py |
DisPFL | DisPFL-master/fedml_experiments/standalone/fedfomo/main_fedfomo.py | import argparse
import logging
import os
import random
import sys
import numpy as np
import torch
sys.path.insert(0, os.path.abspath("/gdata/dairong/DisPFL/"))
from fedml_api.model.cv.vgg import vgg11
from fedml_api.model.cv.lenet5 import LeNet5
from fedml_api.data_preprocessing.cifar10.data_val_loader import load_p... | 8,977 | 44.115578 | 141 | py |
ExU-Net | ExU-Net-main/main.py | import os
import random
import importlib
import numpy as np
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import CosineAnnealingWarmRestarts
import arguments
import trainers.train as train
import data.data_loader as data
from data.voxceleb1 import VoxCeleb1
from utils.util import init_weights
from ... | 6,009 | 32.764045 | 168 | py |
ExU-Net | ExU-Net-main/arguments.py | import os
from itertools import chain
import torch
def get_args():
system_args = {
# expeirment info
'project' : 'ExU-Net',
'name' : 'ExU-Net',
'tags' : ['ExU-Net'],
'description' : 'ExU-Net',
# local
'path_logging' : '/results',
... | 2,832 | 31.193182 | 75 | py |
ExU-Net | ExU-Net-main/models/deep_res_unet.py | import torch
import torch.nn as nn
from models.ResNetBlocks import *
class SE_ResUNet(nn.Module):
def __init__(self, args):
super(SE_ResUNet, self).__init__()
self.l_channel = args['l_channel']
self.l_num_convblocks = args['l_num_convblocks']
self.code_dim = args['code_dim']
self.stride = args['stride']
... | 4,879 | 34.362319 | 147 | py |
ExU-Net | ExU-Net-main/models/exunet.py | import torch
import torch.nn as nn
from models.ResNetBlocks import *
class ExUNet(nn.Module):
def __init__(self, args):
super(ExUNet, self).__init__()
self.l_channel = args.model['l_channel']
self.l_num_convblocks = args.model['l_num_convblocks']
self.code_dim = args.model['code_dim']
self.stride = args.mo... | 5,672 | 34.45625 | 147 | py |
ExU-Net | ExU-Net-main/models/deep_res_znet.py | import torch
import torch.nn as nn
from models.ResNetBlocks import *
class SE_ResZNet(nn.Module):
def __init__(self, args):
super(SE_ResZNet, self).__init__()
self.l_channel = args['l_channel']
self.l_num_convblocks = args['l_num_convblocks']
self.code_dim = args['code_dim']
self.stride = args['stride']
... | 5,666 | 35.095541 | 147 | py |
ExU-Net | ExU-Net-main/models/unet.py | import torch
import torch.nn as nn
from models.ResNetBlocks import *
class UNet(nn.Module):
"""
UNet-based system
"""
def __init__(self, args):
super(UNet, self).__init__()
self.l_channel = args.model['l_channel']
self.l_num_convblocks = args.model['l_num_convblocks']
self.code_dim = args.model['code_di... | 4,804 | 33.078014 | 147 | py |
ExU-Net | ExU-Net-main/models/baseline.py | import torch
import torch.nn as nn
from models.ResNetBlocks import *
class Baseline(nn.Module):
"""
SEResNet
"""
def __init__(self, args):
super(Baseline, self).__init__()
self.l_channel = args.model['l_channel']
self.l_num_convblocks = args.model['l_num_convblocks']
self.code_dim = args.model['code_dim'... | 3,281 | 31.176471 | 147 | py |
ExU-Net | ExU-Net-main/models/se_resnet.py | import torch
import torch.nn as nn
from models.ResNetBlocks import *
class SE_ResNet_Encoder(nn.Module):
"""
SEResNet
"""
def __init__(self, args):
super(SE_ResNet_Encoder, self).__init__()
self.l_channel = args['l_channel']
self.l_num_convblocks = args['l_num_convblocks']
self.code_dim = args['code_dim'... | 3,251 | 31.19802 | 147 | py |
ExU-Net | ExU-Net-main/models/ResNetBlocks.py | import torch.nn as nn
class SEBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
super(SEBasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 =... | 2,762 | 29.032609 | 101 | py |
ExU-Net | ExU-Net-main/speech_features/log_melspectrogram.py | import torch
import torchaudio
class LogMelspectrogram():
"""Extract Log-Melspectrogram from raw waveform using torchaudio.
Note that this module automatically synchronizes device with input tensor.
"""
def __init__(self, winlen, winstep, nfft, samplerate, nfilts, premphasis, winfunc):
super(Lo... | 1,171 | 27.585366 | 87 | py |
ExU-Net | ExU-Net-main/loss/softmax.py | import torch.nn as nn
class LossFunction(nn.Module):
def __init__(self, nOut, nClasses, **kwargs):
super(LossFunction, self).__init__()
self.test_normalize = True
self.criterion = nn.CrossEntropyLoss()
self.fc = nn.Linear(nOut, nClasses, bias=True)
print('Initialised Softmax Loss')
def forward(sel... | 410 | 21.833333 | 50 | py |
ExU-Net | ExU-Net-main/loss/mse.py | import torch.nn as nn
class LossFunction(nn.Module):
def __init__(self, **kwargs):
super(LossFunction, self).__init__()
self.criterion = nn.MSELoss()
print('Initialised Mean Squared Error Loss')
def forward(self, x, label=None):
nloss = self.criterion(x, label)
return nloss | 296 | 20.214286 | 46 | py |
ExU-Net | ExU-Net-main/loss/aam_softmax.py | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class LossFunction(nn.Module):
def __init__(self, nOut, nClasses, margin=0.3, scale=15, easy_margin=False, **kwargs):
super(LossFunction, self).__init__()
self.m = margin
self.s = scale
self.in_feats = ... | 1,761 | 32.884615 | 101 | py |
ExU-Net | ExU-Net-main/loss/angleproto.py | #! /usr/bin/python
# -*- encoding: utf-8 -*-
# Adapted from https://github.com/clovaai/voxceleb_trainer/tree/master/loss
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class LossFunction(nn.Module):
def __init__(self, gpu, init_w=10.0, init_b=-5.0, **kwargs):
super(LossFunct... | 1,059 | 26.179487 | 107 | py |
ExU-Net | ExU-Net-main/utils/ddp_util.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 functools
import logging
import numpy as np
import pickle
import torch
import torch.distributed as dist
_LOCAL_PROCESS_GROUP ... | 8,771 | 26.671924 | 100 | py |
ExU-Net | ExU-Net-main/utils/util.py | import math
import random
import numpy as np
import torch
import torch.nn as nn
__all__=['duplicate', 'subtensor', 'linspace_crop', 'rand_crop']
def duplicate(x, size, dim=-1):
"""duplicate tensor in given dimension
until x is larger than size
params
x - tensor to duplicate
size ... | 2,483 | 25.425532 | 105 | py |
ExU-Net | ExU-Net-main/utils/summary.py | import torch
import torch.nn as nn
from collections import OrderedDict
import numpy as np
def summary_string(model, input_size, batch_size=-1, device=torch.device('cuda:0'), dtypes=None):
if dtypes == None:
dtypes = [torch.FloatTensor]*len(input_size)
summary_str = ''
def register_hook(module):
... | 4,561 | 40.099099 | 97 | py |
ExU-Net | ExU-Net-main/data/data_loader.py | import math
import torch
import torch.utils.data as td
import soundfile as sf
import numpy as np
import warnings
import utils.util as util
from data.musan import MusanNoise
def get_loaders(args, vox1):
train_set = TrainSet(args, vox1)
train_set_sampler = Voxceleb_sampler(dataset=train_set, nb_utt_per_spk=args['nb_u... | 6,059 | 26.798165 | 167 | py |
ExU-Net | ExU-Net-main/data/musan.py | import os
import random
import numpy as np
import soundfile as sf
import torch
class MusanNoise:
Category = ['noise','speech','music']
SNR = {
'noise': (0, 20),
'speech': (0, 20),
'music': (0, 20)
}
NumFile = {
'noise': (1, 1),
'speech': (3, 6),
'music': (1, 1)
}
def __init__(self, path):
# ... | 2,133 | 21 | 63 | py |
ExU-Net | ExU-Net-main/log/local.py | import os
import time
import torch
import shutil
import zipfile
from threading import Thread
from .interface import ExperimentLogger
def zipdir(path, ziph):
for root, dirs, files in os.walk(path):
for file in files:
fn, ext = os.path.splitext(file)
if ext != ".py":
continue
ap = '/'.join(os.path... | 2,618 | 25.454545 | 78 | py |
ExU-Net | ExU-Net-main/trainers/train.py | from tqdm import tqdm
import torch
import torch.nn.functional as F
import numpy as np
import torch.distributed as dist
from utils.ddp_util import all_gather
import utils.metric as metric
class ModelTrainer:
args = None
vox1 = None
model = None
logger = None
criterion = None
optimizer = None
lr_scheduler = Non... | 7,672 | 32.073276 | 191 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/train.py | from typing import List, Optional
import hydra
from omegaconf import DictConfig
from pytorch_lightning import (
Callback,
LightningDataModule,
LightningModule,
Trainer,
seed_everything,
)
from pytorch_lightning.loggers import LightningLoggerBase
from src.utils import utils
log = utils.get_logger(... | 3,224 | 30.617647 | 85 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/datamodules/hecktor_datamodule.py | from typing import Optional, Tuple
from math import pi
from pytorch_lightning import LightningDataModule
from torch.utils.data import ConcatDataset, DataLoader, Dataset, random_split, Subset
from torchvision.datasets import MNIST
from torchvision import transforms
from src.datamodules.transforms import *
from src.d... | 4,563 | 35.222222 | 157 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/datamodules/transforms.py | """Augmentation transforms operating on SimpleITK images.
Credits:
@article{
kim_deep-cr_2020,
title = {Deep-{CR} {MTLR}: a {Multi}-{Modal} {Approach} for {Cancer} {Survival} {Prediction} with {Competing} {Risks}},
shorttitle = {Deep-{CR} {MTLR}},
url = {https://arxiv.org/abs/2012.05765v1},
language = {en},
urld... | 5,367 | 26.111111 | 120 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/datamodules/datasets/hecktor_dataset.py | import os
from typing import Callable, Optional, Tuple
import sys
import SimpleITK as sitk
from pathlib import Path
import torch
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
from joblib import Parallel, delayed
from sklearn.preprocessing import scale
from torchmtlr.utils import make_t... | 4,762 | 30.966443 | 171 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/callbacks/wandb_callbacks.py | import subprocess
from pathlib import Path
from typing import List
import matplotlib.pyplot as plt
import seaborn as sn
import torch
import wandb
from pytorch_lightning import Callback, Trainer
from pytorch_lightning.loggers import LoggerCollection, WandbLogger
from pytorch_lightning.utilities import rank_zero_only
fr... | 9,828 | 33.487719 | 116 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/models/deepmtlr_model.py | from typing import Any, List
import torch
from pytorch_lightning import LightningModule
from torchmetrics.classification.accuracy import Accuracy
from torch.optim import Adam
from torch.optim.lr_scheduler import MultiStepLR
import torch.nn as nn
from torchmtlr import mtlr_neg_log_likelihood, mtlr_survival, mtlr_risk... | 6,697 | 34.818182 | 121 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/models/modules/net.py | import torch
from torch import nn
from torchmtlr import MTLR
#**Update
#Number of clin_var
n_clin_var = 13
def conv_3d_block (in_c, out_c, act='relu', norm='bn', num_groups=8, *args, **kwargs):
activations = nn.ModuleDict ([
['relu', nn.ReLU(inplace=True)],
['lrelu', nn.LeakyReLU(0.1, inplace=True... | 3,111 | 31.757895 | 120 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/src/utils/utils.py | import logging
import os
import warnings
from typing import List, Sequence
import pytorch_lightning as pl
import rich.syntax
import rich.tree
from omegaconf import DictConfig, OmegaConf
from pytorch_lightning.utilities import rank_zero_only
def get_logger(name=__name__, level=logging.INFO) -> logging.Logger:
"""... | 5,401 | 30.045977 | 95 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/tests/unit/test_mnist_datamodule.py | import os
import pytest
import torch
from src.datamodules.mnist_datamodule import MNISTDataModule
@pytest.mark.parametrize("batch_size", [32, 128])
def test_mnist_datamodule(batch_size):
datamodule = MNISTDataModule(batch_size=batch_size)
datamodule.prepare_data()
assert not datamodule.data_train and n... | 1,048 | 27.351351 | 99 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/tests/helpers/runif.py | import sys
from typing import Optional
import pytest
import torch
from packaging.version import Version
from pkg_resources import get_distribution
"""
Adapted from:
https://github.com/PyTorchLightning/pytorch-lightning/blob/master/tests/helpers/runif.py
"""
from tests.helpers.module_available import (
_APEX_... | 3,432 | 29.380531 | 93 | py |
BioMedIA-Hecktor2021 | BioMedIA-Hecktor2021-main/tests/helpers/module_available.py | import platform
from importlib.util import find_spec
"""
Adapted from:
https://github.com/PyTorchLightning/pytorch-lightning/blob/master/pytorch_lightning/utilities/imports.py
"""
def _module_available(module_path: str) -> bool:
"""Check if a path is available in your environment.
>>> _module_available(... | 851 | 26.483871 | 108 | py |
PolarSeg | PolarSeg-master/test_pretrain_nuscenes.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
import errno
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset_nuscenes import Nuscenes, map_n... | 9,705 | 48.520408 | 204 | py |
PolarSeg | PolarSeg-master/train_PL.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset import collate_fn_BEV,spherical_dataset,voxe... | 10,323 | 49.857143 | 191 | py |
PolarSeg | PolarSeg-master/train_nuscenes.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset_nuscenes import Nuscenes, map_name_from_segm... | 10,899 | 49.697674 | 183 | py |
PolarSeg | PolarSeg-master/train_SemanticKITTI.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset import collate_fn_BEV,SemKITTI,SemKITTI_labe... | 9,579 | 47.629442 | 181 | py |
PolarSeg | PolarSeg-master/test_pretrain_SemanticKITTI.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import time
import argparse
import sys
import numpy as np
import torch
import torch.optim as optim
from tqdm import tqdm
from network.BEV_Unet import BEV_Unet
from network.ptBEV import ptBEVnet
from dataloader.dataset import collate_fn_BEV,collate_fn_BEV_test,Se... | 8,927 | 47 | 166 | py |
PolarSeg | PolarSeg-master/network/BEV_Unet.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from dropblock import DropBlock2D
class BEV_Unet(nn.Module):
def __init__(self,n_class,n_height,dilation = 1,group_conv=False,input_batch_norm = False,dropout = 0.,circular_paddin... | 8,273 | 37.305556 | 170 | py |
PolarSeg | PolarSeg-master/network/ptBEV.py | #!/usr/bin/env python3
# -*- 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 ptBEVnet(nn.Module):
def __init__(self, BEV_net, grid_size, pt_model = 'pointnet', fea_dim = 3, pt_pooling =... | 7,147 | 37.430108 | 125 | py |
PolarSeg | PolarSeg-master/network/lovasz_losses.py | """
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 itertools import ifilterfalse
except ImportErro... | 11,484 | 35.230284 | 118 | py |
PolarSeg | PolarSeg-master/dataloader/dataset.py | #!/usr/bin/env python3
# -*- 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
class SemKITTI(data.Dataset):
def __init__(self, data_path, imageset = 'train', return_ref = False):
sel... | 13,051 | 41.239482 | 134 | py |
PolarSeg | PolarSeg-master/dataloader/dataset_nuscenes.py | import os
import numpy as np
import yaml
from pathlib import Path
from torch.utils import data
from nuscenes.nuscenes import NuScenes
from nuscenes.utils import splits
map_name_from_general_to_segmentation_class = {
'human.pedestrian.adult': 'pedestrian',
'human.pedestrian.child': 'pedestrian',
'human.ped... | 6,718 | 37.394286 | 219 | py |
PolarSeg | PolarSeg-master/dataloader/dataset_PL.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import numpy as np
import torch
import random
from plyfile import PlyData
from torch.utils import data
class PLY_dataset(data.Dataset):
def __init__(self, data_path,sample_interval,time_step,label_convert_fun = None,return_ref = False,crop_data = None):
'Initial... | 3,294 | 44.136986 | 150 | py |
CAMB_CPT | CAMB_CPT-master/docs/source/conf.py | # -*- coding: utf-8 -*-
#
# MyProj documentation build configuration file, created by
# sphinx-quickstart on Thu Jun 18 20:57:49 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# Al... | 9,804 | 31.359736 | 100 | py |
CDGAN | CDGAN-master/cdgan.py | import torch
from torch.autograd import Variable
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class CDGAN(BaseModel):
def name(self):
return 'CDGAN'
def initialize(self, opt):
BaseModel.initialize(self, opt)
# specify... | 9,054 | 40.921296 | 228 | py |
CDGAN | CDGAN-master/modules/seg_arch.py | '''
architecture for segmentation
'''
import torch.nn as nn
from . import block as B
class Res131(nn.Module):
def __init__(self, in_nc, mid_nc, out_nc, dilation=1, stride=1):
super(Res131, self).__init__()
conv0 = B.conv_block(in_nc, mid_nc, 1, 1, 1, 1, False, 'zero', 'batch')
conv1 = B.co... | 2,585 | 35.422535 | 98 | py |
CDGAN | CDGAN-master/modules/block.py | from collections import OrderedDict
import torch
import torch.nn as nn
####################
# Basic blocks
####################
def act(act_type, inplace=True, neg_slope=0.2, n_prelu=1):
# helper selecting activation
# neg_slope: for leakyrelu and init of prelu
# n_prelu: for p_relu num_parameters
ac... | 9,760 | 36.114068 | 99 | py |
CDGAN | CDGAN-master/modules/loss.py | import torch
import torch.nn as nn
# Define GAN loss: [vanilla | lsgan | wgan-gp]
class GANLoss(nn.Module):
def __init__(self, gan_type, real_label_val=1.0, fake_label_val=0.0):
super(GANLoss, self).__init__()
self.gan_type = gan_type.lower()
self.real_label_val = real_label_val
se... | 2,260 | 36.065574 | 97 | py |
CDGAN | CDGAN-master/options/base_options.py | import argparse
import os
from util import util
import torch
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
self.initialized = False
def initialize(self):
self.parser.add_argument('--pan_mergin_m', type=int, default=50, help='positive margin of PAN los... | 5,845 | 69.433735 | 345 | py |
CDGAN | CDGAN-master/models/losses.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
import torch.autograd as autograd
import numpy as np
import torchvision.models as models
import util.util as util
from util.image_pool import ImagePool
from torch.autograd import Variable
######################################################... | 5,954 | 29.382653 | 108 | py |
CDGAN | CDGAN-master/models/sgddcycle_gan_model.py | import numpy as np
import torch
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
import util.util as util
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import sys
from .losses import init_loss
def mse_loss(input, target... | 15,567 | 43.735632 | 243 | py |
CDGAN | CDGAN-master/models/pix2pix_model.py | import numpy as np
import torch
import os
from collections import OrderedDict
from torch.autograd import Variable
import util.util as util
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class Pix2PixModel(BaseModel):
def name(self):
return 'Pix2PixModel'
... | 5,656 | 37.482993 | 103 | py |
CDGAN | CDGAN-master/models/models/losses.py | import torch
import torch.nn as nn
from torch.nn import init
import functools
import torch.autograd as autograd
import numpy as np
import torchvision.models as models
import util.util as util
from util.image_pool import ImagePool
from torch.autograd import Variable
######################################################... | 5,954 | 29.382653 | 108 | py |
CDGAN | CDGAN-master/models/models/cdgan.py | import torch
from torch.autograd import Variable
import itertools
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class CDGAN(BaseModel):
def name(self):
return 'CDGAN'
def initialize(self, opt):
BaseModel.initialize(self, opt)
# specify... | 9,054 | 40.921296 | 228 | py |
CDGAN | CDGAN-master/models/models/sgddcycle_gan_model.py | import numpy as np
import torch
import os
from collections import OrderedDict
from torch.autograd import Variable
import itertools
import util.util as util
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
import sys
from .losses import init_loss
def mse_loss(input, target... | 15,567 | 43.735632 | 243 | py |
CDGAN | CDGAN-master/models/models/pix2pix_model.py | import numpy as np
import torch
import os
from collections import OrderedDict
from torch.autograd import Variable
import util.util as util
from util.image_pool import ImagePool
from .base_model import BaseModel
from . import networks
class Pix2PixModel(BaseModel):
def name(self):
return 'Pix2PixModel'
... | 5,656 | 37.482993 | 103 | py |
CDGAN | CDGAN-master/data/custom_dataset_data_loader.py | import torch.utils.data
from data.base_data_loader import BaseDataLoader
def CreateDataset(opt):
dataset = None
if opt.dataset_mode == 'aligned':
from data.aligned_dataset import AlignedDataset
dataset = AlignedDataset()
elif opt.dataset_mode == 'unaligned':
from data.unaligned_dat... | 1,285 | 29.619048 | 75 | py |
CDGAN | CDGAN-master/data/base_dataset.py | import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
def initialize(self, opt):
pass
def get_transform(opt):
... | 1,597 | 33.73913 | 69 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/evaluate.py | """
Evaluation scripts
@author: Zhaoyang Lv
@date: March 2019
"""
import os, sys, argparse, pickle
import os.path as osp
import numpy as np
import pandas as pd
import torch
import torch.utils.data as data
import torchvision.utils as torch_utils
import torch.nn as nn
import models.LeastSquareTracking as ICtracking
... | 9,558 | 32.423077 | 100 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/train_utils.py | """
The training utility functions
@author: Zhaoyang Lv
@date: March 2019
"""
import os, sys
from os.path import join
import torch
import torch.nn as nn
def check_cuda(items):
if torch.cuda.is_available():
return [x.cuda() for x in items]
else:
return items
def initialize_logger(opt, logfile... | 4,568 | 29.059211 | 92 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/Logger.py | """ Logger, wrapped on Tensorboard 1.0.0a6
Tensorboard is not backward compatible since then.
@author: Zhaoyang Lv
@date: March 2019
"""
import sys, os, shutil
import os.path as osp
import tensorboard
import torch
from collections import OrderedDict
class Logger(object):
"""
example usage:
stdout =... | 3,036 | 27.383178 | 112 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/train.py | """
The training script for deep trust region method
@author: Zhaoyang Lv
@date: March 2019
"""
import os, sys, argparse, time
import models.LeastSquareTracking as ICtracking
import models.criterions as criterions
import models.geometry as geometry
import train_utils
import config
from data.dataloader import load_d... | 8,254 | 31.5 | 102 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/run_example.py | """
An extremely simple example to show how to run the algorithm
@author: Zhaoyang Lv
@date: May 2019
"""
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as func
import models.LeastSquareTracking as ICtracking
from tqdm import tqdm
from torch.utils.data import DataLoader
from train_u... | 2,929 | 29.520833 | 97 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/models/LeastSquareTracking.py | """
The learned Inverse Compositional Tracking.
Support both ego-motion and object-motion tracking
@author: Zhaoyang Lv
@Date: March, 2019
"""
import torch
import torch.nn as nn
import numpy as np
from models.submodules import convLayer as conv
from models.submodules import color_normalize
from models.algorithms im... | 9,413 | 39.059574 | 97 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/models/criterions.py | """
Some training criterions
@author: Zhaoyang Lv
@date: March, 2019
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
import torch.nn as nn
import torch.nn.functional as func
import models.geometry as... | 3,553 | 34.188119 | 143 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/models/geometry.py | """
A collection of geometric transformation operations
@author: Zhaoyang Lv
@Date: March, 2019
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torch
import torch.nn as nn
from torch import sin, cos, atan... | 14,882 | 29.686598 | 120 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/models/submodules.py | """
Submodules to build up CNN
@author: Zhaoyang Lv
@date: March, 2019
"""
from __future__ import print_function
import torch.nn as nn
import torch
import numpy as np
from torch.nn import init
from torchvision import transforms
def color_normalize(color):
rgb_mean = torch.Tensor([0.4914, 0.4822, 0.4465]).type_... | 2,823 | 31.45977 | 156 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/models/algorithms.py | """
The algorithm backbone, primarily the three contributions proposed in our paper
@author: Zhaoyang Lv
@date: March, 2019
"""
import torch
import torch.nn as nn
import torchvision.models as models
import torch.nn.functional as func
import models.geometry as geometry
from models.submodules import convLayer as conv
... | 17,644 | 35.607884 | 104 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/data/TUM_RGBD.py | """
Data loader for TUM RGBD benchmark
@author: Zhaoyang Lv
@date: March 2019
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import sys, os, random
import pickle
import numpy as np
import os.path as osp
import ... | 18,636 | 36.574597 | 143 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/data/dataloader.py | """ The dataloaders for training and evaluation
@author: Zhaoyang Lv
@date: March 2019
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import torchvision.transforms as transforms
import numpy as np
def load_data... | 3,778 | 34.317757 | 92 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/data/SimpleLoader.py | """
This Simple loader partially refers to
https://github.com/NVlabs/learningrigidity/blob/master/SimpleLoader.py
@author: Zhaoyang Lv
@date: May, 2019
"""
import sys, os, random
import torch.utils.data as data
import os.path as osp
import numpy as np
from scipy.misc import imread
class SimpleLoader(data.Dataset):... | 2,376 | 29.088608 | 81 | py |
DeeperInverseCompositionalAlgorithm | DeeperInverseCompositionalAlgorithm-master/code/data/MovingObj3D.py | """
Data loader for MovingObjs 3D dataset
@author: Zhaoyang Lv
@date: May 2019
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import sys, os, random
import pickle
import functools
import numpy as np
import torc... | 8,879 | 36.310924 | 107 | py |
emergent_symbols | emergent_symbols-main/train_and_extract_reps.py | import argparse
import os
import sys
import time
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# Prevent python from saving out .pyc files
sys.dont_write_bytecode = True
# Add models and tasks to path
sys.path.in... | 8,150 | 35.388393 | 131 | py |
emergent_symbols | emergent_symbols-main/train_and_eval.py | import argparse
import os
import sys
import time
import numpy as np
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
# Prevent python from saving out .pyc files
sys.dont_write_bytecode = True
# Add models and tasks to path
sys.path.in... | 8,228 | 34.623377 | 192 | py |
emergent_symbols | emergent_symbols-main/models/Transformer.py | import torch
import torch.nn as nn
import math
from util import log
import numpy as np
from modules import *
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len=5000):
super(PositionalEncoding, self).__init__()
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=tor... | 4,177 | 36.303571 | 138 | py |
emergent_symbols | emergent_symbols-main/models/modules.py | import torch
import torch.nn as nn
from util import log
class Encoder_conv(nn.Module):
def __init__(self, args):
super(Encoder_conv, self).__init__()
log.info('Building convolutional encoder...')
# Convolutional layers
log.info('Conv layers...')
self.conv1 = nn.Conv2d(1, 32, 4, stride=2, padding=1)
self.c... | 2,966 | 29.27551 | 70 | py |
emergent_symbols | emergent_symbols-main/models/ESBN_confidence_ablation.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
import pdb
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
eli... | 4,590 | 36.325203 | 124 | py |
emergent_symbols | emergent_symbols-main/models/ESBN_default_memory.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
import pdb
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
eli... | 4,879 | 38.04 | 124 | py |
emergent_symbols | emergent_symbols-main/models/ESBN_return_keys.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 5,061 | 38.24031 | 124 | py |
emergent_symbols | emergent_symbols-main/models/ESBN.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 4,861 | 38.209677 | 124 | py |
emergent_symbols | emergent_symbols-main/models/TRN.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 4,720 | 36.173228 | 118 | py |
emergent_symbols | emergent_symbols-main/models/MNM.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args, n_batch_mem = 1):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)... | 8,180 | 33.812766 | 109 | py |
emergent_symbols | emergent_symbols-main/models/RN.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 3,278 | 34.641304 | 118 | py |
emergent_symbols | emergent_symbols-main/models/NTM.py | import torch
import torch.nn as nn
import numpy as np
from util import log
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 9,685 | 45.344498 | 159 | py |
emergent_symbols | emergent_symbols-main/models/PrediNet.py | import torch
import torch.nn as nn
from util import log
import numpy as np
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 4,455 | 39.509091 | 122 | py |
emergent_symbols | emergent_symbols-main/models/LSTM.py | import torch
import torch.nn as nn
import numpy as np
from util import log
from modules import *
class Model(nn.Module):
def __init__(self, task_gen, args):
super(Model, self).__init__()
# Encoder
log.info('Building encoder...')
if args.encoder == 'conv':
self.encoder = Encoder_conv(args)
elif args.encod... | 3,149 | 36.951807 | 124 | py |
ugrec | ugrec-main/ugrec.py | import functools
import numpy
import tensorflow as tf
import os
from concurrent.futures.process import ProcessPoolExecutor
# os.environ["CUDA_VISIBLE_DEVICES"] = "1"
tf.compat.v1.disable_eager_execution()
#tf.random.set_seed(1)
#numpy.random.seed(10)
from sampler import WarpSampler
from side_inf_sampler import SideInf... | 22,335 | 42.796078 | 128 | py |
synthetic-trees | synthetic-trees-main/synthetic_trees/evaluate.py | import os
import numpy as np
from typing import List, Tuple
from pathlib import Path
import argparse
import torch
import open3d as o3d
from tqdm import tqdm
from data_types.tree import TreeSkeleton, repair_skeleton
from data_types.cloud import Cloud
from util.file import load_data_npz
from util.o3d_abstractions im... | 3,220 | 32.905263 | 147 | py |
synthetic-trees | synthetic-trees-main/synthetic_trees/evaluation/metrics.py | import torch
import frnn
from util.queries import nn_frnn, nn_keops
def recall(gt_points, test_points, gt_radii, thresholds=[0.1]): # recall (completeness)
results = []
dist, idx = nn_keops(gt_points, test_points)
idx = idx.reshape(-1)
dist = dist.reshape(-1)
for t in thresholds:
mask = dist < (gt_r... | 1,550 | 26.696429 | 90 | py |
synthetic-trees | synthetic-trees-main/synthetic_trees/util/queries.py | import numpy as np
import torch
from typing import List
from ..data_types.tube import Tube, CollatedTube, collate_tubes
from pykeops.torch import LazyTensor
"""
For the following :
N : number of pts
M : number of tubes
"""
# N x 3, M x 2
def points_to_collated_tube_projections(pts: np.array, collated_tube:... | 2,727 | 27.416667 | 96 | py |
synthetic-trees | synthetic-trees-main/synthetic_trees/util/misc.py | import numpy as np
import torch
from typing import List
def flatten_list(l):
return [item for sublist in l for item in sublist]
def to_torch(numpy_arrays: List[np.array], device=torch.device("cpu")):
return [torch.from_numpy(np_arr).float().to(device) for np_arr in numpy_arrays] | 288 | 27.9 | 81 | py |
AIRR | AIRR-main/dataloader_synthesis.py | import numpy as np
import os
import torch
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from PIL import Image
import scipy.io as scio
import h5py
import pickle
import copy
import random
import matplotlib.pyplot as plt
class Dataset(torch.utils.data.Dataset):
def __init__(self,... | 3,787 | 38.458333 | 139 | py |
AIRR | AIRR-main/test.py | import torch
import numpy as np
from torch.utils.data import DataLoader
from PIL import Image
import torch.nn.functional as F
import torch.autograd as autograd
import matplotlib.pyplot as plt
import torchvision
import argparse
import os
#options: synthesis, attr, celeba, celebahq
DATASET='celebahq'
#deepfashion synt... | 9,656 | 42.304933 | 128 | py |
AIRR | AIRR-main/model_attr.py | import torch
import torch.nn as nn
import functools
import numpy as np
import torch.nn.functional as F
class residual_block(nn.Module):
def __init__(self,dim):
super(residual_block,self).__init__()
self.block= nn.Sequential(nn.ReflectionPad2d(1),#tf.pad(h, [[0, 0], [1, 1], [1, 1], [0, 0]]... | 8,349 | 34.531915 | 147 | py |
AIRR | AIRR-main/model_celebahq.py | import torch
import torch.nn as nn
import functools
import numpy as np
import torch.nn.functional as F
import math
def get_weight(weight, gain=1, use_wscale=True, lrmul=1):
fan_in = np.prod(weight.size()[1:]) # [kernel, kernel, fmaps_in, fmaps_out] or [in, out]
he_std = gain / np.sqrt(fan_in) # He init
# E... | 7,980 | 33.253219 | 117 | py |
AIRR | AIRR-main/dataloader_celebahq.py | import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import pickle
from PIL import Image
from torchvision import transforms, utils
class Dataset(data.Dataset):
def __init__(self, root='data/celebahq/', split='train',cat='Smiling'):
... | 2,590 | 36.550725 | 284 | py |
AIRR | AIRR-main/dataloader_celeba.py | import numpy as np
import os
import torch
from torch.utils.data import DataLoader
import torchvision.transforms as transforms
from PIL import Image
import scipy.io as scio
import h5py
import pickle
import copy
import random
import matplotlib.pyplot as plt
class Dataset(torch.utils.data.Dataset):#/net/ivcfs4/mnt/data
... | 2,873 | 35.846154 | 122 | py |
AIRR | AIRR-main/model_celeba.py | import torch
import torch.nn as nn
import functools
import numpy as np
import torch.nn.functional as F
class residual_block(nn.Module):
def __init__(self,dim):
super(residual_block,self).__init__()
self.block= nn.Sequential(nn.ReflectionPad2d(1),#tf.pad(h, [[0, 0], [1, 1], [1, 1], [0, 0]]... | 8,201 | 34.353448 | 147 | py |
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