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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VSR-Transformer | VSR-Transformer-main/basicsr/test.py | import logging
import torch
from os import path as osp
from basicsr.data import create_dataloader, create_dataset
from basicsr.models import create_model
from basicsr.train import parse_options
from basicsr.utils import (get_env_info, get_root_logger, get_time_str,
make_exp_dirs)
from basics... | 1,827 | 29.983051 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/train.py | import argparse
import datetime
import logging
import math
import random
import time
import torch
from os import path as osp
from basicsr.data import create_dataloader, create_dataset
from basicsr.data.data_sampler import EnlargedSampler
from basicsr.data.prefetch_dataloader import CPUPrefetcher, CUDAPrefetcher
from b... | 9,685 | 37.436508 | 128 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/lr_scheduler.py | import math
from collections import Counter
from torch.optim.lr_scheduler import _LRScheduler
import pdb
class MultiStepRestartLR(_LRScheduler):
""" MultiStep with restarts learning rate scheme.
Args:
optimizer (torch.nn.optimizer): Torch optimizer.
milestones (list): Iterations that will decr... | 4,373 | 34.852459 | 80 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/base_model.py | import logging
import os
import torch
from collections import OrderedDict
from copy import deepcopy
from torch.nn.parallel import DataParallel, DistributedDataParallel
from basicsr.models import lr_scheduler as lr_scheduler
from basicsr.utils.dist_util import master_only
from basicsr.utils.utils_modelsummary import ge... | 14,045 | 37.168478 | 90 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/video_base_model.py | import importlib
import torch
from collections import Counter
from copy import deepcopy
from os import path as osp
from torch import distributed as dist
from tqdm import tqdm
from basicsr.models.sr_model import SRModel
from basicsr.utils import get_root_logger, imwrite, tensor2img
from basicsr.utils.dist_util import g... | 7,244 | 40.4 | 78 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/count_flop.py | import torch
from torch import nn as nn
from torch.nn import functional as F
import numpy as np
from archs.arch_util import (ResidualBlockNoBN, make_layer, ResidualGroup, default_conv)
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from positional_encodings import PositionalEncodingPerm... | 10,073 | 47.200957 | 161 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/crop_validation.py | import torch
from IPython import embed
import pdb
def lr_crop_index(n, N, D, base_size, overlap):
n_end = D if n == N - 1 else (n + 1) * base_size + overlap
n_start = n_end - base_size - overlap
return n_start, n_end
def hr_crop_index(n, N, D, Dmod, base_size, overlap):
if n == 0:
n_start = 0... | 4,069 | 33.786325 | 126 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/sr_model.py | import importlib
import torch
import torch.nn as nn
from collections import OrderedDict
from copy import deepcopy
from os import path as osp
from tqdm import tqdm
from basicsr.models.archs import define_network
from basicsr.models.base_model import BaseModel
from basicsr.utils import get_root_logger, imwrite, tensor2i... | 10,099 | 37.403042 | 113 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/edvr_model.py | import logging
import torch
from torch.nn.parallel import DistributedDataParallel
from basicsr.models.video_base_model import VideoBaseModel
import pdb
logger = logging.getLogger('basicsr')
class EDVRModel(VideoBaseModel):
"""EDVR Model.
Paper: EDVR: Video Restoration with Enhanced Deformable Convolutional... | 3,523 | 39.976744 | 104 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/archs/vgg_arch.py | import os
import torch
from collections import OrderedDict
from torch import nn as nn
from torchvision.models import vgg as vgg
VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth'
NAMES = {
'vgg11': [
'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2',
'conv3_1', 'relu3... | 6,230 | 35.226744 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/archs/spynet.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from mmcv.cnn import ConvModule
from mmcv.runner import load_checkpoint
from mmedit.models.common import (PixelShufflePack, ResidualBlockNoBN, flow_warp, make_layer)
from mmedit.models.registry import BACKBONES
from mmedit.utils import get_root_logger
... | 6,356 | 39.490446 | 134 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/archs/arch_util.py | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from torch.nn import init as init
from torch.nn.modules.batchnorm import _BatchNorm
from basicsr.utils import get_root_logger
try:
from basicsr.models.ops.dcn import (ModulatedDeformConvPack, modulated_deform_conv)
excep... | 12,223 | 33.727273 | 134 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/archs/vsrTransformer_arch.py | import torch
from torch import nn as nn
from torch.nn import functional as F
import numpy as np
from basicsr.models.archs.arch_util import (ResidualBlockNoBN, make_layer, RCAB, ResidualGroup, default_conv, RCABWithInputConv)
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from positional... | 13,404 | 46.200704 | 140 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/archs/spynet_arch.py | import math
import torch
from torch import nn as nn
from torch.nn import functional as F
from basicsr.models.archs.arch_util import flow_warp
class BasicModule(nn.Module):
"""Basic Module for SpyNet.
"""
def __init__(self):
super(BasicModule, self).__init__()
self.basic_module = nn.Seque... | 9,197 | 36.696721 | 128 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/ops/dcn/deform_conv.py | import math
import torch
from torch import nn as nn
from torch.autograd import Function
from torch.autograd.function import once_differentiable
from torch.nn import functional as F
from torch.nn.modules.utils import _pair, _single
from . import deform_conv_ext
class DeformConvFunction(Function):
@staticmethod
... | 14,808 | 36.87468 | 80 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/ops/upfirdn2d/upfirdn2d.py | # modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py # noqa:E501
import torch
from torch.autograd import Function
from torch.nn import functional as F
from . import upfirdn2d_ext
class UpFirDn2dBackward(Function):
@staticmethod
def forward(ctx, grad_output, kernel, gra... | 5,760 | 29.321053 | 102 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/ops/fused_act/fused_act.py | # modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
import torch
from torch import nn
from torch.autograd import Function
from . import fused_act_ext
class FusedLeakyReLUFunctionBackward(Function):
@staticmethod
def forward(ctx, grad_output, out, negative_s... | 2,496 | 29.45122 | 101 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/losses/losses.py | import math
import torch
from torch import autograd as autograd
from torch import nn as nn
from torch.nn import functional as F
from basicsr.models.archs.vgg_arch import VGGFeatureExtractor
from basicsr.models.losses.loss_util import weighted_loss
import pdb
_reduction_modes = ['none', 'mean', 'sum']
@weighted_loss... | 17,005 | 34.355509 | 108 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/models/losses/loss_util.py | import functools
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are 'none', 'mean' and 'sum'.
Returns:
Tensor: Reduced loss tensor.
"""
reduction_en... | 2,903 | 29.25 | 78 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/metrics/fid.py | import numpy as np
import torch
import torch.nn as nn
from scipy import linalg
from tqdm import tqdm
from basicsr.models.archs.inception import InceptionV3
def load_patched_inception_v3(device='cuda',
resize_input=True,
normalize_input=False):
# we may ... | 3,497 | 32.961165 | 78 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/test_flop_count.py | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-ignore-all-errors[2,3,53]
import typing
import unittest
from collections import Counter, defaultdict
from typing import Any, Dict, Tuple
import torch
import torch.nn as nn
from fvcore.nn.flop_count import _DEFAULT_SUPPORTED_OPS, FlopCountAn... | 24,561 | 30.692903 | 88 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/face_util.py | import cv2
import numpy as np
import os
import torch
from skimage import transform as trans
from basicsr.utils import imwrite
try:
import dlib
except ImportError:
print('Please install dlib before testing face restoration.'
'Reference: https://github.com/davisking/dlib')
class FaceRestorationHelpe... | 9,588 | 42.986239 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/misc.py | import numpy as np
import os
import random
import time
import torch
from os import path as osp
from .dist_util import master_only
from .logger import get_root_logger
def set_random_seed(seed):
"""Set random seeds."""
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual... | 4,480 | 31.007143 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/logger.py | import datetime
import logging
import time
from .dist_util import get_dist_info, master_only
class MessageLogger():
"""Message logger for printing.
Args:
opt (dict): Config. It contains the following keys:
name (str): Exp name.
logger (dict): Contains 'print_freq' (str) for l... | 6,114 | 33.353933 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/img_util.py | import cv2
import math
import numpy as np
import os
import torch
from torchvision.utils import make_grid
def img2tensor(imgs, bgr2rgb=True, float32=True):
"""Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float3... | 5,555 | 32.46988 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/matlab_functions.py | import math
import numpy as np
import torch
def cubic(x):
"""cubic function used for calculate_weights_indices."""
absx = torch.abs(x)
absx2 = absx**2
absx3 = absx**3
return (1.5 * absx3 - 2.5 * absx2 + 1) * (
(absx <= 1).type_as(absx)) + (-0.5 * absx3 + 2.5 * absx2 - 4 * absx +
... | 13,747 | 36.977901 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/dist_util.py | # Modified from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/dist_utils.py # noqa: E501
import functools
import os
import subprocess
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
def init_dist(launcher, backend='nccl', **kwargs):
if mp.get_start_method(allow_none=... | 2,617 | 30.166667 | 102 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/utils/utils_modelsummary.py | import torch.nn as nn
import torch
import numpy as np
import pdb
'''
---- 1) FLOPs: floating point operations
---- 2) #Activations: the number of elements of all ‘Conv2d’ outputs
---- 3) #Conv2d: the number of ‘Conv2d’ layers
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
... | 16,112 | 33.577253 | 129 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/vimeo90k_dataset.py | import random
import torch
from pathlib import Path
from torch.utils import data as data
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.flow_util import flowfrombytes
import pdb
class Vimeo90KDataset(dat... | 8,072 | 37.8125 | 107 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/single_image_dataset.py | from os import path as osp
from torch.utils import data as data
from torchvision.transforms.functional import normalize
from basicsr.data.data_util import paths_from_lmdb
from basicsr.utils import FileClient, imfrombytes, img2tensor, scandir
class SingleImageDataset(data.Dataset):
"""Read only lq images in the t... | 2,554 | 36.573529 | 78 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/reds_dataset.py | import numpy as np
import random
import torch
from pathlib import Path
from torch.utils import data as data
from basicsr.data.transforms import augment, paired_random_crop
from basicsr.utils import FileClient, get_root_logger, imfrombytes, img2tensor
from basicsr.utils.flow_util import dequantize_flow, flowfrombytes
i... | 10,125 | 40.162602 | 107 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/prefetch_dataloader.py | import queue as Queue
import threading
import torch
from torch.utils.data import DataLoader
class PrefetchGenerator(threading.Thread):
"""A general prefetch generator.
Ref:
https://stackoverflow.com/questions/7323664/python-generator-pre-fetch
Args:
generator: Python generator.
num_p... | 3,156 | 23.858268 | 77 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/paired_image_dataset.py | from torch.utils import data as data
from torchvision.transforms.functional import normalize
from basicsr.data.data_util import (paired_paths_from_folder,
paired_paths_from_lmdb,
paired_paths_from_meta_info_file)
from basicsr.data.transforms impor... | 4,804 | 40.068376 | 79 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/data_sampler.py | import math
import torch
from torch.utils.data.sampler import Sampler
class EnlargedSampler(Sampler):
"""Sampler that restricts data loading to a subset of the dataset.
Modified from torch.utils.data.distributed.DistributedSampler
Support enlarging the dataset for iteration-based training, for saving
... | 1,652 | 32.06 | 75 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/data_util.py | import cv2
import numpy as np
import torch
from os import path as osp
from torch.nn import functional as F
from basicsr.data.transforms import mod_crop
from basicsr.utils import img2tensor, scandir
def read_img_seq(path, require_mod_crop=False, scale=1):
"""Read a sequence of images from a given folder path.
... | 11,801 | 34.548193 | 78 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/video_test_dataset.py | import glob
import torch
from os import path as osp
from torch.utils import data as data
from basicsr.data.data_util import (duf_downsample, generate_frame_indices, read_img_seq)
from basicsr.utils import get_root_logger, scandir
import pdb
class VideoTestDataset(data.Dataset):
"""Video test dataset.
Support... | 12,579 | 37.707692 | 89 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/ffhq_dataset.py | from os import path as osp
from torch.utils import data as data
from torchvision.transforms.functional import normalize
from basicsr.data.transforms import augment
from basicsr.utils import FileClient, imfrombytes, img2tensor
class FFHQDataset(data.Dataset):
"""FFHQ dataset for StyleGAN.
Args:
opt (... | 2,369 | 34.909091 | 78 | py |
VSR-Transformer | VSR-Transformer-main/basicsr/data/__init__.py | import importlib
import numpy as np
import random
import torch
import torch.utils.data
from functools import partial
from os import path as osp
from basicsr.data.prefetch_dataloader import PrefetchDataLoader
from basicsr.utils import get_root_logger, scandir
from basicsr.utils.dist_util import get_dist_info
__all__ =... | 4,624 | 35.417323 | 76 | py |
onestage_grounding | onestage_grounding-master/train_yolo.py | import os
import sys
import argparse
import shutil
import time
import random
import gc
import json
from distutils.version import LooseVersion
import scipy.misc
import logging
import matplotlib as mpl
mpl.use('Agg')
from matplotlib import pyplot as plt
from PIL import Image
import numpy as np
import torch
import torch... | 34,104 | 46.8331 | 153 | py |
onestage_grounding | onestage_grounding-master/dataset/referit_loader.py | # -*- coding: utf-8 -*-
"""
ReferIt, UNC, UNC+ and GRef referring image segmentation PyTorch dataset.
Define and group batches of images, segmentations and queries.
Based on:
https://github.com/chenxi116/TF-phrasecut-public/blob/master/build_batches.py
"""
import os
import sys
import cv2
import json
import uuid
impo... | 14,194 | 36.75266 | 120 | py |
onestage_grounding | onestage_grounding-master/utils/misc_utils.py | # -*- coding: utf-8 -*-
"""
Misc download and visualization helper functions and class wrappers.
"""
import sys
import time
import torch
from visdom import Visdom
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
return
duration = ti... | 1,177 | 28.45 | 79 | py |
onestage_grounding | onestage_grounding-master/utils/losses.py | # -*- coding: utf-8 -*-
"""
Custom loss function definitions.
"""
import torch.nn as nn
import torch.nn.functional as F
class IoULoss(nn.Module):
"""
Creates a criterion that computes the Intersection over Union (IoU)
between a segmentation mask and its ground truth.
Rahman, M.A. and Wang, Y:
O... | 987 | 27.228571 | 74 | py |
onestage_grounding | onestage_grounding-master/utils/utils.py | import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
sel... | 5,102 | 31.922581 | 110 | py |
onestage_grounding | onestage_grounding-master/utils/word_utils.py | # -*- coding: utf-8 -*-
"""
Language-related data loading helper functions and class wrappers.
"""
import re
import torch
import codecs
UNK_TOKEN = '<unk>'
PAD_TOKEN = '<pad>'
END_TOKEN = '<eos>'
SENTENCE_SPLIT_REGEX = re.compile(r'(\W+)')
class Dictionary(object):
def __init__(self):
self.word2idx = {... | 3,175 | 30.137255 | 139 | py |
onestage_grounding | onestage_grounding-master/utils/parsing_metrics.py | import torch
import numpy as np
import os
# from plot_util import plot_confusion_matrix
# from makemask import *
def _fast_hist(label_true, label_pred, n_class):
mask = (label_true >= 0) & (label_true < n_class)
hist = np.bincount(
n_class * label_true[mask].astype(int) +
label_pred[mask], minlength=n_class ** ... | 5,879 | 35.75 | 89 | py |
onestage_grounding | onestage_grounding-master/utils/transforms.py | # -*- coding: utf-8 -*-
"""
Generic Image Transform utillities.
"""
import cv2
import random, math
import numpy as np
from collections import Iterable
import torch.nn.functional as F
from torch.autograd import Variable
class ResizePad:
"""
Resize and pad an image to given size.
"""
def __init__(se... | 8,669 | 37.533333 | 114 | py |
onestage_grounding | onestage_grounding-master/model/grounding_model.py | from collections import OrderedDict
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from torch.utils.data import TensorDataset, DataLoader, SequentialSampler
from torch.utils.data.distributed import DistributedSampler
from .darknet import *
import argparse... | 13,670 | 45.030303 | 113 | py |
onestage_grounding | onestage_grounding-master/model/darknet.py | from __future__ import division
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import numpy as np
from collections import defaultdict, OrderedDict
from PIL import Image
# from utils.parse_config import *
from utils.utils import *
# import matplotlib... | 21,948 | 40.102996 | 136 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/main.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 21 14:58:02 2022
@author: ce423
"""
import torch
from time import time as t
from data_generator.struct2d import struct2d_gen
struct_features = {
'box_side': 20,
'hands': [25, 10],
'arrowheads': [8,5],
'delta': 3.5,
... | 5,366 | 23.175676 | 127 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/tools/training.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 17 15:56:40 2022
@author: carlosesteveyague
"""
import torch
class Dataset(torch.utils.data.Dataset):
'Characterizes a dataset for PyTorch'
def __init__(self, eigen_vecs, labels):
'Initialization'
self.labels = labels
s... | 5,237 | 32.793548 | 103 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/tools/graph.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 14 13:52:52 2022
@author: carlosesteveyague
"""
import numpy as np
import torch
def graph_gaussian_kernel(data, sigma_gauss_kernel):
v_size = data.shape[1]*data.shape[2]
data = data.reshape([data.shape[0], v_size])
dists = torch.c... | 1,176 | 19.293103 | 80 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/tools/particle_densities.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 14 09:35:41 2022
@author: carlosesteveyague
"""
import numpy as np
import torch
def density_comp(X, Y, Gamma, sigma, density_type = 'Gaussian'):
Gamma_ext = Gamma.unsqueeze(-1).unsqueeze(-1)
X_diff = X - Gamma_ext[:,0]
Y_diff... | 2,532 | 27.144444 | 83 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/data_generator/dataset.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jun 15 10:26:09 2022
@author: carlosesteveyague
"""
import torch
def dataset_gen(dataset_features, loop = True):
struct = dataset_features['struct']
n = dataset_features['n_imgs']
scaling = 2*torch.pi*torch.Tensor(dataset_f... | 1,994 | 30.171875 | 99 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/data_generator/struct2d.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 14 17:49:50 2022
@author: carlosesteveyague
"""
import numpy as np
import torch
from tools.particle_densities import density_comp, RadonTransform_Gaussians
class struct2d_gen:
def __init__(self, clock_features):
if clo... | 5,532 | 34.696774 | 141 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/2d_struct/model/model_2d.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 17 14:58:20 2022
@author: carlosesteveyague
"""
import torch
import torch.nn as nn
class model_2dCT(nn.Module):
def __init__(self, model_features):
super(model_2dCT, self).__init__()
self.struct = model_features... | 2,480 | 31.644737 | 126 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/main.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 10:22:16 2022
@author: ce423
"""
import torch
import numpy as np
import mdtraj as md
## We load an MD trajectory.
## The molecular structure must be in a pdb or psf file.
## The trajectory is stored in a dcd file.
struct = 2
if struct == 1:
... | 8,743 | 29.788732 | 124 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/fft.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 9 10:30:39 2022
@author: ce423
"""
from torch.fft import fft2, fftshift, ifft2, ifftshift
def fft2_center(img):
img = fftshift(img, dim = (-1,-2))
img = fft2(img)
img = fftshift(img)
return img
def ifft2_center(img):
img = ... | 411 | 17.727273 | 54 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/orientation.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 8 18:29:24 2022
@author: carlosesteveyague
"""
import torch
from scipy.linalg import logm
A_1 = torch.tensor([[[0.,1.,0.],[0.,0.,0.],[0.,0.,1.]]], dtype = torch.float)
A_2 = torch.tensor([[[-1.,0.,0.],[0.,0.,1.],[0.,0.,0.]]], dtype = torch.float)... | 1,838 | 25.271429 | 92 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/training.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 18:22:38 2022
@author: ce423
"""
import torch
class Dataset(torch.utils.data.Dataset):
'Characterizes a dataset for PyTorch'
def __init__(self, eigen_vecs, labels):
'Initialization'
self.labels = labels
self.eigen_ve... | 5,604 | 32.76506 | 111 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/graph.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 18:10:29 2022
@author: ce423
"""
import numpy as np
import torch
def graph_gaussian_kernel(data, sigma_gauss_kernel):
v_size = data.shape[1]*data.shape[2]*data.shape[3]
data = data.reshape([data.shape[0], v_size])
dists = torch... | 1,170 | 20.685185 | 80 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/CTF.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 6 12:31:15 2022
@author: ce423
"""
import torch
import numpy as np
def voltage_to_wavelength(voltage):
Lambda = 12.2643247/np.sqrt(voltage*1e+3 + 0.978466*voltage**2);
return Lambda
def CTF(xi_norm_sq , voltage, Df, C_s, alpha, B_facto... | 1,167 | 28.2 | 94 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/particle_densities.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 14:37:52 2022
@author: ce423
"""
import numpy as np
import torch
def density_comp(X, Y, Gamma, sigma, density_type = 'Gaussian'):
Gamma_ext = Gamma.unsqueeze(-1).unsqueeze(-1)
X_diff = X - Gamma_ext[:,0]
Y_diff = Y - Gamma... | 975 | 26.885714 | 71 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/tools/DFF.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 10:35:59 2022
@author: ce423
"""
import torch
from tools.orientation import params_from_rotation
def compute_DFF(X):
"""
This function computes the Discrete Frenet Frames for each of the discrete curves in a batch of discrete curves.
... | 5,902 | 27.936275 | 116 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/data_generator/Imaging.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Nov 6 11:46:49 2022
@author: ce423
"""
import torch
from tqdm import tqdm
from tools.particle_densities import density_comp
from tools.CTF import CTF_filters
from tools.fft import fft2_center, ifft2_center
def image_CT(struct, orient_diffs, n_p... | 4,858 | 37.563492 | 112 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/data_generator/dataset.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 15:06:13 2022
@author: ce423
"""
import torch
from data_generator.structure_batch import chain_structure
import torch.nn as nn
def dataset(dataset_features):
struct = dataset_features['struct']
n_imgs = dataset_features['n_imgs... | 3,451 | 33.868687 | 180 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/data_generator/structure_batch.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 14:02:38 2022
@author: ce423
"""
import torch
from tools.DFF import R_from_angles
from tools.orientation import rotation_from_params
from tools.particle_densities import density_comp
from data_generator.Imaging import image_CT, CTF_image_CT
... | 4,440 | 36.635593 | 163 | py |
Atomic-structure-reconstruction-for-cryoEM | Atomic-structure-reconstruction-for-cryoEM-main/3d_struct/model/model_chain.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 2 17:52:44 2022
@author: ce423
"""
import torch
import torch.nn as nn
from tools.DFF import R_from_angles
from tools.orientation import rotation_from_params
from tools.particle_densities import density_comp
class model_chain(nn.Module):
... | 7,234 | 35.540404 | 140 | py |
OVE6D-pose | OVE6D-pose-main/evaluation/LMO_RCNN_OVE6D_pipeline.py |
import os
import cv2
import sys
import json
# import yaml
import time
import torch
import warnings
import numpy as np
from PIL import Image
from pathlib import Path
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from os.path import join as pjoi... | 13,771 | 44.302632 | 139 | py |
OVE6D-pose | OVE6D-pose-main/evaluation/utils.py | import os
import time
import glob
import math
import torch
import numpy as np
import torch.nn.functional as F
from lib import geometry, rendering, three, preprocess
from evaluation import pplane_ICP
from lib import preprocess
def rotation_to_position(R):
t = torch.tensor([0, 0, 1], dtype=torch.float32, device=R.d... | 32,071 | 46.095448 | 151 | py |
OVE6D-pose | OVE6D-pose-main/evaluation/LM_RCNN_OVE6D_pipeline.py |
import os
import cv2
import sys
import json
# import yaml
import time
import torch
import warnings
import numpy as np
from PIL import Image
from pathlib import Path
from detectron2 import model_zoo
from detectron2.config import get_cfg
from detectron2.engine import DefaultPredictor
from os.path import join as pjoi... | 13,769 | 42.714286 | 139 | py |
OVE6D-pose | OVE6D-pose-main/evaluation/config.py |
import math
import torch
from pytorch3d.transforms import euler_angles_to_matrix
RANDOM_SEED = 2021 # for reproduce the results of evaluation
VIEWBOOK_BATCHSIZE = 200 # batch size for constructing viewpoint codebook, reduce this if out of GPU memory
RENDER_WIDTH = 640 # the width of rendered images
REND... | 2,732 | 34.493506 | 108 | py |
OVE6D-pose | OVE6D-pose-main/evaluation/TLESS_MPmask_OVE6D_sixd17.py |
import os
import sys
# import glob
import json
import yaml
import time
import torch
import warnings
import numpy as np
from PIL import Image
from pathlib import Path
from os.path import join as pjoin
warnings.filterwarnings("ignore")
base_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.ap... | 9,995 | 43.035242 | 116 | py |
OVE6D-pose | OVE6D-pose-main/evaluation/pplane_ICP.py | """
The code for point-to-plane ICP is modified from the respository https://github.com/pglira/simpleICP/tree/master/python
"""
import time
import torch
import numpy as np
from datetime import datetime
from scipy import spatial, stats
def depth_to_pointcloud(depth, K):
if not isinstance(depth, torch.Tensor):
... | 7,387 | 34.690821 | 143 | py |
OVE6D-pose | OVE6D-pose-main/utility/visualization.py | from collections import namedtuple
import math
from contextlib import contextmanager
from pathlib import Path
import imageio
import numpy as np
import structlog
import tempfile
import torch
import torchvision
from matplotlib import cm
from matplotlib import pyplot as plt
from matplotlib.colors import LinearSegmentedC... | 10,889 | 30.565217 | 94 | py |
OVE6D-pose | OVE6D-pose-main/dataset/TLESS_Dataset.py | import json
import torch
from pathlib import Path
class Dataset():
def __init__(self, data_dir, type='recon'):
"""
type[cad, recon]: using cad model or reconstructed model
"""
super().__init__()
assert(type == 'cad' or type == 'recon'), "only support CAD model (cad) or reco... | 2,256 | 36.616667 | 125 | py |
OVE6D-pose | OVE6D-pose-main/dataset/LineMOD_Dataset.py | import json
import torch
from pathlib import Path
class Dataset():
def __init__(self, data_dir):
self.model_dir = Path(data_dir) / 'models_eval'
self.cam_file = Path(data_dir) / 'camera.json'
with open(self.cam_file, 'r') as cam_f:
self.cam_info = json.load(cam_f)
... | 1,249 | 35.764706 | 84 | py |
OVE6D-pose | OVE6D-pose-main/training/pyrenderer.py | import os
import torch
import random
import structlog
from torch.utils.data import Dataset
from lib import rendering
from lib.three import rigid
from training import data_augment
from training import train_utils
os.environ['PYOPENGL_PLATFORM'] = 'egl'
logger = structlog.get_logger(__name__)
class PyrenderDataset(D... | 11,376 | 45.627049 | 153 | py |
OVE6D-pose | OVE6D-pose-main/training/data_augment.py | import cv2
import torch
import numpy as np
import imgaug.augmenters as iaa
import torchvision.transforms.functional as tf
from lib import geometry
def divergence_depth(anchor_depth, query_depth, min_dep_pixels=100, bins_num=100):
hist_diff = 0
anc_val_idx = anchor_depth>0
que_val_idx = query_depth>0
... | 7,546 | 44.191617 | 143 | py |
OVE6D-pose | OVE6D-pose-main/training/train_utils.py | import torch
import math
import torch.nn.functional as F
from lib import geometry
def background_filter(depths, diameters, dist_factor=0.5):
"""
filter out the outilers beyond the object diameter
"""
new_depths = list()
unsqueeze = False
if not isinstance(diameters, torch.Tensor):
diame... | 18,659 | 37.553719 | 132 | py |
OVE6D-pose | OVE6D-pose-main/training/config.py | import torch
BASE_LR = 1e-3 # starting learning rate
MAX_EPOCHS = 50 # maximum training epochs
NUM_VIEWS = 16 # the sampling number of viewpoint for each object
WARMUP_EPOCHS = 0 # warmup epochs during training
RANKING_MARGIN = 0.1 # the triplet margin for ranking
USE_DATA_AUG = True ... | 2,067 | 46 | 111 | py |
OVE6D-pose | OVE6D-pose-main/training/train_on_shapenet.py | import os
from pickle import load
import sys
import time
import math
import matplotlib
import torch
import random
import shutil
import structlog
import torchvision
import copy
from pytorch3d.transforms import matrix_to_euler_angles
import numpy as np
from pathlib import Path
from torch import optim
from torch.nn i... | 25,399 | 49.8 | 137 | py |
OVE6D-pose | OVE6D-pose-main/example/misc.py | import torch
import numpy as np
from scipy import spatial
def str2dict(ss):
obj_score = dict()
for obj_str in ss.split(','):
obj_s = obj_str.strip()
if len(obj_s) > 0:
obj_id = obj_s.split(':')[0].strip()
obj_s = obj_s.split(':')[1].strip()
if len(obj_s) > 0:... | 4,059 | 33.40678 | 94 | py |
OVE6D-pose | OVE6D-pose-main/lib/network.py |
import torch
import math
from torch import nn
import torch.nn.functional as F
from lib.geometry import inplane_2D_spatial_transform
from lib import preprocess
class OVE6D(nn.Module):
def __init__(self):
super(OVE6D, self).__init__()
###################################### backbone ###############... | 11,408 | 45.190283 | 131 | py |
OVE6D-pose | OVE6D-pose-main/lib/geometry.py | """
This code is borrowed from LatentFusion https://github.com/NVlabs/latentfusion/blob/master/latentfusion/modules/geometry.py
"""
import torch
from skimage import morphology
from torch.nn import functional as F
from lib import three
def inplane_2D_spatial_transform(R, img, mode='nearest', padding_mode='border', ali... | 19,744 | 36.82567 | 123 | py |
OVE6D-pose | OVE6D-pose-main/lib/rendering.py | """
This code is partially borrowed from LatentFusion
"""
import os
import math
import torch
import trimesh
import pyrender
import numpy as np
import torch.nn.functional as F
from pyrender import RenderFlags
from pytorch3d.transforms import matrix_to_euler_angles, euler_angles_to_matrix
from utility import meshutils
... | 14,781 | 37.494792 | 119 | py |
OVE6D-pose | OVE6D-pose-main/lib/preprocess.py | import torch
import torch.nn.functional as F
from lib import geometry
def background_filter(depths, diameters, dist_factor=0.5):
"""
filter out the outilers beyond the object diameter
"""
new_depths = list()
unsqueeze = False
if not isinstance(diameters, torch.Tensor):
diameters = torch... | 18,061 | 37.348195 | 132 | py |
OVE6D-pose | OVE6D-pose-main/lib/three/rigid.py | from typing import Tuple
import torch
from torch.nn import functional as F
from lib.three import core
def intrinsic_to_3x4(matrix):
matrix, unsqueezed = core.ensure_batch_dim(matrix, num_dims=2)
zeros = torch.zeros(1, 3, 1, dtype=matrix.dtype).expand(matrix.shape[0], -1, -1).to(matrix.device)
mat = tor... | 3,819 | 23.487179 | 102 | py |
OVE6D-pose | OVE6D-pose-main/lib/three/core.py | import torch
@torch.jit.script
def acos_safe(t, eps: float = 1e-7):
return torch.acos(torch.clamp(t, min=-1.0 + eps, max=1.0 - eps))
@torch.jit.script
def ensure_batch_dim(tensor, num_dims: int):
unsqueezed = False
if len(tensor.shape) == num_dims:
tensor = tensor.unsqueeze(0)
unsqueezed... | 2,950 | 23.591667 | 68 | py |
OVE6D-pose | OVE6D-pose-main/lib/three/batchview.py | import torch
@torch.jit.script
def bvmm(a, b):
if a.shape[0] != b.shape[0]:
raise ValueError("batch dimension must match")
if a.shape[1] != b.shape[1]:
raise ValueError("view dimension must match")
nbatch, nview, nrow, ncol = a.shape
a = a.view(-1, nrow, ncol)
b = b.view(-1, nrow,... | 1,093 | 26.35 | 79 | py |
hyperspherical_community_detection | hyperspherical_community_detection-main/experiments/observation1_demonstration.py | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from ..algorithms import pair_vector as pv
from ..random_graphs.generators import PPM, HeterogeneousSizedPPM, IndependentLFR
default_lats = np.linspace(0, np.pi / 2, 12)[1:]
def short2mathjax(desc, name='x'):
if desc == 'l(b(T))':
ret... | 8,979 | 38.043478 | 109 | py |
pose-gan | pose-gan-master/test.py | import os
from conditional_gan import make_generator
import cmd
from pose_dataset import PoseHMDataset
from gan.inception_score import get_inception_score
from skimage.io import imread, imsave
from skimage.measure import compare_ssim
import numpy as np
import pandas as pd
from tqdm import tqdm
import re
def l1_sc... | 5,945 | 37.36129 | 119 | py |
pose-gan | pose-gan-master/stn.py | from keras.engine.topology import Layer
import tensorflow as tf
class SpatialTransformer(Layer):
"""Spatial Transformer Layer
Implements a spatial transformer layer as described in [1]_.
Borrowed from [2]_:
downsample_fator : float
A value of 1 will keep the orignal size of the image.
... | 6,874 | 38.97093 | 123 | py |
pose-gan | pose-gan-master/conditional_gan.py | from keras.models import Model, Input, Sequential
from keras.layers import Flatten, Concatenate, Activation, Dropout, Dense
from keras.layers.convolutional import Conv2D, Conv2DTranspose, ZeroPadding2D, Cropping2D
from keras_contrib.layers.normalization import InstanceNormalization
from keras.layers.advanced_activation... | 13,649 | 39.746269 | 119 | py |
pose-gan | pose-gan-master/demo.py | from compute_coordinates import cordinates_from_image_file
from create_pairs_dataset import filter_not_valid
import cmd
import os
from shutil import copy, rmtree
import pandas as pd
from pose_dataset import PoseHMDataset
from conditional_gan import make_generator
from tqdm import tqdm
from test import generate_images, ... | 4,480 | 39.369369 | 115 | py |
pose-gan | pose-gan-master/pose_transform.py | from keras.models import Input, Model
from keras.engine.topology import Layer
from keras.backend import tf as ktf
import pose_utils
import pylab as plt
import numpy as np
from skimage.io import imread
from skimage.transform import warp_coords
import skimage.draw
import skimage.measure
import skimage.transform
from... | 18,211 | 33.041121 | 134 | py |
pose-gan | pose-gan-master/baseline.py | from keras.models import Model
from keras.layers import Dense, Reshape, Flatten, Activation, Input
from keras.layers.convolutional import Conv2D
from keras.layers.normalization import BatchNormalization
from keras_contrib.layers.normalization import InstanceNormalization
from gan.wgan_gp import WGAN_GP
from gan.datase... | 2,214 | 34.15873 | 114 | py |
pose-gan | pose-gan-master/compute_coordinates.py | import pose_utils
import os
import numpy as np
from keras.models import load_model
import skimage.transform as st
import pandas as pd
from tqdm import tqdm
from skimage.io import imread
from skimage.transform import resize
from scipy.ndimage import gaussian_filter
from cmd import args
mapIdx = [[31,32], [39,40], [33... | 9,281 | 40.070796 | 129 | py |
pose-gan | pose-gan-master/sup-mat/search.py | ### Script for retriving images from paper in large datasets, script uses vgg conv5 descriptors for comparizon.
### usage: python search.py /path/to/dataset/folder /path/to/images/from/paper/folder
from skimage.color import rgb2gray, gray2rgb
from skimage.transform import resize
import os
from keras.applications impor... | 1,704 | 33.1 | 111 | py |
pose-gan | pose-gan-master/ssd_score/compute_ssd_score.py | import numpy as np
##Caffe from ssd branc
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
from skimage import img_as_float
from tqdm import tqdm
class SSDScorer(object):
def __init__(self, model_def='deploy.prototxt', model_weights='VGG_VOC0712_SSD_300x300_iter_120000.caffemodel'):
self.net = caffe.N... | 2,857 | 40.42029 | 116 | py |
SSeg | SSeg-master/demo_folder.py | import os
import sys
import time
import argparse
from PIL import Image
import numpy as np
import cv2
import torch
from torch.backends import cudnn
import torchvision.transforms as transforms
import network
from optimizer import restore_snapshot
from datasets import cityscapes
from config import assert_and_infer_cfg
... | 3,199 | 36.209302 | 162 | py |
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