id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
37,691 | import torch
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import torch.nn as nn
import scipy.io as sio
import math
from skimage import io as img
from skimage import color, morphology, filters
from SinGAN.imresize import imresize
import os
import random
from sklearn.cluster imp... | null |
37,692 | import torch
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import torch.nn as nn
import scipy.io as sio
import math
from skimage import io as img
from skimage import color, morphology, filters
from SinGAN.imresize import imresize
import os
import random
from sklearn.cluster imp... | null |
37,693 | import torch
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import torch.nn as nn
import scipy.io as sio
import math
from skimage import io as img
from skimage import color, morphology, filters
from SinGAN.imresize import imresize
import os
import random
from sklearn.cluster imp... | null |
37,694 | import torch
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import torch.nn as nn
import scipy.io as sio
import math
from skimage import io as img
from skimage import color, morphology, filters
from SinGAN.imresize import imresize
import os
import random
from sklearn.cluster imp... | null |
37,695 | import numpy as np
from scipy.ndimage import filters, measurements, interpolation
from skimage import color
from math import pi
import torch
def np2torch(x,opt):
if opt.nc_im == 3:
x = x[:,:,:,None]
x = x.transpose((3, 2, 0, 1))/255
else:
x = color.rgb2gray(x)
x = x[:,:,None,None... | null |
37,696 | from __future__ import print_function
import SinGAN.functions
import SinGAN.models
import argparse
import os
import random
from SinGAN.imresize import imresize
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import... | null |
37,697 | from __future__ import print_function
import SinGAN.functions
import SinGAN.models
import argparse
import os
import random
from SinGAN.imresize import imresize
import torch.nn as nn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
import... | null |
37,698 | import os
import glob
from get_mask.test import *
from get_mask.models.custom import Custom
def get_frames(video_name):
if not video_name:
cap = cv2.VideoCapture(0)
# warmup
for i in range(5):
cap.read()
while True:
ret, frame = cap.read()
if ret:
... | null |
37,699 | import json
import os
import re
import numpy as np
import cv2
from glob import glob
from fire import Fire
def process(dataset_name):
with open(os.path.join(dataset_name, 'list.txt'), 'r') as f:
lines = f.readlines()
videos = [x.strip() for x in lines]
# if dataset_name == 'VOT2016':
meta_data ... | null |
37,700 | import torch.nn as nn
import torch.nn.functional as F
def conv2d_dw_group(x, kernel):
batch, channel = kernel.shape[:2]
x = x.view(1, batch*channel, x.size(2), x.size(3)) # 1 * (b*c) * k * k
kernel = kernel.view(batch*channel, 1, kernel.size(2), kernel.size(3)) # (b*c) * 1 * H * W
out = F.conv2d(x, k... | null |
37,701 | import torch.nn as nn
import torch
from torch.autograd import Variable
import math
import torch.utils.model_zoo as model_zoo
from get_mask.models.features import Features
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, o... | 3x3 convolution with padding |
37,702 | import torch.nn as nn
import torch
from torch.autograd import Variable
import math
import torch.utils.model_zoo as model_zoo
from get_mask.models.features import Features
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resn... | Constructs a ResNet-18 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
37,703 | import torch.nn as nn
import torch
from torch.autograd import Variable
import math
import torch.utils.model_zoo as model_zoo
from get_mask.models.features import Features
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resn... | Constructs a ResNet-34 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
37,704 | import torch.nn as nn
import torch
from torch.autograd import Variable
import math
import torch.utils.model_zoo as model_zoo
from get_mask.models.features import Features
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resn... | Constructs a ResNet-50 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
37,705 | import torch.nn as nn
import torch
from torch.autograd import Variable
import math
import torch.utils.model_zoo as model_zoo
from get_mask.models.features import Features
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resn... | Constructs a ResNet-101 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
37,706 | import torch.nn as nn
import torch
from torch.autograd import Variable
import math
import torch.utils.model_zoo as model_zoo
from get_mask.models.features import Features
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resn... | Constructs a ResNet-152 model. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
37,707 | import numpy as np
from collections import namedtuple
Corner = namedtuple('Corner', 'x1 y1 x2 y2')
Center = namedtuple('Center', 'x y w h')
The provided code snippet includes necessary dependencies for implementing the `corner2center` function. Write a Python function `def corner2center(corner)` to solve the following... | :param corner: Corner or np.array 4*N :return: Center or 4 np.array N |
37,708 | import numpy as np
from collections import namedtuple
Corner = namedtuple('Corner', 'x1 y1 x2 y2')
Center = namedtuple('Center', 'x y w h')
The provided code snippet includes necessary dependencies for implementing the `center2corner` function. Write a Python function `def center2corner(center)` to solve the following... | :param center: Center or np.array 4*N :return: Corner or np.array 4*N |
37,709 | import numpy as np
from collections import namedtuple
def cxy_wh_2_rect(pos, sz):
return np.array([pos[0]-sz[0]/2, pos[1]-sz[1]/2, sz[0], sz[1]]) # 0-index | null |
37,710 | import numpy as np
from collections import namedtuple
def get_axis_aligned_bbox(region):
nv = region.size
if nv == 8:
cx = np.mean(region[0::2])
cy = np.mean(region[1::2])
x1 = min(region[0::2])
x2 = max(region[0::2])
y1 = min(region[1::2])
y2 = max(region[1::2])... | null |
37,711 | import torch
import logging
logger = logging.getLogger('global')
def check_keys(model, pretrained_state_dict):
ckpt_keys = set(pretrained_state_dict.keys())
model_keys = set(model.state_dict().keys())
used_pretrained_keys = model_keys & ckpt_keys
unused_pretrained_keys = ckpt_keys - model_keys
missi... | null |
37,712 | from os.path import join, realpath, dirname, exists, isdir
from os import listdir
import logging
import glob
import numpy as np
import json
from collections import OrderedDict
def get_dataset_zoo():
root = realpath(join(dirname(__file__), '../data'))
zoos = listdir(root)
def valid(x):
y = join(roo... | null |
37,713 | from os.path import join, realpath, dirname, exists, isdir
from os import listdir
import logging
import glob
import numpy as np
import json
from collections import OrderedDict
def load_dataset(dataset):
info = OrderedDict()
if 'VOT' in dataset:
base_path = join(realpath(dirname(__file__)), '../data', d... | null |
37,714 | import json
from os.path import exists
def load_config(args):
assert exists(args.config), '"{}" not exists'.format(args.config)
config = json.load(open(args.config))
# deal with network
if 'network' not in config:
print('Warning: network lost in config. This will be error in next version')
... | null |
37,715 | import numpy as np
The provided code snippet includes necessary dependencies for implementing the `determine_thresholds` function. Write a Python function `def determine_thresholds(confidence, resolution=100)` to solve the following problem:
choose threshold according to confidence Args: confidence: list or numpy arra... | choose threshold according to confidence Args: confidence: list or numpy array or numpy array reolution: number of threshold to choose Restures: threshold: numpy array |
37,716 | import numpy as np
from numba import jit
from . import region
The provided code snippet includes necessary dependencies for implementing the `calculate_failures` function. Write a Python function `def calculate_failures(trajectory)` to solve the following problem:
Calculate number of failures Args: trajectory: list of... | Calculate number of failures Args: trajectory: list of bbox Returns: num_failures: number of failures failures: failures point in trajectory, start with 0 |
37,717 | import numpy as np
from numba import jit
from . import region
The provided code snippet includes necessary dependencies for implementing the `calculate_accuracy` function. Write a Python function `def calculate_accuracy(pred_trajectory, gt_trajectory, burnin=0, ignore_unknown=True, bound=None)` to solve the fo... | Caculate accuracy socre as average overlap over the entire sequence Args: trajectory: list of bbox gt_trajectory: list of bbox burnin: number of frames that have to be ignored after the failure ignore_unknown: ignore frames where the overlap is unknown bound: bounding region Return: acc: average overlap overlaps: per f... |
37,718 | import numpy as np
from numba import jit
from . import region
def overlap_ratio(rect1, rect2):
'''Compute overlap ratio between two rects
Args
rect:2d array of N x [x,y,w,h]
Return:
iou
'''
# if rect1.ndim==1:
# rect1 = rect1[np.newaxis, :]
# if rect2.ndim==1:
# r... | null |
37,719 | import numpy as np
from numba import jit
from . import region
def success_error(gt_center, result_center, thresholds, n_frame):
# n_frame = len(gt_center)
success = np.zeros(len(thresholds))
dist = np.ones(len(gt_center)) * (-1)
mask = np.sum(gt_center > 0, axis=1) == 2
dist[mask] = np.sqrt(np.sum(... | null |
37,720 | import numpy as np
from numba import jit
from . import region
The provided code snippet includes necessary dependencies for implementing the `determine_thresholds` function. Write a Python function `def determine_thresholds(scores, resolution=100)` to solve the following problem:
Args: scores: 1d array of score
Here ... | Args: scores: 1d array of score |
37,721 | import numpy as np
from numba import jit
from . import region
def calculate_f1(overlaps, score, bound, thresholds, N):
overlaps = np.array(overlaps)
overlaps[np.isnan(overlaps)] = 0
score = np.array(score)
score[np.isnan(score)] = 0
precision = np.zeros(len(thresholds))
recall = np.zeros(len(th... | null |
37,722 | import numpy as np
from numba import jit
from . import region
def calculate_expected_overlap(fragments, fweights):
max_len = fragments.shape[1]
expected_overlaps = np.zeros((max_len), np.float32)
expected_overlaps[0] = 1
# TODO Speed Up
for i in range(1, max_len):
mask = np.logical_not(np... | null |
37,723 | from __future__ import division
import os
import logging
import sys
logs = set()
def get_format(logger, level):
if 'SLURM_PROCID' in os.environ:
rank = int(os.environ['SLURM_PROCID'])
if level == logging.INFO:
logger.addFilter(Filter(rank == 0))
else:
rank = 0
format_str ... | null |
37,724 | from __future__ import division
import os
import logging
import sys
def get_format(logger, level):
if 'SLURM_PROCID' in os.environ:
rank = int(os.environ['SLURM_PROCID'])
if level == logging.INFO:
logger.addFilter(Filter(rank == 0))
else:
rank = 0
format_str = '[%(asctime... | null |
37,725 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,726 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,727 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,728 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,729 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,730 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,731 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,732 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,733 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | Wraps hidden states in new Variables, to detach them from their history. |
37,734 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,735 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,736 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,737 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,738 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,739 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,740 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,741 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,742 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,743 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,744 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,745 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
def rotate_image(img, degree, interp=cv2.INTER_LINEAR):
height, width = img... | null |
37,746 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,747 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,748 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | Convert flow into middlebury color code image :param flow: optical flow map :return: optical flow image in middlebury color |
37,749 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
def tensor2img(x,opt):
if opt.no_mean_norm:
x = x.copy() * 255.
... | null |
37,750 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,751 | import csv
import cv2
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
import pdb
import os, sys, random, math, cv2, pickle, subprocess
import numpy as np
from P... | null |
37,752 | import torch.nn as nn
import torch
import numpy as np
def conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1):
if batchNorm:
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=False),
nn.BatchNo... | null |
37,753 | import torch.nn as nn
import torch
import numpy as np
def i_conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1, bias = True):
if batchNorm:
return nn.Sequential(
nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=bias),
... | null |
37,754 | import torch.nn as nn
import torch
import numpy as np
def predict_flow(in_planes):
return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=True) | null |
37,755 | import torch.nn as nn
import torch
import numpy as np
def deconv(in_planes, out_planes):
return nn.Sequential(
nn.ConvTranspose2d(in_planes, out_planes, kernel_size=4, stride=2, padding=1, bias=True),
nn.LeakyReLU(0.1,inplace=True)
) | null |
37,756 | import torch.nn as nn
import torch
import numpy as np
def init_deconv_bilinear(weight):
f_shape = weight.size()
heigh, width = f_shape[-2], f_shape[-1]
f = np.ceil(width/2.0)
c = (2 * f - 1 - f % 2) / (2.0 * f)
bilinear = np.zeros([heigh, width])
for x in range(width):
for y in range(he... | null |
37,757 | import torch.nn as nn
import torch
import numpy as np
def save_grad(grads, name):
def hook(grad):
grads[name] = grad
return hook | null |
37,758 | from __future__ import division
import torch
from torch.utils import data
import cv2
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import math
import time
import tqdm
import os
import random
import argparse
import glob
import json
from scipy import ndimage, signal
import pdb
def temporal_tra... | null |
37,759 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
from time import time
from inpainting.models.correlation_package.modules.correlation import Correlation
from inpainting.models.gated_conv import GatedConvolution, GatedUpConvolution
import pdb
def get_grid... | null |
37,760 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
from time import time
from inpainting.models.correlation_package.modules.correlation import Correlation
from inpainting.models.gated_conv import GatedConvolution, GatedUpConvolution
import pdb
def conv(bat... | null |
37,761 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import sys
from time import time
from inpainting.models.correlation_package.modules.correlation import Correlation
from inpainting.models.gated_conv import GatedConvolution, GatedUpConvolution
import pdb
def conv_(ba... | null |
37,762 | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
def down_sample(x, size=None, scale_factor=None, mode='nearest'):
# define size if user has specified scale... | null |
37,763 | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
def reduce_mean(x):
for i in range(4):
if i==1: continue
x = torch.mean(x, dim=i, keepdim=T... | null |
37,764 | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
def l2_norm(x):
def reduce_sum(x):
for i in range(4):
if i==1: continue
x =... | null |
37,765 | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
def var_to_numpy(obj, for_vis=True):
def show_image(real, masked, stage_1, stage_2, fake, offset_flow):
bat... | null |
37,766 | import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.backends import cudnn
from random import *
import numpy as np
import matplotlib.pyplot as plt
import torch.nn.functional as F
def to_var(x, volatile=False):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, vo... | null |
37,767 | import glob
import cv2
import os
import numpy as np
import subprocess as sp
def createVideoClip(clip, folder, name, size=[256, 256]):
vf = clip.shape[0]
command = ['ffmpeg',
'-y', # overwrite output file if it exists
'-f', 'rawvideo',
'-s', '%dx%d' % (size[1], siz... | null |
37,768 | import time
import subprocess as sp
from torch.utils import data
from inpainting.davis import DAVIS
from inpainting.model import generate_model
from inpainting.utils import *
class Object():
pass
class DAVIS(data.Dataset):
def __init__(self, root, mask_dilation, resolution='480p', size=(256, 256), sample_durat... | null |
37,769 | import peony
from peony import PeonyClient
from hoshino import Service
from hoshino.config import twitter as cfg
from .util import format_tweet
sv = Service('uma-ura9-sniffer', enable_on_default=False, help_='嗅探新鲜出炉的9URA种马', bundle='umamusume')
def format_tweet(tweet):
name = tweet.user.name
# avatar = tweet.u... | null |
37,770 | asyncio
import itertools
import json
import os
import re
from collections import defaultdict
from dataclasses import dataclass, field
from typing import Dict, Iterable, Set
import peony
from peony import PeonyClient
from peony.exceptions import PeonyException
from hoshino import Service, priv
from hoshino.config import... | null |
37,771 | import base64
import os
import time
from hoshino import aiorequests, config
from .. import chara
from . import sv
_last_query_time = 0
quick_key_dic = {}
def refresh_quick_key_dic():
global _last_query_time
now = time.time()
if now - _last_query_time > 300:
quick_key_dic.clear()
_last_query_tim... | null |
37,772 | import base64
import os
import time
from hoshino import aiorequests, config
from .. import chara
from . import sv
logger = sv.logger
def get_likes(id_):
return DB.get(id_, {}).get("like", set())
def get_dislikes(id_):
return DB.get(id_, {}).get("dislike", set())
def gen_quick_key(true_id: str, user_id: int) -> ... | null |
37,773 | import base64
import os
import time
from hoshino import aiorequests, config
from .. import chara
from . import sv
def dump_db():
"""
Dump the arena databese.
json do not accept set object, this function will help to convert.
"""
j = {}
for k in DB:
j[k] = {
"like": list(DB[k]... | null |
37,774 | import os
import re
import random
import asyncio
from urllib.parse import urljoin, urlparse, parse_qs
from hoshino import aiorequests, R, Service
from hoshino.typing import *
f load_index():
with open(R.get('img/priconne/comic/index.json').path, encoding='utf8') as f:
return json.load(f)
def get_pic_name(id... | null |
37,775 | import os
import re
import random
import asyncio
from urllib.parse import urljoin, urlparse, parse_qs
from hoshino import aiorequests, R, Service
from hoshino.typing import *
sv = Service('pcr-comic', help_=sv_help, bundle='pcr订阅')
def load_index():
with open(R.get('img/priconne/comic/index.json').path, encoding='u... | 轮询官方四格漫画更新 若有更新则推送至订阅群 |
37,776 | import random
from hoshino import Service, R
from hoshino.typing import CQEvent
from hoshino.util import DailyNumberLimiter
(1)
login_presents = [
'扫荡券×5', '卢币×1000', '普通EXP药水×5', '宝石×50', '玛那×3000',
'扫荡券×10', '卢币×1500', '普通EXP药水×15', '宝石×80', '白金转蛋券×1',
'扫荡券×15', '卢币×2000', '上级精炼石×3', '宝石×100', '白金转蛋... | null |
37,777 | import asyncio
import importlib
from io import BytesIO
import pygtrie
from fuzzywuzzy import process
from PIL import Image
import hoshino
from hoshino import R, log, sucmd, util, aiorequests
from hoshino.typing import CommandSession
from . import _pcr_data
async def gen_team_pic(team, size=64, star_slot_verbose=True):... | null |
37,778 | import asyncio
import importlib
from io import BytesIO
import pygtrie
from fuzzywuzzy import process
from PIL import Image
import hoshino
from hoshino import R, log, sucmd, util, aiorequests
from hoshino.typing import CommandSession
from . import _pcr_data
logger = log.new_logger('chara', hoshino.config.DEBUG)
UNKNOWN ... | 覆盖更新1、3、6星头像 |
37,779 | import asyncio
import importlib
from io import BytesIO
import pygtrie
from fuzzywuzzy import process
from PIL import Image
import hoshino
from hoshino import R, log, sucmd, util, aiorequests
from hoshino.typing import CommandSession
from . import _pcr_data
logger = log.new_logger('chara', hoshino.config.DEBUG)
def is_n... | 尝试下载缺失的六星头像，已有头像不会被覆盖 |
37,780 | import asyncio
import os
import random
from hoshino import Service, util
from hoshino.modules.priconne import chara
from hoshino.typing import CQEvent, MessageSegment as Seg
from .. import _pcr_data
from . import GameMaster
gm = GameMaster(DB_PATH)
async def description_guess_group_ranking(bot, ev: CQEvent):
ranki... | null |
37,781 | import asyncio
import os
import random
from hoshino import Service, util
from hoshino.modules.priconne import chara
from hoshino.typing import CQEvent, MessageSegment as Seg
from .. import _pcr_data
from . import GameMaster
PREPARE_TIME = 5
ONE_TURN_TIME = 12
TURN_NUMBER = 5
gm = GameMaster(DB_PATH)
= {
1072, # ... | null |
37,782 | import asyncio
import os
import random
from hoshino import Service, util
from hoshino.modules.priconne import chara
from hoshino.typing import CQEvent, MessageSegment as Seg
from .. import _pcr_data
from . import GameMaster
gm = GameMaster(DB_PATH)
async def on_input_chara_name(bot, ev: CQEvent):
game = gm.get_gam... | null |
37,783 | import asyncio
import os
import random
from hoshino import Service, util
from hoshino.modules.priconne import _pcr_data, chara
from hoshino.typing import CQEvent
from hoshino.typing import MessageSegment as Seg
from . import GameMaster
gm = GameMaster(DB_PATH)
async def description_guess_group_ranking(bot, ev: CQEvent... | null |
37,784 | import asyncio
import os
import random
from hoshino import Service, util
from hoshino.modules.priconne import _pcr_data, chara
from hoshino.typing import CQEvent
from hoshino.typing import MessageSegment as Seg
from . import GameMaster
PATCH_SIZE = 32
ONE_TURN_TIME = 20
gm = GameMaster(DB_PATH)
= {
1072, # 可萝爹
... | null |
37,785 | import asyncio
import os
import random
from hoshino import Service, util
from hoshino.modules.priconne import _pcr_data, chara
from hoshino.typing import CQEvent
from hoshino.typing import MessageSegment as Seg
from . import GameMaster
gm = GameMaster(DB_PATH)
async def on_input_chara_name(bot, ev: CQEvent):
game ... | null |
37,786 | from hoshino.service import Service
svtw = Service('pcr-arena-reminder-tw', enable_on_default=False, help_='背刺时间提醒(台B)', bundle='pcr订阅')吗？'
async def pcr_reminder_tw():
await svtw.broadcast(msg, 'pcr-reminder-tw', 0.2) | null |
37,787 | from hoshino.service import Service
cr-arena-reminder-jp', enable_on_default=False, help_='背刺时间提醒(日)', bundle='pcr订阅')
msg = '骑士君、准备好背刺了吗？'
async def pcr_reminder_jp():
await svjp.broadcast(msg, 'pcr-reminder-jp', 0.2) | null |
37,788 | from hoshino import Service, R
sv = Service('buy_potion_reminder', enable_on_default=False, help_='买药提醒')
async def hour_call():
pic = R.img("BuyPotion.jpg").cqcode
msg = f'骑士君，该上线买经验药水啦~\n{pic}'
await sv.broadcast(msg, 'buy_potion_reminder') | null |
37,789 | import numpy as np
from hoshino.typing import CQEvent, MessageSegment as ms
from . import sv
this_season = np.zeros(15001, dtype=int)
all_season = np.zeros(15001, dtype=int)
this_season[1:11] = 50
this_season[11:101] = 10
this_season[101:201] = 5
this_season[201:501] = 3
this_season[501:1001] = 2
this_season[1001:2001]... | null |
37,790 | import itertools
from datetime import datetime
from hoshino import util, R
from hoshino.typing import CQEvent
from . import sv
pcn = R.img(f'priconne/quick/r{rank_cn}-cn-0.png').cqcode
def get_support_rank(t: datetime, server):
if server == 'jp':
delta = t - datetime(2021, 8, 15)
elif server == 'tw':
... | null |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.