id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
12,763 | import sys
MAX = 0
arr = list(map(int, input().split()))
def DFS(x):
global MAX
if len(arr) == 2:
MAX = max(MAX, x)
return
for i in range(1, len(arr)-1):
save = arr[i]
arr.pop(i)
DFS(x + arr[i-1] * arr[i])
arr.insert(i, save) | null |
12,765 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
used = [ 0 for _ in range(10) ]
def dfs(idx, cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
... | null |
12,767 | import sys
N, M = map(int, input().split())
choose = [ 0 for _ in range(10) ]
used = [ 0 for _ in range(10) ]
def dfs(cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(choose[idx], end=' ')
print()
return
for i in range(1, N + 1):
if used[i]:
... | null |
12,768 | import sys
sys.setrecursionlimit(10**4)
def input():
return sys.stdin.readline().rstrip() | null |
12,769 | import sys
n, h, d = map(int, input().split())
visit = [[False] * n for _ in range(n)]
umbs = []
answer = INF
if answer == INF:
print(-1)
else:
print(answer)
def dfs(cur):
global answer, n
y, x, health, durability, cnt = cur
dist = abs(end[0] - y) + abs(end[1] - x)
if dist <= health + durabilit... | null |
12,771 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
used = [ 0 for _ in range(10) ]
def dfs(idx, cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
... | null |
12,773 | import sys
ans = 0
nx = [0, 0, 1, -1]
ny = [1, -1, 0, 0]
visited = [[0 for i in range(31)] for j in range(31)]
N = arr[0]
dir = arr[1:]
visited[14][14] = 1
def DFS(x,y,ct,now):
global ans
if ct == N:
ans += now
return
for i in range(4):
dx = x + nx[i]
dy = y + ny[i]
... | null |
12,774 | import sys
def input():
return sys.stdin.readline().strip() | null |
12,775 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
def dfs(cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
for i in range(0,N):
choose... | null |
12,777 | import sys
N = int(input())
arr = []
visited = [0] * (N+1)
def DFS():
if len(arr) == N:
print(*arr)
return
for i in range(1, N+1):
if visited[i] == 0:
visited[i] = 1
arr.append(i)
DFS()
arr.pop()
visited[i] = 0 | null |
12,779 | import sys
N, M = map(int, input().split())
choose = [ 0 for _ in range(10) ]
def dfs(idx, cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(choose[idx], end=' ')
print()
return
for i in range(idx, N + 1):
choose[cnt] = i
dfs(i, cnt + 1) | null |
12,781 | import sys
N, M = map(int, input().split())
choose = [ 0 for _ in range(10) ]
def dfs(idx, cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(choose[idx], end=' ')
print()
return
for i in range(1, N + 1):
choose[cnt] = i
dfs(i + 1, cnt + 1) | null |
12,783 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
def dfs(cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
pre = -1
for i in range(0, N):
... | null |
12,785 | import sys
from collections import deque
arr = deque(list(map(int, input().split())))
oper = list(map(int, input().split()))
MIN = 1e9+1
MAX = -1e9-1
def DFS(ans):
global MIN, MAX
if not arr:
MIN = min(MIN, ans)
MAX = max(MAX, ans)
return
for i in range(4):
if oper[i] > 0:
... | null |
12,787 | import sys
from collections import deque
result = []
for i in range(10):
result.append(i)
if len(result) > N:
print(result[N])
else:
print(-1)
def bfs(N):
queue = deque()
for i in range(1, 10):
queue.append((i, str(i)))
while queue:
if len(result) == N + 1:
break
... | null |
12,789 | import sys
N, S = map(int, input().split())
arr = list(map(int, input().split()))
poc = []
ans = 0
def backTracking(idx):
global ans
if len(poc) >= N:
if sum(poc) == S:
ans += 1
return
else:
if sum(poc) == S and poc:
ans += 1
for i in range(idx,N):
... | null |
12,791 | import sys
N, M = map(int, input().split())
Map = [ [ 0 for _ in range(M + 1) ] for __ in range(N + 1) ]
answer = 0
def dfs(cnt):
global answer
if cnt == N * M:
answer += 1
return
y = cnt // M + 1
x = cnt % M + 1
dfs(cnt + 1)
if Map[y - 1][x] == 0 or Map[y][x - 1] == ... | null |
12,793 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
used = [ 0 for _ in range(10) ]
def dfs(cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
f... | null |
12,795 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
def dfs(idx, cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
pre = -1
for i in range(id... | null |
12,797 | import sys
N, M = map(int, input().split())
arr = sorted(list(map(int, input().split())))
choose = [ 0 for _ in range(10) ]
used = [ 0 for _ in range(10) ]
def dfs(cnt):
global N, M
if cnt == M:
for idx in range(cnt):
print(arr[choose[idx]], end=' ')
print()
return
p... | null |
12,799 | import sys
for _ in range(F):
a, b = map(int, sys.stdin.readline().split())
relation[a][b] = True
relation[b][a] = True
def dfs(start, relation, friends):
global flag, visit, answer
if flag: # 친구 관계 성립되면 더이상 할 필요 X
return
if len(friends) == K: # 만족하는 친구관계가 K개 일때
flag = True
... | null |
12,803 | import math
import sys
def input():
return sys.stdin.readline().rstrip() | null |
12,809 | import sys
dp = [0] * 101
def recursion(x):
if x == 1 or x == 2 or x == 3:
return 1
if dp[x] == 0:
dp[x] = recursion(x-2) + recursion(x-3)
return dp[x] | null |
12,816 | import sys
sys.setrecursionlimit(1000000)
def input():
return sys.stdin.readline().rstrip() | null |
12,817 | import sys
def dfs(cur):
flag = True
visited[cur] = True
for nei in graph[cur]:
if visited[nei]:
if label[cur] == label[nei]: #만약 neighbor node와 label이 같다면?
return False # Bipartite graph가 아니다!
else:
label[nei] = 3 - label[cur] # 현재 node와 다른 label 저장
... | null |
12,819 | import sys
from collections import deque
dx = [-1,1,0,0]
dy = [0,0,-1,1]
def bfs(x, y):
queue = deque()
queue.append((x, y))
visited[x][y] = True
while queue:
x,y = queue.popleft()
for i in range(4):
nx = x + dx[i]
ny = y + dy[i]
if not (0<=nx<N and 0... | null |
12,822 | import sys
M, N = map(int, input().split())
banner = [list(map(int, input().split())) for _ in range(M)]
dirx = (1, 0, -1, 0, 1, 1, -1, -1)
diry = (0, 1, 0, -1, 1, -1, 1, -1)
def find_string(row, col):
stack = [(row, col)]
banner[row][col] = 0
while stack:
x, y = stack.pop()
for dx, dy in z... | null |
12,824 | import sys
from collections import deque
m, n = map(int,input().split())
board = [list(map(int,input().split())) for _ in range(m)]
start = list(map(int,input().split()))
start[0]-=1
start[1]-=1
end = list(map(int,input().split()))
end[0]-=1
end[1]-=1
def move(dir):
def bfs():
y, x, dir = start
visit = [[[Fals... | null |
12,826 | import sys
from collections import deque
def checkMap():
for z in range(H):
for i in range(N):
for j in range(M):
if arr[z][i][j] == 0:
return False
return True
M, N, H = map(int, input().split())
arr = []
nx = [-1,0,1,0,0,0]
ny = [0,-1,0,1,0,0]
nz = [0,0,... | null |
12,828 | from collections import deque
import sys
q = deque()
fire_q = deque()
def bfs(q, cnt):
global r, c
next_q = deque()
while q:
y, x = q.popleft()
if board[y][x] == 2: continue
for i in range(4):
ny, nx = y + dy[i], x + dx[i]
if ny<0 or ny>=r+2 or nx<0 or nx>=c+2... | null |
12,829 | import sys
from collections import deque
from itertools import combinations
m = map(int, input().split())
arr = [list(map(int, input().split())) for _ in range(n)]
empty, virus = get_pos() ons(empty, 3)
m - (len(empty) + len(virus))
def get_pos():
empty, virus = [], []
for i in range(n):
for j in rang... | null |
12,830 | import sys
from collections import deque
from itertools import combinations
arr = [list(map(int, input().split())) for _ in range(n)]
def set_wall(comb):
for y, x in comb:
arr[y][x] = 1 | null |
12,831 | import sys
from collections import deque
from itertools import combinations
arr = [list(map(int, input().split())) for _ in range(n)]
def collapse_wall(comb):
for y, x in comb:
arr[y][x] = 0 | null |
12,832 | import sys
from collections import deque
from itertools import combinations
direction = [(0, 1), (-1, 0), (0, -1), (1, 0)]
m = map(int, input().split())
arr = [list(map(int, input().split())) for _ in range(n)]
m - (len(empty) + len(virus))
def bfs(virus):
queue = deque(virus)
visited = [[False] * m for _ in r... | null |
12,834 | import sys
arr = [[] for i in range(N+1)]
visited = [0] * (N+1)
for i in range(M):
u,v = map(int, input().split())
arr[u].append(v)
arr[v].append(u)
for i in range(1,N+1):
if visited[i] == 0:
DFS(i)
ans += 1
def DFS(now):
visited[now] = 1
for i in arr[now]:
if visited[i]... | null |
12,836 | from collections import deque
import sys
def answer(G, r):
is_giga_find = False
longest = 0
body = 0
visited = [False for _ in range(len(G))]
q = deque()
q.append((0, r))
visited[r] = True
while q:
dist, node = q.popleft()
if dist > longest:
longest = dist
... | null |
12,838 | import sys
ans = []
if ans:
print(ans[0])
else:
print(-1)
def DFS(x,ct):
if x == B:
ans.append(ct)
return
if x * 10 + 1 <= B:
DFS(x * 10 + 1,ct+1)
if x * 2 <= B:
DFS(x*2,ct+1) | null |
12,840 | from collections import deque
import sys
def bfs(board):
end = (0,7)
que = deque()
que.append((7,0,0))
visit = [[[False] * 8 for _ in range(8)] for _ in range(9)]
visit[0][7][0] = True
dy = [0,0,0,-1,1,-1,1,-1,1]
dx = [0,-1,1,0,0,-1,1,1,-1]
result = 0
while que:
y,x,time = q... | null |
12,842 | import sys
N = int(input())
arr = [list(input()) for _ in range(N)]
def dfs(idx):
global N
visited = [ True for _ in range(N) ]
visited[idx] = False
stack = [ (idx, 0) ]
total = 0
while stack:
curent_x, p_cnt = stack.pop()
for either_x in range(N):
if arr[curent_x][e... | null |
12,843 | import sys
sys.setrecursionlimit(10000)
def input():
return sys.stdin.readline().rstrip() | null |
12,844 | import sys
nx = [-1,-1,-1,0,1,1,1,0]
ny = [-1,0,1,1,1,0,-1,-1]
while True:
w, h = map(int, input().split())
if w == 0 and h == 0:
break
arr = []
ct = 0
for i in range(h):
arr.append(list(map(int, input().split())))
for i in range(h):
for j in range(w):
if arr[... | null |
12,846 | from collections import deque
import sys
n, m = map(int, input().split())
G = []
def answer(row, col):
global m, n
shortest = 10000001
q = deque()
direction = [(1, 0), (0, 1), (-1, 0), (0, -1)]
visited = [[[0] * 2 for _ in range(m)] for _ in range(n)]
q.append((row, col, 1))
visited[row][... | null |
12,848 | import sys
from collections import deque
L, W = map(int, input().split())
arr = []
nx = [-1, 0, 1, 0]
ny = [0, -1, 0, 1]
for i in range(L):
arr.append(input())
for i in range(L):
for j in range(W):
if arr[i][j] == 'L':
visited = [[0 for i in range(W)] for j in range(L)]
ct = BFS(... | null |
12,849 | import os
import sys
import subprocess
import argparse
from bs4 import BeautifulSoup as bs
import requests
def load_arg():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg('--pr_number', type=int, help="Pull Request Number")
arg('--check_solution', action='store_true')
parser.set_de... | null |
12,850 | import os
import sys
import subprocess
import argparse
from bs4 import BeautifulSoup as bs
import requests
def check_alreay_exist_solution(path):
if os.path.exists(path):
raise Exception("Alread Exists Solution")
print("It is a new Solution!!") | null |
12,851 | import os
import sys
import subprocess
import argparse
from bs4 import BeautifulSoup as bs
import requests
def run(command):
ret = subprocess.check_output(command, shell=True).decode('utf8')
return ret
def get_pr_file(pr_number):
run(f"git fetch origin +refs/pull/{pr_number}/merge")
files = run(f"git -... | null |
12,852 | import os
import sys
import subprocess
import argparse
from bs4 import BeautifulSoup as bs
import requests
def run(command):
ret = subprocess.check_output(command, shell=True).decode('utf8')
return ret
def detect_tab(path):
with open(path, 'r') as f:
solution = f.readlines()
f.close()
fo... | null |
12,853 | import os
import subprocess as sp
def getCount(folder):
lines = list()
with open(f'./{folder}/list.md', 'r') as f:
lines = f.readlines()
f.close()
total = 0
cnt = 0
for line in lines:
S = line.split(",")[0]
total += 1
if S != '':
cnt += 1
ret... | null |
12,854 | import os
import subprocess as sp
seq = [ "header.md", "codingtest_info.md", "workbook_header.md", "workbook.md", "workbook_footer.md", "contributors.md", "updatelog.md", "TODO.md", "footer.md" ]
def assemble():
with open('./README.md', 'w') as f:
f.close()
for md in seq:
os.system(f"cat ./mar... | null |
12,855 | import os
import subprocess as sp
def make_contributors():
os.system('python3 ./scripts/make_contributor.py') | null |
12,856 | import datetime
import random
import json
import urllib.request as request
from utils import Date, Communication
class Communication:
def get_json(url):
def get_database(cls):
def get_picked_problem(cls):
def get_today_problem(cls):
def make_table(data: dict, save_file: str) -> None:
database ... | null |
12,857 | import datetime
import random
import json
import urllib.request as request
from utils import Date, Communication
def get_today_date():
year, month, day = Date.get_today_date()
timeformat = f"{year:04d}/{month:02d}/{day:02d}"
return timeformat
class Communication:
__URL = {
"database": "https://... | null |
12,858 | from API import SolvedAPI
import json
def urlProblem(number, name):
FORMAT = f"<a href=\"https://www.acmicpc.net/problem/{number}\" target=\"_blank\">{name}</a>"
return FORMAT | null |
12,859 | from API import SolvedAPI
import json
def urlSolution(link):
if link == "":
return ""
FORMAT = f"<a href=\"{link}\">바로가기</a>"
return FORMAT | null |
12,860 | from API import SolvedAPI
import json
def urlLevel(level):
url = f"https://static.solved.ac/tier_small/{level}.svg"
ret = f"<img height=\"25px\" width=\"25px\" src=\"{url}\"/>"
return ret | null |
12,861 | from urllib import request
import ssl
import json
import atexit
import time
import datetime
import pytz
from utils import Communication
ALPHA = [ 'B', 'S', 'G', 'P', 'D', 'R' ]
return f"{ALPHA[level // 5]}{5 - level % 5}
def changeLevel(level):
ALPHA = [ 'B', 'S', 'G', 'P', 'D', 'R' ]
level -= 1
re... | null |
12,862 | import os
import time
import datetime
import pytz
def getProblem(Dir):
ret = list()
with open(f"{Dir}/list.md", 'r') as f:
ret = f.readlines()
f.close()
return ret | null |
12,863 | import os
import time
import datetime
import pytz
def Assemble(*args):
problems = set()
for i in args:
for problem in i:
problemID = problem.split(',')[-2]
problems.add(problemID)
return problems | null |
12,864 | import os
import time
print("""# Status
간단하게 파이썬으로 진행사항 및 문제 수를 알아보기 위해 만들어 보았습니다.
[메인으로 돌아가기](https://github.com/tony9402/baekjoon)
""")
print(f"총 문제 수 : {len(TotalProblem)} ")
print(f"총 추천 문제 수 : {len(Recommend_List)} ({len(Recommend_List) / len(TotalProblem) * 100. :.2f}%) ")
print(f"알고리즘 Tag 개수 : {len(Folders)} ... | null |
12,865 | import os
import time
import datetime
import pytz
def getTier(Str):
if len(Str) == 2: # Ex p2, P2...
return Str.upper()
else: # Gold5...
return Str[0].upper() + Str[-1] | null |
12,866 | import os
import time
import datetime
import pytz
def getRecommend(*args):
ret = list() # Not Set, Get Problem Info (ProblemID, Problem Name, Tier)
for i in args:
for problem in i:
info = problem.split(",")
rec = info[0].strip()
if not rec == '':
ret.... | null |
12,867 | import os
import time
import datetime
import pytz
def calPercentageOfRec(*args):
total = 0
hasSolution = 0
for i in args:
for problem in i:
info = problem.split(",")
rec = info[0].strip()
link = info[-1].strip()
if rec == '':
contin... | null |
12,868 | import json
def read_json(path):
with open(path, 'r') as f:
data = json.load(f)
f.close()
return data | null |
12,869 | import json
def write_codeowners(data, path = "CODEOWNERS"):
LangtoExt = {
"*": "*",
"c": "c",
"cpp": "cpp",
"python": "py",
"java": "java",
"swift": "swift",
"rust": "rs",
"kotlin": "kt",
"javascript": "js",
"go": "go"
}
f = o... | null |
12,870 | from API import SolvedAPI
from make_table import Table
import json
import argparse
import os
table = None
def updateProblems():
print("update start")
table.run()
print("update end") | null |
12,871 | from API import SolvedAPI
from make_table import Table
import json
import argparse
import os
solution_list = dict()
def getFolder(path, EXCEPT=list()):
def updateSolution():
rootFolder = "./solution"
tagFolder = getFolder(rootFolder) # in ./solution
for tag in tagFolder:
solution_list[tag] = se... | null |
12,872 | from API import SolvedAPI
from make_table import Table
import json
import argparse
import os
config = dict()
solution_list = dict()
solutionRPATH = "./../solution"
rootFolder = "./"
tagFolder = config.get('tags')
for tag in tagFolder:
currentPath = f"{rootFolder}/{tag}"
... | null |
12,873 | from API import SolvedAPI
from make_table import Table
import json
import argparse
import os
def updateStatus():
os.system('python3 ./scripts/arrange.py > status.md') | null |
12,874 | from API import SolvedAPI
from make_table import Table
import json
import argparse
import os
table = None
def updateLevel():
table.run(force = True) | null |
12,875 | import torch
import numpy as np
import os
import cv2
from os.path import join
import pickle
def check_modelpath(paths):
if isinstance(paths, str):
assert os.path.exists(paths), paths
return paths
elif isinstance(paths, list):
for path in paths:
if os.path.exists(path):
... | null |
12,876 | import os
import numpy as np
import cv2
The provided code snippet includes necessary dependencies for implementing the `bbox_from_keypoints` function. Write a Python function `def bbox_from_keypoints(keypoints, rescale=1.2, detection_thresh=0.05, MIN_PIXEL=5)` to solve the following problem:
Get center and scale for b... | Get center and scale for bounding box from openpose detections. |
12,877 | import os
import numpy as np
import math
import cv2
import torch
from ..basetopdown import BaseTopDownModelCache
from .hrnet import HRNet
The provided code snippet includes necessary dependencies for implementing the `get_max_preds` function. Write a Python function `def get_max_preds(batch_heatmaps)` to solve the fol... | get predictions from score maps heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) |
12,878 | import os
import numpy as np
import math
import cv2
import torch
from ..basetopdown import BaseTopDownModelCache
from .hrnet import HRNet
COCO17_IN_BODY25 = [0,16,15,18,17,5,2,6,3,7,4,12,9,13,10,14,11]
def coco17tobody25(points2d):
kpts = np.zeros((points2d.shape[0], 25, 3))
kpts[:, COCO17_IN_BODY25, :2] = poi... | null |
12,879 | import os
import numpy as np
import torch
from functools import partial
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from .layers import drop_path, to_2tuple, trunc_normal_
from ..basetopdown import BaseTopDownModelCache
from ..topdown_keypoints import BaseKeypoints
... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/... |
12,880 | import os
import numpy as np
import torch
from functools import partial
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from .layers import drop_path, to_2tuple, trunc_normal_
from ..basetopdown import BaseTopDownModelCache
from ..topdown_keypoints import BaseKeypoints
... | Calculate absolute positional embeddings. If needed, resize embeddings and remove cls_token dimension for the original embeddings. Args: abs_pos (Tensor): absolute positional embeddings with (1, num_position, C). has_cls_token (bool): If true, has 1 embedding in abs_pos for cls token. hw (Tuple): size of input image to... |
12,881 | import torch
import math
import collections.abc
from itertools import repeat
import warnings
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `drop_path` function. Write a Python function `def drop_path(x, drop_prob: float = 0., training: bool = False, scale_by_keep:... | Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/... |
12,882 | import torch
import math
import collections.abc
from itertools import repeat
import warnings
import torch.nn as nn
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple(repeat(x, n))
return parse | null |
12,883 | import torch
import math
import collections.abc
from itertools import repeat
import warnings
import torch.nn as nn
def _trunc_normal_(tensor, mean, std, a, b):
# Cut & paste from PyTorch official master until it's in a few official releases - RW
# Method based on https://people.sc.fsu.edu/~jburkardt/presentatio... | r"""Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values wo... |
12,884 | import os
import os.path as osp
import torch
import torch.nn as nn
from torchvision.models.resnet import BasicBlock, Bottleneck
from torchvision.models.resnet import model_urls
from ..basetopdown import get_preds_from_heatmaps
def make_conv_layers(feat_dims, kernel=3, stride=1, padding=1, bnrelu_final=True):
layer... | null |
12,885 | import os
import os.path as osp
import torch
import torch.nn as nn
from torchvision.models.resnet import BasicBlock, Bottleneck
from torchvision.models.resnet import model_urls
from ..basetopdown import get_preds_from_heatmaps
def make_deconv_layers(feat_dims, bnrelu_final=True):
layers = []
for i in range(len... | null |
12,886 | import math
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `get_max_preds` function. Write a Python function `def get_max_preds(batch_heatmaps)` to solve the following problem:
get predictions from score maps heatmaps: numpy.ndarray([batch_size, num_joints, height, wi... | get predictions from score maps heatmaps: numpy.ndarray([batch_size, num_joints, height, width]) |
12,887 | import math
import numpy as np
def coco17tobody25(points2d):
kpts = np.zeros((points2d.shape[0], 25, 3))
kpts[:, COCO17_IN_BODY25, :2] = points2d[:, :, :2]
kpts[:, COCO17_IN_BODY25, 2:3] = points2d[:, :, 2:3]
kpts[:, 8, :2] = kpts[:, [9, 12], :2].mean(axis=1)
kpts[:, 8, 2] = kpts[:, [9, 12], 2].min(... | null |
12,888 | import torch
import torch.nn as nn
import torchvision.models.resnet as resnet
import numpy as np
import math
class Bottleneck(nn.Module):
""" Redefinition of Bottleneck residual block
Adapted from the official PyTorch implementation
"""
expansion = 4
def __init__(self, inplanes, planes, stride=1... | Constructs an HMR model with ResNet50 backbone. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet |
12,889 | '''
Date: 2021-10-25 11:51:37 am
Author: dihuangdh
Descriptions:
-----
LastEditTime: 2021-10-25 1:50:40 pm
LastEditors: dihuangdh
'''
import torch
from torchvision.transforms import Normalize
import numpy as np
import cv2
from .models import hmr
class constants:
FOCAL_LENGTH = 5000.
IMG_RES = 224
# Mean an... | Read image, do preprocessing and possibly crop it according to the bounding box. If there are bounding box annotations, use them to crop the image. If no bounding box is specified but openpose detections are available, use them to get the bounding box. |
12,890 |
def solve_translation(X, x, K):
A = np.zeros((2*X.shape[0], 3))
b = np.zeros((2*X.shape[0], 1))
fx, fy = K[0, 0], K[1, 1]
cx, cy = K[0, 2], K[1, 2]
for nj in range(X.shape[0]):
A[2*nj, 0] = 1
A[2*nj + 1, 1] = 1
A[2*nj, 2] = -(x[nj, 0] - cx)/fx
A[2*nj+1, 2] = -(x[nj,... | null |
12,891 | '''
Date: 2021-10-25 11:51:37 am
Author: dihuangdh
Descriptions:
-----
LastEditTime: 2021-10-25 1:50:40 pm
LastEditors: dihuangdh
'''
import torch
from torchvision.transforms import Normalize
import numpy as np
import cv2
from .models import hmr
def estimate_translation_np(S, joints_2d, joints_conf, K):
"""Find ca... | null |
12,892 | import os
from os.path import join
import numpy as np
import cv2
import torch
import torch.nn as nn
import pickle
import math
The provided code snippet includes necessary dependencies for implementing the `get_warp_matrix` function. Write a Python function `def get_warp_matrix(theta, size_input, size_dst, size_target)... | Calculate the transformation matrix under the constraint of unbiased. Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020). Args: theta (float): Rotation angle in degrees. size_input (np.ndarray): Size of input image [w, h]. size_dst (np.ndarra... |
12,893 | import os
from os.path import join
import numpy as np
import cv2
import torch
import torch.nn as nn
import pickle
import math
def generate_patch_image_cv(cvimg, c_x, c_y, bb_width, bb_height, patch_width, patch_height, do_flip, scale, rot):
trans, inv_trans = gen_trans_from_patch_cv(c_x, c_y, bb_width, bb_height, p... | null |
12,894 | import os
from os.path import join
import numpy as np
import cv2
import torch
import torch.nn as nn
import pickle
import math
def xyxy2ccwh(bbox):
w = bbox[:, 2] - bbox[:, 0]
h = bbox[:, 3] - bbox[:, 1]
cx = (bbox[:, 2] + bbox[:, 0])/2
cy = (bbox[:, 3] + bbox[:, 1])/2
return np.stack([cx, cy, w, h]... | null |
12,895 | import os
from os.path import join
import numpy as np
import cv2
import torch
import torch.nn as nn
import pickle
import math
def get_max_preds(batch_heatmaps):
'''
get predictions from score maps
heatmaps: numpy.ndarray([batch_size, num_joints, height, width])
'''
assert isinstance(batch_heatmaps, ... | null |
12,896 | import os
from os.path import join
import numpy as np
import cv2
import torch
import torch.nn as nn
import pickle
import math
def gdown_models(ckpt, url):
print('Try to download model from {} to {}'.format(url, ckpt))
os.makedirs(os.path.dirname(ckpt), exist_ok=True)
cmd = 'gdown "{}" -O {}'.format(url, ck... | null |
12,897 |
def get_backbone_info(backbone):
info = {
'resnet18': {'n_output_channels': 512, 'downsample_rate': 4},
'resnet34': {'n_output_channels': 512, 'downsample_rate': 4},
'resnet50': {'n_output_channels': 2048, 'downsample_rate': 4},
'resnet50_adf_dropout': {'n_output_channels': 2048, '... | null |
12,898 | from torch import nn
The provided code snippet includes necessary dependencies for implementing the `_make_divisible` function. Write a Python function `def _make_divisible(v, divisor, min_value=None)` to solve the following problem:
This function is taken from the original tf repo. It ensures that all layers have a c... | This function is taken from the original tf repo. It ensures that all layers have a channel number that is divisible by 8 It can be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py :param v: :param divisor: :param min_value: :return: |
12,899 | from torch import nn
model_urls = {
'mobilenet_v2': 'https://download.pytorch.org/models/mobilenet_v2-b0353104.pth',
}
class MobileNetV2(nn.Module):
def __init__(self,
num_classes=1000,
width_mult=1.0,
inverted_residual_setting=None,
round_near... | Constructs a MobileNetV2 architecture from `"MobileNetV2: Inverted Residuals and Linear Bottlenecks" <https://arxiv.org/abs/1801.04381>`_. Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
12,900 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from yacs.config import CfgNode as CN
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, stride=1)` to solve the following problem:
3x... | 3x3 convolution with padding |
12,901 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from yacs.config import CfgNode as CN
def get_pose_net(cfg, is_train):
model = PoseHighResolutionNet(cfg)
if is_train and cfg['MODEL']['INIT_WEIGHTS']:
model.init_weights(cfg['MODEL']['PRETRAINED'])
return model
def get_cfg... | null |
12,902 | import os
import torch
import torch.nn as nn
import torch.nn.functional as F
from yacs.config import CfgNode as CN
def get_pose_net(cfg, is_train):
def get_cfg_defaults(pretrained, width=32, downsample=False, use_conv=False):
def hrnet_w48(
pretrained=True,
pretrained_ckpt='data/pretrained_models/pose_... | null |
12,903 | import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `conv3x3` function. Write a Python function `def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1)` to solve the following problem:
3x3 convolution with padding
Here is the function:
def con... | 3x3 convolution with padding |
12,904 | import torch
import torch.nn as nn
The provided code snippet includes necessary dependencies for implementing the `conv1x1` function. Write a Python function `def conv1x1(in_planes, out_planes, stride=1)` to solve the following problem:
1x1 convolution
Here is the function:
def conv1x1(in_planes, out_planes, stride=... | 1x1 convolution |
12,905 | import torch
import torch.nn as nn
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
if norm_layer is None:
norm_layer =... | r"""ResNet-18 model from `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_ Args: pretrained (bool): If True, returns a model pre-trained on ImageNet progress (bool): If True, displays a progress bar of the download to stderr |
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