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import os | |
from huggingface_hub import InferenceClient | |
from typing import List, Literal, TypedDict, Callable | |
Role = Literal["system", "user", "assistant"] | |
class Message(TypedDict): | |
role: Role |
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import numpy as np # version 1.16.1 and python 3.6.7 | |
def nms_cpu_kernel(dets, scores, iou_threshold): | |
x1 = dets[:, 0] | |
y1 = dets[:, 1] | |
x2 = dets[:, 2] | |
y2 = dets[:, 3] | |
areas = (x2 - x1) * (y2 - y1) |
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# Author: Vinicius Arruda | |
# viniciusarruda.github.io | |
# Source code modified from: https://medium.com/@stepanulyanin/implementing-grad-cam-in-pytorch-ea0937c31e82 | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.utils import data | |
from torchvision import transforms | |
from torchvision import datasets |
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EPOCH FITNESS CHROMOSOME | |
0 86 dktzmkeqixkmpzskjqjn | |
1 85 enstfgzpbwrcxoosnfbh | |
2 78 jetvbgqcbpceqtnnctdm | |
3 66 cpujjdaldsjhuuoraejn | |
4 55 enurcichvsoepthmckeh | |
5 44 enstfggohqnewnoncmkk | |
6 43 bosqbgigbwrculrqcmkm | |
7 38 enstfggohqnevqsqcmcj | |
8 38 enstfggohqnevqsqcmcj |
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EPOCH FITNESS CHROMOSOME | |
0 10279 [8, 6, 12, 1, 11, 9, 2, 0, 7, 5, 10, 3, 4] | |
1 9972 [7, 0, 2, 9, 3, 5, 11, 6, 8, 1, 4, 12, 10] | |
2 9972 [7, 0, 2, 9, 3, 5, 11, 6, 8, 1, 4, 12, 10] | |
3 9759 [9, 2, 12, 11, 1, 8, 6, 4, 10, 5, 3, 7, 0] | |
4 9509 [8, 6, 1, 11, 4, 10, 5, 9, 7, 0, 2, 3, 12] | |
5 9309 [7, 0, 2, 4, 9, 3, 5, 10, 12, 11, 1, 8, 6] | |
6 9161 [0, 7, 3, 2, 9, 5, 10, 11, 6, 1, 8, 12, 4] | |
7 9161 [0, 7, 3, 2, 9, 5, 10, 11, 6, 1, 8, 12, 4] | |
8 9161 [0, 7, 3, 2, 9, 5, 10, 11, 6, 1, 8, 12, 4] |
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city_distance = [ | |
[ 0, 2451, 713, 1018, 1631, 1374, 2408, 213, 2571, 875, 1420, 2145, 1972], # New York | |
[2451, 0, 1745, 1524, 831, 1240, 959, 2596, 403, 1589, 1374, 357, 579], # Los Angeles | |
[ 713, 1745, 0, 355, 920, 803, 1737, 851, 1858, 262, 940, 1453, 1260], # Chicago | |
[1018, 1524, 355, 0, 700, 862, 1395, 1123, 1584, 466, 1056, 1280, 987], # Minneapolis | |
[1631, 831, 920, 700, 0, 663, 1021, 1769, 949, 796, 879, 586, 371], # Denver | |
[1374, 1240, 803, 862, 663, 0, 1681, 1551, 1765, 547, 225, 887, 999], # Dallas | |
[2408, 959, 1737, 1395, 1021, 1681, 0, 2493, 678, 1724, 1891, 1114, 701], # Seattle | |
[ 213, 2596, 851, 1123, 1769, 1551, 2493, 0, 2699, 1038, 1605, 2300, 2099], # Boston | |
[2571, 403, 1858, 1584, 949, 1765, 678, 2699, 0, 1744, 1645, 653, 600], # San Francisco |
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def genetic_algorithm(n): | |
population = init_population(n) | |
for _ in xrange(epochs): | |
fn = fitness(population) | |
parents = selection(n, fn) | |
offspring = crossover(parents) | |
offspring = mutation(offspring) | |
population = parents + offspring | |
return best(population) |
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# -*- coding: utf-8 -*- | |
# | |
# Author: Vinicius Ferraco Arruda | |
# Email: viniciusferracoarruda@gmail.com | |
# Website: viniciusarruda.github.io | |
# | |
import argparse | |
from PIL import Image |
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from PIL import Image | |
import os | |
import numpy as np | |
def _square(img): | |
w, h = img.size | |
s = min(h, w) | |
sh = (h - s)/2.0 | |
sw = (w - s)/2.0 | |
cropped_img = img.crop((sw, sh, sw+s, sh+s)) |