40 | {fieldText.title}
41 |
42 |
43 |
44 | {fieldText.text}
45 |
46 |
16 | {/* TODO: A ESTE BOTON HAY QUE CAMBIARLO POR EL BOTON "secundario". */}
17 |
23 |
24 | );
25 | };
26 |
--------------------------------------------------------------------------------
/api/src/generators/swagger.ts:
--------------------------------------------------------------------------------
1 | import swaggerAutogen from 'swagger-autogen'
2 | import fs from 'fs'
3 | import path from 'path'
4 | import config from '../config'
5 |
6 | const doc = {
7 | info: {
8 | title: 'LLA Fraud detection API',
9 | description: 'API for the LLA Fraud detection project'
10 | },
11 | host: `localhost:${config.port}`,
12 | basePath: '/api',
13 | consumes: ['application/json'],
14 | produces: ['application/json']
15 | }
16 |
17 | const routesDir = 'dist/src/routes'
18 | const files = fs.readdirSync(path.resolve(process.cwd(), 'dist/src/routes'))
19 |
20 | const routeFiles = files
21 | .filter(file => file.endsWith('.js') && file !== 'index.js')
22 | .map(file => path.join(routesDir, file))
23 |
24 | const outputFile = path.resolve(process.cwd(), './swagger.json')
25 |
26 | swaggerAutogen(outputFile, routeFiles, doc)
27 |
--------------------------------------------------------------------------------
/frontend/src/assets/icon/menu.svg:
--------------------------------------------------------------------------------
1 |
11 |
--------------------------------------------------------------------------------
/frontend/src/assets/icon/warn-icon.svg:
--------------------------------------------------------------------------------
1 |
--------------------------------------------------------------------------------
/frontend/tailwind.config.js:
--------------------------------------------------------------------------------
1 | // eslint-disable-next-line @typescript-eslint/no-var-requires, no-undef
2 | const defaultTheme = require('tailwindcss/defaultTheme');
3 |
4 | /** @type {import('tailwindcss').Config} */
5 | export default {
6 | content: ['./index.html', './src/**/*.{js,ts,jsx,tsx}'],
7 | theme: {
8 | extend: {
9 | colors: {
10 | 'violet-brand': '#61439D',
11 | 'violet-dark': '#3C2865',
12 | 'violet-light': '#8B6ACE',
13 | 'gray-dark': '#64748B',
14 | 'gray-inactive': '#CACACB',
15 | 'gray-light': '#CBD5E1',
16 | 'text-off': '#363F45',
17 | red: '#AD3459',
18 | black: '#000000',
19 | white: '#FFFFFF',
20 | },
21 | fontFamily: {
22 | sans: ['Poppins', ...defaultTheme.fontFamily.sans],
23 | },
24 | },
25 | },
26 | plugins: [],
27 | };
28 |
--------------------------------------------------------------------------------
/api/bin/setup_localstack.py:
--------------------------------------------------------------------------------
1 | import boto3
2 |
3 | # Initialize clients for S3, SQS, and DynamoDB connecting to LocalStack
4 | s3 = boto3.client('s3', endpoint_url='http://localhost:4566')
5 | sqs = boto3.client('sqs', endpoint_url='http://localhost:4566')
6 | dynamodb = boto3.client('dynamodb', endpoint_url='http://localhost:4566')
7 |
8 | s3.create_bucket(Bucket='lla-reports')
9 |
10 | sqs.create_queue(QueueName='process-telegrams')
11 |
12 | dynamodb.create_table(
13 | TableName='FraudPrevention',
14 | KeySchema=[
15 | {'AttributeName': 'id', 'KeyType': 'HASH'}
16 | ],
17 | AttributeDefinitions=[
18 | {'AttributeName': 'id', 'AttributeType': 'S'}
19 | ],
20 | ProvisionedThroughput={
21 | 'ReadCapacityUnits': 5,
22 | 'WriteCapacityUnits': 5
23 | }
24 | )
25 |
26 | print("Infrastructure set up successfully! 🎉")
27 |
--------------------------------------------------------------------------------
/frontend/src/hooks/utils/use-copy-to-clipboard.tsx:
--------------------------------------------------------------------------------
1 | import { useState } from "react";
2 |
3 | type CopiedValue = string | null;
4 | type CopyFn = (text: string) => Promise
10 |
27 |
11 | 
17 |
19 |
20 |
25 |
26 | 404
12 |
17 | Lo sentimos, página no encontrada
18 |
8 |
34 |
35 | );
36 | };
37 |
38 | export default FormHeader;
39 |
--------------------------------------------------------------------------------
/Fuentes_de_datos/alineamiento_de_imagenes/LICENSE:
--------------------------------------------------------------------------------
1 | MIT License
2 |
3 | Copyright (c) 2023 Felix Hertlein
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
--------------------------------------------------------------------------------
/api/src/utils/errorConstants.ts:
--------------------------------------------------------------------------------
1 | type ErrorCode = {
2 | status: number;
3 | message: string;
4 | };
5 |
6 | export const ERROR_CODES: Record
9 |
10 | 
15 |
16 |
17 |
15 |
16 |
18 |
26 |
19 | 
24 |
25 |
24 |
27 | 
32 |
33 |
32 |
6 |
10 |
11 | );
12 | }
13 |
--------------------------------------------------------------------------------
/api/src/controllers/upload.ts:
--------------------------------------------------------------------------------
1 | import { Request, Response } from 'express';
2 | import { UploadedFile } from 'express-fileupload';
3 | import path from 'path';
4 |
5 | export const uploadFile = async (req: Request, res: Response): Promise
8 | {steps.map((step, index) => (
9 |
10 |
44 | ))}
45 |
46 | );
47 | };
48 |
49 | export default ProgressIndicator;
50 |
--------------------------------------------------------------------------------
/Fuentes_de_datos/alineamiento_de_imagenes/src/inv3d_util/parallel.py:
--------------------------------------------------------------------------------
1 | import concurrent.futures
2 | import math
3 | import multiprocessing
4 | import traceback
5 | from pathlib import Path
6 | from typing import *
7 |
8 | from tqdm import tqdm
9 |
10 |
11 | def process_tasks(task_fn: Callable, tasks: List[Any], num_workers: int, use_indexes: bool = False, **kwargs) -> Dict:
12 | print("Starting parallel execution with {} workers!".format(num_workers))
13 |
14 | with concurrent.futures.ProcessPoolExecutor(max_workers=num_workers) as executor:
15 | futures = {executor.submit(task_fn, task, **kwargs): idx if use_indexes else task for idx, task in enumerate(tasks)}
16 | results = {}
17 |
18 | print("Awaiting completion!".format(num_workers))
19 |
20 | try:
21 | with tqdm(desc="Processing ...", total=len(futures), smoothing=0) as progress_bar:
22 | for f in concurrent.futures.as_completed(futures.keys()):
23 | try:
24 | results[futures[f]] = f.result()
25 | except Exception:
26 | print("EXCEPTION: ", traceback.format_exc())
27 | print(f"FAILED TASK: {futures[f]}")
28 | progress_bar.update(1)
29 | except KeyboardInterrupt:
30 | executor.shutdown(wait=False)
31 | exit(-1)
32 |
33 | return results
34 |
35 | def get_cpus():
36 | try:
37 | cfs_quota_us = int(Path("/sys/fs/cgroup/cpu/cpu.cfs_quota_us").read_text())
38 | cfs_period_us = int(Path("/sys/fs/cgroup/cpu/cpu.cfs_period_us").read_text())
39 | if cfs_quota_us > 0 and cfs_period_us > 0:
40 | return int(math.ceil(cfs_quota_us / cfs_period_us))
41 | except:
42 | pass
43 | return multiprocessing.cpu_count()
44 |
--------------------------------------------------------------------------------
/Fuentes_de_datos/descargador_de_telegramas/descargador.php:
--------------------------------------------------------------------------------
1 | 'https://resultados.gob.ar/backend-difu/scope/data/getTiff/'.$mesa,
22 | CURLOPT_RETURNTRANSFER => true,
23 | CURLOPT_ENCODING => '',
24 | CURLOPT_MAXREDIRS => 10,
25 | CURLOPT_TIMEOUT => 0,
26 | CURLOPT_HTTPHEADER => array(
27 | 'Accept: */*',
28 | 'Connection: keep-alive',
29 | 'Accept-Encoding: gzip, deflate, br',
30 | 'User-Agent: PostmanRuntime/7.33.0',
31 | ),
32 | CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
33 | CURLOPT_CUSTOMREQUEST => 'GET',
34 | ));
35 |
36 | $response = curl_exec($curl);
37 | $responseParsed = json_decode($response);
38 | curl_close($curl);
39 | return $responseParsed->encodingBinary;
40 | }
41 |
42 | function base64_to_jpeg( $base64_string, $output_file ) {
43 | $status = file_put_contents($output_file, base64_decode($base64_string));
44 | chmod($output_file,777);
45 | return($status);
46 | }
47 |
48 |
49 | ?>
--------------------------------------------------------------------------------
/frontend/src/pages/loading-page/loadingPage.tsx:
--------------------------------------------------------------------------------
1 | import { LoadingIndicator } from '#/components/loadingIndicator';
2 |
3 | export const LoadingPage: React.FC = () => {
4 | return (
5 |
19 | {step === ProgressStepStatus.Successful ? (
20 | 
25 | ) : (
26 |
27 | {(index + 1).toString()}
28 |
29 | )}
30 |
31 |
32 | {index != steps.length - 1 && (
33 |
42 | )}
43 |
25 | ) : (
26 |
27 | {(index + 1).toString()}
28 |
29 | )}
30 |
7 |
22 |
23 |
8 |
21 |
9 | 
14 | 
19 |
20 |
14 |
19 |
24 |
26 |
27 |
33 |
34 | {/*
35 | // TODO: FIX FOOTER IMAGE DESIGN
36 | // https://www.figma.com/file/iO7j93Rxbk2nIfYdqpAmv2/%F0%9F%A6%85-APP-Fiscalizaci%C3%B3n-Libertaria-%7C-%F0%9F%93%B1-FINAL?type=design&node-id=59-4193&mode=dev
37 | 
42 |
38 | 
39 |
59 | );
60 | };
61 |
62 | export default FlatList;
63 |
--------------------------------------------------------------------------------
/frontend/src/context/AuthContext.tsx:
--------------------------------------------------------------------------------
1 | import {
2 | createContext,
3 | useContext,
4 | ReactNode,
5 | useState,
6 | useEffect,
7 | } from 'react';
8 | import useAxios from '#/hooks/utils/useAxios';
9 | import { IUser } from '#/interfaces/IUser';
10 | import { useNavigate } from 'react-router-dom';
11 |
12 | interface AuthContextType {
13 | isAuthenticated: boolean;
14 | user: IUser | null;
15 | login: (user: IUser) => void;
16 | logout: () => void;
17 | }
18 |
19 | const AuthContext = createContext
40 |
45 |
50 |
51 | ) => handleVoteChange(Number(e.target.value))}
54 | value={vote}
55 | readOnly={!edit}
56 | className={`border-2 text-center border-gray-300 outline-none cursor-default bg-white text-neutral-700 font-bold rounded-xl h-12 w-32 flex text-2xl ${selectedInputStyle}`}
57 | />
58 |
18 |
27 |
28 | Ubicacion de la mesa
30 |31 | Recopilación de la ubicación precisa del{' '} 32 | centro educativo. 33 |
34 | 35 |
36 |
42 |
43 |
48 |
16 |
51 |
17 |
50 |
18 |
27 |
28 | 
33 |
34 |
33 |
35 |
39 | 36 | {message ?? '¡ MUCHAS GRACIAS POR FISCALIZAR !'} 37 |
38 |
40 | {/* TODO: Mover a Dashboard */}
41 |
42 |
47 |
48 |
49 | {
27 | e.preventDefault();
28 | }}
29 | onDrop={(e) => {
30 | e.preventDefault();
31 | const file = e.dataTransfer.items[0].getAsFile();
32 | onUploadInternal(file);
33 | }}
34 | >
35 |
66 | );
67 | }
68 |
69 | export default UploadImage;
70 |
--------------------------------------------------------------------------------
/frontend/src/components/snackbar/index.tsx:
--------------------------------------------------------------------------------
1 | import { toast, Toaster, ToastPosition } from 'react-hot-toast';
2 | import { ToastiBarProps } from './types';
3 |
4 | const toastiBar = ({
5 | text,
6 | action,
7 | close,
8 | twoLine,
9 | textTwo,
10 | actionText,
11 | }: ToastiBarProps) => {
12 | let longerAction = false;
13 |
14 | if (actionText && actionText?.length > 7) {
15 | longerAction = true;
16 | }
17 |
18 | const config = {
19 | duration: close ? Infinity : 4000,
20 | position: 'top-center' as ToastPosition,
21 | };
22 |
23 | toast.custom(
24 | (t) => (
25 |
36 |
53 |
54 |
65 |
34 |
71 | ),
72 | config,
73 | );
74 | };
75 |
76 | export const SnackBar = (props: ToastiBarProps) => {
77 | toastiBar(props);
78 | return
35 |
38 | {text}
36 | {textTwo &&{textTwo}
} 37 |
43 | {action && (
44 |
70 |
49 |
53 | )}
54 | {close && (
55 | action()}> 50 | {actionText} 51 |
52 |
60 |
68 | )}
69 | toast.dismiss(t.id)}>
61 | 
66 |
11 |
13 |
50 |
51 | );
52 | };
53 |
54 | export const Dashboard = observer(DashboardPage);
55 |
56 | export default Dashboard;
57 |
--------------------------------------------------------------------------------
/frontend/src/pages/upload-certificate/uploadCertificate.tsx:
--------------------------------------------------------------------------------
1 | import { useEffect, useState } from 'react';
2 |
3 | import ProgressIndicator from '#/components/progressIndicator';
4 | import FormHeader from '#/components/formHeader';
5 | import UploadImage from '#/components/uploadImage';
6 | import { ProgressStepStatus } from '#/components/progressIndicator/types';
7 | import './styles.css';
8 | import { useNavigate } from 'react-router-dom';
9 |
10 | const CheckItem = ({ text }: { text: string }) => (
11 |
14 |
49 |
15 |
20 | 16 | ¡Bienvenido {user?.firstName} {user?.lastName}! 17 |
18 |{user?.email}
19 |
21 |
22 |
27 |
28 |
33 | Ver resultados
34 |
35 |
40 |
48 |
41 | ¿No funciona el Formulario?
42 | Realiza la denuncia{' '} 43 | 44 | aquí 45 | 46 |
47 | 42 | Realiza la denuncia{' '} 43 | 44 | aquí 45 | 46 |
12 |
17 | );
18 |
19 | const UploadCertificate = () => {
20 | const navigate = useNavigate();
21 | // TODO: Replace with context useState
22 | const [certificateImage, setCertificateImage] = useState
13 |
14 |
15 |
14 | {text}
16 |
32 |
58 |
33 |
56 |
42 |
55 | 43 | Usá la cámara para subir el certificado del fiscal, o 44 | cargala desde la galería. 45 |
46 |
47 |
54 | Resultados totales
28 |
29 |
72 |
73 |
78 |
83 |
45 |
97 | );
98 | };
99 |
100 | export default Input;
101 |
--------------------------------------------------------------------------------
/frontend/src/assets/icon/mail-person.svg:
--------------------------------------------------------------------------------
1 |
12 |
--------------------------------------------------------------------------------
/frontend/src/assets/react.svg:
--------------------------------------------------------------------------------
1 |
--------------------------------------------------------------------------------
/frontend/src/components/navbar/index.tsx:
--------------------------------------------------------------------------------
1 | import React, { useState } from 'react';
2 | import { Link } from 'react-router-dom';
3 |
4 | const Navbar: React.FC = () => {
5 | const [menuOpen, setMenuOpen] = useState(false);
6 |
7 | return (
8 |
53 |
64 |
96 |
65 |
78 | {type === 'password' &&
79 | (isPasswordVisible ? (
80 | 
setIsPasswordVisible(false)}
85 | />
86 | ) : (
87 | 
setIsPasswordVisible(true)}
92 | />
93 | ))}
94 |
95 | setIsPasswordVisible(false)}
85 | />
86 | ) : (
87 | setIsPasswordVisible(true)}
92 | />
93 | ))}
94 |
9 |
96 | );
97 | };
98 |
99 | export default Navbar;
100 |
--------------------------------------------------------------------------------
/Fuentes_de_datos/alineamiento_de_imagenes/src/inv3d_model/models/geotr/extractor.py:
--------------------------------------------------------------------------------
1 | import torch.nn as nn
2 |
3 |
4 | class ResidualBlock(nn.Module):
5 | def __init__(self, in_planes, planes, norm_fn='group', stride=1):
6 | super(ResidualBlock, self).__init__()
7 |
8 | self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride)
9 | self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1)
10 | self.relu = nn.ReLU(inplace=True)
11 |
12 | num_groups = planes // 8
13 |
14 | if norm_fn == 'group':
15 | self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
16 | self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
17 | if not stride == 1:
18 | self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes)
19 |
20 | elif norm_fn == 'batch':
21 | self.norm1 = nn.BatchNorm2d(planes)
22 | self.norm2 = nn.BatchNorm2d(planes)
23 | if not stride == 1:
24 | self.norm3 = nn.BatchNorm2d(planes)
25 |
26 | elif norm_fn == 'instance':
27 | self.norm1 = nn.InstanceNorm2d(planes)
28 | self.norm2 = nn.InstanceNorm2d(planes)
29 | if not stride == 1:
30 | self.norm3 = nn.InstanceNorm2d(planes)
31 |
32 | elif norm_fn == 'none':
33 | self.norm1 = nn.Sequential()
34 | self.norm2 = nn.Sequential()
35 | if not stride == 1:
36 | self.norm3 = nn.Sequential()
37 |
38 | if stride == 1:
39 | self.downsample = None
40 |
41 | else:
42 | self.downsample = nn.Sequential(
43 | nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3)
44 |
45 | def forward(self, x):
46 | y = x
47 | y = self.relu(self.norm1(self.conv1(y)))
48 | y = self.relu(self.norm2(self.conv2(y)))
49 |
50 | if self.downsample is not None:
51 | x = self.downsample(x)
52 |
53 | return self.relu(x + y)
54 |
55 |
56 | class BasicEncoder(nn.Module):
57 | def __init__(self, output_dim=128, norm_fn='batch'):
58 | super(BasicEncoder, self).__init__()
59 | self.norm_fn = norm_fn
60 |
61 | if self.norm_fn == 'group':
62 | self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64)
63 |
64 | elif self.norm_fn == 'batch':
65 | self.norm1 = nn.BatchNorm2d(64)
66 |
67 | elif self.norm_fn == 'instance':
68 | self.norm1 = nn.InstanceNorm2d(64)
69 |
70 | elif self.norm_fn == 'none':
71 | self.norm1 = nn.Sequential()
72 |
73 | self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3)
74 | self.relu1 = nn.ReLU(inplace=True)
75 |
76 | self.in_planes = 64
77 | self.layer1 = self._make_layer(64, stride=1)
78 | self.layer2 = self._make_layer(128, stride=2)
79 | self.layer3 = self._make_layer(192, stride=2)
80 |
81 | # output convolution
82 | self.conv2 = nn.Conv2d(192, output_dim, kernel_size=1)
83 |
84 | for m in self.modules():
85 | if isinstance(m, nn.Conv2d):
86 | nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
87 | elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
88 | if m.weight is not None:
89 | nn.init.constant_(m.weight, 1)
90 | if m.bias is not None:
91 | nn.init.constant_(m.bias, 0)
92 |
93 | def _make_layer(self, dim, stride=1):
94 | layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride)
95 | layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1)
96 | layers = (layer1, layer2)
97 |
98 | self.in_planes = dim
99 | return nn.Sequential(*layers)
100 |
101 | def forward(self, x):
102 | x = self.conv1(x)
103 | x = self.norm1(x)
104 | x = self.relu1(x)
105 |
106 | x = self.layer1(x)
107 | x = self.layer2(x)
108 | x = self.layer3(x)
109 |
110 | x = self.conv2(x)
111 |
112 | return x
113 |
--------------------------------------------------------------------------------
/Fuentes_de_datos/alineamiento_de_imagenes/src/inv3d_model/models/geotr/position_encoding.py:
--------------------------------------------------------------------------------
1 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
2 | """
3 | Various positional encodings for the transformer.
4 | """
5 | import math
6 | import os
7 | from typing import Optional
8 |
9 | import torch
10 | from torch import Tensor
11 | from torch import nn
12 |
13 |
14 | class NestedTensor(object):
15 | def __init__(self, tensors, mask: Optional[Tensor]):
16 | self.tensors = tensors
17 | self.mask = mask
18 |
19 | def to(self, device):
20 | # type: (Device) -> NestedTensor # noqa
21 | cast_tensor = self.tensors.to(device)
22 | mask = self.mask
23 | if mask is not None:
24 | assert mask is not None
25 | cast_mask = mask.to(device)
26 | else:
27 | cast_mask = None
28 | return NestedTensor(cast_tensor, cast_mask)
29 |
30 | def decompose(self):
31 | return self.tensors, self.mask
32 |
33 | def __repr__(self):
34 | return str(self.tensors)
35 |
36 |
37 | class PositionEmbeddingSine(nn.Module):
38 | """
39 | This is a more standard version of the position embedding, very similar to the one
40 | used by the Attention is all you need paper, generalized to work on images.
41 | """
42 |
43 | def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
44 | super().__init__()
45 | self.num_pos_feats = num_pos_feats
46 | self.temperature = temperature
47 | self.normalize = normalize
48 | if scale is not None and normalize is False:
49 | raise ValueError("normalize should be True if scale is passed")
50 | if scale is None:
51 | scale = 2 * math.pi
52 | self.scale = scale
53 |
54 | def forward(self, mask):
55 | assert mask is not None
56 | y_embed = mask.cumsum(1, dtype=torch.float32)
57 | x_embed = mask.cumsum(2, dtype=torch.float32)
58 | if self.normalize:
59 | eps = 1e-6
60 | y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
61 | x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
62 |
63 | dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32).cuda()
64 | dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
65 |
66 | pos_x = x_embed[:, :, :, None] / dim_t
67 | pos_y = y_embed[:, :, :, None] / dim_t
68 | pos_x = torch.stack((pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4).flatten(3)
69 | pos_y = torch.stack((pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4).flatten(3)
70 | pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
71 | # print(pos.shape)
72 | return pos
73 |
74 |
75 | class PositionEmbeddingLearned(nn.Module):
76 | """
77 | Absolute pos embedding, learned.
78 | """
79 |
80 | def __init__(self, num_pos_feats=256):
81 | super().__init__()
82 | self.row_embed = nn.Embedding(50, num_pos_feats)
83 | self.col_embed = nn.Embedding(50, num_pos_feats)
84 | self.reset_parameters()
85 |
86 | def reset_parameters(self):
87 | nn.init.uniform_(self.row_embed.weight)
88 | nn.init.uniform_(self.col_embed.weight)
89 |
90 | def forward(self, tensor_list: NestedTensor):
91 | x = tensor_list.tensors
92 | h, w = x.shape[-2:]
93 | i = torch.arange(w, device=x.device)
94 | j = torch.arange(h, device=x.device)
95 | x_emb = self.col_embed(i)
96 | y_emb = self.row_embed(j)
97 | pos = torch.cat([
98 | x_emb.unsqueeze(0).repeat(h, 1, 1),
99 | y_emb.unsqueeze(1).repeat(1, w, 1),
100 | ], dim=-1).permute(2, 0, 1).unsqueeze(0).repeat(x.shape[0], 1, 1, 1)
101 | return pos
102 |
103 |
104 | def build_position_encoding(hidden_dim=512, position_embedding='sine'):
105 | N_steps = hidden_dim // 2
106 | if position_embedding in ('v2', 'sine'):
107 | position_embedding = PositionEmbeddingSine(N_steps, normalize=True)
108 | elif position_embedding in ('v3', 'learned'):
109 | position_embedding = PositionEmbeddingLearned(N_steps)
110 | else:
111 | raise ValueError(f"not supported {position_embedding}")
112 |
113 | return position_embedding
114 |
--------------------------------------------------------------------------------
10 |
95 |
11 | 
16 |
17 |
16 |
18 |
94 | setMenuOpen(!menuOpen)}
21 | >
22 | {!menuOpen ? (
23 | 
28 | ) : (
29 |
34 | )}
35 |
36 | {menuOpen && (
37 |
28 | ) : (
29 |
34 | )}
35 |
38 |
92 | )}
93 |
39 |
42 |
43 |
44 | Javier
45 |
46 |
47 | {/* El gris pactado no se parece al de figma */}
48 |
52 | Mi cuenta
53 |
54 |
58 | Cargar resultados de mesa
59 |
60 | alert('No existe la ruta aún')}
64 | >
65 | Impugnar mesa
66 |
67 | alert('No existe la ruta aún')}
71 | >
72 | Denunciar Irregularidades
73 |
74 |
78 | Ver resultados
79 |
80 |
81 |
82 |
91 |
83 | 
88 | Cerrar sesión
89 |
90 |
88 | Cerrar sesión
89 |