deshwalmahesh

# Get top-K accuracy from results. Given a query and its related results, it'll find if any of the Ground Truth are in results.
# Or How many of the results are in Ground Truth

import numpy as np

def get_relative_acc(test_result_indices:[np.ndarray,list],similar_image_indices:[np.ndarray,list])->float:
    '''
    Check HOW MANY similar images are in returned results for single test image
    '''
    # because then only it'll be relative. If an image has 2,3,4 similar images, it won't matter. It'll normalize relative acc

deshwalmahesh

import cv2
from PIL import Image
import numpy as np
import glob

def select_box(results:np.ndarray,method:str)->int:
    '''
    Select a Single BB based on Max Probability or Max area logic
    args:
        results: Pass in the results by detection module in (classes, scores, boxes) format

deshwalmahesh

def process(sentence,use_lemmetization:False, use_stemming:False, add_pos: False, remove_length:bool = False):
    
  if not isinstance(sentence,str):
    return ''

  # convert the characters into lower case
  a = sentence.lower()

  # remomve newline character
  a = re.sub(r"\n+", " ", a)

deshwalmahesh

# pip install selenium

from selenium import webdriver
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from selenium.webdriver.common.by import By
import time
from selenium.webdriver.common.action_chains import ActionChains
from selenium.common.exceptions import NoSuchElementException
from selenium.webdriver.common.keys import Keys

deshwalmahesh

import torch
import torch.nn as nn

class WeightedEMDLossWithConfidencePenalty(nn.Module):
    """
    Original Idea of why this Loss:
    1. If the actual class was "0" and model predicted class "4", then I want it to penalise more than if it would have predicted it class "2": Addressed by the Vanilla EMD Loss itself
    2. Penalization based on classes. For example, if the weights are [2,2,1,1,1] then it penalizes twice if the wrong prediction is from Class "0" or "1" vs when the wrong predictions were from the remaining classes: Addressed by class_weights
    3. If the model is correctly predicting the right class but with a Low confidence, penalise it. For example if the True class was class 0" and my model predicted class "0" BUT I want to penalise it more when it predicted with a probability of 0.6 than when it predicted with a probability of 0.9: Addressed by the `correct_pred_low_prob_penalty` logic
    4. If the model is Wrongly predicting the class and that too with a high confidence, penalise it ev