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

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

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

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

# 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