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# import random
import torch
import math
from torch.nn.utils.rnn import pad_sequence
def find_pad_idx(boxes):
for i, j in enumerate(boxes):
if int(boxes[i].sum().item()) == 0:
return i
return i
# def apply_mask_on_token_bbox(boxes, tokenized_words, only_actual_words = False, span = 4, proportion_to_mask = 0.15, special_token = 103):
# '''
# code taken from here: https://www.geeksforgeeks.org/python-non-overlapping-random-ranges/
# Note: A more robust solution is to be coded
# '''
# length_to_be_masked = int(proportion_to_mask*len(boxes))
# if only_actual_words:
# tot = find_pad_idx(tokenized_words)
# else:
# tot = len(boxes)
# res = set()
# for _ in range(length_to_be_masked):
# temp = random.randint(0, tot - span)
# while any(((temp >= idx) and (temp <= idx + span)) for idx in res):
# temp = random.randint(0, tot - span)
# res.add(temp)
# ## Applying the mask on token
# tokenized_words[temp] = special_token
# ## Applying the masking on the box
# boxes[temp, 0] = torch.min(boxes[temp: temp+span, 0])
# boxes[temp, 1] = torch.min(boxes[temp: temp+span, 1])
# boxes[temp, 2] = torch.max(boxes[temp: temp+span, 2])
# boxes[temp, 3] = torch.max(boxes[temp: temp+span, 3])
# boxes[temp, 4] = boxes[temp, 2] - boxes[temp, 0]
# boxes[temp, 5] = boxes[temp, 3] - boxes[temp, 1]
# return res,boxes, tokenized_words
def convert_ans_to_token(answer, label2id, max_seq_length = 512 ):
## Simple Trick to pad a sequence to deired length
dummy_array = torch.zeros(max_seq_length)
actual_ans_array = []
answer = answer.split(" ")
for token in answer:
actual_ans_array.append(label2id[token]['id'])
actual_ans_array = torch.tensor(actual_ans_array, dtype = torch.int32)
actual_ans_array = pad_sequence([actual_ans_array,dummy_array], batch_first = True)[0]
return actual_ans_array
def convert_ques_to_token(question, tokenizer, pad_token_id = 0, max_seq_len = 512):
question_array = []
question = question.split(" ")
for token in question:
question_array.extend(tokenizer(token, add_special_tokens = False).input_ids)
if len(question_array)< max_seq_len:
question_array.extend([pad_token_id]* (max_seq_len-len(question_array)))
question_array = torch.tensor(question_array, dtype = torch.int32)
return question_array[:max_seq_len]
## To be taken from here
## https://logicatcore.github.io/scratchpad/lidar/sensor-fusion/jupyter/2021/04/20/3D-Oriented-Bounding-Box.html
def rotate(origin, point, angle):
"""
Rotate a point counterclockwise by a given angle around a given origin.
The angle should be given in radians.
modified from answer here: https://stackoverflow.com/questions/34372480/rotate-point-about-another-point-in-degrees-python
"""
# angle = np.deg2rad(angle)
ox, oy = origin
px, py = point
qx = ox + math.cos(angle) * (px - ox) - math.sin(angle) * (py - oy)
qy = oy + math.sin(angle) * (px - ox) + math.cos(angle) * (py - oy)
return int(qx), int(qy)
def convert_token_to_ques(ques, tokenizer):
decoded_ques = tokenizer.decode(ques, skip_special_tokens=True)
return decoded_ques
def convert_token_to_answer(ans, id2label):
non_zero_argument = torch.nonzero(ans,as_tuple = False).view(-1)
actual_answer = ans[non_zero_argument].cpu().numpy()
decoded_answer = []
for token in actual_answer:
decoded_answer.append(id2label[token])
decoded_answer = " ".join(decoded_answer)
return decoded_answer
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