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loader_unified.py
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loader_unified.py
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import os
import csv
import random
import pickle
from tqdm import tqdm
from collections import defaultdict
import pandas as pd
import albumentations
import albumentations.pytorch
import re
import torch
from torch.utils.data import Dataset
import numpy as np
from vae import VQGanVAE
from datetime import datetime
random.seed(42)
#########################################
# ADAM TEMPORAL
from datetime import date
def rem_time(d):
s = date(d.year,d.month, d.day)
return s
#########################################
def datatime_conversion(studydate,studytime):
studytime = str(studytime)
studytime = studytime.split('.')[0]
studytime_str = str(int(studytime)).zfill(6)
studytime_str = studytime_str[:2] + ':' + studytime_str[2:4] + ':' + studytime_str[4:]
studydatetime_str = str(studydate) + ' ' + studytime_str
# print(studydatetime_str)
studydatetime = datetime.strptime(studydatetime_str, '%Y%m%d %H:%M:%S')
return studydatetime
def difference(prevtimestamp, currtimestamp):
difference = currtimestamp - prevtimestamp
years = difference.days // 365
remaining_days = difference.days % 365
months = remaining_days // 30
remaining_days = remaining_days % 30
days = remaining_days
hours = difference.seconds // 3600
sentence = f'\nThe previous scan was taken {years} years, {months} months, {days} days, {hours} hours back.'
return sentence
class UnifiedCXRDataset(Dataset):
def __init__(self,
metadata_file,
label_file,
img_root_dir,
text_root_dir,
vqgan_model_path,
vqgan_config_path,
codebook_indices_path,
vqgan,
max_img_num,
max_text_len,
tokenizer,
target_count,
target_view,
under_sample="fixed"
):
super().__init__()
assert max_img_num <= target_count, f'max_img_num({max_img_num}) should be less than target_count({target_count}).'
self.under_sample = under_sample.split('_')[0] # fixed
self.select_studies = under_sample.split('_')[1] # 'each' or 'all', 'all': using all groups (S w/1, w/2, w/3), 'each': using only selected single group
self.training_mode = under_sample.split('_')[-1] # unified
self.dict_by_subject_id = defaultdict(list)
f = open(metadata_file, 'r')
columns = ['idx','dicom_id','subject_id','study_id','Atelectasis','Cardiomegaly','Consolidation','Edema','Enlarged Cardiomediastinum','Fracture','Lung Lesion','Lung Opacity','No Finding','Pleural Effusion','Pleural Other','Pneumonia','Pneumothorax','Support Devices','total_number_of_studies','study_id_count']
rdr = csv.reader(f)
self.rd2 = pd.read_csv(label_file, names = columns) #santosh
# print(self.rd2)
# self.weights = np.array([0.5023, 0.4156, 0.9114, 0.6777, 0.9858, 0.8778, 0.4067, 0.2331, 0.9221, 1.0000, 0.8601])
for i, line in tqdm(enumerate(rdr)):
idx, dicom_id, subject_id, study_id, ViewPosition, StudyDate, StudyTime, count, curr_state = line # [427446c1-881f5cce-85191ce1-91a58ba9-0a57d3f5,10046166,50051329,LATERAL,2]
if self.select_studies == 'each':
if (int(count) == int(target_count) and ViewPosition in target_view):
self.dict_by_subject_id[subject_id].append(line)
elif self.select_studies == 'all':
if (int(count) <= int(target_count) and ViewPosition in target_view):
self.dict_by_subject_id[subject_id].append(line)
# print('self.dict_by_studyid', self.dict_by_studyid)
if self.select_studies == 'all':
self.dict_by_subject_id = {k: self.dict_by_subject_id[k] for k in self.dict_by_subject_id.keys() if len(self.dict_by_subject_id[k]) == int(self.dict_by_subject_id[k][0][-2])}
elif self.select_studies == 'each':
self.dict_by_subject_id = {k: self.dict_by_subject_id[k] for k in self.dict_by_subject_id.keys() if len(self.dict_by_subject_id[k]) == target_count}
# print('self.dict_by_subject_id ', self.dict_by_subject_id)
self.key_list = list(self.dict_by_subject_id.keys())
self.img_root_dir = img_root_dir
self.text_root_dir = text_root_dir
self.vae = VQGanVAE(vqgan_model_path, vqgan_config_path)
if vqgan == 512:
self.img_fmap_size = 32
self.img_reso = 512 # eg. 256 or 512 in my case
self.img_len = 1024 + 2 # eg. 32**2 = 1024
self.img_vocab_size = self.vae.num_tokens # eg. 1024
else:
NotImplemented
with open(codebook_indices_path, 'rb') as f:
self.indices_dict = pickle.load(f)
# 2 of 3: max_img_num = 2, target_count = 3
self.max_img_num = max_img_num
self.target_count = target_count
self.max_text_len = max_text_len
self.tokenizer = tokenizer
# self.text_vocab_size = self.tokenizer.get_vocab_size()
self.text_vocab_size = len(self.tokenizer)
print(self.text_vocab_size)
# Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.
self.rescaler = albumentations.SmallestMaxSize(max_size=self.img_reso)
self.cropper = albumentations.CenterCrop(height=self.img_reso, width=self.img_reso)
self.totensor = albumentations.pytorch.transforms.ToTensorV2()
self.preprocessor = albumentations.Compose([
self.rescaler,
self.cropper,
])
self.slots = []
self.modes = ['txt']
for i in range(self.max_img_num):
y = [self.img_vocab_size + i] * (self.img_len)
self.slots.extend(y)
self.modes.append(f'img{i + 1}')
self.weights = self.get_weights()
def get_weights(self):
df = self.rd2[self.rd2['study_id_count']==1]
df = df.drop(columns = ['idx', 'dicom_id', 'subject_id', 'study_id', 'total_number_of_studies','study_id_count'])
weights = np.nansum(df, axis=0)
weights = weights.max() - weights + weights.mean()
weights = weights/weights.max()
# print("task weights", weights)
return weights
def clean_report_mimic_cxr(self, report):
report_cleaner = lambda t: t.replace('\n', ' ').replace('__', '_').replace('__', '_').replace('__', '_') \
.replace('__', '_').replace('__', '_').replace('__', '_').replace('__', '_').replace(' ', ' ') \
.replace(' ', ' ').replace(' ', ' ').replace(' ', ' ').replace(' ', ' ').replace(' ', ' ') \
.replace('..', '.').replace('..', '.').replace('..', '.').replace('..', '.').replace('..', '.') \
.replace('..', '.').replace('..', '.').replace('..', '.').replace('1. ', '').replace('. 2. ', '. ') \
.replace('. 3. ', '. ').replace('. 4. ', '. ').replace('. 5. ', '. ').replace(' 2. ', '. ') \
.replace(' 3. ', '. ').replace(' 4. ', '. ').replace(' 5. ', '. ') \
.strip().lower().split('. ')
sent_cleaner = lambda t: re.sub('[.,?;*!%^&_+():-\[\]{}]', '', t.replace('"', '').replace('/', '')
.replace('\\', '').replace("'", '').strip().lower())
tokens = [sent_cleaner(sent) for sent in report_cleaner(report) if sent_cleaner(sent) != []]
report = ' . '.join(tokens) + ' .'
return report
def __len__(self):
return len(self.key_list)
def __getitem__(self, idx):
subject_id = self.key_list[idx]
if self.select_studies == 'each':
assert len(self.dict_by_subject_id[subject_id]) == self.target_count, f'{subject_id} has {len(self.dict_by_subject_id[subject_id])} data, but target_count is {self.target_count}.'
elif self.select_studies == 'all':
assert len(self.dict_by_subject_id[subject_id]) <= self.target_count, f'{subject_id} has {len(self.dict_by_subject_id[subject_id])} data, but target_count is {self.target_count}.'
if self.max_img_num == self.target_count:
imgs_meta = self.dict_by_subject_id[subject_id]
elif self.max_img_num < self.target_count:
if self.under_sample == 'fixed':
imgs_meta = self.dict_by_subject_id[subject_id][self.max_img_num:]
elif self.under_sample == 'random':
imgs_meta = random.sample(self.dict_by_subject_id[subject_id], self.max_img_num)
if self.select_studies == 'all':
num_img_in_subject = int(self.dict_by_subject_id[subject_id][0][-2])
elif self.select_studies == 'each':
num_img_in_subject = self.max_img_num
# imgs
img_paths = ''
image_output = []
view_position = []
img_state = []
for i in range(num_img_in_subject):
# print('helloooooooooooooo',self.rd2)
idx, dicom_id, subject_id, studyid, ViewPosition, StudyDate, StudyTime, count, curr_state = imgs_meta[i]#[:4]
# print('jjjjjjjjjjjjjjj', dicom_id,subject_id,studyid, curr_state)
img_path = os.path.join(self.img_root_dir, 'p' + subject_id[:2], 'p' + subject_id.split('_')[0], 's' + studyid, dicom_id + '.jpg')
image_indices = self.indices_dict[dicom_id].copy() # indices list
if curr_state == '1' and ViewPosition == 'AP':
rs = self.rd2.loc[(self.rd2['dicom_id'] == str(dicom_id)) & (self.rd2['subject_id'] == str(subject_id)) & (self.rd2['study_id']==int(studyid))]
rs1 = rs.values.tolist()[0]
nr1, nr2, nr3, nr4, Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged_Cardiomediastinum, Fracture, Lung_Lesion, Lung_Opacity, No_Finding, Pleural_Effusion, Pleural_Other, Pneumonia, Pneumothorax, Support_Devices, nr5, nr6 = [rs1[i] for i in range(len(rs.axes[1]))] #santosh
if No_Finding == 1: #sansan
Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged_Cardiomediastinum, Fracture, Lung_Lesion, Lung_Opacity, Pleural_Effusion, Pleural_Other, Pneumonia, Pneumothorax, Support_Devices = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
labels = [Atelectasis, Cardiomegaly, Consolidation, Edema, Enlarged_Cardiomediastinum, Fracture, Lung_Lesion, Lung_Opacity, Pleural_Effusion, Pleural_Other, Pneumonia, Pneumothorax, Support_Devices] #sansan
labels = np.array(labels)
labels= labels.astype(float)
labels[labels == -1] = np.nan
image_indices.insert(0, 1025) # self.tokenizer.token_to_id("[SOS1]")
image_indices.append(1026) # self.tokenizer.token_to_id("[EOS1]"
image_output.append(torch.tensor(image_indices))
ViewPosition = 'AP_and_curr'
elif curr_state == '0' and ViewPosition =='AP':
image_indices.insert(0, 1027) # self.tokenizer.token_to_id("[SOS2]")
image_indices.append(1028) # self.tokenizer.token_to_id("[EOS2]")
image_output.append(torch.tensor(image_indices))
ViewPosition = 'AP_and_prev'
# elif ViewPosition == 'LATERAL':
# image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# image_output.append(torch.tensor(image_indices))
# elif ViewPosition == 'LL':
# image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# image_output.append(torch.tensor(image_indices))
else:
raise ValueError
img_paths += (img_path + '|')
view_position.append(ViewPosition)
img_state.append(curr_state)
# PAD imgs
if num_img_in_subject < self.max_img_num:
assert self.select_studies == 'all'
for i in range(self.max_img_num - num_img_in_subject):
image_indices = [1024] * self.img_len
image_output.append(torch.tensor(image_indices))
img_paths += ('PAD' + '|')
view_position.append('PAD')
img_state.append(curr_state)
self.modes = ['txt']
for i in range(num_img_in_subject):
self.modes.append(f'img{i + 1}')
random.shuffle(self.modes)
for i in range(num_img_in_subject, self.max_img_num):
self.modes.append(f'img{i + 1}')
else:
random.shuffle(self.modes)
# report
for i in range(num_img_in_subject):
idx, dicom_id, subject_id, studyid, ViewPosition_text, StudyDate, StudyTime, count, curr_state = imgs_meta[i]#[:4]
if num_img_in_subject == 1:
count = '1'
if curr_state == '0':# and num_img_in_subject == 2:
prev_StudyDate = StudyDate
prev_StudyTime = StudyTime
prevstudy_timestamp = datatime_conversion(prev_StudyDate, prev_StudyTime)
# print('prevvvvvvvvvvvvvvv',prevstudy_timestamp)
continue
else:
# print('in elseeeee', curr_state, num_img_in_subject)
text_path = os.path.join(self.text_root_dir, studyid + '.txt')
with open(text_path, 'r') as f:
data = f.read()
if int(count) == 2:
curr_StudyDate = StudyDate
curr_StudyTime = StudyTime
currstudy_timestamp = datatime_conversion(curr_StudyDate, curr_StudyTime)
# print('currrrrrr',currstudy_timestamp)
timediff = difference(prevstudy_timestamp, currstudy_timestamp)
# print('sentenceeeeeeeeeeeeeeeeeee',prevstudy_timestamp, currstudy_timestamp, timediff)
data += timediff
# ADAM TEMPORAL
dt = rem_time(currstudy_timestamp) - rem_time(prevstudy_timestamp)
dt_in_days = max(1, abs(float(dt.days))) # CALCULATE SOMEHOW
# print(dt_in_days, abs(float(dt.days)), currstudy_timestamp, prevstudy_timestamp)
else:
data+= "\nThere is no previous scan available."
# ADAM TEMPORAL
dt_in_days = -1
# print(data)
#src = data.replace(' ', ' ').replace(' ', ' ').lower()
src = self.clean_report_mimic_cxr(data)
# ids_list = self.tokenizer.encode(src).ids
text_output = self.tokenizer.encode(src, add_special_tokens=True, padding='max_length', max_length = 256, truncation = True, return_tensors='pt') #sansan
# # print(text_output.shape)
text_output = text_output.squeeze(0)
# # print('afterrrrrrr', text_output.shape)
# = torch.tensor(ids_list)
# text_output = torch.tensor(ids_list)
# weights = UnifiedCXRDataset.get_weights(self)
# print(weights, type(weights))
# print(labels, type(labels))
outputs = {'txt': text_output, 'modes': self.modes, 'subject_id': subject_id, 'dicom_id':dicom_id,
'img_paths': img_paths, 'view_position': view_position, 'image_state': img_state, 'labels':labels, 'weights':self.weights,
'dt_in_days': dt_in_days # ADAM TEMPORAL
}
for i in range(self.max_img_num):
outputs[f'img{i+1}'] = image_output[i]
return outputs
# self.dict_by_studyid = defaultdict(list)
# f = open(metadata_file, 'r')
# rdr = csv.reader(f)
# for i, line in tqdm(enumerate(rdr)):
# dicom_id, subject_id, study_id, ViewPosition, count = line # [427446c1-881f5cce-85191ce1-91a58ba9-0a57d3f5,10046166,50051329,LATERAL,2]
# if self.select_studies == 'each':
# if (int(count) == int(target_count) and ViewPosition in target_view):
# self.dict_by_studyid[study_id].append(line)
# elif self.select_studies == 'all':
# if (int(count) <= int(target_count) and ViewPosition in target_view):
# self.dict_by_studyid[study_id].append(line)
# if self.select_studies == 'all':
# self.dict_by_studyid = {k: self.dict_by_studyid[k] for k in self.dict_by_studyid.keys() if len(self.dict_by_studyid[k]) == int(self.dict_by_studyid[k][0][-1])}
# elif self.select_studies == 'each':
# self.dict_by_studyid = {k: self.dict_by_studyid[k] for k in self.dict_by_studyid.keys() if len(self.dict_by_studyid[k]) == target_count}
# self.key_list = list(self.dict_by_studyid.keys())
# self.img_root_dir = img_root_dir
# self.text_root_dir = text_root_dir
# self.vae = VQGanVAE(vqgan_model_path, vqgan_config_path)
# if vqgan == 512:
# self.img_fmap_size = 32
# self.img_reso = 512 # eg. 256 or 512 in my case
# self.img_len = 1024 + 2 # eg. 32**2 = 1024
# self.img_vocab_size = self.vae.num_tokens # eg. 1024
# else:
# NotImplemented
# with open(codebook_indices_path, 'rb') as f:
# self.indices_dict = pickle.load(f)
# # 2 of 3: max_img_num = 2, target_count = 3
# self.max_img_num = max_img_num
# self.target_count = target_count
# self.max_text_len = max_text_len
# self.tokenizer = tokenizer
# self.text_vocab_size = self.tokenizer.get_vocab_size()
# # Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.
# self.rescaler = albumentations.SmallestMaxSize(max_size=self.img_reso)
# self.cropper = albumentations.CenterCrop(height=self.img_reso, width=self.img_reso)
# self.totensor = albumentations.pytorch.transforms.ToTensorV2()
# self.preprocessor = albumentations.Compose([
# self.rescaler,
# self.cropper,
# ])
# self.slots = []
# self.modes = ['txt']
# for i in range(self.max_img_num):
# y = [self.img_vocab_size + i] * (self.img_len)
# self.slots.extend(y)
# self.modes.append(f'img{i + 1}')
# def __len__(self):
# return len(self.key_list)
# def __getitem__(self, idx):
# study_id = self.key_list[idx]
# if self.select_studies == 'each':
# assert len(self.dict_by_studyid[study_id]) == self.target_count, f'{study_id} has {len(self.dict_by_studyid[study_id])} data, but target_count is {self.target_count}.'
# elif self.select_studies == 'all':
# assert len(self.dict_by_studyid[study_id]) <= self.target_count, f'{study_id} has {len(self.dict_by_studyid[study_id])} data, but target_count is {self.target_count}.'
# if self.max_img_num == self.target_count:
# imgs_meta = self.dict_by_studyid[study_id]
# elif self.max_img_num < self.target_count:
# if self.under_sample == 'fixed':
# imgs_meta = self.dict_by_studyid[study_id][:self.max_img_num]
# elif self.under_sample == 'random':
# imgs_meta = random.sample(self.dict_by_studyid[study_id], self.max_img_num)
# if self.select_studies == 'all':
# num_img_in_study = int(self.dict_by_studyid[study_id][0][-1])
# elif self.select_studies == 'each':
# num_img_in_study = self.max_img_num
# # imgs
# img_paths = ''
# image_output = []
# view_position = []
# for i in range(num_img_in_study):
# dicom_id, subject_id, studyid, ViewPosition = imgs_meta[i][:4]
# img_path = os.path.join(self.img_root_dir, 'p' + subject_id[:2], 'p' + subject_id, 's' + studyid, dicom_id + '.jpg')
# image_indices = self.indices_dict[dicom_id].copy() # indices list
# if ViewPosition == 'AP':
# image_indices.insert(0, 1025) # self.tokenizer.token_to_id("[SOS1]")
# image_indices.append(1026) # self.tokenizer.token_to_id("[EOS1]"
# image_output.append(torch.tensor(image_indices))
# elif ViewPosition == 'PA':
# image_indices.insert(0, 1027) # self.tokenizer.token_to_id("[SOS2]")
# image_indices.append(1028) # self.tokenizer.token_to_id("[EOS2]")
# image_output.append(torch.tensor(image_indices))
# elif ViewPosition == 'LATERAL':
# image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# image_output.append(torch.tensor(image_indices))
# elif ViewPosition == 'LL':
# image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# image_output.append(torch.tensor(image_indices))
# else:
# raise ValueError
# img_paths += (img_path + '|')
# view_position.append(ViewPosition)
# # PAD imgs
# if num_img_in_study < self.max_img_num:
# assert self.select_studies == 'all'
# for i in range(self.max_img_num - num_img_in_study):
# image_indices = [1024] * self.img_len
# image_output.append(torch.tensor(image_indices))
# img_paths += ('PAD' + '|')
# view_position.append('PAD')
# self.modes = ['txt']
# for i in range(num_img_in_study):
# self.modes.append(f'img{i + 1}')
# random.shuffle(self.modes)
# for i in range(num_img_in_study, self.max_img_num):
# self.modes.append(f'img{i + 1}')
# else:
# random.shuffle(self.modes)
# # report
# text_path = os.path.join(self.text_root_dir, 's' + study_id + '.txt')
# with open(text_path, 'r') as f:
# data = f.read()
# src = data.replace(' ', ' ').replace(' ', ' ').lower()
# ids_list = self.tokenizer.encode(src).ids
# text_output = torch.tensor(ids_list)
# outputs = {'txt': text_output, 'modes': self.modes, 'study_id': study_id,
# 'img_paths': img_paths, 'view_position': view_position}
# for i in range(self.max_img_num):
# outputs[f'img{i+1}'] = image_output[i]
# return outputs
# '''
# The upper code is for training
# SEPARATION
# The below is for testing
# '''
# import os
# import csv
# import random
# import pickle
# from tqdm import tqdm
# from collections import defaultdict
# import albumentations
# import albumentations.pytorch
# import torch
# from torch.utils.data import Dataset
# from vae import VQGanVAE
# random.seed(42)
# class UnifiedCXRDataset(Dataset):
# def __init__(self,
# metadata_file,
# img_root_dir,
# text_root_dir,
# vqgan_model_path,
# vqgan_config_path,
# codebook_indices_path,
# vqgan,
# max_img_num,
# max_text_len,
# tokenizer,
# target_count,
# target_view,
# under_sample="fixed"
# ):
# super().__init__()
# assert max_img_num <= target_count, f'max_img_num({max_img_num}) should be less than target_count({target_count}).'
# self.under_sample = under_sample.split('_')[0] # fixed
# self.select_studies = under_sample.split('_')[1] # 'each' or 'all', 'all': using all groups (S w/1, w/2, w/3), 'each': using only selected single group
# self.training_mode = under_sample.split('_')[-1] # unified
# self.dict_by_subject_id = defaultdict(list)
# f = open(metadata_file, 'r')
# rdr = csv.reader(f)
# for i, line in tqdm(enumerate(rdr)):
# idx, dicom_id, subject_id, study_id, ViewPosition, StudyDate, StudyTime, count, curr_state = line # [427446c1-881f5cce-85191ce1-91a58ba9-0a57d3f5,10046166,50051329,LATERAL,2]
# if self.select_studies == 'each':
# if (int(count) == int(target_count) and ViewPosition in target_view):
# self.dict_by_subject_id[subject_id].append(line)
# elif self.select_studies == 'all':
# if (int(count) <= int(target_count) and ViewPosition in target_view):
# self.dict_by_subject_id[subject_id].append(line)
# # print('self.dict_by_studyid', self.dict_by_studyid)
# if self.select_studies == 'all':
# self.dict_by_subject_id = {k: self.dict_by_subject_id[k] for k in self.dict_by_subject_id.keys() if len(self.dict_by_subject_id[k]) == int(self.dict_by_subject_id[k][0][-2])}
# elif self.select_studies == 'each':
# self.dict_by_subject_id = {k: self.dict_by_subject_id[k] for k in self.dict_by_subject_id.keys() if len(self.dict_by_subject_id[k]) == target_count}
# # print('self.dict_by_studyid', self.dict_by_studyid)
# self.key_list = list(self.dict_by_subject_id.keys())
# self.img_root_dir = img_root_dir
# self.text_root_dir = text_root_dir
# self.vae = VQGanVAE(vqgan_model_path, vqgan_config_path)
# if vqgan == 512:
# self.img_fmap_size = 32
# self.img_reso = 512 # eg. 256 or 512 in my case
# self.img_len = 1024 + 2 # eg. 32**2 = 1024
# self.img_vocab_size = self.vae.num_tokens # eg. 1024
# else:
# NotImplemented
# with open(codebook_indices_path, 'rb') as f:
# self.indices_dict = pickle.load(f)
# # 2 of 3: max_img_num = 2, target_count = 3
# self.max_img_num = max_img_num
# self.target_count = target_count
# self.max_text_len = max_text_len
# self.tokenizer = tokenizer
# self.text_vocab_size = self.tokenizer.get_vocab_size()
# # Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.
# self.rescaler = albumentations.SmallestMaxSize(max_size=self.img_reso)
# self.cropper = albumentations.CenterCrop(height=self.img_reso, width=self.img_reso)
# self.totensor = albumentations.pytorch.transforms.ToTensorV2()
# self.preprocessor = albumentations.Compose([
# self.rescaler,
# self.cropper,
# ])
# self.slots = []
# self.modes = ['txt']
# for i in range(self.max_img_num):
# y = [self.img_vocab_size + i] * (self.img_len)
# self.slots.extend(y)
# self.modes.append(f'img{i + 1}')
# def __len__(self):
# return len(self.key_list)
# def __getitem__(self, idx):
# subject_id = self.key_list[idx]
# if self.select_studies == 'each':
# assert len(self.dict_by_subject_id[subject_id]) == self.target_count, f'{subject_id} has {len(self.dict_by_subject_id[subject_id])} data, but target_count is {self.target_count}.'
# elif self.select_studies == 'all':
# assert len(self.dict_by_subject_id[subject_id]) <= self.target_count, f'{subject_id} has {len(self.dict_by_subject_id[subject_id])} data, but target_count is {self.target_count}.'
# if self.max_img_num == self.target_count:
# imgs_meta = self.dict_by_subject_id[subject_id]
# elif self.max_img_num < self.target_count:
# if self.under_sample == 'fixed':
# imgs_meta = self.dict_by_subject_id[subject_id][self.max_img_num:]
# elif self.under_sample == 'random':
# imgs_meta = random.sample(self.dict_by_subject_id[subject_id], self.max_img_num)
# if self.select_studies == 'all':
# num_img_in_subject = int(self.dict_by_subject_id[subject_id][0][-2])
# elif self.select_studies == 'each':
# num_img_in_subject = self.max_img_num
# # imgs
# img_paths = ''
# image_output = []
# view_position = []
# img_state = []
# for i in range(num_img_in_subject):
# idx, dicom_id, subject_id, studyid, ViewPosition, StudyDate, StudyTime, count, curr_state = imgs_meta[i]#[:4]
# img_path = os.path.join(self.img_root_dir, 'p' + subject_id[:2], 'p' + subject_id.split('_')[0], 's' + studyid, dicom_id + '.jpg')
# image_indices = self.indices_dict[dicom_id].copy() # indices list
# if curr_state == '1' and ViewPosition == 'AP':
# image_indices.insert(0, 1025) # self.tokenizer.token_to_id("[SOS1]")
# image_indices.append(1026) # self.tokenizer.token_to_id("[EOS1]"
# image_output.append(torch.tensor(image_indices))
# ViewPosition = 'AP_and_curr'
# elif curr_state == '0' and ViewPosition =='AP':
# image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS2]")
# image_indices.append(1030) # self.tokenizer.token_to_id("[EOS2]")
# image_output.append(torch.tensor(image_indices))
# ViewPosition = 'AP_and_prev'
# # elif ViewPosition == 'LATERAL':
# # image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# # image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# # image_output.append(torch.tensor(image_indices))
# # elif ViewPosition == 'LL':
# # image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# # image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# # image_output.append(torch.tensor(image_indices))
# else:
# raise ValueError
# img_paths += (img_path + '|')
# view_position.append(ViewPosition)
# img_state.append(curr_state)
# # PAD imgs
# if num_img_in_subject < self.max_img_num:
# assert self.select_studies == 'all'
# for i in range(self.max_img_num - num_img_in_subject):
# image_indices = [1024] * self.img_len
# image_output.append(torch.tensor(image_indices))
# img_paths += ('PAD' + '|')
# view_position.append('PAD')
# img_state.append(curr_state)
# self.modes = ['txt']
# for i in range(num_img_in_subject):
# self.modes.append(f'img{i + 1}')
# random.shuffle(self.modes)
# for i in range(num_img_in_subject, self.max_img_num):
# self.modes.append(f'img{i + 1}')
# else:
# random.shuffle(self.modes)
# # report
# for i in range(num_img_in_subject):
# idx, dicom_id, subject_id, studyid, ViewPosition_text, StudyDate, StudyTime, count, curr_state = imgs_meta[i]#[:4]
# if curr_state == '0':# and num_img_in_subject == 2:
# continue
# else:
# # print('in elseeeee', curr_state, num_img_in_subject)
# text_path = os.path.join(self.text_root_dir, studyid + '.txt')
# with open(text_path, 'r') as f:
# data = f.read()
# src = data.replace(' ', ' ').replace(' ', ' ').lower()
# ids_list = self.tokenizer.encode(src).ids
# # text_output = self.tokenizer.encode(src, add_special_tokens=True, padding='max_length', max_length = 256, truncation = True, return_tensors='pt')
# # # print(text_output.shape)
# # text_output = text_output.squeeze(0)
# # # print('afterrrrrrr', text_output.shape)
# # = torch.tensor(ids_list)
# text_output = torch.tensor(ids_list)
# outputs = {'txt': text_output, 'modes': self.modes, 'subject_id': subject_id, 'dicom_id':dicom_id,
# 'img_paths': img_paths, 'view_position': view_position, 'image_state': img_state}
# for i in range(self.max_img_num):
# outputs[f'img{i+1}'] = image_output[i]
# return outputs
# # self.dict_by_studyid = defaultdict(list)
# # f = open(metadata_file, 'r')
# # rdr = csv.reader(f)
# # for i, line in tqdm(enumerate(rdr)):
# # dicom_id, subject_id, study_id, ViewPosition, count = line # [427446c1-881f5cce-85191ce1-91a58ba9-0a57d3f5,10046166,50051329,LATERAL,2]
# # if self.select_studies == 'each':
# # if (int(count) == int(target_count) and ViewPosition in target_view):
# # self.dict_by_studyid[study_id].append(line)
# # elif self.select_studies == 'all':
# # if (int(count) <= int(target_count) and ViewPosition in target_view):
# # self.dict_by_studyid[study_id].append(line)
# # if self.select_studies == 'all':
# # self.dict_by_studyid = {k: self.dict_by_studyid[k] for k in self.dict_by_studyid.keys() if len(self.dict_by_studyid[k]) == int(self.dict_by_studyid[k][0][-1])}
# # elif self.select_studies == 'each':
# # self.dict_by_studyid = {k: self.dict_by_studyid[k] for k in self.dict_by_studyid.keys() if len(self.dict_by_studyid[k]) == target_count}
# # self.key_list = list(self.dict_by_studyid.keys())
# # self.img_root_dir = img_root_dir
# # self.text_root_dir = text_root_dir
# # self.vae = VQGanVAE(vqgan_model_path, vqgan_config_path)
# # if vqgan == 512:
# # self.img_fmap_size = 32
# # self.img_reso = 512 # eg. 256 or 512 in my case
# # self.img_len = 1024 + 2 # eg. 32**2 = 1024
# # self.img_vocab_size = self.vae.num_tokens # eg. 1024
# # else:
# # NotImplemented
# # with open(codebook_indices_path, 'rb') as f:
# # self.indices_dict = pickle.load(f)
# # # 2 of 3: max_img_num = 2, target_count = 3
# # self.max_img_num = max_img_num
# # self.target_count = target_count
# # self.max_text_len = max_text_len
# # self.tokenizer = tokenizer
# # self.text_vocab_size = self.tokenizer.get_vocab_size()
# # # Rescale an image so that minimum side is equal to max_size, keeping the aspect ratio of the initial image.
# # self.rescaler = albumentations.SmallestMaxSize(max_size=self.img_reso)
# # self.cropper = albumentations.CenterCrop(height=self.img_reso, width=self.img_reso)
# # self.totensor = albumentations.pytorch.transforms.ToTensorV2()
# # self.preprocessor = albumentations.Compose([
# # self.rescaler,
# # self.cropper,
# # ])
# # self.slots = []
# # self.modes = ['txt']
# # for i in range(self.max_img_num):
# # y = [self.img_vocab_size + i] * (self.img_len)
# # self.slots.extend(y)
# # self.modes.append(f'img{i + 1}')
# # def __len__(self):
# # return len(self.key_list)
# # def __getitem__(self, idx):
# # study_id = self.key_list[idx]
# # if self.select_studies == 'each':
# # assert len(self.dict_by_studyid[study_id]) == self.target_count, f'{study_id} has {len(self.dict_by_studyid[study_id])} data, but target_count is {self.target_count}.'
# # elif self.select_studies == 'all':
# # assert len(self.dict_by_studyid[study_id]) <= self.target_count, f'{study_id} has {len(self.dict_by_studyid[study_id])} data, but target_count is {self.target_count}.'
# # if self.max_img_num == self.target_count:
# # imgs_meta = self.dict_by_studyid[study_id]
# # elif self.max_img_num < self.target_count:
# # if self.under_sample == 'fixed':
# # imgs_meta = self.dict_by_studyid[study_id][:self.max_img_num]
# # elif self.under_sample == 'random':
# # imgs_meta = random.sample(self.dict_by_studyid[study_id], self.max_img_num)
# # if self.select_studies == 'all':
# # num_img_in_study = int(self.dict_by_studyid[study_id][0][-1])
# # elif self.select_studies == 'each':
# # num_img_in_study = self.max_img_num
# # # imgs
# # img_paths = ''
# # image_output = []
# # view_position = []
# # for i in range(num_img_in_study):
# # dicom_id, subject_id, studyid, ViewPosition = imgs_meta[i][:4]
# # img_path = os.path.join(self.img_root_dir, 'p' + subject_id[:2], 'p' + subject_id, 's' + studyid, dicom_id + '.jpg')
# # image_indices = self.indices_dict[dicom_id].copy() # indices list
# # if ViewPosition == 'AP':
# # image_indices.insert(0, 1025) # self.tokenizer.token_to_id("[SOS1]")
# # image_indices.append(1026) # self.tokenizer.token_to_id("[EOS1]"
# # image_output.append(torch.tensor(image_indices))
# # elif ViewPosition == 'PA':
# # image_indices.insert(0, 1027) # self.tokenizer.token_to_id("[SOS2]")
# # image_indices.append(1028) # self.tokenizer.token_to_id("[EOS2]")
# # image_output.append(torch.tensor(image_indices))
# # elif ViewPosition == 'LATERAL':
# # image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# # image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# # image_output.append(torch.tensor(image_indices))
# # elif ViewPosition == 'LL':
# # image_indices.insert(0, 1029) # self.tokenizer.token_to_id("[SOS3]")
# # image_indices.append(1030) # self.tokenizer.token_to_id("[EOS3]")
# # image_output.append(torch.tensor(image_indices))
# # else:
# # raise ValueError
# # img_paths += (img_path + '|')
# # view_position.append(ViewPosition)
# # # PAD imgs
# # if num_img_in_study < self.max_img_num:
# # assert self.select_studies == 'all'
# # for i in range(self.max_img_num - num_img_in_study):
# # image_indices = [1024] * self.img_len
# # image_output.append(torch.tensor(image_indices))
# # img_paths += ('PAD' + '|')
# # view_position.append('PAD')
# # self.modes = ['txt']
# # for i in range(num_img_in_study):
# # self.modes.append(f'img{i + 1}')
# # random.shuffle(self.modes)
# # for i in range(num_img_in_study, self.max_img_num):
# # self.modes.append(f'img{i + 1}')
# # else:
# # random.shuffle(self.modes)
# # # report
# # text_path = os.path.join(self.text_root_dir, 's' + study_id + '.txt')
# # with open(text_path, 'r') as f:
# # data = f.read()
# # src = data.replace(' ', ' ').replace(' ', ' ').lower()
# # ids_list = self.tokenizer.encode(src).ids
# # text_output = torch.tensor(ids_list)
# # outputs = {'txt': text_output, 'modes': self.modes, 'study_id': study_id,
# # 'img_paths': img_paths, 'view_position': view_position}
# # for i in range(self.max_img_num):
# # outputs[f'img{i+1}'] = image_output[i]
# # return outputs