def __init__(self, args, t5_type='t5-base'):
"""
R1 = Raw 1
Training:
R1 + R2 + R3 -> M3
"""
super().__init__()
self.lr = getattr(args, "lr")
self.epochs = getattr(args, "epochs")
self.warmup_steps = getattr(args, "warmup_steps")
self.gpu_id = getattr(args, "gpu_id")
self.transformer = T5_Cond_Gen_Wrapper.from_pretrained(t5_type)
self.tokenizer = T5TokenizerFast.from_pretrained(t5_type)
self.EM_accuracy = CategoricalAccuracy()
self.to('cpu' if self.gpu_id == -1 else f"cuda:{self.gpu_id}")
self.decoder_tokenizer = T5TokenizerFast.from_pretrained(t5_type)
self.decoder_tokenizer.padding_side = 'left' # necessary since initial decoding sequences could have different length
self.validation_scores = []
self.encoder = self.transformer.encoder
self.decoder = self.transformer.decoder
self.lm_head = self.transformer.lm_head
def test_RumorPadaDataset():
split = "train"
data_processor = RumorPadaDataProcessor(["charliehebdo", "ferguson", "germanwings-crash", "ottawashooting"],
"sydneysiege", DATA_DIR, EXP_DIR)
print()
print(data_processor.data[split].keys())
print(len(list(data_processor.data[split].values())[0]))
for k, v in data_processor.data[split].items():
if type(v) is not int:
print(k, v[0])
else:
print(k, v[0], len(v[0]))
dataset = RumorPadaDataset(split, data_processor, T5TokenizerFast.from_pretrained("t5-base"), 64, 0.5, 0.5)
print(len(dataset))
for i, example in enumerate(dataset):
for k, v in example.items():
print(k, v)
if i == 8:
break
print()
dataloader = DataLoader(dataset, 8)
for batch in dataloader:
for k, v in batch.items():
print(k, v)
break
def test_AbsaSeq2SeqPadaDataProcessor():
data_processor = AbsaSeq2SeqPadaDataProcessor(
["device", "laptops", "rest", "service"], "service", DATA_DIR, EXP_DIR)
print()
print(data_processor.data["dev"].keys())
print(len(list(data_processor.data["dev"].values())[0]))
for k, v in data_processor.data["dev"].items():
if type(v) is not int:
print(k, v[-80])
else:
print(k, v[-80], len(v[-80]))
tokenizer = T5TokenizerFast.from_pretrained("t5-base")
percent = 99.8
for split in AbsaSeq2SeqPadaDataProcessor.ALL_SPLITS:
print(split, len(data_processor.data[split]["example_id"]))
print(split, max(data_processor.data[split]["input_tokens_len"]))
print()
tokenizer_lens = tokenizer(data_processor.data[split]["input_tokens"],
is_split_into_words=True,
return_length=True)["length"]
print(split, max(tokenizer_lens))
print(split, percentile(tokenizer_lens, percent))
print()
tokenizer_lens = tokenizer(
data_processor.data[split]["output_labels_tokens"],
is_split_into_words=True,
return_length=True)["length"]
print(split, max(tokenizer_lens))
print(split, percentile(tokenizer_lens, percent))
break
def test_AbsaSeq2SeqPadaDataset():
split = "train"
data_processor = AbsaSeq2SeqPadaDataProcessor(
["device", "laptops", "rest"], "service", DATA_DIR, EXP_DIR)
print()
print(data_processor.data[split].keys())
print(len(list(data_processor.data[split].values())[0]))
for k, v in data_processor.data[split].items():
if type(v) is not int:
print(k, v[0])
else:
print(k, v[0], len(v[0]))
dataset = AbsaSeq2SeqPadaDataset(
split, data_processor, T5TokenizerFast.from_pretrained("t5-base"), 64,
0.5, 0.5)
print(len(dataset))
for i, example in enumerate(dataset):
for k, v in example.items():
print(k, v)
if i == 8:
break
print()
dataloader = DataLoader(dataset, 8)
for batch in dataloader:
for k, v in batch.items():
print(k, v)
break
def _setup_model_and_tokenizer(self):
self.model = T5ForConditionalGeneration.from_pretrained(
self.config.model_name)
if self.config.fast_tokenizer:
self.tokenizer = T5TokenizerFast.from_pretrained(
self.config.model_name)
else:
self.tokenizer = T5Tokenizer.from_pretrained(
self.config.model_name)
def from_pretrained(self, model_name="t5-base"):
"""
Download Model from HF hub
:param model_name: T5
:return: Download the model and tokenizer
"""
self.tokenizer = T5Tokenizer.from_pretrained(f"{model_name}")
self.model = T5ForConditionalGeneration.from_pretrained(
f"{model_name}", return_dict=True)
def __init__(self, model_name, ckpt_path,
num_generations=10):
device, gpu_ids = util.get_available_devices(assert_cuda=True)
logging.info(device, gpu_ids)
self.device = device
self.num_generations = num_generations
self.model = T5ForConditionalGeneration.from_pretrained(model_name)
self.tokenizer = T5TokenizerFast.from_pretrained(model_name)
load_ckpt(ckpt_path, self.model, map_location=device)
self.model.to(device)
def __init__(self,
# model_args
t5_model_name: str,
eval_metrics: List[str],
# model_generate_args
beam_size: int,
repetition_penalty: float,
length_penalty: float,
num_beam_groups: int,
diversity_penalty: float,
skip_special_tokens: bool,
clean_up_tokenization_spaces: bool,
# model_optimizer_args
weight_decay: float,
learning_rate: float,
adam_epsilon: float,
# trainer_args
train_batch_size: int,
eval_batch_size: int,
gradient_accumulation_steps: int,
n_gpu: int,
num_train_epochs: int,
warmup_steps: int,
output_dir: str,
# dataset_args
dataset_obj: Any,
data_procesor_obj: Any,
src_domains: List[str],
trg_domain: str,
data_dir: str,
experiment_dir: str,
max_seq_len: int,
dataset_specific_kwargs: Namespace = None,
num_labels: int = 2):
super().__init__()
self.save_hyperparameters()
self.tokenizer = T5TokenizerFast.from_pretrained(self.hparams.t5_model_name)
self.data_processor, self.datasets = self._init_datasets()
self.hparams.num_labels = len(self.data_processor.labels_dict)
self.loss_fn = CrossEntropyLoss(ignore_index=T5TextClassifier.LOSS_IGNORE_ID)
self.model = T5ForConditionalGeneration.from_pretrained(self.hparams.t5_model_name)
self.classifier = CnnClassifier(num_labels=self.hparams.num_labels,
hidden_size=self.model.config.hidden_size,
max_seq_length=self.hparams.max_seq_len)
self.eval_metric_scorer = T5TextClassifier._init_eval_metric_scorer(self.hparams.eval_metrics)
self.eval_predictions = dict()
def __init__(self, args, t5_type='t5-base'):
"""
R1 = Raw 1
Training:
R1 + R2 + R3 -> M3
"""
super().__init__()
self.lr = getattr(args, "lr")
self.epochs = getattr(args, "epochs")
self.warmup_steps = getattr(args, "warmup_steps")
self.gpu_id = getattr(args, "gpu_id")
self.transformer = T5ForConditionalGeneration.from_pretrained(t5_type)
self.tokenizer = T5TokenizerFast.from_pretrained(t5_type)
self.EM_accuracy = CategoricalAccuracy()
self.to('cpu' if self.gpu_id == -1 else f"cuda:{self.gpu_id}")
def test_RumorPadaDataProcessor():
data_processor = RumorPadaDataProcessor(["charliehebdo", "ferguson", "germanwings-crash", "ottawashooting"],
"sydneysiege", DATA_DIR, EXP_DIR)
print()
print(data_processor.data["dev"].keys())
print(len(list(data_processor.data["dev"].values())[0]))
for k, v in data_processor.data["dev"].items():
if type(v) is not int:
print(k, v[-80])
else:
print(k, v[-80], len(v[-80]))
tokenizer = T5TokenizerFast.from_pretrained("t5-base")
percent = 99.8
for split in RumorPadaDataProcessor.ALL_SPLITS:
print(split, len(data_processor.data[split]["example_id"]))
print()
tokenizer_lens = tokenizer(data_processor.data[split]["input_str"], is_split_into_words=False,
max_length=128, return_length=True)["length"]
print(split, max(tokenizer_lens))
print(split, percentile(tokenizer_lens, percent))
break
def load_model(self, model_dir: str = "outputs", use_gpu: bool = False):
"""
loads a checkpoint for inferencing/prediction
Args:
model_dir (str, optional): path to model directory. Defaults to "outputs".
use_gpu (bool, optional): if True, model uses gpu for inferencing/prediction. Defaults to True.
"""
self.model = T5ForConditionalGeneration.from_pretrained(f"{model_dir}")
self.tokenizer = T5Tokenizer.from_pretrained(f"{model_dir}")
if use_gpu:
if torch.cuda.is_available():
self.device = torch.device("cuda")
else:
raise Exception(
"exception ---> no gpu found. set use_gpu=False, to use CPU"
)
else:
self.device = torch.device("cpu")
self.model = self.model.to(self.device)
class QuestionGenerator:
"""Class loads pipeline for generating questions from text"""
model = T5ForConditionalGeneration.from_pretrained(
"ThomasSimonini/t5-end2end-question-generation")
tokenizer = T5TokenizerFast.from_pretrained("t5-base")
tokenizer.sep_token = '<sep>'
tokenizer.add_tokens(['<sep>'])
@staticmethod
def generate(text: str):
"""
generates questions
for given text
:param text: sentence or paragraph for question generation
:return: list of questions
"""
try:
if len(text) < 50:
raise Exception("input too small")
generator_args = {
'temperature': 1.02,
'num_beams': 1,
'max_length': 70
}
text = "generate questions: " + text + " </s>"
input_ids = QuestionGenerator.tokenizer.encode(text,
return_tensors="pt")
res = QuestionGenerator.model.generate(input_ids, **generator_args)
output = QuestionGenerator.tokenizer.batch_decode(
res, skip_special_tokens=True)
output = output[0].split("<sep>")
if len(output[-1]) == 0 or output[-1][-1] != "?":
output.pop()
output = [" ".join(i.split()) for i in output]
return list(set(output))
except Exception as ex:
raise ex
def __init__(self,
data_dir: str,
batch_size=8,
pre_trained='',
with_answers=False):
super().__init__()
self.batch_size = batch_size
self.data_dir = data_dir
self.with_answers = with_answers
if pre_trained == 't5':
self.tokenizer = T5TokenizerFast.from_pretrained(
't5-base',
extra_ids=0,
additional_special_tokens=['<A>', '<H>', '<R>', '<T>'])
elif pre_trained == 'bart':
self.tokenizer = BartTokenizerFast.from_pretrained(
'facebook/bart-base',
extra_ids=0,
additional_special_tokens=['<A>', '<H>', '<R>', '<T>'])
else:
raise Exception(
f'Unknown pre-trained model {pre_trained}, choose t5 or bart.')
def test_AbsaSeq2SeqDataset():
data_processor = AbsaSeq2SeqDataProcessor(["device", "laptops", "rest"],
"service", DATA_DIR)
print()
print(data_processor.data["dev"].keys())
print(len(list(data_processor.data["dev"].values())[0]))
for k, v in data_processor.data["dev"].items():
if type(v) is not int:
print(k, v[0])
else:
print(k, v[0], len(v[0]))
dataset = AbsaSeq2SeqDataset("dev", data_processor,
T5TokenizerFast.from_pretrained("t5-base"),
64)
print(len(dataset))
for example in dataset:
for k, v in example.items():
print(k, v)
break
dataloader = DataLoader(dataset, 4)
for batch in dataloader:
for k, v in batch.items():
print(k, v)
break
def test_RumorDataset():
data_processor = RumorDataProcessor(
["charliehebdo", "ferguson", "germanwings-crash", "ottawashooting"],
"sydneysiege", DATA_DIR)
print()
print(data_processor.data["dev"].keys())
print(len(list(data_processor.data["dev"].values())[0]))
for k, v in data_processor.data["dev"].items():
if type(v) is not int:
print(k, v[0])
else:
print(k, v[0], len(v[0]))
dataset = RumorDataset("dev", data_processor,
T5TokenizerFast.from_pretrained("t5-base"), 64)
print(len(dataset))
for example in dataset:
for k, v in example.items():
print(k, v)
break
dataloader = DataLoader(dataset, 4)
for batch in dataloader:
for k, v in batch.items():
print(k, v)
break
def __init__(self,
split='train',
raw_dataset=None,
rank=-1,
topk=-1,
verbose=True,
args=None,
mode='train'):
super().__init__()
self.raw_dataset = raw_dataset
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.split = split
if self.verbose:
print('Data source: ', self.split)
data = self.raw_dataset.data
if topk > 0:
data = data[:topk]
if self.verbose:
print(f"Use only {topk} data")
self.n_gpus = torch.cuda.device_count()
self.rank = rank
self.data = data
if self.verbose:
# if 'sent' not in self.data_out:
# print("# all images:", len(self.data))
# else:
print("# all sentences:", len(self.data))
self.n_boxes = args.n_boxes
if 't5' in self.args.backbone:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {
'additional_special_tokens': additional_special_tokens
}
num_added_toks = self.tokenizer.add_special_tokens(
special_tokens_dict)
self.source_to_h5 = {
'train': nlvr_feature_dir.joinpath(f'train_obj36.h5'),
'valid': nlvr_feature_dir.joinpath(f'valid_obj36.h5'),
'test': nlvr_feature_dir.joinpath(f'test_obj36.h5'),
}
def t5_base_tokenizer_fast(self):
return T5TokenizerFast.from_pretrained("t5-base")
tokenizer._tokenizer.post_processor = BertProcessing(
("</s>", tokenizer.token_to_id("</s>")),
("<s>", tokenizer.token_to_id("<s>")),
)
tokenizer.enable_truncation(max_length=512)
config = T5Config(
vocab_size=52_000,
max_position_embeddings=514,
num_attention_heads=12,
num_hidden_layers=6,
type_vocab_size=1,
)
tokenizer = T5TokenizerFast.from_pretrained(tokenizer_dir, max_len=512)
model = T5ForConditionalGeneration(config=config)
model.num_parameters()
train_dataset = LineByLineTextDataset(
tokenizer=tokenizer,
file_path=f"{data_dir}/train_texts.txt",
block_size=128,
)
test_dataset = LineByLineTextDataset(
tokenizer=tokenizer,
file_path=f"{data_dir}/valid_texts.txt",
block_size=128,
)
def __init__(self,
split='train,valid',
raw_dataset=None,
rank=-1,
topk=-1,
verbose=True,
args=None,
mode='train'):
super().__init__()
self.raw_dataset = raw_dataset
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.sources = split.split(',')
if self.verbose:
print('Data sources: ', self.sources)
if 't5' in self.args.backbone:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {
'additional_special_tokens': additional_special_tokens
}
num_added_toks = self.tokenizer.add_special_tokens(
special_tokens_dict)
self.img_ids_to_source = {}
data_info_dicts = []
for source in self.sources:
data_info_path = dataset_dir.joinpath(f'GQA/{source}.json')
with open(data_info_path) as f:
_data_info_dicts = json.load(f)
# source_img_ids.append([d['img_id'] for d in _data_info_dicts])
for _d in _data_info_dicts:
self.img_ids_to_source[_d['img_id']] = source
_d['source'] = source
data_info_dicts.extend(_data_info_dicts)
if self.verbose:
print(f"Loaded {len(_data_info_dicts)} data from", source)
data = data_info_dicts
self.n_gpus = torch.cuda.device_count()
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose:
print("# all sentences:", len(self.data))
self.n_boxes = args.n_boxes
self.source_to_featname = {
'train': 'others',
'valid': 'others',
'submit': 'others',
'testdev': 'testdev'
}
self.featname_to_h5 = {
'others': vg_dir.joinpath('features/vg_gqa_obj36.h5'),
'testdev': gqa_dir.joinpath('features/gqa_testdev_obj36.h5'),
}
import pandas as pd
import torch
from torch.utils.data import Dataset, DataLoader
import pytorch_lightning as pl
from transformers import (Adafactor, T5ForConditionalGeneration,
T5TokenizerFast as T5Tokenizer)
MODEL_NAME = "t5-base"
tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
class TranslationDataset(Dataset):
def __init__(
self,
data: pd.DataFrame,
tokenizer: T5Tokenizer,
text_max_token_len: int = 110,
translation_max_token_len: int = 100,
):
self.tokenizer = tokenizer
self.data = data
self.text_max_token_len = text_max_token_len
self.translation_max_token_len = translation_max_token_len
def __len__(self):
return len(self.data)
def __getitem__(self, index: int):
#Read line of DataFrame
data_files["validation"] = data_args.validation_file
extension = data_args.train_file.split(".")[-1]
if extension == "txt":
extension = "text"
datasets = load_dataset(extension,
data_files=data_files,
cache_dir=model_args.cache_dir)
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
if model_args.tokenizer_name:
tokenizer = T5TokenizerFast.from_pretrained(
model_args.tokenizer_name,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer)
elif model_args.model_name_or_path:
tokenizer = T5TokenizerFast.from_pretrained(
model_args.model_name_or_path,
cache_dir=model_args.cache_dir,
use_fast=model_args.use_fast_tokenizer)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if model_args.config_name:
config = T5Config.from_pretrained(model_args.config_name,
cache_dir=model_args.cache_dir,
def __init__(self,
split='train',
raw_dataset=None,
rank=-1,
topk=-1,
verbose=True,
args=None,
mode='train'):
super().__init__()
self.raw_dataset = raw_dataset
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.source = split
if self.verbose:
print('Data source: ', self.source)
if self.args.tokenizer is None:
self.args.tokenizer = self.args.backbone
if 't5' in self.args.tokenizer:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.tokenizer:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
if args.use_vis_order_embedding:
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {
'additional_special_tokens': additional_special_tokens
}
num_added_toks = self.tokenizer.add_special_tokens(
special_tokens_dict)
if self.args.oscar_tags:
# Load VG Classes
vg_classes = []
with open(vg_dir.joinpath('objects_vocab.txt')) as f:
for obj in f.readlines():
vg_classes.append(obj.split(',')[0].lower().strip())
self.vg_classes = vg_classes
with open(wmt_data_dir.joinpath(f'raw/{self.source}.en')) as f:
source_text_list = f.readlines()
with open(wmt_data_dir.joinpath(f'raw/{self.source}.de')) as f:
target_text_list = f.readlines()
with open(
wmt_data_dir.joinpath(f'image_splits/{self.source}.txt')) as f:
image_ids = f.readlines()
assert len(source_text_list) == len(target_text_list)
assert len(source_text_list) == len(image_ids)
data = []
for source_text, target_text, image_id in zip(source_text_list,
target_text_list,
image_ids):
datum = {
'img_id': image_id.strip().split('.')[0],
'source_text': source_text.strip(),
'target_text': target_text.strip()
}
data.append(datum)
if self.verbose:
print(f"Loaded {len(data)} data from", split)
self.n_gpus = torch.cuda.device_count()
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose:
print("# all sentences:", len(self.data))
self.source_to_h5 = {
'train':
flickr30k_feature_dir.joinpath('trainval_boxes36.h5'),
'val':
flickr30k_feature_dir.joinpath('trainval_boxes36.h5'),
'test_2016_flickr':
flickr30k_feature_dir.joinpath('trainval_boxes36.h5'),
'test_2017_flickr':
flickr30k_feature_dir.joinpath('test2017_boxes36.h5'),
'test_2018_flickr':
flickr30k_feature_dir.joinpath('test2018_boxes36.h5'),
}
def __init__(self,
split='karpathy_train',
raw_dataset=None,
rank=-1,
topk=-1,
verbose=True,
args=None,
mode='train'):
super().__init__()
self.raw_dataset = raw_dataset
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.source = split
if self.verbose:
print('Data source: ', self.source)
if self.args.tokenizer is None:
self.args.tokenizer = self.args.backbone
if 't5' in self.args.tokenizer:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.tokenizer:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {
'additional_special_tokens': additional_special_tokens
}
num_added_toks = self.tokenizer.add_special_tokens(
special_tokens_dict)
if self.args.oscar_tags:
# Load VG Classes
vg_classes = []
with open(vg_dir.joinpath('objects_vocab.txt')) as f:
for obj in f.readlines():
vg_classes.append(obj.split(',')[0].lower().strip())
self.vg_classes = vg_classes
data_info_path = dataset_dir.joinpath('COCO/dataset_coco.json')
with open(data_info_path) as f:
karpathy_data = json.load(f)
split_rename = {
'train': 'train',
'restval': 'train',
'val': 'val',
'test': 'test'
}
n_images = 0
data = []
for datum in karpathy_data['images']:
re_split = split_rename[datum['split']]
if re_split != self.source.split('_')[-1]:
continue
if re_split == 'train':
for d in datum['sentences']:
img_id = datum['filename'].split('.')[0]
new_datum = {
'img_id': img_id,
'sent': d['raw'].strip(),
'targets':
[d['raw'].strip() for d in datum['sentences']],
'is_train': True,
}
data.append(new_datum)
else:
img_id = datum['filename'].split('.')[0]
new_datum = {
'img_id': img_id,
# 'sent': d['raw'],
'targets': [d['raw'].strip() for d in datum['sentences']],
'is_train': False,
}
data.append(new_datum)
n_images += 1
if self.verbose:
print(f"{self.source} has {n_images} images")
print(f"Loaded {len(data)} data from", split)
self.n_gpus = torch.cuda.device_count()
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose:
print("# all sentences:", len(self.data))
self.source_to_h5 = {}
if self.args.max_n_boxes == 36:
self.source_to_h5.update({
'train2014':
coco_dir.joinpath('features').joinpath('train2014_obj36.h5'),
'val2014':
coco_dir.joinpath('features').joinpath('val2014_obj36.h5'),
})
def __init__(self, split='train', rank=-1, topk=-1, verbose=True, args=None, is_train=True):
self.topk = topk
self.verbose = verbose
self.args = args
# Loading datasets to data
self.source = split
if self.verbose:
print('Data sources: ', self.source)
if 't5' in self.args.backbone:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
if args.use_vis_order_embedding:
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {'additional_special_tokens': additional_special_tokens}
num_added_toks = self.tokenizer.add_special_tokens(special_tokens_dict)
self.losses = args.losses.split(',')
data_info_path = dataset_dir.joinpath(f'VCR/{self.source}.jsonl')
with open(data_info_path) as f:
data_info_dicts = [json.loads(s) for s in f]
if self.topk > 0:
data_info_dicts = data_info_dicts[:self.topk]
for datum in data_info_dicts:
datum['backbone'] = self.args.backbone
datum['losses'] = self.losses
with Pool(8) as pool:
if self.verbose:
data = [datum for _data in tqdm(
pool.imap(get_datum, data_info_dicts), total=len(data_info_dicts), ncols=100) for datum in _data]
else:
data = [datum for _data in pool.imap(
get_datum, data_info_dicts) for datum in _data]
if self.verbose:
print(f"Loaded {len(data)} data from", self.source)
self.n_gpus = torch.cuda.device_count()
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose and is_train and ('t5' in self.args.backbone or 'bart' in self.args.backbone):
from collections import Counter
task_counter = Counter()
for datum in data:
try:
task_counter.update([datum['task']])
except KeyError:
print(datum)
exit()
print(task_counter)
for k, v in task_counter.items():
print(k, f'{v/len(data)*100:.1f}%')
if self.verbose:
print("# examples:", len(data))
self.source_to_h5 = {
'train': vcr_feature_dir.joinpath(f'train_boxes36.h5'),
'val': vcr_feature_dir.joinpath(f'val_boxes36.h5'),
'test': vcr_feature_dir.joinpath(f'test_boxes36.h5'),
'train_GT': vcr_feature_dir.joinpath(f'train_boxes_GT.h5'),
'val_GT': vcr_feature_dir.joinpath(f'val_boxes_GT.h5'),
'test_GT': vcr_feature_dir.joinpath(f'test_boxes_GT.h5'),
}
self.n_boxes = args.n_boxes
from torch.utils.data import Dataset, DataLoader
from transformers import T5TokenizerFast
import torch
import yaml
with open('config.yml', 'r') as f:
config = yaml.safe_load(f)
TOKENIZER = T5TokenizerFast.from_pretrained(config['model']['model_name'],
do_lower_case=True)
class ShapingDataset(Dataset):
def __init__(self, texts, summaries):
super().__init__()
self.texts = texts
self.summaries = summaries
self.tokenizer = TOKENIZER
self.summary_length = config['model']['summary_length']
self.token_length = config['model']['token_length']
def __len__(self):
return len(self.texts)
def __getitem__(self, item):
texts = str(self.texts[item])
summaries = str(self.summaries[item])
texts_enconding = self.tokenizer(
texts,
padding='max_length',
def __init__(self,
split='train',
raw_dataset=None,
rank=-1,
topk=-1,
verbose=True,
args=None,
mode='train'):
super().__init__()
self.raw_dataset = raw_dataset
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.split = split
self.sources = split.split(',')
if self.verbose:
print('Data sources: ', self.sources)
if 't5' in self.args.backbone:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(args.backbone)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {
'additional_special_tokens': additional_special_tokens
}
num_added_toks = self.tokenizer.add_special_tokens(
special_tokens_dict)
# mattnet_maskrcnn_detections_path = refcoco_dir.joinpath(
# 'detections/refcocog_umd/res101_coco_minus_refer_notime_dets.json')
# with open(mattnet_maskrcnn_detections_path) as f:
# mattnet_maskrcnn_detections = json.load(f)
data = []
self.refer = REFER('refcocog',
'umd',
img_dir=coco_img_dir,
ref_dir=refcoco_dir,
verbose=verbose)
ref_ids = self.refer.getRefIds(split=split)
for ref_id in ref_ids:
ref = self.refer.Refs[ref_id]
image_id = ref["image_id"]
ref_id = ref["ref_id"]
refBox = self.refer.getRefBox(ref_id)
for sent, sent_id in zip(ref["sentences"], ref["sent_ids"]):
caption = sent["raw"]
data.append({
"caption": caption,
"sent_id": sent_id,
"image_id": image_id,
"refBox": refBox,
"ref_id": ref_id,
})
self.n_gpus = torch.cuda.device_count()
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose:
print("# all sentences:", len(self.data))
self.n_boxes = args.n_boxes
self.source_to_h5 = {
'train': refcocog_feature_dir.joinpath(f'train_boxes_GT.h5')
}
if self.args.RefCOCO_GT:
self.source_to_h5['val'] = refcocog_feature_dir.joinpath(
f'val_boxes_GT.h5')
self.source_to_h5['test'] = refcocog_feature_dir.joinpath(
f'test_boxes_GT.h5')
else:
self.source_to_h5['val'] = refcocog_feature_dir.joinpath(
f'val_boxes_mattnet.h5')
self.source_to_h5['test'] = refcocog_feature_dir.joinpath(
f'test_boxes_mattnet.h5')
def __init__(self, split='vg', rank=-1, topk=-1, verbose=True, args=None, is_train=True):
self.topk = topk
self.verbose = verbose
self.args = args
# Loading datasets to data
self.sources = split.split(',')
if self.verbose:
print('Data sources: ', self.sources)
# Answer Table from LXMERT (Could be removed)
self.answer_table = AnswerTable()
if self.verbose:
print("Load an answer table of size %d." % (len(self.answer_table.ans2id_map())))
self.img_ids_to_source = {}
losses = args.losses.split(',')
data = []
for img_source in self.sources:
data_info_path = dataset_dir.joinpath(f'lxmert/{img_source}.json')
with open(data_info_path) as f:
_data = json.load(f)
if self.verbose:
print(f"Loaded {len(_data)} data from", img_source)
# source_img_ids.append([d['img_id'] for d in _data])
for datum in _data:
self.img_ids_to_source[datum['img_id']] = img_source
# datum['img_source'] = img_source
datum['args'] = args
datum['is_train'] = is_train
datum['caption_only'] = args.caption_only
datum['lm'] = 'lm' in losses
datum['qa'] = 'qa' in losses
datum['ground_caption'] = 'ground_caption' in losses
datum['refer'] = 'refer' in losses
datum['itm'] = 'itm' in losses
datum['caption'] = 'caption' in losses
datum['backbone'] = self.args.backbone
data.extend(_data)
# Modify the answers
if 'qa' in args.losses:
for datum in data:
labelf = datum['labelf']
for _qa_source, labels in labelf.items():
for label in labels:
for ans in list(label.keys()):
new_ans = self.answer_table.convert_ans(ans)
if self.answer_table.used(new_ans):
if ans != new_ans:
label[new_ans] = label.pop(ans)
else:
label.pop(ans)
if self.verbose:
print("# images:", len(data))
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
if 'qa' in args.losses:
self.evaluator = QAEvaluator(data)
with Pool(8) as pool:
if self.verbose:
data = [datum for _data in tqdm(
pool.imap(get_datum, data), total=len(data), ncols=100, desc="Creating pretrainig data examples") for datum in _data]
else:
data = [datum for _data in pool.imap(
get_datum, data) for datum in _data]
if self.args.itm_cocoonly:
caption_sources = ['mscoco']
else:
caption_sources = ['mscoco', 'vg']
self.data_captions = [datum for datum in data if datum['text_source'] in caption_sources]
self.n_data_captions = len(self.data_captions)
if self.verbose:
print('# itm data:', self.n_data_captions)
self.data = data
self.n_data = len(self.data)
if self.verbose and is_train:
from collections import Counter
task_counter = Counter()
for datum in data:
try:
task_counter.update([datum['task']])
except KeyError:
print(datum)
exit()
print(task_counter)
for k, v in task_counter.items():
print(k, f'{v/len(data)*100:.1f}%')
if self.verbose:
print("# examples:", len(data))
self.source_to_h5 = {
'mscoco_resplit_train_train2014': coco_dir.joinpath('features').joinpath('train2014_obj36.h5'),
'mscoco_resplit_train_val2014': coco_dir.joinpath('features').joinpath('val2014_obj36.h5'),
'mscoco_resplit_val': coco_dir.joinpath('features').joinpath('resplit_val_obj36.h5'),
'vgnococo': vg_dir.joinpath('features').joinpath('vg_gqa_obj36.h5'),
}
self.n_boxes = args.n_boxes
if 't5' in self.args.backbone:
if self.args.use_vision:
# self.tokenizer = VLT5Tokenizer.from_pretrained(
# args.backbone, do_lower_case=args.do_lower_case)
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone, do_lower_case=args.do_lower_case)
else:
# self.tokenizer = T5Tokenizer.from_pretrained(
# args.backbone, do_lower_case=args.do_lower_case)
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone, do_lower_case=args.do_lower_case)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(args.backbone)
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {'additional_special_tokens': additional_special_tokens}
self.tokenizer.add_special_tokens(special_tokens_dict)
def __init__(self, split='train', raw_dataset=None, rank=-1, topk=-1, verbose=True, args=None, mode='train'):
super().__init__()
self.raw_dataset = raw_dataset
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.sources = split.split(',')
if self.verbose:
print('Data sources: ', self.sources)
if 't5' in self.args.backbone:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
if args.use_vis_order_embedding:
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {'additional_special_tokens': additional_special_tokens}
num_added_toks = self.tokenizer.add_special_tokens(special_tokens_dict)
self.answer_normalizer = VQAEvaluator()
self.img_ids_to_source = {}
data_info_dicts = []
for source in self.sources:
data_info_path = dataset_dir.joinpath(f'vqa/{source}.json')
with open(data_info_path) as f:
_data_info_dicts = json.load(f)
for _d in _data_info_dicts:
if 'vg_qa_full' == source:
self.img_ids_to_source[_d['img_id']] = 'vg'
elif 'train2014' in _d['img_id']:
self.img_ids_to_source[_d['img_id']] = 'train2014'
elif 'val2014' in _d['img_id']:
self.img_ids_to_source[_d['img_id']] = 'val2014'
else:
self.img_ids_to_source[_d['img_id']] = source
_d['source'] = source
data_info_dicts.extend(_data_info_dicts)
if self.verbose:
print(f"Loaded {len(_data_info_dicts)} data from", source)
data = data_info_dicts
self.n_gpus = torch.cuda.device_count()
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose:
print("# all sentences:", len(self.data))
self.n_boxes = args.n_boxes
self.source_to_h5 = {
'train': coco_feature_dir.joinpath(f'train2014_obj36.h5'),
'minival': coco_feature_dir.joinpath(f'val2014_obj36.h5'),
'nominival': coco_feature_dir.joinpath(f'val2014_obj36.h5'),
'test': coco_feature_dir.joinpath(f'test2015_obj36.h5'),
'vg': dataset_dir.joinpath('VG/features').joinpath('vg_gqa_obj36.h5'),
'train2014': coco_feature_dir.joinpath(f'train2014_obj36.h5'),
'val2014': coco_feature_dir.joinpath(f'val2014_obj36.h5'),
}
def __init__(self,
split='train',
raw_dataset=None,
rank=-1,
topk=-1,
verbose=True,
args=None,
mode='train'):
super().__init__()
self.topk = topk
self.verbose = verbose
self.args = args
self.mode = mode
# Loading datasets to data
self.split = split
self.sources = split.split(',')
if self.verbose:
print('Data sources: ', self.sources)
if 't5' in self.args.backbone:
if self.args.use_vision:
self.tokenizer = VLT5TokenizerFast.from_pretrained(
args.backbone,
max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
else:
self.tokenizer = T5TokenizerFast.from_pretrained(
args.backbone,
max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
elif 'bart' in self.args.backbone:
self.tokenizer = BartTokenizer.from_pretrained(
args.backbone,
# max_length=self.args.max_text_length,
do_lower_case=self.args.do_lower_case)
additional_special_tokens = [f'<extra_id_{i}>' for i in range(100-1, -1, -1)] + \
[f'<vis_extra_id_{i}>' for i in range(100-1, -1, -1)]
special_tokens_dict = {
'additional_special_tokens': additional_special_tokens
}
num_added_toks = self.tokenizer.add_special_tokens(
special_tokens_dict)
self.img_ids_to_source = {}
data_info_dicts = []
for source in self.sources:
data_info_path = dataset_dir.joinpath(f'VCR/{source}.jsonl')
with open(data_info_path) as f:
_data_info_dicts = [json.loads(s) for s in f]
for _d in _data_info_dicts:
self.img_ids_to_source[_d['img_id']] = source
_d['source'] = source
data_info_dicts.extend(_data_info_dicts)
if self.verbose:
print(f"Loaded {len(_data_info_dicts)} data from", source)
data = data_info_dicts
self.rank = rank
if self.topk > 0:
data = data[:self.topk]
if self.verbose:
print(f"Use only {self.topk} data")
self.data = data
if self.verbose:
print("# all sentences:", len(self.data))
self.n_boxes = args.n_boxes
self.source_to_h5 = {
'train': vcr_feature_dir.joinpath(f'train_boxes36.h5'),
'val': vcr_feature_dir.joinpath(f'val_boxes36.h5'),
'test': vcr_feature_dir.joinpath(f'test_boxes36.h5'),
'train_GT': vcr_feature_dir.joinpath(f'train_boxes_GT.h5'),
'val_GT': vcr_feature_dir.joinpath(f'val_boxes_GT.h5'),
'test_GT': vcr_feature_dir.joinpath(f'test_boxes_GT.h5'),
}
def get_tokenizer(self, opt):
return T5TokenizerFast.from_pretrained(opt['t5_model_arch'],
truncation=True)
