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from huggingface_hub import hf_hub_download
from PIL import Image
import torch
from datasets import load_dataset, get_dataset_split_names
import numpy as np
import requests
from transformers import ViltProcessor, ViltForQuestionAnswering
from transformers import AutoProcessor, AutoModelForCausalLM
from transformers import BlipProcessor, BlipForQuestionAnswering
import os
import requests
from tqdm import tqdm
import timm
# VLMO: modify in vlmo/config.py: set test_only -> True
from datasets import load_dataset, get_dataset_split_names
import torch
import torchvision
from torchvision.models import resnet50
import torchvision.transforms as transforms
from transformers import VisualBertForMultipleChoice, VisualBertForQuestionAnswering, BertTokenizerFast, AutoTokenizer, ViltForQuestionAnswering
from PIL import Image
import time
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
VQA_URL = "https://dl.fbaipublicfiles.com/pythia/data/answers_vqa.txt"
# load processor and model
def load_model(name):
if name == "vilt":
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
elif name == "vilt_finetuned":
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
model = ViltForQuestionAnswering.from_pretrained("Minqin/carets_vqa_finetuned")
elif name == "git":
processor = AutoProcessor.from_pretrained("microsoft/git-base-vqav2")
model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-vqav2")
elif name == "blip":
processor = BlipProcessor.from_pretrained('Salesforce/blip-vqa-base')
model = BlipForQuestionAnswering.from_pretrained('Salesforce/blip-vqa-base')
elif name == "vbert":
processor = AutoTokenizer.from_pretrained("bert-base-uncased")
model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa")
else:
raise ValueError("invalid model name: ", name)
return (processor, model)
'''
def load_dataset(type):
if type == "train":
return load_dataset("HuggingFaceM4/VQAv2", split="train", cache_dir="cache", streaming=False)
elif type == "test":
return load_dataset("HuggingFaceM4/VQAv2", split="validation", cache_dir="cache", streaming=False)
else:
raise ValueError("invalid dataset: ", type)
'''
def tokenize_function(examples, processor):
sample = {}
sample['inputs'] = processor(images=examples['image'], text=examples['question'], return_tensors="pt")
sample['outputs'] = examples['multiple_choice_answer']
return sample
def label_count_list(labels):
res = {}
keys = set(labels)
for key in keys:
res[key] = labels.count(key)
return res
def get_item(image, question, tokenizer, image_model, model_name):
inputs = tokenizer(
question,
# padding='max_length',
# truncation=True,
# max_length=128,
return_tensors='pt'
)
visual_embeds = get_img_feats(image, image_model=image_model, name=model_name)\
.squeeze(2, 3).unsqueeze(0)
visual_token_type_ids = torch.ones(visual_embeds.shape[:-1], dtype=torch.long)
visual_attention_mask = torch.ones(visual_embeds.shape[:-1], dtype=torch.float)
upd_dict = {
"visual_embeds": visual_embeds,
"visual_token_type_ids": visual_token_type_ids,
"visual_attention_mask": visual_attention_mask,
}
inputs.update(upd_dict)
return upd_dict, inputs
def get_img_feats(image, image_model, new_size=None, name='resnet50'):
if name == "resnet50":
image_model = torch.nn.Sequential(*list(image_model.children())[:-1])
# apply transforms when necessary
if new_size is not None:
transfrom_f = transforms.Resize((new_size, new_size), interpolation=transforms.InterpolationMode.LANCZOS)
image = transfrom_f(image)
transform = transforms.Compose([
transforms.ToTensor(), # Convert PIL Image back to tensor
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
# get features
image = transform(image)
if name == "resnet50":
image_features = image_model(image.unsqueeze(0))
elif name == "vitb16":
image_features = image_model.forward_features(image.unsqueeze(0))
return image_features
def get_data(query, delim=","):
assert isinstance(query, str)
if os.path.isfile(query):
with open(query) as f:
data = eval(f.read())
else:
req = requests.get(query)
try:
data = requests.json()
except Exception:
data = req.content.decode()
assert data is not None, "could not connect"
try:
data = eval(data)
except Exception:
data = data.split("\n")
req.close()
return data
def err_msg():
print("Load error, try again")
return "[ERROR]"
def get_answer(model_loader_args, img, question, model_name):
processor, model = model_loader_args[0], model_loader_args[1]
if model_name == "vilt":
try:
encoding = processor(images=img, text=question, return_tensors="pt")
except Exception:
return err_msg()
else:
outputs = model(**encoding)
logits = outputs.logits
idx = logits.argmax(-1).item()
pred = model.config.id2label[idx]
elif model_name == "vilt_finetuned":
try:
encoding = processor(images=img, text=question, return_tensors="pt")
except Exception:
return err_msg()
else:
outputs = model(**encoding)
logits = outputs.logits
idx = logits.argmax(-1).item()
pred = model.config.id2label[idx]
elif model_name == "git":
try:
pixel_values = processor(images=img, return_tensors="pt").pixel_values
input_ids = processor(text=question, add_special_tokens=False).input_ids
input_ids = [processor.tokenizer.cls_token_id] + input_ids
input_ids = torch.tensor(input_ids).unsqueeze(0)
except Exception:
return err_msg()
else:
generate_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50)
output = processor.batch_decode(generate_ids, skip_special_tokens=True)
output = output[0]
pred = output.split('?')[-1]
pred = pred.strip()
elif model_name == "vbert":
vqa_answers = get_data(VQA_URL)
try:
# load question and image (processor = tokenizer)
_, inputs = get_item(img, question, processor, model_name)
outputs = model(**inputs)
except Exception:
return err_msg()
else:
answer_idx = torch.argmax(outputs.logits, dim=1).item() # from 3129
pred = vqa_answers[answer_idx]
elif model_name == "blip":
try:
pixel_values = processor(images=img, return_tensors="pt").pixel_values
blip_ques = processor.tokenizer.cls_token + question
batch_input_ids = processor(text=blip_ques, add_special_tokens=False).input_ids
batch_input_ids = torch.tensor(batch_input_ids).unsqueeze(0)
generate_ids = model.generate(pixel_values=pixel_values, input_ids=batch_input_ids, max_length=50)
blip_output = processor.batch_decode(generate_ids, skip_special_tokens=True)
except Exception:
return err_msg()
else:
pred = blip_output[0]
else:
return "Invalid model name"
return pred |