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import argparse
import collections
import gzip
import html
import json
import os
import random
import re
import torch
from tqdm import tqdm
import numpy as np
from utils import *
from PIL import Image
import requests
from transformers import AutoProcessor, MllamaForConditionalGeneration, MllamaForCausalLM, MllamaTextModel, MllamaVisionModel
def load_data(args):
# Musical_Instruments.features.json
# item2feature_path = args.data_path
item2feature = load_json(args.data_path)
# item2order_path = args.order_path
# item2order = load_json(args.order_path)
return item2feature
def generate_feature(item2feature, features):
item_feature_list = []
for item in item2feature:
data = item2feature[item]
text = []
image = []
for meta_key in features:
if meta_key in data:
if 'image' in meta_key:
image.append(data[meta_key][0])
else:
meta_value = clean_text(data[meta_key])
text.append(meta_value.strip())
item_feature_list.append([item, text, image])
return item_feature_list
def preprocess_feature(args):
print('Process feature data ...')
# print('Dataset: ', args.dataset)
item2feature = load_data(args)
# load item text and clean
item_feature_list = generate_feature(item2feature, ['title', 'description', 'imageH'])
# item_text_list = generate_text(item2feature, ['title'])
# return: list of (item_ID, cleaned_item_text)
return item_feature_list
def generate_item_embedding(args, item_text_list, tokenizer, model, word_drop_ratio=-1, save_path = ''):
print('Generate feature embedding ...')
# print(' Dataset: ', args.dataset)
items, texts, images = zip(*item_text_list)
order_texts, order_images = [[0]] * len(items), [[0]] * len(items)
# item_order_mapping = {}
# item_order = 0
# for item in items:
# item_order_mapping[item] = item_order
# item_order += 1
item_order_mapping = load_json(args.order_path)
for item, text, image in zip(items, texts, images):
order_texts[int(item_order_mapping[item])] = text
order_images[int(item_order_mapping[item])] = image
for text in order_texts:
assert text != [0]
for image in order_images:
assert image != [0]
embeddings = []
text_emb_result = []
image_emb_result = []
multi_modal_emb_result = []
start, batch_size = 0, 1
with torch.no_grad():
while start < len(order_texts):
if (start + 1) % 100 == 0:
print("==>", start + 1)
item_text, item_image = ' '.join(order_texts[start: start + 1][0]), order_images[start: start + 1][0][0]
# print(field_texts)
# field_texts = zip(*field_texts)
processed_text = processor(text = item_text, return_tensors = 'pt').to(args.device)
text_output = model.language_model.model(**processed_text)
text_masked_output = text_output.last_hidden_state * processed_text['attention_mask'].unsqueeze(-1)
text_mean_output = text_masked_output.sum(dim = 1) / processed_text['attention_mask'].sum(dim = -1, keepdim = True)
text_mean_output = text_mean_output.detach().cpu()
text_emb_result.append(text_mean_output.numpy().tolist())
open_image = Image.open(requests.get(item_image, stream = True).raw)
processed_image = processor(images = open_image, return_tensors = "pt").to(args.device)
image_output = model.vision_model(**processed_image)
image_mean_output = image_output.last_hidden_state.squeeze().mean(dim = 0)
image_mean_output = image_mean_output.mean(dim = 0,keepdim = True)
image_mean_output = image_mean_output.detach().cpu()
image_emb_result.append(image_mean_output.numpy().tolist())
prompt = '<|image|>' + item_text
inputs = processor(text = prompt, images = open_image, return_tensors = "pt").to(args.device)
multi_modal_output = model(**inputs, output_hidden_states = True)
multi_modal_mean_output = multi_modal_output.hidden_states[-1].mean(dim = 1)
multi_modal_mean_output = multi_modal_mean_output.detach().cpu()
multi_modal_emb_result.append(multi_modal_mean_output.numpy().tolist())
text_embeddings = torch.cat(text_emb_result, dim = 0).numpy()
print('Text-Embeddings shape: ', text_embeddings.shape)
image_embeddings = torch.cat(image_emb_result, dim = 0).numpy()
print('Image-Embeddings shape: ', image_embeddings.shape)
multi_modal_embeddings = torch.cat(multi_modal_emb_result, dim = 0).numpy()
print('Multimodal-Embeddings shape: ', multi_modal_embeddings.shape)
file = os.path.join(args.save_path + "Musical_Instruments.emb.text.npy")
np.save(file, text_embeddings)
file = os.path.join(args.save_path + "Musical_Instruments.emb.imgae.npy")
np.save(file, image_embeddings)
file = os.path.join(args.save_path + "Musical_Instruments.emb.multimodal.npy")
np.save(file, multi_modal_embeddings)
# tokenized_text = tokenizer(
# item_text,
# max_length = args.max_sent_len,
# truncation = True,
# return_tensors = 'pt',
# padding = "longest").to(args.device)
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_id', type=int, default=0, help='ID of running GPU')
parser.add_argument('--plm_name', type=str, default='llama')
parser.add_argument('--plm_checkpoint', type=str, default='')
parser.add_argument('--max_sent_len', type=int, default=2048)
parser.add_argument('--word_drop_ratio', type=float, default=-1, help='word drop ratio, do not drop by default')
parser.add_argument('--data_path', type=str, default='')
parser.add_argument('--order_path', type=str, default='')
parser.add_argument('--save_path', type=str, default='')
return parser.parse_args()
if __name__ == '__main__':
args = parse_args()
device = set_device(args.gpu_id)
args.device = device
item_feature_list = preprocess_feature(args)
model = MllamaForConditionalGeneration.from_pretrained(args.plm_checkpoint, torch_dtype = torch.float16)
processor = AutoProcessor.from_pretrained(args.plm_checkpoint)
# plm_tokenizer, plm_model = load_plm(args.plm_checkpoint)
# if processor.pad_token_id is None:
# processor.pad_token_id = 0
model = model.to(device)
generate_item_embedding(
args,
item_feature_list,
processor,
model,
word_drop_ratio = args.word_drop_ratio,
save_path = args.save_path
) |