[pai-diffusion]pai的easynlp的clip模型训练

EasyNLP带你玩转CLIP图文检索 - 知乎作者：熊兮、章捷、岑鸣、临在导读随着自媒体的不断发展，多种模态数据例如图像、文本、语音、视频等不断增长，创造了互联网上丰富多彩的世界。为了准确建模用户的多模态内容，跨模态检索是跨模态理解的重要任务，…https://zhuanlan.zhihu.com/p/528476134

initialize_easynlp()->train_dataset = CLIPDataset(pretrained_model_name_or_path=get_pretrain_model_path("alibaba-pai/clip_chinese_roberta_base_vit_base"),data_file="MUGE_MR_train_base64_part.tsv",max_seq_length=32,input_schema="text:str:1,image:str:1",first_sequence="text",second_sequence="image",is_training=True)
valid_dataset = CLIPDataset()model = get_application_model(app_name='clip',...)
- easynlp.appzoo.api.ModelMapping->CLIPApp
- easynlp.appzoo.clip.model.py->CLIPApp
- CHINESE_CLIP->
- self.visual = VisualTransformer()
- self.bert = BertModel()trainer = Trainer(model,train_dataset,user_defined_parameters,  evaluator=get_application_evaluator(app_name="clip",valid_dataset=valid_dataset,user_defined_parameters=user_defined_parameters,eval_batch_size=32))trainer.train()
- for _epoch in range(self._first_epoch,int(args.epoch_num)):for _step,batch in enumerate(self._train_loader):    label_ids = batch.pop()forward_outputs = self._model(batch)loss_dict = self.model_module.compute_loss(forward_outputs,label_ids)_loss = loss_dict('loss')_loss.backward()model = get_application_model_evaluation()
evaluator = get_application_evaluator()
evaluator.evaluate(model)

数据处理：

import os
import base64
import multiprocessing
from tqdm import tqdmdef process_image(image_path):# 从图片路径中提取中文描述image_name = os.path.basename(image_path)description = os.path.splitext(image_name)[0]# 将图片转换为 Base64 编码with open(image_path, 'rb') as f:image_data = f.read()base64_data = base64.b64encode(image_data).decode('utf-8')return description, base64_datadef generate_tsv(directory):image_paths = [os.path.join(directory, filename) for filename in os.listdir(directory) iffilename.endswith(('.jpg', '.png'))]with multiprocessing.Pool() as pool, tqdm(total=len(image_paths), desc='Processing Images') as pbar:results = []for result in pool.imap_unordered(process_image, image_paths):results.append(result)pbar.update(1)with open('/home/image_team/image_team_docker_home/lgd/e_commerce_sd/data/vcg_furnitures_text_image/vcg_furnitures_train.tsv','w', encoding='utf-8') as f:for description, base64_data in results:line = f"{description}\t{base64_data}\n"f.write(line)if __name__ == '__main__':target_directory = "/home/image_team/image_team_docker_home/lgd/e_commerce_sd/data/vcg_furnitures_text_image/vcg_furnitures_train/img_download/"# import pdb;pdb.set_trace()generate_tsv(target_directory)

训练代码：

import torch.cuda
from easynlp.appzoo import CLIPDataset
from easynlp.appzoo import get_application_predictor, get_application_model, get_application_evaluator, \get_application_model_for_evaluation
from easynlp.core import Trainer, PredictorManager
from easynlp.utils import initialize_easynlp, get_args, get_pretrain_model_path
from easynlp.utils.global_vars import parse_user_defined_parametersdef main():# /root/.easynlp/modelzoo中train_dataset = CLIPDataset(pretrained_model_name_or_path=get_pretrain_model_path(args.pretrained_model_name_or_path),data_file=args.tables.split(",")[0],max_seq_length=args.sequence_length,input_schema=args.input_schema,first_sequence=args.first_sequence,second_sequence=args.second_sequence,is_training=True)valid_dataset = CLIPDataset(# 预训练模型名称路径，这里我们使用封装好的get_pretrain_model_path函数，来处理模型名称"alibaba-pai/clip_chinese_roberta_base_vit_base"以得到其路径，并自动下载模型pretrained_model_name_or_path=get_pretrain_model_path(args.pretrained_model_name_or_path),data_file=args.tables.split(",")[-1],# "data/pai/MUGE_MR_valid_base64_part.tsv"max_seq_length=args.sequence_length,  # 文本最大长度，超过将截断，不足将paddinginput_schema=args.input_schema,  # 输入tsv数据的格式，逗号分隔的每一项对应数据文件中每行以\t分隔的一项，每项开头为其字段标识，如label、sent1等first_sequence=args.first_sequence,  # 用于说明input_schema中哪些字段作为第一/第二列输入数据second_sequence=args.second_sequence,is_training=False)  # 是否为训练过程，train_dataset为True，valid_dataset为Falsemodel = get_application_model(app_name=args.app_name,  # 任务名称，这里选择文本分类"clip"pretrained_model_name_or_path=get_pretrain_model_path(args.pretrained_model_name_or_path),user_defined_parameters=user_defined_parameters# user_defined_parameters：用户自定义参数，直接填入刚刚处理好的自定义参数user_defined_parameters)trainer = Trainer(model=model,train_dataset=train_dataset,user_defined_parameters=user_defined_parameters,evaluator=get_application_evaluator(app_name=args.app_name,valid_dataset=valid_dataset,user_defined_parameters=user_defined_parameters,eval_batch_size=32))trainer.train()# 模型评估model = get_application_model_for_evaluation(app_name=args.app_name,pretrained_model_name_or_path=args.checkpoint_dir,user_defined_parameters=user_defined_parameters)evaluator = get_application_evaluator(app_name=args.app_name,valid_dataset=valid_dataset,user_defined_parameters=user_defined_parameters,eval_batch_size=32)model.to(torch.cuda.current_device())evaluator.evaluate(model=model)# 模型预测if test:predictor = get_application_predictor(app_name="clip",model_dir="./outputs/clip_model/",first_sequence="text",second_sequence="image",sequence_length=32,user_defined_parameters=user_defined_parameters)predictor_manager = PredictorManager(predictor=predictor,input_file="data/vcg_furnitures_text_image/vcg_furnitures_test.tsv",input_schema="text:str:1",output_file="text_feat.tsv",output_schema="text_feat",append_cols="text",batch_size=2)predictor_manager.run()if __name__ == "__main__":initialize_easynlp()args = get_args()user_defined_parameters = parse_user_defined_parameters('pretrain_model_name_or_path=alibaba-pai/clip_chinese_roberta_base_vit_base')args.checkpoint_dir = "./outputs/clip_model/"args.pretrained_model_name_or_path = "alibaba-pai/clip_chinese_roberta_base_vit_base"# args.n_gpu = 3# args.worker_gpu = "1,2,3"args.app_name = "clip"args.tables = "data/pai/MUGE_MR_train_base64_part.tsv,data/pai/MUGE_MR_valid_base64_part.tsv"# "data/vcg_furnitures_text_image/vcg_furnitures_train.tsv," \#               "data/vcg_furnitures_text_image/vcg_furnitures_test.tsv"# "data/pai/MUGE_MR_train_base64_part.tsv,data/pai/MUGE_MR_valid_base64_part.tsv"args.input_schema = "text:str:1,image:str:1"args.first_sequence = "text"args.second_sequence = "image"args.learning_rate = 1e-4args.epoch_num = 1000args.random_seed = 42args.save_checkpoint_steps = 200args.sequence_length = 32# args.train_batch_size = 2args.micro_batch_size = 32test = Falsemain()# python -m torch.distributed.launch --nproc_per_node 4 tools/train_pai_chinese_clip.py

说一点自己的想法，在我自己工作之初，我很喜欢去拆解一些框架，例如openmm系列，但其实大部分在训练过程上都是相似的，大可不必，在改动上，也没有必要对其进行流程上的大改动，兼具百家之长，了解整体pipeline，更加专注在pipeline实现和效果导向型的结果提交更加有效。