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# Copyright (c) Alibaba, Inc. and its affiliates.
import torch
import os
from modelscope.models.base import TorchModel
from modelscope.preprocessors.base import Preprocessor
from modelscope.pipelines.base import Model, Pipeline
from modelscope.utils.config import Config
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors.builder import PREPROCESSORS
from modelscope.models.builder import MODELS
from modelscope.preprocessors.image import load_image
from vlmo.utils.beit_utils import load_from_config
@PIPELINES.register_module(
"multi-modal-embeddings", module_name="multi-modal-embedding-pipeline"
)
class MyCustomPipeline(Pipeline):
"""Give simple introduction to this pipeline.
Examples:
>>> from modelscope.pipelines import pipeline
>>> input = "Hello, ModelScope!"
>>> my_pipeline = pipeline('my-task', 'my-model-id')
>>> result = my_pipeline(input)
"""
def __init__(self, model, preprocessor=None, **kwargs):
"""
use `model` and `preprocessor` to create a custom pipeline for prediction
Args:
model: model id on modelscope hub.
preprocessor: the class of method be init_preprocessor
"""
super().__init__(model=model, auto_collate=False)
self.model_dir = model
self._device = "cuda" if torch.cuda.is_available() else "cpu"
# model_config = {
# "loss_names": {"itc": 1},
# "encoder_layers": 9,
# "beit3_vl_layers": 3,
# "tokenizer_type": "GLMChineseTokenizer",
# "tokenizer": os.path.join(self.model_dir, "./vlmo/tokenizer"),
# "vocab_size": 115244,
# "whole_word_masking": True,
# "precision": 32,
# "test_only": True,
# "flash_attn": True,
# "model_path": os.path.join(self.model_dir, "m2_encoder_1B.ckpt"),
# "modelscope": {"model_id": "M2Cognition/M2-Encoder-Large"},
# "model_file": "m2_encoder_1B.ckpt",
# }
model_config = {
"loss_names": {"itc": 1},
"beit_version": "large",
"encoder_embed_dim": 1024,
"out_embed_dim": 1024,
"encoder_layers": 21,
"beit3_vl_layers": 3,
# "image_size": 224,
"visual_mask_size": 14,
"tokenizer_type": "GLMChineseTokenizer",
"tokenizer": os.path.join(self.model_dir, "./vlmo/tokenizer"),
"vocab_size": 115244,
"whole_word_masking": False,
"precision": 32,
"test_only": True,
"flash_attn": True,
"model_path": os.path.join(self.model_dir, "m2_encoder_1B.ckpt"),
"modelscope": {
"model_id": "M2Cognition/M2_Encoder_Large"
},
"model_file": "m2_encoder_1B.ckpt"
}
model, processors = load_from_config(model_config)
self.model = model
self.model.to(self._device).eval()
self._tokenizer, self._img_processor = processors
def _sanitize_parameters(self, **pipeline_parameters):
"""
this method should sanitize the keyword args to preprocessor params,
forward params and postprocess params on '__call__' or '_process_single' method
considered to be a normal classmethod with default implementation / output
Default Returns:
Dict[str, str]: preprocess_params = {}
Dict[str, str]: forward_params = {}
Dict[str, str]: postprocess_params = pipeline_parameters
"""
return {}, pipeline_parameters, {}
def _check_input(self, inputs):
pass
def _check_output(self, outputs):
pass
def forward(self, forward_params):
"""Provide default implementation using self.model and user can reimplement it"""
# print("forward_params", forward_params)
labels = forward_params.get("label_list", "")
labels = labels.split(",")
if len(labels) > 1 and labels[0] != "":
txt_encoding = self._tokenizer(
labels,
padding="max_length",
truncation=True,
max_length=self.model.hparams.config["max_text_len"],
return_special_tokens_mask=True,
)
txt_data = {
"text_ids": torch.tensor(txt_encoding["input_ids"]).to(self._device),
"text_masks": torch.tensor(txt_encoding["attention_mask"]).to(
self._device
),
"text_labels": None,
}
txt_feats = self.model.infer_text(txt_data)["cls_vlffn_feats"]
image = forward_params["image"]
image = load_image(image)
img = self._img_processor(image).unsqueeze(0)
img_data = {"image": [img.to(self._device)]}
img_feats = self.model.infer_image(img_data)["cls_vlffn_feats"]
logits_per_image = self.model.logit_scale.exp() * img_feats @ txt_feats.t()
probs = logits_per_image.softmax(dim=-1).detach().cpu()
index = probs.max(dim=-1)[1][0]
label = labels[index]
return {"text": label, "scores": probs.numpy().tolist()[0]}
else:
rets = {}
if "text" in forward_params:
text = forward_params.get("text")
txt_encoding = self._tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.model.hparams.config["max_text_len"],
return_special_tokens_mask=True,
)
txt_data = {
"text_ids": torch.tensor(txt_encoding["input_ids"]).to(
self._device
),
"text_masks": torch.tensor(txt_encoding["attention_mask"]).to(
self._device
),
"text_labels": None,
}
txt_feats = self.model.infer_text(txt_data)["cls_vlffn_feats"]
rets.update({"text_embedding": txt_feats.detach()})
if "img" in forward_params:
input_img = forward_params["img"]
img = self._img_processor(input_img).unsqueeze(0)
img_data = {"image": [img.to(self._device)]}
img_feats = self.model.infer_image(img_data)["cls_vlffn_feats"]
rets.update({"img_embedding": img_feats.detach()})
return rets
def preprocess(self, inputs):
return inputs
def postprocess(self, inputs):
"""If current pipeline support model reuse, common postprocess
code should be write here.
Args:
inputs: input data
Return:
dict of results: a dict containing outputs of model, each
output should have the standard output name.
"""
return inputs
"""
# Tips: usr_config_path is the temporary save configuration location， after upload modelscope hub, it is the model_id
usr_config_path = "/tmp/snapdown/"
config = Config(
{
"framework": "pytorch",
"task": "multi-modal-embeddings",
"model": {"type": "m2-encoder"},
"pipeline": {"type": "multi-modal-embedding-pipeline"},
"allow_remote": True,
}
)
config.dump("/tmp/snapdown/" + "configuration.json")
"""
if __name__ == "__main__":
from modelscope.pipelines import pipeline
from modelscope.preprocessors.image import load_image
model = "M2Cognition/M2-Encoder"
pipe = pipeline("multi-modal-embeddings", model=model)
input = {
"image": "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg",
"label_list": "杰尼龟,妙蛙种子,小火龙,皮卡丘",
}
demo = pipe(input)
print("demo output", demo)
inputs = {"text": ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]}
output = pipe(inputs)
print("text output", output)
input_img = load_image(
"https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
) # 支持皮卡丘示例图片路径/本地图片 返回PIL.Image
inputs = {"img": input_img}
img_embedding = pipe(inputs) # 2D Tensor, [图片数, 特征维度]
print("image output", img_embedding)
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