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# Copyright 2021 Zilliz. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
from pathlib import Path
import torch
from torchvision import transforms
from towhee.types.image_utils import to_pil
from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
from towhee import register
from transformers import CLIPTokenizer, CLIPTextModel ,CLIPModel,CLIPProcessor
from train_clip_with_hf_trainer import train_with_hf_trainer
@register(output_schema=['vec'])
class Clip(NNOperator):
"""
CLIP multi-modal embedding operator
"""
def __init__(self, model_name: str, modality: str):
self.modality = modality
self.device = "cuda" if torch.cuda.is_available() else "cpu"
cfg = self._configs()[model_name]
self.model = CLIPModel.from_pretrained(cfg)
self.tokenizer = CLIPTokenizer.from_pretrained(cfg)
self.processor = CLIPProcessor.from_pretrained(cfg)
def inference_single_data(self, data):
if self.modality == 'image':
vec = self._inference_from_image(data)
elif self.modality == 'text':
vec = self._inference_from_text(data)
else:
raise ValueError("modality[{}] not implemented.".format(self._modality))
return vec.detach().cpu().numpy().flatten()
def __call__(self, data):
if not isinstance(data, list):
data = [data]
else:
data = data
results = []
for single_data in data:
result = self.inference_single_data(single_data)
results.append(result)
if len(data) == 1:
return results[0]
else:
return results
def _inference_from_text(self, text):
tokens = self.tokenizer([text], padding=True, return_tensors="pt")
text_features = self.model.get_text_features(**tokens)
return text_features
@arg(1, to_image_color('RGB'))
def _inference_from_image(self, img):
img = to_pil(img)
inputs = processor(images=img, return_tensors="pt")
image_features = self.model.get_image_features(**inputs)
return image_features
def train(self, **kwargs):
data_args = kwargs.pop('data_args', None)
training_args = kwargs.pop('training_args', None)
train_with_hf_trainer(self.model, self.tokenizer, data_args, training_args)
def _configs(self):
config = {}
config['clip_vit_base_32'] = 'openai/clip-vit-base-patch16'
config['clip_vit_base_16'] = 'openai/clip-vit-base-patch32'
config['clip_vit_large_14'] = 'openai/clip-vit-large-patch14'
config['clip_vit_large_14_336'] ='openai/clip-vit-large-patch14-336'
return config