You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Readme
Files and versions
64 lines
2.4 KiB
64 lines
2.4 KiB
# 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 towhee.models import clip
|
|
|
|
|
|
@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"
|
|
self.model = clip.create_model(model_name=model_name, pretrained=True, jit=True)
|
|
self.tokenize = clip.tokenize
|
|
self.tfms = transforms.Compose([
|
|
transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
|
|
transforms.CenterCrop(224),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(
|
|
(0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
|
|
])
|
|
|
|
def __call__(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 _inference_from_text(self, text):
|
|
text = self.tokenize(text).to(self.device)
|
|
text_features = self.model.encode_text(text)
|
|
return text_features
|
|
|
|
@arg(1, to_image_color('RGB'))
|
|
def _inference_from_image(self, img):
|
|
img = to_pil(img)
|
|
image = self.tfms(img).unsqueeze(0).to(self.device)
|
|
image_features = self.model.encode_image(image)
|
|
return image_features
|