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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 numpy
import towhee
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
@register(output_schema=['vec'])
class Clip(NNOperator):
"""
CLIP multi-modal embedding operator
"""
def __init__(self, name: str, modality: str):
sys.path.append(str(Path(__file__).parent))
#from clip_impl import load
import clip_impl
self.modality = modality
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self._model, self.preprocess = clip_impl.load(name, self.device)
self.tokenize = clip_impl.tokenize
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
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):
image = self.preprocess(to_pil(img)).unsqueeze(0).to(self.device)
image_features = self._model.encode_image(image)
return image_features