isc
copied
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
109 lines
3.5 KiB
109 lines
3.5 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 logging
|
|
import os
|
|
import numpy
|
|
from typing import Union, List
|
|
from pathlib import Path
|
|
|
|
import towhee
|
|
from towhee.operator.base import NNOperator, OperatorFlag
|
|
from towhee.types.arg import arg, to_image_color
|
|
from towhee import register
|
|
from towhee.models import isc
|
|
|
|
import torch
|
|
from torch import nn
|
|
from torchvision import transforms
|
|
from PIL import Image as PILImage
|
|
import timm
|
|
|
|
import warnings
|
|
|
|
warnings.filterwarnings('ignore')
|
|
log = logging.getLogger()
|
|
|
|
|
|
@register(output_schema=['vec'])
|
|
class Isc(NNOperator):
|
|
"""
|
|
The operator uses pretrained ISC model to extract features for an image input.
|
|
|
|
Args:
|
|
skip_preprocess (`bool = False`):
|
|
Whether skip image transforms.
|
|
"""
|
|
|
|
def __init__(self, timm_backbone: str = 'tf_efficientnetv2_m_in21ft1k',
|
|
skip_preprocess: bool = False, checkpoint_path: str = None, device: str = None) -> None:
|
|
super().__init__()
|
|
if device is None:
|
|
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
|
self.device = device
|
|
self.skip_tfms = skip_preprocess
|
|
if checkpoint_path is None:
|
|
checkpoint_path = os.path.join(str(Path(__file__).parent), 'checkpoints', timm_backbone + '.pth')
|
|
|
|
backbone = timm.create_model(timm_backbone, features_only=True, pretrained=True)
|
|
self.model = isc.create_model(pretrained=True, checkpoint_path=checkpoint_path, device=self.device,
|
|
backbone=backbone, p=3.0, eval_p=1.0)
|
|
self.model.eval()
|
|
|
|
self.tfms = transforms.Compose([
|
|
transforms.Resize((512, 512)),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(mean=backbone.default_cfg['mean'],
|
|
std=backbone.default_cfg['std'])
|
|
])
|
|
|
|
def __call__(self, data: Union[List[towhee._types.Image], towhee._types.Image]):
|
|
if isinstance(data, towhee._types.Image):
|
|
imgs = [data]
|
|
else:
|
|
imgs = data
|
|
|
|
img_list = []
|
|
for img in imgs:
|
|
img = self.convert_img(img)
|
|
img = img if self.skip_tfms else self.tfms(img)
|
|
img_list.append(img)
|
|
inputs = torch.stack(img_list)
|
|
inputs = inputs.to(self.device)
|
|
features = self.model(inputs)
|
|
features = features.to('cpu').flatten(1)
|
|
|
|
if isinstance(data, list):
|
|
vecs = list(features.detach().numpy())
|
|
else:
|
|
vecs = features.squeeze(0).detach().numpy()
|
|
return vecs
|
|
|
|
@arg(1, to_image_color('RGB'))
|
|
def convert_img(self, img: towhee._types.Image):
|
|
img = PILImage.fromarray(img.astype('uint8'), 'RGB')
|
|
return img
|
|
|
|
|
|
# if __name__ == '__main__':
|
|
# from towhee import ops
|
|
#
|
|
# path = 'https://github.com/towhee-io/towhee/raw/main/towhee_logo.png'
|
|
#
|
|
# decoder = ops.image_decode.cv2()
|
|
# img = decoder(path)
|
|
#
|
|
# op = Isc()
|
|
# out = op(img)
|
|
# assert out.shape == (256,)
|