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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 logging
import numpy
import towhee
from towhee.operator.base import NNOperator, OperatorFlag
from towhee.types.arg import arg, to_image_color
from towhee import register
import torch
from torch import nn
from PIL import Image as PILImage
from timm.data.transforms_factory import create_transform
from timm.data import resolve_data_config
from timm.models.factory import create_model
import warnings
warnings.filterwarnings('ignore')
log = logging.getLogger()
@register(output_schema=['vec'])
class TimmImage(NNOperator):
"""
Pytorch image embedding operator that uses the Pytorch Image Model (timm) collection.
Args:
model_name (`str`):
Which model to use for the embeddings.
num_classes (`int = 1000`):
Number of classes for classification.
skip_preprocess (`bool = False`):
Whether skip image transforms.
"""
def __init__(self, model_name: str, num_classes: int = 1000, skip_preprocess: bool = False) -> None:
super().__init__()
self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
self.model = create_model(model_name, pretrained=True, num_classes=num_classes)
self.model.to(self.device)
self.model.eval()
config = resolve_data_config({}, model=self.model)
self.tfms = create_transform(**config)
self.skip_tfms = skip_preprocess
@arg(1, to_image_color('RGB'))
def __call__(self, img: towhee._types.Image) -> numpy.ndarray:
img = PILImage.fromarray(img.astype('uint8'), 'RGB')
if not self.skip_tfms:
img = self.tfms(img).unsqueeze(0)
img = img.to(self.device)
features = self.model.forward_features(img)
if features.dim() == 4:
global_pool = nn.AdaptiveAvgPool2d(1)
features = global_pool(features)
features = features.to('cpu')
feature_vector = features.flatten().detach().numpy()
return feature_vector
# if __name__ == '__main__':
# from towhee import ops
#
# path = '/image/path/or/link'
#
# decoder = ops.image_decode.cv2()
# img = decoder(path)
#
# op = TimmImage('resnet50')
# out = op(img)
# print(out)