towhee
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resnet-image-embedding
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68 lines
3.0 KiB
68 lines
3.0 KiB
# Copyright 2021 Zilliz. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy
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import timm
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import torch
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from torch import nn as nn
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from torch.nn import Linear
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from torchvision import transforms
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from typing import NamedTuple
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from torchvision.transforms import InterpolationMode
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from towhee.operator import NNOperator
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from towhee.utils.pil_utils import to_pil
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import warnings
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warnings.filterwarnings("ignore")
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class ResnetImageEmbedding(NNOperator):
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"""
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PyTorch model for image embedding.
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"""
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def __init__(self, model_name: str, num_classes: int = 1000, framework: str = 'pytorch') -> None:
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super().__init__(framework=framework)
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self.model = timm.create_model(model_name, pretrained=True)
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pretrained_dict = None
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if model_name == 'resnet101':
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pretrained_dict = torch.hub.load_state_dict_from_url(
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'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet101_a1h-36d3f2aa.pth')
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if model_name == 'resnet50':
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pretrained_dict = torch.hub.load_state_dict_from_url(
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'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1_0-14fe96d1.pth')
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if pretrained_dict:
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self.model.load_state_dict(pretrained_dict, strict=False)
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if num_classes != 1000:
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self.create_classifier(num_classes=num_classes)
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self.model.eval()
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self.tfms = transforms.Compose([transforms.Resize(235, interpolation=InterpolationMode.BICUBIC),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
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def __call__(self, image: 'towhee.types.Image') -> NamedTuple('Outputs', [('feature_vector', numpy.ndarray)]):
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img_tensor = self.tfms(to_pil(image)).unsqueeze(0)
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self.model.eval()
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features = self.model.forward_features(img_tensor)
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if features.dim() == 4: # if the shape of feature map is [N, C, H, W], where H > 1 and W > 1
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global_pool = nn.AdaptiveAvgPool2d(1)
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features = global_pool(features)
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embedding = features.flatten().detach().numpy()
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Outputs = NamedTuple('Outputs', [('feature_vector', numpy.ndarray)])
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return Outputs(embedding)
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def create_classifier(self, num_classes):
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self.model.fc = Linear(self.model.fc.in_features, num_classes, bias=True)
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