clip-vision/clip_vision.py

# 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 torch
import numpy as np
from torchvision import transforms as T

from towhee.operator import NNOperator, SharedType
from towhee.dc2 import accelerate
from towhee.models import clip


@accelerate
class Model:
    def __init__(self, model_name, device='cpu'):
        self.model = clip.create_model(model_name=model_name, pretrained=True, device=device).visual
        self.model.eval()
        print('Create local model')

    def __call__(self, data: 'Tensor'):
        return self.model(data)


class ClipVision(NNOperator):
    def __init__(self, model_name='clip_vit_b32'):
        super().__init__()
        self._device = None
        
        self.tfms = torch.nn.Sequential(
            T.Resize(224, interpolation=T.InterpolationMode.BICUBIC),
            T.CenterCrop(224),
            T.ConvertImageDtype(torch.float),
            T.Normalize(
                (0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
        ).to(self.device)
        self.model = Model(model_name, self.device)

    @property
    def device(self):
        if self._device is None:
            if self._device_id < 0:
                self._device = torch.device('cpu')
            else:
                self._device = torch.device(self._device_id)
        return self._device

    def __call__(self, image: 'Image'):
        img = np.transpose(image, [2, 0, 1])
        data = torch.from_numpy(img)
        data = data.to(self.device)
        image_tensor = self.tfms(data).unsqueeze(0)
        features = self.model(image_tensor)
        return features.detach().cpu().numpy().flatten()

    def save_model(self, model_type, output_file, args=None):
        if model_type != 'onnx':
            return False
        x = torch.randn((1, 3, 224, 224))

        torch.onnx.export(self.model.model, x, output_file, input_names=['INPUT0'],
                          output_names=['OUTPUT0'],  dynamic_axes={'INPUT0': [0]})
        return True

    @property
    def supported_formats(self):
        return ['onnx']

    @property
    def shared_type(self):
        return SharedType.Shareable