clip4clip
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109 lines
4.5 KiB
109 lines
4.5 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 as np
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import torch
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from typing import List, Union
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from torchvision import transforms
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from towhee.models.clip4clip import convert_tokens_to_id
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from towhee.operator.base import NNOperator
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from towhee import register
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from towhee.models import clip4clip
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from PIL import Image as PILImage
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from towhee.types import VideoFrame
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from pathlib import Path
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@register(output_schema=['vec'])
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class CLIP4Clip(NNOperator):
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"""
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CLIP4Clip multi-modal embedding operator
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"""
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def __init__(self, model_name: str, modality: str, weight_path: str = None, device: str = None):
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super().__init__()
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self.modality = modality
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if weight_path is None:
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weight_path = str(Path(__file__).parent / 'pytorch_model.bin.1')
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# print('weight_path is None, use default path: {}'.format(weight_path))
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if device is None:
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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else:
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self.device = device
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self.model = clip4clip.create_model(model_name=model_name,
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context_length=77,
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pretrained=True,
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weights_path=weight_path,
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device=self.device)
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self.tokenize = clip4clip.SimpleTokenizer()
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self.tfms = transforms.Compose([
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transforms.Resize(224, interpolation=transforms.InterpolationMode.BICUBIC),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(
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(0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
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])
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self.model.eval()
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def __call__(self, data: Union[str, List[VideoFrame]]):
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if self.modality == 'video':
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vec = self._inference_from_video(data)
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elif self.modality == 'text':
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vec = self._inference_from_text(data)
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else:
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raise ValueError("modality[{}] not implemented.".format(self._modality))
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return vec
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def _inference_from_text(self, text: str):
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self.model.eval()
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text_ids = convert_tokens_to_id(self.tokenize, text)
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text_ids = torch.tensor(text_ids).unsqueeze(0).to(self.device)
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text_features = self.model.get_sequence_output(text_ids)
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text_features = text_features / text_features.norm(dim=-1, keepdim=True)
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return text_features.detach().flatten().cpu().numpy()
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def _inference_from_video(self, img_list: List[VideoFrame]):
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self.model.eval()
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max_frames = 12
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video = np.zeros((1, max_frames, 1, 3, 224, 224), dtype=np.float64)
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slice_len = len(img_list)
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max_video_length = 0 if 0 > slice_len else slice_len
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for i, img in enumerate(img_list):
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pil_img = PILImage.fromarray(img, img.mode)
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tfmed_img = self.tfms(pil_img).unsqueeze(0)
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if slice_len >= 1:
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video[0, i, ...] = tfmed_img.cpu().numpy()
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video_mask = np.zeros((1, max_frames), dtype=np.int32)
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video_mask[0, :max_video_length] = [1] * max_video_length
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video = torch.as_tensor(video).float().to(self.device)
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pair, bs, ts, channel, h, w = video.shape
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video = video.view(pair * bs * ts, channel, h, w)
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video_mask = torch.as_tensor(video_mask).float().to(self.device)
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visual_output = self.model.get_visual_output(video, video_mask, shaped=True)
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visual_output = visual_output / visual_output.norm(dim=-1, keepdim=True)
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video_mask_un = video_mask.to(dtype=torch.float).unsqueeze(-1)
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visual_output = visual_output * video_mask_un
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video_mask_un_sum = torch.sum(video_mask_un, dim=1, dtype=torch.float)
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video_mask_un_sum[video_mask_un_sum == 0.] = 1.
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visual_output = torch.sum(visual_output, dim=1) / video_mask_un_sum
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visual_output = visual_output / visual_output.norm(dim=-1, keepdim=True)
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return visual_output.detach().flatten().cpu().numpy()
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