From 327d7e7c0e9ee7f9a87ab530efb1f026e2bcd59a Mon Sep 17 00:00:00 2001 From: ChengZi Date: Thu, 23 Jun 2022 14:54:59 +0800 Subject: [PATCH] collaborative_experts --- README.md | 127 ++++++++++++++++++++++++++++++++++++++- __init__.py | 20 ++++++ collaborative_experts.py | 59 ++++++++++++++++++ img.png | Bin 0 -> 155354 bytes requirements.txt | 3 + 5 files changed, 208 insertions(+), 1 deletion(-) create mode 100644 __init__.py create mode 100644 collaborative_experts.py create mode 100644 img.png create mode 100644 requirements.txt diff --git a/README.md b/README.md index 1855959..ad9330f 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,127 @@ -# collaborative-experts +# Video-Text Retrieval Embedding with Collaborative Experts + +*author: Chen Zhang* + + +
+ + + +## Description + +This operator extracts features for video or text with [Use What You Have: Video Retrieval Using Representations From Collaborative Experts](https://arxiv.org/pdf/1907.13487v2.pdf). +For video encoder, this operator exploits embeddings with different modality information extracted from pre-trained experts models, such as motion, appearance, scene, ASR or OCR. +For text query encoder, it exploits text embeddings extracted from pre-trained models such as word2vec or GPT. +This operator is a collaborative experts model, which aggregates information from these different pre-trained expert models, and output the video embeddings and text embeddings. + + +
+ + +## Code Example + +For video input, load embeddings extracted from different upstream expert networks, such as audio, face, action, RGB, OCR and so on. They can be from upstream operators or from disk . +For text query input, load text embeddings extracted from pre-trained models. They can be from upstream operators or from disk . +For `ind` input, if a data in one of the modalities is invalid(such as NaN) or you do not want use it, the corresponding value in `ind` is 0, else 1. + + + *Write the pipeline code*: + +```python +import torch +from towhee import Entity +import towhee + +torch.manual_seed(42) + +batch_size = 8 +experts = {"audio": torch.rand(batch_size, 29, 128), + "face": torch.rand(batch_size, 512), + "i3d.i3d.0": torch.rand(batch_size, 1024), + "imagenet.resnext101_32x48d.0": torch.rand(batch_size, 2048), + "imagenet.senet154.0": torch.rand(batch_size, 2048), + "ocr": torch.rand(batch_size, 49, 300), + "r2p1d.r2p1d-ig65m.0": torch.rand(batch_size, 512), + "scene.densenet161.0": torch.rand(batch_size, 2208), + "speech": torch.rand(batch_size, 32, 300) + } +ind = { + "r2p1d.r2p1d-ig65m.0": torch.ones(batch_size,), + "imagenet.senet154.0": torch.ones(batch_size,), + "imagenet.resnext101_32x48d.0": torch.ones(batch_size,), + "scene.densenet161.0": torch.ones(batch_size,), + "audio": torch.ones(batch_size,), + "speech": torch.ones(batch_size,), + "ocr": torch.randint(low=0, high=2, size=(batch_size,)), + "face": torch.randint(low=0, high=2, size=(batch_size,)), + "i3d.i3d.0": torch.ones(batch_size,), +} + +text = torch.randn(batch_size, 1, 37, 768) + +towhee.dc([Entity(experts=experts, ind=ind, text=text)]) \ + .video_text_embedding.collaborative_experts[('experts', 'ind', 'text'), ('text_embds', 'vid_embds')]().show() +``` + +![](img.png) + + +
+ + + +## Factory Constructor + +Create the operator via the following factory method + +***collaborative_experts(config: Dict = None, weights_path: str = None, device: str = None)*** + +**Parameters:** + +​ ***config:*** *Optional[dict]* + +​ Default is None, if None, use the [default config](https://github.com/towhee-io/towhee/blob/a713ea2deaa0273f0b6af28354a36572e8eba604/towhee/models/collaborative_experts/collaborative_experts.py#L1130) the same as these in the original paper and repo, + +​ ***weights_path:*** *Optional[str]* + +​ Pretrained model weights path, if None, use the weights in this operator. + +​ ***device:*** *Optional[str]* + +​ cpu or cuda. + + +
+ + + +## Interface + +When video modality, load a video embeddings extracted from different upstream expert networks, such as video, RGB, audio. +When text modality, read the text to generate a text embedding. + + +**Parameters:** + +​ ***experts:*** *dict* + +​ Embeddings extracted from different upstream expert networks, such as audio, face, action, RGB, OCR and so on. They can be from upstream operators or from disk . + +​ ***ind:*** *dict* + +​ If a data in one of the modalities is invalid(such as NaN) or you do not want use it, the corresponding value in `ind` is 0, else 1. + +​ ***text:*** *Tensor* + +​ Text embeddings extracted from pre-trained models. They can be from upstream operators or from disk . + + + + +**Returns:** *numpy.ndarray* + +​ Text embeddings and video embeddings. They are both a dict with different modality, the key is the same as input modality, and the value is a tensor with shape (batch size, 768). + + + diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..dbf0ab0 --- /dev/null +++ b/__init__.py @@ -0,0 +1,20 @@ +# 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. + +from .collaborative_experts import CollaborativeExperts + + +def collaborative_experts(**kwargs): + return CollaborativeExperts(**kwargs) + diff --git a/collaborative_experts.py b/collaborative_experts.py new file mode 100644 index 0000000..032b997 --- /dev/null +++ b/collaborative_experts.py @@ -0,0 +1,59 @@ +# 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. + + +from typing import Dict + +from pathlib import Path +from towhee.operator.base import NNOperator +from towhee import register +from towhee.models import collaborative_experts + +import warnings + +warnings.filterwarnings('ignore') + + +@register(output_schema=['vec']) +class CollaborativeExperts(NNOperator): + """ + CollaborativeExperts embedding operator + """ + + def __init__(self, config: Dict = None, weights_path: str = None, device: str = None): + super().__init__() + if weights_path is None: + weights_path = str(Path(__file__).parent / 'trained_model.pth') + self.ce_net_model = collaborative_experts.create_model(config, weights_path, device) + + def __call__(self, experts, ind, text): + out = self.ce_net_model(experts, ind, text) + text_embds = {k: v.squeeze(1).detach().cpu().numpy() for k, v in out['text_embds'].items()} + vid_embds = {k: v.detach().cpu().numpy() for k, v in out['vid_embds'].items()} + return text_embds, vid_embds + + def get_text_embds(self, experts, ind, text): + out = self.ce_net_model(experts, ind, text) + text_embds = {k: v.squeeze(1).detach().cpu().numpy() for k, v in out['text_embds'].items()} + return text_embds + + def get_vid_embds(self, experts, ind, text): + out = self.ce_net_model(experts, ind, text) + vid_embds = {k: v.detach().cpu().numpy() for k, v in out['vid_embds'].items()} + return vid_embds + + def get_cross_view_conf_matrix(self, experts, 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