model

Signed-off-by: xujinling <jinling.xu@zilliz.com>

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README.md

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-# bridge-former
+# Video-Text Retrieval Embedding with BridgeFormer
+
+*author: Jinling Xu*
+
+<br />
+
+## Description
+
+This operator extracts features for video or text with [BridgeFormer](https://arxiv.org/pdf/2201.04850.pdf) which can generate embeddings for text and video by jointly training a video encoder and text encoder to maximize the cosine similarity.
+
+
+<br />
+
+
+## Code Example
+
+Load a video from path './demo_video.mp4' to generate a video embedding. 
+
+Read the text 'kids feeding and playing with the horse' to generate a text embedding. 
+
+ *Write the pipeline in simplified style*:
+
+- Encode video (default):
+```python
+import towhee
+towhee.dc(['./demo_video.mp4']) \
+      .video_decode.ffmpeg() \
+      .video_text_embedding.bridge_former(model_name='frozen_model', modality='video') \
+      .show()
+
+```
+<img src="./result1.png" width="800px"/>
+
+- Encode text:
+```python
+import towhee
+
+towhee.dc(['kids feeding and playing with the horse']) \
+      .video_text_embedding.bridge_former(model_name='frozen_model', modality='text') \
+      .show()
+```
+<img src="./result2.png" width="800px"/>
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+import towhee
+
+towhee.dc['path'](['./demo_video.mp4']) \
+      .video_decode.ffmpeg['path', 'frames'](sample_type='uniform_temporal_subsample', args={'num_samples': 4}) \
+      .runas_op['frames', 'frames'](func=lambda x: [y for y in x]) \
+      .video_text_embedding.bridge_former['frames', 'vec'](model_name='frozen_model', modality='video') \
+      .select['path', 'vec']() \
+      .show(formatter={'path': 'video_path'})
+
+towhee.dc['text'](["kids feeding and playing with the horse"]) \
+      .video_text_embedding.bridge_former['text','vec'](model_name='frozen_model', modality='text') \
+      .select['text', 'vec']() \
+      .show()
+```
+<img src="./result3.png" width="800px"/>
+<img src="./result4.png" width="800px"/>
+
+
+<br />
+
+
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***bridge_former(model_name, modality, weight_path)***
+
+**Parameters:**
+
+​   ***model_name:*** *str*
+
+​   The model name of frozen in time. Supported model names: 
+- frozen_model
+- clip_initialized_model
+
+
+​   ***modality:*** *str*
+
+​   Which modality(*video* or *text*) is used to generate the embedding. 
+
+​   ***weight_path:*** *str*
+
+​   pretrained model weights path.  
+
+<br />
+
+
+
+## Interface
+
+An video-text embedding operator takes a list of [Towhee VideoFrame](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.
+
+
+**Parameters:**
+
+​	***data:*** *List[towhee.types.Image]*  or *str*
+
+​  The data (list of  Towhee VideoFrame (which is uniform subsampled from a video) or text based on specified modality) to generate embedding.	
+
+
+
+**Returns:** *numpy.ndarray*
+
+​   The data embedding extracted by model.
+
+
+
 

__init__.py

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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.
+
+from .bridge_former import BridgeFormer
+
+
+def bridge_former(**kwargs):
+    return BridgeFormer(**kwargs)
+

bridge_former.py

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+import logging
+import os
+import json
+from pathlib import Path
+from typing import List, Union
+import torch
+import numpy
+from towhee import register
+from towhee.operator.base import NNOperator
+from towhee.types.video_frame import VideoFrame
+from towhee.models.utils.video_transforms import transform_video
+from towhee.models.bridgeformer import bridge_former
+from transformers import AutoTokenizer
+
+from .get_configs import configs
+log = logging.getLogger()
+
+
+@register(output_schema=['labels', 'scores', 'features'])
+class BridgeFormer(NNOperator):
+    """
+    extracts features for video or text with BridgeFormer model
+    Args:
+        model_name (str):
+            BridgeFormer model name to be used in BridgeFormer
+        modality (str):
+            Flag to decide what to return
+                - 'video': return video embedding
+                - 'text': return a dense of text embeddings
+        weights_path (str):
+            Pretrained model weights
+    """
+    def __init__(self,
+                 model_name: str = "frozen_model",
+                 modality: str = 'video',
+                 weights_path: str = None,
+                 framework: str = "pytorch",
+                 skip_preprocess: bool = False,
+
+                 ):
+        super().__init__(framework=framework)
+        self.model_name = model_name
+        self.skip_preprocess = skip_preprocess
+        self.modality = modality
+
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        if weights_path is None:
+            weights_name = {"clip_initialized_model": "MCQ_CLIP.pth", "frozen_model": "MCQ.pth"}
+            weights_path = os.path.join(str(Path(__file__).parent), weights_name[self.model_name])
+        self.model = bridge_former.create_model(pretrained=True,
+                                                weights_path=weights_path,
+                                                model_name=self.model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased', TOKENIZERS_PARALLELISM=False)
+
+        self.transform_cfgs = configs(self.model_name)
+
+    def decoder_video(self,  data: List[VideoFrame]):
+        # Convert list of towhee.types.Image to numpy.ndarray in float32
+        video = numpy.stack([img.astype(numpy.float32) / 255. for img in data], axis=0)
+        assert len(video.shape) == 4
+
+        video = video.transpose(3, 0, 1, 2)  # thwc -> cthw
+
+        video = transform_video(
+            video=video,
+            **self.transform_cfgs
+        )
+        # [B x C x T x H x W]
+        video = video.to(self.device)[None, ...]
+        return video
+
+    def __call__(self, data: Union[List[VideoFrame], List[str]]):
+        if self.modality == 'video':
+            vec = self._inference_from_video(data)
+        elif self.modality == 'text':
+            vec = self._inference_from_text(data)
+        else:
+            raise ValueError("modality[{}] not implemented.".format(self._modality))
+        return vec
+
+    def _inference_from_text(self, text: List[str]):
+        text_data = self.tokenizer(text, return_tensors='pt')
+
+        text_data = text_data.to(self.device)
+        if self.model_name == "clip_initialized_model":
+            text_features = self.model.encode_text(text_data["input_ids"])
+        else:
+            text_features = self.model.compute_text(text_data)
+        return text_features.squeeze(0).detach().flatten().cpu().numpy()
+
+    def _inference_from_video(self, data: List[VideoFrame]):
+        # [B x T x C x H x W]
+        video = self.decoder_video(data).transpose(1, 2)
+        if self.model_name == "clip_initialized_model":
+            visual_features = self.model.encode_image(video)
+        else:
+            visual_features = self.model.compute_video(video)
+        return visual_features.squeeze(0).detach().flatten().cpu().numpy()
+
+

get_configs.py

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+
+
+def configs(model_name):
+    args = {
+        'clip_initialized_model':
+            {"side_size": 224,
+             "crop_size": 256,
+             "num_frames": 8,
+             "mean": [0.48145466, 0.4578275, 0.40821073],
+             "std": [0.26862954, 0.26130258, 0.27577711]},
+        'frozen_model':
+            {"side_size": 224,
+             "crop_size": 256,
+             "num_frames": 4,
+             "mean": [0.485, 0.456, 0.406],
+             "std": [0.229, 0.224, 0.225], }
+    }
+    return args[model_name]
+