init the operator.

Signed-off-by: wxywb <xy.wang@zilliz.com>

README.md

@@ -1,2 +1,97 @@
-# lightningdot
+# Image-Text Retrieval Embdding with LightningDOT
+
+*author: David Wang*
+
+
+<br />
+
+
+
+## Description
+
+This operator extracts features for image or text with [LightningDOT](https://arxiv.org/abs/2103.08784) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity.
+
+
+<br />
+
+
+## Code Example
+
+Load an image from path './teddy.jpg' to generate an image embedding. 
+
+Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding. 
+
+ *Write the pipeline in simplified style*:
+
+```python
+import towhee
+
+towhee.glob('./teddy.jpg') \
+      .image_decode() \
+      .image_text_embedding.lightningdot(modality='image') \
+      .show()
+
+towhee.dc(["A teddybear on a skateboard in Times Square."]) \
+      .image_text_embedding.lightningdot(modality='text') \
+      .show()
+```
+<img src="https://towhee.io/towhee/lightningdot/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
+<img src="https://towhee.io/towhee/lightningdot/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
+
+*Write a same pipeline with explicit inputs/outputs name specifications:*
+
+```python
+import towhee
+
+towhee.glob['path']('./teddy.jpg') \
+      .image_decode['path', 'img']() \
+      .image_text_embedding.lightningdot['img', 'vec'](modality='image') \
+      .select['img', 'vec']() \
+      .show()
+
+towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
+      .image_text_embedding.lightningdot['text','vec'](modality='text') \
+      .select['text', 'vec']() \
+      .show()
+```
+<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
+<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>
+
+
+<br />
+
+
+
+## Factory Constructor
+
+Create the operator via the following factory method
+
+***lightningdot(modality)***
+
+**Parameters:**
+
+​   ***modality:*** *str*
+
+​   Which modality(*image* or *text*) is used to generate the embedding. 
+
+<br />
+
+
+
+## Interface
+
+An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.
+
+
+**Parameters:**
+
+​	***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)*  or *str*
+
+​  The data (image 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 .lightningdot import LightningDOT
+
+def lightningdot(modality: str):
+    return LightningDOT(modality)

lightningdot.py

@@ -0,0 +1,146 @@
+# 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 sys
+import os
+import json
+import torch
+from pathlib import Path
+
+import numpy as np
+from transformers.tokenization_bert import BertTokenizer
+
+from towhee.types.image_utils import to_pil
+from towhee.operator.base import NNOperator, OperatorFlag
+from towhee.types.arg import arg, to_image_color
+from towhee import register
+
+from .utils import Configs, get_gather_index
+
+
+def arg_process(args): 
+    dirname = os.path.dirname(__file__)
+    args.img_checkpoint = dirname + '/' + args.img_checkpoint 
+    args.img_model_config = dirname + '/' + args.img_model_config 
+    return args
+
+@register(output_schema=['vec'])
+class LightningDOT(NNOperator): 
+    """
+    CLIP multi-modal embedding operator
+    """
+    def __init__(self,  modality: str):
+        sys.path.append(str(Path(__file__).parent))
+        from dvl.models.bi_encoder import BiEncoder
+        from detector.faster_rcnn import Net, process_img
+
+        full_path = os.path.dirname(__file__)  +  '/config/flickr30k_ft_config.json' 
+        with open(full_path) as fw:
+            content = fw.read()
+            args = json.loads(content)
+        args = Configs(args)
+        args = arg_process(args)
+        self.bi_encoder = BiEncoder(args, True, True, project_dim=args.project_dim)
+        self.tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
+        img_model, txt_model = self.bi_encoder.img_model, self.bi_encoder.txt_model
+        img_model.eval()
+        txt_model.eval()
+        self.faster_rcnn_preprocess = process_img
+        self.faster_rcnn = Net()
+        self.faster_rcnn.load_state_dict(torch.load(os.path.dirname(__file__) + '/data/model/resnet101_faster_rcnn_final.pth'))
+        self.faster_rcnn.eval()
+        self.modality = modality
+
+    def img_detfeat_extract(self, img):
+        orig_im_scale = [img.shape[1], img.shape[0]]
+        img, im_scale = self.faster_rcnn_preprocess(img)
+        img = np.expand_dims(img.transpose((2,0,1)), 0)
+        img = torch.FloatTensor(img)
+        bboxes, feat, confidence = self.faster_rcnn(img, im_scale)
+        bboxes = self.bbox_feat_process(bboxes, orig_im_scale)
+        img_bb = torch.cat([bboxes, bboxes[:, 4:5]*bboxes[:, 5:]], dim=-1)
+        return img_bb, feat, confidence
+
+    def bbox_feat_process(self, bboxes, im_scale):    
+        image_w, image_h = im_scale
+        box_width = bboxes[:, 2] - bboxes[:, 0]
+        box_height = bboxes[:, 3] - bboxes[:, 1]
+        scaled_width = box_width / image_w
+        scaled_height = box_height / image_h
+        scaled_x = bboxes[:, 0] / image_w
+        scaled_y = bboxes[:, 1] / image_h
+    
+        box_width = box_width.unsqueeze(1)
+        box_height = box_height.unsqueeze(1)
+        scaled_width = scaled_width.unsqueeze(1)
+        scaled_height = scaled_height.unsqueeze(1)
+        scaled_x = scaled_x.unsqueeze(1)
+        scaled_y = scaled_y .unsqueeze(1)
+    
+        normalized_bbox = torch.hstack((scaled_x, scaled_y,
+                                        scaled_x + scaled_width,
+                                        scaled_y + scaled_height,
+                                        scaled_width, scaled_height))
+
+        return normalized_bbox
+
+    def get_img_feat(self, data):
+        img_pos_feat, img_feat, _ = self.img_detfeat_extract(data)
+        num_bb = img_pos_feat.shape[1]
+
+        img_input_ids = torch.Tensor([101]).long()
+
+        return img_feat, img_pos_feat, img_input_ids
+
+    def __call__(self, data):
+        if self.modality == 'image':
+            vec = self._inference_from_image(data)
+        elif self.modality == 'text':
+            vec = self._inference_from_text(data)
+        else:
+            raise ValueError("modality[{}] not implemented.".format(self._modality))
+        return vec.detach().cpu().numpy()
+    
+    def _inference_from_text(self, data):
+        ids = self.tokenizer.encode(data)
+        ids = torch.LongTensor(ids).unsqueeze(0)
+        attn_mask = torch.ones(len(ids), dtype=torch.long).unsqueeze(0)
+        pos_ids = torch.arange(len(ids), dtype=torch.long).unsqueeze(0)
+        _, query_vector, _ = self.bi_encoder.txt_model(ids, None, attn_mask, pos_ids)
+        return query_vector
+
+    def _inference_from_image(self, data):
+        img_pos_feat, img_feat, _ = self.img_detfeat_extract(data)
+        num_bb = img_pos_feat.shape[0]
+
+        attn_masks_img = torch.ones(num_bb+1, dtype=torch.long)
+        bs = 1 
+        num_bbs = [num_bb]
+        out_size = attn_masks_img.size(0)
+        gather_index = get_gather_index([1]*bs, num_bbs, bs, 1, out_size)
+        img_feat, img_pos_feat, img_input_ids = self.get_img_feat(data)
+        fix_txt_encoder = False
+
+        position_ids = torch.arange(0, img_input_ids.size(0), dtype=torch.long).unsqueeze(0)
+        img_input_ids = img_input_ids.unsqueeze(0)
+        attn_masks_img = attn_masks_img.unsqueeze(0)
+        img_feat = img_feat.unsqueeze(0)
+        img_pos_feat = img_pos_feat.unsqueeze(0)
+
+        img_seq, img_pooled, img_hidden = self.bi_encoder.get_representation(self.bi_encoder.img_model, img_input_ids,
+                                                                  attn_masks_img, position_ids,
+                                                                  img_feat, img_pos_feat,
+                                                                  None,
+                                                                  gather_index, fix_txt_encoder)
+    
+        return img_pooled

requirements.txt

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+torch>=1.9.0
+torchvision>=0.10.0
+transformers==2.3.0
+Pillow

utils.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.
+import torch
+from types import SimpleNamespace
+
+class Configs(SimpleNamespace):
+
+    def __init__(self, dictionary, **kwargs):
+        super().__init__(**kwargs)
+        for key, value in dictionary.items():
+            if isinstance(value, dict):
+                self.__setattr__(key, Configs(value))
+            else:
+                self.__setattr__(key, value)
+
+    def __getattribute__(self, value):
+        try:
+            return super().__getattribute__(value)
+        except AttributeError:
+            return None
+
+def get_gather_index(txt_lens, num_bbs, batch_size, max_len, out_size):
+    # assert len(txt_lens) == len(num_bbs) == batch_size
+    gather_index = torch.arange(0, out_size, dtype=torch.long,
+                                ).unsqueeze(0).repeat(len(num_bbs), 1)
+
+    # for i, (tl, nbb) in enumerate(zip(txt_lens, num_bbs)):
+    #    gather_index.data[i, tl:tl+nbb] = torch.arange(max_len, max_len+nbb,
+    #                                                   dtype=torch.long).data
+    return gather_index