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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "35d8939e-909d-45d8-bcf9-0ff1dccacfdf",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n",
"Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['bert.encoder.layer.2.attention.self.key.bias', 'cls.seq_relationship.weight', 'bert.encoder.layer.5.intermediate.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'bert.encoder.layer.7.output.dense.weight', 'bert.encoder.layer.10.output.LayerNorm.bias', 'bert.encoder.layer.2.intermediate.dense.bias', 'bert.encoder.layer.6.attention.self.value.weight', 'bert.encoder.layer.5.attention.self.query.bias', 'bert.encoder.layer.8.intermediate.dense.bias', 'bert.encoder.layer.4.output.dense.bias', 'bert.encoder.layer.8.attention.output.dense.weight', 'bert.encoder.layer.8.attention.self.query.bias', 'bert.encoder.layer.4.attention.output.dense.weight', 'bert.encoder.layer.7.intermediate.dense.weight', 'bert.encoder.layer.5.attention.output.LayerNorm.bias', 'bert.encoder.layer.8.output.LayerNorm.bias', 'bert.encoder.layer.2.output.LayerNorm.bias', 'bert.encoder.layer.3.attention.self.value.weight', 'bert.encoder.layer.2.intermediate.dense.weight', 'bert.encoder.layer.5.attention.output.dense.bias', 'bert.encoder.layer.11.intermediate.dense.weight', 'cls.predictions.transform.dense.weight', 'bert.encoder.layer.4.attention.self.key.bias', 'bert.encoder.layer.2.attention.output.LayerNorm.bias', 'bert.encoder.layer.7.output.LayerNorm.bias', 'bert.encoder.layer.5.intermediate.dense.bias', 'bert.encoder.layer.10.output.dense.weight', 'bert.encoder.layer.10.attention.output.LayerNorm.bias', 'bert.encoder.layer.9.intermediate.dense.weight', 'bert.encoder.layer.3.attention.self.query.bias', 'bert.encoder.layer.11.attention.self.query.bias', 'bert.encoder.layer.7.attention.self.value.bias', 'bert.encoder.layer.6.output.dense.bias', 'bert.encoder.layer.6.attention.output.LayerNorm.bias', 'bert.encoder.layer.4.attention.output.LayerNorm.weight', 'cls.predictions.bias', 'bert.encoder.layer.10.attention.output.dense.weight', 'bert.encoder.layer.8.attention.self.value.weight', 'cls.predictions.transform.dense.bias', 'bert.encoder.layer.11.attention.self.query.weight', 'bert.encoder.layer.8.output.LayerNorm.weight', 'bert.encoder.layer.11.attention.self.value.weight', 'bert.encoder.layer.2.attention.self.key.weight', 'bert.encoder.layer.3.attention.output.LayerNorm.weight', 'bert.encoder.layer.8.attention.output.LayerNorm.weight', 'bert.encoder.layer.8.attention.output.LayerNorm.bias', 'bert.encoder.layer.2.output.dense.weight', 'bert.encoder.layer.3.attention.output.dense.bias', 'bert.encoder.layer.11.attention.output.dense.weight', 'bert.encoder.layer.10.attention.self.value.weight', 'bert.encoder.layer.7.attention.output.dense.bias', 'bert.encoder.layer.11.output.dense.bias', 'bert.pooler.dense.bias', 'bert.encoder.layer.11.attention.self.value.bias', 'bert.encoder.layer.6.attention.self.query.bias', 'bert.encoder.layer.6.output.dense.weight', 'bert.encoder.layer.9.output.LayerNorm.bias', 'bert.encoder.layer.4.output.LayerNorm.weight', 'bert.encoder.layer.9.output.LayerNorm.weight', 'bert.encoder.layer.9.intermediate.dense.bias', 'cls.predictions.decoder.weight', 'bert.encoder.layer.4.attention.output.dense.bias', 'bert.encoder.layer.4.attention.self.value.weight', 'bert.encoder.layer.7.output.LayerNorm.weight', 'bert.encoder.layer.11.attention.self.key.bias', 'bert.encoder.layer.6.attention.output.dense.weight', 'bert.encoder.layer.7.attention.self.key.weight', 'bert.encoder.layer.6.attention.output.dense.bias', 'bert.encoder.layer.10.attention.self.value.bias', 'cls.seq_relationship.bias', 'bert.encoder.layer.3.attention.self.key.weight', 'bert.encoder.layer.10.attention.self.key.bias', 'bert.encoder.layer.9.attention.output.dense.bias', 'bert.encoder.layer.4.attention.output.LayerNorm.bias', 'bert.encoder.layer.7.attention.self.key.bias', 'bert.encoder.layer.4.attention.self.query.weight', 'bert.encoder.layer.4.intermediate.dense.weight', 'bert.encoder.layer.4.attention.self.query.bias', 'bert.encoder.layer.6.output.LayerNorm.weight', 'bert.encoder.layer.3.attention.output.dense.weight', 'bert.encoder.layer.3.intermediate.dense.weight', 'bert.encoder.layer.3.intermediate.dense.bias', 'bert.encoder.layer.4.attention.self.value.bias', 'bert.encoder.layer.9.output.dense.weight', 'bert.pooler.dense.weight', 'bert.encoder.layer.11.attention.output.LayerNorm.bias', 'bert.encoder.layer.9.attention.self.query.weight', 'bert.encoder.layer.5.attention.output.dense.weight', 'bert.encoder.layer.10.attention.self.key.weight', 'bert.encoder.layer.11.output.LayerNorm.weight', 'bert.encoder.layer.9.attention.self.query.bias', 'bert.encoder.layer.6.attention.self.value.bias', 'bert.encoder.layer.8.attention.self.value.bias', 'bert.encoder.layer.7.intermediate.dense.bias', 'bert.encoder.layer.10.output.dense.bias', 'bert.encoder.layer.5.output.LayerNorm.weight', 'bert.encoder.layer.8.attention.output.dense.bias', 'bert.encoder.layer.10.intermediate.dense.bias', 'bert.encoder.layer.7.output.dense.bias', 'bert.encoder.layer.7.attention.output.LayerNorm.weight', 'bert.encoder.layer.6.attention.self.query.weight', 'bert.encoder.layer.6.attention.self.key.bias', 'bert.encoder.layer.3.attention.self.query.weight', 'bert.encoder.layer.11.output.dense.weight', 'bert.encoder.layer.9.attention.self.key.bias', 'bert.encoder.layer.2.attention.output.dense.bias', 'bert.encoder.layer.9.attention.output.dense.weight', 'bert.encoder.layer.2.attention.output.LayerNorm.weight', 'bert.encoder.layer.5.attention.output.LayerNorm.weight', 'bert.encoder.layer.11.attention.self.key.weight', 'bert.encoder.layer.4.output.dense.weight', 'bert.encoder.layer.3.attention.self.key.bias', 'bert.encoder.layer.5.output.dense.bias', 'bert.encoder.layer.3.attention.self.value.bias', 'bert.encoder.layer.9.attention.self.key.weight', 'bert.encoder.layer.3.attention.output.LayerNorm.bias', 'bert.encoder.layer.4.intermediate.dense.bias', 'bert.encoder.layer.3.output.LayerNorm.weight', 'bert.encoder.layer.8.attention.self.query.weight', 'bert.encoder.layer.2.output.LayerNorm.weight', 'bert.encoder.layer.10.intermediate.dense.weight', 'bert.encoder.layer.4.output.LayerNorm.bias', 'bert.encoder.layer.10.attention.self.query.bias', 'bert.encoder.layer.11.attention.output.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'bert.encoder.layer.2.attention.output.dense.weight', 'bert.encoder.layer.6.intermediate.dense.bias', 'bert.encoder.layer.7.attention.output.LayerNorm.bias', 'bert.encoder.layer.2.output.dense.bias', 'bert.encoder.layer.5.attention.self.key.bias', 'bert.encoder.layer.9.output.dense.bias', 'bert.encoder.layer.2.attention.self.query.weight', 'bert.encoder.layer.5.output.dense.weight', 'bert.encoder.layer.5.attention.self.value.weight', 'bert.encoder.layer.3.output.LayerNorm.bias', 'bert.encoder.layer.11.output.LayerNorm.bias', 'bert.encoder.layer.7.attention.self.query.bias', 'bert.encoder.layer.6.output.LayerNorm.bias', 'bert.encoder.layer.9.attention.output.LayerNorm.bias', 'bert.encoder.layer.3.output.dense.weight', 'bert.encoder.layer.7.attention.self.value.weight', 'bert.encoder.layer.8.output.dense.bias', 'bert.encoder.layer.5.attention.self.query.weight', 'bert.encoder.layer.5.output.LayerNorm.bias', 'bert.encoder.layer.2.attention.self.value.weight', 'bert.encoder.layer.5.attention.self.key.weight', 'bert.encoder.layer.6.attention.self.key.weight', 'bert.encoder.layer.11.intermediate.dense.bias', 'bert.encoder.layer.6.intermediate.dense.weight', 'bert.encoder.layer.10.attention.self.query.weight', 'bert.encoder.layer.10.output.LayerNorm.weight', 'bert.encoder.layer.3.output.dense.bias', 'bert.encoder.layer.6.attention.output.LayerNorm.weight', 'bert.encoder.layer.10.attention.output.dense.bias', 'bert.encoder.layer.9.attention.output.LayerNorm.weight', 'bert.encoder.layer.11.attention.output.dense.bias', 'bert.encoder.layer.4.attention.self.key.weight', 'bert.embeddings.token_type_embeddings.weight', 'bert.encoder.layer.7.attention.self.query.weight', 'bert.encoder.layer.8.output.dense.weight', 'bert.encoder.layer.5.attention.self.value.bias', 'bert.encoder.layer.2.attention.self.value.bias', 'bert.encoder.layer.9.attention.self.value.bias', 'bert.encoder.layer.10.attention.output.LayerNorm.weight', 'bert.encoder.layer.2.attention.self.query.bias', 'bert.encoder.layer.7.attention.output.dense.weight', 'bert.encoder.layer.8.attention.self.key.bias', 'bert.encoder.layer.8.intermediate.dense.weight', 'bert.encoder.layer.8.attention.self.key.weight', 'bert.encoder.layer.9.attention.self.value.weight']\n",
"- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
"- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
"Some weights of BertModel were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['bert.encoder.layer.0.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.query.bias', 'bert.encoder.layer.0.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.self.query.weight', 'bert.encoder.layer.1.crossattention.output.dense.weight', 'bert.encoder.layer.1.crossattention.self.value.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.weight', 'bert.encoder.layer.1.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.output.dense.bias', 'bert.encoder.layer.0.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.key.bias', 'bert.encoder.layer.1.crossattention.self.query.bias', 'bert.encoder.layer.1.crossattention.self.value.bias', 'bert.encoder.layer.0.crossattention.self.query.weight', 'bert.encoder.layer.0.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.self.key.weight', 'bert.encoder.layer.1.crossattention.output.LayerNorm.bias', 'bert.encoder.layer.0.crossattention.output.dense.weight']\n",
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"/encoder/layer/0/crossattention/self/query is tied\n",
"/encoder/layer/0/crossattention/self/key is tied\n",
"/encoder/layer/0/crossattention/self/value is tied\n",
"/encoder/layer/0/crossattention/output/dense is tied\n",
"/encoder/layer/0/crossattention/output/LayerNorm is tied\n",
"/encoder/layer/0/intermediate/dense is tied\n",
"/encoder/layer/0/output/dense is tied\n",
"/encoder/layer/0/output/LayerNorm is tied\n",
"/encoder/layer/1/crossattention/self/query is tied\n",
"/encoder/layer/1/crossattention/self/key is tied\n",
"/encoder/layer/1/crossattention/self/value is tied\n",
"/encoder/layer/1/crossattention/output/dense is tied\n",
"/encoder/layer/1/crossattention/output/LayerNorm is tied\n",
"/encoder/layer/1/intermediate/dense is tied\n",
"/encoder/layer/1/output/dense is tied\n",
"/encoder/layer/1/output/LayerNorm is tied\n",
"--------------\n",
"/home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth\n",
"--------------\n",
"load checkpoint from /home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth\n",
"vit: swin_b\n",
"msg_v2 _IncompatibleKeys(missing_keys=['visual_encoder.layers.0.blocks.0.attn.relative_position_index', 'visual_encoder.layers.0.blocks.1.attn_mask', 'visual_encoder.layers.0.blocks.1.attn.relative_position_index', 'visual_encoder.layers.1.blocks.0.attn.relative_position_index', 'visual_encoder.layers.1.blocks.1.attn_mask', 'visual_encoder.layers.1.blocks.1.attn.relative_position_index', 'visual_encoder.layers.2.blocks.0.attn.relative_position_index', 'visual_encoder.layers.2.blocks.1.attn_mask', 'visual_encoder.layers.2.blocks.1.attn.relative_position_index', 'visual_encoder.layers.2.blocks.2.attn.relative_position_index', 'visual_encoder.layers.2.blocks.3.attn_mask', 'visual_encoder.layers.2.blocks.3.attn.relative_position_index', 'visual_encoder.layers.2.blocks.4.attn.relative_position_index', 'visual_encoder.layers.2.blocks.5.attn_mask', 'visual_encoder.layers.2.blocks.5.attn.relative_position_index', 'visual_encoder.layers.2.blocks.6.attn.relative_position_index', 'visual_encoder.layers.2.blocks.7.attn_mask', 'visual_encoder.layers.2.blocks.7.attn.relative_position_index', 'visual_encoder.layers.2.blocks.8.attn.relative_position_index', 'visual_encoder.layers.2.blocks.9.attn_mask', 'visual_encoder.layers.2.blocks.9.attn.relative_position_index', 'visual_encoder.layers.2.blocks.10.attn.relative_position_index', 'visual_encoder.layers.2.blocks.11.attn_mask', 'visual_encoder.layers.2.blocks.11.attn.relative_position_index', 'visual_encoder.layers.2.blocks.12.attn.relative_position_index', 'visual_encoder.layers.2.blocks.13.attn_mask', 'visual_encoder.layers.2.blocks.13.attn.relative_position_index', 'visual_encoder.layers.2.blocks.14.attn.relative_position_index', 'visual_encoder.layers.2.blocks.15.attn_mask', 'visual_encoder.layers.2.blocks.15.attn.relative_position_index', 'visual_encoder.layers.2.blocks.16.attn.relative_position_index', 'visual_encoder.layers.2.blocks.17.attn_mask', 'visual_encoder.layers.2.blocks.17.attn.relative_position_index', 'visual_encoder.layers.3.blocks.0.attn.relative_position_index', 'visual_encoder.layers.3.blocks.1.attn.relative_position_index'], unexpected_keys=[])\n"
]
}
],
"source": [
"from PIL import Image\n",
"import requests\n",
"import torch\n",
"from torchvision import transforms\n",
"from torchvision.transforms.functional import InterpolationMode\n",
"import ruamel_yaml as yaml\n",
"from models.tag2text import tag2text_caption\n",
"\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"\n",
"\n",
"import gradio as gr\n",
"\n",
"image_size = 384\n",
"\n",
"\n",
"normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n",
" std=[0.229, 0.224, 0.225])\n",
"transform = transforms.Compose([transforms.Resize((image_size, image_size)),transforms.ToTensor(),normalize])\n",
"\n",
"\n",
"\n",
"#######Swin Version\n",
"pretrained = '/home/notebook/code/personal/S9049611/BLIP/output/blip_tagtotext_14m/blip_tagtotext_encoderdiv_tar_random_swin/caption_coco_finetune_tagparse_tagfinetune_threshold075_bceloss_tagsingle_5e6_epoch19_negative_1_05_pos_1_10/checkpoint_05.pth'\n",
"\n",
"config_file = 'configs/tag2text_caption.yaml'\n",
"config = yaml.load(open(config_file, 'r'), Loader=yaml.Loader)\n",
"\n",
"\n",
"model = tag2text_caption(pretrained=pretrained, image_size=image_size, vit=config['vit'], \n",
" vit_grad_ckpt=config['vit_grad_ckpt'], vit_ckpt_layer=config['vit_ckpt_layer'],\n",
" prompt=config['prompt'],config=config,threshold = 0.75 )\n",
"\n",
"model.eval()\n",
"model = model.to(device)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9772dc6f-680d-45a7-b39c-23770eb5258e",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Running on local URL: http://127.0.0.1:7864\n",
"Running on public URL: https://a10a3bf9-64b6-49d4.gradio.live\n",
"\n",
"This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co./spaces\n"
]
},
{
"data": {
"text/html": [
"<div><iframe src=\"https://a10a3bf9-64b6-49d4.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": []
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"def inference(raw_image, input_tag):\n",
" raw_image = raw_image.resize((image_size, image_size))\n",
" # print(type(raw_image))\n",
" image = transform(raw_image).unsqueeze(0).to(device) \n",
" model.threshold = 0.69\n",
" if input_tag == '' or input_tag == 'none' or input_tag == 'None':\n",
" input_tag_list = None\n",
" else:\n",
" input_tag_list = []\n",
" input_tag_list.append(input_tag.replace(',',' | '))\n",
" # print(input_tag_list)\n",
" with torch.no_grad():\n",
"\n",
"\n",
" caption, tag_predict = model.generate(image,tag_input = input_tag_list, return_tag_predict = True)\n",
" if input_tag_list == None:\n",
" tag_1 = tag_predict\n",
" tag_2 = ['none']\n",
" else:\n",
" _, tag_1 = model.generate(image,tag_input = None, return_tag_predict = True)\n",
" tag_2 = tag_predict\n",
"\n",
"\n",
" return tag_1[0],tag_2[0],caption[0]\n",
"\n",
" # return 'caption: '+caption[0], tag_predict[0]\n",
"\n",
"\n",
" \n",
"# inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning'], type=\"value\", default=\"Image Captioning\", label=\"Task\"),gr.inputs.Textbox(lines=2, label=\"User Identified Tags (Optional, Enter with commas)\"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type=\"value\", default=\"Beam search\", label=\"Caption Decoding Strategy\")]\n",
"inputs = [gr.inputs.Image(type='pil'),gr.inputs.Textbox(lines=2, label=\"User Specified Tags (Optional, Enter with commas)\")]\n",
"\n",
"# outputs = gr.outputs.Textbox(label=\"Output\")\n",
"# outputs = [gr.outputs.Textbox(label=\"Image Caption\"),gr.outputs.Textbox(label=\"Identified Tags\")]\n",
"outputs = [gr.outputs.Textbox(label=\"Model Identified Tags\"),gr.outputs.Textbox(label=\"User Specified Tags\"), gr.outputs.Textbox(label=\"Image Caption\") ]\n",
"\n",
"title = \"Tag2Text\"\n",
"description = \"Welcome to Tag2Text demo! (Supported by Fudan University, OPPO Research Institute, International Digital Economy Academy) <br/> Upload your image to get the tags and caption of the image. Optional: You can also input specified tags to get the corresponding caption.\"\n",
"\n",
"article = \"<p style='text-align: center'><a href='' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='' target='_blank'>Github Repo</a></p>\"\n",
"\n",
"demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000483108.jpg',\"none\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"electric cable\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"track, train\"] , \n",
" ])\n",
"\n",
"\n",
"demo.launch(share=True)\n",
"# demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0da1f11b-e737-47a9-9b07-4e00c0835f63",
"metadata": {},
"outputs": [],
"source": [
"\n",
"def inference(raw_image, input_tag):\n",
" raw_image = raw_image.resize((image_size, image_size))\n",
" # print(type(raw_image))\n",
" image = transform(raw_image).unsqueeze(0).to(device) \n",
" model.threshold = 0.69\n",
" if input_tag == '' or input_tag == 'none' or input_tag == 'None':\n",
" input_tag_list = None\n",
" else:\n",
" input_tag_list = []\n",
" input_tag_list.append(input_tag.replace(',',' | '))\n",
" # print(input_tag_list)\n",
" with torch.no_grad():\n",
"\n",
"\n",
" caption, tag_predict = model.generate(image,tag_input = input_tag_list, return_tag_predict = True)\n",
" if input_tag_list == None:\n",
" tag_1 = tag_predict\n",
" tag_2 = ['none']\n",
" else:\n",
" _, tag_1 = model.generate(image,tag_input = None, return_tag_predict = True)\n",
" tag_2 = tag_predict\n",
"\n",
"\n",
" return tag_1[0],tag_2[0],caption[0]\n",
"\n",
" # return 'caption: '+caption[0], tag_predict[0]\n",
"\n",
"\n",
" \n",
"# inputs = [gr.inputs.Image(type='pil'),gr.inputs.Radio(choices=['Image Captioning'], type=\"value\", default=\"Image Captioning\", label=\"Task\"),gr.inputs.Textbox(lines=2, label=\"User Identified Tags (Optional, Enter with commas)\"),gr.inputs.Radio(choices=['Beam search','Nucleus sampling'], type=\"value\", default=\"Beam search\", label=\"Caption Decoding Strategy\")]\n",
"inputs = [gr.inputs.Image(type='pil'),gr.inputs.Textbox(lines=2, label=\"User Specified Tags (Optional, Enter with commas)\")]\n",
"\n",
"# outputs = gr.outputs.Textbox(label=\"Output\")\n",
"# outputs = [gr.outputs.Textbox(label=\"Image Caption\"),gr.outputs.Textbox(label=\"Identified Tags\")]\n",
"outputs = [gr.outputs.Textbox(label=\"Model Identified Tags\"),gr.outputs.Textbox(label=\"User Specified Tags\"), gr.outputs.Textbox(label=\"Image Caption\") ]\n",
"\n",
"title = \"Tag2Text\"\n",
"description = \"Welcome to Tag2Text demo! (Supported by Fudan University, OPPO Research Institute, International Digital Economy Academy) <br/> Upload your image to get the tags and caption of the image. Optional: You can also input specified tags to get the corresponding caption.\"\n",
"\n",
"article = \"<p style='text-align: center'><a href='' target='_blank'>Tag2Text: Guiding Language-Image Model via Image Tagging</a> | <a href='' target='_blank'>Github Repo</a></p>\"\n",
"\n",
"demo = gr.Interface(inference, inputs, outputs, title=title, description=description, article=article, examples=[['images/COCO_val2014_000000551338.jpg',\"none\"], \n",
" ['images/COCO_val2014_000000551338.jpg',\"fence, sky\"],\n",
" # ['images/COCO_val2014_000000551338.jpg',\"grass\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"none\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"electric cable\"],\n",
" # ['images/COCO_val2014_000000483108.jpg',\"sky, train\"],\n",
" ['images/COCO_val2014_000000483108.jpg',\"track, train\"] , \n",
" ['images/COCO_val2014_000000483108.jpg',\"grass\"] \n",
" ])\n",
"\n",
"\n",
"demo.launch(share=True)\n",
"# demo.launch()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73a4bb88-4200-4853-b1ba-34f0d4b6dc34",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "3340a61f-c6bc-4ead-87ea-b26aa97b7a68",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "d49e3de4-c3f7-4835-90eb-d0d013fc0ffb",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "205e0317-1701-4afd-8d67-bedb6959f350",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf5301a5-80c5-4e44-835e-0160a97fef66",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "f63d7a06-7625-4e1c-855d-177971217a0d",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "c929e566-1a6e-4280-96eb-c434ef9a35d0",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.12"
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