{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "3f8becd7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2022-06-22 16:01:19.315518: W tensorflow/stream_executor/platform/default/dso_loader.cc:59] Could not load dynamic library 'libcudart.so.10.1'; dlerror: libcudart.so.10.1: cannot open shared object file: No such file or directory\n",
"2022-06-22 16:01:19.315554: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n"
]
}
],
"source": [
"from PIL import Image\n",
"import requests\n",
"import torch, numpy as np\n",
"from torchvision import transforms\n",
"from torchvision.transforms.functional import InterpolationMode\n",
"from models.blip_vqa import blip_vqa\n",
"from keras.preprocessing import image"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1e193909",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "d64bc197cae04268b5496da5d322da16",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "fcab18055e1b47038d1102f8e250b25b",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b00264e7496f4f0cbae704a96d5f52d1",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "94212645b9954e439ce10238528af598",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, description='Downloading', max=570.0, style=ProgressStyle(description_…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"load checkpoint from ./ckpt/model_base_vqa_capfilt_large.pth\n"
]
}
],
"source": [
"image_size = 480\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"model_url = './ckpt/model_base_vqa_capfilt_large.pth'\n",
"model = blip_vqa(pretrained=model_url, image_size=image_size, vit='base')\n",
"model = model.to(device)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7c8bc1e6",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"def run_time(func):\n",
" def inner(model, image, question):\n",
" back = func(model, image, question)\n",
" print(\"Runned time: {} s\".format(round((time.time() - t)/10, 3)))\n",
" return back\n",
" t = time.time()\n",
" return inner"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "38495b44",
"metadata": {},
"outputs": [],
"source": [
"def load_demo_image(img_url, image_size, device):\n",
" if \"http\" in img_url:\n",
" raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')\n",
" else:\n",
" raw_image = Image.open(img_url).convert('RGB')\n",
" \n",
" transform = transforms.Compose([\n",
" transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))\n",
" ]) \n",
" image = transform(raw_image).unsqueeze(0).to(device) \n",
" return image"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "7ce54831",
"metadata": {},
"outputs": [],
"source": [
"@run_time\n",
"def inference(model, image, question = 'what is in the picture?'):\n",
" model.eval()\n",
" with torch.no_grad():\n",
" answer = model(image, question, train=False, inference='generate') \n",
" return answer[0]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "497772e6",
"metadata": {},
"outputs": [],
"source": [
"def handle(conf):\n",
" base64_str = conf['Photo']\n",
" question = conf['Question']\n",
" image = load_demo_image(base64_str, image_size, device)\n",
" res = inference(model, image, question)\n",
" print('Answer :', res)\n",
" # add your code\n",
" return {'Answer': res}"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "158995c0",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Runned time: 1.17 s\n",
"Answer : woman and dog\n"
]
},
{
"data": {
"text/plain": [
"{'Answer': 'woman and dog'}"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"handle({'Photo': './img/demo.jpg', 'Question': 'What is in this image?'})"
]
}
],
"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": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}