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import sys
# if 'google.colab' in sys.modules:
# print('Running in Colab.')
# !pip3 install transformers==4.15.0 timm==0.4.12 fairscale==0.4.4
# !git clone https://github.com/salesforce/BLIP
# %cd BLIP
import gradio as gr
import torch
import requests
from torchvision import transforms
from PIL import Image
import requests
import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
#@title
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")
def predict(inp):
inp = transforms.ToTensor()(inp).unsqueeze(0)
with torch.no_grad():
prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
return confidences
demo = gr.Interface(fn=predict,
inputs=gr.inputs.Image(type="pil"),
outputs=gr.outputs.Label(num_top_classes=3)
)
def load_demo_image(image_size,device,imageurl):
img_url = imageurl
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
w,h = raw_image.size
display(raw_image.resize((w//5,h//5)))
transform = transforms.Compose([
transforms.Resize((image_size,image_size),interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])
image = transform(raw_image).unsqueeze(0).to(device)
return image
from models.blip import blip_decoder
def predict(imageurl):
image_size = 384
image = load_demo_image(image_size=image_size, device=device,imageurl=imageurl)
model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'
model = blip_decoder(pretrained=model_url, image_size=image_size, vit='base')
model.eval()
model = model.to(device)
with torch.no_grad():
# beam search
caption = model.generate(image, sample=False, num_beams=3, max_length=20, min_length=5)
# nucleus sampling
# caption = model.generate(image, sample=True, top_p=0.9, max_length=20, min_length=5)
return('caption: '+caption[0])
demo = gr.Interface(fn=predict,
inputs="text",
outputs=gr.outputs.Label(num_top_classes=3)
)
demo.launch() |