wehub-resource-sync
74 lines
2.2 KiB
Python
74 lines
2.2 KiB
Python
from concurrent.futures import ThreadPoolExecutor, TimeoutError
|
|
from io import BytesIO
|
|
import PIL
|
|
from PIL import Image
|
|
import requests
|
|
import starlette.requests
|
|
import torch
|
|
import torchvision.models as models
|
|
from torchvision.models import ResNet50_Weights
|
|
from torchvision import transforms
|
|
|
|
from ray import serve
|
|
|
|
|
|
@serve.deployment
|
|
class Model:
|
|
def __init__(self):
|
|
self.device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
self.resnet50 = (
|
|
models.resnet50(weights=ResNet50_Weights.DEFAULT).eval().to(self.device)
|
|
)
|
|
self.preprocess = transforms.Compose(
|
|
[
|
|
transforms.Resize(256),
|
|
transforms.CenterCrop(224),
|
|
transforms.ToTensor(),
|
|
transforms.Normalize(
|
|
mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
|
|
),
|
|
]
|
|
)
|
|
with open("imagenet_classes.txt", "r") as f:
|
|
self.categories = [s.strip() for s in f.readlines()]
|
|
|
|
self.model_thread_pool = ThreadPoolExecutor(max_workers=5)
|
|
|
|
async def __call__(self, request: starlette.requests.Request) -> str:
|
|
uri = (await request.json())["uri"]
|
|
|
|
try:
|
|
image_bytes = requests.get(uri, timeout=5).content
|
|
except (
|
|
requests.exceptions.ConnectionError,
|
|
requests.exceptions.ChunkedEncodingError,
|
|
requests.exceptions.Timeout,
|
|
):
|
|
return
|
|
|
|
try:
|
|
image = Image.open(BytesIO(image_bytes)).convert("RGB")
|
|
except PIL.UnidentifiedImageError:
|
|
return
|
|
|
|
images = [image] # Batch size is 1
|
|
|
|
def run_model():
|
|
input_tensor = torch.cat(
|
|
[self.preprocess(img).unsqueeze(0) for img in images]
|
|
).to(self.device)
|
|
with torch.no_grad():
|
|
output = self.resnet50(input_tensor)
|
|
sm_output = torch.nn.functional.softmax(output[0], dim=0)
|
|
return torch.argmax(sm_output)
|
|
|
|
try:
|
|
future = self.model_thread_pool.submit(run_model)
|
|
ind = future.result(timeout=5)
|
|
return self.categories[ind]
|
|
except TimeoutError:
|
|
return
|
|
|
|
|
|
app = Model.bind()
|