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from typing import Dict, List, Any
from torchvision.models import resnet18, ResNet18_Weights
from torchvision.io import read_image
from PIL import Image
import io
import requests
import torchvision.transforms.functional as transform
from torch2trt import torch2trt
from torchvision.models.alexnet import alexnet
import torch
# create some regular pytorch model...
model = alexnet(pretrained=True).eval().cuda()
# create example data
x = torch.ones((1, 3, 224, 224)).cuda()
# convert to TensorRT feeding sample data as input
model_trt = torch2trt(model, [x])
class EndpointHandler():
def __init__(self, path=""):
weights = ResNet18_Weights.DEFAULT
# create some regular pytorch model...
model = resnet18(weights=weights).eval().cuda()
# create example data
x = torch.ones((1, 3, 224, 224)).cuda()
# convert to TensorRT feeding sample data as input
self.pipeline = torch2trt(model, [x])
self.preprocess = weights.transforms()
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
# get inputs
inputs = data.pop("inputs",data)
if inputs.startswith("http") or inputs.startswith("www"):
response = requests.get(inputs).content
img = transform.to_tensor(Image.open(io.BytesIO(response)))
else:
img = read_image(inputs)
batch = self.preprocess(img).unsqueeze(0)
prediction = self.pipeline(batch).squeeze(0).softmax(0)
return prediction.tolist() |