File size: 1,748 Bytes
313f8f8 1161d79 313f8f8 1161d79 686892f 92c4446 1161d79 313f8f8 1161d79 313f8f8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
from typing import Dict, List, Any
from transformers import AutoImageProcessor, Swin2SRForImageSuperResolution, Swin2SRModel
import torch
import base64
import logging
import numpy as np
from PIL import Image
from io import BytesIO
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
class EndpointHandler:
def __init__(self, path=""):
# load the model
self.processor = AutoImageProcessor.from_pretrained("caidas/swin2SR-classical-sr-x2-64")
Swin2SRModel._no_split_modules = ["Swin2SREmbeddings", "Swin2SRStage"]
Swin2SRForImageSuperResolution._no_split_modules = ["Swin2SREmbeddings", "Swin2SRStage"]
model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64", device_map="auto")
logger.info(model.hf_device_map)
model.hf_device_map["swin2sr.conv_after_body"] = model.hf_device_map["swin2sr.embeddings"]
model.hf_device_map["upsample"] = model.hf_device_map["swin2sr.embeddings"]
self.model = Swin2SRForImageSuperResolution.from_pretrained("caidas/swin2SR-classical-sr-x2-64", device_map=model.hf_device_map)
def __call__(self, data: Any):
image = data["inputs"]
inputs = self.processor(image, return_tensors="pt")
with torch.no_grad():
outputs = self.model(**inputs)
output = outputs.reconstruction.data.squeeze().float().cpu().clamp_(0, 1).numpy()
output = np.moveaxis(output, source=0, destination=-1)
output = (output * 255.0).round().astype(np.uint8)
img = Image.fromarray(output)
buffered = BytesIO()
img.save(buffered, format="JPEG")
img_str = base64.b64encode(buffered.getvalue())
return img_str.decode()
|