mirror of
https://github.com/clearml/clearml-serving
synced 2025-01-31 10:56:52 +00:00
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import io
|
|
from typing import Any, Union
|
|
|
|
import numpy as np
|
|
from PIL import Image, ImageOps
|
|
|
|
|
|
from clearml import StorageManager
|
|
|
|
|
|
# Notice Preprocess class Must be named "Preprocess"
|
|
class Preprocess(object):
|
|
def __init__(self):
|
|
# set internal state, this will be called only once. (i.e. not per request)
|
|
pass
|
|
|
|
def preprocess(self, body: Union[bytes, dict], state: dict, collect_custom_statistics_fn=None) -> Any:
|
|
# we expect to get two valid on the dict x0, and x1
|
|
if isinstance(body, bytes):
|
|
# we expect to get a stream of encoded image bytes
|
|
try:
|
|
image = Image.open(io.BytesIO(body)).convert("RGB")
|
|
except Exception:
|
|
raise ValueError("Image could not be decoded")
|
|
|
|
if isinstance(body, dict) and "url" in body.keys():
|
|
# image is given as url, and is fetched
|
|
url = body.get("url")
|
|
local_file = StorageManager.get_local_copy(remote_url=url)
|
|
image = Image.open(local_file)
|
|
|
|
image = ImageOps.grayscale(image).resize((28, 28))
|
|
return np.array([np.array(image).flatten()])
|
|
|
|
def postprocess(self, data: Any, state: dict, collect_custom_statistics_fn=None) -> dict:
|
|
# post process the data returned from the model inference engine
|
|
# data is the return value from model.predict we will put is inside a return value as Y
|
|
if not isinstance(data, np.ndarray):
|
|
# this should not happen
|
|
return dict(digit=-1)
|
|
|
|
# data is returned as probability per class (10 class/digits)
|
|
return dict(digit=int(data.flatten().argmax()))
|