in community-content/cpr-examples/timm_serving/timm_serving/predictor.py [0:0]
def preprocess(self, request_dict: Dict[str, List[str]]) -> torch.Tensor:
"""Performs preprocessing.
By default, the server expects a request body consisting of a valid JSON
object. This will be parsed by the handler before it's evaluated by the
preprocess method.
Args:
request_dict: Parsed request body. We expect that the input consists of
a list of base64-encoded image files under the "instances" key. (Any
image format that PIL.image.open can handle is okay.)
Returns:
torch.Tensor containing the preprocessed images as a batch. If GPU is
available, the result tensor will be stored on GPU.
"""
if "instances" not in request_dict:
raise HTTPException(
status_code=400,
detail='Request must contain "instances" as a top-level key.',
)
tensors = []
for (i, image) in enumerate(request_dict["instances"]):
# We use Base64 encoding to handle image data.
# This is probably the best we can do while still using JSON input.
# Overriding the input format requires building a custom Handler.
try:
image_bytes = base64.b64decode(image, validate=True)
except (binascii.Error, TypeError) as e:
raise HTTPException(
status_code=400,
detail=f"Base64 decoding of the input image at index {i} failed:"
f" {str(e)}",
)
try:
pil_image = PIL.Image.open(io.BytesIO(image_bytes)).convert("RGB")
except PIL.UnidentifiedImageError:
raise HTTPException(
status_code=400,
detail=f"The input image at index {i} could not be identified as an"
" image file.",
)
tensors.append(self._transform(pil_image))
with torch.inference_mode():
result = torch.stack(tensors)
if self._cuda:
result = result.cuda()
return result