99 lines
2.9 KiB
Python
99 lines
2.9 KiB
Python
from logging import getLogger
|
|
from os import path
|
|
from typing import Optional
|
|
|
|
import numpy as np
|
|
from PIL import Image
|
|
|
|
from ..models.onnx import OnnxModel
|
|
from ..params import DeviceParams, ImageParams, StageParams, UpscaleParams
|
|
from ..server import ServerContext
|
|
from ..utils import run_gc
|
|
from ..worker import WorkerContext
|
|
from .stage import BaseStage
|
|
|
|
logger = getLogger(__name__)
|
|
|
|
|
|
class UpscaleSwinIRStage(BaseStage):
|
|
max_tile = 64
|
|
|
|
def load(
|
|
self,
|
|
server: ServerContext,
|
|
_stage: StageParams,
|
|
upscale: UpscaleParams,
|
|
device: DeviceParams,
|
|
):
|
|
# must be within the load function for patch to take effect
|
|
model_path = path.join(server.model_path, "%s.onnx" % (upscale.upscale_model))
|
|
cache_key = (model_path,)
|
|
cache_pipe = server.cache.get("swinir", cache_key)
|
|
|
|
if cache_pipe is not None:
|
|
logger.info("reusing existing SwinIR pipeline")
|
|
return cache_pipe
|
|
|
|
logger.debug("loading SwinIR model from %s", model_path)
|
|
|
|
pipe = OnnxModel(
|
|
server,
|
|
model_path,
|
|
provider=device.ort_provider(),
|
|
sess_options=device.sess_options(),
|
|
)
|
|
|
|
server.cache.set("swinir", cache_key, pipe)
|
|
run_gc([device])
|
|
|
|
return pipe
|
|
|
|
def run(
|
|
self,
|
|
job: WorkerContext,
|
|
server: ServerContext,
|
|
stage: StageParams,
|
|
_params: ImageParams,
|
|
source: Image.Image,
|
|
*,
|
|
upscale: UpscaleParams,
|
|
stage_source: Optional[Image.Image] = None,
|
|
**kwargs,
|
|
) -> Image.Image:
|
|
upscale = upscale.with_args(**kwargs)
|
|
source = stage_source or source
|
|
|
|
if upscale.upscale_model is None:
|
|
logger.warn("no correction model given, skipping")
|
|
return source
|
|
|
|
logger.info("correcting faces with SwinIR model: %s", upscale.upscale_model)
|
|
device = job.get_device()
|
|
swinir = self.load(server, stage, upscale, device)
|
|
|
|
# TODO: add support for grayscale (1-channel) images
|
|
image = np.array(source) / 255.0
|
|
image = image[:, :, [2, 1, 0]].astype(np.float32).transpose((2, 0, 1))
|
|
image = np.expand_dims(image, axis=0)
|
|
logger.info("SwinIR input shape: %s", image.shape)
|
|
|
|
scale = upscale.outscale
|
|
dest = np.zeros(
|
|
(
|
|
image.shape[0],
|
|
image.shape[1],
|
|
image.shape[2] * scale,
|
|
image.shape[3] * scale,
|
|
)
|
|
)
|
|
logger.info("SwinIR output shape: %s", dest.shape)
|
|
|
|
dest = swinir(image)
|
|
dest = np.clip(np.squeeze(dest, axis=0), 0, 1)
|
|
dest = dest[[2, 1, 0], :, :].transpose((1, 2, 0))
|
|
dest = (dest * 255.0).round().astype(np.uint8)
|
|
|
|
output = Image.fromarray(dest, "RGB")
|
|
logger.info("output image size: %s x %s", output.width, output.height)
|
|
return output
|