117 lines
3.2 KiB
Python
117 lines
3.2 KiB
Python
from logging import getLogger
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from os import path
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from typing import Optional
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import numpy as np
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from PIL import Image
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from ..models.onnx import OnnxModel
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from ..params import (
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DeviceParams,
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ImageParams,
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Size,
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SizeChart,
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StageParams,
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UpscaleParams,
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)
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from ..server import ModelTypes, ServerContext
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from ..utils import run_gc
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from ..worker import WorkerContext
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from .base import BaseStage
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from .result import StageResult
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logger = getLogger(__name__)
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class UpscaleBSRGANStage(BaseStage):
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max_tile = SizeChart.micro
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def load(
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self,
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server: ServerContext,
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_stage: StageParams,
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upscale: UpscaleParams,
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device: DeviceParams,
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):
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# must be within the load function for patch to take effect
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model_path = path.join(server.model_path, "%s.onnx" % (upscale.upscale_model))
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cache_key = (model_path,)
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cache_pipe = server.cache.get(ModelTypes.upscaling, cache_key)
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if cache_pipe is not None:
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logger.debug("reusing existing BSRGAN pipeline")
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return cache_pipe
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logger.info("loading BSRGAN model from %s", model_path)
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pipe = OnnxModel(
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server,
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model_path,
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provider=device.ort_provider(),
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sess_options=device.sess_options(),
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)
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server.cache.set(ModelTypes.upscaling, cache_key, pipe)
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run_gc([device])
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return pipe
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def run(
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self,
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worker: WorkerContext,
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server: ServerContext,
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stage: StageParams,
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_params: ImageParams,
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sources: StageResult,
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*,
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upscale: UpscaleParams,
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stage_source: Optional[Image.Image] = None,
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**kwargs,
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) -> StageResult:
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upscale = upscale.with_args(**kwargs)
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if upscale.upscale_model is None:
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logger.warning("no upscaling model given, skipping")
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return sources
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logger.info("upscaling with BSRGAN model: %s", upscale.upscale_model)
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device = worker.get_device()
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bsrgan = self.load(server, stage, upscale, device)
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outputs = []
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for source in sources.as_numpy():
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image = source / 255.0
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image = image[:, :, [2, 1, 0]].astype(np.float32).transpose((2, 0, 1))
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image = np.expand_dims(image, axis=0)
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logger.trace("BSRGAN input shape: %s", image.shape)
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scale = upscale.outscale
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logger.trace(
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"BSRGAN output shape: %s",
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(
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image.shape[0],
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image.shape[1],
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image.shape[2] * scale,
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image.shape[3] * scale,
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),
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)
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output = bsrgan(image)
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output = np.clip(np.squeeze(output, axis=0), 0, 1)
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output = output[[2, 1, 0], :, :].transpose((1, 2, 0))
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output = (output * 255.0).round().astype(np.uint8)
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logger.debug("output image shape: %s", output.shape)
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outputs.append(output)
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return StageResult(arrays=outputs)
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def steps(
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self,
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params: ImageParams,
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size: Size,
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) -> int:
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tile = min(params.unet_tile, self.max_tile)
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return size.width // tile * size.height // tile
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