Merge branch 'feat/113-onnx-resrgan'
This commit is contained in:
commit
400e579491
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@ -14,7 +14,7 @@ logger = getLogger(__name__)
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last_pipeline_instance = None
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last_pipeline_params = (None, None)
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x4_v3_tag = "real-esrgan-x4-v3"
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TAG_X4_V3 = "real-esrgan-x4-v3"
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def load_resrgan(
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@ -37,17 +37,7 @@ def load_resrgan(
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if not path.isfile(model_path):
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raise Exception("Real ESRGAN model not found at %s" % model_path)
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if x4_v3_tag in model_file:
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# the x4-v3 model needs a different network
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model = SRVGGNetCompact(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_conv=32,
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upscale=4,
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act_type="prelu",
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)
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elif params.format == "onnx":
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if params.format == "onnx":
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# use ONNX acceleration, if available
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model = OnnxNet(
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server,
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@ -56,20 +46,31 @@ def load_resrgan(
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sess_options=device.sess_options(),
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)
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elif params.format == "pth":
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=params.scale,
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)
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if TAG_X4_V3 in model_file:
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# the x4-v3 model needs a different network
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model = SRVGGNetCompact(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_conv=32,
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upscale=4,
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act_type="prelu",
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)
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else:
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=params.scale,
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)
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else:
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raise Exception("unknown platform %s" % params.format)
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dni_weight = None
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if params.upscale_model == x4_v3_tag and params.denoise != 1:
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wdn_model_path = model_path.replace(x4_v3_tag, "%s-wdn" % (x4_v3_tag))
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if params.upscale_model == TAG_X4_V3 and params.denoise != 1:
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wdn_model_path = model_path.replace(TAG_X4_V3, "%s-wdn" % TAG_X4_V3)
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model_path = [model_path, wdn_model_path]
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dni_weight = [params.denoise, 1 - params.denoise]
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@ -3,12 +3,15 @@ from os import path
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import torch
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from basicsr.archs.rrdbnet_arch import RRDBNet
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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from torch.onnx import export
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from .utils import ConversionContext, ModelDict
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logger = getLogger(__name__)
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TAG_X4_V3 = "real-esrgan-x4-v3"
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@torch.no_grad()
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def convert_upscale_resrgan(
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@ -28,14 +31,26 @@ def convert_upscale_resrgan(
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return
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logger.info("loading and training model")
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=scale,
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)
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if TAG_X4_V3 in name:
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# the x4-v3 model needs a different network
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model = SRVGGNetCompact(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_conv=32,
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upscale=scale,
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act_type="prelu",
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)
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else:
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model = RRDBNet(
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num_in_ch=3,
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num_out_ch=3,
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num_feat=64,
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num_block=23,
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num_grow_ch=32,
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scale=scale,
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)
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torch_model = torch.load(source, map_location=ctx.map_location)
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if "params_ema" in torch_model:
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@ -1 +1 @@
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from .onnx_net import OnnxImage, OnnxNet
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from .onnx_net import OnnxTensor, OnnxNet
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@ -8,7 +8,7 @@ from onnxruntime import InferenceSession, SessionOptions
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from ..utils import ServerContext
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class OnnxImage:
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class OnnxTensor:
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def __init__(self, source) -> None:
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self.source = source
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self.data = self
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@ -58,7 +58,7 @@ class OnnxNet:
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input_name = self.session.get_inputs()[0].name
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output_name = self.session.get_outputs()[0].name
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output = self.session.run([output_name], {input_name: image.cpu().numpy()})[0]
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return OnnxImage(output)
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return OnnxTensor(output)
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def eval(self) -> None:
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pass
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