119 lines
3.3 KiB
Python
119 lines
3.3 KiB
Python
from logging import getLogger
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from os import path
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import torch
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from torch.onnx import export
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from ...models.srvgg import SRVGGNetCompact
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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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SPECIAL_KEYS = {
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"model.0.bias": "conv_first.bias",
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"model.0.weight": "conv_first.weight",
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"model.1.sub.23.bias": "conv_body.bias",
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"model.1.sub.23.weight": "conv_body.weight",
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"model.3.bias": "conv_up1.bias",
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"model.3.weight": "conv_up1.weight",
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"model.6.bias": "conv_up2.bias",
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"model.6.weight": "conv_up2.weight",
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"model.8.bias": "conv_hr.bias",
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"model.8.weight": "conv_hr.weight",
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"model.10.bias": "conv_last.bias",
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"model.10.weight": "conv_last.weight",
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}
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SUB_NAME = r"model\.1\.sub\.(\d)+\.RDB(\d)\.conv(\d)\.0\.(bias|weight)"
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def fix_resrgan_keys(model):
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original_keys = list(model.keys())
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for key in original_keys:
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if key in SPECIAL_KEYS:
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new_key = SPECIAL_KEYS[key]
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else:
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# convert RDBN keys
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sub_index, rdb_index, conv_index, node_type = key.match(SUB_NAME)
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new_key = f"model.1.sub.{sub_index}.rdb{rdb_index}.{conv_index}.{node_type}"
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model[new_key] = model[key]
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del model[key]
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return model
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@torch.no_grad()
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def convert_upscale_resrgan(
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conversion: ConversionContext,
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model: ModelDict,
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source: str,
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):
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from basicsr.archs.rrdbnet_arch import RRDBNet
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name = model.get("name")
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source = source or model.get("source")
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scale = model.get("scale")
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dest = path.join(conversion.model_path, name + ".onnx")
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logger.info("converting Real ESRGAN model: %s -> %s", name, dest)
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if path.isfile(dest):
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logger.info("ONNX model already exists, skipping")
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return
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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=conversion.map_location)
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if "params_ema" in torch_model:
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model.load_state_dict(torch_model["params_ema"])
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elif "params" in torch_model:
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model.load_state_dict(torch_model["params"], strict=False)
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else:
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# keys need fixed up to match
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model.load_state_dict(fix_resrgan_keys(torch_model), strict=False)
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model.to(conversion.training_device).train(False)
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model.eval()
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rng = torch.rand(1, 3, 64, 64, device=conversion.map_location)
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input_names = ["data"]
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output_names = ["output"]
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dynamic_axes = {
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"data": {2: "width", 3: "height"},
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"output": {2: "width", 3: "height"},
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}
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logger.info("exporting ONNX model to %s", dest)
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export(
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model,
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rng,
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dest,
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input_names=input_names,
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output_names=output_names,
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dynamic_axes=dynamic_axes,
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opset_version=conversion.opset,
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export_params=True,
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)
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logger.info("real ESRGAN exported to ONNX successfully")
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