75 lines
2.0 KiB
Python
75 lines
2.0 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.swinir import SwinIR
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from ..utils import ConversionContext, ModelDict
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logger = getLogger(__name__)
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@torch.no_grad()
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def convert_upscaling_swinir(
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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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name = model.get("name")
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source = source or model.get("source")
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scale = model.get("scale", 1)
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dest = path.join(conversion.model_path, name + ".onnx")
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logger.info("converting SwinIR 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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logger.info("loading and training model")
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img_size = (64, 64) # TODO: does this need to be a fixed value?
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model = SwinIR(
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depths=[6, 6, 6, 6, 6, 6],
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embed_dim=180,
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img_range=1.0,
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img_size=img_size,
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in_chans=3,
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mlp_ratio=2,
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num_heads=[6, 6, 6, 6, 6, 6],
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resi_connection="1conv",
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upscale=scale,
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upsampler="pixelshuffle",
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window_size=8,
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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"], strict=False)
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else:
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model.load_state_dict(torch_model["params"], 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 = ["input"]
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output_names = ["output"]
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dynamic_axes = {
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"input": {2: "h", 3: "w"},
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"output": {2: "h", 3: "w"},
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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("SwinIR exported to ONNX successfully")
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