mirror of
https://github.com/facefusion/facefusion-labs.git
synced 2026-06-25 07:59:55 +02:00
henryruhs
48 lines
1.8 KiB
Python
48 lines
1.8 KiB
Python
import os
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from configparser import ConfigParser
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from typing import Tuple
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import torch
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from torch import Tensor, nn
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from .training import HyperSwapTrainer
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from .types import Embedding, Mask, Module
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CONFIG_PARSER = ConfigParser()
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CONFIG_PARSER.read('config.ini')
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class HalfPrecision(nn.Module):
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def __init__(self, model : Module) -> None:
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super().__init__()
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self.model = model.half()
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def forward(self, source_embedding : Embedding, target_tensor : Tensor) -> Tuple[Tensor, Mask]:
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source_embedding = source_embedding.half()
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target_tensor = target_tensor.half()
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output_tensor, output_mask = self.model(source_embedding, target_tensor)
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output_tensor = output_tensor.float()
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output_mask = output_mask.float()
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return output_tensor, output_mask
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def export() -> None:
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config_directory_path = CONFIG_PARSER.get('exporting', 'directory_path')
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config_source_path = CONFIG_PARSER.get('exporting', 'source_path')
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config_target_path = CONFIG_PARSER.get('exporting', 'target_path')
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config_target_size = CONFIG_PARSER.getint('exporting', 'target_size')
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config_ir_version = CONFIG_PARSER.getint('exporting', 'ir_version')
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config_opset_version = CONFIG_PARSER.getint('exporting', 'opset_version')
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config_precision = CONFIG_PARSER.get('exporting', 'precision')
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os.makedirs(config_directory_path, exist_ok = True)
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model = HyperSwapTrainer.load_from_checkpoint(config_source_path, config_parser = CONFIG_PARSER, map_location ='cpu').eval()
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if config_precision == 'half':
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model = HalfPrecision(model).eval()
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model.ir_version = torch.tensor(config_ir_version)
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source_tensor = torch.randn(1, 512)
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target_tensor = torch.randn(1, 3, config_target_size, config_target_size)
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torch.onnx.export(model, (source_tensor, target_tensor), config_target_path, input_names = [ 'source', 'target' ], output_names = [ 'output', 'mask' ], opset_version = config_opset_version)
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