d95808f4ef
This bad boy is for a workflow where you train a model on disjoint image sets to downsample a "good" set of images like a "bad" set of images looks. You then use that downsampler to generate a training set of paired images for supersampling.
20 lines
705 B
Python
20 lines
705 B
Python
import logging
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logger = logging.getLogger('base')
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def create_model(opt):
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model = opt['model']
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# image restoration
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if model == 'sr': # PSNR-oriented super resolution
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from .SR_model import SRModel as M
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elif model == 'srgan' or model == 'corruptgan': # GAN-based super resolution(SRGAN / ESRGAN), or corruption use same logic
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from .SRGAN_model import SRGANModel as M
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# video restoration
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elif model == 'video_base':
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from .Video_base_model import VideoBaseModel as M
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else:
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raise NotImplementedError('Model [{:s}] not recognized.'.format(model))
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m = M(opt)
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logger.info('Model [{:s}] is created.'.format(m.__class__.__name__))
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return m
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