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DL-Art-School/codes/options/test/test_ESRGAN_adrianna_full.yml

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Add my own configs
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name: RRDB_ESRGAN_x4
suffix: ~ # add suffix to saved images
model: sr
distortion: sr
scale: 4
crop_border: ~ # crop border when evaluation. If None(~), crop the scale pixels
Support inference across batches, support inference on cpu, checkpoint This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator.
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#gpu_ids: [0]
Add my own configs
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datasets:
test_1: # the 1st test dataset
name: set5
mode: LQ
Support inference across batches, support inference on cpu, checkpoint This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator.
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batch_size: 1
dataroot_LQ: E:\4k6k\datasets\adrianna\full_extract
Add my own configs
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#### network structures
network_G:
which_model_G: RRDBNet
in_nc: 3
out_nc: 3
Support inference across batches, support inference on cpu, checkpoint This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator.
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nf: 48
Add my own configs
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nb: 23
#### path
path:
Support inference across batches, support inference on cpu, checkpoint This is a checkpoint of a set of long tests with reduced-complexity networks. Some takeaways: 1) A full GAN using the resnet discriminator does appear to converge, but the quality is capped. 2) Likewise, a combination GAN/feature loss does not converge. The feature loss is optimized but the model appears unable to fight the discriminator, so the G-loss steadily increases. Going forwards, I want to try some bigger models. In particular, I want to change the generator to increase complexity and capacity. I also want to add skip connections between the disc and generator.
2020-05-04 14:48:25 +00:00
pretrain_model_G: ../experiments/rrdb_blacked_gan_g.pth
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