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UMSN-Face-Deblurring
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Update train_face_deblur.py

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rajeevyasarla 6 years ago committed by GitHub
parent 968668af6d
commit aef5088a9b
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1 changed files with 8 additions and 8 deletions
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train_face_deblur.py
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@ -151,20 +151,20 @@ criterionCAE1 = nn.SmoothL1Loss()
target= torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
val_target= torch.FloatTensor(opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize)
val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
val_target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/4), (opt.imageSize/4))
val_target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize/2), (opt.imageSize/2))
val_target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
val_target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
label_d = torch.FloatTensor(opt.batchSize)

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