rename srresnet_12l to resnet_14l because it's not the same as SRResNet

appendix/benchmark.md

@@ -16,7 +16,7 @@ Urban100: https://github.com/jbhuang0604/SelfExSR
 
 ### 2x - PSNR 
 
-| Dataset/Model | Bicubic       | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | srresnet_12l/photo | 
+| Dataset/Model | Bicubic       | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | resnet_14l/photo | 
 |---------------|---------------|---------------|------------------|------------------|--------------------|
 | BSD100        | 29.558        | 31.427        | 31.640           | 31.749           | 31.847             |
 | Urban100      | 26.852        | 30.057        | 30.477           | 30.759           | 31.016             |
@@ -25,21 +25,21 @@ Urban100: https://github.com/jbhuang0604/SelfExSR
 
 Note: TTA is an ensemble technique that is supported by waifu2x. TTA method is 8x slower than non TTA method but it improves PSNR (~+0.1 on photo, ~+0.4 on art).
 
-| Dataset/Model | Bicubic       | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | srresnet_12l/photo | 
+| Dataset/Model | Bicubic       | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | resnet_14l/photo | 
 |---------------|---------------|---------------|------------------|------------------|--------------------|
 | BSD100        | 29.558        | 31.474        | 31.705           | 31.812           | 31.915             |
 | Urban100      | 26.852        | 30.140        | 30.599           | 30.868           | 31.162             |
 
 ### 2x - benchmark elapsed time (sec)
 
-| Dataset/Model | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | srresnet_12l/photo |
+| Dataset/Model | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | resnet_14l/photo |
 |---------------|---------------|------------------|------------------|--------------------|
 | BSD100        | 4.057         | 2.509            | 4.947            | 6.86               |
 | Urban100      | 16.349        | 7.083            | 14.178           | 27.87              |
 
 ### 2x with TTA - benchmark elapsed time (sec)
 
-| Dataset/Model | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | srresnet_12l/photo |
+| Dataset/Model | vgg\_7/photo  | upconv\_7/photo  | upconv\_7l/photo | resnet_14l/photo |
 |---------------|---------------|------------------|------------------|--------------------|
 | BSD100        | 36.611        | 20.219           | 42.486           | 60.38              |
 | Urban100      | 132.416       | 65.125           | 129.916          | 255.20             |

lib/srcnn.lua

@@ -429,7 +429,7 @@ function srcnn.srresnet_2x(backend, ch)
 end
 
 -- large version of srresnet_2x. It's current best model but slow.
-function srcnn.srresnet_12l(backend, ch)
+function srcnn.resnet_14l(backend, ch)
    local function resblock(backend, i, o)
       local seq = nn.Sequential()
       local con = nn.ConcatTable()
@@ -463,7 +463,7 @@ function srcnn.srresnet_12l(backend, ch)
    model:add(SpatialFullConvolution(backend, 256, ch, 4, 4, 2, 2, 3, 3):noBias())
    model:add(w2nn.InplaceClip01())
    model:add(nn.View(-1):setNumInputDims(3))
-   model.w2nn_arch_name = "srresnet_12l"
+   model.w2nn_arch_name = "resnet_14l"
    model.w2nn_offset = 28
    model.w2nn_scale_factor = 2
    model.w2nn_resize = true