From 9a89ddd20e44f3fa18d2db722344a04c210dac2c Mon Sep 17 00:00:00 2001 From: nagadomi Date: Wed, 14 Nov 2018 17:40:48 +0900 Subject: [PATCH] remove unused model --- lib/srcnn.lua | 42 +----------------------------------------- 1 file changed, 1 insertion(+), 41 deletions(-) diff --git a/lib/srcnn.lua b/lib/srcnn.lua index 9d5e734..2fccee1 100644 --- a/lib/srcnn.lua +++ b/lib/srcnn.lua @@ -435,7 +435,7 @@ function srcnn.resnet_14l(backend, ch) return model end --- ResNet_with SEBlock for fast conversion +-- ResNet with SEBlock for fast conversion function srcnn.upresnet_s(backend, ch) local model = nn.Sequential() model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0)) @@ -453,46 +453,6 @@ function srcnn.upresnet_s(backend, ch) return model end --- Cascaded ResNet with SEBlock -function srcnn.upcresnet(backend, ch) - local function resnet(backend, ch, deconv) - local model = nn.Sequential() - model:add(SpatialConvolution(backend, ch, 64, 3, 3, 1, 1, 0, 0)) - model:add(nn.LeakyReLU(0.1, true)) - model:add(ResGroupSE(backend, 2, 64)) - model:add(SpatialConvolution(backend, 64, 64, 3, 3, 1, 1, 0, 0)) - model:add(nn.LeakyReLU(0.1, true)) - if deconv then - model:add(SpatialFullConvolution(backend, 64, ch, 4, 4, 2, 2, 3, 3)) - else - model:add(SpatialConvolution(backend, 64, ch, 3, 3, 1, 1, 0, 0)) - end - return model - end - local model = nn.Sequential() - local con = nn.ConcatTable() - local aux_con = nn.ConcatTable() - - -- 2 cascade - model:add(resnet(backend, ch, true)) - con:add(nn.Sequential():add(resnet(backend, ch, false)):add(nn.SpatialZeroPadding(-1, -1, -1, -1))) -- output size must be odd - con:add(nn.SpatialZeroPadding(-8, -8, -8, -8)) - - aux_con:add(nn.Sequential():add(nn.CAddTable()):add(w2nn.InplaceClip01())) - aux_con:add(nn.Sequential():add(nn.SelectTable(2)):add(w2nn.InplaceClip01())) - - model:add(con) - model:add(aux_con) - model:add(w2nn.AuxiliaryLossTable(1)) - - model.w2nn_arch_name = "upcresnet" - model.w2nn_offset = 22 - model.w2nn_scale_factor = 2 - model.w2nn_resize = true - model.w2nn_channels = ch - - return model -end -- for segmentation function srcnn.fcn_v1(backend, ch)