update README; add "scale_height"; fix resolutions

README.md

@@ -65,7 +65,7 @@ bash ./pretrained_models/download_model.sh style_cezanne
 ```
 - Now, let's generate Paul Cézanne style images:
 ```
-DATA_ROOT=./datasets/ae_photos name=style_cezanne_pretrained model=one_direction_test phase=test th test.lua
+DATA_ROOT=./datasets/ae_photos name=style_cezanne_pretrained model=one_direction_test phase=test loadSize=256 fineSize=256 resize_or_crop=``scale_width`` th test.lua
 ```
 The test results will be saved to `./results/style_cezanne_pretrained/latest_test/index.html`.  
 Please refer to [Model Zoo](#Pre-trained-models) for more pre-trained models.

data/dataset.lua

@@ -342,8 +342,8 @@ end
 -- converts a table of samples (and corresponding labels) to a clean tensor
 local function tableToOutput(self, dataTable, scalarTable)
    local data, scalarLabels, labels
-  if opt.resize_or_crop == 'crop' or opt.resize_or_crop == 'scale_width' then
-    assert(#scalarTable == 1)    
+  if opt.resize_or_crop == 'crop' or opt.resize_or_crop == 'scale_width' or opt.resize_or_crop == 'scale_height' then
+    assert(#scalarTable == 1)
     data = torch.Tensor(1,
                 dataTable[1]:size(1), dataTable[1]:size(2), dataTable[1]:size(3))
     data[1]:copy(dataTable[1])

data/donkey_folder.lua

@@ -120,7 +120,7 @@ local function loadSingleImage(path)
       end
       if iW~=oW then
         w1 = math.ceil(torch.uniform(1e-2, iW-oW))
-      end    
+      end
       if iH ~= oH or iW ~= oW then
         im = image.crop(im, w1, h1, w1 + oW, h1 + oH)
       end
@@ -136,11 +136,15 @@ local function loadSingleImage(path)
       w = math.floor(w/4)*4
       local x = math.floor(torch.uniform(0, iW - w))
       local y = math.floor(torch.uniform(0, iH - w))
-      im = image.crop(im, x, y, x+w, y+w)          
-    elseif (opt.resize_or_crop =='scale_width') then
+      im = image.crop(im, x, y, x+w, y+w)
+    elseif (opt.resize_or_crop == 'scale_width') then
       w = oW
       h = torch.floor(iH * oW/iW)
       im = image.scale(im, w, h)
+    elseif (opt.resize_or_crop == 'scale_height') then
+      h = oH
+      w = torch.floor(iW * oH / iH)
+      im = image.scale(im, 448, h)
     end
 
     if opt.flip == 1 and torch.uniform() > 0.5 then

options.lua

@@ -54,8 +54,8 @@ local opt_train = {
 -- options for test
 opt_test = {
   DATA_ROOT = '',           -- path to images (should have subfolders 'train', 'val', etc)
-  loadSize = 256,           -- scale images to this size
-  fineSize = 256,           --  then crop to this size
+  loadSize = 128,           -- scale images to this size
+  fineSize = 128,           --  then crop to this size
   flip = 0,                  -- horizontal mirroring data augmentation
   display = 1,              -- display samples while training. 0 = false
   display_id = 200,         -- display window id.

test.lua

@@ -103,7 +103,7 @@ for n = 1, math.floor(opt.how_many) do
   visuals = model:GetCurrentVisuals(opt, opt.fineSize)
 
   for i,visual in ipairs(visuals) do
-    if opt.resize_or_crop == 'scale_width' then
+    if opt.resize_or_crop == 'scale_width' or opt.resize_or_crop == 'scale_height' then
       s1 = nil
       s2 = nil
     end