remove batchSize in test.lua

models/one_direction_test_model.lua

@@ -20,14 +20,6 @@ function OneDirectionTestModel:Initialize(opt)
   self.real_A = torch.Tensor(opt.batchSize, opt.input_nc, opt.fineSize, opt.fineSize)
 
   -- load/define models
-  local use_lsgan = ((opt.use_lsgan ~= nil) and (opt.use_lsgan == 1))
-  if not use_lsgan then
-    self.criterionGAN = nn.BCECriterion()
-  else
-    self.criterionGAN = nn.MSECriterion()
-  end
-  self.criterionRec = nn.AbsCriterion()
-
   self.netG_A = util.load_test_model('G', opt)
 
   self:RefreshParameters()

options.lua

@@ -54,7 +54,6 @@ local opt_train = {
 -- options for test
 opt_test = {
   DATA_ROOT = '',           -- path to images (should have subfolders 'train', 'val', etc)
-  batchSize = 1,            -- # images in batch
   loadSize = 256,           -- scale images to this size
   fineSize = 256,           --  then crop to this size
   flip = 0,                  -- horizontal mirroring data augmentation

test.lua

@@ -90,7 +90,7 @@ s2 = opt.fineSize / opt.aspect_ratio
 
 visuals = {}
 
-for n = 1, math.floor(opt.how_many/opt.batchSize) do
+for n = 1, math.floor(opt.how_many) do
   print('processing batch ' .. n)
   local cur_dataA, cur_dataB, cur_pathsA, cur_pathsB = data_loader:GetNextBatch()
 
@@ -130,7 +130,7 @@ for i = 1, #labels do
 end
 io.write('</tr>')
 
-for n = 1,math.floor(opt.how_many/opt.batchSize) do
+for n = 1,math.floor(opt.how_many) do
   io.write('<tr>')
   io.write('<td>' .. tostring(n) .. '</td>')
   for j = 1, #labels do