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Code Issues Releases Wiki Activity

Add oracle_rate option

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nagadomi 2016-05-27 16:49:42 +09:00
parent 8fec6f1b5a
commit 8088460a20
2 changed files with 58 additions and 7 deletions
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2
lib/settings.lua
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@ -56,6 +56,8 @@ cmd:option("-max_training_image_size", -1, 'if training image is larger than N,
cmd:option("-use_transparent_png", 0, 'use transparent png (0|1)')
cmd:option("-resize_blur_min", 0.85, 'min blur parameter for ResizeImage')
cmd:option("-resize_blur_max", 1.05, 'max blur parameter for ResizeImage')
cmd:option("-oracle_rate", 0.0, '')
cmd:option("-oracle_drop_rate", 0.5, '')
local function to_bool(settings, name)
if settings[name] == 1 then

63
train.lua
View file

@ -175,20 +175,48 @@ local function transformer(model, x, is_validation, n, offset)
end
local function resampling(x, y, train_x, transformer, input_size, target_size)
print("## resampling")
local c = 1
local shuffle = torch.randperm(#train_x)
for t = 1, #train_x do
xlua.progress(t, #train_x)
local xy = transformer(train_x[t], false, settings.patches)
local xy = transformer(train_x[shuffle[t]], false, settings.patches)
for i = 1, #xy do
local index = (t - 1) * settings.patches + i
x[index]:copy(xy[i][1])
y[index]:copy(xy[i][2])
x[c]:copy(xy[i][1])
y[c]:copy(xy[i][2])
c = c + 1
if c > x:size(1) then
break
end
end
if c > x:size(1) then
break
end
if t % 50 == 0 then
collectgarbage()
end
end
xlua.progress(#train_x, #train_x)
end
local function get_oracle_data(x, y, instance_loss, k, samples)
local index = torch.LongTensor(instance_loss:size(1))
local dummy = torch.Tensor(instance_loss:size(1))
torch.topk(dummy, index, instance_loss, k, 1, true)
print("average loss: " ..instance_loss:mean() .. ", average oracle loss: " .. dummy:mean())
local shuffle = torch.randperm(k)
local x_s = x:size()
local y_s = y:size()
x_s[1] = samples
y_s[1] = samples
local oracle_x = torch.Tensor(table.unpack(torch.totable(x_s)))
local oracle_y = torch.Tensor(table.unpack(torch.totable(y_s)))
for i = 1, samples do
oracle_x[i]:copy(x[index[shuffle[i]]])
oracle_y[i]:copy(y[index[shuffle[i]]])
end
return oracle_x, oracle_y
end
local function remove_small_image(x)
local new_x = {}
for i = 1, #x do
@ -254,12 +282,33 @@ local function train()
x = torch.Tensor(settings.patches * #train_x,
ch, settings.crop_size, settings.crop_size)
end
local instance_loss = nil
for epoch = 1, settings.epoch do
model:training()
print("# " .. epoch)
resampling(x, y, train_x, pairwise_func)
print("## resampling")
if instance_loss then
-- active learning
local oracle_k = math.min(x:size(1) * (settings.oracle_rate * (1 / (1 - settings.oracle_drop_rate))), x:size(1))
local oracle_n = math.min(x:size(1) * settings.oracle_rate, x:size(1))
if oracle_n > 0 then
local oracle_x, oracle_y = get_oracle_data(x, y, instance_loss, oracle_k, oracle_n)
resampling(x, y, train_x, pairwise_func)
x:narrow(1, 1, oracle_x:size(1)):copy(oracle_x)
y:narrow(1, 1, oracle_y:size(1)):copy(oracle_y)
else
resampling(x, y, train_x, pairwise_func)
end
else
resampling(x, y, train_x, pairwise_func)
end
collectgarbage()
instance_loss = torch.Tensor(x:size(1)):zero()
for i = 1, settings.inner_epoch do
local train_score = minibatch_adam(model, criterion, eval_metric, x, y, adam_config)
local train_score, il = minibatch_adam(model, criterion, eval_metric, x, y, adam_config)
instance_loss:copy(il)
print(train_score)
model:evaluate()
print("# validation")