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neural_paint.lua
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neural_paint.lua
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require 'torch'
require 'nn'
require 'image'
require 'optim'
require 'loadcaffe'
require 'libcuda_utils'
require 'cutorch'
require 'cunn'
local cmd = torch.CmdLine()
-- Basic options
cmd:option('-style_image', 'examples/inputs/seated-nude.jpg', 'Style target image')
cmd:option('-content_image', 'examples/inputs/tubingen.jpg', 'Content target image')
cmd:option('-cnnmrf_image', 'examples/inputs/cnnmrf.jpg', 'CNNMRF image')
cmd:option('-tmask_image', 'examples/inputs/t_mask.jpg', 'Content tight mask image')
cmd:option('-mask_image', 'examples/inputs/t_mask.jpg', 'Content loose mask image')
cmd:option('-image_size', 700, 'Maximum height / width of generated image')
cmd:option('-gpu', 0, 'Zero-indexed ID of the GPU to use; for CPU mode set -gpu = -1')
-- Optimization optins
cmd:option('-init', 'image', 'random|image')
cmd:option('-optimizer', 'lbfgs', 'lbfgs|adam')
cmd:option('-learning_rate', 0.1)
-- Output options
cmd:option('-print_iter', 50)
cmd:option('-save_iter', 100)
cmd:option('-index', 0)
cmd:option('-output_image', 'out.png')
-- Other options
cmd:option('-original_colors', 0)
cmd:option('-pooling', 'max', 'max|avg')
cmd:option('-proto_file', 'models/VGG_ILSVRC_19_layers_deploy.prototxt')
cmd:option('-model_file', 'models/VGG_ILSVRC_19_layers.caffemodel')
cmd:option('-backend', 'nn', 'nn|cudnn|clnn')
cmd:option('-cudnn_autotune', false)
cmd:option('-seed', 316)
cmd:option('-num_iterations', 1000)
-- Patchmatch
cmd:option('-patchmatch_size', 3)
-- RefineNNF
cmd:option('-refine_size', 5)
cmd:option('-refine_iter', 1)
-- Ring
cmd:option('-ring_radius', 1)
-- Wiki Art
cmd:option('-wikiart_fn', 'wikiart_output.txt')
local function main(params)
cutorch.setDevice(params.gpu + 1)
cutorch.setHeapTracking(true)
torch.manualSeed(params.seed)
idx = cutorch.getDevice()
print('Gpu, idx = ', params.gpu, idx)
local layers = string.format('relu1_1,relu2_1,relu3_1,relu4_1'):split(",")
local content_layers = string.format('relu4_1'):split(",")
local style_layers = string.format('relu1_1,relu2_1,relu3_1,relu4_1'):split(",")
local hist_layers = string.format('relu1_1,relu4_1'):split(",")
local content_weight = 1.0
local style_weight = 1.0
local hist_weight = 1.0
local tv_weight = 1.0
local num_iterations = params.num_iterations
local content_image = image.load(params.content_image, 3)
content_image = image.scale(content_image, params.image_size, 'bilinear')
local content_image_caffe = preprocess(content_image):float():cuda()
local style_image = image.load(params.style_image, 3)
style_image = image.scale(style_image, params.image_size, 'bilinear')
local style_image_caffe = preprocess(style_image):float():cuda()
local cnnmrf_image = image.load(params.cnnmrf_image, 3)
cnnmrf_image = image.scale(cnnmrf_image, params.image_size, 'bilinear')
local cnnmrf_image_caffe = preprocess(cnnmrf_image):float():cuda()
-- Loose mask
local mask_image = image.load(params.mask_image, 3)[1]
mask_image = image.scale(mask_image, params.image_size, 'bilinear'):float()
local mask_image_ori = mask_image:clone()
-- Tight mask
local tmask_image = image.load(params.tmask_image, 3)
tmask_image = image.scale(tmask_image, params.image_size, 'bilinear'):float()
local tmask_image_ori = tmask_image:clone()
local tr = 3;
local tkernel = image.gaussian(2*tr+1, tr, 1, true):float()
tmask_image = image.convolve(tmask_image, tkernel, 'same')
-- Note: Modify here for custom painting composites
-- or use our pre-trained model (coming soon) on wikiart dataset...
style_weight, hist_weight, tv_weight = params_wikiart_genre(style_image, params.index, params.wikiart_fn)
-- content_weight = 1.0
-- style_weight = 1.0
-- hist_weight = 1.0
-- tv_weight = 10.0
-- load VGG-19 network
local cnn = loadcaffe.load(params.proto_file, params.model_file, params.backend):float():cuda()
local feature_extractor = nn.Sequential()
local input_features, target_features, match_features, match_masks = {}, {}, {}, {}
local layerIdx = 1
for i = 1, cnn:size() do
if layerIdx <= #layers then
local layer = cnn:get(i)
local name = layer.name
feature_extractor:add(layer)
if name == layers[layerIdx] then
print("Extracting feature layer ", i, ":", layer.name)
local input = feature_extractor:forward(cnnmrf_image_caffe):clone()
local target = feature_extractor:forward(style_image_caffe):clone()
table.insert(input_features, input)
table.insert(target_features, target)
layerIdx = layerIdx + 1
end
end
end
feature_extractor = nil
collectgarbage()
-- Feature matching & manipulation
local curr_corr, corr = nil, nil
local curr_mask, mask = nil, nil
for i = #layers, 1, -1 do
local name = layers[i]
print("Working on patchmatch layer ", i, ":", name)
local A = input_features[i]:clone()
local BP = target_features[i]:clone()
local N_A = normalize_features(A)
local N_BP = normalize_features(BP)
local c, h, w = A:size(1), A:size(2), A:size(3)
local _, h2, w2 = BP:size(1), BP:size(2), BP:size(3)
if h ~= h2 or w ~= w2 then
print(" Input and target should have the same dimension! h, h2, w, w2 = ", h, h2, w, w2)
end
local tmask = image.scale(torch.gt(tmask_image_ori[1], 0.1), w, h, 'simple'):cudaInt()
if i == #layers then -- i = 5, relu5_1
print(" Initializing NNF in layer ", i, ":", name, " with patch ", params.patchmatch_size)
print(" Brute-force patch matching...")
local init_corr = cuda_utils.patchmatch(N_A, N_BP, params.patchmatch_size)
local guide = image.scale(style_image, w, h, 'bilinear'):float():cuda()
print(" Refining NNF...")
corr = cuda_utils.refineNNF(N_A, N_BP, init_corr, guide, tmask, params.refine_size, params.refine_iter)
mask = cuda_utils.Ring2(N_A, N_BP, corr, params.ring_radius, tmask)
curr_corr = corr
curr_mask = mask
else -- i = 4, relu4_1
print(" Upsampling NNF in layer ", i, ":", name)
curr_corr = cuda_utils.upsample_corr(corr, h, w)
curr_mask = image.scale(mask:double(), w, h, 'simple'):cudaInt()
end
table.insert(match_features, BP)
table.insert(match_masks, curr_mask)
end
local gram_features, hist_features = {}, {}
local gram_match_masks, hist_match_masks = {}, {}
local gramIdx, histIdx = 1, 1
for i = 1, #layers do
local name = layers[i]
local features = match_features[#layers - i + 1]
local mask = match_masks[#layers - i + 1]
if gramIdx <= #style_layers or histIdx <= #hist_layers then
if name == style_layers[gramIdx] then
table.insert(gram_features, features)
table.insert(gram_match_masks, mask)
gramIdx = gramIdx + 1
end
if name == hist_layers[histIdx] then
table.insert(hist_features, features)
table.insert(hist_match_masks, mask)
histIdx = histIdx + 1
end
end
end
input_features = nil
target_features = nil
collectgarbage()
-- Set up the network, inserting style and content loss modules
local content_losses, style_losses, hist_losses = {}, {}, {}
local next_content_idx, next_style_idx, next_hist_idx = 1, 1, 1
local net = nn.Sequential()
local tv_mod = nn.TVLoss(tv_weight, mask_image):float():cuda()
net:add(tv_mod)
for i = 1, cnn:size() do
if next_content_idx <= #content_layers or next_style_idx <= #style_layers or next_hist_idx <= #hist_layers then
local layer = cnn:get(i)
local name = layer.name
local layer_type = torch.type(layer)
local is_pooling = (layer_type == 'cudnn.SpatialMaxPooling' or layer_type == 'nn.SpatialMaxPooling')
local is_conv = (layer_type == 'nn.SpatialConvolution' or layer_type == 'cudnn.SpatialConvolution')
net:add(layer)
if is_pooling then
mask_image = image.scale(mask_image, math.ceil(mask_image:size(2)/2), math.ceil(mask_image:size(1)/2))
elseif is_conv then
local sap = nn.SpatialAveragePooling(3,3,1,1,1,1):float()
mask_image = sap:forward(mask_image:repeatTensor(1,1,1))[1]:clone()
end
if name == content_layers[next_content_idx] then
print("Setting up content layer", i, ":", layer.name)
local input = net:forward(content_image_caffe):clone()
local mask = mask_image:float():repeatTensor(1,1,1):expandAs(input):cuda()
local loss_module = nn.ContentLoss(content_weight, input, mask):float():cuda()
net:add(loss_module)
table.insert(content_losses, loss_module)
next_content_idx = next_content_idx + 1
end
if name == style_layers[next_style_idx] then
print("Setting up style layer ", i, ":", layer.name)
local gram = GramMatrix():float():cuda()
local input = net:forward(cnnmrf_image_caffe):clone()
local target = net:forward(style_image_caffe):clone()
local mask = mask_image:clone():repeatTensor(1,1,1):expandAs(target):cuda()
local c, h1, w1 = input:size(1), input:size(2), input:size(3)
local _, h2, w2 = target:size(1), target:size(2), target:size(3)
local gram_feature = gram_features[next_style_idx]
local gram_mask = gram_match_masks[next_style_idx] --mask_image --torch.ones(h1, w1) --gram_match_masks[next_style_idx]
local gram_msk = gram_mask:float():repeatTensor(1,1,1):expandAs(input):cuda()
local target_gram = gram:forward(torch.cmul(gram_feature, gram_msk)):clone()
target_gram:div(gram_mask:sum() * c)
local norm = params.normalize_gradients
local loss_module = nn.StyleLoss(style_weight, target_gram, mask):float():cuda()
net:add(loss_module)
table.insert(style_losses, loss_module)
next_style_idx = next_style_idx + 1
if name == hist_layers[next_hist_idx] then
print("Setting up histogram layer", i, ":", layer.name)
local maskI = torch.gt(mask_image, 0.1)
local maskJ = hist_match_masks[next_hist_idx]:byte() -- maskI:clone() --torch.ones(h1, w1):byte() --
local hist_feature = hist_features[next_hist_idx]
local loss_module = nn.HistLoss(hist_weight, input, hist_feature, 256, maskI, maskJ, mask_image):float():cuda()
net:add(loss_module)
table.insert(hist_losses, loss_module)
next_hist_idx = next_hist_idx + 1
end
end
end
end
-- We don't need the base CNN anymore, so clean it up to save memory.
cnn = nil
for i=1,#net.modules do
local module = net.modules[i]
if torch.type(module) == 'nn.SpatialConvolutionMM' then
-- remove these, not used, but uses gpu memory
module.gradWeight = nil
module.gradBias = nil
end
end
collectgarbage()
-- Initialize the image
if params.seed >= 0 then
torch.manualSeed(params.seed)
end
local img = nil
if params.init == 'random' then
img = torch.randn(content_image:size()):float():cuda():mul(0.001)
elseif params.init == 'image' then
img = content_image_caffe:clone():float():cuda()
else
error('Invalid init type')
end
-- Run it through the network once to get the proper size for the gradient
-- All the gradients will come from the extra loss modules, so we just pass
-- zeros into the top of the net on the backward pass.
local y = net:forward(img)
local dy = img.new(#y):zero()
-- Declaring this here lets us access it in maybe_print
local optim_state = nil
if params.optimizer == 'lbfgs' then
optim_state = {
maxIter = num_iterations,
verbose=true,
tolX=-1,
tolFun=-1,
learningRate=params.learning_rate,
}
elseif params.optimizer == 'adam' then
optim_state = {
learningRate = params.learning_rate,
}
else
error(string.format('Unrecognized optimizer "%s"', params.optimizer))
end
local function maybe_print(t, loss)
local verbose = (params.print_iter > 0 and t % params.print_iter == 0)
if verbose then
print(string.format('Iteration %d / %d', t, num_iterations))
for i, loss_module in ipairs(content_losses) do
print(string.format(' Content %d loss: %f', i, loss_module.loss))
end
for i, loss_module in ipairs(style_losses) do
print(string.format(' Style %d loss: %f', i, loss_module.loss))
end
for i, loss_module in ipairs(hist_losses) do
print(string.format(' Histogram %d loss: %f', i, loss_module.loss))
end
print(string.format(' Total loss: %f', loss))
end
end
local function maybe_save(t)
local should_save = params.save_iter > 0 and t % params.save_iter == 0
should_save = should_save or t == num_iterations
if should_save then
-- local disp = deprocess(img:double())
local disp = torch.cmul(img:double(), tmask_image:double())
disp:add(torch.cmul(style_image_caffe:double(), 1.0 - tmask_image:double()))
disp = deprocess(disp)
disp = image.minmax{tensor=disp, min=0, max=1}
local filename = build_filename(params.output_image, t)
if t == num_iterations then
filename = params.output_image
end
-- Maybe perform postprocessing for color-independent style transfer
if params.original_colors == 1 then
disp = original_colors(content_image, disp)
end
image.save(filename, disp)
end
end
-- Function to evaluate loss and gradient. We run the net forward and
-- backward to get the gradient, and sum up losses from the loss modules.
-- optim.lbfgs internally handles iteration and calls this fucntion many
-- times, so we manually count the number of iterations to handle printing
-- and saving intermediate results.
local num_calls = 0
local function feval(x)
num_calls = num_calls + 1
net:forward(x)
local grad = net:updateGradInput(x, dy)
local msk = mask_image_ori:clone()
msk = msk:repeatTensor(1,1,1):expandAs(x):cuda()
grad:cmul(msk)
local loss = 0
for _, mod in ipairs(content_losses) do
loss = loss + mod.loss
end
for _, mod in ipairs(style_losses) do
loss = loss + mod.loss
end
for _, mod in ipairs(hist_losses) do
loss = loss + mod.loss
end
maybe_print(num_calls, loss)
maybe_save(num_calls)
collectgarbage()
-- optim.lbfgs expects a vector for gradients
return loss, grad:view(grad:nElement())
end
-- Run optimization.
if params.optimizer == 'lbfgs' then
print('Running optimization with L-BFGS')
local x, losses = optim.lbfgs(feval, img, optim_state)
elseif params.optimizer == 'adam' then
print('Running optimization with ADAM')
for t = 1, num_iterations do
local x, losses = optim.adam(feval, img, optim_state)
end
end
end
function build_filename(output_image, iteration)
local ext = paths.extname(output_image)
local basename = paths.basename(output_image, ext)
local directory = paths.dirname(output_image)
return string.format('%s/%s_%d.%s',directory, basename, iteration, ext)
end
-- Preprocess an image before passing it to a Caffe model.
-- We need to rescale from [0, 1] to [0, 255], convert from RGB to BGR,
-- and subtract the mean pixel.
function preprocess(img)
local mean_pixel = torch.DoubleTensor({103.939, 116.779, 123.68})
local perm = torch.LongTensor{3, 2, 1}
img = img:index(1, perm):mul(256.0)
mean_pixel = mean_pixel:view(3, 1, 1):expandAs(img)
img:add(-1, mean_pixel)
return img
end
-- Undo the above preprocessing.
function deprocess(img)
local mean_pixel = torch.DoubleTensor({103.939, 116.779, 123.68})
mean_pixel = mean_pixel:view(3, 1, 1):expandAs(img)
img = img + mean_pixel
local perm = torch.LongTensor{3, 2, 1}
img = img:index(1, perm):div(256.0)
return img
end
-- Combine the Y channel of the generated image and the UV channels of the
-- content image to perform color-independent style transfer.
function original_colors(content, generated)
local generated_y = image.rgb2yuv(generated)[{{1, 1}}]
local content_uv = image.rgb2yuv(content)[{{2, 3}}]
return image.yuv2rgb(torch.cat(generated_y, content_uv, 1))
end
-- Normalize 3D feature map in channel dimension
function normalize_features(x)
local c, h, w = x:size(1), x:size(2), x:size(3)
print("Normalizing feature map with dim3[x] = ", c, h, w)
local x2 = torch.pow(x, 2)
local sum_x2 = torch.sum(x2, 1)
local dis_x2 = torch.sqrt(sum_x2)
local Nx = torch.cdiv(x, dis_x2:expandAs(x) + 1e-8)
-- local Nx = torch.cdiv(x, dis_x2:expandAs(x))
return Nx
end
-- Compute weight map
function compute_weightMap(x)
local c, h, w = x:size(1), x:size(2), x:size(3)
print("Computing weight map with dim3[x] = ", c, h, w)
local x2 = torch.pow(x, 2)
local sum_x2 = torch.sum(x2, 1)[1]
local sum_min, sum_max = sum_x2:min(), sum_x2:max()
local wMap = (sum_x2 - sum_min) / (sum_max - sum_min + 1e-8)
-- local wMap = (sum_x2 - sum_min) / (sum_max - sum_min)
return wMap
end
-- Estimate noise level
function noise_estimate(input)
local C, H, W = input:size(1), input:size(2), input:size(3)
local x_diff = torch.zeros(3, H - 1, W - 1)
local y_diff = torch.zeros(3, H - 1, W - 1)
x_diff:copy(input[{{}, {1, -2}, {1, -2}}])
x_diff:add(-1, input[{{}, {1, -2}, {2, -1}}])
y_diff:copy(input[{{}, {1, -2}, {1, -2}}])
y_diff:add(-1, input[{{}, {2, -1}, {1, -2}}])
local x_diff_sqr = torch.pow(x_diff, 2)
local y_diff_sqr = torch.pow(y_diff, 2)
local diff_sqr = (x_diff_sqr + y_diff_sqr) / 2
local noise, idx = diff_sqr:view(diff_sqr:nElement()):median()
return noise[1]
end
function params_wikiart_genre(style_image, index, wikiart_fn)
-- Estimate painting TV noise level
local tv_nosie = noise_estimate(style_image)
local tv_weight = 10.0 / (1.0 + torch.exp(1e4 * tv_nosie - 25.0))
local hist_weight = 1.0
local content_weight = 1.0
local style_weight = 1.0
local fid = io.open(wikiart_fn)
local sty_idx = 0
-- local label = nil
local sty_lev = nil
for line in fid:lines() do
if sty_idx == index then
print(line)
local terms = line:split("=")
sty_lev = tonumber(terms[4])
end
sty_idx = sty_idx + 1
end
style_weight = sty_lev
hist_weight = (10.0 - tv_weight) * sty_lev
tv_weight = tv_weight * sty_lev
io.close(fid)
return style_weight, hist_weight, tv_weight
end
-- Define an nn Module to compute content loss in-place
local ContentLoss, parent = torch.class('nn.ContentLoss', 'nn.Module')
function ContentLoss:__init(strength, input, msk)
parent.__init(self)
self.strength = strength
self.input = torch.cmul(input, msk)
self.loss = 0
self.crit = nn.MSECriterion()
self.msk = msk
end
function ContentLoss:updateOutput(input)
self.output = input
return self.output
end
function ContentLoss:updateGradInput(input, gradOutput)
self.loss = self.crit:forward(torch.cmul(input, self.msk), self.input) * self.strength
self.gradInput = self.crit:backward(torch.cmul(input, self.msk), self.input)
self.gradInput:cmul(self.msk)
local magnitude = torch.norm(self.gradInput, 2)
self.gradInput:div(magnitude + 1e-8)
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
self.gradInput:cmul(self.msk)
return self.gradInput
end
-- Returns a network that computes the CxC Gram matrix from inputs
-- of size C x H x W
function GramMatrix()
local net = nn.Sequential()
net:add(nn.View(-1):setNumInputDims(2))
local concat = nn.ConcatTable()
concat:add(nn.Identity())
concat:add(nn.Identity())
net:add(concat)
net:add(nn.MM(false, true))
return net
end
-- Define an nn Module to compute style loss in-place
local StyleLoss, parent = torch.class('nn.StyleLoss', 'nn.Module')
function StyleLoss:__init(strength, target_gram, msk)
parent.__init(self)
self.strength = strength
self.target_gram = target_gram
self.loss = 0
self.gram = GramMatrix()
self.G = nil
self.crit = nn.MSECriterion()
self.msk = msk
self.msk_mean = msk:mean()
end
function StyleLoss:updateOutput(input)
self.G = self.gram:forward(torch.cmul(input, self.msk))
self.G:div(self.msk_mean * input:nElement())
self.loss = self.crit:forward(self.G, self.target_gram)
self.loss = self.loss * self.strength
self.output = input
return self.output
end
function StyleLoss:updateGradInput(input, gradOutput)
local dG = self.crit:backward(self.G, self.target_gram)
dG:div(self.msk_mean * input:nElement())
self.gradInput = self.gram:backward(torch.cmul(input, self.msk), dG)
self.gradInput:cmul(self.msk)
local magnitude = torch.norm(self.gradInput, 2)
self.gradInput:div(magnitude + 1e-8)
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
self.gradInput:cmul(self.msk)
return self.gradInput
end
-- Histogram loss from: https://arxiv.org/pdf/1701.08893.pdf
local HistLoss, parent = torch.class('nn.HistLoss', 'nn.Module')
function HistLoss:__init(strength, input, target, nbins, maskI, maskJ, mask)
parent.__init(self)
self.strength = strength
self.loss = 0
self.nbins = nbins
self.maskI = maskI
self.nI = maskI:sum()
local c, h1, w1 = input:size(1), input:size(2), input:size(3)
self.msk = self.maskI:float():repeatTensor(1,1,1):expandAs(input):cuda()
self.msk_sub = torch.cmul(torch.ones(c, h1, w1):float(), 1 - self.msk:float()):cuda()
self.mask = mask:float():repeatTensor(1,1,1):expandAs(input):cuda()
self.nJ = maskJ:sum()
local c, h2, w2 = target:size(1), target:size(2), target:size(3)
local mJ = maskJ:repeatTensor(1,1,1):expandAs(target)
local J = target:float()
local _J = J[mJ]:view(c, self.nJ)
self.minJ, self.maxJ = _J:min(2), _J:max(2)
self.histJ = cuda_utils.histogram(target, self.nbins, self.minJ:cuda(), self.maxJ:cuda(), maskJ:cuda()):float()
self.histJ:mul(self.nI / self.nJ)
self.cumJ = torch.cumsum(self.histJ, 2)
end
function HistLoss:updateOutput(input)
self.output = input
return self.output
end
function HistLoss:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input):zero()
local I = input
local c, h1, w1 = I:size(1), I:size(2), I:size(3)
local _I = torch.cmul(I, self.msk) - self.msk_sub
local sortI, idxI = torch.sort(_I:view(c, h1*w1), 2)
local R = I:clone()
cuda_utils.hist_remap2(I, self.nI, self.maskI:cuda(),
self.histJ:cuda(), self.cumJ:cuda(), self.minJ, self.maxJ,
self.nbins, sortI:cuda(), idxI:cudaInt(), R)
self.gradInput:add(I)
self.gradInput:add(-1, R)
local err = self.gradInput:clone()
err:pow(2.0)
self.loss = err:sum() * self.strength / input:nElement()
local magnitude = torch.norm(self.gradInput, 2)
self.gradInput:div(magnitude + 1e-8)
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
self.gradInput:cmul(self.mask)
return self.gradInput
end
local TVLoss, parent = torch.class('nn.TVLoss', 'nn.Module')
function TVLoss:__init(strength, mask)
parent.__init(self)
self.strength = strength
self.mask = mask
self.x_diff = torch.Tensor()
self.y_diff = torch.Tensor()
self.msk = self.mask:clone():repeatTensor(3,1,1):cuda()
end
function TVLoss:updateOutput(input)
self.output = input
return self.output
end
-- TV loss backward pass inspired by kaishengtai/neuralart
function TVLoss:updateGradInput(input, gradOutput)
self.gradInput:resizeAs(input):zero()
local C, H, W = input:size(1), input:size(2), input:size(3)
self.x_diff:resize(3, H - 1, W - 1)
self.y_diff:resize(3, H - 1, W - 1)
self.x_diff:copy(input[{{}, {1, -2}, {1, -2}}])
self.x_diff:add(-1, input[{{}, {1, -2}, {2, -1}}])
self.y_diff:copy(input[{{}, {1, -2}, {1, -2}}])
self.y_diff:add(-1, input[{{}, {2, -1}, {1, -2}}])
self.gradInput[{{}, {1, -2}, {1, -2}}]:add(self.x_diff):add(self.y_diff)
self.gradInput[{{}, {1, -2}, {2, -1}}]:add(-1, self.x_diff)
self.gradInput[{{}, {2, -1}, {1, -2}}]:add(-1, self.y_diff)
self.gradInput:cmul(self.msk)
local magnitude = torch.norm(self.gradInput, 2)
self.gradInput:div(magnitude + 1e-8)
self.gradInput:mul(self.strength)
self.gradInput:add(gradOutput)
self.gradInput:cmul(self.msk)
return self.gradInput
end
-- min
function min(x, y)
local res = x
if res > y then
res = y
end
return res
end
-- max
function max(x, y)
local res = x
if res < y then
res = y
end
return res
end
-- clamp
function clamp(x, x_min, x_max)
local res = x
if x < x_min then
res = x_min
end
if x > x_max then
res = x_max
end
return res
end
local params = cmd:parse(arg)
main(params)