Add RiseML Demo

README.md

@@ -1,5 +1,7 @@
 # fast-neural-style
 
+<a href="https://riseml.com/jcjohnson/fast-neural-style/live-demo"><img align="right" src="http://cdn.riseml.com/btn.png"></a>
+
 This is the code for the paper
 
 **[Perceptual Losses for Real-Time Style Transfer and Super-Resolution](http://cs.stanford.edu/people/jcjohns/eccv16/)**

demo.lua

@@ -0,0 +1,91 @@
+require 'torch'
+require 'nn'
+require 'image'
+require 'riseml'
+
+require 'fast_neural_style.ShaveImage'
+require 'fast_neural_style.TotalVariation'
+require 'fast_neural_style.InstanceNormalization'
+local utils = require 'fast_neural_style.utils'
+local preprocess = require 'fast_neural_style.preprocess'
+
+
+--[[
+Use a trained feedforward model to stylize either a single image or an entire
+directory of images.
+--]]
+
+local cmd = torch.CmdLine()
+
+-- Model options
+cmd:option('-model', 'models/instance_norm/candy.t7')
+cmd:option('-image_size', 320)
+cmd:option('-median_filter', 0)
+cmd:option('-timing', 0)
+
+-- GPU options
+cmd:option('-gpu', -1)
+cmd:option('-backend', 'cuda')
+cmd:option('-use_cudnn', 1)
+cmd:option('-cudnn_benchmark', 0)
+
+
+local function main()
+  local opt = cmd:parse(arg)
+
+  local dtype, use_cudnn = utils.setup_gpu(opt.gpu, opt.backend, opt.use_cudnn == 1)
+  local ok, checkpoint = pcall(function() return torch.load(opt.model) end)
+  if not ok then
+    print('ERROR: Could not load model from ' .. opt.model)
+    print('You may need to download the pretrained models by running')
+    print('bash models/download_style_transfer_models.sh')
+    return
+  end
+  local model = checkpoint.model
+  model:evaluate()
+  model:type(dtype)
+  if use_cudnn then
+    cudnn.convert(model, cudnn)
+    if opt.cudnn_benchmark == 0 then
+      cudnn.benchmark = false
+      cudnn.fastest = true
+    end
+  end
+
+  local preprocess_method = checkpoint.opt.preprocessing or 'vgg'
+  local preprocess = preprocess[preprocess_method]
+
+  local function run_image(img_data)
+    local byte_tensor = torch.ByteTensor(torch.ByteStorage():string(img_data))
+    local img = image.decompressJPG(byte_tensor, 3)
+    if opt.image_size > 0 then
+      img = image.scale(img, opt.image_size)
+    end
+    local H, W = img:size(2), img:size(3)
+
+    local img_pre = preprocess.preprocess(img:view(1, 3, H, W)):type(dtype)
+    local timer = nil
+    if opt.timing == 1 then
+      -- Do an extra forward pass to warm up memory and cuDNN
+      model:forward(img_pre)
+      timer = torch.Timer()
+      if cutorch then cutorch.synchronize() end
+    end
+    local img_out = model:forward(img_pre)
+    if opt.timing == 1 then
+      if cutorch then cutorch.synchronize() end
+      local time = timer:time().real
+      print(string.format('Image %s (%d x %d) took %f',
+            in_path, H, W, time))
+    end
+    local img_out = preprocess.deprocess(img_out)[1]
+
+    if opt.median_filter > 0 then
+      img_out = utils.median_filter(img_out, opt.median_filter)
+    end
+    return image.compressJPG(img_out):storage():string()
+  end
+
+  riseml.serve(run_image)
+end
+main()

riseml.yml

@@ -0,0 +1,15 @@
+deploy:
+  image:
+    name: riseml/torch:latest
+    install:
+    - wget -nc http://cs.stanford.edu/people/jcjohns/fast-neural-style/models/instance_norm/candy.t7
+  run:
+    - th demo.lua -model candy.t7 -gpu 0
+  gpu: yes
+  input: 
+    image: image/jpeg
+  output: 
+    image: image/jpeg
+  demo:
+    title: Real-Time Style Transfer Demo
+    description: Demo for the implementation of 'Perceptual Losses for Real-Time Style Transfer and Super-Resolution'.