Upload ENet-BDD100K-Torch

ENet-BDD100K-Torch/LICENSE

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+BSD License
+
+For fb.resnet.torch software
+
+Copyright (c) 2016, Facebook, Inc. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification,
+are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+ * Neither the name Facebook nor the names of its contributors may be used to
+   endorse or promote products derived from this software without specific
+   prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

ENet-BDD100K-Torch/ParallelCriterion2.lua

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+local ParallelCriterion2, parent = torch.class('nn.ParallelCriterion2', 'nn.Criterion')
+
+function ParallelCriterion2:__init(repeatTarget)
+   parent.__init(self)
+   self.criterions = {}
+   self.weights = {}
+   self.gradInput = {}
+   self.repeatTarget = repeatTarget
+end
+
+function ParallelCriterion2:add(criterion, weight)
+   assert(criterion, 'no criterion provided')
+   weight = weight or 1
+   table.insert(self.criterions, criterion)
+   table.insert(self.weights, weight)
+   return self
+end
+
+function ParallelCriterion2:updateOutput(input, target)
+   self.output = 0
+   local output = {}
+   for i,criterion in ipairs(self.criterions) do
+      local target = self.repeatTarget and target or target[i]
+      self.output = self.output + self.weights[i]*criterion:updateOutput(input[i],target)
+      table.insert(output, self.weights[i]*criterion:updateOutput(input[i],target))
+   end
+   return self.output, output
+end
+
+function ParallelCriterion2:updateGradInput(input, target)
+   self.gradInput = nn.utils.recursiveResizeAs(self.gradInput, input)
+   nn.utils.recursiveFill(self.gradInput, 0)
+   for i,criterion in ipairs(self.criterions) do
+      local target = self.repeatTarget and target or target[i]
+      nn.utils.recursiveAdd(self.gradInput[i], self.weights[i], criterion:updateGradInput(input[i], target))
+   end
+   return self.gradInput
+end
+
+function ParallelCriterion2:type(type, tensorCache)
+   self.gradInput = {}
+   return parent.type(self, type, tensorCache)
+end

ENet-BDD100K-Torch/README.md

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+### Before Start
+
+Please follow [list](./list) to put the BDD100K dataset (train, val, test) in the desired folder. The labels generated by our method are provided here ([train](https://drive.google.com/open?id=1wjoOQT6OJlLETz0ZYThBWjSt1Tgzn6_j) and [val](https://drive.google.com/open?id=1WaUjdgI4CMXkYFfi8Lz2rHiYYWLU5hav)). We'll call the directory that you cloned ENet-BDD100K-Torch as `$ENet_BDD100K_ROOT`. Note that this model only uses ENet as backbone, and if you use ENet-SAD, the performance will be better.
+
+### Testing
+1. Run test script
+    ```Shell
+    cd $ENet_BDD100K_ROOT
+    sh ./experiments/test_ENet.sh
+    ```
+    By now, you should be able to reproduce the result (Accuracy: 0.3656, mIoU: 16.02).
+
+### Training
+1. Training ENet model
+    ```Shell
+    cd $ENet_BDD100K_ROOT
+    sh ./experiments/train_ENet.sh
+    ```
+    The training process should start and trained models would be saved in `experiments/models/ENet-new/` by default.  
+    Then you can test the trained model following the Testing steps above. If your model position or name is changed, remember to set them to yours accordingly.
+

ENet-BDD100K-Torch/checkpoints.lua

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+--
+--  Copyright (c) 2016, Facebook, Inc.
+--  All rights reserved.
+--
+--  This source code is licensed under the BSD-style license found in the
+--  LICENSE file in the root directory of this source tree. An additional grant
+--  of patent rights can be found in the PATENTS file in the same directory.
+--
+local checkpoint = {}
+
+local function deepCopy(tbl)
+   -- creates a copy of a network with new modules and the same tensors
+   local copy = {}
+   for k, v in pairs(tbl) do
+      if type(v) == 'table' then
+         copy[k] = deepCopy(v)
+      else
+         copy[k] = v
+      end
+   end
+   if torch.typename(tbl) then
+      torch.setmetatable(copy, torch.typename(tbl))
+   end
+   return copy
+end
+
+function checkpoint.latest(opt)
+   if opt.resume == 'none' then
+      return nil
+   end
+
+   local latestPath = paths.concat(opt.resume, 'latest.t7')
+   if not paths.filep(latestPath) then
+      return nil
+   end
+
+   print('=> Loading checkpoint ' .. latestPath)
+   local latest = torch.load(latestPath)
+   local optimState = torch.load(paths.concat(opt.resume, latest.optimFile))
+
+   return latest, optimState
+end
+
+function checkpoint.save(epoch, model, optimState, isBestModel, opt, iter, bestLoss)
+   -- don't save the DataParallelTable for easier loading on other machines
+   if torch.type(model) == 'nn.DataParallelTable' then
+      model = model:get(1)
+   end
+
+   -- create a clean copy on the CPU without modifying the original network
+   model = deepCopy(model):float():clearState()
+
+   local modelFile = 'model_new.t7'
+   local optimFile = 'optimState_new.t7'
+
+   torch.save(paths.concat(opt.save, modelFile), model)
+   torch.save(paths.concat(opt.save, optimFile), optimState)
+   torch.save(paths.concat(opt.save, 'latest.t7'), {
+      iter = iter,
+      epoch = epoch,
+      bestLoss = bestLoss,
+      modelFile = modelFile,
+      optimFile = optimFile,
+   })
+
+   if isBestModel then
+      torch.save(paths.concat(opt.save, 'model_best.t7'), model)
+   end
+end
+
+return checkpoint

ENet-BDD100K-Torch/dataloader.lua

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+--
+--  Copyright (c) 2016, Facebook, Inc.
+--  All rights reserved.
+--
+--  This source code is licensed under the BSD-style license found in the
+--  LICENSE file in the root directory of this source tree. An additional grant
+--  of patent rights can be found in the PATENTS file in the same directory.
+--
+--  Multi-threaded data loader
+--
+
+local datasets = require 'datasets/init'
+local Threads = require 'threads'
+Threads.serialization('threads.sharedserialize')
+
+local M = {}
+local DataLoader = torch.class('resnet.DataLoader', M)
+
+function DataLoader.create(opt)
+   -- The train and val loader
+   local loaders = {}
+   local data
+   if opt.dataset == 'lane' then
+      data = {'train', 'val'}
+   elseif opt.dataset == 'laneTest' then
+      data = {'val'}
+   else
+      cmd:error('unknown dataset: ' .. opt.dataset)
+   end
+   for i, split in ipairs(data) do
+      local dataset = datasets.create(opt, split)
+      print("data created")
+      loaders[i] = M.DataLoader(dataset, opt, split)
+      print("data loaded")
+   end
+
+   return table.unpack(loaders)
+end
+
+function DataLoader:__init(dataset, opt, split)
+   local manualSeed = opt.manualSeed
+   local function init()
+      require('datasets/' .. opt.dataset)
+   end
+   local function main(idx)
+      if manualSeed ~= 0 then
+         torch.manualSeed(manualSeed + idx)
+      end
+      torch.setnumthreads(1)
+      _G.dataset = dataset
+      _G.preprocess = dataset:preprocess()
+      _G.preprocess_aug = dataset:preprocess_aug()
+      return dataset:size()
+   end
+
+   local threads, sizes = Threads(opt.nThreads, init, main)
+   -- self.nCrops = (split == 'val' and opt.tenCrop) and 10 or 1
+   self.nCrops = 1
+   self.threads = threads
+   self.__size = sizes[1][1]
+   self.batchSize = math.floor(opt.batchSize / self.nCrops)
+   self.split = split
+   self.dataset = opt.dataset
+end
+
+function DataLoader:size()
+   return math.ceil(self.__size / self.batchSize)
+end
+
+function DataLoader:run()
+   local threads = self.threads
+   local size, batchSize = self.__size, self.batchSize
+   local dataset = self.dataset
+   --if self.split == 'val' then
+      --batchSize = torch.round(batchSize / 2)
+   --end
+   local perm
+   if self.split == 'val' then
+      perm = torch.Tensor(size)
+      for i = 1, size do
+         perm[i] = i
+      end
+   else
+      perm = torch.randperm(size)
+   end
+
+   local idx, sample = 1, nil
+   local function enqueue()
+      while idx <= size and threads:acceptsjob() do
+         local indices = perm:narrow(1, idx, math.min(batchSize, size - idx + 1))
+         threads:addjob(
+            function(indices, nCrops)
+               local sz = indices:size(1)
+               local batch, segLabels, exists, imgpaths
+               for i, idx in ipairs(indices:totable()) do
+                  local sample = _G.dataset:get(idx)
+                  local input, segLabel, exist
+                  if dataset=='laneTest' then
+                     input = _G.preprocess(sample.input)
+                  elseif dataset=='lane' then
+                     input, segLabel, exist = _G.preprocess_aug(sample.input, sample.segLabel, sample.exist)
+                     --print(segLabel:size())
+                     segLabel:resize(segLabel:size(2),segLabel:size(3))
+                  else
+                     cmd:error('unknown dataset: ' .. dataset)
+                  end
+                  if not batch then
+                     local imageSize = input:size():totable()
+                     local pathSize = sample.imgpath:size():totable()
+                     batch = torch.FloatTensor(sz, table.unpack(imageSize))
+                     imgpaths = torch.CharTensor(sz, table.unpack(pathSize))
+                     if dataset=='lane' then
+                        local labelSize = segLabel:size():totable()
+                        local existSize = exist:size():totable()
+                        segLabels = torch.FloatTensor(sz, table.unpack(labelSize))
+                        exists = torch.FloatTensor(sz, table.unpack(existSize))
+                     end
+                  end
+                  batch[i]:copy(input)
+                  imgpaths[i]:copy(sample.imgpath)
+                  if dataset=='lane' then
+                     segLabels[i]:copy(segLabel)
+                     exists[i]:copy(exist)
+                  end
+               end
+               local targets
+               if dataset=='laneTest' then
+                  targets = nil
+               elseif dataset=='lane' then
+                  targets = {segLabels, exists}
+               else
+                  cmd:error('unknown dataset: ' .. dataset)
+               end
+               collectgarbage(); collectgarbage()
+
+               return {
+                  input = batch,
+                  target = targets,
+                  imgpath = imgpaths, -- used in test
+               }
+            end,
+            function(_sample_)
+               sample = _sample_
+            end,
+            indices,
+            self.nCrops
+         )
+         idx = idx + batchSize
+      end
+   end
+
+   local n = 0
+   local function loop()
+      enqueue()
+      if not threads:hasjob() then
+         return nil
+      end
+      threads:dojob()
+      if threads:haserror() then
+         threads:synchronize()
+      end
+      enqueue()
+      n = n + 1
+      return n, sample
+   end
+   return loop
+end
+
+return M.DataLoader