Knet1.0.1 - Compat

Author: ekinakyurek

Manifest.toml

@@ -0,0 +1,6 @@
+[deps]
+Images = "916415d5-f1e6-5110-898d-aaa5f9f070e0"
+JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+JSON = "682c06a0-de6a-54ab-a142-c8b1cf79cde6"
+Knet = "1902f260-5fb4-5aff-8c31-6271790ab950"
+Tqdm = "a73858fe-bd08-11e8-065c-3dfff236a6cd"

Project.toml

@@ -1,4 +1,4 @@
-using JSON,JLD
+using JSON
 dhome    = ARGS[1]
 trgthome = dhome * "demo/"
 valfile  = dhome * "val.json"
@@ -14,14 +14,14 @@ function loadFeatures(dhome,set)
 end
 
 function getdemo(file;part=100)
-    info("Validation feats are loading...")
+    println("Validation feats are loading...")
     valfeats  = loadFeatures(dhome,"val")
     demofeats = Any[];
-    info("Validation data are loading...")
+    println("Validation data are loading...")
     data      = JSON.parsefile(file)
     demo      = data[randperm(length(data))[1:part]]
 
-    info("Random selection are collecting...")
+    println("Random selection are collecting...")
     for d in demo
         imgname = d[1]
     	cp(oclvr*imgname ,valimgs*imgname;remove_destination=true)
@@ -30,17 +30,17 @@ function getdemo(file;part=100)
     end
     demofeats = cat(4,demofeats...)
 
-    info("Demo feats are writed...")
+    println("Demo feats are writed...")
     f = open(trgthome*"demo.bin","w")
     write(f,demofeats); close(f)
 
-    info("Demo data is writed...")
+    println("Demo data is writed...")
     open(trgthome * "demo.json","w") do f
         write(f,json(demo))
     end
 end
 
-info("Creating demo folders if necessary...")
+println("Creating demo folders if necessary...")
 !isdir(trgthome) && mkdir(trgthome)
 !isdir(clvrhome) && mkdir(clvrhome)
 !isdir(imgshome) && mkdir(imgshome)

demoprepare.jl

@@ -1,11 +1,3 @@
-# TO-DO Julia7 dependency installment
-# if ENV["HOME"] == "/mnt/juliabox"
-#     Pkg.dir(path...)=joinpath("/home/jrun/.julia/v0.6",path...)
-# else
-#     for p in ("Knet","JLD","JSON","Images") # ,"WordTokenizers")
-#         Pkg.installed(p) == nothing && Pkg.add(p)
-#     end
-# end
 server="ai.ku.edu.tr/"
 if !isdir("data/demo")
     println("Downloading sample questions and images from CLEVR dataset...")
@@ -17,3 +9,4 @@ if !isfile("models/macnet.jld2")
     println("Downloading pre-trained model from our servers...")
     download(server*"models/mac-network/macnet.jld2","models/macnet.jld2")
 end
+println("Demo setup is completed")

demosetup.jl

@@ -55,10 +55,10 @@ function extract(dhome,set,params;atype=Array{Float32})
 end
 
 function extract_features(dhome)
-    atype = gpu()<0 ? Array{Float32}:KnetArray{Float32}
+    atype = gpu()<0 ? Array{Float32} : KnetArray{Float32}
     params = loadparams(atype)
-    info("Extracting training features")
+    println("Extracting training features")
     extract(dhome,"train",params;atype=atype)
-    info("Extracting validation features")
+    println("Extracting validation features")
     extract(dhome,"val",params;atype=atype)
 end

extract_features.jl

@@ -1,5 +1,6 @@
 #!/bin/sh
 #SBATCH --partition=ai
+#SBATCH --account=ai
 #SBATCH --gres=gpu:1
 #SBATCH --time=56:00:00
 #SBATCH --nodes=1
@@ -8,5 +9,5 @@
 #SBATCH --job-name="clevr"
 #SBATCH --output=clevr.out
 #SBATCH --error=clevr.error
-
-julia train.jl src/macnet.jl configs/config2.jl
+#SBATCH --nodelist=dy02
+julia train.jl src/main.jl configs/config2.jl

job.sh

@@ -1,9 +1,9 @@
 using JSON, WordTokenizers, ProgressMeter
 
 function processquestions(dhome, set; word_dic=Dict(), answer_dic=Dict())
-    info("original data is loading")
+    println("original data is loading")
     data = JSON.parsefile(joinpath(dhome,"questions","CLEVR_$(set)_questions.json"))
-    info("parsing starts...")
+    println("parsing starts...")
     result = []
     p = Progress(length(data["questions"]))
     for question in data["questions"]
@@ -29,9 +29,9 @@ function preprocess(args)
         wdic = dics["word_dic"]
         adic = dics["answer_dic"]
     end
-    info("Parsing training questions...")
+    println("Parsing training questions...")
     processquestions(root, "train";word_dic=wdic, answer_dic=adic)
-    info("Parsing validation questions...")
+    println("Parsing validation questions...")
     processquestions(root, "val"  ;word_dic=wdic, answer_dic=adic)
 
     open("data/dic.json", "w") do f

preprocess.jl

@@ -45,15 +45,15 @@ end
 # target probabilities, q is estimated probabilities. Read left column
 # down, right column (loss gradients) back up.
 
-# x			dx = -p + qz/z = -p + exp(logq)
-# xmax  = max(x,1)	-sum(db)=0
-# logqz = x .- xmax	-p + qz/z
-# qz    = exp(logqz)	rep(1/z)
-# z     = sum(qz,1)	1/z
-# logz  = log(z)	sum(p)=1
-# logq  = logqz.-logz	-p
-# plogq = p .* logq	-1
-# loss  = -sum(plogq)	1
+# xdx = -p + qz/z = -p + exp(logq)
+# xmax  = max(x,1)-sum(db)=0
+# logqz = x .- xmax-p + qz/z
+# qz    = exp(logqz)rep(1/z)
+# z     = sum(qz,1)1/z
+# logz  = log(z)sum(p)=1
+# logq  = logqz.-logz-p
+# plogq = p .* logq-1
+# loss  = -sum(plogq)1
 
 # We keep the old implementation _logp for CPU arrays, slow cases and
 # cases of d not handled by cudnn.