trainsetup tested

Author: ekinakyurek

Manifest.toml

@@ -5,10 +5,12 @@ uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c"
 version = "0.3.2"
 
 [[AutoGrad]]
-deps = ["LinearAlgebra", "Pkg", "Printf", "Random", "SpecialFunctions", "Statistics", "Test"]
-git-tree-sha1 = "bd5bab7aa42b9ce1d5a054495b58c8f928227b8e"
+deps = ["LinearAlgebra", "Printf", "Random", "SpecialFunctions", "Statistics", "Test", "TimerOutputs"]
+git-tree-sha1 = "24a4f2faa56dcf1ebf73194be086ebca5782ee6d"
+repo-rev = "dy/1.1"
+repo-url = "https://github.com/denizyuret/AutoGrad.jl.git"
 uuid = "6710c13c-97f1-543f-91c5-74e8f7d95b35"
-version = "1.1.0"
+version = "1.1.0+"
 
 [[AxisAlgorithms]]
 deps = ["Compat", "WoodburyMatrices"]
@@ -97,6 +99,12 @@ git-tree-sha1 = "47f05d0b7f4999609f92e657147df000818c1f24"
 uuid = "150eb455-5306-5404-9cee-2592286d6298"
 version = "0.5.0"
 
+[[Crayons]]
+deps = ["Pkg", "Test"]
+git-tree-sha1 = "3017c662a988bcb8a3f43306a793617c6524d476"
+uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
+version = "1.0.0"
+
 [[CustomUnitRanges]]
 deps = ["Pkg", "Test"]
 git-tree-sha1 = "0a106457a1831555857e18ac9617279c22fc393b"
@@ -452,6 +460,12 @@ git-tree-sha1 = "58f6f07d3b54a363ec283a8f5fc9fb4ecebde656"
 uuid = "06e1c1a7-607b-532d-9fad-de7d9aa2abac"
 version = "0.2.3"
 
+[[TimerOutputs]]
+deps = ["Crayons", "Printf", "Test", "Unicode"]
+git-tree-sha1 = "89a9bd610d6bfd62a7c2b85112762b99b979fe5f"
+uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
+version = "0.4.0"
+
 [[Tqdm]]
 deps = ["Printf"]
 git-tree-sha1 = "59dcc6bf73d11e91b6d8ddbe77546a822e827098"

Project.toml

@@ -1,6 +1,6 @@
 [deps]
+AutoGrad = "6710c13c-97f1-543f-91c5-74e8f7d95b35"
 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"

src/main.jl

@@ -1,5 +1,6 @@
+using Pkg; Pkg.activate("../")
 #This implementation is very similar to original implementation in https://github.com/stanfordnlp/mac-network
-using JSON,Knet,Images
+using JSON,Knet,Images,HDF5
 using Printf,Random,Tqdm
 include("model.jl")
 savemodel(filename,m,mrun,o) = Knet.save(filename,"m",m,"mrun",mrun,"o",o)
@@ -152,7 +153,7 @@ function modelrun(M,data,feats,o,Mrun=nothing;train=false)
         if train
                 J = @diff M(questions,batchSizes,xS,xB,xP;answers=answers,p=o[:p],selfattn=o[:selfattn],gating=o[:gating])
                 flush(Base.stdout)
-	        cnt   += value(J)*B; total += B;
+                    cnt   += value(J)*B; total += B;
             if acc===nothing
                 acc = atype[];
             end

src/model.jl

@@ -2,7 +2,7 @@ using Knet, Random
 if !isdefined(Main,:atype)
     global atype = gpu() < 0 ? Array{Float32} : KnetArray{Float32}
 end
-include("loss2.jl")
+include("loss.jl")
 abstract type Model;end;
 struct ResNet <: Model; w; end
 function (M::ResNet)(m,imgurl::String,avgimg;stage=3)
@@ -272,7 +272,7 @@ function (M::MACNetwork)(qs,batchSizes,xS,xB,xP;answers=nothing,p=12,selfattn=fa
         if selfattn; push!(cj,ci); push!(mj,mi); end
         tap!=nothing && (tap["cnt"]+=1)
     end
-    
+
     y = M.output(q,mi;train=train)
 
     if answers==nothing

train.jl

@@ -1,18 +1,13 @@
 include(ARGS[1])
 include(ARGS[2])
-# o=Dict(:h5=>false,:mfile=>nothing,:epochs=>16,
-#                 :lr=>0.0001,:p=>12,:ema=>0.999f0,:batchsize=>32,
-#                 :selfattn=>false,:gating=>false,
-#                 :shuffle=>true,:sorted=>false,:prefix=>string(now())[1:10],
-#                 :vocab_size=>90,:embed_size=>300, :dhome=>"data/", :loadresnet=>false,:d=>512)
 println("Loading questions ...")
 trnqstns = getQdata(o[:dhome],"train")
 valqstns = getQdata(o[:dhome],"val")
 println("Loading dictionaries ... ")
 qvoc,avoc,i2w,i2a = getDicts(o[:dhome],"dic")
 sets = []
-push!(sets,shuffle!(miniBatch(trnqstns;B=48,srtd=true)))
-push!(sets,shuffle!(miniBatch(valqstns;B=48,srtd=true)))
+push!(sets,shuffle!(miniBatch(trnqstns;B=32,srtd=true)))
+push!(sets,shuffle!(miniBatch(valqstns;B=32,srtd=true)))
 trnqstns=nothing;
 valqstns=nothing;
 

trainsetup.jl

@@ -1,25 +1,22 @@
 using Pkg; Pkg.activate("."); Pkg.instantiate(); #install required packages
 server = "ai.ku.edu.tr/"
 if length(ARGS)==0
-    error("clevr home folder is not specified. Pre-processed data will be downloaded from the servers(70GB)? (yes or no)")
+    println("clevr home folder is not specified. Pre-processed data will be downloaded from the servers(70GB) if they are not in the data folder? (yes or no)")
     if readline() == "yes"
         !isfile("data/train.bin")  && download(server*"data/mac-network/train.bin","data/train.bin")
         !isfile("data/val.bin")    && download(server*"data/mac-network/val.bin","data/val.bin")
         !isfile("data/train.json") && download(server*"data/mac-network/train.json","data/train.json")
         !isfile("data/val.json")   && download(server*"data/mac-network/val.json","data/val.json")
         !isfile("data/dic.json")   && download(server*"data/mac-network/dic.json","data/dic.json")
+        exit()
     else
         error("No data available!")
     end
 else
     CLEVR_HOME = ARGS[1]
+    println(CLEVR_HOME)
 end
 
-println(CLEVR_HOME)
-
-println("Checking dependencies...")
-include("requirements.jl")
-
 include("preprocess.jl")
 println("Starting to preprocess CLEVR questions @: $(CLEVR_HOME)/questions ")
 println("Training and validation questions will be processed...")