New test

FilterEvaluator/Evaluator.py

@@ -28,7 +28,7 @@ from tools import utils, Train, Loader, WebVisual
 import EvaluatorUnsuper
 
 
-batchsize = 64
+batchsize = 128
 
 # model = utils.SetDevice(Model.Net5Grad35())
 # model = utils.SetDevice(Model.Net31535())
@@ -57,48 +57,18 @@ traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=False
 
 
 
-
-# a = []
-# for batch_idx, (data, target) in enumerate(traindata):
-#     a = torch.jit.trace(model, data)
-#     break
-# print(a.graph)
-
-
-
-
-
-
-
-
-# for batch_idx, (data, target) in enumerate(traindata):
-#     utils.NumpyToImage(data.cpu().detach().numpy(), CurrentPath+"image", title="TrainData")
-#     break
-
-# weight, active = EvaluatorUnsuper.UnsuperLearnSearchWeight(
-#     model, layer, traindata, NumSearch=10, SaveChannel=4000, SearchChannelRatio=32, Interation=10)
-# utils.NumpyToImage(weight, CurrentPath+"image",title="SearchWeight")
-
-# b =0
-
-
-
-
-
-
-
-
-weight,active = EvaluatorUnsuper.UnsuperLearnSearchWeight(model, layer, traindata, NumSearch=100000, SearchChannelRatio=32, Interation=10)
-np.save("WeightSearch.npy", weight)
-weight = np.load(CurrentPath+"WeightSearch.npy")
-utils.NumpyToImage(weight, CurrentPath+"image",title="SearchWeight")
+# weight,active = EvaluatorUnsuper.UnsuperLearnSearchWeight(model, layer, traindata, NumSearch=1000000, SearchChannelRatio=32, Interation=5)
+# np.save("WeightSearch.npy", weight)
 # weight = np.load(CurrentPath+"WeightSearch.npy")
-# bestweight,index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model,layer,weight,traindata,128,100000)
-# np.save(CurrentPath+"bestweightSearch.npy", bestweight)
-# bestweight = np.load(CurrentPath+"bestweightSearch.npy")
-# utils.NumpyToImage(bestweight, CurrentPath+"image",title="SearchWerightBest")
-# EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)
-# utils.SaveModel(model,CurrentPath+"/checkpointSearch.pkl")
+# utils.NumpyToImage(weight, CurrentPath+"image",title="SearchWeight")
+weight = np.load(CurrentPath+"WeightSearch.npy")
+weight = weight[0:256]
+bestweight,index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model,layer,weight,traindata,32,400000)
+np.save(CurrentPath+"bestweightSearch.npy", bestweight)
+bestweight = np.load(CurrentPath+"bestweightSearch.npy")
+utils.NumpyToImage(bestweight, CurrentPath+"image",title="SearchWerightBest")
+EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)
+utils.SaveModel(model,CurrentPath+"/checkpointSearch.pkl")
 
 
 

FilterEvaluator/TrainNetwork.py

@@ -57,10 +57,10 @@ linePretrainTrain = WebVisual.Line(window, "PretrainTrain")
 
 
 
-model = utils.SetDevice(Model.Net3Grad335())
-model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
-optimizer = optim.SGD(model.parameters(), lr=0.1)
-Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,linePretrainTrain)
+# model = utils.SetDevice(Model.Net3Grad335())
+# model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
+# optimizer = optim.SGD(model.parameters(), lr=0.1)
+# Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,linePretrainTrain)
 
 
 model = utils.SetDevice(Model.Net3335())
@@ -69,10 +69,10 @@ optimizer = optim.SGD(model.parameters(), lr=0.1)
 Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,lineNoPre)
 
 
-# model = utils.SetDevice(Model.Net3Grad335())
-# model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
-# optimizer = optim.SGD(model.parameters(), lr=0.1)
-# Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,linePretrainSearch)
+model = utils.SetDevice(Model.Net3Grad335())
+model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
+optimizer = optim.SGD(model.parameters(), lr=0.1)
+Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,linePretrainSearch)