Test 7x7 image of Mnist.

FilterEvaluator/Evaluator.py

@@ -35,7 +35,8 @@ layer = 0
 # model = utils.SetDevice(Model.Net5Grad35())
 # model = utils.SetDevice(Model.Net31535())
 # model = utils.SetDevice(Model.Net3Grad335())
-model = utils.SetDevice(Model.Net3Grad334())
+# model = utils.SetDevice(Model.Net3Grad334())
+model = utils.SetDevice(Model.Net3Grad33())
 # model = utils.SetDevice(Model.Net3())
 model.PrintLayer()
 
@@ -43,7 +44,8 @@ model.PrintLayer()
 
 
 
-traindata, testdata = Loader.MNIST(batchsize)
+# traindata, testdata = Loader.MNIST(batchsize)
+traindata, testdata = Loader.MNIST(batchsize, resize=7)
 # traindata, testdata = Loader.RandomMnist(batchsize, style="Vertical")
 # traindata, testdata = Loader.RandomMnist(batchsize, style="Horizontal")
 # traindata, testdata = Loader.RandomMnist(batchsize, style="VerticalOneLine")
@@ -57,12 +59,12 @@ traindata, testdata = Loader.MNIST(batchsize)
 
 # weight,active = EvaluatorUnsuper.UnsuperLearnSearchWeight(model, layer, traindata, NumSearch=500000, SearchChannelRatio=32, Interation=5)
 # np.save("WeightSearch.npy", weight)
-# weight = np.load(CurrentPath+"WeightSearch.npy")
-# utils.NumpyToImage(weight, CurrentPath+"image",title="SearchWeight")
-# weight = np.load(CurrentPath+"WeightSearch.npy")
+weight = np.load(CurrentPath+"WeightSearch.npy")
+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,32,4000000)
-# np.save(CurrentPath+"bestweightSearch.npy", bestweight)
+bestweight,index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model,layer,weight,traindata,32,4000000)
+np.save(CurrentPath+"bestweightSearch.npy", bestweight)
 bestweight = np.load(CurrentPath+"bestweightSearch.npy")
 utils.NumpyToImage(bestweight, CurrentPath+"image",title="SearchWerightBest")
 EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)

FilterEvaluator/Model.py

@@ -60,6 +60,33 @@ class Net3335(UniModule.ModuleBase):
         x = x.view(-1, 1*10)
         return F.log_softmax(x, dim=1)
 
+class Net333(UniModule.ModuleBase):
+    def __init__(self):
+        super(Net333, self).__init__()
+        layers = []
+        layers += [nn.Conv2d(1,  8, kernel_size=3,bias=False),nn.Sigmoid()]
+        layers += [nn.Conv2d(8,  8, kernel_size=3,bias=False),nn.Sigmoid()]
+        layers += [nn.Conv2d(8,  10, kernel_size=3,bias=False)]
+        self.features = nn.Sequential(*layers)
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(-1, 1*10)
+        return F.log_softmax(x, dim=1)
+
+class Net3Grad33(UniModule.ModuleBase):
+    def __init__(self):
+        super(Net3Grad33, self).__init__()
+        layers = []
+        layers += [nn.Conv2d(1,  8, kernel_size=3,bias=False),nn.Sigmoid()]
+        layers += [nn.Conv2d(8,  8, kernel_size=3,bias=False),nn.Sigmoid()]
+        layers += [nn.Conv2d(8,  10, kernel_size=3,bias=False)]
+        self.features = nn.Sequential(*layers)
+        self.SetConvRequiresGrad(0,False)
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(-1, 1*10)
+        return F.log_softmax(x, dim=1)
+
 class Net3334(UniModule.ModuleBase):
     def __init__(self):
         super(Net3334, self).__init__()

FilterEvaluator/TrainNetwork.py

@@ -39,7 +39,8 @@ batchsize = 128
 # model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
 
 
-traindata, testdata = Loader.MNIST(batchsize, num_workers=4)
+# traindata, testdata = Loader.MNIST(batchsize, num_workers=4, trainsize=5000)
+traindata, testdata = Loader.MNIST(batchsize, resize=7, trainsize=5000)
 # traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Vertical")
 # traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Horizontal")
 # traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="VerticalOneLine")
@@ -48,7 +49,6 @@ traindata, testdata = Loader.MNIST(batchsize, num_workers=4)
 # traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=0)
 
 
-
 WebVisual.InitVisdom()
 window = WebVisual.LineWin()
 lineNoPre = WebVisual.Line(window, "NoPre")
@@ -67,22 +67,33 @@ linePretrainTrain = WebVisual.Line(window, "PretrainTrain")
  
 
 #model = utils.SetDevice(Model.Net3335BN())
-## model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
 #optimizer = optim.SGD(model.parameters(), lr=0.1)
 #Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,lineNoPreBN)
 
 
-model = utils.SetDevice(Model.Net3334())
-# model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
+# model = utils.SetDevice(Model.Net3334())
+# optimizer = optim.SGD(model.parameters(), lr=0.1)
+# Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,lineNoPre)
+
+
+# model = utils.SetDevice(Model.Net3Grad334())
+# model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
+# optimizer = optim.SGD(model.parameters(), lr=0.1)
+# Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,linePretrainSearch)
+
+
+
+
+model = utils.SetDevice(Model.Net333())
 optimizer = optim.SGD(model.parameters(), lr=0.1)
-Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,lineNoPre)
+Train.TrainEpochs(model,traindata,optimizer,testdata,1000,15,lineNoPre)
 
 
-
-model = utils.SetDevice(Model.Net3Grad334())
+model = utils.SetDevice(Model.Net3Grad33())
 model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
 optimizer = optim.SGD(model.parameters(), lr=0.1)
-Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,linePretrainSearch)
+Train.TrainEpochs(model,traindata,optimizer,testdata,1000,15,linePretrainSearch)
+
 
 
 

tools/Loader.py

@@ -8,14 +8,21 @@ from torch.utils.data import Dataset, DataLoader, Subset
 import random
 
 
-def MNIST(batchsize=8, num_workers=0, shuffle=False, trainsize=0):
+def MNIST(batchsize=8, num_workers=0, shuffle=False, trainsize=0, resize=0):
   CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
+  if resize == 0:
+    resize = 28
+  if shuffle:
+    trans = transforms.Compose([transforms.ColorJitter(0.2, 0.2),
+                                transforms.RandomRotation(30),
+                                transforms.RandomResizedCrop(28),
+                                transforms.Resize(resize),
+                                transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
+  else:
+    trans = transforms.Compose(
+        [transforms.Resize(resize), transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
   traindata = datasets.MNIST(root=CurrentPath+'../Dataset/', train=True, download=True,
-                             transform=transforms.Compose([
-                                 transforms.ColorJitter(0.2, 0.2),
-                                 transforms.RandomRotation(30),
-                                 transforms.RandomResizedCrop(28),
-                                 transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]))
+                             transform=trans)
   if trainsize == 0:
     trainsize = traindata.data.shape[0]
   train_loader = torch.utils.data.DataLoader(
@@ -23,6 +30,7 @@ def MNIST(batchsize=8, num_workers=0, shuffle=False, trainsize=0):
       batch_size=batchsize, shuffle=shuffle, num_workers=num_workers, drop_last=True)
   test_loader = torch.utils.data.DataLoader(
       datasets.MNIST(root=CurrentPath+'../Dataset/', train=False, transform=transforms.Compose([
+          transforms.Resize(resize),
           transforms.ToTensor(),
           transforms.Normalize((0.1307,), (0.3081,))
       ])), batch_size=batchsize, shuffle=shuffle, num_workers=num_workers, drop_last=True)