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Try deep search ,and test in Minist DataSet

This commit is contained in:
c 2019-11-28 21:06:06 +08:00
parent f105932c26
commit a368c4eae5
10 changed files with 75 additions and 50 deletions
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26
FilterEvaluator/Evaluator.py
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@ -29,42 +29,40 @@ import EvaluatorUnsuper
batchsize = 128
layer = 0
# model = utils.SetDevice(Model.Net5Grad35())
# model = utils.SetDevice(Model.Net31535())
model = utils.SetDevice(Model.Net3Grad335())
# model = utils.SetDevice(Model.Net3Grad335())
model = utils.SetDevice(Model.Net3Grad334())
# model = utils.SetDevice(Model.Net3())
model.PrintLayer()
layers = model.PrintLayer()
layer = 0
# model = utils.LoadModel(model, CurrentPath+"/checkpoint.pkl")
# traindata, testdata = Loader.MNIST(batchsize)
traindata, testdata = Loader.MNIST(batchsize)
# traindata, testdata = Loader.RandomMnist(batchsize, style="Vertical")
# traindata, testdata = Loader.RandomMnist(batchsize, style="Horizontal")
# traindata, testdata = Loader.RandomMnist(batchsize, style="VerticalOneLine")
# traindata, testdata = Loader.RandomMnist(batchsize, style="VerticalZebra")
# traindata, testdata = Loader.Cifar10Mono(batchsize)
traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=False)
# traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=False)
# weight,active = EvaluatorUnsuper.UnsuperLearnSearchWeight(model, layer, traindata, NumSearch=1000000, SearchChannelRatio=32, Interation=5)
# 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 = weight[0:256]
bestweight,index = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model,layer,weight,traindata,32,400000)
np.save(CurrentPath+"bestweightSearch.npy", bestweight)
# 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 = np.load(CurrentPath+"bestweightSearch.npy")
utils.NumpyToImage(bestweight, CurrentPath+"image",title="SearchWerightBest")
EvaluatorUnsuper.SetModelConvWeight(model,layer,bestweight)

29
FilterEvaluator/Model.py
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@ -60,6 +60,35 @@ class Net3335(UniModule.ModuleBase):
x = x.view(-1, 1*10)
return F.log_softmax(x, dim=1)
class Net3334(UniModule.ModuleBase):
def __init__(self):
super(Net3334, self).__init__()
layers = []
layers += [nn.Conv2d(1, 8, kernel_size=3,bias=False,padding=1),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.Sigmoid()]
layers += [nn.Conv2d(8, 10, kernel_size=4,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 Net3Grad334(UniModule.ModuleBase):
def __init__(self):
super(Net3Grad334, self).__init__()
layers = []
layers += [nn.Conv2d(1, 8, kernel_size=3,bias=False,padding=1),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.MaxPool2d(kernel_size=2, stride=2),nn.Sigmoid()]
layers += [nn.Conv2d(8, 8, kernel_size=3,bias=False),nn.Sigmoid()]
layers += [nn.Conv2d(8, 10, kernel_size=4,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 Net3335BN(UniModule.ModuleBase):
def __init__(self):
super(Net3335BN, self).__init__()

33
FilterEvaluator/TrainNetwork.py
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@ -39,13 +39,14 @@ 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)
# 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")
# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="VerticalZebra")
# traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=500)
traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=0)
# traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=135)
# traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=0)
WebVisual.InitVisdom()
@ -62,26 +63,26 @@ 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)
# Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,linePretrainTrain)
#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.Net3335BN())
model = utils.SetDevice(Model.Net3334())
# model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
optimizer = optim.SGD(model.parameters(), lr=0.1)
Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,lineNoPreBN)
Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,lineNoPre)
model = utils.SetDevice(Model.Net3335())
# model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
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,3000,30,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)
Train.TrainEpochs(model,traindata,optimizer,testdata,3000,15,linePretrainSearch)

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FilterEvaluator/WeightSearch.npy
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FilterEvaluator/bestweightSearch.npy
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FilterEvaluator/checkpoint.pkl
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19
tools/Loader.py
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@ -8,21 +8,26 @@ from torch.utils.data import Dataset, DataLoader, Subset
import random
def MNIST(batchsize=8, num_workers=0, shuffle=False):
def MNIST(batchsize=8, num_workers=0, shuffle=False, trainsize=0):
CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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,))]))
if trainsize == 0:
trainsize = traindata.data.shape[0]
train_loader = torch.utils.data.DataLoader(
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,))])),
Subset(traindata, range(0, trainsize)),
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.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])), batch_size=batchsize, shuffle=shuffle, num_workers=num_workers, drop_last=True)
train_loader.batch_sampler.sampler.num_samples = 50000
test_loader.batch_sampler.sampler.num_samples = 10000
return train_loader, test_loader
def Cifar10(batchsize=8, num_workers=0, shuffle=False):

14
tools/Train.py
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@ -14,14 +14,13 @@ from tqdm import tqdm
def train(model, train_loader, optimizer, epoch=0):
model.train()
batchsize = int(train_loader.sampler.num_samples /
train_loader.batch_size / 5)+1
pbar = tqdm(total=train_loader.sampler.num_samples)
totalsize = train_loader.batch_sampler.sampler.num_samples
batchsize = int(totalsize / train_loader.batch_size / 5)+1
pbar = tqdm(totalsize)
for batch_idx, (data, target) in enumerate(train_loader):
data = utils.SetDevice(data)
target = utils.SetDevice(target)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
@ -30,15 +29,8 @@ def train(model, train_loader, optimizer, epoch=0):
pbar.update(train_loader.batch_size)
if batch_idx % batchsize == 0 and batch_idx > 0:
pbar.set_description("Loss:"+str(loss.item()))
# print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'
# .format(epoch, batch_idx * len(data),
# len(train_loader.dataset),
# 100. * batch_idx /
# len(train_loader),
# loss.item()))
pbar.close()
def test(model, test_loader):
with torch.no_grad():
model.eval()

4
tools/WebVisual.py
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@ -17,7 +17,7 @@ def InitVisdom():
print("Connect http://localhost:8097")
class Line():
class Line(object):
def __init__(self, windows, name, size):
super(Line, self).__init__()
self.name = name
@ -40,7 +40,7 @@ class Line():
self.win.AppendData(self, y, x)
class LineWin():
class LineWin(object):
def __init__(self):
super(LineWin, self).__init__()
self.viz = Visdom()