Refine find best 8
This commit is contained in:
colin
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f858ae1821
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@ -60,7 +60,7 @@ traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=True)
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# weight = EvaluatorUnsuper.UnsuperLearnSearchWeight(model, layer, traindata, NumSearch=100000, SearchChannelRatio=8, Interation=10)
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# np.save("WeightSearch.npy", weight)
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# weight = EvaluatorUnsuper.UnsuperLearnTrainWeight(model, layer, traindata)
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# weight = EvaluatorUnsuper.UnsuperLearnTrainWeight(model, layer, traindata, NumTrain=5000)
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# np.save("WeightTrain.npy", weight)
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@ -72,20 +72,21 @@ traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=0, shuffle=True)
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# utils.NumpyToImage(bestweight, CurrentPath+"image")
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# weight = np.load(CurrentPath+"WeightTrain.npy")
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# bestweight = EvaluatorUnsuper.UnsuperLearnFindBestWeight(model,layer,weight,traindata,128,100000)
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# np.save(CurrentPath+"bestweightTrain.npy", bestweight)
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# utils.NumpyToImage(bestweight, CurrentPath+"image")
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weight = np.load(CurrentPath+"WeightTrain.npy")
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bestweight = EvaluatorUnsuper.UnsuperLearnFindBestWeight(
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model, layer, weight, traindata, databatchs=128, interation=100000)
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np.save(CurrentPath+"bestweightTrain.npy", bestweight)
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utils.NumpyToImage(bestweight, CurrentPath+"image")
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weight = np.load(CurrentPath+"bestweightSearch.npy")
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EvaluatorUnsuper.SetModelConvWeight(model,layer,weight)
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utils.SaveModel(model,CurrentPath+"/checkpointSearch.pkl")
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# weight = np.load(CurrentPath+"bestweightSearch.npy")
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# EvaluatorUnsuper.SetModelConvWeight(model,layer,weight)
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# utils.SaveModel(model,CurrentPath+"/checkpointSearch.pkl")
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weight = np.load(CurrentPath+"bestweightTrain.npy")
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EvaluatorUnsuper.SetModelConvWeight(model,layer,weight)
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utils.SaveModel(model,CurrentPath+"/checkpointTrain.pkl")
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# weight = np.load(CurrentPath+"bestweightTrain.npy")
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# EvaluatorUnsuper.SetModelConvWeight(model,layer,weight)
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# utils.SaveModel(model,CurrentPath+"/checkpointTrain.pkl")
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@ -14,6 +14,8 @@ from torch.utils.data import Dataset, DataLoader
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from PIL import Image
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import random
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import cv2
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from tqdm import tqdm
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CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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sys.path.append(CurrentPath+'../tools')
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@ -91,7 +93,6 @@ def UnsuperLearnSearchWeight(model, layer, dataloader, NumSearch=10000, SaveChan
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tl.data=utils.SetDevice(torch.from_numpy(minweight[0:tl.out_channels]))
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return minweight
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def TrainLayer(netmodel, layer, SearchLayer, DataSet, Epoch=100):
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netmodel.eval()
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sample = utils.SetDevice(torch.empty((SearchLayer.out_channels,0)))
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@ -133,8 +134,8 @@ def UnsuperLearnTrainWeight(model, layer, dataloader, NumTrain=500, TrainChannel
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print("search :" + str(i))
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return minweight
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def UnsuperLearnFindBestWeight(netmodel, layer, weight, dataloader, databatchs=128,interation=10000):
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interationbar = tqdm(total=interation)
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weight = weight.astype("float32")
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netmodel.eval()
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tl = netmodel.features[layer]
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@ -155,28 +156,26 @@ def UnsuperLearnFindBestWeight(netmodel, layer, weight, dataloader, databatchs=1
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for i in range(outchannels):
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shift.append(1<<i)
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shift = utils.SetDevice(torch.from_numpy(np.array(shift).astype("uint8")))
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datasnetout = []
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for d in datas:
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datasnetout.append(netmodel.ForwardLayer(d,forwardlayer))
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datasnetout = torch.cat(datasnetout)
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for i in range(len(indexs)):
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newlayer.weight.data=utils.SetDevice(torch.from_numpy(weight[indexs[i]]))
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outputs = []
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for d in datas:
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outputs.append(newlayer(netmodel.ForwardLayer(d,forwardlayer)))
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outputs = torch.cat(outputs).transpose(0,1)
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outputs = outputs.reshape(outputs.shape[0],-1)
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mean = outputs.mean(1)
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outputs = outputs.transpose(0,1)
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posi = outputs > mean
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posi = posi*shift
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sums = posi.sum(1).type(torch.float32)
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outputs = newlayer(datasnetout).transpose(0,1)
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outputs = outputs.reshape(tl.out_channels,-1).transpose(0,1)
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sums = ((outputs > outputs.mean(0)) * shift).sum(1).type(torch.float32)
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histc = sums.histc(256,0,255).type(torch.float32)
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histc = histc[histc>0]
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histc = histc/histc.sum()
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entropys.append((histc.log2()*histc).sum())
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print("search index : " + str(i))
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entropy = (histc.log2()*histc).sum()
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entropys.append(entropy.detach().cpu().numpy())
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interationbar.update(1)
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argmin = np.argmin(entropys)
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bestweight = weight[indexs[argmin]]
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interationbar.close()
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return bestweight
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def SetModelConvWeight(model, layer, weight):
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w = utils.SetDevice(torch.from_numpy(weight))
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model.features[layer].weight.data = w
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@ -15,6 +15,7 @@ from PIL import Image
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import random
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import cv2
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from visdom import Visdom
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from tqdm import tqdm
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CurrentPath = os.path.split(os.path.realpath(__file__))[0]+"/"
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@ -36,45 +37,44 @@ batchsize = 128
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# model = utils.SetDevice(Model.Net31535())
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# optimizer = optim.Adam(model.parameters(), lr=0.01)
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# model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
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# traindata, testdata = Loader.MNIST(batchsize, num_workers=4)
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# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Vertical")
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# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="Horizontal")
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# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="VerticalOneLine")
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# traindata, testdata = Loader.RandomMnist(batchsize, num_workers=4, style="VerticalZebra")
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traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=500)
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traindata, testdata = Loader.Cifar10Mono(batchsize, num_workers=4,shuffle=True,trainsize=140)
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# WebVisual.InitVisdom()
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WebVisual.InitVisdom()
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window = WebVisual.LineWin()
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lineNoPre = WebVisual.Line("NoPre")
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linePretrain = WebVisual.Line("Pretrain")
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lineNoPre = WebVisual.Line(window, "NoPre")
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linePretrainSearch = WebVisual.Line(window, "PretrainSearch")
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linePretrainTrain = WebVisual.Line(window, "PretrainTrain")
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# model = utils.SetDevice(Model.Net3335())
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model = utils.SetDevice(Model.Net3Grad335())
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model = utils.LoadModel(model, CurrentPath+"/checkpoint.pkl")
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model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
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optimizer = optim.SGD(model.parameters(), lr=0.1)
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for i in range(1000):
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Train.train(model,traindata,optimizer,epoch=i)
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window.AppendData(linePretrain,Train.test(model,testdata))
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Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,linePretrainTrain)
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model = utils.SetDevice(Model.Net3335())
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model = utils.LoadModel(model, CurrentPath+"/checkpoint.pkl")
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model = utils.LoadModel(model, CurrentPath+"/checkpointTrain.pkl")
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optimizer = optim.SGD(model.parameters(), lr=0.1)
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for i in range(1000):
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Train.train(model, traindata, optimizer, epoch=i)
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window.AppendData(lineNoPre, Train.test(model, testdata))
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Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,lineNoPre)
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model = utils.SetDevice(Model.Net3Grad335())
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model = utils.LoadModel(model, CurrentPath+"/checkpointSearch.pkl")
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optimizer = optim.SGD(model.parameters(), lr=0.1)
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Train.TrainEpochs(model,traindata,optimizer,testdata,3000,30,linePretrainSearch)
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Binary file not shown.
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@ -9,12 +9,14 @@ import torch.nn as nn
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import torch
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import os
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import utils as utils
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from tqdm import tqdm
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def train(model, train_loader, optimizer, epoch=0):
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model.train()
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batchsize = int(train_loader.sampler.num_samples /
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train_loader.batch_size / 5)+1
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pbar = tqdm(total=train_loader.sampler.num_samples)
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for batch_idx, (data, target) in enumerate(train_loader):
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data = utils.SetDevice(data)
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target = utils.SetDevice(target)
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@ -25,13 +27,16 @@ def train(model, train_loader, optimizer, epoch=0):
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loss.backward()
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optimizer.step()
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pbar.update(train_loader.batch_size)
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if batch_idx % batchsize == 0 and batch_idx > 0:
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print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'
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.format(epoch, batch_idx * len(data),
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len(train_loader.dataset),
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100. * batch_idx /
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len(train_loader),
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loss.item()))
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pbar.set_description("Loss:"+str(loss.item()))
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# print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'
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# .format(epoch, batch_idx * len(data),
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# len(train_loader.dataset),
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# 100. * batch_idx /
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# len(train_loader),
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# loss.item()))
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pbar.close()
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def test(model, test_loader):
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@ -43,15 +48,23 @@ def test(model, test_loader):
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data = utils.SetDevice(data)
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target = utils.SetDevice(target)
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output = model(data)
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# sum up batch loss
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test_loss += F.nll_loss(output, target, reduction='sum').item()
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# get the index of the max log-probability
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pred = output.max(1, keepdim=True)[1]
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correct += pred.eq(target.view_as(pred)).sum().item()
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test_loss /= len(test_loader.dataset)
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accu = 100. * correct / len(test_loader.dataset)
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print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
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.format(test_loss, correct, len(test_loader.dataset), accu))
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# print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'
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# .format(test_loss, correct, len(test_loader.dataset), accu))
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return accu
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def TrainEpochs(model, traindata, optimizer, testdata, epoch=100,testepoch=10, line=None):
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epochbar = tqdm(total=epoch)
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for i in range(epoch):
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train(model, traindata, optimizer, epoch=i)
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if line and i % testepoch == 0 and i > 0:
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line.AppendData(test(model, testdata))
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epochbar.update(1)
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epochbar.close()
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@ -1,27 +1,43 @@
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from visdom import Visdom
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import random
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import numpy as np
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import os
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import time
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def InitVisdom():
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viz = Visdom()
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assert viz.check_connection()
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viz.close()
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# nohub python3 -m visdom.server > visdom.server.log 2 > &1
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# subprocess.Popen("python3 -m visdom.server > visdom.server.log 2>&1", shell=True)
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os.popen("python3 -m visdom.server > visdom.server.log 2>&1")
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time.sleep(3) # to wait vindom.server started
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# viz = Visdom()
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# assert viz.check_connection()
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# viz.close()
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print("Connect http://localhost:8097")
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class Line():
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def __init__(self,name,size):
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def __init__(self, windows, name, size):
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super(Line, self).__init__()
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self.name=name
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self.size=size
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def __init__(self):
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self.name = name
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self.size = size
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self.win = windows
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def __init__(self, windows):
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super(Line, self).__init__()
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self.name=str(random.random())
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self.size=0
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def __init__(self,name):
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self.name = str(random.random())
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self.size = 0
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self.win = windows
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def __init__(self, windows, name):
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super(Line, self).__init__()
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self.name=name
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self.size=0
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self.name = name
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self.size = 0
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self.win = windows
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def AppendData(self, y):
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self.win.AppendData(self, y)
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class LineWin():
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def __init__(self):
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@ -35,23 +51,23 @@ class LineWin():
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)
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self.data = np.array([])
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def AppendLine(self,y):
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if not isinstance(y,list):
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def AppendLine(self, y):
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if not isinstance(y, list):
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y = [y]
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linename = str(random.random())
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self.viz.line(
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X=np.array(range(len(y))),
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Y=np.array(y),
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opts=dict(markercolor=np.array([50]),
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markersymbol='dot',),
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markersymbol='dot',),
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win=self.win,
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update="new",
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name=linename,
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)
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return Line(linename,len(y))
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return Line(linename, len(y))
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def AppendData(self,line, y):
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if not isinstance(y,list):
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def AppendData(self, line, y):
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if not isinstance(y, list):
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y = [y]
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self.viz.line(
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X=np.array(range(line.size, line.size+len(y))),
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@ -61,4 +77,3 @@ class LineWin():
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name=line.name
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)
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line.size = line.size + len(y)
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@ -0,0 +1,25 @@
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nohup: ignoring input
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/home/colin/anaconda3/lib/python3.7/site-packages/visdom/server.py:30: DeprecationWarning: zmq.eventloop.ioloop is deprecated in pyzmq 17. pyzmq now works with default tornado and asyncio eventloops.
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ioloop.install() # Needs to happen before any tornado imports!
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Traceback (most recent call last):
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File "/home/colin/anaconda3/lib/python3.7/runpy.py", line 193, in _run_module_as_main
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"__main__", mod_spec)
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File "/home/colin/anaconda3/lib/python3.7/runpy.py", line 85, in _run_code
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exec(code, run_globals)
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File "/home/colin/anaconda3/lib/python3.7/site-packages/visdom/server.py", line 1308, in <module>
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download_scripts_and_run()
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File "/home/colin/anaconda3/lib/python3.7/site-packages/visdom/server.py", line 1304, in download_scripts_and_run
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main()
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File "/home/colin/anaconda3/lib/python3.7/site-packages/visdom/server.py", line 1299, in main
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print_func=print_func, user_credential=user_credential)
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File "/home/colin/anaconda3/lib/python3.7/site-packages/visdom/server.py", line 1219, in start_server
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app.listen(port, max_buffer_size=1024 ** 3)
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File "/home/colin/anaconda3/lib/python3.7/site-packages/tornado/web.py", line 2042, in listen
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server.listen(port, address)
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File "/home/colin/anaconda3/lib/python3.7/site-packages/tornado/tcpserver.py", line 143, in listen
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sockets = bind_sockets(port, address=address)
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File "/home/colin/anaconda3/lib/python3.7/site-packages/tornado/netutil.py", line 168, in bind_sockets
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sock.bind(sockaddr)
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OSError: [Errno 98] Address already in use
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Checking for scripts.
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It's Alive!
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