witnn/Other/InvertCNN.py

from __future__ import print_function
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms
import torchvision.models as models
import matplotlib.pyplot as plt
import numpy as np
from visdom import Visdom


# viz=Visdom()
# viz.delete_env('main')


batchsize=128

DATA_FOLDER = os.path.split(os.path.realpath(__file__))[0]+'/Dataset/'
print("Dataset Path :" + DATA_FOLDER)

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

        # Spatial transformer localization-network
        self.localization = nn.Sequential(
            nn.Conv2d(1, 8, kernel_size=7),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True),
            nn.Conv2d(8, 10, kernel_size=5),
            nn.MaxPool2d(2, stride=2),
            nn.ReLU(True)
        )

        # Regressor for the 3 * 2 affine matrix
        self.fc_loc = nn.Sequential(
            nn.Linear(10 * 3 * 3, 32),
            nn.ReLU(True),
            nn.Linear(32, 3 * 2)
        )

        # Initialize the weights/bias with identity transformation
        self.fc_loc[2].weight.data.zero_()
        self.fc_loc[2].bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))

    # Spatial transformer network forward function
    def stn(self, x):
        xs = self.localization(x)
        xs = xs.view(-1, 10 * 3 * 3)
        theta = self.fc_loc(xs)
        theta = theta.view(-1, 2, 3)

        grid = F.affine_grid(theta, x.size())
        x = F.grid_sample(x, grid)

        return x

    def forward(self, x):
        # transform the input
        x = self.stn(x)

        # Perform the usual forward pass
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

#99.01
class NetMnist(nn.Module):
    def __init__(self):
        super(NetMnist, self).__init__()
        channels=32
        self.conv1_1 = nn.Conv2d(1, channels, kernel_size=5 , padding=0)

        self.conv2_1 = nn.Conv2d(channels, channels, kernel_size=3, padding=0)
        self.conv2_2 = nn.Conv2d(channels, channels, kernel_size=3, padding=0)
        # self.conv2_3 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        # self.conv2_4 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)

        self.conv3_1 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        self.conv3_2 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        self.conv3_3 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        self.conv3_4 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)

        self.conv3_5 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        self.conv3_6 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        self.conv3_7 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)
        self.conv3_8 = nn.Conv2d(channels, channels, kernel_size=5, padding=0)

        self.fc1 = nn.Linear(256, 10)

    def forward(self, x):

        con1 = F.relu(F.max_pool2d(self.conv1_1(x), 2))

        con2_1 = F.relu(F.max_pool2d(self.conv2_1(con1), 2))
        con2_2 = F.relu(F.max_pool2d(self.conv2_2(con1), 2))
        # con2_3 = F.relu(F.max_pool2d(self.conv2_3(con1), 2))
        # con2_4 = F.relu(F.max_pool2d(self.conv2_4(con1), 2))

        con3_1 = F.relu((self.conv3_1(con2_1)))
        con3_2 = F.relu((self.conv3_2(con2_1)))
        con3_3 = F.relu((self.conv3_3(con2_1)))
        con3_4 = F.relu((self.conv3_4(con2_1)))

        con3_5 = F.relu((self.conv3_1(con2_2)))
        con3_6 = F.relu((self.conv3_2(con2_2)))
        con3_7 = F.relu((self.conv3_3(con2_2)))
        con3_8 = F.relu((self.conv3_4(con2_2)))

        cc=[con3_1,con3_2,con3_3,con3_4,con3_5,con3_6,con3_7,con3_8]
        #cc=[con4_1,con4_2,con4_3,con4_4,con4_5,con4_6,con4_7,con4_8]
        ccs=torch.cat(cc,1)
        ccs=ccs.view(-1,256)
        x = self.fc1(ccs)
        return x
        #return F.log_softmax(x, dim=1)

class NetMnistRatio(nn.Module):
    def __init__(self):
        super(NetMnistRatio, self).__init__()
        channels=32

        self.conv1 = nn.Conv2d(1, channels, kernel_size=5)
        self.conv2 = nn.Conv2d(channels, channels * 2, kernel_size=3)
        self.conv3 = nn.Conv2d(channels*2, channels * 4, kernel_size=5)

        self.fc1 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = F.relu(self.conv3(x))

        x = x.view(-1, 128)
        x = self.fc1(x)
        return x

#96.4 train slow
class NetMnistNormal(nn.Module):
    def __init__(self):
        super(NetMnistNormal, self).__init__()
        channels=128

        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return x

#98.9
class NetMnistFCNN(nn.Module):
    def __init__(self):
        super(NetMnistFCNN, self).__init__()
        channels=128
        self.conv1 = nn.Conv2d(1, channels, kernel_size=3 , padding=1)

        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=0)

        self.conv3 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)

        self.conv4 = nn.Conv2d(channels, 10, kernel_size=3, padding=0)

    def forward(self, x):

        con1 = F.relu(F.max_pool2d(self.conv1(x), 2))

        con2 = F.relu(F.max_pool2d(self.conv2(con1), 2))

        con3 = F.relu(F.max_pool2d((self.conv3(con2)),2))

        con4 = self.conv4(con3)

        x = con4.view(-1,10)

        return x


#region loder dataset
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root=DATA_FOLDER, train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])), batch_size=batchsize, shuffle=True, num_workers=8)
# Test dataset
val_loader = torch.utils.data.DataLoader(
    datasets.MNIST(root=DATA_FOLDER, train=False, transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
    ])), batch_size=batchsize, shuffle=True, num_workers=8)
#endregion loder dataset


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = (NetMnistRatio()).to(device)
#########################################################
optimizer = optim.Adadelta(model.parameters(), lr=0.000001)
lossfunc=torch.nn.CrossEntropyLoss().to(device)
#lossfunc=torch.nn.NLLLoss()

# gpu_ids=[0,1,2,3]
# model = torch.nn.DataParallel(model, device_ids = gpu_ids)
# optimizer = torch.nn.DataParallel(optimizer, device_ids = gpu_ids)

def train(epoch):
    model.train()
    correct = 0
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)

        

        output = model(data)
        loss = lossfunc(output, target)
        loss.backward()
        optimizer.step()

        pred = output.max(1, keepdim=True)[1]
        correct += pred.eq(target.view_as(pred)).sum().item()

        if batch_idx == (len(train_loader) - 2):
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(epoch, batch_idx * len(data), len(train_loader.dataset),100. * batch_idx / len(train_loader), loss.item()))
    lens = len(train_loader.dataset)
    correct = float(correct) / lens
    testtxt.write(str(correct)+'\n')
    print('Test set: val: {:.6f}'.format(correct))
def val():
    with torch.no_grad():
        model.eval()
        correct = 0
        for data, target in val_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            # get the index of the max log-probability
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()
        lens=len(val_loader.dataset)
        correct = float(correct)/lens
        valtxt.write(str(correct) + '\n')
        print('Val  set: val: {:.6f}'.format(correct))


testtxt=open('TestPrecision.txt','w')
valtxt=open('ValPrecision.txt','w')

# optimizer.zero_grad()

for epoch in range(1, 1000):
    train(epoch)
    val()





# mm = model.conv1(ad)
# datodis = mm * 256 + 128
# datodis = datodis.view(64, 1, 24 * 10, 24)
# imaglis.append(datodis.detach().cpu().numpy()[0:8,:,:,:])


#cdsa=np.reshape(np.array(imaglis),newshape=[-1,1,240,24])
#viz.images(cdsa, win=imagewin, opts=dict(title='Random!', caption='How random.'), nrow=8,padding=2)


# Visualize the STN transformation on some input batch
# visualize_stn()

#plt.ioff()
#plt.show()