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syncNet_v2_1024.py
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import sys
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
#import matplotlib.pyplot as plt
import numpy as np
import random
import torch.optim as optim
from tensorboardX import SummaryWriter
writer = SummaryWriter(comment=str(random.randint(0,100)))
class RandomDatasetShift(Dataset):
def __init__(self, nsample, size):
length = 60*100000
self.len = 3 * nsample
self.data_v = torch.randn(nsample, size, length)
self.data_s = torch.sin(self.data_v)
self.v_neg_inc = np.random.randint(1, nsample, (nsample,))
self.s_neg_inc = np.random.randint(1, nsample, (nsample,))
def __getitem__(self, index):
if index % 3 == 0:
return torch.cat((self.data_v[index // 3], self.data_s[index // 3]), 0), 1
elif index % 3 == 1:
return torch.cat((self.data_v[index // 3], self.data_s[(index // 3 + self.v_neg_inc[index // 3]) % (self.len // 3)]), 0), 0
else:
return torch.cat((self.data_v[(index // 3 + self.s_neg_inc[index // 3]) % (self.len // 3)], self.data_s[index // 3]), 0), 0
def __len__(self):
return self.len
class RandomDataset(Dataset):
def __init__(self, nsample, size):
length = 60
self.len = 3 * nsample
self.data_v = torch.randn(nsample, size, length)
self.data_s = torch.sin(self.data_v)
self.v_neg_inc = np.random.randint(1, nsample, (nsample,))
self.s_neg_inc = np.random.randint(1, nsample, (nsample,))
def __getitem__(self, index):
if index % 3 == 0:
return torch.cat((self.data_v[index // 3], self.data_s[index // 3]), 0), 1
elif index % 3 == 1:
return torch.cat((self.data_v[index // 3], self.data_s[(index // 3 + self.v_neg_inc[index // 3]) % (self.len // 3)]), 0), 0
else:
return torch.cat((self.data_v[(index // 3 + self.s_neg_inc[index // 3]) % (self.len // 3)], self.data_s[index // 3]), 0), 0
def __len__(self):
return self.len
class RandomDataset2d(Dataset):
def __init__(self, nsample, width, noise_ratio):
height = 40
self.len = 3 * nsample
self.data_v = torch.randn(nsample, 1, height, width) # channel: 1
self.data_s = torch.sin(self.data_v) + noise_ratio * torch.randn(nsample, 1, height, width)
self.v_neg_inc = np.random.randint(1, nsample, (nsample,))
self.s_neg_inc = np.random.randint(1, nsample, (nsample,))
def __getitem__(self, index):
if index % 3 == 0:
return torch.cat((self.data_v[index // 3], self.data_s[index // 3]), 0), 1
elif index % 3 == 1:
return torch.cat((self.data_v[index // 3], self.data_s[(index // 3 + self.v_neg_inc[index // 3]) % (self.len // 3)]), 0), 0
else:
return torch.cat((self.data_v[(index // 3 + self.s_neg_inc[index // 3]) % (self.len // 3)], self.data_s[index // 3]), 0), 0
def __len__(self):
return self.len
# class Net(nn.Module):
# def __init__(self):
# super(Net, self).__init__()
# # 1 input image channel, 6 output channels, 5x5 square convolution
# # visual branch
# self.conv1a = nn.Conv1d(256, 64, 5)
# self.conv2a = nn.Conv1d(64, 512, 5)
# self.conv3a = nn.Conv1d(512, 1024, 5)
# # sensor branch
# self.conv1b = nn.Conv1d(256, 64, 5)
# self.conv2b = nn.Conv1d(64, 512, 5)
# self.conv3b = nn.Conv1d(512, 1024, 5)
# def forward(self, input_data):
# size = input_data.size()[1]
# x_v = input_data[:, :size // 2]
# x_s = input_data[:, size // 2:]
# # visual branch
# # Max pooling over a 4 window
# x_v = F.max_pool1d(F.relu(self.conv1a(x_v)), 4, stride=2)
# x_v = F.max_pool1d(F.relu(self.conv2a(x_v)), 4, stride=2)
# x_v = F.relu(self.conv3a(x_v))
# x_v = F.max_pool1d(x_v, x_v.size()[2])
# # sensor branch
# x_s = F.max_pool1d(F.relu(self.conv1b(x_s)), 4, stride=2)
# x_s = F.max_pool1d(F.relu(self.conv2b(x_s)), 4, stride=2)
# x_s = F.relu(self.conv3b(x_s))
# x_s = F.max_pool1d(x_s, x_s.size()[2])
# x_v = x_v.view(input_data.size()[0], 1, -1)
# x_v_norm = x_v.norm(p=2, dim=2, keepdim=True)
# x_v_normalized = x_v.div(x_v_norm.expand_as(x_v))
# x_s = x_s.view(input_data.size()[0], -1, 1)
# x_s_norm = x_s.norm(p=2, dim=1, keepdim=True)
# x_s_normalized = x_s.div(x_s_norm.expand_as(x_s))
# #x_out = torch.bmm(x_v_normalized, x_s_normalized).reshape((-1, 1))
# x_out = torch.bmm(x_v, x_s).reshape((-1, 1))
# return torch.cat((1 - x_out, x_out), 1)
class Net2d(nn.Module):
def __init__(self):
super(Net2d, self).__init__()
# 1 input image channel, 6 output channels, 5x5 square convolution
# visual branch
self.conv1a = nn.Conv2d(1, 64, (40, 5), stride=1)
self.conv2a = nn.Conv2d(64, 512, (1, 25), stride=1)
self.conv3a = nn.Conv2d(512, 1024, (1, 25), stride=1)
# sensor branch
self.conv1b = nn.Conv2d(1, 64, (40, 5), stride=1)
self.conv2b = nn.Conv2d(64, 512, (1, 25), stride=1)
self.conv3b = nn.Conv2d(512, 1024, (1, 25), stride=1)
def forward(self, input_data):
size = input_data.size()[1]
x_v = input_data[:, :size // 2]
x_s = input_data[:, size // 2:]
# visual branch
# Max pooling over a 4 window
x_v = F.max_pool2d(F.relu(self.conv1a(x_v)), (1, 4), stride=2) # relu+maxpool or maxpool+relu
x_v = F.max_pool2d(F.relu(self.conv2a(x_v)), (1, 4), stride=2)
x_v = F.relu(self.conv3a(x_v))
x_v = F.max_pool2d(x_v, x_v.size()[2])
# sensor branch
x_s = F.max_pool2d(F.relu(self.conv1b(x_s)), (1, 4), stride=2)
x_s = F.max_pool2d(F.relu(self.conv2b(x_s)), (1, 4), stride=2)
x_s = F.relu(self.conv3b(x_s))
x_s = F.max_pool2d(x_s, x_s.size()[2])
x_v = x_v.view(input_data.size()[0], 1, -1)
x_v_norm = x_v.norm(p=2, dim=2, keepdim=True)
x_v_normalized = x_v.div(x_v_norm.expand_as(x_v))
x_s = x_s.view(input_data.size()[0], -1, 1)
x_s_norm = x_s.norm(p=2, dim=1, keepdim=True)
x_s_normalized = x_s.div(x_s_norm.expand_as(x_s))
x_out = torch.bmm(x_v_normalized, x_s_normalized).reshape((-1, 1))
# x_out = torch.bmm(x_v, x_s).reshape((-1, 1))
return torch.cat((1 - x_out, x_out), 1)
net = Net2d()
print(net)
def train_net(net, trainloader, valloader=None, epochs=1):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.0001, momentum=0.9)
running_total = running_correct = 0
for epoch in range(epochs): # loop over the dataset multiple times
correct = 0
total = 0
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
loss = criterion(outputs, labels)
writer.add_scalar('data/training_loss', loss, i+epoch*len(trainloader))
loss.backward()
optimizer.step()
_, predicted = torch.max(outputs.data, 1)
running_total += labels.size(0)
running_correct += (predicted == labels).sum().item()
# print(predicted)
# print statistics
running_loss += loss.item()
if (i + epoch * len(trainloader)) % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f, training accuracy: %d %%' %
(epoch + 1, i + 1, running_loss / 2000, 100 * running_correct / running_total))
running_loss = 0.0
running_total = running_correct = 0
# save model parameters
if epoch%5 == 0:
torch.save(net.state_dict(), 'save_checkpoint_epoch'+str(epoch)+'.pt')
with torch.no_grad():
for data in valloader:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = net(inputs)
loss = criterion(outputs, labels)
writer.add_scalar('data/val_loss', loss, epoch*len(trainloader))
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the %d val images: %d %%' % (len(
valloader), 100 * correct / total))
print('Finished Training')
def test_net(net, testloader):
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net.to(device)
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
outputs = net(inputs)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
def main():
batch_size = 1
trainloader = DataLoader(dataset=RandomDataset(10000, 256),
batch_size=batch_size, shuffle=True)
valloader = DataLoader(dataset=RandomDataset(2000, 256),
batch_size=batch_size, shuffle=True)
testloader = DataLoader(dataset=RandomDataset(2000, 256), batch_size=batch_size,
shuffle=False, num_workers=2)
net = Net()
train_net(net, trainloader, valloader=valloader, epochs=50)
test_net(net, testloader)
def main2d():
batch_size = 1
trainloader = DataLoader(dataset=RandomDataset2d(10000, 1024, 0),
batch_size=batch_size, shuffle=True)
valloader = DataLoader(dataset=RandomDataset2d(2000, 1024, 0),
batch_size=batch_size, shuffle=True)
testloader = DataLoader(dataset=RandomDataset2d(2000, 1024, 0), batch_size=batch_size,
shuffle=False, num_workers=2)
net = Net2d()
train_net(net, trainloader, valloader=valloader, epochs=20)
test_net(net, testloader)
#params = list(net.parameters())
# print(len(params))
# print(params[0].size())
if __name__ == "__main__":
main2d()
#load model parameters
# net = Net2d()
# net.load_state_dict(torch.load(PATH))