Usage¶
How to use laymon to monitor layers¶
To use PyLaymon in a project:
# Import laymon
import laymon
# Initialize the feature map monitoring class
fMonitor = laymon.FeatureMapMonitoring()
# Add your model whose feature maps are to be monitored.
fMonitor.add_model(net)
# To start monitoring, call the `start` method during training of the model.
fMonitor.start()
To get the list of layers being monitored:
fMonitor.monitor.get_registered_observers()
Instead of adding the whole model, one can add specific layers, whose feature maps are to be monitored.:
fMonitor.add_layer(net.conv2, 'conv2')
To remove a layer from the monitoring:
fMonitor.remove_layer(net.conv2)
Example¶
Below is a sample example:
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from laymon import FeatureMapMonitoring
# Creating a neural network
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
# Initializing the neural network
net = Net()
# Initialize the feature map monitor
fMonitor = FeatureMapMonitoring()
# Add the neural network model to be monitored
fmonitoring.add_model(net)
# Add your loss and optimizers
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
for epoch in range(1): # loop over the dataset multiple times
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = net(inputs)
# Start monitoring the feature maps
fmonitoring.start()
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
if i % 2000 == 1999: # print every 2000 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0
print('Finished Training')
The full example notebook can be found here.
Custom Displays¶
One may feel the need to have a custom display class for visualizing the feature maps. One the applications could be displaying additional meta info or using a third partly application for visualization (e.g. Grafana) instead of the matplotlib library.
In order to accomplish this one can write his/her own custom display class and plugin it into the FeatureMapMonitoring instance.
# Import the Display abstract class
from laymon.interfaces import Display
from laymon import FeatureMapMonitoring
# Create your own custom class by inheriting
# and implementing the methods of the Display class.
class MyCustomDisplay(Display):
def custom_method(self, params):
// My Custom method
def update_display(self, parameters, display_title):
// Implement this method.
// This method needs to be implemented,
// as this method is invoked by the observers/layer object being monitored to
// send the updated parameters to the display function
// Your Custom Logic.
// Calling your custom methods.
# Create instance of the FeatureMapMonitoring class
f_map_monitor = FeatureMapMonitoring()
# Overwrite the display class with your custom display class.
f_map_monitor.observer_factory.display_object = MyCustomDisplay
# Now the observers/layers being monitored point to your custom display method.