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capsule_network.py
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capsule_network.py
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#
# Dynamic Routing Between Capsules
# https://arxiv.org/pdf/1710.09829.pdf
#
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torchvision import datasets, transforms
import torchvision.utils as vutils
import torch.nn.functional as F
from capsule_conv_layer import CapsuleConvLayer
from capsule_layer import CapsuleLayer
class CapsuleNetwork(nn.Module):
def __init__(self,
image_width,
image_height,
image_channels,
conv_inputs,
conv_outputs,
num_primary_units,
primary_unit_size,
num_output_units,
output_unit_size):
super(CapsuleNetwork, self).__init__()
self.reconstructed_image_count = 0
self.image_channels = image_channels
self.image_width = image_width
self.image_height = image_height
self.conv1 = CapsuleConvLayer(in_channels=conv_inputs,
out_channels=conv_outputs)
self.primary = CapsuleLayer(in_units=0,
in_channels=conv_outputs,
num_units=num_primary_units,
unit_size=primary_unit_size,
use_routing=False)
self.digits = CapsuleLayer(in_units=num_primary_units,
in_channels=primary_unit_size,
num_units=num_output_units,
unit_size=output_unit_size,
use_routing=True)
reconstruction_size = image_width * image_height * image_channels
self.reconstruct0 = nn.Linear(num_output_units*output_unit_size, int((reconstruction_size * 2) / 3))
self.reconstruct1 = nn.Linear(int((reconstruction_size * 2) / 3), int((reconstruction_size * 3) / 2))
self.reconstruct2 = nn.Linear(int((reconstruction_size * 3) / 2), reconstruction_size)
self.relu = nn.ReLU(inplace=True)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
return self.digits(self.primary(self.conv1(x)))
def loss(self, images, input, target, size_average=True):
return self.margin_loss(input, target, size_average) + self.reconstruction_loss(images, input, size_average)
def margin_loss(self, input, target, size_average=True):
batch_size = input.size(0)
# ||vc|| from the paper.
v_mag = torch.sqrt((input**2).sum(dim=2, keepdim=True))
# Calculate left and right max() terms from equation 4 in the paper.
zero = Variable(torch.zeros(1)).cuda()
m_plus = 0.9
m_minus = 0.1
max_l = torch.max(m_plus - v_mag, zero).view(batch_size, -1)**2
max_r = torch.max(v_mag - m_minus, zero).view(batch_size, -1)**2
# This is equation 4 from the paper.
loss_lambda = 0.5
T_c = target
L_c = T_c * max_l + loss_lambda * (1.0 - T_c) * max_r
L_c = L_c.sum(dim=1)
if size_average:
L_c = L_c.mean()
return L_c
def reconstruction_loss(self, images, input, size_average=True):
# Get the lengths of capsule outputs.
v_mag = torch.sqrt((input**2).sum(dim=2))
# Get index of longest capsule output.
_, v_max_index = v_mag.max(dim=1)
v_max_index = v_max_index.data
# Use just the winning capsule's representation (and zeros for other capsules) to reconstruct input image.
batch_size = input.size(0)
all_masked = [None] * batch_size
for batch_idx in range(batch_size):
# Get one sample from the batch.
input_batch = input[batch_idx]
# Copy only the maximum capsule index from this batch sample.
# This masks out (leaves as zero) the other capsules in this sample.
batch_masked = Variable(torch.zeros(input_batch.size())).cuda()
batch_masked[v_max_index[batch_idx]] = input_batch[v_max_index[batch_idx]]
all_masked[batch_idx] = batch_masked
# Stack masked capsules over the batch dimension.
masked = torch.stack(all_masked, dim=0)
# Reconstruct input image.
masked = masked.view(input.size(0), -1)
output = self.relu(self.reconstruct0(masked))
output = self.relu(self.reconstruct1(output))
output = self.sigmoid(self.reconstruct2(output))
output = output.view(-1, self.image_channels, self.image_height, self.image_width)
# Save reconstructed images occasionally.
if self.reconstructed_image_count % 10 == 0:
if output.size(1) == 2:
# handle two-channel images
zeros = torch.zeros(output.size(0), 1, output.size(2), output.size(3))
output_image = torch.cat([zeros, output.data.cpu()], dim=1)
else:
# assume RGB or grayscale
output_image = output.data.cpu()
vutils.save_image(output_image, "reconstruction.png")
self.reconstructed_image_count += 1
# The reconstruction loss is the sum squared difference between the input image and reconstructed image.
# Multiplied by a small number so it doesn't dominate the margin (class) loss.
error = (output - images).view(output.size(0), -1)
error = error**2
error = torch.sum(error, dim=1) * 0.0005
# Average over batch
if size_average:
error = error.mean()
return error