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nets.py
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nets.py
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import numpy as np
import chainer
from chainer import cuda
import chainer.functions as F
import chainer.links as L
def _augmentation(x):
xp = cuda.get_array_module(x)
MAX_SHIFT = 2
batchsize, ch, h, w = x.shape
h_shift, w_shift = xp.random.randint(-MAX_SHIFT, MAX_SHIFT + 1, size=2)
a_h_sl = slice(max(0, h_shift), h_shift + h)
a_w_sl = slice(max(0, w_shift), w_shift + w)
x_h_sl = slice(max(0, - h_shift), - h_shift + h)
x_w_sl = slice(max(0, - w_shift), - w_shift + w)
a = xp.zeros(x.shape)
a[:, :, a_h_sl, a_w_sl] = x[:, :, x_h_sl, x_w_sl]
return a.astype(x.dtype)
def _count_params(m, n_grids=6):
print('# of params', sum(param.size for param in m.params()))
# The number of parameters in the paper (11.36M) might be
# of the model with unshared matrices over primary capsules in a same grid
# when input data are 36x36 images of MultiMNIST (n_grids = 10).
# Our model with n_grids=10 has 11.349008M parameters.
# (In the Sec. 4, the paper says "each capsule in the [6, 6] grid
# is sharing their weights with each other.")
print('# of params if unshared',
sum(param.size for param in m.params()) +
sum(param.size for param in m.Ws.params()) *
(n_grids * n_grids - 1))
def squash(ss):
ss_norm2 = F.sum(ss ** 2, axis=1, keepdims=True)
"""
# ss_norm2 = F.broadcast_to(ss_norm2, ss.shape)
# vs = ss_norm2 / (1. + ss_norm2) * ss / F.sqrt(ss_norm2): naive
"""
norm_div_1pnorm2 = F.sqrt(ss_norm2) / (1. + ss_norm2)
norm_div_1pnorm2 = F.broadcast_to(norm_div_1pnorm2, ss.shape)
vs = norm_div_1pnorm2 * ss # :efficient
# (batchsize, 16, 10)
return vs
def get_norm(vs):
return F.sqrt(F.sum(vs ** 2, axis=1))
init = chainer.initializers.Uniform(scale=0.05)
class CapsNet(chainer.Chain):
def __init__(self, use_reconstruction=False):
super(CapsNet, self).__init__()
self.n_iterations = 3 # dynamic routing
self.n_grids = 6 # grid width of primary capsules layer
self.n_raw_grids = self.n_grids
self.use_reconstruction = use_reconstruction
with self.init_scope():
self.conv1 = L.Convolution2D(1, 256, ksize=9, stride=1,
initialW=init)
self.conv2 = L.Convolution2D(256, 32 * 8, ksize=9, stride=2,
initialW=init)
self.Ws = chainer.ChainList(
*[L.Convolution2D(8, 16 * 10, ksize=1, stride=1, initialW=init)
for i in range(32)])
self.fc1 = L.Linear(16 * 10, 512, initialW=init)
self.fc2 = L.Linear(512, 1024, initialW=init)
self.fc3 = L.Linear(1024, 784, initialW=init)
_count_params(self, n_grids=self.n_grids)
self.results = {'N': 0., 'loss': [], 'correct': [],
'cls_loss': [], 'rcn_loss': []}
def pop_results(self):
merge = dict()
merge['mean_loss'] = sum(self.results['loss']) / self.results['N']
merge['cls_loss'] = sum(self.results['cls_loss']) / self.results['N']
merge['rcn_loss'] = sum(self.results['rcn_loss']) / self.results['N']
merge['accuracy'] = sum(self.results['correct']) / self.results['N']
self.results = {'N': 0., 'loss': [], 'correct': [],
'cls_loss': [], 'rcn_loss': []}
return merge
def __call__(self, x, t):
if chainer.config.train:
x = _augmentation(x)
vs_norm, vs = self.output(x)
self.loss = self.calculate_loss(vs_norm, t, vs, x)
self.results['loss'].append(self.loss.data * t.shape[0])
self.results['correct'].append(self.calculate_correct(vs_norm, t))
self.results['N'] += t.shape[0]
return self.loss
def output(self, x):
batchsize = x.shape[0]
n_iters = self.n_iterations
gg = self.n_grids * self.n_grids
# h1 = F.relu(self.conv1(x))
h1 = F.leaky_relu(self.conv1(x), 0.05)
pr_caps = F.split_axis(self.conv2(h1), 32, axis=1)
# shapes if MNIST. -> if MultiMNIST
# x (batchsize, 1, 28, 28) -> (:, :, 36, 36)
# h1 (batchsize, 256, 20, 20) -> (:, :, 28, 28)
# pr_cap (batchsize, 8, 6, 6) -> (:, :, 10, 10)
Preds = []
for i in range(32):
pred = self.Ws[i](pr_caps[i])
Pred = pred.reshape((batchsize, 16, 10, gg))
Preds.append(Pred)
Preds = F.stack(Preds, axis=3)
assert(Preds.shape == (batchsize, 16, 10, 32, gg))
bs = self.xp.zeros((batchsize, 10, 32, gg), dtype='f')
for i_iter in range(n_iters):
cs = F.softmax(bs, axis=1)
Cs = F.broadcast_to(cs[:, None], Preds.shape)
assert(Cs.shape == (batchsize, 16, 10, 32, gg))
ss = F.sum(Cs * Preds, axis=(3, 4))
vs = squash(ss)
assert(vs.shape == (batchsize, 16, 10))
if i_iter != n_iters - 1:
Vs = F.broadcast_to(vs[:, :, :, None, None], Preds.shape)
assert(Vs.shape == (batchsize, 16, 10, 32, gg))
bs = bs + F.sum(Vs * Preds, axis=1)
assert(bs.shape == (batchsize, 10, 32, gg))
vs_norm = get_norm(vs)
return vs_norm, vs
def reconstruct(self, vs, t):
xp = self.xp
batchsize = t.shape[0]
I = xp.arange(batchsize)
mask = xp.zeros(vs.shape, dtype='f')
mask[I, :, t] = 1.
masked_vs = mask * vs
x_recon = F.sigmoid(
self.fc3(F.relu(
self.fc2(F.relu(
self.fc1(masked_vs)))))).reshape((batchsize, 1, 28, 28))
return x_recon
def calculate_loss(self, vs_norm, t, vs, x):
class_loss = self.calculate_classification_loss(vs_norm, t)
self.results['cls_loss'].append(class_loss.data * t.shape[0])
if self.use_reconstruction:
recon_loss = self.calculate_reconstruction_loss(vs, t, x)
self.results['rcn_loss'].append(recon_loss.data * t.shape[0])
return class_loss + 0.0005 * recon_loss
else:
return class_loss
def calculate_classification_loss(self, vs_norm, t):
xp = self.xp
batchsize = t.shape[0]
I = xp.arange(batchsize)
T = xp.zeros(vs_norm.shape, dtype='f')
T[I, t] = 1.
m = xp.full(vs_norm.shape, 0.1, dtype='f')
m[I, t] = 0.9
loss = T * F.relu(m - vs_norm) ** 2 + \
0.5 * (1. - T) * F.relu(vs_norm - m) ** 2
return F.sum(loss) / batchsize
def calculate_reconstruction_loss(self, vs, t, x):
batchsize = t.shape[0]
x_recon = self.reconstruct(vs, t)
loss = (x_recon - x) ** 2
return F.sum(loss) / batchsize
def calculate_correct(self, v, t):
return (self.xp.argmax(v.data, axis=1) == t).sum()