pytorchテストcapsnet
4019 ワード
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
batch_size = 1
test_batch_size = 1
epochs = 10
lr = 0.01
momentum = 0.5
no_cuda = True
seed = 1
log_interval = 10
cuda = not no_cuda and torch.cuda.is_available()
torch.manual_seed(seed)
if cuda:
torch.cuda.manual_seed(seed)
kwargs = {'num_workers': 1, 'pin_memory': True} if cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('../data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=test_batch_size, shuffle=True, **kwargs)
class CapsNet(nn.Module):
def __init__(self):
super(CapsNet, self).__init__()
self.conv1 = nn.Conv2d(1, 256, 9) # First Conv
conv_caps = [nn.Conv2d(256, 8, 9, stride = 2) for i in range(32)]
self.conv_caps = nn.ModuleList(conv_caps) # Primary caps
self.weight_matrices = nn.ModuleList([nn.ModuleList([nn.ModuleList([nn.Linear(8, 16) for i in range(6)]) for i in range(6)]) for i in range(32)]) # From primary caps to digit caps
self.bij = Variable(torch.FloatTensor(32, 6, 6, 10).zero_()) # routing weights
def forward(self, x):
x = F.relu((self.conv1(x)))
prim_caps_layer = [self.conv_caps[i](x).resize(8, 6, 6).permute(1, 2, 0) for i in range(32)]
for k in range(len(prim_caps_layer)):
for i in range(prim_caps_layer[k].size()[0]):
for j in range(prim_caps_layer[k].size()[1]):
tmp = self.non_linearity(prim_caps_layer[k][i, j].clone())
prim_caps_layer[k][i, j] = tmp
tmp = torch.stack(prim_caps_layer)
out = Variable(torch.FloatTensor(32, 6, 6, 16))
for i in range(32):
for j in range(6):
for k in range(6):
t = self.weight_matrices[i][j][k](tmp[i, j, k].clone())
out[i, j, k] = t
# print (self.bij[0][0][0])
for loop in range(10):
si = Variable(torch.FloatTensor(10, 16).zero_())
for i in range(32):
for j in range(6):
for k in range(6):
ci = F.softmax(self.bij[i,j,k].clone())
for m in range(10):
t = si[m].clone() + ci[m].clone() * out[i,j,k].clone()
si[m] = t
for i in range(10):
tmp = self.non_linearity(si[i].clone())
si[i] = tmp
for i in range(32):
for j in range(6):
for k in range(6):
for m in range(10):
tmp = self.bij[i, j, k, m].clone() + si[m].dot(out[i,j,k].clone())
self.bij[i, j, k, m] = tmp
# print (self.bij[0][0][0])
norms = Variable(torch.FloatTensor(10))
for i in range(10):
norms[i] = si[i].norm()
return norms, self.bij
def non_linearity(self, vec):
nm = vec.norm()
nm2 = nm ** 2
vec = vec * nm2 / ((1 + nm2) * nm)
return vec
import numpy as np
if __name__ == '__main__':
model = CapsNet()
model.eval()
for i in range(101):
t1 = time.time()
x = torch.rand(1,1, 28, 28)
out3 = model(x)
# print(out3)
if i != 0:
cnt = time.time() - t1
print(cnt)