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train.py
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train.py
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# -*- coding: utf-8 -*-
'''
The Capsules Network.
@author: Yuxian Meng
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.optim import lr_scheduler
from Capsules import PrimaryCaps, ConvCaps
from utils import get_args, get_dataloader
class CapsNet(nn.Module):
def __init__(self,A=32,B=32,C=32,D=32, E=10,r = 3):
super(CapsNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=A,
kernel_size=5, stride=2)
self.primary_caps = PrimaryCaps(A,B)
self.convcaps1 = ConvCaps(B, C, kernel = 3, stride=2,iteration=r,
coordinate_add=False, transform_share = False)
self.convcaps2 = ConvCaps(C, D, kernel = 3, stride=1,iteration=r,
coordinate_add=False, transform_share = False)
self.classcaps = ConvCaps(D, E, kernel = 0, stride=1,iteration=r,
coordinate_add=True, transform_share = True)
def forward(self,x,lambda_): #b,1,28,28
x = F.relu(self.conv1(x)) #b,32,12,12
x = self.primary_caps(x) #b,32*(4*4+1),12,12
x = self.convcaps1(x,lambda_) #b,32*(4*4+1),5,5
x = self.convcaps2(x,lambda_) #b,32*(4*4+1),3,3
x = self.classcaps(x,lambda_).view(-1,10*16+10) #b,10*16+10
return x
def loss(self, x, target, m): #x:b,10 target:b
b = x.size(0)
a_t = torch.cat([x[i][target[i]] for i in range(b)]) #b
a_t_stack = a_t.view(b,1).expand(b,10).contiguous() #b,10
u = m-(a_t_stack-x) #b,10
mask = u.ge(0).float() #max(u,0) #b,10
loss = ((mask*u)**2).sum()/b - m**2 #float
return loss
def loss2(self,x ,target):
loss = F.cross_entropy(x,target)
return loss
if __name__ == "__main__":
args = get_args()
train_loader, test_loader = get_dataloader(args)
use_cuda = args.use_cuda
steps = len(train_loader.dataset)//args.batch_size
lambda_ = 1e-3 #TODO:find a good schedule to increase lambda and m
m = 0.2
A,B,C,D,E,r = 64,8,16,16,10,args.r # a small CapsNet
# A,B,C,D,E,r = 32,32,32,32,10,args.r # a classic CapsNet
model = CapsNet(A,B,C,D,E,r)
with torch.cuda.device(args.gpu):
# print(args.gpu, type(args.gpu))
if args.pretrained:
model.load_state_dict(torch.load(args.pretrained))
m = 0.8
lambda_ = 0.9
if use_cuda:
print("activating cuda")
model.cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'max',patience = 1)
for epoch in range(args.num_epochs):
#Train
print("Epoch {}".format(epoch))
b = 0
correct = 0
for data in train_loader:
b += 1
if lambda_ < 1:
lambda_ += 2e-1/steps
if m < 0.9:
m += 2e-1/steps
optimizer.zero_grad()
imgs,labels = data #b,1,28,28; #b
imgs,labels = Variable(imgs),Variable(labels)
if use_cuda:
imgs = imgs.cuda()
labels = labels.cuda()
out = model(imgs,lambda_) #b,10,17
out_poses, out_labels = out[:,:-10],out[:,-10:] #b,16*10; b,10
loss = model.loss(out_labels, labels, m)
torch.nn.utils.clip_grad_norm(model.parameters(), args.clip)
loss.backward()
optimizer.step()
#stats
pred = out_labels.max(1)[1] #b
acc = pred.eq(labels).cpu().sum().data[0]
correct += acc
if b % args.print_freq == 0:
print("batch:{}, loss:{:.4f}, acc:{:}/{}".format(
b, loss.data[0],acc, args.batch_size))
acc = correct/len(train_loader.dataset)
print("Epoch{} Train acc:{:4}".format(epoch, acc))
scheduler.step(acc)
torch.save(model.state_dict(), "./model_{}.pth".format(epoch))
#Test
print('Testing...')
correct = 0
for data in test_loader:
imgs,labels = data #b,1,28,28; #b
imgs,labels = Variable(imgs),Variable(labels)
if use_cuda:
imgs = imgs.cuda()
labels = labels.cuda()
out = model(imgs,lambda_) #b,10,17
out_poses, out_labels = out[:,:-10],out[:,-10:] #b,16*10; b,10
loss = model.loss(out_labels, labels, m)
#stats
pred = out_labels.max(1)[1] #b
acc = pred.eq(labels).cpu().sum().data[0]
correct += acc
acc = correct/len(test_loader.dataset)
print("Epoch{} Test acc:{:4}".format(epoch, acc))