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CUB-200-2011.py
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CUB-200-2011.py
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'''PyTorch CUB-200-2011 Training with VGG16 (TRAINED FROM SCRATCH).'''
from __future__ import print_function
import os
# import nni
import time
import torch
import logging
import argparse
import torchvision
import random
import torch.nn as nn
import numpy as np
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
import torchvision
from my_pooling import my_MaxPool2d,my_AvgPool2d
import torchvision.transforms as transforms
logger = logging.getLogger('MC_VGG_224')
os.environ["CUDA_VISIBLE_DEVICES"] = "2,3"
lr = 0.1
nb_epoch = 300
criterion = nn.CrossEntropyLoss()
#Data
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.Scale((224,224)),
transforms.RandomCrop(224, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
transform_test = transforms.Compose([
transforms.Scale((224,224)),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
])
trainset = torchvision.datasets.ImageFolder(root='/home/data/Birds/train', transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True, num_workers=16, drop_last = True)
testset = torchvision.datasets.ImageFolder(root='/home/data/Birds/test', transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=True, num_workers=16)
print('==> Building model..')
cfg = {
'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 600, 'M', 512, 512, 600],
'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}
class VGG(nn.Module):
def __init__(self, vgg_name):
super(VGG, self).__init__()
self.features = self._make_layers(cfg[vgg_name])
self.classifier = nn.Linear(512, 10)
def forward(self, x):
out = self.features(x)
out = out.view(out.size(0), -1)
out = self.classifier(out)
return out
def _make_layers(self, cfg):
layers = []
in_channels = 3
for x in cfg:
if x == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
layers += [nn.Conv2d(in_channels, x, kernel_size=3, padding=1),
nn.BatchNorm2d(x),
nn.ReLU(inplace=True)]
in_channels = x
layers += [nn.AvgPool2d(kernel_size=1, stride=1)]
return nn.Sequential(*layers)
def Mask(nb_batch, channels):
foo = [1] * 2 + [0] * 1
bar = []
for i in range(200):
random.shuffle(foo)
bar += foo
bar = [bar for i in range(nb_batch)]
bar = np.array(bar).astype("float32")
bar = bar.reshape(nb_batch,200*channels,1,1)
bar = torch.from_numpy(bar)
bar = bar.cuda()
bar = Variable(bar)
return bar
def supervisor(x,targets,height,cnum):
mask = Mask(x.size(0), cnum)
branch = x
branch = branch.reshape(branch.size(0),branch.size(1), branch.size(2) * branch.size(3))
branch = F.softmax(branch,2)
branch = branch.reshape(branch.size(0),branch.size(1), x.size(2), x.size(2))
branch = my_MaxPool2d(kernel_size=(1,cnum), stride=(1,cnum))(branch)
branch = branch.reshape(branch.size(0),branch.size(1), branch.size(2) * branch.size(3))
loss_2 = 1.0 - 1.0*torch.mean(torch.sum(branch,2))/cnum # set margin = 3.0
branch_1 = x * mask
branch_1 = my_MaxPool2d(kernel_size=(1,cnum), stride=(1,cnum))(branch_1)
branch_1 = nn.AvgPool2d(kernel_size=(height,height))(branch_1)
branch_1 = branch_1.view(branch_1.size(0), -1)
loss_1 = criterion(branch_1, targets)
return [loss_1, loss_2]
class model_bn(nn.Module):
def __init__(self, feature_size=512,classes_num=200):
super(model_bn, self).__init__()
self.features_1 = nn.Sequential(*list(VGG('VGG16').features.children())[:34])
self.features_2 = nn.Sequential(*list(VGG('VGG16').features.children())[34:])
self.max = nn.MaxPool2d(kernel_size=2, stride=2)
self.num_ftrs = 600*7*7
self.classifier = nn.Sequential(
nn.BatchNorm1d(self.num_ftrs),
#nn.Dropout(0.5),
nn.Linear(self.num_ftrs, feature_size),
nn.BatchNorm1d(feature_size),
nn.ELU(inplace=True),
#nn.Dropout(0.5),
nn.Linear(feature_size, classes_num),
)
def forward(self, x, targets):
x = self.features_1(x)
x = self.features_2(x)
if self.training:
MC_loss = supervisor(x,targets,height=14,cnum=3)
x = self.max(x)
x = x.view(x.size(0), -1)
x = self.classifier(x)
loss = criterion(x, targets)
if self.training:
return x, loss, MC_loss
else:
return x, loss
use_cuda = torch.cuda.is_available()
net =model_bn(512, 200)
if use_cuda:
net.classifier.cuda()
net.features_1.cuda()
net.features_2.cuda()
net.classifier = torch.nn.DataParallel(net.classifier)
net.features_1 = torch.nn.DataParallel(net.features_1)
net.features_2 = torch.nn.DataParallel(net.features_2)
cudnn.benchmark = True
def train(epoch,net, args, trainloader,optimizer):
print('\nEpoch: %d' % epoch)
net.train()
train_loss = 0
correct = 0
total = 0
idx = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
idx = batch_idx
inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
out, ce_loss, MC_loss = net(inputs, targets)
loss = ce_loss + args["alpha_1"] * MC_loss[0] + args["beta_1"] * MC_loss[1]
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(out.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum().item()
train_acc = 100.*correct/total
train_loss = train_loss/(idx+1)
logging.info('Iteration %d, train_acc = %.5f,train_loss = %.6f' % (epoch, train_acc,train_loss))
return train_acc, train_loss
def test(epoch,net,testloader,optimizer):
net.eval()
test_loss = 0
correct = 0
total = 0
idx = 0
for batch_idx, (inputs, targets) in enumerate(testloader):
with torch.no_grad():
idx = batch_idx
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
inputs, targets = Variable(inputs), Variable(targets)
out, ce_loss = net(inputs,targets)
test_loss += ce_loss.item()
_, predicted = torch.max(out.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum().item()
test_acc = 100.*correct/total
test_loss = test_loss/(idx+1)
logging.info('test, test_acc = %.4f,test_loss = %.4f' % (test_acc,test_loss))
return test_acc
def cosine_anneal_schedule(t):
cos_inner = np.pi * (t % (nb_epoch )) # t - 1 is used when t has 1-based indexing.
cos_inner /= (nb_epoch )
cos_out = np.cos(cos_inner) + 1
return float( 0.1 / 2 * cos_out)
optimizer = optim.SGD([
{'params': net.classifier.parameters(), 'lr': 0.1},
{'params': net.features_1.parameters(), 'lr': 0.1},
{'params': net.features_2.parameters(), 'lr': 0.1},
],
momentum=0.9, weight_decay=5e-4)
def get_params():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MC2_AutoML Example')
parser.add_argument('--alpha_1', type=float, default=1.5, metavar='ALPHA',
help='alpha_1 value (default: 2.0)')
parser.add_argument('--beta_1', type=float, default=20.0, metavar='BETA',
help='beta_1 value (default: 20.0)')
args, _ = parser.parse_known_args()
return args
if __name__ == '__main__':
try:
args = vars(get_params())
print(args)
# main(params)
max_val_acc = 0
for epoch in range(1, nb_epoch+1):
if epoch ==150:
lr = 0.01
if epoch ==225:
lr = 0.001
optimizer.param_groups[0]['lr'] = lr
optimizer.param_groups[1]['lr'] = lr
optimizer.param_groups[2]['lr'] = lr
train(epoch, net, args,trainloader,optimizer)
test_acc = test(epoch, net,testloader,optimizer)
if test_acc >max_val_acc:
max_val_acc = test_acc
print("max_val_acc", max_val_acc)
except Exception as exception:
logger.exception(exception)
raise