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HBP_all.py
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HBP_all.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Fine-tune all layers only for HBP(Hierarchical Bilinear Pooling for Fine-Grained Visual Recognition).
Usage:
CUDA_VISIBLE_DEVICES=0,1 python HBP_all.py --base_lr 0.001 --batch_size 24 --epochs 200 --weight_decay 0.0005 --model 'HBP_fc_epoch_*.pth' | tee 'hbp_all.log'
"""
import os
import torch
import torchvision
import cub200
import visdom
import argparse
vis = visdom.Visdom(env=u'HBP_all',use_incoming_socket=False)
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
class HBP(torch.nn.Module):
def __init__(self):
"""Declare all needed layers."""
torch.nn.Module.__init__(self)
# Convolution and pooling layers of VGG-16.
self.features = torchvision.models.vgg16(pretrained=False).features
self.features_conv5_1 = torch.nn.Sequential(*list(self.features.children())
[:-5])
self.features_conv5_2 = torch.nn.Sequential(*list(self.features.children())
[-5:-3])
self.features_conv5_3 = torch.nn.Sequential(*list(self.features.children())
[-3:-1])
self.bilinear_proj = torch.nn.Sequential(torch.nn.Conv2d(512,8192,kernel_size=1,bias=False),
torch.nn.BatchNorm2d(8192),
torch.nn.ReLU(inplace=True))
# Linear classifier.
self.fc = torch.nn.Linear(8192*3, 200)
def hbp(self,conv1,conv2):
N = conv1.size()[0]
proj_1 = self.bilinear_proj(conv1)
proj_2 = self.bilinear_proj(conv2)
assert(proj_1.size() == (N,8192,28,28))
X = proj_1 * proj_2
assert(X.size() == (N,8192,28,28))
X = torch.sum(X.view(X.size()[0],X.size()[1],-1),dim = 2)
X = X.view(N, 8192)
X = torch.sqrt(X + 1e-5)
X = torch.nn.functional.normalize(X)
return X
def forward(self, X):
N = X.size()[0]
assert X.size() == (N, 3, 448, 448)
X_conv5_1 = self.features_conv5_1(X)
X_conv5_2 = self.features_conv5_2(X_conv5_1)
X_conv5_3 = self.features_conv5_3(X_conv5_2)
X_branch_1 = self.hbp(X_conv5_1,X_conv5_2)
X_branch_2 = self.hbp(X_conv5_2,X_conv5_3)
X_branch_3 = self.hbp(X_conv5_1,X_conv5_3)
X_branch = torch.cat([X_branch_1,X_branch_2,X_branch_3],dim=1)
assert X_branch.size() == (N,8192*3)
X = self.fc(X_branch)
assert X.size() == (N, 200)
return X
class HBPManager(object):
def __init__(self, options, path):
print('Prepare the network and data.')
self._options = options
self._path = path
# Network.
self._net = torch.nn.DataParallel(HBP()).cuda()
print(self._net)
self._net.load_state_dict(torch.load(self._path['model']))
# Criterion.
self._criterion = torch.nn.CrossEntropyLoss().cuda()
# Solver.
param_to_optim = []
for param in self._net.parameters():
param_to_optim.append(param)
self._solver = torch.optim.SGD(
param_to_optim, lr=self._options['base_lr'],
momentum=0.9, weight_decay=self._options['weight_decay'])
milestones = [100]
self._scheduler = torch.optim.lr_scheduler.MultiStepLR(self._solver,milestones = milestones,gamma=0.25)
train_transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=448), # Let smaller edge match
torchvision.transforms.RandomHorizontalFlip(),
torchvision.transforms.RandomCrop(size=448),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
test_transforms = torchvision.transforms.Compose([
torchvision.transforms.Resize(size=448),
torchvision.transforms.CenterCrop(size=448),
torchvision.transforms.ToTensor(),
torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225))
])
train_data = cub200.CUB200(
root=self._path['cub200'], train=True, download=True,
transform=train_transforms)
test_data = cub200.CUB200(
root=self._path['cub200'], train=False, download=True,
transform=test_transforms)
self._train_loader = torch.utils.data.DataLoader(
train_data, batch_size=self._options['batch_size'],
shuffle=True, num_workers=4, pin_memory=True)
self._test_loader = torch.utils.data.DataLoader(
test_data, batch_size=16,
shuffle=False, num_workers=4, pin_memory=True)
def train(self):
print('Training.')
best_acc = 0.0
best_epoch = None
print('Epoch\tTrain loss\tTrain acc\tTest acc')
ii = 0
for t in range(self._options['epochs']):
epoch_loss = []
num_correct = 0
num_total = 0
for X, y in self._train_loader:
# Data.
X = torch.autograd.Variable(X.cuda())
y = torch.autograd.Variable(y.cuda(non_blocking = True))
# Clear the existing gradients.
self._solver.zero_grad()
# Forward pass.
score = self._net(X)
loss = self._criterion(score, y)
epoch_loss.append(loss.data[0])
# Prediction.
_, prediction = torch.max(score.data, 1)
num_total += y.size(0)
num_correct += torch.sum(prediction == y.data)
# Backward pass.
loss.backward()
self._solver.step()
ii += 1
x = torch.Tensor([ii])
y = torch.Tensor([loss.data[0]])
vis.line(X=x, Y=y, win='polynomial', update='append' if ii > 0 else None)
num_correct = torch.tensor(num_correct).float().cuda()
num_total = torch.tensor(num_total).float().cuda()
train_acc = 100 * num_correct / num_total
test_acc = self._accuracy(self._test_loader)
self._scheduler.step(test_acc)
if test_acc > best_acc:
best_acc = test_acc
best_epoch = t + 1
print('*', end='')
# Save model onto disk.
torch.save(self._net.state_dict(),
os.path.join('./model/'
'HBP_all_epoch_%d.pth' % (t + 1)))
print('%d\t%4.3f\t\t%4.2f%%\t\t%4.2f%%' %
(t+1, sum(epoch_loss) / len(epoch_loss), train_acc, test_acc))
print('Best at epoch %d, test accuaray %f' % (best_epoch, best_acc))
def _accuracy(self, data_loader):
self._net.train(False)
num_correct = 0
num_total = 0
for X, y in data_loader:
# Data.
X = torch.autograd.Variable(X.cuda())
y = torch.autograd.Variable(y.cuda(non_blocking = True))
# Prediction.
score = self._net(X)
_, prediction = torch.max(score.data, 1)
num_total += y.size(0)
num_correct += torch.sum(prediction == y.data)
self._net.train(True) # Set the model to training phase
num_correct = torch.tensor(num_correct).float().cuda()
num_total = torch.tensor(num_total).float().cuda()
return 100 * num_correct / num_total
def getStat(self):
print('Compute mean and variance for training data.')
train_data = cub200.CUB200(
root=self._path['cub200'], train=True,
transform=torchvision.transforms.ToTensor(), download=True)
train_loader = torch.utils.data.DataLoader(
train_data, batch_size=1, shuffle=False, num_workers=4,
pin_memory=True)
mean = torch.zeros(3)
std = torch.zeros(3)
for X, _ in train_loader:
for d in range(3):
mean[d] += X[:, d, :, :].mean()
std[d] += X[:, d, :, :].std()
mean.div_(len(train_data))
std.div_(len(train_data))
print(mean)
print(std)
def main():
parser = argparse.ArgumentParser(
description='Train HBP on CUB200.')
parser.add_argument('--base_lr', dest='base_lr', type=float, required=True,
help='Base learning rate for training.')
parser.add_argument('--batch_size', dest='batch_size', type=int,
required=True, help='Batch size.')
parser.add_argument('--epochs', dest='epochs', type=int,
required=True, help='Epochs for training.')
parser.add_argument('--weight_decay', dest='weight_decay', type=float,
required=True, help='Weight decay.')
parser.add_argument('--model', dest='model', type=str, required=True,
help='Model for fine-tuning.')
args = parser.parse_args()
if args.base_lr <= 0:
raise AttributeError('--base_lr parameter must >0.')
if args.batch_size <= 0:
raise AttributeError('--batch_size parameter must >0.')
if args.epochs < 0:
raise AttributeError('--epochs parameter must >=0.')
if args.weight_decay <= 0:
raise AttributeError('--weight_decay parameter must >0.')
options = {
'base_lr': args.base_lr,
'batch_size': args.batch_size,
'epochs': args.epochs,
'weight_decay': args.weight_decay,
}
project_root = os.popen('pwd').read().strip()
path = {
'cub200': os.path.join(project_root, 'data/cub200'),
'model': os.path.join(project_root, 'model', args.model),
}
for d in path:
if d == 'model':
assert os.path.isfile(path[d])
else:
assert os.path.isdir(path[d])
manager = HBPManager(options, path)
manager.getStat()
manager.train()
if __name__ == '__main__':
main()