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train_bap.py
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############################################################
# File: train_bap.py #
# Created: 2019-11-06 13:22:23 #
# Author : wvinzh #
# Email : [email protected] #
# ------------------------------------------ #
# Description:train_bap.py #
# Copyright@2019 wvinzh, HUST #
############################################################
# system
import os
import time
import shutil
import random
import numpy as np
# my implementation
from model.inception_bap import inception_v3_bap
from model.resnet import resnet50
from dataset.custom_dataset import CustomDataset
from utils import calculate_pooling_center_loss, mask2bbox
from utils import attention_crop, attention_drop, attention_crop_drop
from utils import getDatasetConfig, getConfig
from utils import accuracy, get_lr, save_checkpoint, AverageMeter, set_seed
from utils import Engine
# pytorch
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import torchvision.models as models
import torch.nn.functional as F
from tensorboardX import SummaryWriter
GLOBAL_SEED = 1231
def _init_fn(worker_id):
set_seed(GLOBAL_SEED+worker_id)
def train():
# input params
set_seed(GLOBAL_SEED)
config = getConfig()
data_config = getDatasetConfig(config.dataset)
sw_log = 'logs/%s' % config.dataset
sw = SummaryWriter(log_dir=sw_log)
best_prec1 = 0.
rate = 0.875
# define train_dataset and loader
transform_train = transforms.Compose([
transforms.Resize((int(config.input_size//rate), int(config.input_size//rate))),
transforms.RandomCrop((config.input_size,config.input_size)),
transforms.RandomHorizontalFlip(),
transforms.ColorJitter(brightness=32./255.,saturation=0.5),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
train_dataset = CustomDataset(
data_config['train'], data_config['train_root'], transform=transform_train)
train_loader = DataLoader(
train_dataset, batch_size=config.batch_size, shuffle=True, num_workers=config.workers, pin_memory=True, worker_init_fn=_init_fn)
transform_test = transforms.Compose([
transforms.Resize((config.image_size, config.image_size)),
transforms.CenterCrop(config.input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
val_dataset = CustomDataset(
data_config['val'], data_config['val_root'], transform=transform_test)
val_loader = DataLoader(
val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=config.workers, pin_memory=True, worker_init_fn=_init_fn)
# logging dataset info
print('Dataset Name:{dataset_name}, Train:[{train_num}], Val:[{val_num}]'.format(
dataset_name=config.dataset,
train_num=len(train_dataset),
val_num=len(val_dataset)))
print('Batch Size:[{0}], Total:::Train Batches:[{1}],Val Batches:[{2}]'.format(
config.batch_size, len(train_loader), len(val_loader)
))
# define model
if config.model_name == 'inception':
net = inception_v3_bap(pretrained=True, aux_logits=False,num_parts=config.parts)
elif config.model_name == 'resnet50':
net = resnet50(pretrained=True,use_bap=True)
in_features = net.fc_new.in_features
new_linear = torch.nn.Linear(
in_features=in_features, out_features=train_dataset.num_classes)
net.fc_new = new_linear
# feature center
feature_len = 768 if config.model_name == 'inception' else 512
center_dict = {'center': torch.zeros(
train_dataset.num_classes, feature_len*config.parts)}
# gpu config
use_gpu = torch.cuda.is_available() and config.use_gpu
if use_gpu:
net = net.cuda()
center_dict['center'] = center_dict['center'].cuda()
gpu_ids = [int(r) for r in config.gpu_ids.split(',')]
if use_gpu and config.multi_gpu:
net = torch.nn.DataParallel(net, device_ids=gpu_ids)
# define optimizer
assert config.optim in ['sgd', 'adam'], 'optim name not found!'
if config.optim == 'sgd':
optimizer = torch.optim.SGD(
net.parameters(), lr=config.lr, momentum=config.momentum, weight_decay=config.weight_decay)
elif config.optim == 'adam':
optimizer = torch.optim.Adam(
net.parameters(), lr=config.lr, weight_decay=config.weight_decay)
# define learning scheduler
assert config.scheduler in ['plateau',
'step'], 'scheduler not supported!!!'
if config.scheduler == 'plateau':
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer, 'min', patience=3, factor=0.1)
elif config.scheduler == 'step':
scheduler = torch.optim.lr_scheduler.StepLR(
optimizer, step_size=2, gamma=0.9)
# define loss
criterion = torch.nn.CrossEntropyLoss()
if use_gpu:
criterion = criterion.cuda()
# train val parameters dict
state = {'model': net, 'train_loader': train_loader,
'val_loader': val_loader, 'criterion': criterion,
'center': center_dict['center'], 'config': config,
'optimizer': optimizer}
## train and val
engine = Engine()
print(config)
for e in range(config.epochs):
if config.scheduler == 'step':
scheduler.step()
lr_val = get_lr(optimizer)
print("Start epoch %d ==========,lr=%f" % (e, lr_val))
train_prec, train_loss = engine.train(state, e)
prec1, val_loss = engine.validate(state)
is_best = prec1 > best_prec1
best_prec1 = max(prec1, best_prec1)
save_checkpoint({
'epoch': e + 1,
'state_dict': net.state_dict(),
'best_prec1': best_prec1,
'optimizer': optimizer.state_dict(),
'center': center_dict['center']
}, is_best, config.checkpoint_path)
sw.add_scalars("Accurancy", {'train': train_prec, 'val': prec1}, e)
sw.add_scalars("Loss", {'train': train_loss, 'val': val_loss}, e)
if config.scheduler == 'plateau':
scheduler.step(val_loss)
def test():
##
engine = Engine()
config = getConfig()
data_config = getDatasetConfig(config.dataset)
# define dataset
transform_test = transforms.Compose([
transforms.Resize((config.image_size, config.image_size)),
transforms.CenterCrop(config.input_size),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
])
val_dataset = CustomDataset(
data_config['val'], data_config['val_root'], transform=transform_test)
val_loader = DataLoader(
val_dataset, batch_size=config.batch_size, shuffle=False, num_workers=config.workers, pin_memory=True)
# define model
if config.model_name == 'inception':
net = inception_v3_bap(pretrained=True, aux_logits=False)
elif config.model_name == 'resnet50':
net = resnet50(pretrained=True)
in_features = net.fc_new.in_features
new_linear = torch.nn.Linear(
in_features=in_features, out_features=val_dataset.num_classes)
net.fc_new = new_linear
# load checkpoint
use_gpu = torch.cuda.is_available() and config.use_gpu
if use_gpu:
net = net.cuda()
gpu_ids = [int(r) for r in config.gpu_ids.split(',')]
if use_gpu and len(gpu_ids) > 1:
net = torch.nn.DataParallel(net, device_ids=gpu_ids)
#checkpoint_path = os.path.join(config.checkpoint_path,'model_best.pth.tar')
net.load_state_dict(torch.load(config.checkpoint_path)['state_dict'])
# define loss
# define loss
criterion = torch.nn.CrossEntropyLoss()
if use_gpu:
criterion = criterion.cuda()
prec1, prec5 = engine.test(val_loader, net, criterion)
if __name__ == '__main__':
config = getConfig()
engine = Engine()
if config.action == 'train':
train()
else:
test()