import os
from torch.autograd import Variable
import torch.utils.data
from torch.nn import DataParallel
from config import BATCH_SIZE, PROPOSAL_NUM, test_model
from core import model, dataset
from core.utils import progress_bar
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
if not test_model:
raise NameError('please set the test_model file to choose the checkpoint!')
# read dataset
trainset = dataset.CUB(root='./CUB_200_2011', is_train=True, data_len=None)
trainloader = torch.utils.data.DataLoader(trainset,
batch_size=BATCH_SIZE,
shuffle=True,
num_workers=8,
drop_last=False)
testset = dataset.CUB(root='./CUB_200_2011', is_train=False, data_len=None)
testloader = torch.utils.data.DataLoader(testset,
batch_size=BATCH_SIZE,
shuffle=False,
num_workers=8,
drop_last=False)
# define model
net = model.attention_net(topN=PROPOSAL_NUM)
ckpt = torch.load(test_model)
net.load_state_dict(ckpt['net_state_dict'])
net = net.cuda()
net = DataParallel(net)
creterion = torch.nn.CrossEntropyLoss()
parser.add_option("--cutout", action="store_true", dest="cutout", default=False, help="applying cutout")
parser.add_option("--focal", action="store_true", dest="focal", default=False, help="applying focal loss")
# Parse command line options
(options, args) = parser.parse_args()
print(options)
start_epoch = 1
if os.path.exists(options.save_dir):
raise NameError('model dir exists!')
os.makedirs(options.save_dir)
logging = init_log(options.save_dir)
_print = logging.info
# read dataset
trainset = dataset.CUB(root_dir='core/2016train', is_train=True)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=options.batchSize,
shuffle=True, num_workers=0, drop_last=False)
testset = dataset.CUB(root_dir='core/2016test', is_train=False)
testloader = torch.utils.data.DataLoader(testset, batch_size=options.batchSize,
shuffle=False, num_workers=0, drop_last=False)
# define model
net = model.attention_net(topN=PROPOSAL_NUM)
if resume:
ckpt = torch.load(resume)
net.load_state_dict(ckpt['net_state_dict'])
start_epoch = ckpt['epoch'] + 1
creterion = torch.nn.CrossEntropyLoss()
# define optimizers
raw_parameters = list(net.pretrained_model.parameters())
from core import model, dataset
from core.utils import init_log, progress_bar
from tensorboardX import SummaryWriter
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
start_epoch = 1
save_dir = os.path.join(save_dir, datetime.now().strftime('%Y%m%d_%H%M%S'))
if os.path.exists(save_dir):
raise NameError('model dir exists!')
os.makedirs(save_dir)
logging = init_log(save_dir)
_print = logging.info
# read dataset
trainset = dataset.CUB(root=img_dir, is_train=True, data_len=None)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=8, drop_last=False)
testset = dataset.CUB(root=img_dir, is_train=False, data_len=None)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,
shuffle=False, num_workers=8, drop_last=False)
# define model
net = model.attention_net(topN=PROPOSAL_NUM)
if resume:
ckpt = torch.load(resume)
net.load_state_dict(ckpt['net_state_dict'])
start_epoch = ckpt['epoch'] + 1
creterion = torch.nn.CrossEntropyLoss()
# define optimizers
raw_parameters = list(net.pretrained_model.parameters())
import os
from torch.autograd import Variable
import torch.utils.data
from torch.nn import DataParallel
from config import BATCH_SIZE, PROPOSAL_NUM, test_model
from core import model, dataset
from core.utils import progress_bar
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
if not test_model:
raise NameError('please set the test_model file to choose the checkpoint!')
# read dataset
trainset = dataset.CUB(is_train=True, data_len=None)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE,
shuffle=True, num_workers=0, drop_last=False)
testset = dataset.CUB(is_train=False, data_len=None)
testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE,
shuffle=False, num_workers=0, drop_last=False)
# define model
net = model.attention_net(topN=PROPOSAL_NUM)
ckpt = torch.load(test_model)
net.load_state_dict(ckpt['net_state_dict'])
net = net.cuda()
net = DataParallel(net)
creterion = torch.nn.CrossEntropyLoss()
########################## evaluate net on train set ###############################
train_loss = 0
train_correct = 0
total = 0
net.eval()
