def val(args, model=None, current_epoch=0):
top1 = AverageMeter()
top5 = AverageMeter()
top1.reset()
top5.reset()
if model is None:
model = get_model(args)
model.eval()
_, val_loader = data_loader(args, test_path=True)
save_atten = SAVE_ATTEN(save_dir=args.save_atten_dir)
global_counter = 0
prob = None
gt = None
for idx, dat in tqdm(enumerate(val_loader)):
img_path, img, label_in = dat
global_counter += 1
if args.tencrop == 'True':
bs, ncrops, c, h, w = img.size()
img = img.view(-1, c, h, w)
label_input = label_in.repeat(10, 1)
label = label_input.view(-1)
else:
label = label_in
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var, label_var)
logits0 = logits[0]
logits0 = F.softmax(logits0, dim=1)
if args.tencrop == 'True':
logits0 = logits0.view(bs, ncrops, -1).mean(1)
# Calculate classification results
prec1_1, prec5_1 = Metrics.accuracy(logits0.cpu().data,
label_in.long(),
topk=(1, 5))
# prec3_1, prec5_1 = Metrics.accuracy(logits[1].data, label.long(), topk=(1,5))
top1.update(prec1_1[0], img.size()[0])
top5.update(prec5_1[0], img.size()[0])
# save_atten.save_heatmap_segmentation(img_path, np_last_featmaps, label.cpu().numpy(),
# save_dir='./save_bins/heatmaps', size=(0,0), maskedimg=True)
#
np_last_featmaps = logits[2].cpu().data.numpy()
np_last_featmaps = logits[-1].cpu().data.numpy()
np_scores, pred_labels = torch.topk(logits0, k=args.num_classes, dim=1)
pred_np_labels = pred_labels.cpu().data.numpy()
save_atten.save_top_5_pred_labels(pred_np_labels[:, :5], img_path,
global_counter)
# pred_np_labels[:,0] = label.cpu().numpy() #replace the first label with gt label
# save_atten.save_top_5_atten_maps(np_last_featmaps, pred_np_labels, img_path)
print('Top1:', top1.avg, 'Top5:', top5.avg)
def val(args, model=None, current_epoch=0):
top1 = AverageMeter()
top5 = AverageMeter()
top1.reset()
top5.reset()
if model is None:
model, _ = get_model(args)
model.eval()
train_loader, val_loader = data_loader(args, test_path=True)
save_atten = SAVE_ATTEN(save_dir='../save_bins/')
global_counter = 0
prob = None
gt = None
for idx, dat in tqdm(enumerate(val_loader)):
img_path, img, label_in = dat
global_counter += 1
if args.tencrop == 'True':
bs, ncrops, c, h, w = img.size()
img = img.view(-1, c, h, w)
label_input = label_in.repeat(10, 1)
label = label_input.view(-1)
else:
label = label_in
img, label = img.cuda(), label.cuda()
img_var, label_var = Variable(img), Variable(label)
logits = model(img_var, label_var)
logits0 = logits[0]
logits0 = F.softmax(logits0, dim=1)
if args.tencrop == 'True':
logits0 = logits0.view(bs, ncrops, -1).mean(1)
# Calculate classification results
if args.onehot == 'True':
val_mAP, prob, gt = cal_mAP(logits0, label_var, prob, gt)
# print val_mAP
else:
prec1_1, prec5_1 = Metrics.accuracy(logits0.cpu().data,
label_in.long(),
topk=(1, 5))
# prec3_1, prec5_1 = Metrics.accuracy(logits[1].data, label.long(), topk=(1,5))
top1.update(prec1_1[0], img.size()[0])
top5.update(prec5_1[0], img.size()[0])
# model.module.save_erased_img(img_path)
last_featmaps = model.module.get_localization_maps()
np_last_featmaps = last_featmaps.cpu().data.numpy()
# Save 100 sample masked images by heatmaps
# if idx < 100/args.batch_size:
save_atten.get_masked_img(img_path,
np_last_featmaps,
label_in.numpy(),
size=(0, 0),
maps_in_dir=True,
only_map=True)
# save_atten.save_heatmap_segmentation(img_path, np_last_featmaps, label.cpu().numpy(),
# save_dir='./save_bins/heatmaps', size=(0,0), maskedimg=True)
# save_atten.get_masked_img(img_path, np_last_featmaps, label_in.numpy(),size=(0,0),
# maps_in_dir=True, save_dir='../heatmaps',only_map=True )
# np_scores, pred_labels = torch.topk(logits0,k=args.num_classes,dim=1)
# # print pred_labels.size(), label.size()
# pred_np_labels = pred_labels.cpu().data.numpy()
# save_atten.save_top_5_pred_labels(pred_np_labels[:,:5], img_path, global_counter)
# # pred_np_labels[:,0] = label.cpu().numpy() #replace the first label with gt label
# # save_atten.save_top_5_atten_maps(np_last_featmaps, pred_np_labels, img_path)
if args.onehot == 'True':
print val_mAP
print 'AVG:', np.mean(val_mAP)
else:
print('Top1:', top1.avg, 'Top5:', top5.avg)
from ss_datalayer import SSDatalayer
from oneshot import *
from utils.Restore import restore
from utils import AverageMeter
from utils.save_atten import SAVE_ATTEN
from utils.segscorer import SegScorer
from utils import Metrics
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
ROOT_DIR = '/'.join(os.getcwd().split('/'))
print ROOT_DIR
save_atten = SAVE_ATTEN()
SNAPSHOT_DIR = os.path.join(ROOT_DIR, 'snapshots_1way1shot_heat')
DISP_INTERVAL = 20
def get_arguments():
parser = argparse.ArgumentParser(description='OneShot')
parser.add_argument("--arch", type=str, default='onemodel_sg_one')
parser.add_argument("--disp_interval", type=int, default=100)
parser.add_argument(
"--snapshot_dir",
type=str,
default='/home/liruimin/SG-One-master/snapshots_1way1shot_heat')
parser.add_argument("--lr", type=float, default=1e-5)