class WeightEMA(object):
def __init__(self, model, ema_model, alpha=0.999):
self.model = model
self.ema_model = ema_model
self.alpha = alpha
self.tmp_model = MobileNet(num_classes=16).cuda()
self.wd = 0.02 * args.lr
for param, ema_param in zip(self.model.parameters(),
self.ema_model.parameters()):
ema_param.data.copy_(param.data)
def step(self, bn=False):
if bn:
# copy batchnorm stats to ema model
for ema_param, tmp_param in zip(self.ema_model.parameters(),
self.tmp_model.parameters()):
tmp_param.data.copy_(ema_param.data.detach())
self.ema_model.load_state_dict(self.model.state_dict())
for ema_param, tmp_param in zip(self.ema_model.parameters(),
self.tmp_model.parameters()):
ema_param.data.copy_(tmp_param.data.detach())
else:
one_minus_alpha = 1.0 - self.alpha
for param, ema_param in zip(self.model.parameters(),
self.ema_model.parameters()):
ema_param.data.mul_(self.alpha)
ema_param.data.add_(param.data.detach() * one_minus_alpha)
# customized weight decay
param.data.mul_(1 - self.wd)
def create_model(num_classes, ema=False):
model = MobileNet(num_classes)
model = torch.nn.DataParallel(model).cuda()
if ema:
for param in model.parameters():
param.detach_()
return model
def create_model(num_classes, ema=False):
model = MobileNet(num_classes)
#model = WideResNet(num_classes)
model.cuda()
if ema:
for param in model.parameters():
param.detach_()
return model
def __init__(self, model, ema_model, alpha=0.999):
self.model = model
self.ema_model = ema_model
self.alpha = alpha
self.tmp_model = MobileNet(num_classes=16).cuda()
self.wd = 0.02 * args.lr
for param, ema_param in zip(self.model.parameters(),
self.ema_model.parameters()):
ema_param.data.copy_(param.data)
def create_model(num_classes, ema=False):
model = MobileNet(num_classes)
#model = WideResNet(num_classes)
model = torch.nn.DataParallel(model).cuda()
if ema:
for param in model.parameters():
param.detach_()
#param.requires_grad = False
return model
parser.add_argument('--target-layer', type=int, default=13,
help='Target layer')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = get_args()
random.seed(1)
torch.manual_seed(1)
torch.backends.cudnn.deterministic = True
model = MobileNet(16)
checkpoint = torch.load('/home/jingyi/cxr-jingyi/Age/result/supervised/model_best.pth.tar')
model.load_state_dict(checkpoint['state_dict'])
grad_cam = GradCam(model=model, target_layer=args.target_layer)
img = imageio.imread(args.image_path)
cxr_test_transforms = tfms.Compose([
tfms.ToPILImage(),
tfms.Resize((512,512), interpolation=3),
tfms.CenterCrop(256),
tfms.ToTensor()
])
img_mask = get_mask(img)
cropped_img = segment(img, img_mask)
# transformation
preprocessed_img = cxr_test_transforms(cropped_img)
import torch.nn.functional as F
# import model
from dnn121 import DenseNet121, MobileNet
import wisenet as models
# import dataset
from mixmatch_dataset import train_val_split, NIH_CXR_BASE, CxrDataset, CXR_unlabeled
from utils import Bar, Logger, AverageMeter, accuracy, mkdir_p, savefig
from tensorboardX import SummaryWriter
test_set = CxrDataset(NIH_CXR_BASE, "~/cxr-jingyi/Age/NIH_test_2500.csv")
test_loader = data.DataLoader(test_set, batch_size=32, shuffle=False, num_workers=32)
model = MobileNet(16)
model = model.cuda()
#checkpoint = torch.load('/home/jingyi/cxr-jingyi/Age/result/supervised/model_best.pth.tar')
checkpoint = torch.load('/home/jingyi/cxr-jingyi/Age/checkpoint/cifar10-semi/exp/ckpt.pth.tar')
#model.load_state_dict(checkpoint['state_dict'])
model.load_state_dict(checkpoint['net'])
def validate(val_loader, model, mode = 'valid'):
top1 = AverageMeter()
top5 = AverageMeter()
predict = []
# switch to evaluate mode
model.eval()