img_name = img_name[0]; label = label[0]
img_path = voc12.data.get_img_path(img_name, args.voc12_root)
orig_img = np.asarray(Image.open(img_path))
orig_img_size = orig_img.shape[:2]
def _work(i, img):
with torch.no_grad():
_, cam = model(img.cuda())
cam = F.upsample(cam[:,1:,:,:], orig_img_size, mode='bilinear', align_corners=False)[0]
cam = cam.cpu().numpy() * label.clone().view(20, 1, 1).numpy()
if i % 2 == 1:
cam = np.flip(cam, axis=-1)
return cam
thread_pool = pyutils.BatchThreader(_work, list(enumerate(img_list)),
batch_size=12, prefetch_size=0, processes=args.num_workers)
cam_list = thread_pool.pop_results()
sum_cam = np.sum(cam_list, axis=0)
sum_cam[sum_cam < 0] = 0
cam_max = np.max(sum_cam, (1,2), keepdims=True)
cam_min = np.min(sum_cam, (1,2), keepdims=True)
sum_cam[sum_cam < cam_min+1e-5] = 0
norm_cam = (sum_cam-cam_min-1e-5) / (cam_max - cam_min + 1e-5)
cam_dict = {}
for i in range(20):
if label[i] > 1e-5:
cam_dict[i] = norm_cam[i]
def get_iou(weights):
model = getattr(importlib.import_module(args.network), 'Net')()
model.load_state_dict(torch.load(weights))
model.eval()
model.cuda()
infer_dataset = voc12.data.VOC12ClsDatasetMSFseg(
args.infer_list,
voc12_root=args.voc12_root,
scales=(1, 0.5, 1.5, 2.0),
inter_transform=torchvision.transforms.Compose(
[np.asarray, model.normalize, imutils.HWC_to_CHW]))
infer_data_loader = DataLoader(infer_dataset,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
n_gpus = torch.cuda.device_count()
model_replicas = torch.nn.parallel.replicate(model,
list(range(n_gpus)))
preds, gts = [], []
for iter, (img_name, img_list, label,
labelseg) in enumerate(infer_data_loader):
img_name = img_name[0]
label = label[0]
img_path = voc12.data.get_img_path(img_name, args.voc12_root)
orig_img = np.asarray(Image.open(img_path))
orig_img_size = orig_img.shape[:2]
# print(len(img_list))
# for im in img_list:
# print (im.shape)
def _work(i, img):
with torch.no_grad():
with torch.cuda.device(i % n_gpus):
cam = model_replicas[i % n_gpus].forward_cam(
img.cuda())
cam = F.upsample(cam,
orig_img_size,
mode='bilinear',
align_corners=False)[0]
cam = cam.cpu().numpy() * label.clone().view(
20, 1, 1).numpy()
if i % 2 == 1:
cam = np.flip(cam, axis=-1)
return cam
thread_pool = pyutils.BatchThreader(_work,
list(enumerate(img_list)),
batch_size=12,
prefetch_size=0,
processes=args.num_workers)
cam_list = thread_pool.pop_results()
sum_cam = np.sum(cam_list, axis=0)
print(sum_cam.shape)
norm_cam = sum_cam / (np.max(sum_cam,
(1, 2), keepdims=True) + 1e-5)
bg_score = [np.ones_like(norm_cam[0]) * 0.2]
pred = np.argmax(np.concatenate((bg_score, norm_cam)), 0)
if iter % 50 == 0:
print(iter, weights)
preds += list(pred)
gts += list(labelseg[0].cpu().numpy())
score = scores(gts, preds, n_class=21)
print(weights)
print(score)