def visualize_image(self, writer, dataset, image, target, output,
global_step):
grid_image = make_grid(image[:3].clone().cpu().data, 3, normalize=True)
writer.add_image('Image', grid_image, global_step)
grid_image = make_grid(decode_seg_map_sequence(torch.max(
output[:3], 1)[1].detach().cpu().numpy(),
dataset=dataset),
3,
normalize=False,
range=(0, 255))
writer.add_image('Predicted label', grid_image, global_step)
grid_image = make_grid(decode_seg_map_sequence(torch.squeeze(
target[:3], 1).detach().cpu().numpy(),
dataset=dataset),
3,
normalize=False,
range=(0, 255))
writer.add_image('Groundtruth label', grid_image, global_step)
def main():
args = parse_args()
if args.dataset == 'Cityscapes':
num_class = 19
if args.net == 'resnet101':
blocks = [2, 4, 23, 3]
model = FPN(blocks, num_class, back_bone=args.net)
if args.checkname is None:
args.checkname = 'fpn-' + str(args.net)
#evaluator = Evaluator(num_class)
# Trained model path and name
experiment_dir = args.experiment_dir
#load_name = os.path.join(experiment_dir, 'checkpoint.pth.tar')
load_name = os.path.join(
r'/home/home_data/zjw/SemanticSegmentationUsingFPN_PanopticFeaturePyramidNetworks-master/run/Cityscapes/fpn-resnet101/model_best.pth.tar'
)
# Load trained model
if not os.path.isfile(load_name):
raise RuntimeError("=> no checkpoint found at '{}'".format(load_name))
print('====>loading trained model from ' + load_name)
checkpoint = torch.load(load_name)
checkepoch = checkpoint['epoch']
if args.cuda:
model.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint['state_dict'])
# test
img_path = r'./s1.jpeg'
image = scipy.misc.imread(img_path, mode='RGB')
image = image[:, :, ::-1]
image = np.transpose(image, (2, 0, 1))
#image[0] -= means[0]
#image[1] -= means[1]
#image[2] -= means[2]
image = torch.from_numpy(image.copy()).float()
image = image.unsqueeze(0)
if args.cuda:
image, model = image.cuda(), model.cuda()
with torch.no_grad():
output = model(image)
pred = output.data.cpu().numpy()
pred = np.argmax(pred, axis=1)
# show result
pred_rgb = decode_seg_map_sequence(pred, args.dataset, args.plot)
#results.append(pred_rgb)
save_image(pred_rgb, r'./testjpg.png')
def main():
args = parse_args()
if args.dataset == 'CamVid':
num_class = 32
elif args.dataset == 'Cityscapes':
num_class = 19
if args.net == 'resnet101':
blocks = [2, 4, 23, 3]
model = FPN(blocks, num_class, back_bone=args.net)
if args.checkname is None:
args.checkname = 'fpn-' + str(args.net)
evaluator = Evaluator(num_class)
# Trained model path and name
experiment_dir = args.experiment_dir
load_name = os.path.join(experiment_dir, 'checkpoint.pth.tar')
# Load trained model
if not os.path.isfile(load_name):
raise RuntimeError("=> no checkpoint found at '{}'".format(load_name))
print('====>loading trained model from ' + load_name)
checkpoint = torch.load(load_name)
checkepoch = checkpoint['epoch']
if args.cuda:
model.load_state_dict(checkpoint['state_dict'])
else:
model.load_state_dict(checkpoint['state_dict'])
# Load image and save in test_imgs
test_imgs = []
test_label = []
if args.dataset == "CamVid":
root_dir = Path.db_root_dir('CamVid')
test_file = os.path.join(root_dir, "val.csv")
test_data = CamVidDataset(csv_file=test_file, phase='val')
test_loader = torch.utils.data.DataLoader(test_data, batch_size=args.batch_size, shuffle=False, num_workers=args.num_workers)
elif args.dataset == "Cityscapes":
kwargs = {'num_workers': args.num_workers, 'pin_memory': True}
#_, test_loader, _, _ = make_data_loader(args, **kwargs)
_, val_loader, test_loader, _ = make_data_loader(args, **kwargs)
else:
raise RuntimeError("dataset {} not found.".format(args.dataset))
# test
Acc = []
Acc_class = []
mIoU = []
FWIoU = []
results = []
for iter, batch in enumerate(val_loader):
if args.dataset == 'CamVid':
image, target = batch['X'], batch['l']
elif args.dataset == 'Cityscapes':
image, target = batch['image'], batch['label']
else:
raise NotImplementedError
if args.cuda:
image, target, model = image.cuda(), target.cuda(), model.cuda()
with torch.no_grad():
output = model(image)
pred = output.data.cpu().numpy()
pred = np.argmax(pred, axis=1)
target = target.cpu().numpy()
evaluator.add_batch(target, pred)
# show result
pred_rgb = decode_seg_map_sequence(pred, args.dataset, args.plot)
results.append(pred_rgb)
Acc = evaluator.Pixel_Accuracy()
Acc_class = evaluator.Pixel_Accuracy_Class()
mIoU = evaluator.Mean_Intersection_over_Union()
FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union()
print('Mean evaluate result on dataset {}'.format(args.dataset))
print('Acc:{:.3f}\tAcc_class:{:.3f}\nmIoU:{:.3f}\tFWIoU:{:.3f}'.format(Acc, Acc_class, mIoU, FWIoU))
pic = pic.resize((1024,512),Image.BILINEAR)
print('pic shape:{}'.format(pic.size))
pic = np.array(pic)
pic = Normalize(pic)
pic = np.transpose(pic,(2,0,1))
pic = torch.from_numpy(pic.copy()).float()
pic = pic.unsqueeze(0)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
pic = pic.to(device)
danet = torch.load('./danet.pth')
danet = danet.to(device)
danet = danet.eval()
out = danet(pic)
out_all = out[0]
out_p = out[1]
out_c = out[2]
out = out_all.data.cpu().numpy()
out = np.argmax(out,axis=1)
pre = decode_seg_map_sequence(out, plot=True)
save_image(pre,r'./testjpg.png')