if whereIam == "wdtim719z":
sys.path.append("/home/optimom/github/EfficientNet-PyTorch")
sys.path.append("/home/optimom/github/pytorch-image-models")
sys.path.append("/home/optimom/github/pretrained-models.pytorch")
sys.path.append("/home/optimom/github/segmentation_models.pytorch")
if whereIam in ["calculon", "astroboy", "flexo", "bender", "baymax"]:
sys.path.append("/d/achanhon/github/EfficientNet-PyTorch")
sys.path.append("/d/achanhon/github/pytorch-image-models")
sys.path.append("/d/achanhon/github/pretrained-models.pytorch")
sys.path.append("/d/achanhon/github/segmentation_models.pytorch")
import segmentation_models_pytorch as smp
import digitanie
print("load data")
miniworld = digitanie.DigitanieALL()
print("load model")
with torch.no_grad():
net = torch.load("build/model.pth")
net = net.cuda()
net.eval()
print("test")
def largeforward(net, image, tilesize=128, stride=64):
pred = torch.zeros(1, 2, image.shape[2], image.shape[3]).cuda()
image = image.cuda()
for row in range(0, image.shape[2] - tilesize + 1, stride):
for col in range(0, image.shape[3] - tilesize + 1, stride):
if whereIam == "wdtim719z":
sys.path.append("/home/optimom/github/EfficientNet-PyTorch")
sys.path.append("/home/optimom/github/pytorch-image-models")
sys.path.append("/home/optimom/github/pretrained-models.pytorch")
sys.path.append("/home/optimom/github/segmentation_models.pytorch")
if whereIam in ["calculon", "astroboy", "flexo", "bender", "baymax"]:
sys.path.append("/d/achanhon/github/EfficientNet-PyTorch")
sys.path.append("/d/achanhon/github/pytorch-image-models")
sys.path.append("/d/achanhon/github/pretrained-models.pytorch")
sys.path.append("/d/achanhon/github/segmentation_models.pytorch")
import segmentation_models_pytorch as smp
import digitanie
print("load data")
dataset = digitanie.DigitanieALL()
print("load model")
with torch.no_grad():
net = torch.load("build/model.pth")
net = net.cuda()
net.eval()
print("test")
globalcm, globalcm1 = torch.zeros((2, 2)).cuda(), torch.zeros((2, 2)).cuda()
with torch.no_grad():
for city in dataset.cities:
print(city)
cm, cm1 = torch.zeros((2, 2)).cuda(), torch.zeros((2, 2)).cuda()
for i in range(10):