], [np.mean(image_std[-3:]).tolist()]
if __name__ == '__main__':
# train on the GPU or on the CPU, if a GPU is not available
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
device = torch.device('cuda:0')
# our dataset has three classes only - background, non-damaged, and damaged
num_classes = 2
input_c = 1
# use our dataset and defined transformations
dataset = Dataset("./datasets/iros/bishop/aug/",
transforms=get_transform(train=True),
include_name=False,
input_channel=input_c)
##dataset_test = Dataset("./datasets/Rock/data_test/", transforms=get_transform(train=False), include_name=False, input_channel=input_c)
#dataset_test = Dataset("./datasets/Rock_test/mult/", transforms=get_transform(train=False), include_name=False, input_channel=input_c)
dataset_test = Dataset("./datasets/iros/bishop_test/mult_masks/",
transforms=get_transform(train=False),
include_name=False,
input_channel=input_c)
# image_mean, image_std, _, _ = dataset.imageStat()
image_mean = [
0.2635908247051704, 0.2565450032962188, 0.24311759802366928,
0.3007502338171828, 0.35368477144149774, 0.35639093071269307,
0.5402165474345183, 0.24508291731782375
]
image_std = [
0.14736204788409055, 0.13722317885795837, 0.12990199087409235,
elif input_channel == 'dem':
return image_mean[-3:], image_std[-3:]
if __name__ == '__main__':
# train on the GPU or on the CPU, if a GPU is not available
device = torch.device(
'cuda') if torch.cuda.is_available() else torch.device('cpu')
device = torch.device('cuda:1')
# our dataset has three classes only - background, non-damaged, and damaged
num_classes = 2
input_c = 3
dataset_test = Dataset("./datasets/Rock/mult_10/",
transforms=get_transform(train=False),
include_name=True,
input_channel=input_c)
# dataset = Dataset("./datasets/Rock_test/mult/", transforms=get_transform(train=True), input_channel=8)
# image_mean, image_std, _, _ = dataset.imageStat()
image_mean = [
0.23924888725523394, 0.2180423480395164, 0.2118836715688813,
0.26721142156890876, 0.32996910784324385, 0.1461123186277879,
0.5308107499991753, 0.28652559313771186
]
image_std = [
0.1459739643338365, 0.1311105424825076, 0.12715888419418298,
0.149469170605332, 0.15553466224696225, 0.10533129832132752,
0.24088403135495345, 0.24318892151508417
]
image_mean, image_std = get_mean_std(input_c, image_mean, image_std)