def get_transform_fun(resized=False):
if resized == True:
transform_function = et.ExtCompose([
et.ExtRandomCrop(size=2048),
et.ExtRandomCrop(scale=0.7, size=None),
et.ExtEnhanceContrast(),
et.ExtRandomCrop(size=2048, pad_if_needed=True),
et.ExtResize(scale=0.5),
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomCrop(size=512),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtToTensor()
])
else:
transform_function = et.ExtCompose([
et.ExtRandomCrop(size=256),
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtEnhanceContrast(),
et.ExtToTensor()
])
return transform_function
file_names_train = file_names_train[file_names_train != ".DS_S"]
file_names_val = np.array([
image_name[:-4] for image_name in os.listdir(path_val)
if image_name[-5] != "k"
])
N_files = len(file_names_val)
shuffled_index = np.random.permutation(len(file_names_val))
file_names_val = file_names_val[shuffled_index]
file_names_val = file_names_val[file_names_val != ".DS_S"]
# #FOR EXTENDED DATASET EXPERIMENT
transform_function = et.ExtCompose([
et.ExtRandomHorizontalFlip(p=0.5),
et.ExtRandomCrop(size=SIZE),
et.ExtEnhanceContrast(),
et.ExtRandomVerticalFlip(p=0.5),
et.ExtToTensor(),
et.ExtNormalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
if binary:
color_dict = data_loader.color_dict_binary
target_dict = data_loader.get_target_dict()
annotations_dict = data_loader.annotations_dict
train_dst = LeatherData(path_mask=path_train,
path_img=path_train,
list_of_filenames=file_names_train,
transform=transform_function,
color_dict=color_dict,
device = torch.device('cpu')
cpu_device = torch.device('cpu')
model_name = 'resnet50'
path_original_data = r'C:\Users\johan\OneDrive\Skrivebord\leather_patches'
path_meta_data = r'samples/model_comparison.csv'
optim = "SGD"
tif_path = r'C:\Users\johan\iCloudDrive\DTU\KID\BA\HPC\TIF\good_area1.png'
save_path = r'C:\Users\johan\iCloudDrive\DTU\KID\BA\HPC\last_round\predictions\vda4'
resize = False
if resize:
patch_size = 512
else:
path_size = 256
transform_function = et.ExtCompose([et.ExtEnhanceContrast(),
et.ExtToTensor()])
print("Device: %s" % device)
print("Exp: ", exp)
if brevetti:
print("REDHALF")
else:
print("WALKNAPPA")
data_loader = DataLoader(data_path=path_original_data,
metadata_path=path_meta_data)
array = load_tif_as_numpy_array(tif_path)
print("Shape array: ", np.shape(array))
if resize == True:
cpu_device = torch.device('cpu')
model_name = 'resnet50'
path_original_data = r'C:\Users\johan\OneDrive\Skrivebord\leather_patches'
path_meta_data = r'samples/model_comparison.csv'
optim = "SGD"
tif_path = r'C:\Users\johan\iCloudDrive\DTU\KID\BA\HPC\TIF\good_area1.png'
save_path = r'C:\Users\johan\iCloudDrive\DTU\KID\BA\HPC\last_round\predictions\vda4'
resize = False
if resize:
patch_size = 512
else:
path_size = 256
transform_function = et.ExtCompose(
[et.ExtEnhanceContrast(), et.ExtToTensor()])
print("Device: %s" % device)
print("Exp: ", exp)
if brevetti:
print("REDHALF")
else:
print("WALKNAPPA")
data_loader = DataLoader(data_path=path_original_data,
metadata_path=path_meta_data)
array = load_tif_as_numpy_array(tif_path)
print("Shape array: ", np.shape(array))
if resize == True:
resize_function = et.ExtCompose([et.ExtResize(scale=0.5)])