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)])