unable to get repr for <class 'albumentations.core.composition.compose'> - albumentations

Question

I am trying to run a code repository downloaded from GitHub as it is mentioned in the instruction of that but getting following error.
TypeError: init() missing 1 required positional argument: 'image_paths'
I am having this error at the code line 63 (preprocessing = preprocessing).
When I srat the program in debug mode I shows following error
unable to get repr for <class 'albumentations.core.composition.compose'>
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import torch
from skimage import io
from utils import adjust_sar_contrast, compute_building_score, plot_images
sys.path.append('/home/salman/Downloads/SpaceNet_SAR_Buildings_Solutions-master/4-motokimura/tmp/work')
from spacenet6_model.configs.load_config import get_config_with_previous_experiment
from spacenet6_model.datasets import SpaceNet6TestDataset
from spacenet6_model.models import get_model
from spacenet6_model.transforms import get_augmentation, get_preprocess
# select previous experiment to load
exp_id = 14
exp_log_dir = "/home/salman/Downloads/SpaceNet_SAR_Buildings_Solutions-master/4-motokimura/tmp/logs" # None: use default
# select device to which the model is loaded
cuda = True
if cuda:
device = 'cuda'
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
else:
device = 'cpu'
os.environ['CUDA_VISIBLE_DEVICES'] = ''
# overwrite default config with previous experiment
config = get_config_with_previous_experiment(exp_id=exp_id, exp_log_dir=exp_log_dir)
# overwrite additional hyper parameters
config.MODEL.DEVICE = device
config.WEIGHT_ROOT = "/home/salman/Downloads/SpaceNet_SAR_Buildings_Solutions-master/4-motokimura/tmp/weights/"
config.MODEL.WEIGHT = f"/home/salman/Downloads/SpaceNet_SAR_Buildings_Solutions-master/4-motokimura/tmp/weights/exp_{exp_id:04d}/model_best.pth"
config.INPUT.MEAN_STD_DIR = "/home/salman/Downloads/SpaceNet_SAR_Buildings_Solutions-master/4-motokimura/tmp/work/models/image_mean_std/"
config.INPUT.TEST_IMAGE_DIR = "/home/salman/data/SN6_buildings_AOI_11_Rotterdam_test_public/test_public/AOI_11_Rotterdam/SAR-Intensity"
config.INPUT.SAR_ORIENTATION="/home/salman/Downloads/SpaceNet_SAR_Buildings_Solutions-master/4-motokimura/tmp/work/static/SAR_orientations.txt"
config.TRAIN_VAL_SPLIT_DIR="/home/salman/Downloads/data/spacenet6/split"
config.PREDICTION_ROOT="/home/salman/Downloads/data/spacenet6/predictions"
config.POLY_CSV_ROOT="/home/salman/Downloads/data/spacenet6/polygons"
config.CHECKPOINT_ROOT="/home/salman/Downloads/data/spacenet6/ceckpoints"
config.POLY_OUTPUT_PATH="/home/salman/Downloads/data/spacenet6/val_polygons"
config.freeze()
print(config)
model = get_model(config)
model.eval();
from glob import glob
image_paths = glob(os.path.join(config.INPUT.TEST_IMAGE_DIR, "*.tif"))
#print(image_paths)
preprocessing = get_preprocess(config, is_test=True)
augmentation = get_augmentation(config, is_train=False)
test_dataset = SpaceNet6TestDataset(
config,
augmentation=augmentation,
preprocessing=preprocessing
)
test_dataset_vis = SpaceNet6TestDataset(
config,
augmentation=augmentation,
preprocessing=None
)
channel_footprint = config.INPUT.CLASSES.index('building_footprint')
channel_boundary = config.INPUT.CLASSES.index('building_boundary')
score_thresh = 0.5
alpha = 1.0
start_index = 900
N = 20
for i in range(start_index, start_index + N):
image_vis = test_dataset_vis[i]['image']
image = test_dataset[i]['image']
x_tensor = image.unsqueeze(0).to(config.MODEL.DEVICE)
pr_score = model.module.predict(x_tensor)
pr_score = pr_score.squeeze().cpu().numpy()
pr_score_building = compute_building_score(
pr_score[channel_footprint],
pr_score[channel_boundary],
alpha=alpha
)
pr_mask = pr_score_building > score_thresh
rotated = test_dataset[i]['rotated']
if rotated:
image_vis = np.flipud(np.fliplr(image_vis))
pr_mask = np.flipud(np.fliplr(pr_mask))
plot_images(
SAR_intensity_0=(adjust_sar_contrast(image_vis[:, :, 0]), 'gray'),
building_mask_pr=(pr_mask, 'viridis')
)
The function which this code calls is given below:
def get_spacenet6_preprocess(config, is_test):
"""
"""
mean_path = os.path.join(
config.INPUT.MEAN_STD_DIR,
config.INPUT.IMAGE_TYPE,
'mean.npy'
)
mean = np.load(mean_path)
mean = mean[np.newaxis, np.newaxis, :]
std_path = os.path.join(
config.INPUT.MEAN_STD_DIR,
config.INPUT.IMAGE_TYPE,
'std.npy'
)
std = np.load(std_path)
std = std[np.newaxis, np.newaxis, :]
if is_test:
to_tensor = albu.Lambda(
image=functools.partial(_to_tensor)
)
else:
to_tensor = albu.Lambda(
image=functools.partial(_to_tensor),
mask=functools.partial(_to_tensor)
)
preprocess = [
albu.Lambda(
image=functools.partial(
_normalize_image,
mean=mean,
std=std
)
),
to_tensor,
]
return albu.Compose(preprocess)