run_pipeline.py

from utils import *
from model_utils import *
from data import *
from c2d_models import *
from test_pipeline import *

model_path = "trained_models/C2D_AE_128_3x3_MVTec/model.h5"
train_data = MVTec(isTrain = True)
output_path = join_paths(["results/", train_data.__name__, ""])
create_directory(output_path)

config = {
#     'lime': {
#         'num_features': 128*128,
#         'mask_features': 5,
#         'num_samples': 1000,
#     },
#     'counterfactual': {
#         'threshold_pct': 0.98,
#         'block_size': 32
#     },
    'shap': {
        'blend_alpha': 0.7,
        'background_samples': 100
    },
#     'elrp': {
#         'max_loss_value': 1000
#     },
    'output_path': output_path,
    'test_data_path': join_paths(["TEST_SETS/", train_data.__name__, ""]),
    'train_data': train_data.data, # np.array of normal images
    'max_loss_value': 1000,
    'batch_size': 64,
    'data_name': train_data.__name__,
    'model_path': model_path
}

rec_model = get_model(model_path)
loss_model = get_model(model_path, rec=False)

# p = Pipeline(rec_model, loss_model, config)
# p.run()

output_path = config["output_path"]
test_path = config['test_data_path']
for category_dir in read_directory_contents(test_path):
    category_type = os.path.split(category_dir)[-1]
    INFO(category_type)
    config["output_path"] = join_paths([output_path, category_type, ""])
    config['data_name'] = category_type
    create_directory(config["output_path"])
    config['test_data_path'] = category_dir
    
    _pipline = Pipeline(rec_model, loss_model, config)
    _pipline.run()