Spaces:
Build error
Build error
File size: 3,507 Bytes
c8c12e9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 |
"""Anomalib Traning Script.
This script reads the name of the model or config file from command
line, train/test the anomaly model to get quantitative and qualitative
results.
"""
# Copyright (C) 2020 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions
# and limitations under the License.
import logging
import warnings
from argparse import ArgumentParser, Namespace
from pytorch_lightning import Trainer, seed_everything
from anomalib.config import get_configurable_parameters
from anomalib.data import get_datamodule
from anomalib.models import get_model
from anomalib.utils.callbacks import LoadModelCallback, get_callbacks
from anomalib.utils.loggers import configure_logger, get_experiment_logger
logger = logging.getLogger("anomalib")
def get_args() -> Namespace:
"""Get command line arguments.
Returns:
Namespace: List of arguments.
"""
parser = ArgumentParser()
parser.add_argument("--model", type=str, default="padim", help="Name of the algorithm to train/test")
# --model_config_path will be deprecated in 0.2.8 and removed in 0.2.9
parser.add_argument("--model_config_path", type=str, required=False, help="Path to a model config file")
parser.add_argument("--config", type=str, required=False, help="Path to a model config file")
parser.add_argument("--log-level", type=str, default="INFO", help="<DEBUG, INFO, WARNING, ERROR>")
args = parser.parse_args()
if args.model_config_path is not None:
warnings.warn(
message="--model_config_path will be deprecated in v0.2.8 and removed in v0.2.9. Use --config instead.",
category=DeprecationWarning,
stacklevel=2,
)
args.config = args.model_config_path
return args
def train():
"""Train an anomaly classification or segmentation model based on a provided configuration file."""
args = get_args()
configure_logger(level=args.log_level)
config = get_configurable_parameters(model_name=args.model, config_path=args.config)
if config.project.seed != 0:
seed_everything(config.project.seed)
if args.log_level == "ERROR":
warnings.filterwarnings("ignore")
logger.info("Loading the datamodule")
datamodule = get_datamodule(config)
logger.info("Loading the model.")
model = get_model(config)
logger.info("Loading the experiment logger(s)")
experiment_logger = get_experiment_logger(config)
logger.info("Loading the callbacks")
callbacks = get_callbacks(config)
trainer = Trainer(**config.trainer, logger=experiment_logger, callbacks=callbacks)
logger.info("Training the model.")
trainer.fit(model=model, datamodule=datamodule)
logger.info("Loading the best model weights.")
load_model_callback = LoadModelCallback(weights_path=trainer.checkpoint_callback.best_model_path)
trainer.callbacks.insert(0, load_model_callback)
logger.info("Testing the model.")
trainer.test(model=model, datamodule=datamodule)
if __name__ == "__main__":
train()
|