Refactor/samet/data

README.md

@@ -4,7 +4,6 @@
 [![pytorch](https://img.shields.io/badge/pytorch-1.7.1%2B-orange)]()
 [![openvino](https://img.shields.io/badge/openvino-2021.4-purple)]()
 ![example branch parameter](https://github.com/openvinotoolkit/anomalib/actions/workflows/tox.yml/badge.svg?branch=development)
-[![coverage report](https://gitlab-icv.inn.intel.com/algo_rnd_team/anomaly/badges/development/coverage.svg)](https://gitlab-icv.inn.intel.com/algo_rnd_team/anomaly/-/commits/development)
 
 
 This repository contains state-of-the art anomaly detection algorithms trained and evaluated on both public and private

anomalib/core/callbacks/nncf_callback.py

@@ -15,7 +15,7 @@
 from pytorch_lightning import Callback
 from torch.utils.data.dataloader import DataLoader
 
-from anomalib.datasets import get_datamodule
+from anomalib.data import get_datamodule
 
 
 def criterion_fn(outputs, criterion):

anomalib/core/callbacks/visualizer_callback.py

@@ -1,6 +1,4 @@
-"""
-Visualizer Callback
-"""
+"""Visualizer Callback."""
 from pathlib import Path
 from warnings import warn
 
@@ -11,23 +9,24 @@
 from anomalib import loggers
 from anomalib.core.model import AnomalyModule
 from anomalib.core.results import SegmentationResults
-from anomalib.datasets.utils import Denormalize
+from anomalib.data.transforms import Denormalize
 from anomalib.utils.metrics import compute_threshold_and_f1_score
 from anomalib.utils.post_process import compute_mask, superimpose_anomaly_map
 from anomalib.utils.visualizer import Visualizer
 
 
 class VisualizerCallback(Callback):
-    """
-    Callback that visualizes the inference results of a model. The callback generates a figure showing the original
+    """Callback that visualizes the inference results of a model.
+
+    The callback generates a figure showing the original
     image, the ground truth segmentation mask, the predicted error heat map, and the predicted segmentation mask.
 
     To save the images to the filesystem, add the 'local' keyword to the project.log_images_to parameter in the
-    config.yaml file. .
+    config.yaml file.
     """
 
     def __init__(self):
-        """Visualizer callback"""
+        """Visualizer callback."""
 
     def _add_images(
         self,
@@ -59,13 +58,13 @@ def _add_images(
             visualizer.save(Path(module.hparams.project.path) / "images" / filename.parent.name / filename.name)
 
     def on_test_epoch_end(self, _trainer: Trainer, pl_module: LightningModule) -> None:
-        """Log images at the end of training
+        """Log images at the end of training.
+
         Args:
             _trainer (Trainer): Pytorch lightning trainer object (unused)
             pl_module (LightningModule): Lightning modules derived from BaseAnomalyLightning object as
             currently only they support logging images.
         """
-
         if isinstance(pl_module.results, SegmentationResults):
             results = pl_module.results
         else:

anomalib/core/model/inference.py

@@ -29,8 +29,8 @@
 from torch import Tensor, nn
 
 from anomalib.core.model import AnomalyModule
-from anomalib.datasets.transforms.pre_process import PreProcessor
-from anomalib.datasets.utils import read_image
+from anomalib.data.transforms.pre_process import PreProcessor
+from anomalib.data.utils import read_image
 from anomalib.models import get_model
 from anomalib.utils.post_process import superimpose_anomaly_map
 

anomalib/datasets/__init__.py → anomalib/data/__init__.py

@@ -1,6 +1,4 @@
-"""
-Anomalib Datasets
-"""
+"""Anomalib Datasets."""
 
 # Copyright (C) 2020 Intel Corporation
 #
@@ -21,13 +19,11 @@
 from omegaconf import DictConfig, ListConfig
 from pytorch_lightning import LightningDataModule
 
-from .anomaly_dataset import AnomalyDataModule
 from .mvtec import MVTecDataModule
 
 
 def get_datamodule(config: Union[DictConfig, ListConfig]):
-    """
-    Get Anomaly Datamodule
+    """Get Anomaly Datamodule.
 
     Args:
         config: Configuration of the anomaly model
@@ -39,27 +35,15 @@ def get_datamodule(config: Union[DictConfig, ListConfig]):
     """
     datamodule: LightningDataModule
 
-    if config.dataset.format.lower() == "mvtec":
+    if config.dataset.name.lower() == "mvtec":
         datamodule = MVTecDataModule(
+            # TODO: Remove config values. IAAALD-211
             root=config.dataset.path,
             category=config.dataset.category,
-            image_size=config.dataset.image_size,
-            crop_size=config.dataset.image_size,
-            train_batch_size=config.dataset.train_batch_size,
-            test_batch_size=config.dataset.test_batch_size,
-            num_workers=config.dataset.num_workers,
-        )
-    elif config.dataset.format.lower() == "anomaly_dataset":
-        datamodule = AnomalyDataModule(
-            root=config.dataset.path,
-            url=config.dataset.url,
-            category=config.dataset.category,
-            task=config.dataset.task,
-            label_format=config.dataset.label_format,
+            image_size=(config.dataset.image_size[0], config.dataset.image_size[0]),
             train_batch_size=config.dataset.train_batch_size,
             test_batch_size=config.dataset.test_batch_size,
             num_workers=config.dataset.num_workers,
-            image_size=(config.dataset.image_size[0], config.dataset.image_size[1]),
         )
     else:
         raise ValueError("Unknown dataset!")