➕ Add FastFlow Model

anomalib/models/__init__.py

@@ -42,7 +42,7 @@ def get_model(config: Union[DictConfig, ListConfig]) -> AnomalyModule:
     Returns:
         AnomalyModule: Anomaly Model
     """
-    model_list: List[str] = ["cflow", "dfkde", "dfm", "ganomaly", "padim", "patchcore", "stfpm"]
+    model_list: List[str] = ["cflow", "dfkde", "dfm", "fastflow", "ganomaly", "padim", "patchcore", "stfpm"]
     model: AnomalyModule
 
     if config.model.name in model_list:

anomalib/models/components/base/anomaly_module.py

@@ -171,6 +171,8 @@ def _outputs_to_cpu(self, output):
 
     def _log_metrics(self):
         """Log computed performance metrics."""
-        self.log_dict(self.image_metrics)
         if self.pixel_metrics.update_called:
-            self.log_dict(self.pixel_metrics)
+            self.log_dict(self.pixel_metrics, prog_bar=True)
+            self.log_dict(self.image_metrics, prog_bar=False)
+        else:
+            self.log_dict(self.image_metrics, prog_bar=True)

anomalib/models/components/freia/__init__.py

@@ -9,3 +9,8 @@
 # Copyright (c) 2018-2022 Lynton Ardizzone, Visual Learning Lab Heidelberg.
 # SPDX-License-Identifier: MIT
 #
+
+from .framework import SequenceINN
+from .modules import AllInOneBlock
+
+__all__ = ["SequenceINN", "AllInOneBlock"]

anomalib/models/dfkde/config.yaml

@@ -7,7 +7,7 @@ dataset:
   image_size: 256
   train_batch_size: 32
   test_batch_size: 32
-  num_workers: 36
+  num_workers: 8
   transform_config:
     train: null
     val: null

anomalib/models/dfm/config.yaml

@@ -7,7 +7,7 @@ dataset:
   image_size: 256
   train_batch_size: 32
   test_batch_size: 32
-  num_workers: 36
+  num_workers: 8
   transform_config:
     train: null
     val: null

anomalib/models/fastflow/README.md

@@ -0,0 +1,117 @@
+# FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows
+
+This is the implementation of the [FastFlow](https://arxiv.org/abs/2111.07677) paper. This code is developed inspired by [https://github.com/gathierry/FastFlow](https://github.com/gathierry/FastFlow).
+
+Model Type: Segmentation
+
+## Description
+
+FastFlow is a two-dimensional normalizing flow-based probability distribution estimator. It can be used as a plug-in module with any deep feature extractor, such as ResNet and vision transformer, for unsupervised anomaly detection and localisation. In the training phase, FastFlow learns to transform the input visual feature into a tractable distribution, and in the inference phase, it assesses the likelihood of identifying anomalies.
+
+## Architecture
+
+![FastFlow Architecture](../../../docs/source/images/fastflow/architecture.jpg "FastFlow Architecture")
+
+## Usage
+
+`python tools/train.py --model fastflow`
+
+## Benchmark
+
+All results gathered with seed `0`.
+
+## [MVTec AD Dataset](https://www.mvtec.com/company/research/datasets/mvtec-ad)
+
+### Image-Level AUC
+
+|            | ResNet-18 | Wide ResNet50 | DeiT  | CaiT  |
+| ---------- | --------- | ------------- | ----- | ----- |
+| Bottle     | 1.000     | 1.000         | 0.905 | 0.986 |
+| Cable      | 0.891     | 0.962         | 0.942 | 0.839 |
+| Capsule    | 0.900     | 0.963         | 0.819 | 0.913 |
+| Carpet     | 0.979     | 0.994         | 0.999 | 1.000 |
+| Grid       | 0.988     | 1.000         | 0.991 | 0.979 |
+| Hazelnut   | 0.846     | 0.994         | 0.900 | 0.948 |
+| Leather    | 1.000     | 0.999         | 0.999 | 0.991 |
+| Metal_nut  | 0.963     | 0.995         | 0.911 | 0.963 |
+| Pill       | 0.916     | 0.942         | 0.910 | 0.916 |
+| Screw      | 0.521     | 0.839         | 0.705 | 0.791 |
+| Tile       | 0.744     | 1.000         | 0.993 | 0.998 |
+| Toothbrush | 0.844     | 0.836         | 0.850 | 0.886 |
+| Transistor | 0.916     | 0.979         |       | 0.983 |
+| Wood       | 0.978     | 0.992         | 0.979 | 0.989 |
+| Zipper     | 0.878     | 0.951         | 0.981 |       |
+| Average    |           |               |       |       |
+
+
+### Pixel-Level AUC
+
+|            | ResNet-18 | Wide ResNet50 | DeiT  | CaiT  |
+| ---------- | --------- | ------------- | ----- | ----- |
+| Bottle     | 0.983     | 0.986         | 0.991 | 0.984 |
+| Cable      | 0.954     | 0.972         | 0.973 | 0.981 |
+| Capsule    | 0.985     | 0.990         | 0.979 | 0.991 |
+| Carpet     | 0.983     | 0.991         | 0.991 | 0.992 |
+| Grid       | 0.985     | 0.992         | 0.980 | 0.979 |
+| Hazelnut   | 0.953     | 0.980         | 0.989 |       |
+| Leather    | 0.996     | 0.996         | 0.995 | 0.996 |
+| Metal_nut  | 0.972     | 0.988         | 0.978 | 0.973 |
+| Pill       | 0.972     | 0.976         | 0.985 | 0.992 |
+| Screw      | 0.920     | 0.966         | 0.945 | 0.979 |
+| Tile       | 0.893     | 0.966         | 0.951 | 0.960 |
+| Toothbrush | 0.979     | 0.980         | 0.985 | 0.992 |
+| Transistor | 0.958     | 0.971         |       | 0.960 |
+| Wood       | 0.956     | 0.941         | 0.952 | 0.954 |
+| Zipper     | 0.965     | 0.985         | 0.978 |       |
+| Average    |           |               |       |       |
+
+
+
+### Image F1 Score
+|            | ResNet-18 | Wide ResNet50 | DeiT  | CaiT  |
+| ---------- | --------- | ------------- | ----- | ----- |
+| Bottle     | 0.976     | 0.952         | 0.741 | 0.977 |
+| Cable      | 0.851     | 0.918         | 0.848 | 0.835 |
+| Capsule    | 0.937     | 0.952         | 0.905 | 0.928 |
+| Carpet     | 0.955     | 0.983         | 0.994 | 0.973 |
+| Grid       | 0.941     | 0.974         | 0.982 | 0.948 |
+| Hazelnut   | 0.852     | 0.979         | 0.828 | 0.900 |
+| Leather    | 0.995     | 0.974         | 0.995 | 0.963 |
+| Metal_nut  | 0.925     | 0.969         | 0.899 | 0.916 |
+| Pill       | 0.946     | 0.949         | 0.949 | 0.616 |
+| Screw      | 0.853     | 0.893         | 0.868 | 0.979 |
+| Tile       | 0.875     | 0.994         | 0.976 | 0.994 |
+| Toothbrush | 0.875     | 0.870         | 0.833 | 0.833 |
+| Transistor | 0.769     | 0.854         |       | 0.909 |
+| Wood       | 0.983     | 0.968         | 0.944 | 0.967 |
+| Zipper     | 0.921     | 0.975         | 0.958 |       |
+| Average    |           |               |       |       |
+
+### Pixel F1 Score
+|            | ResNet-18 | Wide ResNet50 | DeiT  | CaiT  |
+| ---------- | --------- | ------------- | ----- | ----- |
+| Bottle     | 0.670     | 0.733         | 0.753 | 0.725 |
+| Cable      | 0.547     | 0.564         | 0.487 | 0.608 |
+| Capsule    | 0.472     | 0.490         | 0.399 | 0.497 |
+| Carpet     | 0.573     | 0.598         | 0.586 | 0.606 |
+| Grid       | 0.412     | 0.481         | 0.393 | 0.410 |
+| Hazelnut   | 0.522     | 0.545         | 0.643 | 0.706 |
+| Leather    | 0.560     | 0.576         | 0.504 | 0.516 |
+| Metal_nut  | 0.728     | 0.754         | 0.766 | 0.737 |
+| Pill       | 0.589     | 0.611         | 0.709 | 0.617 |
+| Screw      | 0.061     | 0.660         | 0.269 | 0.370 |
+| Tile       | 0.053     | 0.660         | 0.655 | 0.660 |
+| Toothbrush | 0.479     | 0.481         | 0.524 | 0.535 |
+| Transistor | 0.560     | 0.573         |       | 0.567 |
+| Wood       | 0.557     | 0.488         | 0.614 | 0.572 |
+| Zipper     | 0.492     | 0.621         | 0.522 |       |
+| Average    |           |               |       |       |
+
+
+### Sample Results
+
+![Sample Result 1](../../../docs/source/images/fastflow/results/0.png "Sample Result 1")
+
+![Sample Result 2](../../../docs/source/images/fastflow/results/1.png "Sample Result 2")
+
+![Sample Result 3](../../../docs/source/images/fastflow/results/2.png "Sample Result 3")

anomalib/models/fastflow/__init__.py

@@ -0,0 +1,10 @@
+"""FastFlow Algorithm Implementation."""
+
+# Copyright (C) 2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+from .lightning_model import Fastflow, FastflowLightning
+from .torch_model import FastflowLoss, FastflowModel
+
+__all__ = ["FastflowModel", "FastflowLoss", "FastflowLightning", "Fastflow"]

anomalib/models/fastflow/anomaly_map.py

@@ -0,0 +1,49 @@
+"""FastFlow Anomaly Map Generator Implementation."""
+
+# Copyright (C) 2022 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+from typing import List, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+from omegaconf import ListConfig
+from torch import Tensor
+
+
+class AnomalyMapGenerator:
+    """Generate Anomaly Heatmap."""
+
+    def __init__(self, input_size: Union[ListConfig, Tuple]):
+        self.input_size = input_size if isinstance(input_size, tuple) else tuple(input_size)
+
+    def __call__(self, hidden_variables: List[Tensor]) -> Tensor:
+        """Generate Anomaly Heatmap.
+
+        This implementation generates the heatmap based on the flow maps
+        computed from the normalizing flow (NF) FastFlow blocks. Each block
+        yields a flow map, which overall is stacked and averaged to an anomaly
+        map.
+
+        Args:
+            hidden_variables (List[Tensor]): List of hidden variables from each NF FastFlow block.
+
+        Returns:
+            Tensor: Anomaly Map.
+        """
+        flow_maps: List[Tensor] = []
+        for hidden_variable in hidden_variables:
+            log_prob = -torch.mean(hidden_variable**2, dim=1, keepdim=True) * 0.5
+            prob = torch.exp(log_prob)
+            flow_map = F.interpolate(
+                input=-prob,
+                size=self.input_size,
+                mode="bilinear",
+                align_corners=False,
+            )
+            flow_maps.append(flow_map)
+        flow_maps = torch.stack(flow_maps, dim=-1)
+        anomaly_map = torch.mean(flow_maps, dim=-1)
+
+        return anomaly_map

anomalib/models/fastflow/config.yaml

@@ -0,0 +1,102 @@
+dataset:
+  name: mvtec #options: [mvtec, btech, folder]
+  format: mvtec
+  path: ./datasets/MVTec
+  task: segmentation
+  category: bottle
+  image_size: 256 # options: [256, 256, 448, 384] - for each supported backbone
+  train_batch_size: 32
+  test_batch_size: 32
+  num_workers: 8
+  transform_config:
+    train: null
+    val: null
+  create_validation_set: false
+  tiling:
+    apply: false
+    tile_size: null
+    stride: null
+    remove_border_count: 0
+    use_random_tiling: False
+    random_tile_count: 16
+
+model:
+  name: fastflow
+  backbone: resnet18 # options: [resnet18, wide_resnet50_2, cait_m48_448, deit_base_distilled_patch16_384]
+  flow_steps: 8 # options: [8, 8, 20, 20] - for each supported backbone
+  hidden_ratio: 1.0 # options: [1.0, 1.0, 0.16, 0.16] - for each supported backbone
+  conv3x3_only: True # options: [True, False, False, False] - for each supported backbone
+  lr: 0.001
+  weight_decay: 0.00001
+  early_stopping:
+    patience: 3
+    metric: pixel_AUROC
+    mode: max
+  normalization_method: min_max # options: [null, min_max, cdf]
+
+metrics:
+  image:
+    - F1Score
+    - AUROC
+  pixel:
+    - F1Score
+    - AUROC
+  threshold:
+    image_default: 0
+    pixel_default: 0
+    adaptive: true
+
+project:
+  seed: 0
+  path: ./results
+  log_images_to: [local]
+  logger: false # options: [tensorboard, wandb, csv] or combinations.
+
+# PL Trainer Args. Don't add extra parameter here.
+trainer:
+  accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
+  accumulate_grad_batches: 1
+  amp_backend: native
+  auto_lr_find: false
+  auto_scale_batch_size: false
+  auto_select_gpus: false
+  benchmark: false
+  check_val_every_n_epoch: 1 # Don't validate before extracting features.
+  default_root_dir: null
+  detect_anomaly: false
+  deterministic: false
+  devices: 1
+  enable_checkpointing: true
+  enable_model_summary: true
+  enable_progress_bar: true
+  fast_dev_run: false
+  gpus: null # Set automatically
+  gradient_clip_val: 0
+  ipus: null
+  limit_predict_batches: 1.0
+  limit_test_batches: 1.0
+  limit_train_batches: 1.0
+  limit_val_batches: 1.0
+  log_every_n_steps: 50
+  log_gpu_memory: null
+  max_epochs: 500
+  max_steps: -1
+  max_time: null
+  min_epochs: null
+  min_steps: null
+  move_metrics_to_cpu: false
+  multiple_trainloader_mode: max_size_cycle
+  num_nodes: 1
+  num_processes: null
+  num_sanity_val_steps: 0
+  overfit_batches: 0.0
+  plugins: null
+  precision: 32
+  profiler: null
+  reload_dataloaders_every_n_epochs: 0
+  replace_sampler_ddp: true
+  strategy: null
+  sync_batchnorm: false
+  tpu_cores: null
+  track_grad_norm: -1
+  val_check_interval: 1.0 # Don't validate before extracting features.