From 90625bb58e5eb3d5b311854273ddfa17c85231ca Mon Sep 17 00:00:00 2001 From: Blaz Rolih Date: Tue, 29 Oct 2024 18:12:43 +0100 Subject: [PATCH 01/23] Initial code structure Signed-off-by: Blaz Rolih Signed-off-by: blaz.rolih --- .../models/image/supersimplenet/LICENSE | 29 ++++++++++++ .../models/image/supersimplenet/README.md | 20 +++++++++ .../models/image/supersimplenet/__init__.py | 8 ++++ .../image/supersimplenet/anomaly_generator.py | 18 ++++++++ .../image/supersimplenet/lightning_model.py | 44 +++++++++++++++++++ .../models/image/supersimplenet/loss.py | 15 +++++++ .../image/supersimplenet/torch_model.py | 24 ++++++++++ 7 files changed, 158 insertions(+) create mode 100644 src/anomalib/models/image/supersimplenet/LICENSE create mode 100644 src/anomalib/models/image/supersimplenet/README.md create mode 100644 src/anomalib/models/image/supersimplenet/__init__.py create mode 100644 src/anomalib/models/image/supersimplenet/anomaly_generator.py create mode 100644 src/anomalib/models/image/supersimplenet/lightning_model.py create mode 100644 src/anomalib/models/image/supersimplenet/loss.py create mode 100644 src/anomalib/models/image/supersimplenet/torch_model.py diff --git a/src/anomalib/models/image/supersimplenet/LICENSE b/src/anomalib/models/image/supersimplenet/LICENSE new file mode 100644 index 0000000000..7adaf2cd5c --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/LICENSE @@ -0,0 +1,29 @@ +Copyright (c) 2024 Intel Corporation +SPDX-License-Identifier: Apache-2.0 + +Some files in this folder are based on the original SuperSimpleNet implementation by Blaž Rolih + +Original license: +----------------- + + MIT License + + Copyright (c) 2024 Blaž Rolih + + Permission is hereby granted, free of charge, to any person obtaining a copy + of this software and associated documentation files (the "Software"), to deal + in the Software without restriction, including without limitation the rights + to use, copy, modify, merge, publish, distribute, sublicense, and/or sell + copies of the Software, and to permit persons to whom the Software is + furnished to do so, subject to the following conditions: + + The above copyright notice and this permission notice shall be included in all + copies or substantial portions of the Software. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + SOFTWARE. \ No newline at end of file diff --git a/src/anomalib/models/image/supersimplenet/README.md b/src/anomalib/models/image/supersimplenet/README.md new file mode 100644 index 0000000000..f0d65ed64a --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/README.md @@ -0,0 +1,20 @@ +# SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection + +This is the implementation of the [SuperSimpleNet](https://arxiv.org/pdf/2408.03143) paper. + +Model Type: Segmentation + +## Description + +TODO + +## Architecture + +![SuperSimpleNet architecture](/docs/source/images/supersimplenet/architecture.png "SuperSimpleNet architecture") + +## Usage + +`anomalib train --model SuperSimpleNet --data MVTec --data.category ` + +## Benchmark + diff --git a/src/anomalib/models/image/supersimplenet/__init__.py b/src/anomalib/models/image/supersimplenet/__init__.py new file mode 100644 index 0000000000..2f1e7d990b --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/__init__.py @@ -0,0 +1,8 @@ +"""SuperSimpleNet model.""" + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from .lightning_model import SuperSimpleNet + +__all__ = ["SuperSimpleNet"] diff --git a/src/anomalib/models/image/supersimplenet/anomaly_generator.py b/src/anomalib/models/image/supersimplenet/anomaly_generator.py new file mode 100644 index 0000000000..d2651060e4 --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/anomaly_generator.py @@ -0,0 +1,18 @@ +"""Anomaly generator for the SuperSimplenet model implementation.""" + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from torch import nn + + +class SSNAnomalyGenerator(nn.Module): + """Anomaly generator of the SuperSimpleNet model. + + """ + + def __init__( + self, + ) -> None: + super().__init__() + pass diff --git a/src/anomalib/models/image/supersimplenet/lightning_model.py b/src/anomalib/models/image/supersimplenet/lightning_model.py new file mode 100644 index 0000000000..8f5cc49f8d --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/lightning_model.py @@ -0,0 +1,44 @@ +"""SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection + +Paper https://arxiv.org/pdf/2408.03143 +""" + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from typing import Any + +from lightning.pytorch.utilities.types import STEP_OUTPUT, OptimizerLRScheduler + +from anomalib import LearningType +from anomalib.data import Batch + +from anomalib.models import AnomalyModule + + +class SuperSimpleNet(AnomalyModule): + def __init__(self): + super().__init__() + pass + + def training_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: + pass + + def validation_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: + pass + + @property + def trainer_arguments(self) -> dict[str, Any]: + pass + + def configure_optimizers(self) -> OptimizerLRScheduler: + pass + + @property + def learning_type(self) -> LearningType: + """Return the learning type of the model. + + Returns: + LearningType: Learning type of the model. + """ + return LearningType.ONE_CLASS diff --git a/src/anomalib/models/image/supersimplenet/loss.py b/src/anomalib/models/image/supersimplenet/loss.py new file mode 100644 index 0000000000..89161dc124 --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/loss.py @@ -0,0 +1,15 @@ +"""Loss function for the SuperSimpleNet model implementation.""" +from torch import nn + + +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +class SSNLoss(nn.Module): + """SuperSimpleNet loss function. + + """ + + def __init__(self): + super().__init__() + pass \ No newline at end of file diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py new file mode 100644 index 0000000000..48c09e6f8d --- /dev/null +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -0,0 +1,24 @@ +"""PyTorch model for the SuperSimpleNet model implementation.""" + +from torch import nn + + +# Original Code +# Copyright (c) 2024 Blaž Rolih +# https://github.com/blaz-r/SuperSimpleNet. +# SPDX-License-Identifier: MIT +# +# Modified +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + + +class SuperSimpleNet(nn.Module): + """SuperSimpleNet Pytorch model. + + It consists of feature extractor, feature adaptor, anomaly generation mechanism and segmentation-detection module. + + """ + + def __init__(self): + super().__init__() From 4d5e480ffb07dc72a89648d9621d1e264f5ec26e Mon Sep 17 00:00:00 2001 From: Blaz Rolih Date: Tue, 29 Oct 2024 21:29:42 +0100 Subject: [PATCH 02/23] Add feature extraction Signed-off-by: Blaz Rolih Signed-off-by: blaz.rolih --- .../image/supersimplenet/torch_model.py | 53 ++++++++++++++++++- 1 file changed, 51 insertions(+), 2 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index 48c09e6f8d..bb332e1c41 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -1,6 +1,10 @@ """PyTorch model for the SuperSimpleNet model implementation.""" - +import torch from torch import nn +import torch.nn.functional as F + +from anomalib.models.components import TorchFXFeatureExtractor +from torchvision.models import Wide_ResNet50_2_Weights # Original Code @@ -20,5 +24,50 @@ class SuperSimpleNet(nn.Module): """ - def __init__(self): + def __init__(self, patch_size: int): super().__init__() + + self.feature_extractor = TorchFXFeatureExtractor( + backbone="wide_resnet50_2", + return_nodes=["layer2", "layer3"], + weights=Wide_ResNet50_2_Weights.IMAGENET1K_V1 + ) + self.pooler = nn.AvgPool2d( + kernel_size=patch_size, stride=1, padding=patch_size // 2 + ) + + def get_features(self, input_tensor: torch.Tensor): + # extract features + self.feature_extractor.eval() + with torch.no_grad(): + features = self.feature_extractor(input_tensor) + + features = list(features.values()) + + _, _, h, w = features[0].shape + feature_map = [] + for layer in features: + # upscale all to 2x the size of the first (largest) + resized = F.interpolate( + layer, size=(h * 2, w * 2), mode="bilinear" + ) + feature_map.append(resized) + # channel-wise concat + feature_map = torch.cat(feature_map, dim=1) + + # neighboring patch aggregation + feature_map = self.pooler(feature_map) + + return feature_map + + def forward(self, input_tensor: torch.Tensor): + features = self.get_features(input_tensor) + + return features + + +if __name__ == '__main__': + ssn = SuperSimpleNet(patch_size=3) + x = torch.rand(1, 3, 256, 256) + y = ssn(x) + print(y.shape) From 742e0cab5a73981da2c57ffddf984fc1736ae8a6 Mon Sep 17 00:00:00 2001 From: blaz-r Date: Fri, 1 Nov 2024 21:43:41 +0100 Subject: [PATCH 03/23] Add adaptor and segdec Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/torch_model.py | 177 ++++++++++++++++-- 1 file changed, 159 insertions(+), 18 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index bb332e1c41..ddfc6d1cb9 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -1,11 +1,11 @@ """PyTorch model for the SuperSimpleNet model implementation.""" + import torch -from torch import nn import torch.nn.functional as F - -from anomalib.models.components import TorchFXFeatureExtractor +from torch import nn from torchvision.models import Wide_ResNet50_2_Weights +from anomalib.models.components import TorchFXFeatureExtractor # Original Code # Copyright (c) 2024 Blaž Rolih @@ -24,19 +24,49 @@ class SuperSimpleNet(nn.Module): """ - def __init__(self, patch_size: int): + def __init__(self): + super().__init__() + self.feature_extractor = FeatureExtractor(backbone="wide_resnet50_2", layers=["layer2", "layer3"]) + + channels = self.feature_extractor.get_channels_dim() + self.adaptor = FeatureAdaptor(channels) + self.segdec = SegmentationDetectionModule(input_dim=channels, stop_grad=True) + + def forward(self, input_tensor: torch.Tensor): + features = self.feature_extractor(input_tensor) + adapted = self.adaptor(features) + + if self.training: + pass + else: + anomaly_map, anomaly_score = self.segdec(adapted) + + return anomaly_map, anomaly_score + + +def init_weights(m: nn.Module): + if isinstance(m, (nn.Linear, nn.Conv2d)): + nn.init.xavier_normal_(m.weight) + elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)): + nn.init.constant_(m.weight, 1) + + +class FeatureExtractor(nn.Module): + def __init__(self, backbone: str, layers: list[str], patch_size: int = 3): super().__init__() self.feature_extractor = TorchFXFeatureExtractor( - backbone="wide_resnet50_2", - return_nodes=["layer2", "layer3"], - weights=Wide_ResNet50_2_Weights.IMAGENET1K_V1 - ) + backbone=backbone, + return_nodes=layers, + weights=Wide_ResNet50_2_Weights.IMAGENET1K_V1, + ) self.pooler = nn.AvgPool2d( - kernel_size=patch_size, stride=1, padding=patch_size // 2 + kernel_size=patch_size, + stride=1, + padding=patch_size // 2, ) - def get_features(self, input_tensor: torch.Tensor): + def forward(self, input_tensor: torch.Tensor): # extract features self.feature_extractor.eval() with torch.no_grad(): @@ -49,7 +79,9 @@ def get_features(self, input_tensor: torch.Tensor): for layer in features: # upscale all to 2x the size of the first (largest) resized = F.interpolate( - layer, size=(h * 2, w * 2), mode="bilinear" + layer, + size=(h * 2, w * 2), + mode="bilinear", ) feature_map.append(resized) # channel-wise concat @@ -60,14 +92,123 @@ def get_features(self, input_tensor: torch.Tensor): return feature_map - def forward(self, input_tensor: torch.Tensor): - features = self.get_features(input_tensor) + def get_channels_dim(self) -> int: + # dryrun + self.feature_extractor.eval() + with torch.no_grad(): + features = self.feature_extractor(torch.rand(1, 3, 256, 256)) + # sum channels + channels = sum(feature.shape[1] for feature in features.values()) + return channels + + +class FeatureAdaptor(nn.Module): + def __init__(self, projection_dim: int): + super().__init__() + # linear layer equivalent + self.projection = nn.Conv2d( + in_channels=projection_dim, + out_channels=projection_dim, + kernel_size=1, + stride=1, + ) + self.apply(init_weights) + + def forward(self, features: torch.Tensor) -> torch.Tensor: + return self.projection(features) + + +class SegmentationDetectionModule(nn.Module): + def __init__( + self, + input_dim: int, + stop_grad: bool = False, + ): + super().__init__() + self.stop_grad = stop_grad + + # 1x1 conv - linear layer equivalent + self.seg = nn.Sequential( + nn.Conv2d( + in_channels=input_dim, + out_channels=1024, + kernel_size=1, + stride=1, + ), + nn.BatchNorm2d(1024), + nn.LeakyReLU(0.2, inplace=True), + nn.Conv2d( + in_channels=1024, + out_channels=1, + kernel_size=1, + stride=1, + bias=False, + ), + ) + + self.dec_block = nn.Sequential( + nn.Conv2d( + in_channels=input_dim + 1, + out_channels=128, + kernel_size=5, + padding="same", + ), + nn.BatchNorm2d(128), + nn.ReLU(inplace=True), + ) + + self.map_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) + self.map_max_pool = nn.AdaptiveMaxPool2d(output_size=(1, 1)) + + self.dec_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) + self.dec_max_pool = nn.AdaptiveMaxPool2d(output_size=(1, 1)) + + # 128 from dec and 2 from map, * 2 due to max and avg pool + self.fc_score = nn.Linear(in_features=128 * 2 + 2, out_features=1) + + self.apply(init_weights) + + def get_params(self): + seg_params = self.seg.parameters() + dec_params = list(self.dec_block.parameters()) + list(self.fc_score.parameters()) + return seg_params, dec_params + + def forward(self, input: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + # get anomaly map from seg head + map = self.seg(input) + + map_dec_copy = map + if self.stop_grad: + map_dec_copy = map_dec_copy.detach() + # dec conv layer takes feat + map + mask_cat = torch.cat((input, map_dec_copy), dim=1) + dec_out = self.dec_block(mask_cat) + + # conv block result pooling + dec_max = self.dec_max_pool(dec_out) + dec_avg = self.dec_avg_pool(dec_out) + + # predicted map pooling (and stop grad) + map_max = self.map_max_pool(map) + if self.stop_grad: + map_max = map_max.detach() + + map_avg = self.map_avg_pool(map) + if self.stop_grad: + map_avg = map_avg.detach() + + # final dec layer: conv channel max and avg and map max and avg + dec_cat = torch.cat((dec_max, dec_avg, map_max, map_avg), dim=1).squeeze( + dim=(2, 3), + ) + score = self.fc_score(dec_cat).squeeze(dim=1) - return features + return map, score -if __name__ == '__main__': - ssn = SuperSimpleNet(patch_size=3) +if __name__ == "__main__": + ssn = SuperSimpleNet() + ssn.eval() x = torch.rand(1, 3, 256, 256) - y = ssn(x) - print(y.shape) + p_map, p_score = ssn(x) + print(p_map.shape, p_score.shape) From fd95793817305471595170d1b52d04606374308d Mon Sep 17 00:00:00 2001 From: blaz-r Date: Fri, 1 Nov 2024 21:43:52 +0100 Subject: [PATCH 04/23] Format files Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- src/anomalib/models/image/supersimplenet/LICENSE | 2 +- src/anomalib/models/image/supersimplenet/README.md | 3 +-- .../models/image/supersimplenet/anomaly_generator.py | 5 +---- .../models/image/supersimplenet/lightning_model.py | 2 -- src/anomalib/models/image/supersimplenet/loss.py | 8 +++----- 5 files changed, 6 insertions(+), 14 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/LICENSE b/src/anomalib/models/image/supersimplenet/LICENSE index 7adaf2cd5c..7578a7064b 100644 --- a/src/anomalib/models/image/supersimplenet/LICENSE +++ b/src/anomalib/models/image/supersimplenet/LICENSE @@ -26,4 +26,4 @@ Original license: AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - SOFTWARE. \ No newline at end of file + SOFTWARE. diff --git a/src/anomalib/models/image/supersimplenet/README.md b/src/anomalib/models/image/supersimplenet/README.md index f0d65ed64a..f866c780d3 100644 --- a/src/anomalib/models/image/supersimplenet/README.md +++ b/src/anomalib/models/image/supersimplenet/README.md @@ -1,4 +1,4 @@ -# SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection +# SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection This is the implementation of the [SuperSimpleNet](https://arxiv.org/pdf/2408.03143) paper. @@ -17,4 +17,3 @@ TODO `anomalib train --model SuperSimpleNet --data MVTec --data.category ` ## Benchmark - diff --git a/src/anomalib/models/image/supersimplenet/anomaly_generator.py b/src/anomalib/models/image/supersimplenet/anomaly_generator.py index d2651060e4..2db395dc69 100644 --- a/src/anomalib/models/image/supersimplenet/anomaly_generator.py +++ b/src/anomalib/models/image/supersimplenet/anomaly_generator.py @@ -7,12 +7,9 @@ class SSNAnomalyGenerator(nn.Module): - """Anomaly generator of the SuperSimpleNet model. - - """ + """Anomaly generator of the SuperSimpleNet model.""" def __init__( self, ) -> None: super().__init__() - pass diff --git a/src/anomalib/models/image/supersimplenet/lightning_model.py b/src/anomalib/models/image/supersimplenet/lightning_model.py index 8f5cc49f8d..2a3e9b889a 100644 --- a/src/anomalib/models/image/supersimplenet/lightning_model.py +++ b/src/anomalib/models/image/supersimplenet/lightning_model.py @@ -12,14 +12,12 @@ from anomalib import LearningType from anomalib.data import Batch - from anomalib.models import AnomalyModule class SuperSimpleNet(AnomalyModule): def __init__(self): super().__init__() - pass def training_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: pass diff --git a/src/anomalib/models/image/supersimplenet/loss.py b/src/anomalib/models/image/supersimplenet/loss.py index 89161dc124..7b396effaa 100644 --- a/src/anomalib/models/image/supersimplenet/loss.py +++ b/src/anomalib/models/image/supersimplenet/loss.py @@ -1,15 +1,13 @@ """Loss function for the SuperSimpleNet model implementation.""" -from torch import nn +from torch import nn # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 -class SSNLoss(nn.Module): - """SuperSimpleNet loss function. - """ +class SSNLoss(nn.Module): + """SuperSimpleNet loss function.""" def __init__(self): super().__init__() - pass \ No newline at end of file From 26e6f5a37a42b3d2d6a56599f6522d3268e173e0 Mon Sep 17 00:00:00 2001 From: blaz-r Date: Fri, 1 Nov 2024 23:03:50 +0100 Subject: [PATCH 05/23] Add anomaly generation Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/anomaly_generator.py | 138 +++++++++++++++++- .../image/supersimplenet/torch_model.py | 69 +++++++-- 2 files changed, 195 insertions(+), 12 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/anomaly_generator.py b/src/anomalib/models/image/supersimplenet/anomaly_generator.py index 2db395dc69..4f3fdda28b 100644 --- a/src/anomalib/models/image/supersimplenet/anomaly_generator.py +++ b/src/anomalib/models/image/supersimplenet/anomaly_generator.py @@ -3,13 +3,149 @@ # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 +import torch +import torch.nn.functional as F from torch import nn +from anomalib.data.utils.generators.perlin import _rand_perlin_2d + class SSNAnomalyGenerator(nn.Module): """Anomaly generator of the SuperSimpleNet model.""" def __init__( self, - ) -> None: + noise_mean: float, + noise_std: float, + threshold: float, + perlin_range: tuple[int, int] = (0, 6), + ): super().__init__() + + self.noise_mean = noise_mean + self.noise_std = noise_std + + self.threshold = threshold + + self.min_perlin_scale = perlin_range[0] + self.max_perlin_scale = perlin_range[1] + + @staticmethod + def next_power_2(num: int) -> int: + """Get the next power of 2 for given number. + + Args: + num (int): value of interest + + Returns: + next power of 2 value for given number + """ + return 1 << (num - 1).bit_length() + + def generate_perlin(self, batches: int, height: int, width: int) -> torch.Tensor: + """Generate 2d perlin noise masks with dims [b, 1, h, w] + + Args: + batches (int): number of batches (different masks) + height (int): height of features + width (int): width of features + + Returns: + tensor with b perlin binarized masks + """ + perlin = [] + for _ in range(batches): + # get scale of perlin in x and y direction + perlin_scalex = 2 ** ( + torch.randint( + self.min_perlin_scale, + self.max_perlin_scale, + (1,), + ).item() + ) + perlin_scaley = 2 ** ( + torch.randint( + self.min_perlin_scale, + self.max_perlin_scale, + (1,), + ).item() + ) + + perlin_height = self.next_power_2(height) + perlin_width = self.next_power_2(width) + + perlin_noise = _rand_perlin_2d( + (perlin_height, perlin_width), + (perlin_scalex, perlin_scaley), + ) + # original is power of 2 scale, so fit to our size + perlin_noise = F.interpolate( + perlin_noise.reshape(1, 1, perlin_height, perlin_width), + size=(height, width), + mode="bilinear", + ) + # binarize + perlin_thr = torch.where(perlin_noise > self.threshold, 1, 0) + + # 50% of anomaly + if torch.rand(1).item() > 0.5: + perlin_thr = torch.zeros_like(perlin_thr) + + perlin.append(perlin_thr) + return torch.cat(perlin) + + def forward( + self, + features: torch.Tensor, + mask: torch.Tensor, + labels: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + b, _, h, w = features.shape + + # duplicate + features = torch.cat((features, features)) + mask = torch.cat((mask, mask)) + labels = torch.cat((labels, labels)) + + noise = torch.normal( + mean=self.noise_mean, + std=self.noise_std, + size=features.shape, + device=features.device, + requires_grad=False, + ) + + # mask indicating which regions will have noise applied + # [B * 2, 1, H, W] initial all masked as anomalous + noise_mask = torch.ones( + b * 2, + 1, + h, + w, + device=features.device, + requires_grad=False, + ) + + # no overlap: don't apply to already anomalous regions (mask=1 -> bad) + noise_mask = noise_mask * (1 - mask) + + # [B * 2, 1, H, W] + perlin_mask = self.generate_perlin(b * 2, h, w).to(features.device) + # only apply where perlin mask is 1 + noise_mask = noise_mask * perlin_mask + + # update gt mask + mask = mask + noise_mask + # binarize + mask = torch.where(mask > 0, 1, 0) + + # make new labels. 1 if any part of mask is 1, 0 otherwise + new_anomalous = noise_mask.reshape(b * 2, -1).any(dim=1).type(torch.float32) + labels = labels + new_anomalous + # binarize + labels = torch.where(labels > 0, 1, 0) + + # apply masked noise + perturbed = features + noise * noise_mask + + return perturbed, mask, labels diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index ddfc6d1cb9..c49c8daaf3 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -1,11 +1,14 @@ """PyTorch model for the SuperSimpleNet model implementation.""" +import math + import torch import torch.nn.functional as F from torch import nn from torchvision.models import Wide_ResNet50_2_Weights -from anomalib.models.components import TorchFXFeatureExtractor +from anomalib.models.components import GaussianBlur2d, TorchFXFeatureExtractor +from anomalib.models.image.supersimplenet.anomaly_generator import SSNAnomalyGenerator # Original Code # Copyright (c) 2024 Blaž Rolih @@ -24,24 +27,53 @@ class SuperSimpleNet(nn.Module): """ - def __init__(self): + def __init__(self, perlin_threshold: float): super().__init__() self.feature_extractor = FeatureExtractor(backbone="wide_resnet50_2", layers=["layer2", "layer3"]) channels = self.feature_extractor.get_channels_dim() self.adaptor = FeatureAdaptor(channels) self.segdec = SegmentationDetectionModule(input_dim=channels, stop_grad=True) + self.anomaly_generator = SSNAnomalyGenerator(noise_mean=0, noise_std=0.015, threshold=perlin_threshold) + + self.anomaly_map_generator = AnomalyMapGenerator(sigma=4) + + def forward(self, input_tensor: torch.Tensor, masks: torch.Tensor, labels: torch.Tensor): + output_size = input_tensor.shape[-2:] - def forward(self, input_tensor: torch.Tensor): features = self.feature_extractor(input_tensor) adapted = self.adaptor(features) if self.training: - pass - else: - anomaly_map, anomaly_score = self.segdec(adapted) + masks = self.downsample_mask(masks, *features.shape[-2:]) - return anomaly_map, anomaly_score + features, masks, labels = self.anomaly_generator( + adapted, + masks, + labels, + ) + + anomaly_map, anomaly_score = self.segdec(features) + return anomaly_map, anomaly_score, masks, labels + + anomaly_map, anomaly_score = self.segdec(adapted) + anomaly_map = self.anomaly_map_generator(anomaly_map, output_size=output_size) + + return anomaly_map, anomaly_score + + @staticmethod + def downsample_mask(masks: torch.Tensor, feat_h: int, feat_w: int) -> torch.Tensor: + # best downsampling proposed by DestSeg + masks = F.interpolate( + masks.unsqueeze(1), + size=(feat_h, feat_w), + mode="bilinear", + ) + return torch.where( + masks < 0.5, + torch.zeros_like(masks), + torch.ones_like(masks), + ) def init_weights(m: nn.Module): @@ -206,9 +238,24 @@ def forward(self, input: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: return map, score +class AnomalyMapGenerator(nn.Module): + def __init__(self, sigma: float): + super().__init__() + kernel_size = 2 * math.ceil(3 * sigma) + 1 + self.blur = GaussianBlur2d(kernel_size=kernel_size, sigma=4) + + def forward(self, out_map: torch.Tensor, output_size: tuple[int, int]) -> torch.Tensor: + # upscale & smooth + anomaly_map = F.interpolate(out_map, size=output_size, mode="bilinear") + anomaly_map = self.blur(anomaly_map) + return anomaly_map + + if __name__ == "__main__": - ssn = SuperSimpleNet() - ssn.eval() - x = torch.rand(1, 3, 256, 256) - p_map, p_score = ssn(x) + ssn = SuperSimpleNet(perlin_threshold=0.2) + ssn.train() + x = torch.rand(2, 3, 256, 256) + mask = torch.rand(2, 256, 256) + label = torch.rand(2) + p_map, p_score, m, l = ssn(x, mask, label) print(p_map.shape, p_score.shape) From eeddb15e984ad67ac4eac95a3a310ff32632e5a8 Mon Sep 17 00:00:00 2001 From: blaz-r Date: Fri, 8 Nov 2024 20:59:52 +0100 Subject: [PATCH 06/23] Add configurable params and docstrings Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/torch_model.py | 185 ++++++++++++++---- 1 file changed, 148 insertions(+), 37 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index c49c8daaf3..178419b906 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -3,9 +3,9 @@ import math import torch -import torch.nn.functional as F +import torch.nn.functional as F # noqa: N812 from torch import nn -from torchvision.models import Wide_ResNet50_2_Weights +from torch.nn import Parameter from anomalib.models.components import GaussianBlur2d, TorchFXFeatureExtractor from anomalib.models.image.supersimplenet.anomaly_generator import SSNAnomalyGenerator @@ -25,20 +25,49 @@ class SuperSimpleNet(nn.Module): It consists of feature extractor, feature adaptor, anomaly generation mechanism and segmentation-detection module. + Args: + perlin_threshold (float): threshold value for Perlin noise thresholding during anomaly generation. + backbone (str): backbone name + layers (list[str]): backbone layers utilised + stop_grad (bool): whether to stop gradient from class. to seg. head. """ - def __init__(self, perlin_threshold: float): + def __init__( + self, + perlin_threshold: float = 0.2, + backbone: str = "wide_resnet50_2", + layers: list[str] = ["layer2", "layer3"], # noqa: B006 + stop_grad: bool = True, + ) -> None: super().__init__() - self.feature_extractor = FeatureExtractor(backbone="wide_resnet50_2", layers=["layer2", "layer3"]) + self.feature_extractor = FeatureExtractor(backbone=backbone, layers=layers) channels = self.feature_extractor.get_channels_dim() self.adaptor = FeatureAdaptor(channels) - self.segdec = SegmentationDetectionModule(input_dim=channels, stop_grad=True) + self.segdec = SegmentationDetectionModule(channel_dim=channels, stop_grad=stop_grad) self.anomaly_generator = SSNAnomalyGenerator(noise_mean=0, noise_std=0.015, threshold=perlin_threshold) self.anomaly_map_generator = AnomalyMapGenerator(sigma=4) - def forward(self, input_tensor: torch.Tensor, masks: torch.Tensor, labels: torch.Tensor): + def forward( + self, + input_tensor: torch.Tensor, + masks: torch.Tensor, + labels: torch.Tensor, + ) -> tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + """SuperSimpleNet forward pass. + + Extract and process features, adapt them, generate anomalies (train only) and predict anomaly map and score. + + Args: + input_tensor (torch.Tensor): Input images. + masks (torch.Tensor): GT masks. + labels (torch.Tensor): GT labels. + + Returns: + inference: anomaly map and score + training: anomaly map, score and GT masks and labels + """ output_size = input_tensor.shape[-2:] features = self.feature_extractor(input_tensor) @@ -63,6 +92,18 @@ def forward(self, input_tensor: torch.Tensor, masks: torch.Tensor, labels: torch @staticmethod def downsample_mask(masks: torch.Tensor, feat_h: int, feat_w: int) -> torch.Tensor: + """Downsample the masks according to the feature dimensions. + + Primarily used in supervised setting. + + Args: + masks (torch.Tensor): input GT masks + feat_h (int): feature height. + feat_w (int): feature width. + + Returns: + (torch.Tensor): downsampled masks. + """ # best downsampling proposed by DestSeg masks = F.interpolate( masks.unsqueeze(1), @@ -76,21 +117,33 @@ def downsample_mask(masks: torch.Tensor, feat_h: int, feat_w: int) -> torch.Tens ) -def init_weights(m: nn.Module): - if isinstance(m, (nn.Linear, nn.Conv2d)): +def init_weights(m: nn.Module) -> None: + """Init weight of the model. + + Args: + m (nn.Module): torch module. + """ + if isinstance(m, nn.Linear | nn.Conv2d): nn.init.xavier_normal_(m.weight) - elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)): + elif isinstance(m, nn.BatchNorm1d | nn.BatchNorm2d): nn.init.constant_(m.weight, 1) class FeatureExtractor(nn.Module): - def __init__(self, backbone: str, layers: list[str], patch_size: int = 3): + """Feature extractor module. + + Args: + backbone (str): backbone name. + layers (list[str]): list of layers used for extraction. + """ + + def __init__(self, backbone: str, layers: list[str], patch_size: int = 3) -> None: super().__init__() self.feature_extractor = TorchFXFeatureExtractor( backbone=backbone, return_nodes=layers, - weights=Wide_ResNet50_2_Weights.IMAGENET1K_V1, + weights="IMAGENET1K_V1", ) self.pooler = nn.AvgPool2d( kernel_size=patch_size, @@ -98,7 +151,15 @@ def __init__(self, backbone: str, layers: list[str], patch_size: int = 3): padding=patch_size // 2, ) - def forward(self, input_tensor: torch.Tensor): + def forward(self, input_tensor: torch.Tensor) -> torch.Tensor: + """Extract features from input tensor. + + Args: + input_tensor: input tensor (images) + + Returns: + (torch.Tensor): extracted feature map. + """ # extract features self.feature_extractor.eval() with torch.no_grad(): @@ -120,49 +181,72 @@ def forward(self, input_tensor: torch.Tensor): feature_map = torch.cat(feature_map, dim=1) # neighboring patch aggregation - feature_map = self.pooler(feature_map) - - return feature_map + return self.pooler(feature_map) def get_channels_dim(self) -> int: + """Get feature channel dimension. + + Returns: + (int): feature channel dimension. + """ # dryrun self.feature_extractor.eval() with torch.no_grad(): features = self.feature_extractor(torch.rand(1, 3, 256, 256)) # sum channels - channels = sum(feature.shape[1] for feature in features.values()) - return channels + return sum(feature.shape[1] for feature in features.values()) class FeatureAdaptor(nn.Module): - def __init__(self, projection_dim: int): + """Feature adaptor used to adapt raw features for the task of anomaly detection. + + Args: + channel_dim (int): channel dimension of features. + """ + + def __init__(self, channel_dim: int) -> None: super().__init__() # linear layer equivalent self.projection = nn.Conv2d( - in_channels=projection_dim, - out_channels=projection_dim, + in_channels=channel_dim, + out_channels=channel_dim, kernel_size=1, stride=1, ) self.apply(init_weights) def forward(self, features: torch.Tensor) -> torch.Tensor: + """Adapt features. + + Args: + features (torch.Tensor): input features + + Returns: + (torch.Tensor) adapted features + """ return self.projection(features) class SegmentationDetectionModule(nn.Module): + """SegmentationDetection module responsible for prediction of anomaly map and score. + + Args: + channel_dim (int): channel dimension of features. + stop_grad (bool): whether to stop gradient from class. head to seg. head. + """ + def __init__( self, - input_dim: int, + channel_dim: int, stop_grad: bool = False, - ): + ) -> None: super().__init__() self.stop_grad = stop_grad # 1x1 conv - linear layer equivalent self.seg = nn.Sequential( nn.Conv2d( - in_channels=input_dim, + in_channels=channel_dim, out_channels=1024, kernel_size=1, stride=1, @@ -180,7 +264,7 @@ def __init__( self.dec_block = nn.Sequential( nn.Conv2d( - in_channels=input_dim + 1, + in_channels=channel_dim + 1, out_channels=128, kernel_size=5, padding="same", @@ -200,20 +284,33 @@ def __init__( self.apply(init_weights) - def get_params(self): - seg_params = self.seg.parameters() + def get_params(self) -> tuple[list[Parameter], list[Parameter]]: + """Get segmentation and classification head parameters. + + Returns: + seg. head parameters and class. head parameters. + """ + seg_params = list(self.seg.parameters()) dec_params = list(self.dec_block.parameters()) + list(self.fc_score.parameters()) return seg_params, dec_params - def forward(self, input: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + def forward(self, features: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """Predict anomaly map and anomaly score. + + Args: + features: adapted features. + + Returns: + predicted anomaly map and score. + """ # get anomaly map from seg head - map = self.seg(input) + ano_map = self.seg(features) - map_dec_copy = map + map_dec_copy = ano_map if self.stop_grad: map_dec_copy = map_dec_copy.detach() # dec conv layer takes feat + map - mask_cat = torch.cat((input, map_dec_copy), dim=1) + mask_cat = torch.cat((features, map_dec_copy), dim=1) dec_out = self.dec_block(mask_cat) # conv block result pooling @@ -221,11 +318,11 @@ def forward(self, input: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: dec_avg = self.dec_avg_pool(dec_out) # predicted map pooling (and stop grad) - map_max = self.map_max_pool(map) + map_max = self.map_max_pool(ano_map) if self.stop_grad: map_max = map_max.detach() - map_avg = self.map_avg_pool(map) + map_avg = self.map_avg_pool(ano_map) if self.stop_grad: map_avg = map_avg.detach() @@ -239,16 +336,30 @@ def forward(self, input: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: class AnomalyMapGenerator(nn.Module): - def __init__(self, sigma: float): + """Final anomaly map generator, responsible for upscaling and smoothing. + + Args: + sigma (float) Gaussian kernel sigma value. + """ + + def __init__(self, sigma: float) -> None: super().__init__() kernel_size = 2 * math.ceil(3 * sigma) + 1 self.blur = GaussianBlur2d(kernel_size=kernel_size, sigma=4) - def forward(self, out_map: torch.Tensor, output_size: tuple[int, int]) -> torch.Tensor: + def forward(self, out_map: torch.Tensor, final_size: tuple[int, int]) -> torch.Tensor: + """Upscale and smooth anomaly map to get final anomaly map of same size as input image. + + Args: + out_map (torch.Tensor): output anomaly map from seg. head. + final_size (tuple[int, int]): size (h, w) of final anomaly map. + + Returns: + torch.Tensor: final anomaly map. + """ # upscale & smooth - anomaly_map = F.interpolate(out_map, size=output_size, mode="bilinear") - anomaly_map = self.blur(anomaly_map) - return anomaly_map + anomaly_map = F.interpolate(out_map, size=final_size, mode="bilinear") + return self.blur(anomaly_map) if __name__ == "__main__": From 090120d63132f93baddbd798b1d49585ecb3290c Mon Sep 17 00:00:00 2001 From: blaz-r Date: Fri, 8 Nov 2024 21:00:13 +0100 Subject: [PATCH 07/23] Add dosctring and fix lint issue Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/anomaly_generator.py | 20 +++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/anomaly_generator.py b/src/anomalib/models/image/supersimplenet/anomaly_generator.py index 4f3fdda28b..a432a4bc5e 100644 --- a/src/anomalib/models/image/supersimplenet/anomaly_generator.py +++ b/src/anomalib/models/image/supersimplenet/anomaly_generator.py @@ -4,7 +4,7 @@ # SPDX-License-Identifier: Apache-2.0 import torch -import torch.nn.functional as F +import torch.nn.functional as F # noqa: N812 from torch import nn from anomalib.data.utils.generators.perlin import _rand_perlin_2d @@ -19,7 +19,7 @@ def __init__( noise_std: float, threshold: float, perlin_range: tuple[int, int] = (0, 6), - ): + ) -> None: super().__init__() self.noise_mean = noise_mean @@ -43,7 +43,7 @@ def next_power_2(num: int) -> int: return 1 << (num - 1).bit_length() def generate_perlin(self, batches: int, height: int, width: int) -> torch.Tensor: - """Generate 2d perlin noise masks with dims [b, 1, h, w] + """Generate 2d perlin noise masks with dims [b, 1, h, w]. Args: batches (int): number of batches (different masks) @@ -100,6 +100,18 @@ def forward( mask: torch.Tensor, labels: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + """Generate anomaly on features using thresholded perlin noise and Gaussian noise. + + Also update GT masks and labels with new anomaly information. + + Args: + features: input features. + mask: GT masks. + labels: GT labels. + + Returns: + perturbed features, updated GT masks and labels. + """ b, _, h, w = features.shape # duplicate @@ -129,7 +141,7 @@ def forward( # no overlap: don't apply to already anomalous regions (mask=1 -> bad) noise_mask = noise_mask * (1 - mask) - # [B * 2, 1, H, W] + # shape: [B * 2, 1, H, W] perlin_mask = self.generate_perlin(b * 2, h, w).to(features.device) # only apply where perlin mask is 1 noise_mask = noise_mask * perlin_mask From 2be15bd2c22f5439d2aaf0e37a868eff20824512 Mon Sep 17 00:00:00 2001 From: blaz-r Date: Fri, 8 Nov 2024 21:00:29 +0100 Subject: [PATCH 08/23] Implement loss and training step Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/lightning_model.py | 45 ++++++++++++- .../models/image/supersimplenet/loss.py | 65 ++++++++++++++++++- 2 files changed, 105 insertions(+), 5 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/lightning_model.py b/src/anomalib/models/image/supersimplenet/lightning_model.py index 2a3e9b889a..b74d189a69 100644 --- a/src/anomalib/models/image/supersimplenet/lightning_model.py +++ b/src/anomalib/models/image/supersimplenet/lightning_model.py @@ -1,4 +1,4 @@ -"""SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection +"""SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection. Paper https://arxiv.org/pdf/2408.03143 """ @@ -13,20 +13,59 @@ from anomalib import LearningType from anomalib.data import Batch from anomalib.models import AnomalyModule +from anomalib.models.image.supersimplenet.loss import SSNLoss class SuperSimpleNet(AnomalyModule): - def __init__(self): + """PL Lightning Module for the SuperSimpleNet model. + + Args: + perlin_threshold (float): threshold value for Perlin noise thresholding during anomaly generation. + backbone (str): backbone name + layers (list[str]): backbone layers utilised + stop_grad (bool): whether to stop gradient from class. to seg. head. + """ + + def __init__( + self, + perlin_threshold: float = 0.2, + backbone: str = "wide_resnet50_2", + layers: list[str] = ["layer2", "layer3"], # noqa: B006 + stop_grad: bool = True, + ) -> None: super().__init__() + self.model = SuperSimpleNet( + perlin_threshold=perlin_threshold, + backbone=backbone, + layers=layers, + stop_grad=stop_grad, + ) + self.loss = SSNLoss() def training_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: - pass + """Perform the training step input and return the loss. + + Args: + batch (batch: dict[str, str | torch.Tensor]): Input batch + args: Additional arguments. + kwargs: Additional keyword arguments. + + Returns: + STEP_OUTPUT: Dictionary containing the loss value. + """ + del args, kwargs # These variables are not used. + + anomaly_map, anomaly_score, masks, labels = self.model(batch.image) + loss = self.loss(pred_map=anomaly_map, pred_score=anomaly_score, target_mask=masks, target_label=labels) + self.log("train_loss", loss.item(), on_epoch=True, prog_bar=True, logger=True) + return {"loss": loss} def validation_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: pass @property def trainer_arguments(self) -> dict[str, Any]: + # TODO - normclip pass def configure_optimizers(self) -> OptimizerLRScheduler: diff --git a/src/anomalib/models/image/supersimplenet/loss.py b/src/anomalib/models/image/supersimplenet/loss.py index 7b396effaa..e4ff551ef1 100644 --- a/src/anomalib/models/image/supersimplenet/loss.py +++ b/src/anomalib/models/image/supersimplenet/loss.py @@ -1,13 +1,74 @@ """Loss function for the SuperSimpleNet model implementation.""" +from functools import partial + +import torch from torch import nn +from torchvision.ops.focal_loss import sigmoid_focal_loss # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 class SSNLoss(nn.Module): - """SuperSimpleNet loss function.""" + """SuperSimpleNet loss function. + + Args: + truncation_term (float): L1 loss truncation term preventing overfitting. + """ - def __init__(self): + def __init__(self, truncation_term: float = 0.5) -> None: super().__init__() + self.focal_loss = partial(sigmoid_focal_loss, alpha=-1, gamma=4.0, reduction="mean") + self.th = truncation_term + + def trunc_l1_loss(self, pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: + """Calculate the truncated L1 loss between `pred` and `target`. + + Args: + pred (torch.Tensor): predicted values. + target (torch.Tensor): target GT values. + + Returns: + torch.Tensor: L1 truncated loss value. + """ + normal_scores = pred[target == 0] + anomalous_scores = pred[target > 0] + # push normal towards negative numbers + true_loss = torch.clip(normal_scores + self.th, min=0) + # push anomalous towards positive numbers + fake_loss = torch.clip(-anomalous_scores + self.th, min=0) + + true_loss = true_loss.mean() if len(true_loss) else torch.Tensor(0) + fake_loss = fake_loss.mean() if len(fake_loss) else torch.Tensor(0) + + return true_loss + fake_loss + + def forward( + self, + pred_map: torch.Tensor, + pred_score: torch.Tensor, + target_mask: torch.Tensor, + target_label: torch.Tensor, + ) -> torch.Tensor: + """Calculate loss based on predicted anomaly maps and scores. + + Total loss = Lseg and Lcls + where + Lseg = Lfocal(map) + Ltruncl1(map) + Lcls = Lfocal(score) + + Args: + pred_map: predicted anomaly maps. + pred_score: predicted anomaly scores. + target_mask: GT anomaly masks. + target_label: GT anomaly labels. + + Returns: + torch.Tensor: loss value. + """ + map_focal = self.focal_loss(pred_map, target_mask) + map_trunc_l1 = self.trunc_l1_loss(pred_score, target_label) + score_focal = self.focal_loss(pred_score, target_mask) + + return map_focal + map_trunc_l1 + score_focal From f986c8e371e42774f47b7aa0486f7fb8e48a6a6f Mon Sep 17 00:00:00 2001 From: blaz-r Date: Mon, 11 Nov 2024 19:55:44 +0100 Subject: [PATCH 09/23] Update torchFX to also support str weights Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../models/components/feature_extractors/torchfx.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/anomalib/models/components/feature_extractors/torchfx.py b/src/anomalib/models/components/feature_extractors/torchfx.py index 600f2a961d..485441fa50 100644 --- a/src/anomalib/models/components/feature_extractors/torchfx.py +++ b/src/anomalib/models/components/feature_extractors/torchfx.py @@ -166,7 +166,11 @@ class can be provided and it will try to load the weights from the provided weig backbone_class = backbone.class_path backbone_model = backbone_class(**backbone.init_args) - if isinstance(weights, WeightsEnum): # torchvision models + if isinstance(weights, WeightsEnum) or weights in { + "IMAGENET1K_V1", + "IMAGENET1K_V2", + "DEFAULT", + }: # torchvision models feature_extractor = create_feature_extractor(model=backbone_model, return_nodes=return_nodes) elif weights is not None: if not isinstance(weights, str): From e08366d097a17690849dd852f7332cdc6d238d9e Mon Sep 17 00:00:00 2001 From: blaz-r Date: Sun, 17 Nov 2024 17:10:39 +0100 Subject: [PATCH 10/23] Update names Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/anomaly_generator.py | 2 +- .../image/supersimplenet/torch_model.py | 42 ++++++++++--------- 2 files changed, 23 insertions(+), 21 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/anomaly_generator.py b/src/anomalib/models/image/supersimplenet/anomaly_generator.py index a432a4bc5e..ea6dd65ea4 100644 --- a/src/anomalib/models/image/supersimplenet/anomaly_generator.py +++ b/src/anomalib/models/image/supersimplenet/anomaly_generator.py @@ -141,7 +141,7 @@ def forward( # no overlap: don't apply to already anomalous regions (mask=1 -> bad) noise_mask = noise_mask * (1 - mask) - # shape: [B * 2, 1, H, W] + # shape of noise is [B * 2, 1, H, W] perlin_mask = self.generate_perlin(b * 2, h, w).to(features.device) # only apply where perlin mask is 1 noise_mask = noise_mask * perlin_mask diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index 178419b906..7e9790d16e 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -20,7 +20,7 @@ # SPDX-License-Identifier: Apache-2.0 -class SuperSimpleNet(nn.Module): +class SuperSimpleNetModel(nn.Module): """SuperSimpleNet Pytorch model. It consists of feature extractor, feature adaptor, anomaly generation mechanism and segmentation-detection module. @@ -244,7 +244,7 @@ def __init__( self.stop_grad = stop_grad # 1x1 conv - linear layer equivalent - self.seg = nn.Sequential( + self.seg_head = nn.Sequential( nn.Conv2d( in_channels=channel_dim, out_channels=1024, @@ -262,7 +262,15 @@ def __init__( ), ) - self.dec_block = nn.Sequential( + # pooling for cls. conv out and map + self.map_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) + self.map_max_pool = nn.AdaptiveMaxPool2d(output_size=(1, 1)) + + self.dec_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) + self.dec_max_pool = nn.AdaptiveMaxPool2d(output_size=(1, 1)) + + # cls. head conv block + self.cls_conv = nn.Sequential( nn.Conv2d( in_channels=channel_dim + 1, out_channels=128, @@ -273,14 +281,8 @@ def __init__( nn.ReLU(inplace=True), ) - self.map_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) - self.map_max_pool = nn.AdaptiveMaxPool2d(output_size=(1, 1)) - - self.dec_avg_pool = nn.AdaptiveAvgPool2d(output_size=(1, 1)) - self.dec_max_pool = nn.AdaptiveMaxPool2d(output_size=(1, 1)) - - # 128 from dec and 2 from map, * 2 due to max and avg pool - self.fc_score = nn.Linear(in_features=128 * 2 + 2, out_features=1) + # cls. head fc block: 128 from dec and 2 from map, * 2 due to max and avg pool + self.cls_fc = nn.Linear(in_features=128 * 2 + 2, out_features=1) self.apply(init_weights) @@ -290,8 +292,8 @@ def get_params(self) -> tuple[list[Parameter], list[Parameter]]: Returns: seg. head parameters and class. head parameters. """ - seg_params = list(self.seg.parameters()) - dec_params = list(self.dec_block.parameters()) + list(self.fc_score.parameters()) + seg_params = list(self.seg_head.parameters()) + dec_params = list(self.cls_conv.parameters()) + list(self.cls_fc.parameters()) return seg_params, dec_params def forward(self, features: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: @@ -304,14 +306,14 @@ def forward(self, features: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: predicted anomaly map and score. """ # get anomaly map from seg head - ano_map = self.seg(features) + ano_map = self.seg_head(features) map_dec_copy = ano_map if self.stop_grad: map_dec_copy = map_dec_copy.detach() # dec conv layer takes feat + map mask_cat = torch.cat((features, map_dec_copy), dim=1) - dec_out = self.dec_block(mask_cat) + dec_out = self.cls_conv(mask_cat) # conv block result pooling dec_max = self.dec_max_pool(dec_out) @@ -330,9 +332,9 @@ def forward(self, features: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: dec_cat = torch.cat((dec_max, dec_avg, map_max, map_avg), dim=1).squeeze( dim=(2, 3), ) - score = self.fc_score(dec_cat).squeeze(dim=1) + ano_score = self.cls_fc(dec_cat).squeeze(dim=1) - return map, score + return ano_map, ano_score class AnomalyMapGenerator(nn.Module): @@ -345,7 +347,7 @@ class AnomalyMapGenerator(nn.Module): def __init__(self, sigma: float) -> None: super().__init__() kernel_size = 2 * math.ceil(3 * sigma) + 1 - self.blur = GaussianBlur2d(kernel_size=kernel_size, sigma=4) + self.smoothing = GaussianBlur2d(kernel_size=kernel_size, sigma=4) def forward(self, out_map: torch.Tensor, final_size: tuple[int, int]) -> torch.Tensor: """Upscale and smooth anomaly map to get final anomaly map of same size as input image. @@ -359,11 +361,11 @@ def forward(self, out_map: torch.Tensor, final_size: tuple[int, int]) -> torch.T """ # upscale & smooth anomaly_map = F.interpolate(out_map, size=final_size, mode="bilinear") - return self.blur(anomaly_map) + return self.smoothing(anomaly_map) if __name__ == "__main__": - ssn = SuperSimpleNet(perlin_threshold=0.2) + ssn = SuperSimpleNetModel(perlin_threshold=0.2) ssn.train() x = torch.rand(2, 3, 256, 256) mask = torch.rand(2, 256, 256) From e45b6d4fc47d49ffebb3ea61bcfb5c622ef3a54e Mon Sep 17 00:00:00 2001 From: blaz-r Date: Sun, 17 Nov 2024 17:30:25 +0100 Subject: [PATCH 11/23] Add supervision based param settings and validation step Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/lightning_model.py | 45 +++++++++++++++---- .../image/supersimplenet/torch_model.py | 29 +++++------- 2 files changed, 47 insertions(+), 27 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/lightning_model.py b/src/anomalib/models/image/supersimplenet/lightning_model.py index b74d189a69..336d93dc82 100644 --- a/src/anomalib/models/image/supersimplenet/lightning_model.py +++ b/src/anomalib/models/image/supersimplenet/lightning_model.py @@ -13,7 +13,9 @@ from anomalib import LearningType from anomalib.data import Batch from anomalib.models import AnomalyModule -from anomalib.models.image.supersimplenet.loss import SSNLoss + +from .loss import SSNLoss +from .torch_model import SuperSimpleNetModel class SuperSimpleNet(AnomalyModule): @@ -23,7 +25,7 @@ class SuperSimpleNet(AnomalyModule): perlin_threshold (float): threshold value for Perlin noise thresholding during anomaly generation. backbone (str): backbone name layers (list[str]): backbone layers utilised - stop_grad (bool): whether to stop gradient from class. to seg. head. + supervised (bool): whether the model will be trained in supervised mode. False by default (unsupervised). """ def __init__( @@ -31,10 +33,19 @@ def __init__( perlin_threshold: float = 0.2, backbone: str = "wide_resnet50_2", layers: list[str] = ["layer2", "layer3"], # noqa: B006 - stop_grad: bool = True, + supervised: bool = False, ) -> None: super().__init__() - self.model = SuperSimpleNet( + self.supervised = supervised + # stop grad in unsupervised + if supervised: + stop_grad = False + self.norm_clip_val = 1 + else: + stop_grad = True + self.norm_clip_val = 0 + + self.model = SuperSimpleNetModel( perlin_threshold=perlin_threshold, backbone=backbone, layers=layers, @@ -55,18 +66,36 @@ def training_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: """ del args, kwargs # These variables are not used. - anomaly_map, anomaly_score, masks, labels = self.model(batch.image) + anomaly_map, anomaly_score, masks, labels = self.model( + images=batch.image, masks=batch.gt_mask, labels=batch.gt_label + ) loss = self.loss(pred_map=anomaly_map, pred_score=anomaly_score, target_mask=masks, target_label=labels) self.log("train_loss", loss.item(), on_epoch=True, prog_bar=True, logger=True) return {"loss": loss} def validation_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: - pass + """Perform the validation step and return the anomaly map and anomaly score. + + Args: + batch (dict[str, str | torch.Tensor]): Input batch + args: Additional arguments. + kwargs: Additional keyword arguments. + + Returns: + STEP_OUTPUT | None: batch dictionary containing anomaly-maps. + """ + # These variables are not used. + del args, kwargs + + # Get anomaly maps and predicted scores from the model. + predictions = self.model(batch.image) + + return batch.update(**predictions._asdict()) @property def trainer_arguments(self) -> dict[str, Any]: - # TODO - normclip - pass + """Return SuperSimpleNet trainer arguments.""" + return {"gradient_clip_val": self.norm_clip_val, "num_sanity_val_steps": 0} def configure_optimizers(self) -> OptimizerLRScheduler: pass diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index 7e9790d16e..850faece5c 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -7,6 +7,7 @@ from torch import nn from torch.nn import Parameter +from anomalib.data import InferenceBatch from anomalib.models.components import GaussianBlur2d, TorchFXFeatureExtractor from anomalib.models.image.supersimplenet.anomaly_generator import SSNAnomalyGenerator @@ -51,16 +52,16 @@ def __init__( def forward( self, - input_tensor: torch.Tensor, - masks: torch.Tensor, - labels: torch.Tensor, - ) -> tuple[torch.Tensor, torch.Tensor] | tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + images: torch.Tensor, + masks: torch.Tensor | None = None, + labels: torch.Tensor | None = None, + ) -> InferenceBatch | tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: """SuperSimpleNet forward pass. Extract and process features, adapt them, generate anomalies (train only) and predict anomaly map and score. Args: - input_tensor (torch.Tensor): Input images. + images (torch.Tensor): Input images. masks (torch.Tensor): GT masks. labels (torch.Tensor): GT labels. @@ -68,9 +69,9 @@ def forward( inference: anomaly map and score training: anomaly map, score and GT masks and labels """ - output_size = input_tensor.shape[-2:] + output_size = images.shape[-2:] - features = self.feature_extractor(input_tensor) + features = self.feature_extractor(images) adapted = self.adaptor(features) if self.training: @@ -86,9 +87,9 @@ def forward( return anomaly_map, anomaly_score, masks, labels anomaly_map, anomaly_score = self.segdec(adapted) - anomaly_map = self.anomaly_map_generator(anomaly_map, output_size=output_size) + anomaly_map = self.anomaly_map_generator(anomaly_map, final_size=output_size) - return anomaly_map, anomaly_score + return InferenceBatch(anomaly_map=anomaly_map, pred_score=anomaly_score) @staticmethod def downsample_mask(masks: torch.Tensor, feat_h: int, feat_w: int) -> torch.Tensor: @@ -362,13 +363,3 @@ def forward(self, out_map: torch.Tensor, final_size: tuple[int, int]) -> torch.T # upscale & smooth anomaly_map = F.interpolate(out_map, size=final_size, mode="bilinear") return self.smoothing(anomaly_map) - - -if __name__ == "__main__": - ssn = SuperSimpleNetModel(perlin_threshold=0.2) - ssn.train() - x = torch.rand(2, 3, 256, 256) - mask = torch.rand(2, 256, 256) - label = torch.rand(2) - p_map, p_score, m, l = ssn(x, mask, label) - print(p_map.shape, p_score.shape) From 8768607a70e4b23c4273c7f5b4e7a49033b07076 Mon Sep 17 00:00:00 2001 From: blaz-r Date: Sun, 17 Nov 2024 18:04:54 +0100 Subject: [PATCH 12/23] Add optimizer configs Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../image/supersimplenet/lightning_model.py | 27 +++++++++++++++++-- 1 file changed, 25 insertions(+), 2 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/lightning_model.py b/src/anomalib/models/image/supersimplenet/lightning_model.py index 336d93dc82..5723a255bc 100644 --- a/src/anomalib/models/image/supersimplenet/lightning_model.py +++ b/src/anomalib/models/image/supersimplenet/lightning_model.py @@ -9,6 +9,8 @@ from typing import Any from lightning.pytorch.utilities.types import STEP_OUTPUT, OptimizerLRScheduler +from torch.optim import AdamW +from torch.optim.lr_scheduler import MultiStepLR from anomalib import LearningType from anomalib.data import Batch @@ -67,7 +69,9 @@ def training_step(self, batch: Batch, *args, **kwargs) -> STEP_OUTPUT: del args, kwargs # These variables are not used. anomaly_map, anomaly_score, masks, labels = self.model( - images=batch.image, masks=batch.gt_mask, labels=batch.gt_label + images=batch.image, + masks=batch.gt_mask, + labels=batch.gt_label, ) loss = self.loss(pred_map=anomaly_map, pred_score=anomaly_score, target_mask=masks, target_label=labels) self.log("train_loss", loss.item(), on_epoch=True, prog_bar=True, logger=True) @@ -98,7 +102,26 @@ def trainer_arguments(self) -> dict[str, Any]: return {"gradient_clip_val": self.norm_clip_val, "num_sanity_val_steps": 0} def configure_optimizers(self) -> OptimizerLRScheduler: - pass + """Configure AdamW optimizer and MultiStepLR scheduler.""" + optim = AdamW( + [ + { + "params": self.model.adaptor.parameters(), + "lr": 0.0001, + }, + { + "params": self.model.segdec.parameters(), + "lr": 0.0002, + "weight_decay": 0.00001, + }, + ], + ) + sched = MultiStepLR( + optim, + milestones=[int(self.trainer.max_epochs * 0.8), int(self.trainer.max_epochs * 0.9)], + gamma=0.4, + ) + return [optim], [sched] @property def learning_type(self) -> LearningType: From 0e5a6c7d0e0e006682a363a60dbdd60516498aef Mon Sep 17 00:00:00 2001 From: blaz-r Date: Sun, 17 Nov 2024 18:05:06 +0100 Subject: [PATCH 13/23] Fix loss and types Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- src/anomalib/models/image/supersimplenet/anomaly_generator.py | 4 ++-- src/anomalib/models/image/supersimplenet/loss.py | 4 ++-- src/anomalib/models/image/supersimplenet/torch_model.py | 2 ++ 3 files changed, 6 insertions(+), 4 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/anomaly_generator.py b/src/anomalib/models/image/supersimplenet/anomaly_generator.py index ea6dd65ea4..0ed8ed3992 100644 --- a/src/anomalib/models/image/supersimplenet/anomaly_generator.py +++ b/src/anomalib/models/image/supersimplenet/anomaly_generator.py @@ -149,13 +149,13 @@ def forward( # update gt mask mask = mask + noise_mask # binarize - mask = torch.where(mask > 0, 1, 0) + mask = torch.where(mask > 0, torch.ones_like(mask), torch.zeros_like(mask)) # make new labels. 1 if any part of mask is 1, 0 otherwise new_anomalous = noise_mask.reshape(b * 2, -1).any(dim=1).type(torch.float32) labels = labels + new_anomalous # binarize - labels = torch.where(labels > 0, 1, 0) + labels = torch.where(labels > 0, torch.ones_like(labels), torch.zeros_like(labels)) # apply masked noise perturbed = features + noise * noise_mask diff --git a/src/anomalib/models/image/supersimplenet/loss.py b/src/anomalib/models/image/supersimplenet/loss.py index e4ff551ef1..3635c9fd12 100644 --- a/src/anomalib/models/image/supersimplenet/loss.py +++ b/src/anomalib/models/image/supersimplenet/loss.py @@ -68,7 +68,7 @@ def forward( torch.Tensor: loss value. """ map_focal = self.focal_loss(pred_map, target_mask) - map_trunc_l1 = self.trunc_l1_loss(pred_score, target_label) - score_focal = self.focal_loss(pred_score, target_mask) + map_trunc_l1 = self.trunc_l1_loss(pred_map, target_mask) + score_focal = self.focal_loss(pred_score, target_label) return map_focal + map_trunc_l1 + score_focal diff --git a/src/anomalib/models/image/supersimplenet/torch_model.py b/src/anomalib/models/image/supersimplenet/torch_model.py index 850faece5c..e583dfa271 100644 --- a/src/anomalib/models/image/supersimplenet/torch_model.py +++ b/src/anomalib/models/image/supersimplenet/torch_model.py @@ -76,6 +76,7 @@ def forward( if self.training: masks = self.downsample_mask(masks, *features.shape[-2:]) + labels = labels.type(torch.float32) features, masks, labels = self.anomaly_generator( adapted, @@ -105,6 +106,7 @@ def downsample_mask(masks: torch.Tensor, feat_h: int, feat_w: int) -> torch.Tens Returns: (torch.Tensor): downsampled masks. """ + masks = masks.type(torch.float32) # best downsampling proposed by DestSeg masks = F.interpolate( masks.unsqueeze(1), From 123851207999c1c332ab4d949b89f38d10c79cec Mon Sep 17 00:00:00 2001 From: blaz-r Date: Sun, 17 Nov 2024 18:05:23 +0100 Subject: [PATCH 14/23] Add SSN to init Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- src/anomalib/models/image/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/src/anomalib/models/image/__init__.py b/src/anomalib/models/image/__init__.py index f3a5435038..3b7e96929a 100644 --- a/src/anomalib/models/image/__init__.py +++ b/src/anomalib/models/image/__init__.py @@ -19,6 +19,7 @@ from .reverse_distillation import ReverseDistillation from .rkde import Rkde from .stfpm import Stfpm +from .supersimplenet import SuperSimpleNet from .uflow import Uflow from .winclip import WinClip @@ -41,4 +42,5 @@ "Stfpm", "Uflow", "WinClip", + "SuperSimpleNet", ] From 28006f18b74c3febc5b889b31b07548646b31edc Mon Sep 17 00:00:00 2001 From: Blaz Rolih Date: Tue, 19 Nov 2024 21:53:35 +0100 Subject: [PATCH 15/23] Add SSN description Signed-off-by: Blaz Rolih Signed-off-by: blaz.rolih --- src/anomalib/models/image/supersimplenet/README.md | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/src/anomalib/models/image/supersimplenet/README.md b/src/anomalib/models/image/supersimplenet/README.md index f866c780d3..5c6b8f5219 100644 --- a/src/anomalib/models/image/supersimplenet/README.md +++ b/src/anomalib/models/image/supersimplenet/README.md @@ -6,7 +6,17 @@ Model Type: Segmentation ## Description -TODO +SuperSimpleNet is a discriminative defect / anomaly detection model evolved from the SimpleNet architecture. It consists of four components: +feature extractor with upscaling, feature adaptor, synthetic feature-level anomaly generation module, and +segmentation-detection module. A ResNet-like feature extractor first extracts features, which are then upscaled and +average-pooled to capture neighboring context. Features are further refined for anomaly detection task in the adaptor module. +During training, synthetic anomalies are generated at the feature level by adding Gaussian noise to regions defined by the +binary Perlin noise mask. The perturbed features are then fed into the segmentation-detection +module, which produces the anomaly map and the anomaly score. During inference, anomaly generation is skipped, and the model +directly predicts the anomaly map and score. The predicted anomaly map is upscaled to match the input image size +and refined with a Gaussian filter. + +This model can be trained in both unsupervised and supervised setting, but Anomalib currently supports only unsupervised training. ## Architecture From 40e7475dc2d580b006978fd773c59f762d5b37ec Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bla=C5=BE=20Rolih?= <61357777+blaz-r@users.noreply.github.com> Date: Wed, 20 Nov 2024 07:46:32 +0100 Subject: [PATCH 16/23] Update supersimplenet README.md Signed-off-by: blaz.rolih --- src/anomalib/models/image/supersimplenet/README.md | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/src/anomalib/models/image/supersimplenet/README.md b/src/anomalib/models/image/supersimplenet/README.md index 5c6b8f5219..bb5cac1a41 100644 --- a/src/anomalib/models/image/supersimplenet/README.md +++ b/src/anomalib/models/image/supersimplenet/README.md @@ -1,6 +1,6 @@ # SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection -This is the implementation of the [SuperSimpleNet](https://arxiv.org/pdf/2408.03143) paper. +This is the implementation of the [SuperSimpleNet](https://arxiv.org/pdf/2408.03143) paper, based on the [official code](https://github.com/blaz-r/SuperSimpleNet). Model Type: Segmentation @@ -20,10 +20,19 @@ This model can be trained in both unsupervised and supervised setting, but Anoma ## Architecture +TODO ![SuperSimpleNet architecture](/docs/source/images/supersimplenet/architecture.png "SuperSimpleNet architecture") ## Usage `anomalib train --model SuperSimpleNet --data MVTec --data.category ` +> It is recommended to train the model for 300 epochs with batch size of 32 to achieve stable training with random anomaly generation. Training with lower parameter values will still work, but might not yield the optimal results. +> +> For supervised learning, refer to the [official code](https://github.com/blaz-r/SuperSimpleNet). + ## Benchmark + +The following results were obtained with seed 42, default params, batch size 32, and model trained for 300 epochs. + +TODO From ce6c7f54d5d15acf41f283e88550d311aa4a6b40 Mon Sep 17 00:00:00 2001 From: blaz-r Date: Wed, 20 Nov 2024 22:20:04 +0100 Subject: [PATCH 17/23] Update readme with arch and results Signed-off-by: blaz-r Signed-off-by: blaz.rolih --- .../images/supersimplenet/architecture.png | Bin 0 -> 681252 bytes .../models/image/supersimplenet/README.md | 24 +++++++++++++++--- 2 files changed, 20 insertions(+), 4 deletions(-) create mode 100644 docs/source/images/supersimplenet/architecture.png diff --git a/docs/source/images/supersimplenet/architecture.png b/docs/source/images/supersimplenet/architecture.png new file mode 100644 index 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unsupervised and supervised setting, but Anoma ## Architecture -TODO ![SuperSimpleNet architecture](/docs/source/images/supersimplenet/architecture.png "SuperSimpleNet architecture") ## Usage @@ -28,11 +27,28 @@ TODO `anomalib train --model SuperSimpleNet --data MVTec --data.category ` > It is recommended to train the model for 300 epochs with batch size of 32 to achieve stable training with random anomaly generation. Training with lower parameter values will still work, but might not yield the optimal results. -> +> > For supervised learning, refer to the [official code](https://github.com/blaz-r/SuperSimpleNet). -## Benchmark +## MVTec AD results The following results were obtained with seed 42, default params, batch size 32, and model trained for 300 epochs. -TODO +| | **Image AUROC** | **Pixel AUPRO** | +| ----------- | :-------------: | :-------------: | +| Bottle | 1.000 | 0.914 | +| Cable | 0.981 | 0.895 | +| Capsule | 0.990 | 0.926 | +| Carpet | 0.987 | 0.936 | +| Grid | 0.998 | 0.935 | +| Hazelnut | 0.999 | 0.946 | +| Leather | 1.000 | 0.972 | +| Metal_nut | 0.996 | 0.923 | +| Pill | 0.960 | 0.942 | +| Screw | 0.903 | 0.952 | +| Tile | 0.989 | 0.817 | +| Toothbrush | 0.917 | 0.861 | +| Transistor | 1.000 | 0.909 | +| Wood | 0.996 | 0.868 | +| Zipper | 0.996 | 0.944 | +| **Average** | 0.981 | 0.916 | From a47fc50dc05681ebbd9e14f5737b434e40c52fee Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Bla=C5=BE=20Rolih?= <61357777+blaz-r@users.noreply.github.com> Date: Thu, 21 Nov 2024 09:36:34 +0100 Subject: [PATCH 18/23] Update SuperSimpleNet README.md Signed-off-by: blaz.rolih --- .../models/image/supersimplenet/README.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/src/anomalib/models/image/supersimplenet/README.md b/src/anomalib/models/image/supersimplenet/README.md index 12eabd1a08..5553292770 100644 --- a/src/anomalib/models/image/supersimplenet/README.md +++ b/src/anomalib/models/image/supersimplenet/README.md @@ -1,14 +1,16 @@ # SuperSimpleNet: Unifying Unsupervised and Supervised Learning for Fast and Reliable Surface Defect Detection -This is the implementation of the [SuperSimpleNet](https://arxiv.org/pdf/2408.03143) paper, based on the [official code](https://github.com/blaz-r/SuperSimpleNet). +This is an implementation of the [SuperSimpleNet](https://arxiv.org/pdf/2408.03143) paper, based on the [official code](https://github.com/blaz-r/SuperSimpleNet). Model Type: Segmentation ## Description -SuperSimpleNet is a discriminative defect / anomaly detection model evolved from the SimpleNet architecture. It consists of four components: +**SuperSimpleNet** is a simple yet strong discriminative defect / anomaly detection model evolved from the SimpleNet architecture. It consists of four components: feature extractor with upscaling, feature adaptor, synthetic feature-level anomaly generation module, and -segmentation-detection module. A ResNet-like feature extractor first extracts features, which are then upscaled and +segmentation-detection module. + +A ResNet-like feature extractor first extracts features, which are then upscaled and average-pooled to capture neighboring context. Features are further refined for anomaly detection task in the adaptor module. During training, synthetic anomalies are generated at the feature level by adding Gaussian noise to regions defined by the binary Perlin noise mask. The perturbed features are then fed into the segmentation-detection @@ -16,7 +18,7 @@ module, which produces the anomaly map and the anomaly score. During inference, directly predicts the anomaly map and score. The predicted anomaly map is upscaled to match the input image size and refined with a Gaussian filter. -This model can be trained in both unsupervised and supervised setting, but Anomalib currently supports only unsupervised training. +This implementation supports both unsupervised and supervised setting, but Anomalib currently supports only unsupervised learning. ## Architecture @@ -32,8 +34,7 @@ This model can be trained in both unsupervised and supervised setting, but Anoma ## MVTec AD results -The following results were obtained with seed 42, default params, batch size 32, and model trained for 300 epochs. - +The following results were obtained using this Anomalib implementation trained for 300 epochs with seed 42, default params, and batch size 32. | | **Image AUROC** | **Pixel AUPRO** | | ----------- | :-------------: | :-------------: | | Bottle | 1.000 | 0.914 | @@ -52,3 +53,5 @@ The following results were obtained with seed 42, default params, batch size 32, | Wood | 0.996 | 0.868 | | Zipper | 0.996 | 0.944 | | **Average** | 0.981 | 0.916 | + +For other results on VisA, SensumSODF, and KSDD2, refer to the [paper](https://arxiv.org/pdf/2408.03143). 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