From d65dab241e8902821a79f0387680ca127cc59faa Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sun, 15 Oct 2023 14:55:33 -0300 Subject: [PATCH 01/21] Added uflow model --- src/anomalib/models/uflow/__init__.py | 8 + src/anomalib/models/uflow/anomaly_map.py | 160 ++++++++++++++++++ src/anomalib/models/uflow/config.yaml | 108 ++++++++++++ .../models/uflow/feature_extraction.py | 141 +++++++++++++++ src/anomalib/models/uflow/lightning_model.py | 124 ++++++++++++++ src/anomalib/models/uflow/loss.py | 27 +++ src/anomalib/models/uflow/torch_model.py | 107 ++++++++++++ 7 files changed, 675 insertions(+) create mode 100644 src/anomalib/models/uflow/__init__.py create mode 100644 src/anomalib/models/uflow/anomaly_map.py create mode 100644 src/anomalib/models/uflow/config.yaml create mode 100644 src/anomalib/models/uflow/feature_extraction.py create mode 100644 src/anomalib/models/uflow/lightning_model.py create mode 100644 src/anomalib/models/uflow/loss.py create mode 100644 src/anomalib/models/uflow/torch_model.py diff --git a/src/anomalib/models/uflow/__init__.py b/src/anomalib/models/uflow/__init__.py new file mode 100644 index 0000000000..df5786900d --- /dev/null +++ b/src/anomalib/models/uflow/__init__.py @@ -0,0 +1,8 @@ +"""U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold.""" + +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from .lightning_model import Uflow, UflowLightning + +__all__ = ["Uflow", "UflowLightning"] diff --git a/src/anomalib/models/uflow/anomaly_map.py b/src/anomalib/models/uflow/anomaly_map.py new file mode 100644 index 0000000000..3be614abdd --- /dev/null +++ b/src/anomalib/models/uflow/anomaly_map.py @@ -0,0 +1,160 @@ +"""UFlow Anomaly Map Generator Implementation.""" + +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +from typing import List + +import numpy as np +import scipy.integrate as integrate +import scipy.stats as st +import torch +import torch.nn.functional as F +from mpmath import mp, binomial +from omegaconf import ListConfig +from torch import Tensor, nn + +mp.dps = 15 # Set precision for NFA computation (in case of high_precision=True) + + +class AnomalyMapGenerator(nn.Module): + """Generate Anomaly Heatmap and segmentation.""" + + def __init__(self, input_size: ListConfig | tuple) -> None: + super().__init__() + self.input_size = input_size if isinstance(input_size, tuple) else tuple(input_size) + + def forward(self, latent_variables: list[Tensor]) -> Tensor: + return self.compute_anomaly_map(latent_variables) + + def compute_anomaly_map(self, latent_variables: list[Tensor]) -> Tensor: + """ + Generate a likelihood-based anomaly map, from latent variables. + Args: + latent_variables: List of latent variables from the UFlow model. Each element is a tensor of shape + (N, Cl, Hl, Wl), where N is the batch size, Cl is the number of channels, and Hl and Wl are the height and + width of the latent variables, respectively, for each scale l. + + Returns: + Final Anomaly Map. Tensor of shape (N, 1, H, W), where N is the batch size, and H and W are the height and + width of the input image, respectively. + """ + + likelihoods = [] + for z in latent_variables: + # Mean prob by scale. Likelihood is actually with sum instead of mean. Using mean to avoid numerical issues. + # Also, this way all scales have the same weight, and it does not depend on the number of channels + log_prob_i = -torch.mean(z ** 2, dim=1, keepdim=True) * 0.5 + prob_i = torch.exp(log_prob_i) + likelihoods.append(F.interpolate( + prob_i, + size=self.input_size, + mode="bilinear", + align_corners=False, + )) + anomaly_map = 1 - torch.mean(torch.stack(likelihoods, dim=-1), dim=-1) + return anomaly_map + + def compute_anomaly_mask( + self, + z: List[torch.Tensor], + win_size: int = 7, + binomial_probability_thr: float = 0.5, + high_precision: bool = False + ): + """ + This method is not used in the basic functionality of training and testing. It is a bit slow, so we decided to + leave it as an option for the user. It is included as it is part of the U-Flow paper, and can be called + separately if an unsupervised anomaly segmentation is needed. + + Generate an anomaly mask, from latent variables. It is based on the NFA (Number of False Alarms) method, which + is a statistical method to detect anomalies. The NFA is computed as the log of the probability of the null + hypothesis, which is that all pixels are normal. First, we compute a list of candidate pixels, with + suspiciously high values of z^2, by applying a binomial test to each pixel, looking at a window around it. + Then, to compute the NFA values (actually the log-NFA), we evaluate how probable is that a pixel belongs to the + normal distribution. The null-hypothesis is that under normality assumptions, all candidate pixels are uniformly + distributed. Then, the detection is based on the concentration of candidate pixels. + + Args: + z: List of latent variables from the UFlow model. Each element is a tensor of shape + (N, Cl, Hl, Wl), where N is the batch size, Cl is the number of channels, and Hl and Wl are the height and + width of the latent variables, respectively, for each scale l. + win_size: Window size for the binomial test. + binomial_probability_thr: Probability threshold for the binomial test. + high_precision: Whether to use high precision for the binomial test. + + Returns: + Anomaly mask. Tensor of shape (N, 1, H, W), where N is the batch size, and H and W are the height and + width of the input image, respectively. + """ + log_prob_l = [ + self.binomial_test(zi, win_size / (2 ** scale), binomial_probability_thr, high_precision) + for scale, zi in enumerate(z) + ] + + log_prob_l_up = torch.cat([ + F.interpolate(lpl, size=self.input_size, mode='bicubic', align_corners=True) for lpl in log_prob_l + ], dim=1) + + log_prob = torch.sum(log_prob_l_up, dim=1, keepdim=True) + + log_number_of_tests = torch.log10(torch.sum(torch.tensor([zi.shape[-2] * zi.shape[-1] for zi in z]))) + log_nfa = log_number_of_tests + log_prob + + anomaly_score = -log_nfa + anomaly_mask = anomaly_score < 0 + + return anomaly_mask + + @staticmethod + def binomial_test(z: torch.Tensor, win, probability_thr: float, high_precision: bool = False): + """ + The binomial test applied to validate or reject the null hypothesis that the pixel is normal. The null + hypothesis is that the pixel is normal, and the alternative hypothesis is that the pixel is anomalous. The + binomial test is applied to a window around the pixel, and the number of pixels in the window that are + anomalous is compared to the number of pixels that are expected to be anomalous under the null hypothesis. + Args: + z: Latent variable from the UFlow model. Tensor of shape (N, Cl, Hl, Wl), where N is the batch size, Cl is + the number of channels, and Hl and Wl are the height and width of the latent variables, respectively. + win: Window size for the binomial test. + probability_thr: Probability threshold for the binomial test. + high_precision: Whether to use high precision for the binomial test. + + Returns: + Log of the probability of the null hypothesis. + + """ + tau = st.chi2.ppf(probability_thr, 1) + half_win = np.max([int(win // 2), 1]) + + n_chann = z.shape[1] + + # Candidates + z2 = F.pad(z ** 2, tuple(4 * [half_win]), 'reflect').detach().cpu() + z2_unfold_h = z2.unfold(-2, 2 * half_win + 1, 1) + z2_unfold_hw = z2_unfold_h.unfold(-2, 2 * half_win + 1, 1).numpy() + observed_candidates_k = np.sum(z2_unfold_hw >= tau, axis=(-2, -1)) + + # All volume together + observed_candidates = np.sum(observed_candidates_k, axis=1, keepdims=True) + x = observed_candidates / n_chann + n = int((2 * half_win + 1) ** 2) + + # Low precision + if not high_precision: + log_prob = torch.tensor(st.binom.logsf(x, n, 1 - probability_thr) / np.log(10)) + # High precision - good and slow + else: + to_mp = np.frompyfunc(mp.mpf, 1, 1) + mpn = mp.mpf(n) + mpp = probability_thr + binomial_density = lambda k: binomial(mpn, to_mp(k)) * ((1 - mpp) ** k) * (mpp ** (mpn - k)) + + integral = lambda xx: integrate.quad(binomial_density, xx, n)[0] + integral_array = np.vectorize(integral) + prob = integral_array(x) + log_prob = torch.tensor(np.log10(prob)) + + return log_prob diff --git a/src/anomalib/models/uflow/config.yaml b/src/anomalib/models/uflow/config.yaml new file mode 100644 index 0000000000..2f0a501d8c --- /dev/null +++ b/src/anomalib/models/uflow/config.yaml @@ -0,0 +1,108 @@ +dataset: + name: mvtec + format: mvtec + path: ./datasets/MVTec + category: bottle + task: segmentation + train_batch_size: 16 + eval_batch_size: 16 + inference_batch_size: 16 + num_workers: 8 + image_size: 448 # or 256 for ResNet extractors # dimensions to which images are resized (mandatory) + center_crop: null # dimensions to which images are center-cropped after resizing (optional) + normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet] + transform_config: + train: null + eval: null + test_split_mode: from_dir # options: [from_dir, synthetic] + test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode) + val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic] + val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode) + +model: + name: uflow + flow_steps: 4 + permute_soft: false + affine_clamp: 2.0 + affine_subnet_channels_ratio: 1.0 + backbone: mcait # official: mcait, other extractors tested: resnet18, wide_resnet50_2. Could use others... + lr: 1e-4 + weight_decay: 1e-5 + early_stopping: + patience: 20 + metric: pixel_AUROC + mode: max + normalization_method: min_max # options: [null, min_max, cdf] + +metrics: + image: + - F1Score + - AUROC + pixel: + - F1Score + - AUROC + threshold: # TODO: add NFA + method: adaptive # options: [adaptive, manual] + manual_image: null + manual_pixel: null + +visualization: + show_images: False # show images on the screen + save_images: True # save images to the file system + log_images: True # log images to the available loggers (if any) + image_save_path: null # path to which images will be saved + mode: full # options: ["full", "simple"] + +project: + seed: 0 + path: ./results + +logging: + logger: [] # options: [comet, tensorboard, wandb, csv] or combinations. + log_graph: false # Logs the model graph to respective logger. + +optimization: + export_mode: null # options: torch, onnx, openvino + +# PL Trainer Args. Don't add extra parameter here. +trainer: + enable_checkpointing: true + default_root_dir: null + gradient_clip_val: 0 + gradient_clip_algorithm: norm + num_nodes: 1 + devices: 1 + enable_progress_bar: true + overfit_batches: 0.0 + track_grad_norm: -1 + check_val_every_n_epoch: 1 # Don't validate before extracting features. + fast_dev_run: false + accumulate_grad_batches: 1 + max_epochs: 50 + min_epochs: null + max_steps: -1 + min_steps: null + max_time: null + limit_train_batches: 1.0 + limit_val_batches: 1.0 + limit_test_batches: 1.0 + limit_predict_batches: 1.0 + val_check_interval: 1.0 # Don't validate before extracting features. + log_every_n_steps: 50 + accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto"> + strategy: null + sync_batchnorm: false + precision: 32 + enable_model_summary: true + num_sanity_val_steps: 0 + profiler: null + benchmark: false + deterministic: false + reload_dataloaders_every_n_epochs: 0 + auto_lr_find: false + replace_sampler_ddp: true + detect_anomaly: false + auto_scale_batch_size: false + plugins: null + move_metrics_to_cpu: false + multiple_trainloader_mode: max_size_cycle diff --git a/src/anomalib/models/uflow/feature_extraction.py b/src/anomalib/models/uflow/feature_extraction.py new file mode 100644 index 0000000000..8740fc6099 --- /dev/null +++ b/src/anomalib/models/uflow/feature_extraction.py @@ -0,0 +1,141 @@ +import pytorch_lightning as pl +import timm +import torch +import torch.nn as nn +import torch.nn.functional as F + +AVAILABLE_EXTRACTORS = ['mcait', 'resnet18', 'wide_resnet50_2'] + + +def get_feature_extractor(backbone, input_size=256, **kwargs): + """ + Get feature extractor. Currently, is restricted to AVAILABLE_EXTRACTORS. + Args: + backbone (str): Backbone name. + input_size (int): Input size. + **kwargs: Additional arguments. + + Returns: + FeatureExtractorInterface: Feature extractor. + """ + assert backbone in AVAILABLE_EXTRACTORS, f"Feature extractor must be one of {AVAILABLE_EXTRACTORS}." + if backbone in ["resnet18", "wide_resnet50_2"]: + return ResNetFeatureExtractor(backbone, input_size, **kwargs) + elif backbone == "mcait": + return MCaitFeatureExtractor(**kwargs) + raise ValueError("`backbone` must be one of `[mcait, resnet18, wide_resnet50_2]`") + + +class FeatureExtractorInterface(pl.LightningModule): + + def __init__(self): + super(FeatureExtractorInterface, self).__init__() + + def forward(self, img, **kwargs): + raise NotImplementedError + + def extract_features(self, img, **kwargs): + raise NotImplementedError + + def normalize_features(self, x, **kwargs): + raise NotImplementedError + + +class ResNetFeatureExtractor(FeatureExtractorInterface): + """Feature extractor based on ResNet backbone.""" + def __init__(self, backbone, input_size, max_downsampling_factor=32, **kwargs): + super(ResNetFeatureExtractor, self).__init__() + self.extractor = timm.create_model( + backbone, + pretrained=True, + features_only=True, + out_indices=[1, 2, 3], + output_stride=max_downsampling_factor + ) + self.channels = self.extractor.feature_info.channels() + self.scale_factors = self.extractor.feature_info.reduction() + self.scales = range(len(self.scale_factors)) + + self.feature_normalizations = nn.ModuleList() + for in_channels, scale in zip(self.channels, self.scale_factors): + self.feature_normalizations.append(nn.LayerNorm( + [in_channels, int(input_size / scale), int(input_size / scale)], + elementwise_affine=True + )) + + for param in self.extractor.parameters(): + param.requires_grad = False + + def forward(self, img, **kwargs): + features = self.extract_features(img) + normalized_features = self.normalize_features(features, **kwargs) + return normalized_features + + def extract_features(self, img, **kwargs): + self.extractor.eval() + return self.extractor(img) + + def normalize_features(self, features, **kwargs): + return [self.feature_normalizations[i](feature) for i, feature in enumerate(features)] + + +class MCaitFeatureExtractor(FeatureExtractorInterface): + """ + Feature extractor based on MCait backbone. This is the proposed feature extractor in the paper. It uses two + independently trained Cait models, at different scales, with input sizes 448 and 224, respectively. + It also includes a normalization layer for each scale. + """ + def __init__(self, **kwargs): + super(MCaitFeatureExtractor, self).__init__() + self.input_size = 448 + self.extractor1 = timm.create_model("cait_m48_448", pretrained=True) + self.extractor2 = timm.create_model("cait_s24_224", pretrained=True) + self.channels = [768, 384] + self.scale_factors = [16, 32] + self.scales = range(len(self.scale_factors)) + + for param in self.extractor1.parameters(): + param.requires_grad = False + for param in self.extractor2.parameters(): + param.requires_grad = False + + def forward(self, img, training=True): + features = self.extract_features(img) + normalized_features = self.normalize_features(features, training=training) + return normalized_features + + def extract_features(self, img, **kwargs): + self.extractor1.eval() + self.extractor2.eval() + + # Scale 1 --> Extractor 1 + x1 = self.extractor1.patch_embed(img) + x1 = x1 + self.extractor1.pos_embed + x1 = self.extractor1.pos_drop(x1) + for i in range(41): # paper Table 6. Block Index = 40 + x1 = self.extractor1.blocks[i](x1) + + # Scale 2 --> Extractor 2 + img_sub = F.interpolate(torch.Tensor(img), size=(224, 224), mode='bicubic', align_corners=True) + x2 = self.extractor2.patch_embed(img_sub) + x2 = x2 + self.extractor2.pos_embed + x2 = self.extractor2.pos_drop(x2) + for i in range(21): + x2 = self.extractor2.blocks[i](x2) + + features = [x1, x2] + return features + + def normalize_features(self, features, **kwargs): + + normalized_features = [] + for i, extractor in enumerate([self.extractor1, self.extractor2]): + batch, _, channels = features[i].shape + scale_factor = self.scale_factors[i] + + x = extractor.norm(features[i].contiguous()) + x = x.permute(0, 2, 1) + x = x.reshape(batch, channels, self.input_size // scale_factor, self.input_size // scale_factor) + normalized_features.append(x) + + return normalized_features diff --git a/src/anomalib/models/uflow/lightning_model.py b/src/anomalib/models/uflow/lightning_model.py new file mode 100644 index 0000000000..633176d715 --- /dev/null +++ b/src/anomalib/models/uflow/lightning_model.py @@ -0,0 +1,124 @@ +"""U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold. + +https://arxiv.org/pdf/2211.12353.pdf +""" + +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import torch +from omegaconf import DictConfig, ListConfig +from pytorch_lightning.callbacks import EarlyStopping +from pytorch_lightning.utilities.types import STEP_OUTPUT +from torch import Tensor + +from anomalib.models.components import AnomalyModule +from anomalib.models.uflow.loss import UFlowLoss +from anomalib.models.uflow.torch_model import UflowModel + +__all__ = ["Uflow", "UflowLightning"] + + +class Uflow(AnomalyModule): + """PL Lightning Module for the UFLOW algorithm.""" + + def __init__( + self, + input_size: tuple[int, int], + backbone: str, + flow_steps: int = 4, + affine_clamp: float = 2.0, + affine_subnet_channels_ratio: float = 1.0, + permute_soft: bool = False + ) -> None: + super().__init__() + self.model: UflowModel = UflowModel( + input_size=input_size, + backbone=backbone, + flow_steps=flow_steps, + affine_clamp=affine_clamp, + affine_subnet_channels_ratio=affine_subnet_channels_ratio, + permute_soft=permute_soft, + ) + self.loss = UFlowLoss() + + def step(self, batch): + z, ljd = self.model(batch['image']) + loss = self.loss(z, ljd) + return {"loss": loss} + + def training_step(self, batch: dict[str, str | Tensor], *args, **kwargs) -> STEP_OUTPUT: + losses = self.step(batch) + self.log_dict({"loss": losses['loss']}, on_step=True, on_epoch=False, prog_bar=False, logger=True) + return losses + + def validation_step(self, batch: dict[str, str | Tensor], *args, **kwargs) -> STEP_OUTPUT: + anomaly_maps = self.model(batch["image"]) + batch["anomaly_maps"] = anomaly_maps + return batch + + +class UflowLightning(Uflow): + """PL Lightning Module for the UFLOW algorithm. + + Args: + hparams (DictConfig | ListConfig): Model params + """ + + def __init__(self, hparams: DictConfig | ListConfig) -> None: + super().__init__( + input_size=hparams.model.input_size, + backbone=hparams.model.backbone, + flow_steps=hparams.model.flow_steps, + affine_clamp=hparams.model.affine_clamp, + affine_subnet_channels_ratio=hparams.model.affine_subnet_channels_ratio, + permute_soft=hparams.model.permute_soft + ) + self.lr = hparams.model.lr + self.weight_decay = hparams.model.weight_decay + self.hparams: DictConfig | ListConfig # type: ignore + self.save_hyperparameters(hparams) + + def configure_callbacks(self) -> list[EarlyStopping]: + """Configure model-specific callbacks. + + Note: + This method is used for the existing CLI. + When PL CLI is introduced, configure callback method will be + deprecated, and callbacks will be configured from either + config.yaml file or from CLI. + """ + early_stopping = EarlyStopping( + monitor=self.hparams.model.early_stopping.metric, + patience=self.hparams.model.early_stopping.patience, + mode=self.hparams.model.early_stopping.mode, + ) + return [early_stopping] + + def configure_optimizers(self): + def get_total_number_of_iterations(): + try: + self.trainer.reset_train_dataloader() + number_of_training_examples = len(self.trainer.train_dataloader.dataset) + batch_size = self.trainer.train_dataloader.loaders.batch_size + drop_last = 1 * self.trainer.train_dataloader.loaders.drop_last + iterations_per_epoch = number_of_training_examples // batch_size + 1 - drop_last + total_iterations = iterations_per_epoch * (self.trainer.max_epochs - 1) + except: + total_iterations = 25000 + return total_iterations + + # Optimizer + optimizer = torch.optim.Adam( + [{"params": self.parameters(), "initial_lr": self.lr}], + lr=self.lr, + weight_decay=self.weight_decay + ) + + # Scheduler for slowly reducing learning rate + scheduler = torch.optim.lr_scheduler.LinearLR( + optimizer, start_factor=1., end_factor=0.4, total_iters=get_total_number_of_iterations() + ) + return [optimizer], [scheduler] diff --git a/src/anomalib/models/uflow/loss.py b/src/anomalib/models/uflow/loss.py new file mode 100644 index 0000000000..4c068f272f --- /dev/null +++ b/src/anomalib/models/uflow/loss.py @@ -0,0 +1,27 @@ +"""Loss function for the UFlow Model Implementation.""" + +# Copyright (C) 2022 Intel Corporation +# SPDX-License-Identifier: Apache-2.0 + +from __future__ import annotations + +import torch +from torch import Tensor, nn + + +class UFlowLoss(nn.Module): + """UFlow Loss.""" + + def forward(self, hidden_variables: list[Tensor], jacobians: list[Tensor]) -> Tensor: + """Calculate the UFlow loss. + + Args: + hidden_variables (list[Tensor]): Hidden variables from the fastflow model. f: X -> Z + jacobians (list[Tensor]): Log of the jacobian determinants from the fastflow model. + + Returns: + Tensor: UFlow loss computed based on the hidden variables and the log of the Jacobians. + """ + lpz = torch.sum(torch.stack([0.5 * torch.sum(z_i ** 2, dim=(1, 2, 3)) for z_i in hidden_variables], dim=0)) + flow_loss = torch.mean(lpz - jacobians) + return flow_loss diff --git a/src/anomalib/models/uflow/torch_model.py b/src/anomalib/models/uflow/torch_model.py new file mode 100644 index 0000000000..c129356357 --- /dev/null +++ b/src/anomalib/models/uflow/torch_model.py @@ -0,0 +1,107 @@ +import torch.nn as nn +from FrEIA import framework as ff, modules as fm + +from .feature_extraction import get_feature_extractor +from .anomaly_map import AnomalyMapGenerator + + +class UflowModel(nn.Module): + def __init__( + self, + input_size=(448, 448), + flow_steps=4, + backbone="mcait", + affine_clamp=2.0, + affine_subnet_channels_ratio=1.0, + permute_soft=False + ): + super(UflowModel, self).__init__() + + self.input_size = input_size + self.affine_clamp = affine_clamp + self.affine_subnet_channels_ratio = affine_subnet_channels_ratio + self.permute_soft = permute_soft + + self.feature_extractor = get_feature_extractor(backbone, input_size) + self.flow = self.build_flow(flow_steps) + self.anomaly_map_generator = AnomalyMapGenerator(input_size) + + def build_flow(self, flow_steps): + input_nodes = [] + for channel, s_factor in zip(self.feature_extractor.channels, self.feature_extractor.scale_factors): + input_nodes.append( + ff.InputNode(channel, self.input_size[0] // s_factor, self.input_size[1] // s_factor, name=f"cond_{channel}") + ) + + nodes, output_nodes = [], [] + last_node = input_nodes[-1] + for i in reversed(range(1, len(input_nodes))): + flows = self.get_flow_stage(last_node, flow_steps) + volume_size = flows[-1].output_dims[0][0] + split = ff.Node( + flows[-1], fm.Split, + {'section_sizes': (volume_size // 8 * 4, volume_size - volume_size // 8 * 4), 'dim': 0}, + name=f'split_{i + 1}' + ) + output = ff.OutputNode(split.out1, name=f'output_scale_{i + 1}') + up = ff.Node(split.out0, fm.IRevNetUpsampling, {}, name=f'up_{i + 1}') + last_node = ff.Node([input_nodes[i - 1].out0, up.out0], fm.Concat, {'dim': 0}, name=f'cat_{i}') + + output_nodes.append(output) + nodes.extend([*flows, split, up, last_node]) + + flows = self.get_flow_stage(last_node, flow_steps) + output = ff.OutputNode(flows[-1], name='output_scale_1') + + output_nodes.append(output) + nodes.extend(flows) + + return ff.GraphINN(input_nodes + nodes + output_nodes[::-1]) + + def get_flow_stage(self, in_node, flow_steps, condition_node=None): + + def get_affine_coupling_subnet(kernel_size, subnet_channels_ratio): + def affine_coupling_subnet(in_channels, out_channels): + mid_channels = int(in_channels * subnet_channels_ratio) + return nn.Sequential( + nn.Conv2d(in_channels, mid_channels, kernel_size, padding="same"), + nn.ReLU(), + nn.Conv2d(mid_channels, out_channels, kernel_size, padding="same"), + ) + + return affine_coupling_subnet + + def glow_params(k): + return { + 'subnet_constructor': get_affine_coupling_subnet(k, self.affine_subnet_channels_ratio), + 'affine_clamping': self.affine_clamp, + 'permute_soft': self.permute_soft + } + + flow_size = in_node.output_dims[0][-1] + nodes = [] + for step in range(flow_steps): + nodes.append(ff.Node( + in_node, + fm.AllInOneBlock, + glow_params(3 if step % 2 == 0 else 1), + conditions=condition_node, + name=f"flow{flow_size}_step{step}" + )) + in_node = nodes[-1] + return nodes + + def forward(self, image): + features = self.feature_extractor(image) + z, ljd = self.encode(features) + + if self.training: + return z, ljd + else: + return self.anomaly_map_generator(z) + + def encode(self, features): + z, ljd = self.flow(features, rev=False) + if len(self.feature_extractor.scales) == 1: + z = [z] + return z, ljd From b8a07eb37b11f7ae346cb8c2ba7c56c59b7b23be Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sun, 15 Oct 2023 14:55:57 -0300 Subject: [PATCH 02/21] Added documentation (README) for uflow model --- docs/source/images/uflow/diagram.png | Bin 0 -> 4045933 bytes docs/source/images/uflow/teaser.jpg | Bin 0 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anomalies](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-others-anomalies.jpg "BeanTech, LGG MRI, STC results - anomalies") + +### Normal images + +#### MVTec + +![MVTec results - normal](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-mvtec-good.jpg "MVTec results - normal") + +#### BeanTech, LGG MRI, STC + +![BeanTech, LGG MRI, STC results - normal](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-others-good.jpg "BeanTech, LGG MRI, STC results - normal") From 81c049c650aaa1e40e777dc7acea1c8dd7aaa5d6 Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sun, 15 Oct 2023 14:56:31 -0300 Subject: [PATCH 03/21] Added uflow to the list of available models, and main README updated --- README.md | 1 + src/anomalib/models/__init__.py | 2 ++ 2 files changed, 3 insertions(+) diff --git a/README.md b/README.md index f28594ef5d..0cf4397403 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,7 @@ where the currently available models are: - [PatchCore](src/anomalib/models/patchcore) - [Reverse Distillation](src/anomalib/models/reverse_distillation) - [STFPM](src/anomalib/models/stfpm) +- [UFlow](src/anomalib/models/uflow) ## Feature extraction & (pre-trained) backbones diff --git a/src/anomalib/models/__init__.py b/src/anomalib/models/__init__.py index c242eed881..bfee908fb1 100644 --- a/src/anomalib/models/__init__.py +++ b/src/anomalib/models/__init__.py @@ -29,6 +29,7 @@ from anomalib.models.reverse_distillation import ReverseDistillation from anomalib.models.rkde import Rkde from anomalib.models.stfpm import Stfpm +from anomalib.models.uflow import Uflow __all__ = [ "Cfa", @@ -44,6 +45,7 @@ "ReverseDistillation", "Rkde", "Stfpm", + "Uflow", "AiVad", "EfficientAd", ] From b0eb9568aad16acef007c20324b431fcc917b885 Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Tue, 17 Oct 2023 08:29:46 -0300 Subject: [PATCH 04/21] Added missing images for the documentation --- docs/source/images/uflow/iou.png | Bin 0 -> 162416 bytes docs/source/images/uflow/more-results.png | Bin 0 -> 134011 bytes docs/source/images/uflow/pixel-aupro.png | Bin 0 -> 167256 bytes docs/source/images/uflow/pixel-auroc.png | Bin 0 -> 155440 bytes .../images/uflow/results-mvtec-anomalies.jpg | Bin 0 -> 677990 bytes .../source/images/uflow/results-mvtec-good.jpg | Bin 0 -> 732747 bytes .../images/uflow/results-others-anomalies.jpg | Bin 0 -> 556385 bytes .../images/uflow/results-others-good.jpg | Bin 0 -> 502849 bytes 8 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/source/images/uflow/iou.png create mode 100644 docs/source/images/uflow/more-results.png create mode 100644 docs/source/images/uflow/pixel-aupro.png create mode 100644 docs/source/images/uflow/pixel-auroc.png create mode 100644 docs/source/images/uflow/results-mvtec-anomalies.jpg create mode 100644 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z7trb0R?e6b%68A?v$n=;qHDhJ@5;~b6^p+sKmWAgcLQPj#j$L$n(BAu=f5Z^-caB-XXss87PvA-u2xJ&jtY5Z-f1NHU}+LE9e@}Z0GbFsD7PfN{kEfD8K*ic}Yu*_NVQq|oeiLJxg4al>(7wDBll`*92U@NYRevVmkDVA?uDBG7Pln&a zai26k$U{rv1Lrrrf1HgmIH&HS3$gL&iXTXz4RUKvM9sHMD93E+7 zF7^|vj5#M z%s-os+T9fCtVCY6GpizPD}FCSA|p)hC`6~v3gf&1GnTmhZo>O{|F()@u` z1zjZiTt&5wnAw?_$j#{BF~s}jBoPOz(aczNEtMTE6EyHjt&+|&@3zzI+n(2R_FaoE zmvh61BRRZb_nls%Rma*i;;}1eP9Eh_Mgq;cww@>5+YsP0K;zD{mUgoTJL-9=AA^ zsVFRvW7QELHEj|kR5bP>@uE=3)r|zl$#W06#Dg52MSp`aCgZ81YT;iFq8FS1(+PxV zw~-epR975kS=&3XDsBVb18x6#6hn^pp2H=SeS?ia?o8()5`^x2*F$%piVgD$J|yn} z7DSUkax1&_HyCyd>{tLu!+c8BVUPm{YQ@7!!OrgXLBBio|B0kF?JM&66EhQ5e}tRG zmsz0w`VH%--l(@X8xQs-Nt#FTnvY8E+}6rwUSbCpn>LL&gi=eJby0Ss8bXO0&xkha zBJiFY)erzuzSL%2+^mKmp@h5b7YYBOhQB-Y>VK!CHlQHDTwAREd|AB_BQ_{ys<$*? z*_~Emjt^0C_N(*RDG?pE1*t(DkdE@!Mx@y0cMfJXvN~1=EHCXn@EeXlrF!vjKA=>C=paIB>Vx za54#tl>W&+o)VtNYoRT(fD?sdIV0 zY{4~E zlYR+l;Ba*Y$RrM?^j+DqI@M;KZ=#+CVM8 Date: Tue, 17 Oct 2023 08:47:29 -0300 Subject: [PATCH 05/21] Update src/anomalib/models/uflow/anomaly_map.py Co-authored-by: Samet Akcay --- src/anomalib/models/uflow/anomaly_map.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/anomalib/models/uflow/anomaly_map.py b/src/anomalib/models/uflow/anomaly_map.py index 3be614abdd..e024ff75c0 100644 --- a/src/anomalib/models/uflow/anomaly_map.py +++ b/src/anomalib/models/uflow/anomaly_map.py @@ -8,7 +8,7 @@ from typing import List import numpy as np -import scipy.integrate as integrate +from scipy import integrate import scipy.stats as st import torch import torch.nn.functional as F From 6bf1d16f98c652fe3a87935d6033f89e99ee4abf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mat=C3=ADas=20Tailanian?= <895687+mtailanian@users.noreply.github.com> Date: Tue, 17 Oct 2023 08:48:26 -0300 Subject: [PATCH 06/21] Update src/anomalib/models/uflow/anomaly_map.py Co-authored-by: Samet Akcay --- src/anomalib/models/uflow/anomaly_map.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/anomalib/models/uflow/anomaly_map.py b/src/anomalib/models/uflow/anomaly_map.py index e024ff75c0..d3f953f234 100644 --- a/src/anomalib/models/uflow/anomaly_map.py +++ b/src/anomalib/models/uflow/anomaly_map.py @@ -109,7 +109,7 @@ def compute_anomaly_mask( return anomaly_mask @staticmethod - def binomial_test(z: torch.Tensor, win, probability_thr: float, high_precision: bool = False): + def binomial_test(z: torch.Tensor, win, probability_thr: float, high_precision: bool = False) -> torch.Tensor: """ The binomial test applied to validate or reject the null hypothesis that the pixel is normal. The null hypothesis is that the pixel is normal, and the alternative hypothesis is that the pixel is anomalous. The From 6e1fe6c955749ea4f37a86aa9c759474a2a624fe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mat=C3=ADas=20Tailanian?= <895687+mtailanian@users.noreply.github.com> Date: Tue, 17 Oct 2023 08:48:39 -0300 Subject: [PATCH 07/21] Update src/anomalib/models/uflow/feature_extraction.py Co-authored-by: Samet Akcay --- src/anomalib/models/uflow/feature_extraction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/anomalib/models/uflow/feature_extraction.py b/src/anomalib/models/uflow/feature_extraction.py index 8740fc6099..e9db3a5409 100644 --- a/src/anomalib/models/uflow/feature_extraction.py +++ b/src/anomalib/models/uflow/feature_extraction.py @@ -1,7 +1,7 @@ import pytorch_lightning as pl import timm import torch -import torch.nn as nn +from torch import nn import torch.nn.functional as F AVAILABLE_EXTRACTORS = ['mcait', 'resnet18', 'wide_resnet50_2'] From 13a1b05038c43a9bb6d2371ad2341d664fb81d55 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mat=C3=ADas=20Tailanian?= <895687+mtailanian@users.noreply.github.com> Date: Tue, 17 Oct 2023 09:20:44 -0300 Subject: [PATCH 08/21] Update src/anomalib/models/uflow/torch_model.py Co-authored-by: Samet Akcay --- src/anomalib/models/uflow/torch_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/anomalib/models/uflow/torch_model.py b/src/anomalib/models/uflow/torch_model.py index c129356357..2fe640b3ef 100644 --- a/src/anomalib/models/uflow/torch_model.py +++ b/src/anomalib/models/uflow/torch_model.py @@ -15,7 +15,7 @@ def __init__( affine_subnet_channels_ratio=1.0, permute_soft=False ): - super(UflowModel, self).__init__() + super().__init__() self.input_size = input_size self.affine_clamp = affine_clamp From 9fe38cb415b2aa40e0a92efa81a038b3b231bde3 Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 20:43:42 -0300 Subject: [PATCH 09/21] Added uflow to the reference guide in docs --- .../reference_guide/algorithms/index.rst | 1 + .../reference_guide/algorithms/uflow.rst | 44 +++++++++++++++++++ 2 files changed, 45 insertions(+) create mode 100644 docs/source/reference_guide/algorithms/uflow.rst diff --git a/docs/source/reference_guide/algorithms/index.rst b/docs/source/reference_guide/algorithms/index.rst index 3c4ce0a271..02391bd305 100644 --- a/docs/source/reference_guide/algorithms/index.rst +++ b/docs/source/reference_guide/algorithms/index.rst @@ -17,6 +17,7 @@ Algorithms patchcore reverse_distillation stfpm + uflow Feature extraction & (pre-trained) backbones diff --git a/docs/source/reference_guide/algorithms/uflow.rst b/docs/source/reference_guide/algorithms/uflow.rst new file mode 100644 index 0000000000..659f55446c --- /dev/null +++ b/docs/source/reference_guide/algorithms/uflow.rst @@ -0,0 +1,44 @@ +U-Flow +--------- + +This is the implementation of the `U-Flow `_ paper. + +Model Type: Segmentation + +Description +*********** + +U-Flow is a U-Shaped normalizing flow-based probability distribution estimator. +The method consists of three phases. +(1) Multi-scale feature extraction: a rich multi-scale representation is obtained with MSCaiT, by combining pre-trained image Transformers acting at different image scales. It can also be used any other feature extractor, such as ResNet. +(2) U-shaped Normalizing Flow: by adapting the widely used U-like architecture to NFs, a fully invertible architecture is designed. This architecture is capable of merging the information from different scales while ensuring independence both intra- and inter-scales. To make it fully invertible, split and invertible up-sampling operations are used. +(3) Anomaly score and segmentation computation: besides generating the anomaly map based on the likelihood of test data, we also propose to adapt the a contrario framework to obtain an automatic threshold by controlling the allowed number of false alarms. + +Architecture +************ + +.. image:: ../../images/uflow/diagram.png + :alt: U-Flow Architecture + +Usage +***** + +.. code-block:: bash + + $ python tools/train.py --model uflow + + +.. automodule:: anomalib.models.uflow.torch_model + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: anomalib.models.uflow.lightning_model + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: anomalib.models.uflow.anomaly_map + :members: + :undoc-members: + :show-inheritance: From 09bdf61a04244694348acfd28f4aa7cd2d206bfd Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 20:44:24 -0300 Subject: [PATCH 10/21] Added uflow to the pre-merge tests --- tests/pre_merge/deploy/test_inferencer.py | 1 + tests/pre_merge/models/test_model_premerge.py | 1 + 2 files changed, 2 insertions(+) diff --git a/tests/pre_merge/deploy/test_inferencer.py b/tests/pre_merge/deploy/test_inferencer.py index 86bd89f785..354add6795 100644 --- a/tests/pre_merge/deploy/test_inferencer.py +++ b/tests/pre_merge/deploy/test_inferencer.py @@ -87,6 +87,7 @@ def make( ("patchcore", "segmentation"), ("reverse_distillation", "segmentation"), ("stfpm", "segmentation"), + ("uflow", "segmentation"), # also test different task types for a single model ("padim", "classification"), ("padim", "detection"), diff --git a/tests/pre_merge/models/test_model_premerge.py b/tests/pre_merge/models/test_model_premerge.py index 231c0fd6a2..71df268952 100644 --- a/tests/pre_merge/models/test_model_premerge.py +++ b/tests/pre_merge/models/test_model_premerge.py @@ -30,6 +30,7 @@ class TestModel: ("reverse_distillation", False), ("rkde", False), ("stfpm", False), + ("uflow", False), ], ) @TestDataset(num_train=20, num_test=10) From 4ca19fc8f552813afc4118c3245389ff2894f833 Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 20:46:00 -0300 Subject: [PATCH 11/21] removed the _step function, and merged the code with training_step --- src/anomalib/models/uflow/lightning_model.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/src/anomalib/models/uflow/lightning_model.py b/src/anomalib/models/uflow/lightning_model.py index 633176d715..7fbb6da9ce 100644 --- a/src/anomalib/models/uflow/lightning_model.py +++ b/src/anomalib/models/uflow/lightning_model.py @@ -44,16 +44,12 @@ def __init__( ) self.loss = UFlowLoss() - def step(self, batch): + def training_step(self, batch: dict[str, str | Tensor], *args, **kwargs) -> STEP_OUTPUT: z, ljd = self.model(batch['image']) loss = self.loss(z, ljd) + self.log_dict({"loss": loss}, on_step=True, on_epoch=False, prog_bar=False, logger=True) return {"loss": loss} - def training_step(self, batch: dict[str, str | Tensor], *args, **kwargs) -> STEP_OUTPUT: - losses = self.step(batch) - self.log_dict({"loss": losses['loss']}, on_step=True, on_epoch=False, prog_bar=False, logger=True) - return losses - def validation_step(self, batch: dict[str, str | Tensor], *args, **kwargs) -> STEP_OUTPUT: anomaly_maps = self.model(batch["image"]) batch["anomaly_maps"] = anomaly_maps From 858c589157411c830a2345cf108992791e7fea3e Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 20:46:36 -0300 Subject: [PATCH 12/21] added as a comment the values used in the paper --- src/anomalib/models/uflow/config.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/anomalib/models/uflow/config.yaml b/src/anomalib/models/uflow/config.yaml index 2f0a501d8c..fd121aef0c 100644 --- a/src/anomalib/models/uflow/config.yaml +++ b/src/anomalib/models/uflow/config.yaml @@ -4,11 +4,11 @@ dataset: path: ./datasets/MVTec category: bottle task: segmentation - train_batch_size: 16 + train_batch_size: 14 # values used in paper: bottle: 23, cable: 14, capsule: 14, carpet: 13, grid: 12, hazelnut: 21, leather: 15, metal_nut: 11, pill: 11, screw: 11, tile: 30, toothbrush: 21, transistor: 20, wood: 22, zipper: 24 eval_batch_size: 16 inference_batch_size: 16 num_workers: 8 - image_size: 448 # or 256 for ResNet extractors # dimensions to which images are resized (mandatory) + image_size: 448 # 448 for mcait or 256 for ResNet extractors # dimensions to which images are resized (mandatory) center_crop: null # dimensions to which images are center-cropped after resizing (optional) normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet] transform_config: @@ -26,7 +26,7 @@ model: affine_clamp: 2.0 affine_subnet_channels_ratio: 1.0 backbone: mcait # official: mcait, other extractors tested: resnet18, wide_resnet50_2. Could use others... - lr: 1e-4 + lr: 1e-3 # values used in paper: bottle: 0.0001128999, cable: 0.0016160391, capsule: 0.0012118892, carpet: 0.0012118892, grid: 0.0000362248, hazelnut: 0.0013268899, leather: 0.0006124724, metal_nut: 0.0008148858, pill: 0.0010756100, screw: 0.0004155987, tile: 0.0060457548, toothbrush: 0.0001287313, transistor: 0.0011212904, wood: 0.0002466546, zipper: 0.0000455247 weight_decay: 1e-5 early_stopping: patience: 20 From 04ce7e501fb2b7b91dd910d72cc36a25954bb5c9 Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 20:47:13 -0300 Subject: [PATCH 13/21] re-factorized feature extractors to use the TimmFeatureExtractor class --- .../models/uflow/feature_extraction.py | 66 +++++++------------ 1 file changed, 25 insertions(+), 41 deletions(-) diff --git a/src/anomalib/models/uflow/feature_extraction.py b/src/anomalib/models/uflow/feature_extraction.py index e9db3a5409..ef96d4c5f9 100644 --- a/src/anomalib/models/uflow/feature_extraction.py +++ b/src/anomalib/models/uflow/feature_extraction.py @@ -1,18 +1,21 @@ -import pytorch_lightning as pl +from typing import List + import timm import torch -from torch import nn import torch.nn.functional as F +from torch import nn + +from anomalib.models.components.feature_extractors import TimmFeatureExtractor AVAILABLE_EXTRACTORS = ['mcait', 'resnet18', 'wide_resnet50_2'] -def get_feature_extractor(backbone, input_size=256, **kwargs): +def get_feature_extractor(backbone, input_size: List[int] = (256, 256), **kwargs): """ Get feature extractor. Currently, is restricted to AVAILABLE_EXTRACTORS. Args: backbone (str): Backbone name. - input_size (int): Input size. + input_size (tuple[int, int]): Input size. **kwargs: Additional arguments. Returns: @@ -20,50 +23,31 @@ def get_feature_extractor(backbone, input_size=256, **kwargs): """ assert backbone in AVAILABLE_EXTRACTORS, f"Feature extractor must be one of {AVAILABLE_EXTRACTORS}." if backbone in ["resnet18", "wide_resnet50_2"]: - return ResNetFeatureExtractor(backbone, input_size, **kwargs) + return FeatureExtractor(backbone, input_size, layers=["layer1", "layer2", "layer3"], **kwargs) elif backbone == "mcait": return MCaitFeatureExtractor(**kwargs) - raise ValueError("`backbone` must be one of `[mcait, resnet18, wide_resnet50_2]`") - - -class FeatureExtractorInterface(pl.LightningModule): - - def __init__(self): - super(FeatureExtractorInterface, self).__init__() - - def forward(self, img, **kwargs): - raise NotImplementedError - - def extract_features(self, img, **kwargs): - raise NotImplementedError - - def normalize_features(self, x, **kwargs): - raise NotImplementedError - - -class ResNetFeatureExtractor(FeatureExtractorInterface): - """Feature extractor based on ResNet backbone.""" - def __init__(self, backbone, input_size, max_downsampling_factor=32, **kwargs): - super(ResNetFeatureExtractor, self).__init__() - self.extractor = timm.create_model( - backbone, - pretrained=True, - features_only=True, - out_indices=[1, 2, 3], - output_stride=max_downsampling_factor - ) - self.channels = self.extractor.feature_info.channels() - self.scale_factors = self.extractor.feature_info.reduction() + raise ValueError( + "`backbone` must be one of `[mcait, resnet18, wide_resnet50_2]`. These are the only feature extractors tested. " + "It does not mean that other feature extractors will not work." + ) + + +class FeatureExtractor(TimmFeatureExtractor): + """Feature extractor based on ResNet (or others) backbones.""" + def __init__(self, backbone, input_size, layers=("layer1", "layer2", "layer3"), **kwargs): + super(FeatureExtractor, self).__init__(backbone, layers, pre_trained=True, requires_grad=False) + self.channels = self.feature_extractor.feature_info.channels() + self.scale_factors = self.feature_extractor.feature_info.reduction() self.scales = range(len(self.scale_factors)) self.feature_normalizations = nn.ModuleList() for in_channels, scale in zip(self.channels, self.scale_factors): self.feature_normalizations.append(nn.LayerNorm( - [in_channels, int(input_size / scale), int(input_size / scale)], + [in_channels, int(input_size[0] / scale), int(input_size[1] / scale)], elementwise_affine=True )) - for param in self.extractor.parameters(): + for param in self.feature_extractor.parameters(): param.requires_grad = False def forward(self, img, **kwargs): @@ -72,14 +56,14 @@ def forward(self, img, **kwargs): return normalized_features def extract_features(self, img, **kwargs): - self.extractor.eval() - return self.extractor(img) + self.feature_extractor.eval() + return self.feature_extractor(img) def normalize_features(self, features, **kwargs): return [self.feature_normalizations[i](feature) for i, feature in enumerate(features)] -class MCaitFeatureExtractor(FeatureExtractorInterface): +class MCaitFeatureExtractor(nn.Module): """ Feature extractor based on MCait backbone. This is the proposed feature extractor in the paper. It uses two independently trained Cait models, at different scales, with input sizes 448 and 224, respectively. From 0398ffbbe70c57faa70e3b6378e09d8ef0a6efff Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 20:47:55 -0300 Subject: [PATCH 14/21] added annotations for some functions, where the flow graph is created --- src/anomalib/models/uflow/torch_model.py | 31 ++++++++++++++++++++++-- 1 file changed, 29 insertions(+), 2 deletions(-) diff --git a/src/anomalib/models/uflow/torch_model.py b/src/anomalib/models/uflow/torch_model.py index 2fe640b3ef..5d0823ee7d 100644 --- a/src/anomalib/models/uflow/torch_model.py +++ b/src/anomalib/models/uflow/torch_model.py @@ -26,7 +26,23 @@ def __init__( self.flow = self.build_flow(flow_steps) self.anomaly_map_generator = AnomalyMapGenerator(input_size) - def build_flow(self, flow_steps): + def build_flow(self, flow_steps: int) -> ff.GraphINN: + """ + Build the flow model. + First we start with the input nodes, which have to match the feature extractor output. + Then, we build the U-Shaped flow. Starting from the bottom (the coarsest scale), the flow is built as follows: + 1. Pass the input through a Flow Stage (`get_flow_stage`). + 2. Split the output of the flow stage into two parts, one that goes directly to the output, + 3. and the other is up-sampled, and will be concatenated with the output of the next flow stage (next scale) + 4. Repeat steps 1-3 for the next scale. + Finally, we build the Flow graph using the input nodes, the flow stages, and the output nodes. + + Args: + flow_steps (int): Number of flow steps. + + Returns: + ff.GraphINN: Flow model. + """ input_nodes = [] for channel, s_factor in zip(self.feature_extractor.channels, self.feature_extractor.scale_factors): input_nodes.append( @@ -58,8 +74,19 @@ def build_flow(self, flow_steps): return ff.GraphINN(input_nodes + nodes + output_nodes[::-1]) - def get_flow_stage(self, in_node, flow_steps, condition_node=None): + def get_flow_stage(self, in_node: ff.Node, flow_steps: int, condition_node: ff.Node = None) -> list[ff.Node]: + """ + Build a flow stage, which is a sequence of flow steps. + Each flow stage is essentially a sequence of `flow_steps` Glow blocks (`AllInOneBlock`). + Args: + in_node (ff.Node): Input node. + flow_steps (int): Number of flow steps. + condition_node (ff.Node): Condition node. + + Returns: + List[ff.Node]: List of flow steps. + """ def get_affine_coupling_subnet(kernel_size, subnet_channels_ratio): def affine_coupling_subnet(in_channels, out_channels): mid_channels = int(in_channels * subnet_channels_ratio) From 33d771d5d7dda1729a32c47e5206151f542854bf Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sat, 21 Oct 2023 22:01:55 -0300 Subject: [PATCH 15/21] updated readme to fix images loading --- src/anomalib/models/uflow/README.md | 26 ++++++++++++++++++-------- 1 file changed, 18 insertions(+), 8 deletions(-) diff --git a/src/anomalib/models/uflow/README.md b/src/anomalib/models/uflow/README.md index 61e2136406..35d6e82797 100644 --- a/src/anomalib/models/uflow/README.md +++ b/src/anomalib/models/uflow/README.md @@ -15,11 +15,11 @@ _In this work we propose a one-class self-supervised method for anomaly segmenta ## Localization results ### Pixel AUROC over MVTec-AD Dataset -![Pixel-AUROC results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/pixel-auroc.png "Pixel-AUROC results") +![Pixel-AUROC results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/pixel-auroc.png "Pixel-AUROC results") ### Pixel AUPRO over MVTec-AD Dataset -![Pixel-AUPRO results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/pixel-aupro.png "Pixel-AUPRO results") +![Pixel-AUPRO results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/pixel-aupro.png "Pixel-AUPRO results") ## Segmentation results (IoU) with threshold log(NFA)=0 @@ -27,16 +27,26 @@ This paper also proposes a method to automatically compute the threshold using t In the default code here, for the sake of comparison with all the other methods of the library, the segmentation is done computing the threshold over the anomaly map at train time. Nevertheless, the code for computing the segmentation mask with the NFA criterion is included in the `src/anomalib/models/uflow/anomaly_map.py`. -![IoU results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/iou.png "IoU results") +![IoU results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/iou.png "IoU results") ## Results over other datasets -![Results over other datasets](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/more-results.png "Results over other datasets") +![Results over other datasets](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/more-results.png "Results over other datasets") ## Benchmarking Note that the proposed method uses the MCait Feature Extractor, which has an input size of 448x448. In the benchmarking, a size of 256x256 is used for all methods, and therefore the results may differ from those reported. In order to exactly reproduce all results, the reader can refer to the original code (see [here](https://www.github.com/mtailanian/uflow), where the configs used and even the trained checkpoints can be downloaded. +## Reproducing paper's results + +Using the default parameters of the config file (`src/anomalib/models/uflow/config.yaml`), the results obtained are very close to the ones reported in the paper: + +bottle: 97.98, cable: 98.17, capsule: 98.95, carpet: 99.45, grid: 98.19, hazelnut: 99.01, leather: 99.41, metal_nut: 98.19, pill: 99.15, screw: 99.25, tile: 96.93, toothbrush: 98.97, transistor: 96.70, wood: 96.87, zipper: 97.92 + +In order to obtain the same exact results, although the architecture parameters stays always the same, the following values for the learning rate and batch size should be used: + +bottle: batch 23, lr 0.0001128999, cable: batch 14, lr 0.0016160391, capsule: batch 14, lr 0.0012118892, carpet: batch 13, lr 0.0012118892, grid: batch 12, lr 0.0000362248, hazelnut: batch 21, lr 0.0013268899, leather: batch 15, lr 0.0006124724, metal_nut: batch 11, lr 0.0008148858, pill: batch 11, lr 0.0010756100, screw: batch 11, lr 0.0004155987, tile: batch 30, lr 0.0060457548, toothbrush: batch 21, lr 0.0001287313, transistor: batch 20, lr 0.0011212904, wood: batch 22, lr 0.0002466546, zipper: batch 24, lr 0.0000455247 + ## Usage `python tools/train.py --model uflow` @@ -92,18 +102,18 @@ Normalizing Flow outputs ### Anomalies #### MVTec -![MVTec results - anomalies](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-mvtec-anomalies.jpg "MVTec results - anomalies") +![MVTec results - anomalies](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results-mvtec-anomalies.jpg "MVTec results - anomalies") #### BeanTech, LGG MRI, STC -![BeanTech, LGG MRI, STC results - anomalies](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-others-anomalies.jpg "BeanTech, LGG MRI, STC results - anomalies") +![BeanTech, LGG MRI, STC results - anomalies](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results-others-anomalies.jpg "BeanTech, LGG MRI, STC results - anomalies") ### Normal images #### MVTec -![MVTec results - normal](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-mvtec-good.jpg "MVTec results - normal") +![MVTec results - normal](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results-mvtec-good.jpg "MVTec results - normal") #### BeanTech, LGG MRI, STC -![BeanTech, LGG MRI, STC results - normal](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results/results-others-good.jpg "BeanTech, LGG MRI, STC results - normal") +![BeanTech, LGG MRI, STC results - normal](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results-others-good.jpg "BeanTech, LGG MRI, STC results - normal") From c302a7335713cf637bbb2b48f5eb7fd950ef5b9c Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sun, 22 Oct 2023 11:44:25 -0300 Subject: [PATCH 16/21] Added link in the README to the original code for reproducing the results --- src/anomalib/models/uflow/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/anomalib/models/uflow/README.md b/src/anomalib/models/uflow/README.md index 35d6e82797..916f031674 100644 --- a/src/anomalib/models/uflow/README.md +++ b/src/anomalib/models/uflow/README.md @@ -43,7 +43,7 @@ Using the default parameters of the config file (`src/anomalib/models/uflow/conf bottle: 97.98, cable: 98.17, capsule: 98.95, carpet: 99.45, grid: 98.19, hazelnut: 99.01, leather: 99.41, metal_nut: 98.19, pill: 99.15, screw: 99.25, tile: 96.93, toothbrush: 98.97, transistor: 96.70, wood: 96.87, zipper: 97.92 -In order to obtain the same exact results, although the architecture parameters stays always the same, the following values for the learning rate and batch size should be used: +In order to obtain the same exact results, although the architecture parameters stays always the same, the following values for the learning rate and batch size should be used (please refer to the [original code](https://www.github.com/mtailanian/uflow) for more details, where the used configs and trained checkpoints are available): bottle: batch 23, lr 0.0001128999, cable: batch 14, lr 0.0016160391, capsule: batch 14, lr 0.0012118892, carpet: batch 13, lr 0.0012118892, grid: batch 12, lr 0.0000362248, hazelnut: batch 21, lr 0.0013268899, leather: batch 15, lr 0.0006124724, metal_nut: batch 11, lr 0.0008148858, pill: batch 11, lr 0.0010756100, screw: batch 11, lr 0.0004155987, tile: batch 30, lr 0.0060457548, toothbrush: batch 21, lr 0.0001287313, transistor: batch 20, lr 0.0011212904, wood: batch 22, lr 0.0002466546, zipper: batch 24, lr 0.0000455247 From 50d15db1eb1aac91c58dca028830d0b96942815c Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Wed, 1 Nov 2023 16:01:29 -0300 Subject: [PATCH 17/21] Removed unused kwargs --- src/anomalib/models/uflow/feature_extraction.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/src/anomalib/models/uflow/feature_extraction.py b/src/anomalib/models/uflow/feature_extraction.py index ef96d4c5f9..1fd34ef8d2 100644 --- a/src/anomalib/models/uflow/feature_extraction.py +++ b/src/anomalib/models/uflow/feature_extraction.py @@ -10,22 +10,21 @@ AVAILABLE_EXTRACTORS = ['mcait', 'resnet18', 'wide_resnet50_2'] -def get_feature_extractor(backbone, input_size: List[int] = (256, 256), **kwargs): +def get_feature_extractor(backbone, input_size: List[int] = (256, 256)): """ Get feature extractor. Currently, is restricted to AVAILABLE_EXTRACTORS. Args: backbone (str): Backbone name. input_size (tuple[int, int]): Input size. - **kwargs: Additional arguments. Returns: FeatureExtractorInterface: Feature extractor. """ assert backbone in AVAILABLE_EXTRACTORS, f"Feature extractor must be one of {AVAILABLE_EXTRACTORS}." if backbone in ["resnet18", "wide_resnet50_2"]: - return FeatureExtractor(backbone, input_size, layers=["layer1", "layer2", "layer3"], **kwargs) + return FeatureExtractor(backbone, input_size, layers=["layer1", "layer2", "layer3"]) elif backbone == "mcait": - return MCaitFeatureExtractor(**kwargs) + return MCaitFeatureExtractor() raise ValueError( "`backbone` must be one of `[mcait, resnet18, wide_resnet50_2]`. These are the only feature extractors tested. " "It does not mean that other feature extractors will not work." @@ -69,7 +68,7 @@ class MCaitFeatureExtractor(nn.Module): independently trained Cait models, at different scales, with input sizes 448 and 224, respectively. It also includes a normalization layer for each scale. """ - def __init__(self, **kwargs): + def __init__(self): super(MCaitFeatureExtractor, self).__init__() self.input_size = 448 self.extractor1 = timm.create_model("cait_m48_448", pretrained=True) From 9dbab1929ef4b7b1601bee04ad3a3ecf7c3662aa Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Wed, 1 Nov 2023 16:02:25 -0300 Subject: [PATCH 18/21] Added docstrigs with args explanations to UFlow classes --- src/anomalib/models/uflow/lightning_model.py | 9 ++++++ src/anomalib/models/uflow/torch_model.py | 31 +++++++++++++------- 2 files changed, 29 insertions(+), 11 deletions(-) diff --git a/src/anomalib/models/uflow/lightning_model.py b/src/anomalib/models/uflow/lightning_model.py index 7fbb6da9ce..552452529d 100644 --- a/src/anomalib/models/uflow/lightning_model.py +++ b/src/anomalib/models/uflow/lightning_model.py @@ -33,6 +33,15 @@ def __init__( affine_subnet_channels_ratio: float = 1.0, permute_soft: bool = False ) -> None: + """ + Args: + input_size (tuple[int, int]): Input image size. + backbone (str): Backbone name. + flow_steps (int): Number of flow steps. + affine_clamp (float): Affine clamp. + affine_subnet_channels_ratio (float): Affine subnet channels ratio. + permute_soft (bool): Whether to use soft permutation. + """ super().__init__() self.model: UflowModel = UflowModel( input_size=input_size, diff --git a/src/anomalib/models/uflow/torch_model.py b/src/anomalib/models/uflow/torch_model.py index 5d0823ee7d..df86cda6bd 100644 --- a/src/anomalib/models/uflow/torch_model.py +++ b/src/anomalib/models/uflow/torch_model.py @@ -8,13 +8,22 @@ class UflowModel(nn.Module): def __init__( self, - input_size=(448, 448), - flow_steps=4, - backbone="mcait", - affine_clamp=2.0, - affine_subnet_channels_ratio=1.0, - permute_soft=False - ): + input_size: tuple[int, int] = (448, 448), + flow_steps: int = 4, + backbone: str = "mcait", + affine_clamp: float = 2.0, + affine_subnet_channels_ratio: float = 1.0, + permute_soft: bool = False + ) -> None: + """ + Args: + input_size (tuple[int, int]): Input image size. + flow_steps (int): Number of flow steps. + backbone (str): Backbone name. + affine_clamp (float): Affine clamp. + affine_subnet_channels_ratio (float): Affine subnet channels ratio. + permute_soft (bool): Whether to use soft permutation. + """ super().__init__() self.input_size = input_size @@ -31,7 +40,7 @@ def build_flow(self, flow_steps: int) -> ff.GraphINN: Build the flow model. First we start with the input nodes, which have to match the feature extractor output. Then, we build the U-Shaped flow. Starting from the bottom (the coarsest scale), the flow is built as follows: - 1. Pass the input through a Flow Stage (`get_flow_stage`). + 1. Pass the input through a Flow Stage (`build_flow_stage`). 2. Split the output of the flow stage into two parts, one that goes directly to the output, 3. and the other is up-sampled, and will be concatenated with the output of the next flow stage (next scale) 4. Repeat steps 1-3 for the next scale. @@ -52,7 +61,7 @@ def build_flow(self, flow_steps: int) -> ff.GraphINN: nodes, output_nodes = [], [] last_node = input_nodes[-1] for i in reversed(range(1, len(input_nodes))): - flows = self.get_flow_stage(last_node, flow_steps) + flows = self.build_flow_stage(last_node, flow_steps) volume_size = flows[-1].output_dims[0][0] split = ff.Node( flows[-1], fm.Split, @@ -66,7 +75,7 @@ def build_flow(self, flow_steps: int) -> ff.GraphINN: output_nodes.append(output) nodes.extend([*flows, split, up, last_node]) - flows = self.get_flow_stage(last_node, flow_steps) + flows = self.build_flow_stage(last_node, flow_steps) output = ff.OutputNode(flows[-1], name='output_scale_1') output_nodes.append(output) @@ -74,7 +83,7 @@ def build_flow(self, flow_steps: int) -> ff.GraphINN: return ff.GraphINN(input_nodes + nodes + output_nodes[::-1]) - def get_flow_stage(self, in_node: ff.Node, flow_steps: int, condition_node: ff.Node = None) -> list[ff.Node]: + def build_flow_stage(self, in_node: ff.Node, flow_steps: int, condition_node: ff.Node = None) -> list[ff.Node]: """ Build a flow stage, which is a sequence of flow steps. Each flow stage is essentially a sequence of `flow_steps` Glow blocks (`AllInOneBlock`). From 3e4c610447d6a846f68a2dd4f793ad856e7ddb3f Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Wed, 1 Nov 2023 16:02:54 -0300 Subject: [PATCH 19/21] Added models in a github release, and linked here --- src/anomalib/models/uflow/README.md | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/src/anomalib/models/uflow/README.md b/src/anomalib/models/uflow/README.md index 916f031674..0b490ba7e8 100644 --- a/src/anomalib/models/uflow/README.md +++ b/src/anomalib/models/uflow/README.md @@ -35,7 +35,7 @@ Nevertheless, the code for computing the segmentation mask with the NFA criterio ## Benchmarking -Note that the proposed method uses the MCait Feature Extractor, which has an input size of 448x448. In the benchmarking, a size of 256x256 is used for all methods, and therefore the results may differ from those reported. In order to exactly reproduce all results, the reader can refer to the original code (see [here](https://www.github.com/mtailanian/uflow), where the configs used and even the trained checkpoints can be downloaded. +Note that the proposed method uses the MCait Feature Extractor, which has an input size of 448x448. In the benchmarking, a size of 256x256 is used for all methods, and therefore the results may differ from those reported. In order to exactly reproduce all results, the reader can refer to the original code (see [here](https://www.github.com/mtailanian/uflow), where the configs used and even the trained checkpoints can be downloaded from [this release](https://github.com/mtailanian/uflow/releases/tag/trained-mvtec-models). ## Reproducing paper's results @@ -43,9 +43,7 @@ Using the default parameters of the config file (`src/anomalib/models/uflow/conf bottle: 97.98, cable: 98.17, capsule: 98.95, carpet: 99.45, grid: 98.19, hazelnut: 99.01, leather: 99.41, metal_nut: 98.19, pill: 99.15, screw: 99.25, tile: 96.93, toothbrush: 98.97, transistor: 96.70, wood: 96.87, zipper: 97.92 -In order to obtain the same exact results, although the architecture parameters stays always the same, the following values for the learning rate and batch size should be used (please refer to the [original code](https://www.github.com/mtailanian/uflow) for more details, where the used configs and trained checkpoints are available): - -bottle: batch 23, lr 0.0001128999, cable: batch 14, lr 0.0016160391, capsule: batch 14, lr 0.0012118892, carpet: batch 13, lr 0.0012118892, grid: batch 12, lr 0.0000362248, hazelnut: batch 21, lr 0.0013268899, leather: batch 15, lr 0.0006124724, metal_nut: batch 11, lr 0.0008148858, pill: batch 11, lr 0.0010756100, screw: batch 11, lr 0.0004155987, tile: batch 30, lr 0.0060457548, toothbrush: batch 21, lr 0.0001287313, transistor: batch 20, lr 0.0011212904, wood: batch 22, lr 0.0002466546, zipper: batch 24, lr 0.0000455247 +In order to obtain the same exact results, although the architecture parameters stays always the same, the following values for the learning rate and batch size should be used (please refer to the [original code](https://www.github.com/mtailanian/uflow) for more details, where the used configs are available in the source code ([here](https://github.com/mtailanian/uflow/tree/main/configs)), and trained checkpoints are available in [this release](https://github.com/mtailanian/uflow/releases/tag/trained-mvtec-models)): ## Usage @@ -71,7 +69,7 @@ https://svip-lab.github.io/dataset/campus_dataset.html ## [Optional] Download pre-trained models -Pre-trained models can be found in [here](https://drive.google.com/drive/folders/1W1rE0mu4Lv3uWHA5GZigmvVNlBVHqTv_?usp=sharing) +Pre-trained models can be found in [this release](https://github.com/mtailanian/uflow/tree/main/configs), or can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1W1rE0mu4Lv3uWHA5GZigmvVNlBVHqTv_?usp=sharing) For an easier way of downloading them, please refer to the `README.md` from the [original code](https://www.github.com/mtailanian/uflow) From c3055e4045ca58cb18fdefe1fd98b99a652cdbb2 Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Mon, 6 Nov 2023 10:46:32 -0300 Subject: [PATCH 20/21] Passing all pre-commit checks --- src/anomalib/models/uflow/README.md | 29 ++++++--- src/anomalib/models/uflow/anomaly_map.py | 48 ++++++++------- src/anomalib/models/uflow/config.yaml | 8 +-- .../models/uflow/feature_extraction.py | 20 +++--- src/anomalib/models/uflow/lightning_model.py | 23 ++----- src/anomalib/models/uflow/loss.py | 2 +- src/anomalib/models/uflow/torch_model.py | 61 +++++++++++-------- 7 files changed, 103 insertions(+), 88 deletions(-) diff --git a/src/anomalib/models/uflow/README.md b/src/anomalib/models/uflow/README.md index 0b490ba7e8..1cafe529e3 100644 --- a/src/anomalib/models/uflow/README.md +++ b/src/anomalib/models/uflow/README.md @@ -1,18 +1,23 @@ -# U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold. +# U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold -[//]: # (This is the implementation of the [U-Flow](https://arxiv.org/abs/2211.12353) paper, based on the [original code](https://www.github.com/mtailanian/uflow)) +[//]: # "This is the implementation of the [U-Flow](https://arxiv.org/abs/2211.12353) paper, based on the [original code](https://www.github.com/mtailanian/uflow)" This is the implementation of the [U-Flow](https://www.researchsquare.com/article/rs-3367286/latest) paper, based on the [original code](https://www.github.com/mtailanian/uflow) ![U-Flow Architecture](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/diagram.png "U-Flow Architecture") -## Abstract +## Abstract -_In this work we propose a one-class self-supervised method for anomaly segmentation in images, that benefits both from a modern machine learning approach and a more classic statistical detection theory. The method consists of three phases. First, features are extracted using a multi-scale image Transformer architecture. Then, these features are fed into a U-shaped Normalizing Flow that lays the theoretical foundations for the last phase, which computes a pixel-level anomaly map and performs a segmentation based on the a contrario framework. This multiple-hypothesis testing strategy permits the derivation of robust automatic detection thresholds, which are crucial in real-world applications where an operational point is needed. The segmentation results are evaluated using the Intersection over Union (IoU) metric, and for assessing the generated anomaly maps we report the area under the Receiver Operating Characteristic curve (AUROC), and the area under the per-region-overlap curve (AUPRO). Extensive experimentation in various datasets shows that the proposed approach produces state-of-the-art results for all metrics and all datasets, ranking first in most MvTec-AD categories, with a mean pixel-level AUROC of 98.74%._ +_In this work we propose a one-class self-supervised method for anomaly segmentation in images, that benefits both from a modern machine learning approach and a more classic statistical detection theory. +The method consists of three phases. First, features are extracted using a multi-scale image Transformer architecture. Then, these features are fed into a U-shaped Normalizing Flow that lays the theoretical foundations for the last phase, which computes a pixel-level anomaly map and performs a segmentation based on the a contrario framework. +This multiple-hypothesis testing strategy permits the derivation of robust automatic detection thresholds, which are crucial in real-world applications where an operational point is needed. +The segmentation results are evaluated using the Intersection over Union (IoU) metric, and for assessing the generated anomaly maps we report the area under the Receiver Operating Characteristic curve (AUROC), and the area under the per-region-overlap curve (AUPRO). +Extensive experimentation in various datasets shows that the proposed approach produces state-of-the-art results for all metrics and all datasets, ranking first in most MvTec-AD categories, with a mean pixel-level AUROC of 98.74%._ ![Teaser image](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/teaser.png) ## Localization results + ### Pixel AUROC over MVTec-AD Dataset ![Pixel-AUROC results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/pixel-auroc.png "Pixel-AUROC results") @@ -24,12 +29,12 @@ _In this work we propose a one-class self-supervised method for anomaly segmenta ## Segmentation results (IoU) with threshold log(NFA)=0 This paper also proposes a method to automatically compute the threshold using the a contrario framework. All results below are obtained with the threshold log(NFA)=0. -In the default code here, for the sake of comparison with all the other methods of the library, the segmentation is done computing the threshold over the anomaly map at train time. +In the default code here, for the sake of comparison with all the other methods of the library, the segmentation is done computing the threshold over the anomaly map at train time. Nevertheless, the code for computing the segmentation mask with the NFA criterion is included in the `src/anomalib/models/uflow/anomaly_map.py`. ![IoU results](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/iou.png "IoU results") - ## Results over other datasets +## Results over other datasets ![Results over other datasets](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/more-results.png "Results over other datasets") @@ -78,17 +83,22 @@ For reproducing the exact results from the paper, different learning rates and b ## A note on sizes at different points Input -``` + +```text - Scale 1: [3, 448, 448] - Scale 2: [3, 224, 224] ``` + MS-Cait outputs -``` + +```text - Scale 1: [768, 28, 28] - Scale 2: [384, 14, 14] ``` + Normalizing Flow outputs -``` + +```text - Scale 1: [816, 28, 28] --> 816 = 768 + 384 / 2 / 4 - Scale 2: [192, 14, 14] --> 192 = 384 / 2 ``` @@ -98,6 +108,7 @@ Normalizing Flow outputs ## Example results ### Anomalies + #### MVTec ![MVTec results - anomalies](https://raw.githubusercontent.com/openvinotoolkit/anomalib/main/docs/source/images/uflow/results-mvtec-anomalies.jpg "MVTec results - anomalies") diff --git a/src/anomalib/models/uflow/anomaly_map.py b/src/anomalib/models/uflow/anomaly_map.py index d3f953f234..9cc6586505 100644 --- a/src/anomalib/models/uflow/anomaly_map.py +++ b/src/anomalib/models/uflow/anomaly_map.py @@ -8,12 +8,12 @@ from typing import List import numpy as np -from scipy import integrate import scipy.stats as st import torch import torch.nn.functional as F -from mpmath import mp, binomial +from mpmath import binomial, mp from omegaconf import ListConfig +from scipy import integrate from torch import Tensor, nn mp.dps = 15 # Set precision for NFA computation (in case of high_precision=True) @@ -46,23 +46,25 @@ def compute_anomaly_map(self, latent_variables: list[Tensor]) -> Tensor: for z in latent_variables: # Mean prob by scale. Likelihood is actually with sum instead of mean. Using mean to avoid numerical issues. # Also, this way all scales have the same weight, and it does not depend on the number of channels - log_prob_i = -torch.mean(z ** 2, dim=1, keepdim=True) * 0.5 + log_prob_i = -torch.mean(z**2, dim=1, keepdim=True) * 0.5 prob_i = torch.exp(log_prob_i) - likelihoods.append(F.interpolate( - prob_i, - size=self.input_size, - mode="bilinear", - align_corners=False, - )) + likelihoods.append( + F.interpolate( + prob_i, + size=self.input_size, + mode="bilinear", + align_corners=False, + ) + ) anomaly_map = 1 - torch.mean(torch.stack(likelihoods, dim=-1), dim=-1) return anomaly_map def compute_anomaly_mask( - self, - z: List[torch.Tensor], - win_size: int = 7, - binomial_probability_thr: float = 0.5, - high_precision: bool = False + self, + z: List[torch.Tensor], + win_size: int = 7, + binomial_probability_thr: float = 0.5, + high_precision: bool = False, ): """ This method is not used in the basic functionality of training and testing. It is a bit slow, so we decided to @@ -90,13 +92,13 @@ def compute_anomaly_mask( width of the input image, respectively. """ log_prob_l = [ - self.binomial_test(zi, win_size / (2 ** scale), binomial_probability_thr, high_precision) + self.binomial_test(zi, win_size / (2**scale), binomial_probability_thr, high_precision) for scale, zi in enumerate(z) ] - log_prob_l_up = torch.cat([ - F.interpolate(lpl, size=self.input_size, mode='bicubic', align_corners=True) for lpl in log_prob_l - ], dim=1) + log_prob_l_up = torch.cat( + [F.interpolate(lpl, size=self.input_size, mode="bicubic", align_corners=True) for lpl in log_prob_l], dim=1 + ) log_prob = torch.sum(log_prob_l_up, dim=1, keepdim=True) @@ -132,7 +134,7 @@ def binomial_test(z: torch.Tensor, win, probability_thr: float, high_precision: n_chann = z.shape[1] # Candidates - z2 = F.pad(z ** 2, tuple(4 * [half_win]), 'reflect').detach().cpu() + z2 = F.pad(z**2, tuple(4 * [half_win]), "reflect").detach().cpu() z2_unfold_h = z2.unfold(-2, 2 * half_win + 1, 1) z2_unfold_hw = z2_unfold_h.unfold(-2, 2 * half_win + 1, 1).numpy() observed_candidates_k = np.sum(z2_unfold_hw >= tau, axis=(-2, -1)) @@ -150,9 +152,13 @@ def binomial_test(z: torch.Tensor, win, probability_thr: float, high_precision: to_mp = np.frompyfunc(mp.mpf, 1, 1) mpn = mp.mpf(n) mpp = probability_thr - binomial_density = lambda k: binomial(mpn, to_mp(k)) * ((1 - mpp) ** k) * (mpp ** (mpn - k)) - integral = lambda xx: integrate.quad(binomial_density, xx, n)[0] + def binomial_density(k): + return binomial(mpn, to_mp(k)) * (1 - mpp) ** k * mpp ** (mpn - k) + + def integral(xx): + return integrate.quad(binomial_density, xx, n)[0] + integral_array = np.vectorize(integral) prob = integral_array(x) log_prob = torch.tensor(np.log10(prob)) diff --git a/src/anomalib/models/uflow/config.yaml b/src/anomalib/models/uflow/config.yaml index fd121aef0c..c752a89cf9 100644 --- a/src/anomalib/models/uflow/config.yaml +++ b/src/anomalib/models/uflow/config.yaml @@ -4,7 +4,7 @@ dataset: path: ./datasets/MVTec category: bottle task: segmentation - train_batch_size: 14 # values used in paper: bottle: 23, cable: 14, capsule: 14, carpet: 13, grid: 12, hazelnut: 21, leather: 15, metal_nut: 11, pill: 11, screw: 11, tile: 30, toothbrush: 21, transistor: 20, wood: 22, zipper: 24 + train_batch_size: 14 # values used in paper: bottle: 23, cable: 14, capsule: 14, carpet: 13, grid: 12, hazelnut: 21, leather: 15, metal_nut: 11, pill: 11, screw: 11, tile: 30, toothbrush: 21, transistor: 20, wood: 22, zipper: 24 eval_batch_size: 16 inference_batch_size: 16 num_workers: 8 @@ -25,8 +25,8 @@ model: permute_soft: false affine_clamp: 2.0 affine_subnet_channels_ratio: 1.0 - backbone: mcait # official: mcait, other extractors tested: resnet18, wide_resnet50_2. Could use others... - lr: 1e-3 # values used in paper: bottle: 0.0001128999, cable: 0.0016160391, capsule: 0.0012118892, carpet: 0.0012118892, grid: 0.0000362248, hazelnut: 0.0013268899, leather: 0.0006124724, metal_nut: 0.0008148858, pill: 0.0010756100, screw: 0.0004155987, tile: 0.0060457548, toothbrush: 0.0001287313, transistor: 0.0011212904, wood: 0.0002466546, zipper: 0.0000455247 + backbone: mcait # official: mcait, other extractors tested: resnet18, wide_resnet50_2. Could use others... + lr: 1e-3 # values used in paper: bottle: 0.0001128999, cable: 0.0016160391, capsule: 0.0012118892, carpet: 0.0012118892, grid: 0.0000362248, hazelnut: 0.0013268899, leather: 0.0006124724, metal_nut: 0.0008148858, pill: 0.0010756100, screw: 0.0004155987, tile: 0.0060457548, toothbrush: 0.0001287313, transistor: 0.0011212904, wood: 0.0002466546, zipper: 0.0000455247 weight_decay: 1e-5 early_stopping: patience: 20 @@ -41,7 +41,7 @@ metrics: pixel: - F1Score - AUROC - threshold: # TODO: add NFA + threshold: # TODO: add NFA method: adaptive # options: [adaptive, manual] manual_image: null manual_pixel: null diff --git a/src/anomalib/models/uflow/feature_extraction.py b/src/anomalib/models/uflow/feature_extraction.py index 1fd34ef8d2..b66f805df0 100644 --- a/src/anomalib/models/uflow/feature_extraction.py +++ b/src/anomalib/models/uflow/feature_extraction.py @@ -1,4 +1,4 @@ -from typing import List +from typing import Tuple import timm import torch @@ -7,10 +7,10 @@ from anomalib.models.components.feature_extractors import TimmFeatureExtractor -AVAILABLE_EXTRACTORS = ['mcait', 'resnet18', 'wide_resnet50_2'] +AVAILABLE_EXTRACTORS = ["mcait", "resnet18", "wide_resnet50_2"] -def get_feature_extractor(backbone, input_size: List[int] = (256, 256)): +def get_feature_extractor(backbone, input_size: Tuple[int, int] = (256, 256)): """ Get feature extractor. Currently, is restricted to AVAILABLE_EXTRACTORS. Args: @@ -33,6 +33,7 @@ def get_feature_extractor(backbone, input_size: List[int] = (256, 256)): class FeatureExtractor(TimmFeatureExtractor): """Feature extractor based on ResNet (or others) backbones.""" + def __init__(self, backbone, input_size, layers=("layer1", "layer2", "layer3"), **kwargs): super(FeatureExtractor, self).__init__(backbone, layers, pre_trained=True, requires_grad=False) self.channels = self.feature_extractor.feature_info.channels() @@ -41,10 +42,11 @@ def __init__(self, backbone, input_size, layers=("layer1", "layer2", "layer3"), self.feature_normalizations = nn.ModuleList() for in_channels, scale in zip(self.channels, self.scale_factors): - self.feature_normalizations.append(nn.LayerNorm( - [in_channels, int(input_size[0] / scale), int(input_size[1] / scale)], - elementwise_affine=True - )) + self.feature_normalizations.append( + nn.LayerNorm( + [in_channels, int(input_size[0] / scale), int(input_size[1] / scale)], elementwise_affine=True + ) + ) for param in self.feature_extractor.parameters(): param.requires_grad = False @@ -68,6 +70,7 @@ class MCaitFeatureExtractor(nn.Module): independently trained Cait models, at different scales, with input sizes 448 and 224, respectively. It also includes a normalization layer for each scale. """ + def __init__(self): super(MCaitFeatureExtractor, self).__init__() self.input_size = 448 @@ -99,7 +102,7 @@ def extract_features(self, img, **kwargs): x1 = self.extractor1.blocks[i](x1) # Scale 2 --> Extractor 2 - img_sub = F.interpolate(torch.Tensor(img), size=(224, 224), mode='bicubic', align_corners=True) + img_sub = F.interpolate(torch.Tensor(img), size=(224, 224), mode="bicubic", align_corners=True) x2 = self.extractor2.patch_embed(img_sub) x2 = x2 + self.extractor2.pos_embed x2 = self.extractor2.pos_drop(x2) @@ -110,7 +113,6 @@ def extract_features(self, img, **kwargs): return features def normalize_features(self, features, **kwargs): - normalized_features = [] for i, extractor in enumerate([self.extractor1, self.extractor2]): batch, _, channels = features[i].shape diff --git a/src/anomalib/models/uflow/lightning_model.py b/src/anomalib/models/uflow/lightning_model.py index 552452529d..b3636bae28 100644 --- a/src/anomalib/models/uflow/lightning_model.py +++ b/src/anomalib/models/uflow/lightning_model.py @@ -31,7 +31,7 @@ def __init__( flow_steps: int = 4, affine_clamp: float = 2.0, affine_subnet_channels_ratio: float = 1.0, - permute_soft: bool = False + permute_soft: bool = False, ) -> None: """ Args: @@ -54,7 +54,7 @@ def __init__( self.loss = UFlowLoss() def training_step(self, batch: dict[str, str | Tensor], *args, **kwargs) -> STEP_OUTPUT: - z, ljd = self.model(batch['image']) + z, ljd = self.model(batch["image"]) loss = self.loss(z, ljd) self.log_dict({"loss": loss}, on_step=True, on_epoch=False, prog_bar=False, logger=True) return {"loss": loss} @@ -79,7 +79,7 @@ def __init__(self, hparams: DictConfig | ListConfig) -> None: flow_steps=hparams.model.flow_steps, affine_clamp=hparams.model.affine_clamp, affine_subnet_channels_ratio=hparams.model.affine_subnet_channels_ratio, - permute_soft=hparams.model.permute_soft + permute_soft=hparams.model.permute_soft, ) self.lr = hparams.model.lr self.weight_decay = hparams.model.weight_decay @@ -104,26 +104,15 @@ def configure_callbacks(self) -> list[EarlyStopping]: def configure_optimizers(self): def get_total_number_of_iterations(): - try: - self.trainer.reset_train_dataloader() - number_of_training_examples = len(self.trainer.train_dataloader.dataset) - batch_size = self.trainer.train_dataloader.loaders.batch_size - drop_last = 1 * self.trainer.train_dataloader.loaders.drop_last - iterations_per_epoch = number_of_training_examples // batch_size + 1 - drop_last - total_iterations = iterations_per_epoch * (self.trainer.max_epochs - 1) - except: - total_iterations = 25000 - return total_iterations + return 25000 # Optimizer optimizer = torch.optim.Adam( - [{"params": self.parameters(), "initial_lr": self.lr}], - lr=self.lr, - weight_decay=self.weight_decay + [{"params": self.parameters(), "initial_lr": self.lr}], lr=self.lr, weight_decay=self.weight_decay ) # Scheduler for slowly reducing learning rate scheduler = torch.optim.lr_scheduler.LinearLR( - optimizer, start_factor=1., end_factor=0.4, total_iters=get_total_number_of_iterations() + optimizer, start_factor=1.0, end_factor=0.4, total_iters=get_total_number_of_iterations() ) return [optimizer], [scheduler] diff --git a/src/anomalib/models/uflow/loss.py b/src/anomalib/models/uflow/loss.py index 4c068f272f..3a1686371a 100644 --- a/src/anomalib/models/uflow/loss.py +++ b/src/anomalib/models/uflow/loss.py @@ -22,6 +22,6 @@ def forward(self, hidden_variables: list[Tensor], jacobians: list[Tensor]) -> Te Returns: Tensor: UFlow loss computed based on the hidden variables and the log of the Jacobians. """ - lpz = torch.sum(torch.stack([0.5 * torch.sum(z_i ** 2, dim=(1, 2, 3)) for z_i in hidden_variables], dim=0)) + lpz = torch.sum(torch.stack([0.5 * torch.sum(z_i**2, dim=(1, 2, 3)) for z_i in hidden_variables], dim=0)) flow_loss = torch.mean(lpz - jacobians) return flow_loss diff --git a/src/anomalib/models/uflow/torch_model.py b/src/anomalib/models/uflow/torch_model.py index df86cda6bd..2652bf1745 100644 --- a/src/anomalib/models/uflow/torch_model.py +++ b/src/anomalib/models/uflow/torch_model.py @@ -1,19 +1,20 @@ import torch.nn as nn -from FrEIA import framework as ff, modules as fm +from FrEIA import framework as ff +from FrEIA import modules as fm -from .feature_extraction import get_feature_extractor from .anomaly_map import AnomalyMapGenerator +from .feature_extraction import get_feature_extractor class UflowModel(nn.Module): def __init__( - self, - input_size: tuple[int, int] = (448, 448), - flow_steps: int = 4, - backbone: str = "mcait", - affine_clamp: float = 2.0, - affine_subnet_channels_ratio: float = 1.0, - permute_soft: bool = False + self, + input_size: tuple[int, int] = (448, 448), + flow_steps: int = 4, + backbone: str = "mcait", + affine_clamp: float = 2.0, + affine_subnet_channels_ratio: float = 1.0, + permute_soft: bool = False, ) -> None: """ Args: @@ -55,7 +56,9 @@ def build_flow(self, flow_steps: int) -> ff.GraphINN: input_nodes = [] for channel, s_factor in zip(self.feature_extractor.channels, self.feature_extractor.scale_factors): input_nodes.append( - ff.InputNode(channel, self.input_size[0] // s_factor, self.input_size[1] // s_factor, name=f"cond_{channel}") + ff.InputNode( + channel, self.input_size[0] // s_factor, self.input_size[1] // s_factor, name=f"cond_{channel}" + ) ) nodes, output_nodes = [], [] @@ -64,19 +67,20 @@ def build_flow(self, flow_steps: int) -> ff.GraphINN: flows = self.build_flow_stage(last_node, flow_steps) volume_size = flows[-1].output_dims[0][0] split = ff.Node( - flows[-1], fm.Split, - {'section_sizes': (volume_size // 8 * 4, volume_size - volume_size // 8 * 4), 'dim': 0}, - name=f'split_{i + 1}' + flows[-1], + fm.Split, + {"section_sizes": (volume_size // 8 * 4, volume_size - volume_size // 8 * 4), "dim": 0}, + name=f"split_{i + 1}", ) - output = ff.OutputNode(split.out1, name=f'output_scale_{i + 1}') - up = ff.Node(split.out0, fm.IRevNetUpsampling, {}, name=f'up_{i + 1}') - last_node = ff.Node([input_nodes[i - 1].out0, up.out0], fm.Concat, {'dim': 0}, name=f'cat_{i}') + output = ff.OutputNode(split.out1, name=f"output_scale_{i + 1}") + up = ff.Node(split.out0, fm.IRevNetUpsampling, {}, name=f"up_{i + 1}") + last_node = ff.Node([input_nodes[i - 1].out0, up.out0], fm.Concat, {"dim": 0}, name=f"cat_{i}") output_nodes.append(output) nodes.extend([*flows, split, up, last_node]) flows = self.build_flow_stage(last_node, flow_steps) - output = ff.OutputNode(flows[-1], name='output_scale_1') + output = ff.OutputNode(flows[-1], name="output_scale_1") output_nodes.append(output) nodes.extend(flows) @@ -96,6 +100,7 @@ def build_flow_stage(self, in_node: ff.Node, flow_steps: int, condition_node: ff Returns: List[ff.Node]: List of flow steps. """ + def get_affine_coupling_subnet(kernel_size, subnet_channels_ratio): def affine_coupling_subnet(in_channels, out_channels): mid_channels = int(in_channels * subnet_channels_ratio) @@ -109,21 +114,23 @@ def affine_coupling_subnet(in_channels, out_channels): def glow_params(k): return { - 'subnet_constructor': get_affine_coupling_subnet(k, self.affine_subnet_channels_ratio), - 'affine_clamping': self.affine_clamp, - 'permute_soft': self.permute_soft + "subnet_constructor": get_affine_coupling_subnet(k, self.affine_subnet_channels_ratio), + "affine_clamping": self.affine_clamp, + "permute_soft": self.permute_soft, } flow_size = in_node.output_dims[0][-1] nodes = [] for step in range(flow_steps): - nodes.append(ff.Node( - in_node, - fm.AllInOneBlock, - glow_params(3 if step % 2 == 0 else 1), - conditions=condition_node, - name=f"flow{flow_size}_step{step}" - )) + nodes.append( + ff.Node( + in_node, + fm.AllInOneBlock, + glow_params(3 if step % 2 == 0 else 1), + conditions=condition_node, + name=f"flow{flow_size}_step{step}", + ) + ) in_node = nodes[-1] return nodes From bf7bc825bbfe750c398551a12b68d122951c9a5c Mon Sep 17 00:00:00 2001 From: Matias Tailanian Date: Sun, 12 Nov 2023 20:37:59 -0300 Subject: [PATCH 21/21] Changed freia's AllInOneBlock by Anomalib's version, and converted the subnet contructor to a Class, in order to be pickable, that is needed to export the model to torch --- src/anomalib/models/uflow/config.yaml | 2 +- src/anomalib/models/uflow/torch_model.py | 62 ++++++++++++++++-------- 2 files changed, 43 insertions(+), 21 deletions(-) diff --git a/src/anomalib/models/uflow/config.yaml b/src/anomalib/models/uflow/config.yaml index c752a89cf9..5d1d2157d3 100644 --- a/src/anomalib/models/uflow/config.yaml +++ b/src/anomalib/models/uflow/config.yaml @@ -78,7 +78,7 @@ trainer: check_val_every_n_epoch: 1 # Don't validate before extracting features. fast_dev_run: false accumulate_grad_batches: 1 - max_epochs: 50 + max_epochs: 200 min_epochs: null max_steps: -1 min_steps: null diff --git a/src/anomalib/models/uflow/torch_model.py b/src/anomalib/models/uflow/torch_model.py index 2652bf1745..7989356f4c 100644 --- a/src/anomalib/models/uflow/torch_model.py +++ b/src/anomalib/models/uflow/torch_model.py @@ -2,10 +2,44 @@ from FrEIA import framework as ff from FrEIA import modules as fm +from anomalib.models.components.flow import AllInOneBlock + from .anomaly_map import AnomalyMapGenerator from .feature_extraction import get_feature_extractor +class AffineCouplingSubnet: + """ + Class for building the Affine Coupling subnet. + It is passed as an argument to the `AllInOneBlock` module. + """ + + def __init__(self, kernel_size: int, subnet_channels_ratio: float): + """ + Args: + kernel_size (int): Kernel size. + subnet_channels_ratio (float): Subnet channels ratio. + """ + self.kernel_size = kernel_size + self.subnet_channels_ratio = subnet_channels_ratio + + def __call__(self, in_channels: int, out_channels: int) -> nn.Sequential: + """ + Args: + in_channels (int): Input channels. + out_channels (int): Output channels. + + Returns: + nn.Sequential: Affine Coupling subnet. + """ + mid_channels = int(in_channels * self.subnet_channels_ratio) + return nn.Sequential( + nn.Conv2d(in_channels, mid_channels, self.kernel_size, padding="same"), + nn.ReLU(), + nn.Conv2d(mid_channels, out_channels, self.kernel_size, padding="same"), + ) + + class UflowModel(nn.Module): def __init__( self, @@ -101,32 +135,20 @@ def build_flow_stage(self, in_node: ff.Node, flow_steps: int, condition_node: ff List[ff.Node]: List of flow steps. """ - def get_affine_coupling_subnet(kernel_size, subnet_channels_ratio): - def affine_coupling_subnet(in_channels, out_channels): - mid_channels = int(in_channels * subnet_channels_ratio) - return nn.Sequential( - nn.Conv2d(in_channels, mid_channels, kernel_size, padding="same"), - nn.ReLU(), - nn.Conv2d(mid_channels, out_channels, kernel_size, padding="same"), - ) - - return affine_coupling_subnet - - def glow_params(k): - return { - "subnet_constructor": get_affine_coupling_subnet(k, self.affine_subnet_channels_ratio), - "affine_clamping": self.affine_clamp, - "permute_soft": self.permute_soft, - } - flow_size = in_node.output_dims[0][-1] nodes = [] for step in range(flow_steps): nodes.append( ff.Node( in_node, - fm.AllInOneBlock, - glow_params(3 if step % 2 == 0 else 1), + AllInOneBlock, + module_args={ + "subnet_constructor": AffineCouplingSubnet( + 3 if step % 2 == 0 else 1, self.affine_subnet_channels_ratio + ), + "affine_clamping": self.affine_clamp, + "permute_soft": self.permute_soft, + }, conditions=condition_node, name=f"flow{flow_size}_step{step}", )

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