SHE (Simplified Hopfield Energy) method¶

This method first computes the mean of the internal layer representation of ID data for each ID class. This mean is seen as the average of the ID activation patterns as defined in the original paper. The method then returns the maximum value of the dot product between the internal layer representation of the input and the average patterns, which is a simplified version of Hopfield energy as defined in the original paper.

Reference Out-of-Distribution Detection based on In-Distribution Data Patterns Memorization with Modern Hopfield Energy, ICLR 2023

Imports¶

In [1]:

%load_ext autoreload
%autoreload 2

import warnings
warnings.filterwarnings("ignore")
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
os.environ["CUDA_VISIBLE_DEVICES"] = "1"

from IPython.display import clear_output
from sklearn.metrics import accuracy_score
import matplotlib.pyplot as plt
import torch
from torchvision import transforms
import numpy as np

from oodeel.methods import SHE
from oodeel.eval.metrics import bench_metrics
from oodeel.eval.plots import plot_ood_scores, plot_roc_curve, plot_2D_features
from oodeel.datasets import OODDataset
from oodeel.utils.torch_training_tools import train_torch_model
from oodeel.preprocess import TorchRandomPatchPermutation

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

%load_ext autoreload %autoreload 2 import warnings warnings.filterwarnings("ignore") import os os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" os.environ["CUDA_VISIBLE_DEVICES"] = "1" from IPython.display import clear_output from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt import torch from torchvision import transforms import numpy as np from oodeel.methods import SHE from oodeel.eval.metrics import bench_metrics from oodeel.eval.plots import plot_ood_scores, plot_roc_curve, plot_2D_features from oodeel.datasets import OODDataset from oodeel.utils.torch_training_tools import train_torch_model from oodeel.preprocess import TorchRandomPatchPermutation device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

Note that models are saved at ~/.oodeel/saved_models and data is supposed to be found at ~/.oodeel/datasets by default. Change the following cell for a custom path.

In [2]:

model_path = os.path.expanduser("~/") + ".oodeel/saved_models"
data_path = os.path.expanduser("~/") + ".oodeel/datasets"
os.makedirs(model_path, exist_ok=True)
os.makedirs(data_path, exist_ok=True)

model_path = os.path.expanduser("~/") + ".oodeel/saved_models" data_path = os.path.expanduser("~/") + ".oodeel/datasets" os.makedirs(model_path, exist_ok=True) os.makedirs(data_path, exist_ok=True)

First exp: MNIST[0-4] vs MNIST[5-9]¶

For this first experiment, we train a toy convolutional network on the MNIST dataset restricted to digits 0 to 4. After fitting the train subset of this dataset to the Gram method, we will compare the scores returned for MNIST[0-4] (in-distrubtion) and MNIST[5-9] (out-of-distribution) test subsets.

Data loading¶

• In-distribution data: MNIST[0-4]
• Out-of-distribution data: MNIST[5-9]

Note: We denote In-Distribution (ID) data with _in and Out-Of-Distribution (OOD) data with _out to avoid confusion with OOD detection which is the name of the task, and is therefore used to denote core classes such as OODDataset and OODBaseDetector.

In [3]:

# === load ID and OOD data ===
batch_size = 128
in_labels = [0, 1, 2, 3, 4]

# 1- load train/test MNIST dataset
mnist_train = OODDataset(
    dataset_id="MNIST",
    backend="torch",
    load_kwargs={"root": data_path, "train": True, "download": True},
)
mnist_test = OODDataset(
    dataset_id="MNIST",
    backend="torch",
    load_kwargs={"root": data_path, "train": False, "download": True},
)

# 2- split ID / OOD data depending on label value:
# in-distribution: MNIST[0-4] / out-of-distribution: MNIST[5-9]
oods_fit, _ = mnist_train.split_by_class(in_labels=in_labels)
oods_in, oods_out = mnist_test.split_by_class(in_labels=in_labels)


# 3- prepare data (preprocess, shuffle, batch) => torch dataloaders
def preprocess_fn(*inputs):
    """Simple preprocessing function to normalize images in [0, 1]."""
    x = inputs[0] / 255.0
    return tuple([x] + list(inputs[1:]))


ds_fit = oods_fit.prepare(
    batch_size=batch_size, preprocess_fn=preprocess_fn, shuffle=True
)
ds_in = oods_in.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn)
ds_out = oods_out.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn)

clear_output()

# === load ID and OOD data === batch_size = 128 in_labels = [0, 1, 2, 3, 4] # 1- load train/test MNIST dataset mnist_train = OODDataset( dataset_id="MNIST", backend="torch", load_kwargs={"root": data_path, "train": True, "download": True}, ) mnist_test = OODDataset( dataset_id="MNIST", backend="torch", load_kwargs={"root": data_path, "train": False, "download": True}, ) # 2- split ID / OOD data depending on label value: # in-distribution: MNIST[0-4] / out-of-distribution: MNIST[5-9] oods_fit, _ = mnist_train.split_by_class(in_labels=in_labels) oods_in, oods_out = mnist_test.split_by_class(in_labels=in_labels) # 3- prepare data (preprocess, shuffle, batch) => torch dataloaders def preprocess_fn(*inputs): """Simple preprocessing function to normalize images in [0, 1].""" x = inputs[0] / 255.0 return tuple([x] + list(inputs[1:])) ds_fit = oods_fit.prepare( batch_size=batch_size, preprocess_fn=preprocess_fn, shuffle=True ) ds_in = oods_in.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn) ds_out = oods_out.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn) clear_output()

Model training¶

Now let's train a simple model on MNIST[0-4] using train_torch_model function.

In [4]:

# === Train / Load model ===
# model path
model_path_mnist_04 = os.path.join(model_path, "mnist_mlp_0-4")

try:
    # if the model exists, load it
    model = torch.load(os.path.join(model_path_mnist_04, "best.pt")).to(device)
except OSError:
    # else, train a new model
    train_config = {
        "model": "toy_mlp",
        "num_classes": 10,
        "epochs": 5,
        "save_dir": model_path_mnist_04,
        "validation_data": ds_in,
    }
    model = train_torch_model(ds_fit, **train_config).to(device)
    clear_output()

# evaluate model
model.eval()
labels, preds = [], []
for x, y in ds_in:
    x = x.to(device)
    preds.append(torch.argmax(model(x), dim=-1).detach().cpu())
    labels.append(y)
print(f"Test accuracy:\t{accuracy_score(torch.cat(labels), torch.cat(preds)):.6f}")

# penultimate features 2d visualization
print("\n=== Penultimate features viz ===")
plt.figure(figsize=(4.5, 3))
plot_2D_features(
    model=model,
    in_dataset=ds_in,
    out_dataset=ds_out,
    output_layer_id=-2,
)
plt.tight_layout()
plt.show()

# === Train / Load model === # model path model_path_mnist_04 = os.path.join(model_path, "mnist_mlp_0-4") try: # if the model exists, load it model = torch.load(os.path.join(model_path_mnist_04, "best.pt")).to(device) except OSError: # else, train a new model train_config = { "model": "toy_mlp", "num_classes": 10, "epochs": 5, "save_dir": model_path_mnist_04, "validation_data": ds_in, } model = train_torch_model(ds_fit, **train_config).to(device) clear_output() # evaluate model model.eval() labels, preds = [], [] for x, y in ds_in: x = x.to(device) preds.append(torch.argmax(model(x), dim=-1).detach().cpu()) labels.append(y) print(f"Test accuracy:\t{accuracy_score(torch.cat(labels), torch.cat(preds)):.6f}") # penultimate features 2d visualization print("\n=== Penultimate features viz ===") plt.figure(figsize=(4.5, 3)) plot_2D_features( model=model, in_dataset=ds_in, out_dataset=ds_out, output_layer_id=-2, ) plt.tight_layout() plt.show()

Test accuracy:	0.991438

=== Penultimate features viz ===

SHE score¶

We now fit a SHE detector with MNIST[0-4] train dataset, and compare OOD scores returned for MNIST[0-4] (ID) and MNIST[5-9] (OOD) test datasets.

In [5]:

%autoreload 2

# === gram scores ===
she = SHE()
she.fit(model, ds_fit, feature_layers_id=["relu1", "relu2"])
scores_in, _ = she.score(ds_in)
scores_out, _ = she.score(ds_out)

# === metrics ===
# auroc / fpr95
metrics = bench_metrics(
    (scores_in, scores_out),
    metrics=["auroc", "fpr95tpr"],
)
print("=== Metrics ===")
for k, v in metrics.items():
    print(f"{k:<10} {v:.6f}")

%autoreload 2 # === gram scores === she = SHE() she.fit(model, ds_fit, feature_layers_id=["relu1", "relu2"]) scores_in, _ = she.score(ds_in) scores_out, _ = she.score(ds_out) # === metrics === # auroc / fpr95 metrics = bench_metrics( (scores_in, scores_out), metrics=["auroc", "fpr95tpr"], ) print("=== Metrics ===") for k, v in metrics.items(): print(f"{k:<10} {v:.6f}")

=== Metrics ===
auroc      0.736096
fpr95tpr   0.645845

In [6]:

print("\n=== Plots ===")
# hists / roc
plt.figure(figsize=(9, 3))
plt.subplot(121)
plot_ood_scores(scores_in, scores_out)
plt.subplot(122)
plot_roc_curve(scores_in, scores_out)
plt.tight_layout()
plt.show()

print("\n=== Plots ===") # hists / roc plt.figure(figsize=(9, 3)) plt.subplot(121) plot_ood_scores(scores_in, scores_out) plt.subplot(122) plot_roc_curve(scores_in, scores_out) plt.tight_layout() plt.show()

=== Plots ===

Second exp: CIFAR-10 vs SVHN¶

For this second experiment, we oppose CIFAR-10 (in-distribution dataset) to SVHN (out-of-distribution dataset).

Data loading¶

• In-distribution data: CIFAR-10
• Out-of-distribution data: SVHN

In [7]:

# === load ID and OOD data ===
batch_size = 128

# 1a- load in-distribution dataset: CIFAR-10
oods_fit = OODDataset(
    dataset_id="CIFAR10",
    backend="torch",
    load_kwargs={"root": data_path, "train": True, "download": True},
)
oods_in = OODDataset(
    dataset_id="CIFAR10",
    backend="torch",
    load_kwargs={"root": data_path, "train": False, "download": True},
)
# 1b- load out-of-distribution dataset: SVHN
oods_out = OODDataset(
    dataset_id="SVHN",
    backend="torch",
    load_kwargs={"root": data_path, "split": "test", "download": True},
)


# 2- prepare data (preprocess, shuffle, batch) => torch dataloaders


def preprocess_fn(*inputs):
    """Preprocessing function from
    https://github.com/chenyaofo/pytorch-cifar-models
    """
    x = inputs[0] / 255.0
    x = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))(x)
    return tuple([x] + list(inputs[1:]))


patch_permutation = TorchRandomPatchPermutation(patch_size=(4, 4))


def preprocess_fn_ood(*inputs):
    """Simple preprocessing function to normalize images in [0, 1]."""
    x = inputs[0] / 255.0
    x = patch_permutation(x)
    return tuple([x] + list(inputs[1:]))


ds_fit = oods_fit.prepare(
    batch_size=batch_size, preprocess_fn=preprocess_fn, shuffle=True
)
ds_fit_ood = oods_fit.prepare(
    batch_size=batch_size, preprocess_fn=preprocess_fn_ood, shuffle=True
)
ds_in = oods_in.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn)
ds_out = oods_out.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn)

clear_output()

# === load ID and OOD data === batch_size = 128 # 1a- load in-distribution dataset: CIFAR-10 oods_fit = OODDataset( dataset_id="CIFAR10", backend="torch", load_kwargs={"root": data_path, "train": True, "download": True}, ) oods_in = OODDataset( dataset_id="CIFAR10", backend="torch", load_kwargs={"root": data_path, "train": False, "download": True}, ) # 1b- load out-of-distribution dataset: SVHN oods_out = OODDataset( dataset_id="SVHN", backend="torch", load_kwargs={"root": data_path, "split": "test", "download": True}, ) # 2- prepare data (preprocess, shuffle, batch) => torch dataloaders def preprocess_fn(*inputs): """Preprocessing function from https://github.com/chenyaofo/pytorch-cifar-models """ x = inputs[0] / 255.0 x = transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))(x) return tuple([x] + list(inputs[1:])) patch_permutation = TorchRandomPatchPermutation(patch_size=(4, 4)) def preprocess_fn_ood(*inputs): """Simple preprocessing function to normalize images in [0, 1].""" x = inputs[0] / 255.0 x = patch_permutation(x) return tuple([x] + list(inputs[1:])) ds_fit = oods_fit.prepare( batch_size=batch_size, preprocess_fn=preprocess_fn, shuffle=True ) ds_fit_ood = oods_fit.prepare( batch_size=batch_size, preprocess_fn=preprocess_fn_ood, shuffle=True ) ds_in = oods_in.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn) ds_out = oods_out.prepare(batch_size=batch_size, preprocess_fn=preprocess_fn) clear_output()

Model loading¶

The model is a ResNet20 pretrained on CIFAR-10 and getting an accuracy score of 92.60%, loaded from pytorch-cifar-models repository.

In [8]:

# === load model ===
# resnet20 pretrained on CIFAR-10
model = torch.hub.load(
    repo_or_dir="chenyaofo/pytorch-cifar-models",
    model="cifar10_resnet20",
    pretrained=True,
    verbose=False,
).to(device)
model.eval()
clear_output()

# === load model === # resnet20 pretrained on CIFAR-10 model = torch.hub.load( repo_or_dir="chenyaofo/pytorch-cifar-models", model="cifar10_resnet20", pretrained=True, verbose=False, ).to(device) model.eval() clear_output()

In [9]:

# evaluate model
labels, preds = [], []
for x, y in ds_in:
    x = x.to(device)
    preds.append(torch.argmax(model(x), dim=-1).detach().cpu())
    labels.append(y)
print(f"Test accuracy:\t{accuracy_score(torch.cat(labels), torch.cat(preds)):.6f}")

# penultimate features 2d visualization
print("\n=== Penultimate features viz ===")
plt.figure(figsize=(4.5, 3))
plot_2D_features(
    model=model,
    in_dataset=ds_in,
    out_dataset=ds_out,
    output_layer_id=-2,
)
plt.tight_layout()
plt.show()

# evaluate model labels, preds = [], [] for x, y in ds_in: x = x.to(device) preds.append(torch.argmax(model(x), dim=-1).detach().cpu()) labels.append(y) print(f"Test accuracy:\t{accuracy_score(torch.cat(labels), torch.cat(preds)):.6f}") # penultimate features 2d visualization print("\n=== Penultimate features viz ===") plt.figure(figsize=(4.5, 3)) plot_2D_features( model=model, in_dataset=ds_in, out_dataset=ds_out, output_layer_id=-2, ) plt.tight_layout() plt.show()

Test accuracy:	0.925900

=== Penultimate features viz ===

SHE score¶

We now fit a SHE detector with CIFAR-10 train dataset, and compare OOD scores returned for CIFAR-10 (ID) and SVHN (OOD) test datasets.

In [10]:

%autoreload 2

# === gram scores ===
she = SHE()
she.fit(model, ds_fit, feature_layers_id=["layer1.2.conv2", "layer2.2.conv2", "layer3.2.conv2"])

scores_in, _ = she.score(ds_in)
scores_out, _ = she.score(ds_out)

%autoreload 2 # === gram scores === she = SHE() she.fit(model, ds_fit, feature_layers_id=["layer1.2.conv2", "layer2.2.conv2", "layer3.2.conv2"]) scores_in, _ = she.score(ds_in) scores_out, _ = she.score(ds_out)

In [12]:

# === metrics ===
# auroc / fpr95
metrics = bench_metrics(
    (scores_in, scores_out),
    metrics=["auroc", "fpr95tpr"],
)
print("=== Metrics ===")
for k, v in metrics.items():
    print(f"{k:<10} {v:.6f}")

print("\n=== Plots ===")
# hists / roc
plt.figure(figsize=(9, 3))
plt.subplot(121)
plot_ood_scores(scores_in, scores_out)
plt.subplot(122)
plot_roc_curve(scores_in, scores_out)
plt.tight_layout()
plt.show()

# === metrics === # auroc / fpr95 metrics = bench_metrics( (scores_in, scores_out), metrics=["auroc", "fpr95tpr"], ) print("=== Metrics ===") for k, v in metrics.items(): print(f"{k:<10} {v:.6f}") print("\n=== Plots ===") # hists / roc plt.figure(figsize=(9, 3)) plt.subplot(121) plot_ood_scores(scores_in, scores_out) plt.subplot(122) plot_roc_curve(scores_in, scores_out) plt.tight_layout() plt.show()

=== Metrics ===
auroc      0.990859
fpr95tpr   0.040200

=== Plots ===