Maximum Logit Score / Maximum Softmax Probability¶

This notebook aims at evaluating the MLS and MSP methods.

These methods return an OOD score based on the maximum value of the output logits or softmax activations.

Here, we focus on a toy convolutional network trained on MNIST[0-4] and a ResNet20 model trained on CIFAR-10, respectively challenged on MNIST[5-9] and SVHN OOD datasets.

References

• MLS: Open-Set Recognition: a Good Closed-Set Classifier is All You Need?, ICLR 2022.
• MSP: A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks, ICLR 2017.

Imports¶

In [1]:

%load_ext autoreload
%autoreload 2

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

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

from oodeel.methods import MLS
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

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" from IPython.display import clear_output from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt import numpy as np import torch from torchvision import transforms from oodeel.methods import MLS 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 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 MLS 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_model_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_convnet",
        "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_model_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_convnet", "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:	0.995914

=== Penultimate features viz ===

MLS score¶

We now fit an MLS 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]:

# === mls scores ===
mls = MLS()
mls.fit(model)
scores_in, _ = mls.score(ds_in)
scores_out, _ = mls.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}")

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

# === mls scores === mls = MLS() mls.fit(model) scores_in, _ = mls.score(ds_in) scores_out, _ = mls.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}") print("\n=== Plots ===") # hists / roc plt.figure(figsize=(9, 3)) plt.subplot(121) plot_ood_scores(scores_in, scores_out, log_scale=False) plt.subplot(122) plot_roc_curve(scores_in, scores_out) plt.tight_layout() plt.show()

=== Metrics ===
auroc      0.896637
fpr95tpr   0.514886

=== Plots ===

MSP score¶

Using the softmax activations instead, we get the MSP scores for MNIST[0-4] (ID) and MNIST[5-9] (OOD) test datasets.

In [6]:

# === msp scores ===
msp = MLS(output_activation="softmax")
msp.fit(model)
scores_in, _ = msp.score(ds_in)
scores_out, _ = msp.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}")

print("\n=== Plots ===")
# Normalize scores for a better hist visualization
minim = np.min([np.min(scores_in), np.min(scores_out)])
scores_in_ = scores_in - 2 * minim + np.min(scores_in[np.where(scores_in != minim)])
scores_out_ = scores_out - 2 * minim + np.min(scores_in[np.where(scores_in != minim)])

# hists / roc
plt.figure(figsize=(9, 3))
plt.subplot(121)
plot_ood_scores(scores_in_, scores_out_, log_scale=True)
plt.subplot(122)
plot_roc_curve(scores_in, scores_out)
plt.tight_layout()
plt.show()

# === msp scores === msp = MLS(output_activation="softmax") msp.fit(model) scores_in, _ = msp.score(ds_in) scores_out, _ = msp.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}") print("\n=== Plots ===") # Normalize scores for a better hist visualization minim = np.min([np.min(scores_in), np.min(scores_out)]) scores_in_ = scores_in - 2 * minim + np.min(scores_in[np.where(scores_in != minim)]) scores_out_ = scores_out - 2 * minim + np.min(scores_in[np.where(scores_in != minim)]) # hists / roc plt.figure(figsize=(9, 3)) plt.subplot(121) plot_ood_scores(scores_in_, scores_out_, log_scale=True) plt.subplot(122) plot_roc_curve(scores_in, scores_out) plt.tight_layout() plt.show()

=== Metrics ===
auroc      0.907393
fpr95tpr   0.616073

=== 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:]))

ds_fit = oods_fit.prepare(batch_size=batch_size, shuffle=True, preprocess_fn=preprocess_fn)
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:])) ds_fit = oods_fit.prepare(batch_size=batch_size, shuffle=True, preprocess_fn=preprocess_fn) 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();

# 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()

# === 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(); # 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:	0.926000

=== Penultimate features viz ===

MLS score¶

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

In [9]:

# === mls scores ===
mls = MLS()
mls.fit(model)
scores_in, _ = mls.score(ds_in)
scores_out, _ = mls.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}")

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

# === mls scores === mls = MLS() mls.fit(model) scores_in, _ = mls.score(ds_in) scores_out, _ = mls.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}") print("\n=== Plots ===") # hists / roc plt.figure(figsize=(9, 3)) plt.subplot(121) plot_ood_scores(scores_in, scores_out, log_scale=False) plt.subplot(122) plot_roc_curve(scores_in, scores_out) plt.tight_layout() plt.show()

=== Metrics ===
auroc      0.904943
fpr95tpr   0.303800

=== Plots ===

MSP score¶

Using the softmax activations instead, we get the MSP scores for CIFAR-10 (ID) and SVHN (OOD) test datasets.

In [10]:

# === msp scores ===
msp = MLS(output_activation="softmax")
msp.fit(model)
scores_in, _ = msp.score(ds_in)
scores_out, _ = msp.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}")

print("\n=== Plots ===")
# Normalize scores for a better hist visualization
minim = np.min([np.min(scores_in), np.min(scores_out)])
scores_in_ = scores_in - 2 * minim + np.min(scores_in[np.where(scores_in != minim)])
scores_out_ = scores_out - 2 * minim + np.min(scores_in[np.where(scores_in != minim)])

# hists / roc
plt.figure(figsize=(9, 3))
plt.subplot(121)
plot_ood_scores(scores_in_, scores_out_, log_scale=True)
plt.subplot(122)
plot_roc_curve(scores_in, scores_out)
plt.tight_layout()
plt.show()

# === msp scores === msp = MLS(output_activation="softmax") msp.fit(model) scores_in, _ = msp.score(ds_in) scores_out, _ = msp.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}") print("\n=== Plots ===") # Normalize scores for a better hist visualization minim = np.min([np.min(scores_in), np.min(scores_out)]) scores_in_ = scores_in - 2 * minim + np.min(scores_in[np.where(scores_in != minim)]) scores_out_ = scores_out - 2 * minim + np.min(scores_in[np.where(scores_in != minim)]) # hists / roc plt.figure(figsize=(9, 3)) plt.subplot(121) plot_ood_scores(scores_in_, scores_out_, log_scale=True) plt.subplot(122) plot_roc_curve(scores_in, scores_out) plt.tight_layout() plt.show()

=== Metrics ===
auroc      0.874827
fpr95tpr   0.342100

=== Plots ===