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evaluate.py
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evaluate.py
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"""
Script to evaluate a single model.
"""
import os
import json
import math
import torch
import argparse
import torch.backends.cudnn as cudnn
# Import dataloaders
import data.ood_detection.cifar10 as cifar10
import data.ood_detection.cifar100 as cifar100
import data.ood_detection.svhn as svhn
# Import network models
from net.resnet import resnet50
from net.wide_resnet import wrn
from net.vgg import vgg16
# Import metrics to compute
from metrics.classification_metrics import (
test_classification_net,
test_classification_net_logits,
test_classification_net_ensemble
)
from metrics.calibration_metrics import expected_calibration_error
from metrics.uncertainty_confidence import entropy, logsumexp
from metrics.ood_metrics import get_roc_auc, get_roc_auc_logits, get_roc_auc_ensemble
# Import GMM utils
from utils.gmm_utils import get_embeddings, gmm_evaluate, gmm_fit
from utils.ensemble_utils import load_ensemble, ensemble_forward_pass
from utils.eval_utils import model_load_name
from utils.train_utils import model_save_name
from utils.args import eval_args
# Temperature scaling
from utils.temperature_scaling import ModelWithTemperature
# Dataset params
dataset_num_classes = {"cifar10": 10, "cifar100": 100, "svhn": 10}
dataset_loader = {"cifar10": cifar10, "cifar100": cifar100, "svhn": svhn}
# Mapping model name to model function
models = {"resnet50": resnet50, "wide_resnet": wrn, "vgg16": vgg16}
model_to_num_dim = {"resnet50": 2048, "wide_resnet": 640, "vgg16": 512}
if __name__ == "__main__":
args = eval_args().parse_args()
# Checking if GPU is available
cuda = torch.cuda.is_available()
# Setting additional parameters
print("Parsed args", args)
print("Seed: ", args.seed)
torch.manual_seed(args.seed)
device = torch.device("cuda" if cuda else "cpu")
# Taking input for the dataset
num_classes = dataset_num_classes[args.dataset]
test_loader = dataset_loader[args.dataset].get_test_loader(batch_size=args.batch_size, pin_memory=args.gpu)
ood_test_loader = dataset_loader[args.ood_dataset].get_test_loader(batch_size=args.batch_size, pin_memory=args.gpu)
# Evaluating the models
accuracies = []
# Pre temperature scaling
# m1 - Uncertainty/Confidence Metric 1
# for deterministic model: logsumexp, for ensemble: entropy
# m2 - Uncertainty/Confidence Metric 2
# for deterministic model: entropy, for ensemble: MI
eces = []
m1_aurocs = []
m1_auprcs = []
m2_aurocs = []
m2_auprcs = []
# Post temperature scaling
t_eces = []
t_m1_aurocs = []
t_m1_auprcs = []
t_m2_aurocs = []
t_m2_auprcs = []
topt = None
for i in range(args.runs):
print (f"Evaluating run: {(i+1)}")
# Loading the model(s)
if args.model_type == "ensemble":
val_loaders = []
for j in range(args.ensemble):
train_loader, val_loader = dataset_loader[args.dataset].get_train_valid_loader(
batch_size=args.batch_size, augment=args.data_aug, val_seed=(args.seed+(5*i)+j), val_size=0.1, pin_memory=args.gpu,
)
val_loaders.append(val_loader)
# Evaluate an ensemble
ensemble_loc = os.path.join(args.load_loc, ("Run" + str(i + 1)))
net_ensemble = load_ensemble(
ensemble_loc=ensemble_loc,
model_name=args.model,
device=device,
num_classes=num_classes,
spectral_normalization=args.sn,
mod=args.mod,
coeff=args.coeff,
seed=(5*i+1)
)
else:
train_loader, val_loader = dataset_loader[args.dataset].get_train_valid_loader(
batch_size=args.batch_size, augment=args.data_aug, val_seed=(args.seed+i), val_size=0.1, pin_memory=args.gpu,
)
saved_model_name = os.path.join(
args.load_loc,
"Run" + str(i + 1),
model_load_name(args.model, args.sn, args.mod, args.coeff, args.seed, i) + "_350.model",
)
net = models[args.model](
spectral_normalization=args.sn, mod=args.mod, coeff=args.coeff, num_classes=num_classes, temp=1.0,
)
if args.gpu:
net.cuda()
net = torch.nn.DataParallel(net, device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True
net.load_state_dict(torch.load(str(saved_model_name)))
net.eval()
# Evaluating the model(s)
if args.model_type == "ensemble":
(conf_matrix, accuracy, labels_list, predictions, confidences,) = test_classification_net_ensemble(
net_ensemble, test_loader, device
)
ece = expected_calibration_error(confidences, predictions, labels_list, num_bins=15)
(_, _, _), (_, _, _), m1_auroc, m1_auprc = get_roc_auc_ensemble(
net_ensemble, test_loader, ood_test_loader, "mutual_information", device
)
(_, _, _), (_, _, _), m2_auroc, m2_auprc = get_roc_auc_ensemble(
net_ensemble, test_loader, ood_test_loader, "entropy", device
)
# Temperature scale the ensemble
t_ensemble = []
for model, val_loader in zip(net_ensemble, val_loaders):
t_model = ModelWithTemperature(model)
t_model.set_temperature(val_loader)
t_ensemble.append(t_model)
(
t_conf_matrix,
t_accuracy,
t_labels_list,
t_predictions,
t_confidences,
) = test_classification_net_ensemble(t_ensemble, test_loader, device)
t_ece = expected_calibration_error(t_confidences, t_predictions, t_labels_list, num_bins=15)
(_, _, _), (_, _, _), t_m1_auroc, t_m1_auprc = get_roc_auc_ensemble(
t_ensemble, test_loader, ood_test_loader, "mutual_information", device
)
(_, _, _), (_, _, _), t_m2_auroc, t_m2_auprc = get_roc_auc_ensemble(
t_ensemble, test_loader, ood_test_loader, "entropy", device
)
else:
(conf_matrix, accuracy, labels_list, predictions, confidences,) = test_classification_net(
net, test_loader, device
)
ece = expected_calibration_error(confidences, predictions, labels_list, num_bins=15)
temp_scaled_net = ModelWithTemperature(net)
temp_scaled_net.set_temperature(val_loader)
topt = temp_scaled_net.temperature
(t_conf_matrix, t_accuracy, t_labels_list, t_predictions, t_confidences,) = test_classification_net(
temp_scaled_net, test_loader, device
)
t_ece = expected_calibration_error(t_confidences, t_predictions, t_labels_list, num_bins=15)
if (args.model_type == "gmm"):
# Evaluate a GMM model
print("GMM Model")
embeddings, labels = get_embeddings(
net,
train_loader,
num_dim=model_to_num_dim[args.model],
dtype=torch.double,
device=device,
storage_device=device,
)
try:
gaussians_model, jitter_eps = gmm_fit(embeddings=embeddings, labels=labels, num_classes=num_classes)
logits, labels = gmm_evaluate(
net, gaussians_model, test_loader, device=device, num_classes=num_classes, storage_device=device,
)
ood_logits, ood_labels = gmm_evaluate(
net, gaussians_model, ood_test_loader, device=device, num_classes=num_classes, storage_device=device,
)
(_, _, _), (_, _, _), m1_auroc, m1_auprc = get_roc_auc_logits(
logits, ood_logits, logsumexp, device, confidence=True
)
(_, _, _), (_, _, _), m2_auroc, m2_auprc = get_roc_auc_logits(logits, ood_logits, entropy, device)
t_m1_auroc = m1_auroc
t_m1_auprc = m1_auprc
t_m2_auroc = m2_auroc
t_m2_auprc = m2_auprc
except RuntimeError as e:
print("Runtime Error caught: " + str(e))
continue
else:
# Evaluate a normal Softmax model
print("Softmax Model")
(_, _, _), (_, _, _), m1_auroc, m1_auprc = get_roc_auc(
net, test_loader, ood_test_loader, logsumexp, device, confidence=True
)
(_, _, _), (_, _, _), m2_auroc, m2_auprc = get_roc_auc(net, test_loader, ood_test_loader, entropy, device)
(_, _, _), (_, _, _), t_m1_auroc, t_m1_auprc = get_roc_auc(
temp_scaled_net, test_loader, ood_test_loader, logsumexp, device, confidence=True,
)
(_, _, _), (_, _, _), t_m2_auroc, t_m2_auprc = get_roc_auc(
temp_scaled_net, test_loader, ood_test_loader, entropy, device
)
accuracies.append(accuracy)
# Pre-temperature results
eces.append(ece)
m1_aurocs.append(m1_auroc)
m1_auprcs.append(m1_auprc)
m2_aurocs.append(m2_auroc)
m2_auprcs.append(m2_auprc)
# Post-temperature results
t_eces.append(t_ece)
t_m1_aurocs.append(t_m1_auroc)
t_m1_auprcs.append(t_m1_auprc)
t_m2_aurocs.append(t_m2_auroc)
t_m2_auprcs.append(t_m2_auprc)
accuracy_tensor = torch.tensor(accuracies)
ece_tensor = torch.tensor(eces)
m1_auroc_tensor = torch.tensor(m1_aurocs)
m1_auprc_tensor = torch.tensor(m1_auprcs)
m2_auroc_tensor = torch.tensor(m2_aurocs)
m2_auprc_tensor = torch.tensor(m2_auprcs)
t_ece_tensor = torch.tensor(t_eces)
t_m1_auroc_tensor = torch.tensor(t_m1_aurocs)
t_m1_auprc_tensor = torch.tensor(t_m1_auprcs)
t_m2_auroc_tensor = torch.tensor(t_m2_aurocs)
t_m2_auprc_tensor = torch.tensor(t_m2_auprcs)
mean_accuracy = torch.mean(accuracy_tensor)
mean_ece = torch.mean(ece_tensor)
mean_m1_auroc = torch.mean(m1_auroc_tensor)
mean_m1_auprc = torch.mean(m1_auprc_tensor)
mean_m2_auroc = torch.mean(m2_auroc_tensor)
mean_m2_auprc = torch.mean(m2_auprc_tensor)
mean_t_ece = torch.mean(t_ece_tensor)
mean_t_m1_auroc = torch.mean(t_m1_auroc_tensor)
mean_t_m1_auprc = torch.mean(t_m1_auprc_tensor)
mean_t_m2_auroc = torch.mean(t_m2_auroc_tensor)
mean_t_m2_auprc = torch.mean(t_m2_auprc_tensor)
std_accuracy = torch.std(accuracy_tensor) / math.sqrt(accuracy_tensor.shape[0])
std_ece = torch.std(ece_tensor) / math.sqrt(ece_tensor.shape[0])
std_m1_auroc = torch.std(m1_auroc_tensor) / math.sqrt(m1_auroc_tensor.shape[0])
std_m1_auprc = torch.std(m1_auprc_tensor) / math.sqrt(m1_auprc_tensor.shape[0])
std_m2_auroc = torch.std(m2_auroc_tensor) / math.sqrt(m2_auroc_tensor.shape[0])
std_m2_auprc = torch.std(m2_auprc_tensor) / math.sqrt(m2_auprc_tensor.shape[0])
std_t_ece = torch.std(t_ece_tensor) / math.sqrt(t_ece_tensor.shape[0])
std_t_m1_auroc = torch.std(t_m1_auroc_tensor) / math.sqrt(t_m1_auroc_tensor.shape[0])
std_t_m1_auprc = torch.std(t_m1_auprc_tensor) / math.sqrt(t_m1_auprc_tensor.shape[0])
std_t_m2_auroc = torch.std(t_m2_auroc_tensor) / math.sqrt(t_m2_auroc_tensor.shape[0])
std_t_m2_auprc = torch.std(t_m2_auprc_tensor) / math.sqrt(t_m2_auprc_tensor.shape[0])
res_dict = {}
res_dict["mean"] = {}
res_dict["mean"]["accuracy"] = mean_accuracy.item()
res_dict["mean"]["ece"] = mean_ece.item()
res_dict["mean"]["m1_auroc"] = mean_m1_auroc.item()
res_dict["mean"]["m1_auprc"] = mean_m1_auprc.item()
res_dict["mean"]["m2_auroc"] = mean_m2_auroc.item()
res_dict["mean"]["m2_auprc"] = mean_m2_auprc.item()
res_dict["mean"]["t_ece"] = mean_t_ece.item()
res_dict["mean"]["t_m1_auroc"] = mean_t_m1_auroc.item()
res_dict["mean"]["t_m1_auprc"] = mean_t_m1_auprc.item()
res_dict["mean"]["t_m2_auroc"] = mean_t_m2_auroc.item()
res_dict["mean"]["t_m2_auprc"] = mean_t_m2_auprc.item()
res_dict["std"] = {}
res_dict["std"]["accuracy"] = std_accuracy.item()
res_dict["std"]["ece"] = std_ece.item()
res_dict["std"]["m1_auroc"] = std_m1_auroc.item()
res_dict["std"]["m1_auprc"] = std_m1_auprc.item()
res_dict["std"]["m2_auroc"] = std_m2_auroc.item()
res_dict["std"]["m2_auprc"] = std_m2_auprc.item()
res_dict["std"]["t_ece"] = std_t_ece.item()
res_dict["std"]["t_m1_auroc"] = std_t_m1_auroc.item()
res_dict["std"]["t_m1_auprc"] = std_t_m1_auprc.item()
res_dict["std"]["t_m2_auroc"] = std_t_m2_auroc.item()
res_dict["std"]["t_m2_auprc"] = std_t_m2_auprc.item()
res_dict["mean"] = {}
res_dict["mean"]["accuracy"] = mean_accuracy.item()
res_dict["mean"]["ece"] = mean_ece.item()
res_dict["mean"]["m1_auroc"] = mean_m1_auroc.item()
res_dict["mean"]["m1_auprc"] = mean_m1_auprc.item()
res_dict["mean"]["m2_auroc"] = mean_m2_auroc.item()
res_dict["mean"]["m2_auprc"] = mean_m2_auprc.item()
res_dict["mean"]["t_ece"] = mean_t_ece.item()
res_dict["mean"]["t_m1_auroc"] = mean_t_m1_auroc.item()
res_dict["mean"]["t_m1_auprc"] = mean_t_m1_auprc.item()
res_dict["mean"]["t_m2_auroc"] = mean_t_m2_auroc.item()
res_dict["mean"]["t_m2_auprc"] = mean_t_m2_auprc.item()
res_dict["values"] = {}
res_dict["values"]["accuracy"] = accuracies
res_dict["values"]["ece"] = eces
res_dict["values"]["m1_auroc"] = m1_aurocs
res_dict["values"]["m1_auprc"] = m1_auprcs
res_dict["values"]["m2_auroc"] = m2_aurocs
res_dict["values"]["m2_auprc"] = m2_auprcs
res_dict["values"]["t_ece"] = t_eces
res_dict["values"]["t_m1_auroc"] = t_m1_aurocs
res_dict["values"]["t_m1_auprc"] = t_m1_auprcs
res_dict["values"]["t_m2_auroc"] = t_m2_aurocs
res_dict["values"]["t_m2_auprc"] = t_m2_auprcs
res_dict["info"] = vars(args)
with open(
"res_"
+ model_save_name(args.model, args.sn, args.mod, args.coeff, args.seed)
+ "_"
+ args.model_type
+ "_"
+ args.dataset
+ "_"
+ args.ood_dataset
+ ".json",
"w",
) as f:
json.dump(res_dict, f)