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robustness_standalone.py
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robustness_standalone.py
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# Test robustness of model using CIFAR100-c dataset
# References:
# 1. https://github.com/hendrycks/robustness/blob/master/ImageNet-C/test.py
# 2. https://github.com/psh150204/AugMix/blob/master/main.py
import os
import torch
import numpy as np
import argparse
import yaml
import torchvision.datasets as datasets
import torchvision.transforms as transforms
#import torchvision.models as models
import torch.utils.model_zoo as model_zoo
#from utils.get_all_models import get_model
#Step I: # *********Import model definition file here *******
# Example: loading resnet here
from models import resnet
from models.mobilenet import mobilenetv2
# set parser
parser = argparse.ArgumentParser(description="Evaluates robustness of CNNs")
parser.add_argument("-wp", "--weights_pth", type=str,
default="pretrained/r50_bestmodel.pth",
help="model weights file path")
parser.add_argument("-dp", "--dataset_pth", type=str,
default="dataset/CIFAR100-C",
help="Robustness evaluation dataset folder path")
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = '0' # '0,1,2' for 3 gpus
def show_performance_cifar(model, dataloader,
distortion_name=None,
device='cuda'):
# Calculate error
model.to(device).eval() # Put model in eval mode
err, correct, total = 0,0,0
with torch.no_grad():
for batch_idx, (data, target) in enumerate(dataloader):
data = data.to(device, non_blocking=True)
target = target.to(device, non_blocking=True)
output = model(data)
_, pred = torch.max(output.data, 1)
correct += (pred==target).sum().item()
total += target.size(0)
err = 1 - correct / total
correct = correct / total
#print(f"Total correct prediction (%): {correct*100}")
if distortion_name is not None: # For robustness
print(f"Distortion: {distortion_name}, Err: {err}")
print(f"Distortion: {distortion_name}, Correct: {correct}")
print(f"Total images in {distortion_name}: {total}")
return err, correct
def cal_mCE(model, dataset_root,
dataset_transforms,
dataset_name,
device='cuda'):
# All the distortions: Total 15
distortions = ['gaussian_noise', 'shot_noise', 'impulse_noise',
'defocus_blur', 'glass_blur', 'motion_blur',
'zoom_blur', 'snow', 'frost',
'brightness', 'contrast', 'elastic_transform',
'pixelate', 'jpeg_compression', 'speckle_noise',
'gaussian_blur', 'spatter', 'saturate']
# Creating dummy object of datasets.CIFAR100 class and replacing later with
# cifa100-c data and labels
if dataset_name=="cifar100":
test_data = datasets.CIFAR100("./dataset",
train=False,
transform=dataset_transforms,
download=True)
elif dataset_name=="cifar10":
test_data = datasets.CIFAR10("./dataset",
train=False,
transform=dataset_transforms,
download=True)
else:
raise NotImplementedError("Only for CIFAR100 and CIFAR10")
# Standard dataset accuracy:
standard_test_loader = torch.utils.data.DataLoader(test_data,
batch_size=32,
shuffle=False,
num_workers=8,
pin_memory=True)
err, correct = show_performance_cifar(model,
standard_test_loader,
device=device
)
print(f"Standard Err (%): {err*100}")
print(f"Standard Correct (%): {correct*100}")
# Calculate errors: mCE
errors = []
corrects = []
for distortion_name in distortions:
full_data_pth = os.path.join(dataset_root, f"{distortion_name}.npy")
full_labels_pth = os.path.join(dataset_root, "labels.npy")
test_data.data = np.load(full_data_pth)
test_data.targets = torch.LongTensor(np.load(full_labels_pth))
testloader = torch.utils.data.DataLoader(test_data,
batch_size=32,
shuffle=False,
num_workers=8,
pin_memory=True)
# error rate for a distortion
err, correct = show_performance_cifar(model,
testloader,
distortion_name,
device=device
)
# Collect all distortion rates to calculate mCE later
errors.append(err)
corrects.append(correct)
print('Distortion: {:15s} | CE (unnormalized) (%): {:.2f}'.format(distortion_name, 100*err))
# Calculate and print mCE
print('mCE (unnormalized) (%): {:.2f}'.format(100 * np.mean(errors)))
def load_best_model(cfg, model):
bestmodelpth = os.path.join(cfg['bestmodel']['path'], cfg['bestmodel']['name'])
bestmodel = torch.load(bestmodelpth) # load .pth file
model.load_state_dict(torch.load(bestmodel['model']))
print("Best model loaded!")
return model
def mCE_cifar100(dataset_root, model, device='cuda'):
print("Calculating Errors on CIFAR100 and CIFAR100-C")
dataset_name = "cifar100"
cifar_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.50707516, 0.48654887, 0.44091784),
(0.26733429, 0.25643846, 0.27615047))
])
# test_dataset = torchvision.datasets.CIFAR100(root='./dataset',
# train=False,
# download=True,
# transform=cifar_transforms)
cal_mCE(model, dataset_root,
dataset_transforms=cifar_transforms,
dataset_name=dataset_name,
device=device)
def mCE_cifar10(dataset_root, model, device='cuda'):
# Calculate err
print("Calculating Errors on CIFAR10 and CIFAR10-C")
dataset_name = "cifar10"
cifar_transforms = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.49139968, 0.48215841, 0.44653091),
(0.24703223, 0.24348513, 0.26158784))
])
# test_dataset = torchvision.datasets.CIFAR100(root='./dataset',
# train=False,
# download=True,
# transform=cifar_transforms)
cal_mCE(model, dataset_root,
dataset_transforms=cifar_transforms,
dataset_name=dataset_name,
device=device)
if __name__== "__main__":
# Step II: # Create model : Assumes that model definition file is imported
#model = resnet.resnet50(100)
model = mobilenetv2.MobileNetV2Wrapper(num_class=10)
model = torch.nn.DataParallel(model).cuda() # modules.layername saved if dataparallel was used while saving the model. Therefore need to wrap again with DataParallel when loading weights
print("Model created!")
# Step III: Load model with weights
model.load_state_dict(torch.load(args.weights_pth)['model'])
print("Model loaded with weights!")
# Step IV:
# calculate mCE on cifar
print(f"Evaluating ...")
mCE_cifar10(args.dataset_pth, model, device='cuda')