diff --git a/data/saved_models/mnist/adversarial_fgsm_LeNet.pth b/data/saved_models/mnist/adversarial_fgsm_LeNet.pth new file mode 100644 index 0000000..73f42b2 Binary files /dev/null and b/data/saved_models/mnist/adversarial_fgsm_LeNet.pth differ diff --git a/data/saved_models/mnist/noisy_0.4_LeNet.pth b/data/saved_models/mnist/noisy_0.4_LeNet.pth new file mode 100644 index 0000000..136bbb3 Binary files /dev/null and b/data/saved_models/mnist/noisy_0.4_LeNet.pth differ diff --git a/data/saved_models/mnist/normal_LeNet.pth b/data/saved_models/mnist/normal_LeNet.pth new file mode 100644 index 0000000..3d3b046 Binary files /dev/null and b/data/saved_models/mnist/normal_LeNet.pth differ diff --git a/explorations/mnist/cap_vol_stats.py b/explorations/mnist/cap_vol_stats.py new file mode 100644 index 0000000..accc358 --- /dev/null +++ b/explorations/mnist/cap_vol_stats.py @@ -0,0 +1,187 @@ +import sys +sys.path.append("../../") +import math +import torch +import numpy as np +import torchvision +from torchvision.transforms import transforms +from torch.autograd import Variable +from torch.utils.tensorboard import SummaryWriter +from torch.distributions.normal import Normal +from torch.distributions.multivariate_normal import MultivariateNormal +import torch.nn as nn +from models.mnist.models import LeNet5, CNN + +from utils.attacks import Adversary +import utils.config as cf +from utils.utils import get_one_vol, get_one_cap + + +def mean_dist(x, radius, dist_samples=500, dist_iter=2): + dim = 28 * 28 + dim_sqrt = math.sqrt(dim) + sigma = radius / dim_sqrt + dists = torch.zeros(dist_samples).float().to(device) + for i in range(dist_iter): + x_exp_orig = x.repeat(dist_samples, 1, 1, 1) + y_exp = y.repeat(dist_samples) + x_exp = x_exp_orig + torch.randn_like(x_exp_orig) * sigma + #print((x_exp-x_exp_orig).norm(dim=1).norm(dim=1).norm(dim=1)) + dists += adv.get_distances(model, x_exp, y_exp, device, eps=1., alpha=6e-1)[0] # original for pgd_linf 1e-3 + #dist = adv.get_distances(model, x, y, device, eps=1.0e-1, alpha=1.0e-3)[0] + dists /= dist_iter + return dists + + +if __name__=="__main__": + print("Test Distance") + torch.manual_seed(0) + transform = transforms.Compose([ + #transforms.RandomCrop(32, padding=4), + #transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + #transforms.Normalize(cf.mean['cifar10'], cf.std['cifar10']), + ]) + num_examples = 1000 + batch_size = 1 + dataset = torchvision.datasets.MNIST(root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + arch = "CNN" + curr_type = "data extraction" + curr_model = "adversarial_fgsm" + model_path = str("../../train/mnist/trained_models/"+curr_model+"_"+arch+".pth") + + device = "cuda" + torch.cuda.set_device(0) + if arch == 'LeNet': + model = LeNet5().to(device) + elif arch == 'CNN': + model = CNN.to(device) + model.load_state_dict(torch.load(model_path)) + #model = nn.DataParallel(model) + model.eval() + + dim = 28 * 28 + dim_sqrt = math.sqrt(dim) + radius_init = torch.tensor(30.) + radius_step = torch.tensor(0.1) + radius_iter = 1000 + radius = torch.tensor(40.) + alpha = 2.0e0 + num_steps = int(100 * alpha) + step = 1.0e-1 * radius / (dim_sqrt * math.sqrt(alpha)) + c = 0.1 + + normal_1d = Normal(torch.tensor(0.0), torch.tensor(1.0)) + t = torch.tensor(num_steps * step**2) + rmsd = torch.sqrt(dim * t) + #dist = c * rmsd + + print("Runtime: ", t) + print("RMSD: ", rmsd) + #print("Dist to hyperplane", dist) + + it = iter(loader) + adv = Adversary("pgd_linf", device) + + vol_data = [] + cap_data = [] + tau_data = [] + dist_data_bogdan_method = [] + dist_data_adv_method = [] + radius_data = [] + iso_bound_data = [] + + for i in range(num_examples): + data = next(it) + x, y = data[0].to(device), data[1].to(device) + radius = radius_init.clone() + sigma = radius / dim_sqrt + vol = 0. + vol_iter = 3 + r_iter = 0 + + upper = 0.011 + lower = 0.009 + + while (vol > upper) or (vol < lower): + if vol > upper: + radius -= radius_step + else: + radius += radius_step + + sigma = radius / dim_sqrt + vol = 0. + for j in range(vol_iter): + vol += get_one_vol(model, x, y, device, + radius=radius, num_samples=600, sample_full_ball=True) + vol = torch.tensor(vol / vol_iter) + r_iter += 1 + if r_iter > radius_iter: + break + #dists = mean_dist(x, radius) + dists = torch.mean(mean_dist(x, radius)) + #dist = adv.get_distances(model, x, y, device, eps=1.0e-1, alpha=1.0e-3, max_iter=100)[0] + + step = 1.0e-1 * radius / (dim_sqrt * math.sqrt(alpha)) + t = torch.tensor(num_steps * step**2) + rmsd = torch.sqrt(dim * t) + cap = 0. + cap_iter = 2 + for j in range(cap_iter): + cap += get_one_cap(model, x, y, device, + step=step, num_steps=num_steps, num_walks=1000, j="") + cap = torch.tensor(cap / cap_iter) + + iso_bound = 0 + if vol < 0.5: + iso_bound = -sigma * normal_1d.icdf(vol) + + vol_data += [vol.data] + cap_data += [cap.data] + tau_data += [cap.data/vol.data] + dist_data_bogdan_method += [dists.cpu().detach().numpy()] + #dist_data_adv_method += [dist.cpu().detach().numpy()] + radius_data += [radius.data] + iso_bound_data += [iso_bound] + + if (i+1) % 5 == 0: + np.save("saves/vol_"+curr_model+"_"+arch, np.array(vol_data)) + np.save("saves/cap_"+curr_model+"_"+arch, np.array(cap_data)) + np.save("saves/distance_"+curr_model+"_"+arch, np.array(dist_data_bogdan_method)) + #np.save("saves/testing_distance_normal_LeNet", np.array(dist_data_adv_method)) + np.save("saves/radius_"+curr_model+"_"+arch, np.array(radius_data)) + np.save('saves/iso_bounds_'+curr_model+'_'+arch, np.array(iso_bound_data)) + + #print("Dist to Hyperplane ", dist) + #print("Sigma ", sigma) + #print("rmsq ", rmsd) + print("Radius of Ball ", radius) + #print("Initial Radius ", radius_init) + print("Vol ", vol) + print("Cap ", cap) + print("Tau", cap/vol) + print("Dist bogdan: ", dists) + #print("Dist adv: ", dist) + print("Isoperimetric Bound:", iso_bound) + #print("BM reaches sphere: ", rmsd) + print("ratio iso v avg dist bogdan: ", dists / iso_bound) + #print("ratio iso v avg dist adv: ", iso_bound / dist) + #print("Mean Dist", dists.mean()) + #print("Dist from center", dist) + print("------------- "+arch+" -- "+curr_model+" --------------- "+curr_type+" --------") + """ + f = open('saves/cap_vol_tau_untrained_LeNet.txt', 'w') + for i in range(num_examples): + f.write(str(i+1)+' '+ + str(cap_data[i].item())+' '+ + str(vol_data[i].item())+' '+ + str(tau_data[i].item())+' '+ + str(radius_data[i].item())+'\n' + ) + f.close() + """ + + + diff --git a/explorations/mnist/saves/01_DNN4.txt b/explorations/mnist/saves/01_DNN4.txt new file mode 100644 index 0000000..0c64ca7 --- /dev/null +++ b/explorations/mnist/saves/01_DNN4.txt @@ -0,0 +1,500 @@ +1 0.017500001937150955 0.009999999776482582 1.750000238418579 10.000004768371582 3.7595138549804688 +2 0.013500000350177288 0.010555555112659931 1.2789474725723267 12.7000150680542 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import MultivariateNormal +import torch.nn as nn +from models.mnist.models import LeNet5, CNN, DNN4 + +from utils.attacks import Adversary +import utils.config as cf +from utils.utils import get_one_vol, get_one_cap +from utils.utils import get_one_sliced_vol, get_one_sliced_cap + +def mean_dist(x, radius, dist_samples=500, dist_iter=2): + # currently investigating between layer one and two + dim = 32 * 14 * 14 + dim_sqrt = math.sqrt(dim) + sigma = radius / dim_sqrt + dists = torch.zeros(dist_samples).float().to(device) + for i in range(dist_iter): + x_exp_orig = x.repeat(dist_samples, 1, 1, 1) + y_exp = y.repeat(dist_samples) + #x_exp = x_exp_orig + torch.randn_like(x_exp_orig) * sigma + #print((x_exp-x_exp_orig).norm(dim=1).norm(dim=1).norm(dim=1)) + #dists += adv.get_distances(model, x_exp, y_exp, device, eps=1.0, alpha=1.0e-3)[0] + + # after the first layer + pert = torch.randn(dist_samples, 32, 14, 14).to(device) * sigma + dists += adv.get_sliced_distances( + model, x_exp_orig, pert, y_exp, device, eps=1.0, alpha=1.0e-3)[0] + + #dist = adv.get_distances(model, x, y, device, eps=1.0e-1, alpha=1.0e-3)[0] + dists /= dist_iter + return dists + + +if __name__=="__main__": + print("Test Distance") + torch.manual_seed(0) + transform = transforms.Compose([ + #transforms.RandomCrop(32, padding=4), + #transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + #transforms.Normalize(cf.mean['cifar10'], cf.std['cifar10']), + ]) + num_examples = 500 + batch_size = 1 + dataset = torchvision.datasets.MNIST(root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + model_path = str("../../models/mnist/trained_models/slice_DNN4.pth") + + device = "cuda" + torch.cuda.set_device(0) + model = DNN4().to(device) + #model = CNN.to(device) + model.load_state_dict(torch.load(model_path)) + #model = nn.DataParallel(model) + model.eval() + + # layer dependent dimension + dim = 1 * 100 + pert = torch.zeros(1, 100) + + dim_sqrt = math.sqrt(dim) + radius_init = torch.tensor(5.) + radius_step = torch.tensor(0.1) + radius_iter = 1000 + radius = torch.tensor(40.) + alpha = 2.0e0 + num_steps = int(100 * alpha) + step = 1.0e-1 * radius / (dim_sqrt * math.sqrt(alpha)) + c = 0.1 + + normal_1d = Normal(torch.tensor(0.0), torch.tensor(1.0)) + t = torch.tensor(num_steps * step**2) + rmsd = torch.sqrt(dim * t) + #dist = c * rmsd + + print("Runtime: ", t) + print("RMSD: ", rmsd) + #print("Dist to hyperplane", dist) + + it = iter(loader) + adv = Adversary("pgd_linf", device) + + vol_data = [] + cap_data = [] + tau_data = [] + dist_data = [] + radius_data = [] + + for i in range(num_examples): + data = next(it) + x, y = data[0].to(device), data[1].to(device) + radius = radius_init.clone() + sigma = radius / dim_sqrt + vol = 0. + vol_iter = 3 + r_iter = 0 + + upper = 0.011 + lower = 0.009 + + while (vol > upper) or (vol < lower): + if vol > upper: + radius -= radius_step + else: + radius += radius_step + + sigma = radius / dim_sqrt + vol = 0. + for j in range(vol_iter): + vol += get_one_sliced_vol(model, x, pert, y, device, + radius=radius, num_samples=600, sample_full_ball=True) + vol = torch.tensor(vol / vol_iter ) + r_iter += 1 + if r_iter > radius_iter: + break + #dists = mean_dist(x, radius) + + dist = adv.get_sliced_distances( + model, x, pert, y, device, eps=5.0e-1, alpha=1.0e-3, max_iter=10000)[0] + + step = 1.0e-1 * radius / (dim_sqrt * math.sqrt(alpha)) + t = torch.tensor(num_steps * step**2) + rmsd = torch.sqrt(dim * t) + cap = 0. + cap_iter = 2 + for j in range(cap_iter): + cap += get_one_sliced_cap(model, x, pert, y, device, + step=step, num_steps=num_steps, num_walks=1000, j="") + cap = torch.tensor(cap / cap_iter ) + + iso_bound = 0 + if vol < 0.5: + iso_bound = -sigma * normal_1d.icdf(vol) + + vol_data += [vol.data] + cap_data += [cap.data] + tau_data += [cap.data/vol.data] + dist_data += [dist.cpu().detach().numpy()] + radius_data += [radius.data] + + """ + if (i+1) % 5 == 0: + i np.save("../../data/cap_vol_stats_mnist/vol_data_untrained_LeNet", np.array(vol_data)) + np.save("../../data/cap_vol_stats_mnist/cap_data_untrained_LeNet", np.array(cap_data)) + #np.save("dist_data_2", np.array(dist_data)) + np.save("../../data/cap_vol_stats_mnist/radius_LeNet", np.array(radius_data)) + """ + + #print("Dist to Hyperplane ", dist) + print("Sigma ", sigma) + print("Radius of Ball ", radius) + print("Initial Radius ", radius_init) + print("Vol ", vol) + print("Cap ", cap) + print("Tau", cap/vol) + print("Normed dist", dist.cpu().detach().numpy()) + print("Isoperimetric Bound:", iso_bound) + print("BM reaches sphere: ", rmsd) + #print("Mean Dist", dists.mean()) + #print("Dist from center", dist) + print("----------------------------------") + + f = open('saves/34_DNN4.txt', 'w') + for i in range(num_examples): + f.write(str(i+1)+' '+ + str(cap_data[i].item())+' '+ + str(vol_data[i].item())+' '+ + str(tau_data[i].item())+' '+ + str(radius_data[i].item())+' '+ + str(dist_data[i].item())+'\n' + ) + f.close() + + + + diff --git a/explorations/mnist/test_robustness.py b/explorations/mnist/test_robustness.py new file mode 100644 index 0000000..b647f1e --- /dev/null +++ b/explorations/mnist/test_robustness.py @@ -0,0 +1,159 @@ +import sys +sys.path.append("../../") +import torch +from torch.autograd import Variable +import torchvision +import torchvision.transforms as transforms +import numpy as np + +from models.mnist.models import LeNet5 +from utils.attacks import Adversary + + +device = 'cuda' +num_examples = 1000 + +model_name = [ + 'normal', + 'adv_fgsm', + 'noisy 04', + 'adv_random_1', + 'adv_random_2', + 'adv_random_4' + ] +models = [ + '../../train/mnist/trained_models/normal_LeNet.pth', + '../../train/mnist/trained_models/adversarial_fgsm_LeNet.pth', + '../../train/mnist/trained_models/noisy_0.4_LeNet.pth', + '../../train/mnist/trained_models/adversarial_random_1_ex_LeNet.pth', + '../../train/mnist/trained_models/adversarial_random_2_ex_LeNet.pth', + '../../train/mnist/trained_models/adversarial_random_4_ex_LeNet.pth', + ] + +f = open('saves/Lenet_robustness_random_adv_models.txt', "w") + +for path, name in zip(models, model_name): + model = LeNet5().to(device) + model.load_state_dict(torch.load(path)) + model.eval() + + # test accuracy on clean dataset + + transform = transforms.ToTensor() + batch_size = 200 + dataset = torchvision.datasets.MNIST( + root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + acc = 0. + total = 0. + for _, (x, y) in enumerate(loader): + x, y = Variable(x).to(device), Variable(y).to(device) + outcome = model(x) + _, pred = torch.max(outcome.data, 1) + acc += np.sum(pred.cpu().numpy() == y.cpu().numpy()) + total += y.size(0) + acc = acc/total + print('clean data accuracy for '+name+': ', acc) + acc_clean = acc + + # test accuary during pgd attack: epsilon = 0.5 + + batch_size = 100 + dataset = torchvision.datasets.MNIST( + root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + adversary = Adversary('pgd', device) + epsilon = 0.5 + step_size = epsilon / 25. + num_steps = 100 + acc = 0. + total = 0. + for _, (x, y) in enumerate(loader): + x, y = Variable(x).to(device), Variable(y).to(device) + delta = adversary.pgd(model, x, y, epsilon, step_size, num_steps).to(device) + outcome = model(x+delta) + _, pred = torch.max(outcome, 1) + acc += np.sum(pred.cpu().numpy() == y.cpu().numpy()) + total += y.size(0) + acc = acc/total + print('epsilon 0.5 pgd attack accuracy for '+name+': ', acc) + acc_pgd_05 = acc + + # test accuary during pgd attack: epsilon = 1. + + batch_size = 100 + dataset = torchvision.datasets.MNIST( + root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + adversary = Adversary('pgd', device) + epsilon = 1.0 + step_size = epsilon / 25. + num_steps = 100 + acc = 0. + total = 0. + for _, (x, y) in enumerate(loader): + x, y = Variable(x).to(device), Variable(y).to(device) + delta = adversary.pgd(model, x, y, epsilon, step_size, num_steps).to(device) + outcome = model(x+delta) + _, pred = torch.max(outcome, 1) + acc += np.sum(pred.cpu().numpy() == y.cpu().numpy()) + total += y.size(0) + acc = acc/total + print('epsilon 1.0 pgd attack accuracy for '+name+': ', acc) + acc_pgd_1 = acc + + # test accuary during gaussian perturbation: variance = 0.4 + + batch_size = 100 + dataset = torchvision.datasets.MNIST( + root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + var = 0.4 + acc = 0. + total = 0. + for _, (x, y) in enumerate(loader): + x, y = Variable(x).to(device), Variable(y).to(device) + x += torch.randn(x.size()).to(device) * var + outcome = model(x+delta) + _, pred = torch.max(outcome, 1) + acc += np.sum(pred.cpu().numpy() == y.cpu().numpy()) + total += y.size(0) + acc = acc/total + print('var 0.4 gaussian perturbation on '+name+': ', acc) + acc_var_04 = acc + + # test accuary during fog corruption: + + batch_size = 100 + dataset = torchvision.datasets.MNIST( + root='../../data/datasets/mnist/', train=False, download=True, transform=transform) + loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False) + + severity = 4 + acc = 0. + total = 0. + for _, (x, y) in enumerate(loader): + fog = adversary.fog(x, severity=severity).to(device) + x = x.to(device) + x, y = Variable(x+fog).to(device), Variable(y).to(device) + x += torch.randn(x.size()).to(device) * var + outcome = model(x+delta) + _, pred = torch.max(outcome, 1) + acc += np.sum(pred.cpu().numpy() == y.cpu().numpy()) + total += y.size(0) + acc = acc/total + print('fog corruption (Gilmer: MNIST-C) on '+name+': ', acc) + acc_fog = acc + + f.write("for model "+name+':\n') + f.write('clean data: '+str(acc_clean)+'\n') + f.write('pdg attack 0.5: '+str(acc_pgd_05)+'\n') + f.write('pgd_attack 1.0: '+str(acc_pgd_1)+'\n') + f.write('gaussian noise 0.4 var: '+str(acc_var_04)+'\n') + f.write('fog corruption: '+str(acc_fog)+"\n") +f.close() + diff --git a/models/ImageNet/dog.jpg b/models/ImageNet/dog.jpg new file mode 100644 index 0000000..12f0e0d Binary files /dev/null and b/models/ImageNet/dog.jpg differ diff --git a/models/ImageNet/models.py b/models/ImageNet/models.py new file mode 100644 index 0000000..6f5b22b --- /dev/null +++ b/models/ImageNet/models.py @@ -0,0 +1,431 @@ +from collections import namedtuple +import warnings +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.jit.annotations import Optional +from torch import Tensor +#from .utils import load_state_dict_from_url +from torch.utils.model_zoo import load_url as load_state_dict_from_url + + +__all__ = ['Inception3', 'inception_v3', 'InceptionOutputs', '_InceptionOutputs'] + + +model_urls = { + # Inception v3 ported from TensorFlow + 'inception_v3_google': 'https://download.pytorch.org/models/inception_v3_google-1a9a5a14.pth', +} + +InceptionOutputs = namedtuple('InceptionOutputs', ['logits', 'aux_logits']) +InceptionOutputs.__annotations__ = {'logits': torch.Tensor, 'aux_logits': Optional[torch.Tensor]} + +# Script annotations failed with _GoogleNetOutputs = namedtuple ... +# _InceptionOutputs set here for backwards compat +_InceptionOutputs = InceptionOutputs + + +def inception_v3(pretrained=False, progress=True, **kwargs): + r"""Inception v3 model architecture from + `"Rethinking the Inception Architecture for Computer Vision" `_. + .. note:: + **Important**: In contrast to the other models the inception_v3 expects tensors with a size of + N x 3 x 299 x 299, so ensure your images are sized accordingly. + Args: + pretrained (bool): If True, returns a model pre-trained on ImageNet + progress (bool): If True, displays a progress bar of the download to stderr + aux_logits (bool): If True, add an auxiliary branch that can improve training. + Default: *True* + transform_input (bool): If True, preprocesses the input according to the method with which it + was trained on ImageNet. Default: *False* + """ + if pretrained: + if 'transform_input' not in kwargs: + kwargs['transform_input'] = True + if 'aux_logits' in kwargs: + original_aux_logits = kwargs['aux_logits'] + kwargs['aux_logits'] = True + else: + original_aux_logits = True + model = Inception3(**kwargs) + state_dict = load_state_dict_from_url(model_urls['inception_v3_google'], + progress=progress) + model.load_state_dict(state_dict) + if not original_aux_logits: + model.aux_logits = False + del model.AuxLogits + return model + + return Inception3(**kwargs) + + +class Inception3(nn.Module): + + def __init__(self, num_classes=1000, aux_logits=True, transform_input=False, + inception_blocks=None, init_weights=True): + super(Inception3, self).__init__() + if inception_blocks is None: + inception_blocks = [ + BasicConv2d, InceptionA, InceptionB, InceptionC, + InceptionD, InceptionE, InceptionAux + ] + assert len(inception_blocks) == 7 + conv_block = inception_blocks[0] + inception_a = inception_blocks[1] + inception_b = inception_blocks[2] + inception_c = inception_blocks[3] + inception_d = inception_blocks[4] + inception_e = inception_blocks[5] + inception_aux = inception_blocks[6] + + self.aux_logits = aux_logits + self.transform_input = transform_input + self.Conv2d_1a_3x3 = conv_block(3, 32, kernel_size=3, stride=2) + self.Conv2d_2a_3x3 = conv_block(32, 32, kernel_size=3) + self.Conv2d_2b_3x3 = conv_block(32, 64, kernel_size=3, padding=1) + self.Conv2d_3b_1x1 = conv_block(64, 80, kernel_size=1) + self.Conv2d_4a_3x3 = conv_block(80, 192, kernel_size=3) + self.Mixed_5b = inception_a(192, pool_features=32) + self.Mixed_5c = inception_a(256, pool_features=64) + self.Mixed_5d = inception_a(288, pool_features=64) + self.Mixed_6a = inception_b(288) + self.Mixed_6b = inception_c(768, channels_7x7=128) + self.Mixed_6c = inception_c(768, channels_7x7=160) + self.Mixed_6d = inception_c(768, channels_7x7=160) + self.Mixed_6e = inception_c(768, channels_7x7=192) + if aux_logits: + self.AuxLogits = inception_aux(768, num_classes) + self.Mixed_7a = inception_d(768) + self.Mixed_7b = inception_e(1280) + self.Mixed_7c = inception_e(2048) + self.fc = nn.Linear(2048, num_classes) + if init_weights: + for m in self.modules(): + if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear): + import scipy.stats as stats + stddev = m.stddev if hasattr(m, 'stddev') else 0.1 + X = stats.truncnorm(-2, 2, scale=stddev) + values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype) + values = values.view(m.weight.size()) + with torch.no_grad(): + m.weight.copy_(values) + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + def _transform_input(self, x): + if self.transform_input: + x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5 + x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5 + x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5 + x = torch.cat((x_ch0, x_ch1, x_ch2), 1) + return x + + def _forward(self, x): + # N x 3 x 299 x 299 + x = self.Conv2d_1a_3x3(x) + # N x 32 x 149 x 149 + x = self.Conv2d_2a_3x3(x) + # N x 32 x 147 x 147 + x = self.Conv2d_2b_3x3(x) + # N x 64 x 147 x 147 + x = F.max_pool2d(x, kernel_size=3, stride=2) + # N x 64 x 73 x 73 + x = self.Conv2d_3b_1x1(x) + # N x 80 x 73 x 73 + x = self.Conv2d_4a_3x3(x) + # N x 192 x 71 x 71 + x = F.max_pool2d(x, kernel_size=3, stride=2) + # N x 192 x 35 x 35 + x = self.Mixed_5b(x) + # N x 256 x 35 x 35 + x = self.Mixed_5c(x) + # N x 288 x 35 x 35 + x = self.Mixed_5d(x) + # N x 288 x 35 x 35 + x = self.Mixed_6a(x) + # N x 768 x 17 x 17 + x = self.Mixed_6b(x) + # N x 768 x 17 x 17 + x = self.Mixed_6c(x) + # N x 768 x 17 x 17 + x = self.Mixed_6d(x) + # N x 768 x 17 x 17 + x = self.Mixed_6e(x) + # N x 768 x 17 x 17 + aux_defined = self.training and self.aux_logits + if aux_defined: + aux = self.AuxLogits(x) + else: + aux = None + # N x 768 x 17 x 17 + x = self.Mixed_7a(x) + # N x 1280 x 8 x 8 + x = self.Mixed_7b(x) + # N x 2048 x 8 x 8 + x = self.Mixed_7c(x) + # N x 2048 x 8 x 8 + # Adaptive average pooling + x = F.adaptive_avg_pool2d(x, (1, 1)) + # N x 2048 x 1 x 1 + x = F.dropout(x, training=self.training) + # N x 2048 x 1 x 1 + x = torch.flatten(x, 1) + # N x 2048 + x = self.fc(x) + # N x 1000 (num_classes) + return x, aux + + @torch.jit.unused + def eager_outputs(self, x, aux): + # type: (Tensor, Optional[Tensor]) -> InceptionOutputs + if self.training and self.aux_logits: + return InceptionOutputs(x, aux) + else: + return x + + def forward(self, x): + x = self._transform_input(x) + x, aux = self._forward(x) + aux_defined = self.training and self.aux_logits + if torch.jit.is_scripting(): + if not aux_defined: + warnings.warn("Scripted Inception3 always returns Inception3 Tuple") + return InceptionOutputs(x, aux) + else: + return self.eager_outputs(x, aux) + + +class InceptionA(nn.Module): + + def __init__(self, in_channels, pool_features, conv_block=None): + super(InceptionA, self).__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1x1 = conv_block(in_channels, 64, kernel_size=1) + + self.branch5x5_1 = conv_block(in_channels, 48, kernel_size=1) + self.branch5x5_2 = conv_block(48, 64, kernel_size=5, padding=2) + + self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) + self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) + self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, padding=1) + + self.branch_pool = conv_block(in_channels, pool_features, kernel_size=1) + + def _forward(self, x): + branch1x1 = self.branch1x1(x) + + branch5x5 = self.branch5x5_1(x) + branch5x5 = self.branch5x5_2(branch5x5) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch5x5, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x): + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionB(nn.Module): + + def __init__(self, in_channels, conv_block=None): + super(InceptionB, self).__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch3x3 = conv_block(in_channels, 384, kernel_size=3, stride=2) + + self.branch3x3dbl_1 = conv_block(in_channels, 64, kernel_size=1) + self.branch3x3dbl_2 = conv_block(64, 96, kernel_size=3, padding=1) + self.branch3x3dbl_3 = conv_block(96, 96, kernel_size=3, stride=2) + + def _forward(self, x): + branch3x3 = self.branch3x3(x) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = self.branch3x3dbl_3(branch3x3dbl) + + branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) + + outputs = [branch3x3, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x): + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionC(nn.Module): + + def __init__(self, in_channels, channels_7x7, conv_block=None): + super(InceptionC, self).__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1x1 = conv_block(in_channels, 192, kernel_size=1) + + c7 = channels_7x7 + self.branch7x7_1 = conv_block(in_channels, c7, kernel_size=1) + self.branch7x7_2 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7_3 = conv_block(c7, 192, kernel_size=(7, 1), padding=(3, 0)) + + self.branch7x7dbl_1 = conv_block(in_channels, c7, kernel_size=1) + self.branch7x7dbl_2 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7dbl_3 = conv_block(c7, c7, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7dbl_4 = conv_block(c7, c7, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7dbl_5 = conv_block(c7, 192, kernel_size=(1, 7), padding=(0, 3)) + + self.branch_pool = conv_block(in_channels, 192, kernel_size=1) + + def _forward(self, x): + branch1x1 = self.branch1x1(x) + + branch7x7 = self.branch7x7_1(x) + branch7x7 = self.branch7x7_2(branch7x7) + branch7x7 = self.branch7x7_3(branch7x7) + + branch7x7dbl = self.branch7x7dbl_1(x) + branch7x7dbl = self.branch7x7dbl_2(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_3(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_4(branch7x7dbl) + branch7x7dbl = self.branch7x7dbl_5(branch7x7dbl) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch7x7, branch7x7dbl, branch_pool] + return outputs + + def forward(self, x): + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionD(nn.Module): + + def __init__(self, in_channels, conv_block=None): + super(InceptionD, self).__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch3x3_1 = conv_block(in_channels, 192, kernel_size=1) + self.branch3x3_2 = conv_block(192, 320, kernel_size=3, stride=2) + + self.branch7x7x3_1 = conv_block(in_channels, 192, kernel_size=1) + self.branch7x7x3_2 = conv_block(192, 192, kernel_size=(1, 7), padding=(0, 3)) + self.branch7x7x3_3 = conv_block(192, 192, kernel_size=(7, 1), padding=(3, 0)) + self.branch7x7x3_4 = conv_block(192, 192, kernel_size=3, stride=2) + + def _forward(self, x): + branch3x3 = self.branch3x3_1(x) + branch3x3 = self.branch3x3_2(branch3x3) + + branch7x7x3 = self.branch7x7x3_1(x) + branch7x7x3 = self.branch7x7x3_2(branch7x7x3) + branch7x7x3 = self.branch7x7x3_3(branch7x7x3) + branch7x7x3 = self.branch7x7x3_4(branch7x7x3) + + branch_pool = F.max_pool2d(x, kernel_size=3, stride=2) + outputs = [branch3x3, branch7x7x3, branch_pool] + return outputs + + def forward(self, x): + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionE(nn.Module): + + def __init__(self, in_channels, conv_block=None): + super(InceptionE, self).__init__() + if conv_block is None: + conv_block = BasicConv2d + self.branch1x1 = conv_block(in_channels, 320, kernel_size=1) + + self.branch3x3_1 = conv_block(in_channels, 384, kernel_size=1) + self.branch3x3_2a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3_2b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch3x3dbl_1 = conv_block(in_channels, 448, kernel_size=1) + self.branch3x3dbl_2 = conv_block(448, 384, kernel_size=3, padding=1) + self.branch3x3dbl_3a = conv_block(384, 384, kernel_size=(1, 3), padding=(0, 1)) + self.branch3x3dbl_3b = conv_block(384, 384, kernel_size=(3, 1), padding=(1, 0)) + + self.branch_pool = conv_block(in_channels, 192, kernel_size=1) + + def _forward(self, x): + branch1x1 = self.branch1x1(x) + + branch3x3 = self.branch3x3_1(x) + branch3x3 = [ + self.branch3x3_2a(branch3x3), + self.branch3x3_2b(branch3x3), + ] + branch3x3 = torch.cat(branch3x3, 1) + + branch3x3dbl = self.branch3x3dbl_1(x) + branch3x3dbl = self.branch3x3dbl_2(branch3x3dbl) + branch3x3dbl = [ + self.branch3x3dbl_3a(branch3x3dbl), + self.branch3x3dbl_3b(branch3x3dbl), + ] + branch3x3dbl = torch.cat(branch3x3dbl, 1) + + branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1) + branch_pool = self.branch_pool(branch_pool) + + outputs = [branch1x1, branch3x3, branch3x3dbl, branch_pool] + return outputs + + def forward(self, x): + outputs = self._forward(x) + return torch.cat(outputs, 1) + + +class InceptionAux(nn.Module): + + def __init__(self, in_channels, num_classes, conv_block=None): + super(InceptionAux, self).__init__() + if conv_block is None: + conv_block = BasicConv2d + self.conv0 = conv_block(in_channels, 128, kernel_size=1) + self.conv1 = conv_block(128, 768, kernel_size=5) + self.conv1.stddev = 0.01 + self.fc = nn.Linear(768, num_classes) + self.fc.stddev = 0.001 + + def forward(self, x): + # N x 768 x 17 x 17 + x = F.avg_pool2d(x, kernel_size=5, stride=3) + # N x 768 x 5 x 5 + x = self.conv0(x) + # N x 128 x 5 x 5 + x = self.conv1(x) + # N x 768 x 1 x 1 + # Adaptive average pooling + x = F.adaptive_avg_pool2d(x, (1, 1)) + # N x 768 x 1 x 1 + x = torch.flatten(x, 1) + # N x 768 + x = self.fc(x) + # N x 1000 + return x + + +class BasicConv2d(nn.Module): + + def __init__(self, in_channels, out_channels, **kwargs): + super(BasicConv2d, self).__init__() + self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs) + self.bn = nn.BatchNorm2d(out_channels, eps=0.001) + + def forward(self, x): + x = self.conv(x) + x = self.bn(x) + return F.relu(x, inplace=True) diff --git a/models/ImageNet/test_model.py b/models/ImageNet/test_model.py new file mode 100644 index 0000000..98e7528 --- /dev/null +++ b/models/ImageNet/test_model.py @@ -0,0 +1,46 @@ +from models import inception_v3, Inception3 +import torch +import urllib +url, filename = ("https://github.com/pytorch/hub/raw/master/dog.jpg", "dog.jpg") +try: urllib.URLopener().retrieve(url, filename) +except: urllib.request.urlretrieve(url, filename) +from PIL import Image +from torchvision import transforms + +input_image = Image.open(filename) +preprocess = transforms.Compose([ + transforms.Resize(299), + transforms.CenterCrop(299), + transforms.ToTensor(), + transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), +]) +input_tensor = preprocess(input_image) +input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model + +print('commencing loading model') + +model = Inception3() +state_dict = torch.load('pretrained_models/inception_v3_torchvision.pth') +model.load_state_dict(state_dict) +model.eval() +print('loaded!') + +# move the input and model to GPU for speed if available +if torch.cuda.is_available(): + input_batch = input_batch.to('cuda') + model.to('cuda') + +with torch.no_grad(): + output = model(input_batch) + +print('output shape: ', output[0].shape) +print('output shape ', output.shape) + +# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes +print(output[0]) +# The output has unnormalized scores. To get probabilities, you can run a softmax on it. +print(torch.nn.functional.softmax(output[0], dim=0)) + + + + diff --git a/models/cifar10/models.py b/models/cifar10/models.py index 87322b5..f3b7f9d 100644 --- a/models/cifar10/models.py +++ b/models/cifar10/models.py @@ -154,9 +154,131 @@ def forward(self, x): nn.Linear(100, 10)) +######### RESNET MODELS ############################# +__all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202'] + +def _weights_init(m): + classname = m.__class__.__name__ + #print(classname) + if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d): + init.kaiming_normal_(m.weight) + +class LambdaLayer(nn.Module): + def __init__(self, lambd): + super(LambdaLayer, self).__init__() + self.lambd = lambd + + def forward(self, x): + return self.lambd(x) + + +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1, option='A'): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + + self.shortcut = nn.Sequential() + if stride != 1 or in_planes != planes: + if option == 'A': + """ + For CIFAR10 ResNet paper uses option A. + """ + self.shortcut = LambdaLayer(lambda x: + F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0)) + elif option == 'B': + self.shortcut = nn.Sequential( + nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class ResNet(nn.Module): + def __init__(self, block, num_blocks, num_classes=10): + super(ResNet, self).__init__() + self.in_planes = 16 + + self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(16) + self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2) + self.linear = nn.Linear(64, num_classes) + + self.apply(_weights_init) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1]*(num_blocks-1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + + return nn.Sequential(*layers) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = F.avg_pool2d(out, out.size()[3]) + out = out.view(out.size(0), -1) + out = self.linear(out) + return out + + +def resnet20(): + return ResNet(BasicBlock, [3, 3, 3]) + + +def resnet32(): + return ResNet(BasicBlock, [5, 5, 5]) + + +def resnet44(): + return ResNet(BasicBlock, [7, 7, 7]) + + +def resnet56(): + return ResNet(BasicBlock, [9, 9, 9]) + + +def resnet110(): + return ResNet(BasicBlock, [18, 18, 18]) + + +def resnet1202(): + return ResNet(BasicBlock, [200, 200, 200]) + + +def test(net): + import numpy as np + total_params = 0 + + for x in filter(lambda p: p.requires_grad, net.parameters()): + total_params += np.prod(x.data.numpy().shape) + print("Total number of params", total_params) + print("Total layers", len(list(filter(lambda p: p.requires_grad and len(p.data.size())>1, net.parameters())))) +if __name__ == "__main__": + for net_name in __all__: + if net_name.startswith('resnet'): + print(net_name) + test(globals()[net_name]()) + print() diff --git a/models/mnist/models.py b/models/mnist/models.py new file mode 100644 index 0000000..ae81270 --- /dev/null +++ b/models/mnist/models.py @@ -0,0 +1,133 @@ +import torch +import torch.nn as nn + +import numpy as np + +class LeNet5(nn.Module): + def __init__(self): + super(LeNet5, self).__init__() + self.conv1 = nn.Conv2d(1, 32, kernel_size=3, padding=1, stride=1) + self.relu1 = nn.ReLU(inplace=True) + self.maxpool1 = nn.MaxPool2d(2) + self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1) + self.relu2 = nn.ReLU(inplace=True) + self.maxpool2 = nn.MaxPool2d(2) + self.linear1 = nn.Linear(7*7*64, 200) + self.relu3 = nn.ReLU(inplace=True) + self.linear2 = nn.Linear(200, 10) + + def forward(self, x, pert=None): + if pert == None: + pert = torch.zeros(len(x), 32, 14, 14).to('cuda:0') + out = self.maxpool1(self.relu1(self.conv1(x))) + out = self.maxpool2(self.relu2(self.conv2(out))) + #out = self.maxpool2(self.relu2(self.conv2(out))) + out = out.view(out.size(0), -1) + out = self.relu3(self.linear1(out)) + out = self.linear2(out) + return out + + +class SubstituteModel(nn.Module): + + def __init__(self): + super(SubstituteModel, self).__init__() + self.linear1 = nn.Linear(28*28, 200) + self.relu1 = nn.ReLU(inplace=True) + self.linear2 = nn.Linear(200, 200) + self.relu2 = nn.ReLU(inplace=True) + self.linear3 = nn.Linear(200, 10) + + def forward(self, x): + out = x.view(x.size(0), -1) + out = self.relu1(self.linear1(out)) + out = self.relu2(self.linear2(out)) + out = self.linear3(out) + return out + + +# some simpler models +class Flatten(nn.Module): + def forward(self, x): + return x.view(x.shape[0], -1) + + + +DNN2 = nn.Sequential(Flatten(), nn.Linear(784, 200), nn.ReLU(), + nn.Linear(200, 10)) + + +""" +DNN4 = nn.Sequential(Flatten(), nn.Linear(784, 200), nn.ReLU(), + nn.Linear(200, 100), nn.ReLU(), + nn.Linear(100, 100), nn.ReLU(), + nn.Linear(100, 10)) + + +""" +""" +CNN = nn.Sequential(nn.Conv2d(1, 32, 3, padding=1), nn.ReLU(), + nn.Conv2d(32, 32, 3, padding=1, stride=2), nn.ReLU(), + nn.Conv2d(32, 64, 3, padding=1), nn.ReLU(), + nn.Conv2d(64, 64, 3, padding=1, stride=2), nn.ReLU(), + Flatten(), + nn.Linear(7*7*64, 100), nn.ReLU(), + nn.Linear(100, 10)) +""" + + +class DNN4(nn.Module): + def __init__(self): + super(DNN4, self).__init__() + self.linear1 = nn.Linear(784, 200) + self.relu1 = nn.ReLU() + self.linear2 = nn.Linear(200, 100) + self.relu2 = nn.ReLU() + self.linear3 = nn.Linear(100, 100) + self.relu3 = nn.ReLU() + self.linear4 = nn.Linear(100, 10) + + def forward(self, x, pert=None): + x = x.view(x.shape[0], -1) + x = self.relu1(self.linear1(x)) + x = self.relu2(self.linear2(x)) + x = self.relu3(self.linear3(x)) + x = self.linear4(x+pert) + return x + + + +class CNN(nn.Module): + def __init__(self): + super(CNN, self).__init__() + self.conv1 = nn.Conv2d(1, 32, 3, padding=1) + self.relu1 = nn.ReLU() + self.conv2 = nn.Conv2d(32, 32, 3, padding=1, stride=2) + self.relu2 = nn.ReLU() + self.conv3 = nn.Conv2d(32, 64, 3, padding=1) + self.relu3 = nn.ReLU() + self.conv4 = nn.Conv2d(64, 64, 3, padding=1, stride=2) + self.relu4 = nn.ReLU() + self.linear1 = nn.Linear(7*7*64, 100) + self.relu5 = nn.ReLU() + self.linear2 = nn.Linear(100, 10) + + + def forward(self, x, pert=None): + x = self.relu1(self.conv1(x)) + print('layer12: ', x.size()) + x = self.relu2(self.conv2(x)) + print('layer23: ', x.size()) + x = self.relu3(self.conv3(x)) + print('layer34: ', x.size()) + x = self.relu4(self.conv4(x)) + print('layer45: ', x.size()) + x = x.view(x.shape[0], -1) + print('layer56: ', x.size()) + x = self.relu5(self.linear1(x)) + print('layer67: ', x.size()) + x = self.linear2(x) + print('-------------------------') + return x + + diff --git a/tests/dist_acc_test.py b/tests/dist_acc_test.py new file mode 100644 index 0000000..7f64fdf --- /dev/null +++ b/tests/dist_acc_test.py @@ -0,0 +1,119 @@ +import torch +import torch.nn as nn +import torchvision +import torchvision.transforms as transforms +from collections import OrderedDict +import sys +import numpy as np +sys.path += ['../'] +sys.path += ['../utils/'] +from models.cifar10.models import Wide_ResNet, resnet110, resnet20, resnet32 +from models.cifar10.models import resnet44, resnet56, resnet1202 +import config as cf +from attacks import * +from torch.nn.parallel import DistributedDataParallel as DDP + +def mean(liste): + summe = 0. + for el in liste: + summe += el + return summe/len(liste) + +get_accuracy = False +get_distances = True +device = torch.device("cuda") +num_examples = 10 +batch_size = 10 +eps = 1. +alpha = 1e-3 +max_iter = 10000 +steps = np.zeros(num_examples) +criterion = nn.CrossEntropyLoss() +adv = Adversary('fgsm', device) +model_address = '../models/cifar10/trained_models/noisy_0.1_WideResNet_28_10_run_1.pth' +#model_address = '../models/cifar10/trained_models/ResNet_1202.th' + + +state_dict = torch.load(model_address) + +""" +####### the following is for ResNet loading only ##### +new_state_dict = OrderedDict() +for k, v in state_dict['state_dict'].items(): + name = k[7:] + new_state_dict[name] = v +###################################################### +""" + +model = Wide_ResNet(28, 10, 0.3, 10).to(device) +model.load_state_dict(state_dict) + +#model = resnet1202().to(device) +#model.load_state_dict(new_state_dict) + +model.eval() + +transform = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(cf.mean['cifar10'], cf.std['cifar10']), + ]) + +dataset = torchvision.datasets.CIFAR10(root='../data/datasets/cifar10', train=False, download=False, transform=transform) +loader = torch.utils.data.DataLoader(dataset, batch_size=num_examples, shuffle=False) +test_loader = torch.utils.data.DataLoader(dataset, batch_size = 100, shuffle=False) + +it = iter(loader) +data = next(it) +x, y = data[0].to(device), data[1].to(device) + +if get_distances: + distances = [] + step_counter = [] + + for i in range(int(num_examples/batch_size)): + hold, _, steps = adv.get_distances(model, x[i*batch_size:(i+1)*batch_size], y[i*batch_size:(i+1)*batch_size], device, + eps=eps, alpha=alpha, max_iter=max_iter) + distances += hold.tolist() + step_counter += steps.tolist() + print('recently found distances: ', hold.tolist()) + print('in eps {} iteration {}/{} with mean {}'.format(eps, i+1, int(num_examples/batch_size), mean(distances[-batch_size:]))) + + print('distances: ', distances) + print('steps taken: ', step_counter) + + avg_distances = mean(distances) + + f = open('distances.txt', 'w') + for i in range(num_examples): + f.write(str(eps)+' '+str(distances[i])+' '+str(step_counter[i])+'\n') + f.close() + + +if get_accuracy: + with torch.no_grad(): + # testing + model.eval() + #self.training = False + loss = 0. + correct = 0 + total = 0 + for curr_batch, (x, y) in enumerate(test_loader): + x_var, y_var = Variable(x), Variable(y) + x_var, y_var = x_var.to(device), y_var.to(device) + outcome = model(x_var) + curr_loss = criterion(outcome, y_var) + loss += curr_loss + _, pred = torch.max(outcome.data, 1) + correct += pred.eq(y_var.data).cpu().sum() + total += y_var.size(0) + acc = 100.*correct/total + print("\n \t\t\tLoss: %.4f Acc@1: %.2f%%" %(loss.item(), acc)) + + f = open('accuracy.txt', 'w') + f.write(str(acc.item())) + f.close() + + +print('Experiment conducted, find results in saves!') diff --git a/tests/saves/WRN_acc.txt b/tests/saves/WRN_acc.txt new file mode 100644 index 0000000..a57840c --- /dev/null +++ b/tests/saves/WRN_acc.txt @@ -0,0 +1 @@ +94.56999969482422 diff --git a/tests/saves/WRN_dist.txt b/tests/saves/WRN_dist.txt new file mode 100644 index 0000000..644b7c9 --- /dev/null +++ b/tests/saves/WRN_dist.txt @@ -0,0 +1,1000 @@ +1.0 1.3554108142852783 30 +1.0 0.9447942972183228 20 +1.0 0.6061559319496155 12 +1.0 0.6445476412773132 13 +1.0 2.663245439529419 62 +1.0 1.2554000616073608 29 +1.0 1.1615839004516602 24 +1.0 0.9550446271896362 20 +1.0 1.9009852409362793 45 +1.0 0.8099802136421204 16 +1.0 0.7816656231880188 17 +1.0 3.4945106506347656 83 +1.0 0.7001088261604309 14 +1.0 2.7277743816375732 61 +1.0 1.4359266757965088 30 +1.0 0.943139374256134 19 +1.0 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torchvision.transforms as transforms +from torch.autograd import Variable +#from torch.utils.tensorboard import SummaryWriter +import torch.nn.functional as F +import time +import numpy as np + +from attacks import Adversary + +from models import LeNet5, DNN2, DNN4, CNN + +# parameters +param = { + 'train_batch_size': 64, + 'test_batch_size': 100, + 'num_epochs': 50, + 'delay': 10, + 'learning_rate': 1e-3, + 'weight_decay': 5e-4, + 'num_runs': 1, + #training types: 'normal', 'noisy', 'adversarial' + 'training_type': 'adversarial', + 'save_filename': 'save_results.txt', + # available strategies: 'fgsm', 'pgd', 'pgd_linf', 'pgd_linf_rand' + 'attack': 'random_walk', + # variance for noisy training + 'variance': 0.8 +} + + +class Experiment: + def __init__(self, param): + self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.train_batch_size = param['train_batch_size'] + self.test_batch_size = param['test_batch_size'] + self.num_epochs = param['num_epochs'] + self.curr_epoch = None + self.curr_batch = None + self.learning_rate = param['learning_rate'] + self.weight_decay = param['weight_decay'] + self.num_runs = param['num_runs'] + self.training_type = param['training_type'] + # attacks and data; also provides method 'perturb' to add noise to data + self.adversary = Adversary(param['attack'], self.device) + self.adv_num_examples = 4 + self.adv_step_size = 0.25 + self.adv_max_iter = 100 + self.attack = param['attack'] + self.variance = param['variance'] + self.model = None + self.optimizer = None + self.criterion = None + self.train_loader = None + self.test_loader = None + # self.writer = None + self.model_save_path = './trained_models/' + + + def save_model(self): + torch.save(self.model.state_dict(), self.model_save_path+'adversarial_random_'+str(self.adv_num_examples)+'_ex_LeNet.pth') + + + def conduct(self): + for self.curr_run in range(1, self.num_runs+1): + self.reset() + self.train() + self.save_model() + print('run {} finished!'.format(self.curr_run)) + print('model trained and saved!') + return + + + def reset(self): + self.load_model() + self.load_data() + self.curr_epoch = None + self.curr_batch = None + + + def load_data(self): + train_dataset = MNIST(root='../data/',train=True, download=True, + transform=transforms.ToTensor()) + self.train_loader = torch.utils.data.DataLoader(train_dataset, + batch_size=self.train_batch_size, shuffle=True) + test_dataset = MNIST(root='../data/', train=False, download=True, + transform=transforms.ToTensor()) + self.test_loader = torch.utils.data.DataLoader(test_dataset, + batch_size=self.test_batch_size, shuffle=True) + + + def load_model(self): + self.model = LeNet5().to(self.device) + # self.model = DNN2.to(self.device) + # self.model = DNN4.to(self.device) + # self.model = CNN() + # self.model = DNN4() + self.model = self.model.to(self.device) + # self.adversary = LinfPGDAttack() + self.criterion = nn.CrossEntropyLoss() + self.optimizer = torch.optim.RMSprop(self.model.parameters(), lr=self.learning_rate, + weight_decay=self.weight_decay) + + # (also does the testing) + def train(self): + + # regular training + self.model.train() + + for self.curr_epoch in range(1, self.num_epochs+1): + train_loss = 0. + train_correct = 0 + total = 0 + + for self.curr_batch, (x, y) in enumerate(self.train_loader): + x, y = x.to(self.device), y.to(self.device) + # perturb data during noisy training + if self.training_type == 'noisy': + perturbation = torch.randn(x.size()).to(self.device)*self.variance + x += perturbation + x_var, y_var = Variable(x).to(self.device), Variable(y).to(self.device) + outcome = self.model(x_var) + prediction = torch.max(outcome, 1) + train_correct += np.sum(prediction[1].cpu().numpy() == y_var.cpu().numpy()) + total += y_var.size(0) + loss = self.criterion(outcome, y_var) + train_loss += loss.item() + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + # add training on adversarial perturbation during adv training + if self.training_type == 'adversarial': + x, y = self.adversary.get_adversarial_examples( + self.model, x, y, + step_size=self.adv_step_size, + num_examples=self.adv_num_examples, + max_iter=self.adv_max_iter) + x_var, y_var = Variable(x).to(self.device), Variable(y).to(self.device) + outcome = self.model(x_var) + prediction = torch.max(outcome, 1) + train_correct += np.sum(prediction[1].cpu().numpy() == y_var.cpu().numpy()) + total += y_var.size(0) + loss = self.criterion(outcome, y_var) + train_loss += loss.item() + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + + train_correct, train_loss = train_correct/total, train_loss/total + with torch.no_grad(): + # testing + test_loss = 0. + test_correct = 0 + total = 0 + for self.curr_batch, (x, y) in enumerate(self.test_loader): + #x, y = x.to(self.device), y.to(self.device) + x_var, y_var = Variable(x).to(self.device), Variable(y).to(self.device) + outcome = self.model(x_var) + loss = self.criterion(outcome, y_var) + test_loss += loss.item() + prediction = torch.max(outcome, 1) + test_correct += np.sum(prediction[1].cpu().numpy() == y_var.cpu().numpy()) + total += y_var.size(0) + test_correct, test_loss = test_correct / total, test_loss / total + + print('epoch {}/{}, train loss: {}, val loss: {}'.format( + self.curr_epoch, self.num_epochs, train_loss, test_loss)) + + print('train correct: {}, test correct: {}'.format(train_correct, test_correct)) + # self.save(train_loss, train_correct.item(), test_loss, test_correct.item(), spikiness) + # self.write_tb(train_loss, train_correct, test_loss, test_correct, spikiness, avg_cap, avg_vol) + + + def save(self, train_loss, train_class, test_loss, test_class, spikiness): + self.save_quantities.run += [self.curr_run] + self.save_quantities.epoch += [self.curr_epoch] + self.save_quantities.train_loss += [train_loss] + self.save_quantities.train_class += [train_class] + self.save_quantities.test_loss += [test_loss] + self.save_quantities.test_class += [test_class] + self.save_quantities.spikiness += [spikiness] + + + def write_tb(self, train_loss, train_correct, test_loss, test_correct, spikiness, avg_cap, avg_vol): + self.writer.add_scalar('Loss/train', train_loss, self.curr_epoch) + self.writer.add_scalar('Loss/test', test_loss, self.curr_epoch) + self.writer.add_scalar('Accuracy/train', train_correct, self.curr_epoch) + self.writer.add_scalar('Accuracy/test', test_correct, self.curr_epoch) + + +experiment = Experiment(param) +experiment.conduct() + + diff --git a/utils/attacks.py b/utils/attacks.py index 7550e4e..62f498d 100644 --- a/utils/attacks.py +++ b/utils/attacks.py @@ -84,7 +84,6 @@ def pgd_linf_rand(self, model, x, y, eps, alpha, num_iter, restarts): max_loss = torch.max(max_loss, all_loss) return max_delta - # return array with l_2 distances to closest adv examples of x def get_distances(self, model, x, y, device, eps=0.1, alpha=1.0e-2, max_iter=1000): # travel in adversarial direction until adversary is found @@ -95,20 +94,21 @@ def get_distances(self, model, x, y, device, eps=0.1, alpha=1.0e-2, max_iter=100 step_counter = torch.zeros_like(y).to(device) distances = torch.zeros(y.size()).to(device).float() delta = torch.zeros_like(x, requires_grad=True, device=self.device) - org_x = x + org_x = x for i in range(max_iter): x.requires_grad = True model.zero_grad() loss = self.criterion(model(x), y).to(device) loss.backward() - adv_x = x + alpha * x.grad.sign() + adv_x = x + alpha * x.grad.sign() eta = torch.clamp(adv_x - org_x, min=-eps, max=eps) x = (org_x + eta).detach_() # eval current predictions _, pred = torch.max(model(x).data, 1) - killed = (pred != y) * (tracker == 0) + killed = pred != y + killed[tracker == 1] = False tracker[killed] = 1 step_counter[killed] = i+1 distances[killed] = torch.norm((x - org_x).view(len(y), -1), dim=1)[killed] @@ -118,6 +118,50 @@ def get_distances(self, model, x, y, device, eps=0.1, alpha=1.0e-2, max_iter=100 return distances, tracker, step_counter + + # return array with l_2 distances to closest adv examples of x + def get_sliced_distances(self, model, x, pert, y, device, eps=0.1, alpha=1.0e-2, max_iter=1000): + # travel in adversarial direction until adversary is found + # using pgd_linf method + self.device = device + # 0: not yet found, 1: found + tracker = torch.zeros_like(y).to(device) + step_counter = torch.zeros_like(y).to(device) + distances = torch.zeros(y.size()).to(device).float() + #delta = torch.zeros_like(x, requires_grad=True, device=self.device) + #delta = torch.zeros_like(pert, requires_grad=True, device=self.device) + + layer_dim = len(pert.view(len(y),-1)[0]) + delta = pert.to(device) + org_delta = pert.to(device) + + for i in range(max_iter): + delta.requires_grad = True + #x.requires_grad = False + model.zero_grad() + loss = self.criterion(model(x, pert=delta), y).to(device) + loss.backward() + adv_delta = delta + alpha * delta.grad.sign() + eta = torch.clamp(org_delta - adv_delta, min=-eps, max=eps) + delta = (org_delta + eta).detach_() + #adv_x = x + alpha * x.grad.sign() + #eta = torch.clamp(adv_x - org_x, min=-eps, max=eps) + #x = (org_x + eta).detach_() + # eval current predictions + _, pred = torch.max(model(x, pert=delta).data, 1) + killed = pred != y + killed[tracker == 1] = False + tracker[killed] = 1 + step_counter[killed] = i+1 + distances[killed] = torch.norm(delta.view(len(y), -1), dim=1)[killed] + #distances[killed] = torch.norm((x - org_x).view(len(y), -1), dim=1)[killed] + #print('iter: ', i) + #print('tracker: ', tracker) + #print('distances: ', distances) + if sum(tracker) == len(tracker): + break + + return distances, tracker, step_counter def perturb(self, data, device, variance): return data + torch.randn(data.size()).to(device)*np.sqrt(variance) diff --git a/utils/utils.py b/utils/utils.py index b740592..4ea3248 100644 --- a/utils/utils.py +++ b/utils/utils.py @@ -128,7 +128,27 @@ def get_one_cap(net, x, y, device, step, num_steps, num_walks, j): if killed_walks.sum() == 0.: break cap = (num_walks - killed_walks.sum()) / num_walks - print('Example ', j, ' Capacity ', cap.item()) + #print('Example ', j, ' Capacity ', cap.item()) + return cap.item() + + +def get_one_sliced_cap(net, x, init_walk, y, device, step, num_steps, num_walks, j): + + walks_in_layer = expand(num_walks, device, init_walk)[0] + + points, labels = expand(num_walks, device, x, y) + killed_walks = torch.ones(num_walks).to(device) + + for i in range(num_steps): + #walks = walks + torch.randn(walks.size(), device=device) * step + walks_in_layer = walks_in_layer + torch.randn(walks_in_layer.size(), device=device) * step + outcome = net(points, pert=walks_in_layer) + _, pred = torch.max(outcome.data, 1) + killed_walks[pred != labels] *= 0. + if killed_walks.sum() == 0.: + break + cap = (num_walks - killed_walks.sum()) / num_walks + #print('Example ', j, ' Capacity ', cap.item()) return cap.item() @@ -143,19 +163,50 @@ def get_vols(net, x, y, device, radius, num_samples): # new volume method (sampling unformly from ball) -def get_one_vol(net, x, y, device, radius, num_samples): - dim = 3*32*32 - example = expand(num_samples, device, x)[0] - label = expand(num_samples, device, y)[0] +def get_one_vol(net, x, y, device, radius, num_samples, sample_full_ball=False): + """ + x: 1 x dim + y: 1 x num_labels + """ + dim = 28*28 + + if sample_full_ball: + sample_step = torch.randn(num_samples, dim + 2).to(device) + sample_step = radius * sample_step / sample_step.norm(dim=1).unsqueeze(1) + sample_step = sample_step[:, :-2] + else: + sample_step = radius * torch.randn(num_samples, dim).to(device) + + sample_step = sample_step.view(num_samples, 1, 28, 28) - sample_step = torch.randn(num_samples, dim + 2).to(device) - sample_step = radius * sample_step / sample_step.norm(dim=1).unsqueeze(1) - sample_step = sample_step[:, :-2] - sample_step = sample_step.view(num_samples, 3, 32, 32) + _, pred = torch.max(net(x + sample_step), 1) + correct = pred.eq(y).sum() + vol = (num_samples - correct.item()) / num_samples + + return vol + + +# new volume method (sampling unformly from ball) +def get_one_sliced_vol(net, x, curr_dim, y, device, radius, num_samples, sample_full_ball=False): + """ + x: 1 x dim + y: 1 x num_labels + """ + dim = len(curr_dim.view(1,-1)[0]) + curr_dim = expand(num_samples, device, curr_dim)[0] + + if sample_full_ball: + sample_step = torch.randn(num_samples, dim + 2).to(device) + sample_step = radius * sample_step / sample_step.norm(dim=1).unsqueeze(1) + sample_step = sample_step[:, :-2] + else: + sample_step = radius * torch.randn(num_samples, dim).to(device) + + sample_step = sample_step.resize_(curr_dim.size()) + #sample_step = sample_step.view(num_samples, 1, 28, 28) - outcome = net(example + sample_step) - _, pred = torch.max(outcome.data, 1) - correct = pred.eq(label.data).sum() + _, pred = torch.max(net(x, sample_step), 1) + correct = pred.eq(y).sum() vol = (num_samples - correct.item()) / num_samples return vol