From c8935c6d22cd76e137b1a9b1b3ea67f7d234601d Mon Sep 17 00:00:00 2001 From: Kaidi Xu Date: Tue, 29 Dec 2020 23:20:41 -0800 Subject: [PATCH] Jan 2021 Release Co-authored-by: Zhouxing Shi Co-authored-by: Huan Zhang Co-authored-by: Yihan Wang --- README.md | 4 +- auto_LiRPA/bound_general.py | 67 ++++-- auto_LiRPA/bound_ops.py | 27 ++- examples/language/train.py | 19 +- examples/sequence/train.py | 112 ++++++---- examples/vision/cifar_training.py | 16 +- examples/vision/imagenet_training.py | 12 +- examples/vision/models/__init__.py | 1 + examples/vision/models/feedforward.py | 30 +++ examples/vision/models/resnet.py | 18 +- examples/vision/models/resnet18.py | 26 +-- examples/vision/patch_convolution.py | 35 +-- examples/vision/pretrain/kw_mnist.pth | Bin 0 -> 666692 bytes examples/vision/simple_verification.py | 79 +++---- examples/vision/tinyimagenet_training.py | 12 +- examples/vision/weights_training.py | 102 ++++----- tests/data/.gitignore | 3 + .../weight_ptb.pkl => bound_ops_data} | Bin 40150 -> 40255 bytes tests/data/language.pkl | Bin 914889 -> 0 bytes tests/data/resnet_patches_test_data | Bin 0 -> 1623 bytes tests/test_bound_ops.py | 208 +++++++++--------- tests/test_constant.py | 60 +++-- tests/test_conv.py | 78 ++++--- tests/test_linear_cnn_model.py | 145 ++++-------- tests/test_linear_model.py | 191 +++++++--------- tests/test_resnet_patches.py | 185 ++++------------ tests/test_state_dict_name.py | 39 ++-- tests/test_vision_models.py | 3 - tests/test_weight_perturbation.py | 191 +++++++--------- tests/testcase.py | 61 +++++ 30 files changed, 812 insertions(+), 912 deletions(-) create mode 100644 examples/vision/pretrain/kw_mnist.pth rename tests/data/{bound_ops/weight_ptb.pkl => bound_ops_data} (89%) delete mode 100644 tests/data/language.pkl create mode 100644 tests/data/resnet_patches_test_data create mode 100644 tests/testcase.py diff --git a/README.md b/README.md index 4d3bb09..18ed711 100644 --- a/README.md +++ b/README.md @@ -115,10 +115,10 @@ model = BoundedModule(model, my_input) ptb = PerturbationLpNorm(norm=np.inf, eps=0.1) # Make the input a BoundedTensor with perturbation my_input = BoundedTensor(my_input, ptb) -# Forward propagation using BoundedTensor +# Regular forward propagation using BoundedTensor works as usual. prediction = model(my_input) # Compute LiRPA bounds -lb, ub = model.compute_bounds(method="backward") +lb, ub = model.compute_bounds(x=(my_input,), method="backward") ``` Checkout diff --git a/auto_LiRPA/bound_general.py b/auto_LiRPA/bound_general.py index 9031a45..3ee349e 100644 --- a/auto_LiRPA/bound_general.py +++ b/auto_LiRPA/bound_general.py @@ -13,7 +13,7 @@ class BoundedModule(nn.Module): - def __init__(self, model, global_input, bound_opts={}, auto_batch_dim=True, device='cpu', + def __init__(self, model, global_input, bound_opts={}, auto_batch_dim=True, device='auto', verbose=False): super(BoundedModule, self).__init__() if isinstance(model, BoundedModule): @@ -23,7 +23,7 @@ def __init__(self, model, global_input, bound_opts={}, auto_batch_dim=True, devi self.verbose = verbose self.bound_opts = bound_opts self.auto_batch_dim = auto_batch_dim - self.device = device + self.device = device if device != 'auto' else next(model.parameters()).device self.global_input = global_input if auto_batch_dim: # logger.warning('Using automatic batch dimension inferring, which may not be correct') @@ -41,10 +41,11 @@ def __call__(self, *input, **kwargs): kwargs.pop("method_opt") else: opt = "forward" - if "disable_multi_gpu" in kwargs: - kwargs.pop("disable_multi_gpu") - if "no_replicas" in kwargs: - kwargs.pop("no_replicas") + for kwarg in [ + 'disable_multi_gpu', 'no_replicas', 'get_property', + 'node_class', 'att_name']: + if kwarg in kwargs: + kwargs.pop(kwarg) if opt == "compute_bounds": return self.compute_bounds(**kwargs) else: @@ -471,6 +472,9 @@ def _convert(self, model, global_input): model.load_state_dict(self.ori_state_dict) delattr(self, 'ori_state_dict') + # The final node used in the last time calling `compute_bounds` + self.last_final_node = None + logger.debug('NodesOP:') for node in nodesOP: logger.debug('{}'.format(node._replace(param=None))) @@ -531,11 +535,27 @@ def compute_bounds(self, x=None, aux=None, C=None, IBP=False, forward=False, met A_dict = {} if return_A else None if x is not None: self._set_input(*x, new_interval=new_interval) + + # Several shortcuts. + method = method.lower() if method is not None else method + if method == 'ibp': + # Pure IBP bounds. + method = None + IBP = True + elif method == 'ibp+backward' or method == 'ibp+crown' or method == 'crown-ibp': + method = 'backward' + IBP = True + elif method == 'crown': + method = 'backward' + elif method == 'forward': + forward = True + elif method == 'forward+backward': + method = 'backward' + forward = True + if IBP and method is None and reuse_ibp: # directly return the previously saved ibp bounds return self.ibp_lower, self.ibp_upper - if method == 'forward': - forward = True root = [self._modules[name] for name in self.root_name] batch_size = root[0].fv.shape[0] dim_in = 0 @@ -578,14 +598,32 @@ def compute_bounds(self, x=None, aux=None, C=None, IBP=False, forward=False, met # check whether weights are perturbed and set nonlinear for the BoundMatMul operation for n in self._modules.values(): if isinstance(n, (BoundLinear, BoundConv, BoundBatchNormalization)): + n.nonlinear = False for l_name in n.input_name[1:]: node = self._modules[l_name] if hasattr(node, 'perturbation'): if node.perturbation is not None: n.nonlinear = True + # BFS to find out whether each node is used given the current final node + if final != self.last_final_node: + self.last_final_node = final + for i in self._modules.values(): + i.used = False + final.used = True + queue = deque([final]) + while len(queue) > 0: + n = queue.popleft() + for n_pre_name in n.input_name: + n_pre = self._modules[n_pre_name] + if not n_pre.used: + n_pre.used = True + queue.append(n_pre) + for i in self._modules.values(): # for all nodes - if hasattr(i, 'nonlinear') and i.nonlinear: # if node.nonlinear + if not i.used: + continue + if hasattr(i, 'nonlinear') and i.nonlinear: for l_name in i.input_name: node = self._modules[l_name] if not hasattr(node, 'lower'): @@ -607,7 +645,8 @@ def compute_bounds(self, x=None, aux=None, C=None, IBP=False, forward=False, met node.lower = node.forward(self._modules[node.input_name[0]].lower) node.upper = node.forward(self._modules[node.input_name[0]].upper) elif isinstance(node, BoundReshape) and \ - hasattr(self._modules[node.input_name[0]], 'lower'): + hasattr(self._modules[node.input_name[0]], 'lower') and \ + hasattr(self._modules[node.input_name[1]], 'value'): # Node for input value. val_input = self._modules[node.input_name[0]] # Node for input parameter (e.g., shape, permute) @@ -633,7 +672,7 @@ def compute_bounds(self, x=None, aux=None, C=None, IBP=False, forward=False, met newC = Patches(None, 1, 0, [batch_size, node.default_shape[-2] * node.default_shape[-1], node.default_shape[-3], node.default_shape[-3], 1, 1], 1) elif isinstance(node, BoundAdd) and node.mode == "patches": num_channel = node.default_shape[-3] - patches = (torch.eye(num_channel)).unsqueeze(0).unsqueeze(0).unsqueeze(4).unsqueeze(5) # now [1 * 1 * in_C * in_C * 1 * 1] + patches = (torch.eye(num_channel, device=self.device)).unsqueeze(0).unsqueeze(0).unsqueeze(4).unsqueeze(5) # now [1 * 1 * in_C * in_C * 1 * 1] newC = Patches(patches, 1, 0, [batch_size] + list(patches.shape[1:])) else: newC = torch.eye(dim, device=self.device)\ @@ -641,7 +680,7 @@ def compute_bounds(self, x=None, aux=None, C=None, IBP=False, forward=False, met .view(batch_size, dim, *node.default_shape[1:]) if return_A: _, _, A_dict = self._backward_general(C=newC, node=node, root=root, - return_A=return_A, A_dict=A_dict) + return_A=return_A, A_dict=A_dict) else: self._backward_general(C=newC, node=node, root=root, return_A=return_A) @@ -1085,7 +1124,9 @@ def forward(self, *inputs, **kwargs): kwargs.pop("no_replicas") if not self.device_ids or disable_multi_gpu: - return self.module(*inputs, **kwargs) + if kwargs.pop("get_property", False): + return self.get_property(self, *inputs, **kwargs) + return self.module(*inputs, **kwargs) if kwargs.pop("get_property", False): if self._replicas is None: diff --git a/auto_LiRPA/bound_ops.py b/auto_LiRPA/bound_ops.py index 42a99ef..66b2ea4 100644 --- a/auto_LiRPA/bound_ops.py +++ b/auto_LiRPA/bound_ops.py @@ -220,7 +220,7 @@ def get_bias(self, A, bias): # the shape of A.patches is [batch, L, out_c, in_c, K, K] - if self.batch_dim != -1: # Here we only support batch_dim == 0 + if self.batch_dim != -1: batch_size = bias.shape[0] bias = F.unfold(bias, kernel_size=A.patches.size(-1), stride=A.stride, padding=A.padding).transpose(-2, -1).unsqueeze(-2) # Here the size of bias is [batch_size, L, 1, in_c * K * K] @@ -232,7 +232,12 @@ def get_bias(self, A, bias): bias_new = prod.sum(-1).transpose(-2, -1) bias_new = bias_new.view(batch_size, bias_new.size(-2), int(math.sqrt(bias_new.size(-1))), int(math.sqrt(bias_new.size(-1)))) else: - raise NotImplementedError() + # Similar to BoundConstant + patches = A.patches + patches_reshape = torch.sum(patches, dim=(-1, -2, -3)) * bias.to(self.device) + patches_reshape = patches_reshape.transpose(-1, -2) + return patches_reshape.view(patches_reshape.size(0), patches_reshape.size(1), int(math.sqrt(patches_reshape.size(2))), -1).transpose(0, 1) + return bias_new else: return NotImplementedError() @@ -914,7 +919,7 @@ def _bound_oneside(last_A): else: # we should create a real identity Patch num_channel = tmp_weight.view(-1).size(0) - patches = (torch.eye(num_channel) * tmp_weight.view(-1)).unsqueeze(0).unsqueeze(0).unsqueeze(4).unsqueeze(5) # now [1 * 1 * in_C * in_C * 1 * 1] + patches = (torch.eye(num_channel, device=tmp_weight.device) * tmp_weight.view(-1)).unsqueeze(0).unsqueeze(0).unsqueeze(4).unsqueeze(5) # now [1 * 1 * in_C * in_C * 1 * 1] next_A = Patches(patches, 1, 0, [1, 1, num_channel, 1, 1]) sum_bias = tmp_bias.unsqueeze(1).unsqueeze(2).unsqueeze(3) # squeezing batch dim, now [C * 1 * 1 * 1] else: @@ -2119,8 +2124,16 @@ def bound_backward(self, last_lA, last_uA): def _bound_oneside(A): if A is None: return 0.0 - while A.ndim > 2: - A = torch.sum(A, dim=-1) + + if type(A) == torch.Tensor: + while A.ndim > 2: + A = torch.sum(A, dim=-1) + elif type(A) == Patches: + patches = A.patches + patches_reshape = torch.sum(patches, dim=(-1, -2, -3)) * self.value.to(self.device) + patches_reshape = patches_reshape.transpose(-1, -2) + return patches_reshape.view(patches_reshape.size(0), patches_reshape.size(1), int(math.sqrt(patches_reshape.size(2))), -1).transpose(0, 1) + return A * self.value.to(self.device) lbias = _bound_oneside(last_lA) @@ -2255,10 +2268,10 @@ def _bound_oneside(last_A): if last_A is None: return None A = torch.zeros( - last_A.shape[0], last_A.shape[1], *x.lower.shape[1:], device=last_A.device) + last_A.shape[0], last_A.shape[1], *x.default_shape[1:], device=last_A.device) A.scatter_( dim=dim + 1, - index=index.lower.unsqueeze(0).repeat(A.shape[0], *([1] * (A.ndim - 1))), + index=self.index.unsqueeze(0).repeat(A.shape[0], *([1] * (A.ndim - 1))), src=last_A) return A diff --git a/examples/language/train.py b/examples/language/train.py index 84c9770..4666ccf 100644 --- a/examples/language/train.py +++ b/examples/language/train.py @@ -167,7 +167,7 @@ def step(model, ptb, batch, eps=1.0, train=False): if args.method == 'IBP+backward_train': lb, ub = model_loss.compute_bounds( x=(labels, embeddings, mask), aux=aux, - IBP=True, C=None, method='backward', bound_lower=False) + C=None, method='IBP+backward', bound_lower=False) else: raise NotImplementedError loss_robust = torch.log(ub).mean() @@ -184,21 +184,16 @@ def step(model, ptb, batch, eps=1.0, train=False): torch.eye(num_class).type_as(embeddings).unsqueeze(0) I = (~(labels.data.unsqueeze(1) == torch.arange(num_class).type_as(labels.data).unsqueeze(0))) c = (c[I].view(embeddings.size(0), num_class - 1, num_class)) - if args.method == 'IBP': - lb, ub = model_bound.compute_bounds(aux=aux, IBP=True, C=c, method=None) - elif args.method == 'IBP+backward': - lb, ub = model_bound.compute_bounds(aux=aux, IBP=True, C=c, method='backward', bound_upper=False) + if args.method in ['IBP', 'IBP+backward', 'forward', 'forward+backward']: + lb, ub = model_bound.compute_bounds(aux=aux, C=c, method=args.method, bound_upper=False) elif args.method == 'IBP+backward_train': + # CROWN-IBP if 1 - eps > 1e-4: - lb, ub = model_bound.compute_bounds(aux=aux, IBP=True, C=c, method='backward', bound_upper=False) - ilb, iub = model_bound.compute_bounds(aux=aux, IBP=True, C=c, method=None, reuse_ibp=True) + lb, ub = model_bound.compute_bounds(aux=aux, C=c, method='IBP+backward', bound_upper=False) + ilb, iub = model_bound.compute_bounds(aux=aux, C=c, method='IBP', reuse_ibp=True) lb = eps * ilb + (1 - eps) * lb else: - lb, ub = model_bound.compute_bounds(aux=aux, IBP=True, C=c, method=None) - elif args.method == 'forward': - lb, ub = model_bound.compute_bounds(aux=aux, IBP=False, C=c, method='forward', bound_upper=False) - elif args.method == 'forward+backward': - lb, ub = model_bound.compute_bounds(aux=aux, IBP=False, forward=True, C=c, method='backward', bound_upper=False) + lb, ub = model_bound.compute_bounds(aux=aux, C=c, method='IBP') else: raise NotImplementedError lb_padded = torch.cat((torch.zeros(size=(lb.size(0),1), dtype=lb.dtype, device=lb.device), lb), dim=1) diff --git a/examples/sequence/train.py b/examples/sequence/train.py index 90251bc..edcba84 100644 --- a/examples/sequence/train.py +++ b/examples/sequence/train.py @@ -1,10 +1,5 @@ import argparse import random -import pickle -import os -import pdb -import time -import math import torch import torch.nn as nn import torch.nn.functional as F @@ -17,97 +12,126 @@ parser = argparse.ArgumentParser() parser.add_argument("--seed", type=int, default=0) parser.add_argument("--device", type=str, default="cuda", choices=["cuda", "cpu"]) -parser.add_argument("--norm", type=int, default=2) +parser.add_argument("--norm", type=int, default=np.inf) parser.add_argument("--eps", type=float, default=0.1) parser.add_argument("--num_epochs", type=int, default=20) -parser.add_argument("--batch_size", type=int, default=128) +parser.add_argument("--batch_size", type=int, default=512) parser.add_argument("--num_slices", type=int, default=8) -parser.add_argument("--hidden_size", type=int, default=64) +parser.add_argument("--hidden_size", type=int, default=256) parser.add_argument("--num_classes", type=int, default=10) parser.add_argument("--input_size", type=int, default=784) -parser.add_argument("--lr", type=float, default=5e-3) +parser.add_argument("--lr", type=float, default=1e-2) parser.add_argument("--dir", type=str, default="model", help="directory to load or save the model") -parser.add_argument("--num_epochs_warmup", type=int, default=1, help="number of epochs for the warmup stage when eps is linearly increased from 0 to the full value") +parser.add_argument("--num_epochs_warmup", type=int, default=10, help="number of epochs for the warmup stage when eps is linearly increased from 0 to the full value") parser.add_argument("--log_interval", type=int, default=10, help="interval of printing the log during training") args = parser.parse_args() + +## Train or test one batch. def step(model, ptb, batch, eps=args.eps, train=False): - ptb.set_eps(eps) + # We increase the perturbation each batch. + ptb.set_eps(eps) + # We create a BoundedTensor object with current batch of data. X, y = model.get_input(batch) X = BoundedTensor(X, ptb) logits = model.core(X) + # Form the linear speicifications, which are margins of ground truth class and other classes. num_class = args.num_classes c = torch.eye(num_class).type_as(X)[y].unsqueeze(1) - \ torch.eye(num_class).type_as(X).unsqueeze(0) I = (~(y.data.unsqueeze(1) == torch.arange(num_class).type_as(y.data).unsqueeze(0))) c = (c[I].view(X.size(0), num_class - 1, num_class)) - lb, ub = model.core.compute_bounds(IBP=True, C=c, method='backward', bound_upper=False) + # Compute CROWN-IBP (IBP+backward) bounds for training. We only need the lower bound. + # Here we can omit the x=(X,) argument because we have just used X for forward propagation. + lb, ub = model.core.compute_bounds(C=c, method='CROWN-IBP', bound_upper=False) + # Compute robust cross entropy loss. lb_padded = torch.cat((torch.zeros(size=(lb.size(0),1), dtype=lb.dtype, device=lb.device), lb), dim=1) fake_labels = torch.zeros(size=(lb.size(0),), dtype=torch.int64, device=lb.device) + loss = nn.CrossEntropyLoss()(-lb_padded, fake_labels) + + # Report accuracy and robust accuracy. acc = (torch.argmax(logits, dim=-1) == y).float().mean() acc_robust = 1 - torch.mean((lb < 0).any(dim=1).float()) - loss = nn.CrossEntropyLoss()(-lb_padded, fake_labels) if train: loss.backward() return acc.detach(), acc_robust.detach(), loss.detach() -data_train, data_test = load_data() -logger.info("Dataset sizes: {}/{}".format(len(data_train), len(data_test))) - -random.seed(args.seed) -np.random.seed(args.seed) -torch.manual_seed(args.seed) -torch.cuda.manual_seed_all(args.seed) - -model = LSTM(args).to(args.device) -test_batches = get_batches(data_test, args.batch_size) -X, y = model.get_input(test_batches[0]) -ptb = PerturbationLpNorm(norm=args.norm, eps=args.eps) -X = BoundedTensor(X, ptb) -model.core = BoundedModule(model.core, (X,), device=args.device) -optimizer = model.build_optimizer() - -avg_acc, avg_acc_robust, avg_loss = avg = [AverageMeter() for i in range(3)] +## Train one epoch. def train(epoch): model.train() + # Load data for a epoch. train_batches = get_batches(data_train, args.batch_size) for a in avg: a.reset() + eps_inc_per_step = 1.0 / (args.num_epochs_warmup * len(train_batches)) + for i, batch in enumerate(train_batches): + # We increase eps linearly every batch. eps = args.eps * min(eps_inc_per_step * ((epoch - 1) * len(train_batches) + i + 1), 1.0) + # Call the main training loop. acc, acc_robust, loss = res = step(model, ptb, batch, eps=eps, train=True) + # Optimize the loss. torch.nn.utils.clip_grad_norm_(model.core.parameters(), 5.0) optimizer.step() optimizer.zero_grad() + # Print training statistics. for k in range(3): avg[k].update(res[k], len(batch)) if (i + 1) % args.log_interval == 0: - logger.info("Epoch {}, training step {}/{}: acc {:.3f}, acc_robust {:.3f}, loss {:.3f}, eps {:.3f}".format( + logger.info("Epoch {}, training step {}/{}: acc {:.3f}, robust acc {:.3f}, loss {:.3f}, eps {:.3f}".format( epoch, i + 1, len(train_batches), avg_acc.avg, avg_acc_robust.avg, avg_loss.avg, eps)) model.save(epoch) -def infer(epoch, batches, type): + +## Test accuracy and robust accuracy. +def test(epoch, batches): model.eval() for a in avg: a.reset() for i, batch in enumerate(batches): acc, acc_robust, loss = res = step(model, ptb, batch) for k in range(3): avg[k].update(res[k], len(batch)) - logger.info("Epoch {}, {}: acc {:.3f}, acc_robust {:.3f}, loss {:.5f}".format( - epoch, type, avg_acc.avg, avg_acc_robust.avg, avg_loss.avg)) - -def main(): - for t in range(model.checkpoint, args.num_epochs): - train(t + 1) - infer(t + 1, test_batches, "test") - if model.checkpoint == args.num_epochs: - infer(args.num_epochs, test_batches, "test") - -if __name__ == "__main__": - main() + logger.info("Epoch {} test: acc {:.3f}, robust acc {:.3f}, loss {:.5f}".format( + epoch, avg_acc.avg, avg_acc_robust.avg, avg_loss.avg)) + +# Load MNIST dataset +logger.info("Loading data...") +data_train, data_test = load_data() +logger.info("Dataset sizes: {}/{}".format(len(data_train), len(data_test))) +test_batches = get_batches(data_test, args.batch_size) + +# Set all random seeds. +random.seed(args.seed) +np.random.seed(args.seed) +torch.manual_seed(args.seed) +torch.cuda.manual_seed_all(args.seed) + +# Create a LSTM sequence classifier. +logger.info("Creating LSTM model...") +model = LSTM(args).to(args.device) +X, y = model.get_input(test_batches[0]) +# Create the perturbation object once here, and we can reuse it. +ptb = PerturbationLpNorm(norm=args.norm, eps=args.eps) +# Convert the LSTM to BoundedModule +X = BoundedTensor(X, ptb) +model.core = BoundedModule(model.core, (X,), device=args.device) +optimizer = model.build_optimizer() + +# Averaging accuracym robust accuracy and loss. +avg_acc, avg_acc_robust, avg_loss = avg = [AverageMeter() for i in range(3)] + +# Main training loop. +for t in range(model.checkpoint, args.num_epochs): + train(t + 1) + test(t + 1, test_batches) + +# If the loaded model has already reached the last epoch, test it directly. +if model.checkpoint == args.num_epochs: + test(args.num_epochs, test_batches) + diff --git a/examples/vision/cifar_training.py b/examples/vision/cifar_training.py index 2917755..a8c4aa0 100644 --- a/examples/vision/cifar_training.py +++ b/examples/vision/cifar_training.py @@ -55,14 +55,14 @@ def get_exp_module(bounded_module): parser.add_argument("--lr_decay_milestones", nargs='+', type=int, default=[1400, 1700], help='learning rate dacay milestones') parser.add_argument("--scheduler_name", type=str, default="SmoothedScheduler", choices=["LinearScheduler", "AdaptiveScheduler", "SmoothedScheduler"], help='epsilon scheduler') -parser.add_argument("--scheduler_opts", type=str, default="start=101,length=801, mid=0.4", help='options for epsilon scheduler') +parser.add_argument("--scheduler_opts", type=str, default="start=101,length=801,mid=0.4", help='options for epsilon scheduler') parser.add_argument("--bound_opts", type=str, default=None, choices=["same-slope", "zero-lb", "one-lb"], help='bound options for relu') parser.add_argument('--clip_grad_norm', type=float, default=8.0) args = parser.parse_args() -exp_name = args.model+'_b'+str(args.batch_size) + str(args.bound_type)+'_epoch'+str(args.num_epochs)+'_'+args.scheduler_opts+'_'+str(args.eps)[:6] +exp_name = args.model + '_b' + str(args.batch_size) + '_' + str(args.bound_type) + '_epoch' + str(args.num_epochs) + '_' + args.scheduler_opts + '_' + str(args.eps)[:6] os.makedirs('saved_models/', exist_ok=True) if args.verify: logger = Logger(open('saved_models/' + exp_name + '_test.log', "w")) @@ -312,7 +312,7 @@ def main(args): Train(model, 1, test_data, eps_scheduler, norm, False, None, 'IBP', loss_fusion=False, final_node_name=None) else: timer = 0.0 - best_acc = 1e10 + best_err = 1e10 # with torch.autograd.detect_anomaly(): for t in range(epoch + 1, args.num_epochs+1): logger.log("Epoch {}, learning rate {}".format(t, lr_scheduler.get_last_lr())) @@ -347,10 +347,10 @@ def main(args): if t < int(eps_scheduler.params['start']): torch.save(save_dict, 'saved_models/natural_' + exp_name) elif t > int(eps_scheduler.params['start']) + int(eps_scheduler.params['length']): - current_acc = m.avg('Verified_Err') - if current_acc < best_acc: - best_acc = current_acc - torch.save(save_dict, 'saved_models/' + exp_name + '_best_' + str(best_acc)[:6]) + current_err = m.avg('Verified_Err') + if current_err < best_err: + best_err = current_err + torch.save(save_dict, 'saved_models/' + exp_name + '_best_' + str(best_err)[:6]) else: torch.save(save_dict, 'saved_models/' + exp_name) else: @@ -360,4 +360,4 @@ def main(args): if __name__ == "__main__": logger.log(args) - main(args) \ No newline at end of file + main(args) diff --git a/examples/vision/imagenet_training.py b/examples/vision/imagenet_training.py index 43d9ab4..14a429d 100644 --- a/examples/vision/imagenet_training.py +++ b/examples/vision/imagenet_training.py @@ -60,7 +60,7 @@ def get_exp_module(bounded_module): args = parser.parse_args() -exp_name = args.model + '_b' + str(args.batch_size) + str(args.bound_type) + '_epoch' + str( +exp_name = args.model + '_b' + str(args.batch_size) + '_' + str(args.bound_type) + '_epoch' + str( args.num_epochs) + '_' + args.scheduler_opts + '_' + str(args.eps)[:6] if args.verify: @@ -285,7 +285,7 @@ def main(args): Train(model, 1, test_data, eps_scheduler, norm, False, None, 'IBP', loss_fusion=False, final_node_name=None) else: timer = 0.0 - best_acc = 1e10 + best_err = 1e10 # with torch.autograd.detect_anomaly(): for t in range(epoch + 1, args.num_epochs + 1): logger.log("Epoch {}, learning rate {}".format(t, lr_scheduler.get_last_lr())) @@ -321,10 +321,10 @@ def main(args): if t < int(eps_scheduler.params['start']): torch.save(save_dict, 'saved_models/natural_' + exp_name) elif t > int(eps_scheduler.params['start']) + int(eps_scheduler.params['length']): - current_acc = m.avg('Verified_Err') - if current_acc < best_acc: - best_acc = current_acc - torch.save(save_dict, 'saved_models/' + exp_name + '_best_' + str(best_acc)[:6]) + current_err = m.avg('Verified_Err') + if current_err < best_err: + best_err = current_err + torch.save(save_dict, 'saved_models/' + exp_name + '_best_' + str(best_err)[:6]) else: torch.save(save_dict, 'saved_models/' + exp_name) torch.cuda.empty_cache() diff --git a/examples/vision/models/__init__.py b/examples/vision/models/__init__.py index fd6d472..3fedd35 100644 --- a/examples/vision/models/__init__.py +++ b/examples/vision/models/__init__.py @@ -14,6 +14,7 @@ Models = { 'mlp_2layer': mlp_2layer, 'mlp_3layer': mlp_3layer, + 'mlp_3layer_weight_perturb': mlp_3layer_weight_perturb, 'mlp_5layer': mlp_5layer, 'cnn_4layer': cnn_4layer, 'cnn_5layer': cnn_5layer, diff --git a/examples/vision/models/feedforward.py b/examples/vision/models/feedforward.py index 9bee160..e90f829 100644 --- a/examples/vision/models/feedforward.py +++ b/examples/vision/models/feedforward.py @@ -1,6 +1,7 @@ import torch import torch.nn as nn import torch.nn.functional as F +from auto_LiRPA import PerturbationLpNorm, BoundedParameter # CNN, relatively large 4-layer @@ -70,6 +71,35 @@ def forward(self, x): x = self.fc3(x) return x + +class mlp_3layer_weight_perturb(nn.Module): + def __init__(self, in_ch=1, in_dim=28, width=1, pert_weight=True, pert_bias=False, norm=2): + super(mlp_3layer_weight_perturb, self).__init__() + self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 64 * width) + self.fc2 = nn.Linear(64 * width, 64 * width) + self.fc3 = nn.Linear(64 * width, 10) + + eps = 0.01 + self.ptb = PerturbationLpNorm(norm=norm, eps=eps) + + if pert_weight: + self.fc1.weight = BoundedParameter(self.fc1.weight.data, self.ptb) + self.fc2.weight = BoundedParameter(self.fc2.weight.data, self.ptb) + self.fc3.weight = BoundedParameter(self.fc3.weight.data, self.ptb) + + if pert_bias: + self.fc1.bias = BoundedParameter(self.fc1.bias.data, self.ptb) + self.fc2.bias = BoundedParameter(self.fc2.bias.data, self.ptb) + self.fc3.bias = BoundedParameter(self.fc3.bias.data, self.ptb) + + def forward(self, x): + x = x.view(-1, 784) + x = F.relu(self.fc1(x)) + x = F.relu(self.fc2(x)) + x = self.fc3(x) + return x + + class mlp_5layer(nn.Module): def __init__(self, in_ch, in_dim, width=1): super(mlp_5layer, self).__init__() diff --git a/examples/vision/models/resnet.py b/examples/vision/models/resnet.py index 4721646..d058f3c 100644 --- a/examples/vision/models/resnet.py +++ b/examples/vision/models/resnet.py @@ -36,7 +36,7 @@ def forward(self, x): return x.view(x.size(0), -1) -def model_resnet(in_ch=3, in_dim=32, width=1, N=1): +def model_resnet(in_ch=3, in_dim=32, width=1, mult=16, N=1): def block(in_filters, out_filters, k, downsample): if not downsample: k_first = 3 @@ -55,23 +55,23 @@ def block(in_filters, out_filters, k, downsample): nn.ReLU() ] - conv1 = [nn.Conv2d(in_ch, 16, 3, stride=1, padding=3 if in_dim == 28 else 1), nn.ReLU()] - conv2 = block(16, 16 * width, 3, False) + conv1 = [nn.Conv2d(in_ch, mult, 3, stride=1, padding=3 if in_dim == 28 else 1), nn.ReLU()] + conv2 = block(mult, mult * width, 3, False) for _ in range(N): - conv2.extend(block(16 * width, 16 * width, 3, False)) - conv3 = block(16 * width, 32 * width, 3, True) + conv2.extend(block(mult * width, mult * width, 3, False)) + conv3 = block(mult * width, mult * 2 * width, 3, True) for _ in range(N - 1): - conv3.extend(block(32 * width, 32 * width, 3, False)) - conv4 = block(32 * width, 64 * width, 3, True) + conv3.extend(block(mult * 2 * width, mult * 2 * width, 3, False)) + conv4 = block(mult * 2 * width, mult * 4 * width, 3, True) for _ in range(N - 1): - conv4.extend(block(64 * width, 64 * width, 3, False)) + conv4.extend(block(mult * 4 * width, mult * 4 * width, 3, False)) layers = ( conv1 + conv2 + conv3 + conv4 + [Flatten(), - nn.Linear(64 * width * 8 * 8, 1000), + nn.Linear(mult * 4 * width * 8 * 8, 1000), nn.ReLU(), nn.Linear(1000, 10)] ) diff --git a/examples/vision/models/resnet18.py b/examples/vision/models/resnet18.py index 46fe89c..6c66152 100644 --- a/examples/vision/models/resnet18.py +++ b/examples/vision/models/resnet18.py @@ -71,18 +71,18 @@ def forward(self, x): class ResNet(nn.Module): - def __init__(self, block, num_blocks, num_classes=10): + def __init__(self, block, num_blocks, num_classes=10, in_planes=64): super(ResNet, self).__init__() - self.in_planes = 64 + self.in_planes = in_planes - self.conv1 = nn.Conv2d(3, 64, kernel_size=3, + self.conv1 = nn.Conv2d(3, in_planes, kernel_size=3, stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(64) - self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1) - self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2) - self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2) - self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) - self.linear = nn.Linear(512*block.expansion, num_classes) + self.bn1 = nn.BatchNorm2d(in_planes) + self.layer1 = self._make_layer(block, in_planes, num_blocks[0], stride=1) + self.layer2 = self._make_layer(block, in_planes * 2, num_blocks[1], stride=2) + self.layer3 = self._make_layer(block, in_planes * 4, num_blocks[2], stride=2) + self.layer4 = self._make_layer(block, in_planes * 8, num_blocks[3], stride=2) + self.linear = nn.Linear(in_planes * 8 * block.expansion, num_classes) def _make_layer(self, block, planes, num_blocks, stride): strides = [stride] + [1]*(num_blocks-1) @@ -104,8 +104,8 @@ def forward(self, x): return out -def ResNet18(): - return ResNet(BasicBlock, [2, 2, 2, 2]) +def ResNet18(in_planes=64): + return ResNet(BasicBlock, [2, 2, 2, 2], in_planes=in_planes) ''' def ResNet34(): @@ -127,10 +127,10 @@ def ResNet152(): if __name__ == "__main__": from thop import profile - net = ResNet18() + net = ResNet18(in_planes=64) x = torch.randn(1,3,32,32) y = net(x) print(net) macs, params = profile(net, (torch.randn(1, 3, 32, 32),)) print(macs / 1000000, params / 1000000) # 556M, 11M - print(y) \ No newline at end of file + print(y) diff --git a/examples/vision/patch_convolution.py b/examples/vision/patch_convolution.py index 81b20a0..e7c3901 100644 --- a/examples/vision/patch_convolution.py +++ b/examples/vision/patch_convolution.py @@ -17,6 +17,7 @@ # BoundeModule object. In this test we show the difference between Patches # mode and Matrix mode in memory consumption. +device = 'cuda' conv_mode = 'patches' # conv_mode can be set as 'matrix' or 'patches' seed = 1234 @@ -25,25 +26,9 @@ random.seed(seed) np.random.seed(seed) -class Flatten(nn.Module): - def forward(self, x): - return x.view(x.size(0), -1) - -## Step 1: Define computational graph by implementing forward() -def cifar_model(): - model = nn.Sequential( - nn.Conv2d(3, 8, 4, stride=2, padding=1), - nn.ReLU(), - nn.Conv2d(8, 16, 4, stride=2, padding=1), - nn.ReLU(), - Flatten(), - nn.Linear(1024, 100), - nn.ReLU(), - nn.Linear(100, 10) - ) - return model - -model_ori = cifar_model() +## Step 1: Define the model +# model_ori = models.model_resnet(width=1, mult=4) +model_ori = models.ResNet18(in_planes=2) # model_ori.load_state_dict(torch.load("data/cifar_base_kw.pth")['state_dict'][0]) ## Step 2: Prepare dataset as usual @@ -52,19 +37,20 @@ def cifar_model(): normalize = torchvision.transforms.Normalize(mean = [0.4914, 0.4822, 0.4465], std = [0.2023, 0.1994, 0.2010]) test_data = torchvision.datasets.CIFAR10("./data", train=False, download=True, transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor(), normalize])) -# For illustration we only use 2 image from dataset -N = 2 +# For illustration we only use 1 image from dataset +N = 1 n_classes = 10 image = torch.Tensor(test_data.data[:N]).reshape(N,3,32,32) # Convert to float image = image.to(torch.float32) / 255.0 -image = image.cuda() +if device == 'cuda': + image = image.cuda() ## Step 3: wrap model with auto_LiRPA # The second parameter is for constructing the trace of the computational graph, and its content is not important. # The new "patches" conv_mode provides an more efficient implementation for convolutional neural networks. -model = BoundedModule(model_ori, image, bound_opts={"conv_mode": conv_mode}, device="cuda") +model = BoundedModule(model_ori, image, bound_opts={"conv_mode": conv_mode}, device=device) ## Step 4: Compute bounds using LiRPA given a perturbation eps = 0.03 @@ -77,7 +63,7 @@ def cifar_model(): # Compute bounds torch.cuda.empty_cache() print('Using {} mode to compute convolution.'.format(conv_mode)) -lb, ub = model.compute_bounds(x=(image,), IBP=False, C=None, method='backward') +lb, ub = model.compute_bounds(IBP=False, C=None, method='backward') ## Step 5: Final output # pred = pred.detach().cpu().numpy() @@ -92,4 +78,3 @@ def cifar_model(): # Print the GPU memory usage print('Memory usage in "{}" mode:'.format(conv_mode)) print(torch.cuda.memory_summary()) - diff --git a/examples/vision/pretrain/kw_mnist.pth b/examples/vision/pretrain/kw_mnist.pth new file mode 100644 index 0000000000000000000000000000000000000000..eb48bfcdd10828165b01d0faa520aee64a8a6e96 GIT binary patch literal 666692 zcmZUac|2Cn`~U4^r;v&eiDW6uecxy1+#({ONTjr&M3!z_(Sq!2)?~@P6eW_l?=y35 zv}@l)DWy%ev~Q}P-tX_{`}lkxzxm@l?m720&w0(6IoF&sbDdbBG|9lJQrqtu0ms*< 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zLuc%Q$LlckjMacOf2-hrJ8>>7?J(DeYv6+6iS&N~!~i@0IDq-Qf~cUpPp>CFC#su1 z52>X-x){?vmtC^FOxoGKE5KX56rX#%Czhu>e4Xd1 literal 0 HcmV?d00001 diff --git a/examples/vision/simple_verification.py b/examples/vision/simple_verification.py index 635f3b0..7486e08 100644 --- a/examples/vision/simple_verification.py +++ b/examples/vision/simple_verification.py @@ -6,39 +6,27 @@ import models ## Step 1: Define computational graph by implementing forward() -class cnn_MNIST(nn.Module): - def __init__(self): - super(cnn_MNIST, self).__init__() - self.conv1 = nn.Conv2d(1, 8, 4, stride=2, padding=1) - self.conv2 = nn.Conv2d(8, 16, 4, stride=2, padding=1) - self.fc1 = nn.Linear(784, 256) - self.fc2 = nn.Linear(256, 10) - +class Flatten(nn.Module): def forward(self, x): - x = F.relu(self.conv1(x)) - x = F.relu(self.conv2(x)) - # print(x.shape) - x = x.view(-1, 784) - x = F.relu(self.fc1(x)) - x = self.fc2(x) - return x + return x.view(x.size(0), -1) -class simple_MNIST(nn.Module): - def __init__(self): - super(simple_MNIST, self).__init__() - self.fc1 = nn.Linear(2, 2) - self.fc2 = nn.Linear(2, 2) - self.fc3 = nn.Linear(2, 1) - - def forward(self, x): - x = F.relu(self.f1(x)) - x = F.relu(self.f2(x)) - x = self.fc3(x) - return x +# This simple model comes from https://github.com/locuslab/convex_adversarial +def mnist_model(): + model = nn.Sequential( + nn.Conv2d(1, 16, 4, stride=2, padding=1), + nn.ReLU(), + nn.Conv2d(16, 32, 4, stride=2, padding=1), + nn.ReLU(), + Flatten(), + nn.Linear(32*7*7,100), + nn.ReLU(), + nn.Linear(100, 10) + ) + return model -model = cnn_MNIST() +model = mnist_model() # Load the pretrained weights -checkpoint = torch.load(os.path.join(os.path.dirname(__file__),"pretrain/mnist_cnn_small.pth")) +checkpoint = torch.load(os.path.join(os.path.dirname(__file__),"pretrain/kw_mnist.pth")) model.load_state_dict(checkpoint) ## Step 2: Prepare dataset as usual @@ -46,33 +34,38 @@ def forward(self, x): # For illustration we only use 2 image from dataset N = 2 n_classes = 10 -image = test_data.data[:N].view(N,1,28,28).cuda() +image = test_data.data[:N].view(N,1,28,28) +true_label = test_data.targets[:N] # Convert to float image = image.to(torch.float32) / 255.0 ## Step 3: wrap model with auto_LiRPA # The second parameter is for constructing the trace of the computational graph, and its content is not important. -model = BoundedModule(model, torch.empty_like(image), device="cuda") +model = BoundedModule(model, torch.empty_like(image)) +# For larger convolutional models, setting bound_opts={"conv_mode": "patches"} is more efficient. +# model = BoundedModule(model, torch.empty_like(image), bound_opts={"conv_mode": "patches"}) ## Step 4: Compute bounds using LiRPA given a perturbation -eps = 0.3 +eps = 0.1 norm = np.inf # ptb = PerturbationL0Norm(eps=eps) ptb = PerturbationLpNorm(norm = norm, eps = eps) image = BoundedTensor(image, ptb) # Get model prediction as usual pred = model(image) -# label = torch.argmax(pred, dim=1).cpu().numpy() -# Compute bounds -lb, ub = model.compute_bounds(IBP=False) +label = torch.argmax(pred, dim=1).cpu().numpy() -## Step 5: Final output -# pred = pred.detach().cpu().numpy() -lb = lb.detach().cpu().numpy() -ub = ub.detach().cpu().numpy() -for i in range(N): - # print("Image {} top-1 prediction {}".format(i, label[i])) - for j in range(n_classes): - print("f_{j}(x_0): {l:8.3f} <= f_{j}(x_0+delta) <= {u:8.3f}".format(j=j, l=lb[i][j], u=ub[i][j])) +## Step 5: Compute bounds for final output +for method in ['IBP', 'IBP+backward (CROWN-IBP)', 'backward (CROWN)']: + lb, ub = model.compute_bounds(x=(image,), method=method.split()[0]) + lb = lb.detach().cpu().numpy() + ub = ub.detach().cpu().numpy() + print("Bounding method:", method) + for i in range(N): + print("Image {} top-1 prediction {} ground-truth {}".format(i, label[i], true_label[i])) + for j in range(n_classes): + indicator = '(ground-truth)' if j == true_label[i] else '' + print("f_{j}(x_0): {l:8.3f} <= f_{j}(x_0+delta) <= {u:8.3f} {ind}".format( + j=j, l=lb[i][j], u=ub[i][j], ind=indicator)) print() diff --git a/examples/vision/tinyimagenet_training.py b/examples/vision/tinyimagenet_training.py index 8996bc4..bb1156c 100644 --- a/examples/vision/tinyimagenet_training.py +++ b/examples/vision/tinyimagenet_training.py @@ -62,7 +62,7 @@ def get_exp_module(bounded_module): args = parser.parse_args() -exp_name = args.model + '_b' + str(args.batch_size) + str(args.bound_type) + '_epoch' + str( +exp_name = args.model + '_b' + str(args.batch_size) + '_' + str(args.bound_type) + '_epoch' + str( args.num_epochs) + '_' + args.scheduler_opts + '_ImageNet_' + str(args.eps)[:6] os.makedirs('saved_models/', exist_ok=True) if args.verify: @@ -292,7 +292,7 @@ def main(args): Train(model, 1, test_data, eps_scheduler, norm, False, None, 'IBP', loss_fusion=False, final_node_name=None) else: timer = 0.0 - best_acc = 1e10 + best_err = 1e10 # with torch.autograd.detect_anomaly(): for t in range(epoch + 1, args.num_epochs + 1): logger.log("Epoch {}, learning rate {}".format(t, lr_scheduler.get_last_lr())) @@ -327,10 +327,10 @@ def main(args): if t < int(eps_scheduler.params['start']): torch.save(save_dict, 'saved_models/natural_' + exp_name) elif t > int(eps_scheduler.params['start']) + int(eps_scheduler.params['length']): - current_acc = m.avg('Verified_Err') - if current_acc < best_acc: - best_acc = current_acc - torch.save(save_dict, 'saved_models/' + exp_name + '_best_' + str(best_acc)[:6]) + current_err = m.avg('Verified_Err') + if current_err < best_err: + best_err = current_err + torch.save(save_dict, 'saved_models/' + exp_name + '_best_' + str(best_err)[:6]) else: torch.save(save_dict, 'saved_models/' + exp_name) torch.cuda.empty_cache() diff --git a/examples/vision/weights_training.py b/examples/vision/weights_training.py index 30c502c..bb98314 100644 --- a/examples/vision/weights_training.py +++ b/examples/vision/weights_training.py @@ -10,29 +10,9 @@ import torch.nn.functional as F from datasets import mnist_loaders import torchvision.datasets as datasets +import models from auto_LiRPA.eps_scheduler import LinearScheduler, AdaptiveScheduler, SmoothedScheduler, FixedScheduler -class mlp_3layer(nn.Module): - def __init__(self, in_ch=1, in_dim=28, width=1): - super(mlp_3layer, self).__init__() - self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 64 * width) - self.fc2 = nn.Linear(64 * width, 64 * width) - self.fc3 = nn.Linear(64 * width, 10) - - eps = 0.01 - norm = 2 - global ptb - ptb = PerturbationLpNorm(norm=norm, eps=eps) - self.fc1.weight = BoundedParameter(self.fc1.weight.data, ptb) - self.fc2.weight = BoundedParameter(self.fc2.weight.data, ptb) - self.fc3.weight = BoundedParameter(self.fc3.weight.data, ptb) - - def forward(self, x): - x = x.view(x.size(0), -1) - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - x = self.fc3(x) - return x class Logger(object): def __init__(self, log_file=None): @@ -76,18 +56,21 @@ def get_exp_module(bounded_module): parser.add_argument("--bound_opts", type=str, default=None, choices=["same-slope", "zero-lb", "one-lb"], help='bound options') parser.add_argument('--clip_grad_norm', type=float, default=8.0) +parser.add_argument('--truncate_data', type=int, help='Truncate the training/test batches in unit test') +parser.add_argument('--multigpu', action='store_true', help='MultiGPU training') +num_class = 10 args = parser.parse_args() -exp_name = 'mlp_MNIST'+'_b'+str(args.batch_size)+str(args.bound_type)+'_epoch'+str(args.num_epochs)+'_'+args.scheduler_opts+'_'+str(args.eps)[:6] +exp_name = 'mlp_MNIST'+'_b'+str(args.batch_size)+'_'+str(args.bound_type)+'_epoch'+str(args.num_epochs)+'_'+args.scheduler_opts+'_'+str(args.eps)[:6] if args.verify: logger = Logger(open(exp_name + '_test.log', "w")) else: logger = Logger(open(exp_name+'.log', "w")) +## Training one epoch. def Train(model, t, loader, eps_scheduler, norm, train, opt, bound_type, method='robust', loss_fusion=True, final_node_name=None): - num_class = 10 meter = MultiAverageMeter() if train: model.train() @@ -97,36 +80,31 @@ def Train(model, t, loader, eps_scheduler, norm, train, opt, bound_type, method= else: model.eval() eps_scheduler.eval() - + + # Used for loss-fusion. Get the exp operation in computational graph. exp_module = get_exp_module(model) def get_bound_loss(x=None, c=None): if loss_fusion: + # When loss fusion is used, we need the upper bound for the final loss function. bound_lower, bound_upper = False, True else: + # When loss fusion is not used, we need the lower bound for the logit layer. bound_lower, bound_upper = True, False if bound_type == 'IBP': - lb, ub = model(method_opt="compute_bounds", x=x, IBP=True, C=c, method=None, final_node_name=final_node_name, no_replicas=True) + lb, ub = model(method_opt="compute_bounds", x=x, C=c, method="IBP", final_node_name=final_node_name, no_replicas=True) elif bound_type == 'CROWN': - lb, ub = model(method_opt="compute_bounds", x=x, IBP=False, C=c, method='backward', + lb, ub = model(method_opt="compute_bounds", x=x, C=c, method="backward", bound_lower=bound_lower, bound_upper=bound_upper) elif bound_type == 'CROWN-IBP': - # lb, ub = model.compute_bounds(ptb=ptb, IBP=True, x=data, C=c, method='backward') # pure IBP bound # we use a mixed IBP and CROWN-IBP bounds, leading to better performance (Zhang et al., ICLR 2020) # factor = (eps_scheduler.get_max_eps() - eps_scheduler.get_eps()) / eps_scheduler.get_max_eps() - ilb, iub = model(method_opt="compute_bounds", x=x, IBP=True, C=c, method=None, final_node_name=final_node_name, no_replicas=True) - # if factor < 1e-50: - # lb, ub = ilb, iub - # else: - lb, ub = model(method_opt="compute_bounds", IBP=False, C=c, method='backward', + ilb, iub = model(method_opt="compute_bounds", x=x, C=c, method="IBP", final_node_name=final_node_name, no_replicas=True) + lb, ub = model(method_opt="compute_bounds", C=c, method="CROWN-IBP", bound_lower=bound_lower, bound_upper=bound_upper, final_node_name=final_node_name, average_A=True, no_replicas=True) - # if loss_fusion: - # ub = cub * factor + iub * (1 - factor) - # else: - # lb = clb * factor + ilb * (1 - factor) - if loss_fusion: + # When loss fusion is enabled, we need to get the common factor before softmax. if isinstance(model, BoundDataParallel): max_input = model(get_property=True, node_class=BoundExp, att_name='max_input') else: @@ -140,6 +118,11 @@ def get_bound_loss(x=None, c=None): return lb, robust_ce for i, (data, labels) in enumerate(loader): + # For unit test. We only use a small number of batches + if args.truncate_data: + if i >= args.truncate_data: + break + start = time.time() eps_scheduler.step_batch() eps = eps_scheduler.get_eps() @@ -149,21 +132,11 @@ def get_bound_loss(x=None, c=None): batch_method = "natural" if train: opt.zero_grad() - # bound input for Linf norm used only - if norm == np.inf: - data_max = torch.reshape((1. - loader.mean) / loader.std, (1, -1, 1, 1)) - data_min = torch.reshape((0. - loader.mean) / loader.std, (1, -1, 1, 1)) - data_ub = torch.min(data + (eps / loader.std).view(1,-1,1,1), data_max) - data_lb = torch.max(data - (eps / loader.std).view(1,-1,1,1), data_min) - else: - data_ub = data_lb = data if list(model.parameters())[0].is_cuda: data, labels = data.cuda(), labels.cuda() - data_lb, data_ub = data_lb.cuda(), data_ub.cuda() - ptb.eps = eps - # x = BoundedTensor(data, ptb) + model.ptb.eps = eps x = data if loss_fusion: if batch_method == 'natural' or not train: @@ -176,9 +149,10 @@ def get_bound_loss(x=None, c=None): x = (x, labels) c = None else: + # Generate speicification matrix (when loss fusion is not used). c = torch.eye(num_class).type_as(data)[labels].unsqueeze(1) - torch.eye(num_class).type_as(data).unsqueeze( 0) - # remove specifications to self + # remove specifications to self. I = (~(labels.data.unsqueeze(1) == torch.arange(num_class).type_as(labels.data).unsqueeze(0))) c = (c[I].view(data.size(0), num_class - 1, num_class)) x = (x, labels) @@ -226,9 +200,11 @@ def main(args): random.seed(args.seed) np.random.seed(args.seed) - model_ori = mlp_3layer() + ## Load the model with BoundedParameter for weight perturbation. + model_ori = models.Models['mlp_3layer_weight_perturb']() epoch = 0 + ## Load a checkpoint, if requested. if args.load: checkpoint = torch.load(args.load) epoch, state_dict = checkpoint['epoch'], checkpoint['state_dict'] @@ -254,10 +230,13 @@ def main(args): final_name1 = model.final_name model_loss = BoundedModule(CrossEntropyWrapper(model_ori), (dummy_input, torch.zeros(1, dtype=torch.long)), bound_opts= { 'relu': args.bound_opts, 'loss_fusion': True }, device=args.device) + # after CrossEntropyWrapper, the final name will change because of one more input node in CrossEntropyWrapper final_name2 = model_loss._modules[final_name1].output_name[0] assert type(model._modules[final_name1]) == type(model_loss._modules[final_name2]) - model_loss = BoundDataParallel(model_loss) + if args.multigpu: + model_loss = BoundDataParallel(model_loss) + model_loss.ptb = model.ptb = model_ori.ptb # Perturbation on the parameters ## Step 4 prepare optimizer, epsilon scheduler and learning rate scheduler if args.opt == 'ADAM': @@ -270,7 +249,7 @@ def main(args): eps_scheduler = eval(args.scheduler_name)(args.eps, args.scheduler_opts) logger.log(str(model_ori)) - # skip epochs + # Skip epochs if we continue training from a checkpoint. if epoch > 0: epoch_length = int((len(train_data.dataset) + train_data.batch_size - 1) / train_data.batch_size) eps_scheduler.set_epoch_length(epoch_length) @@ -287,7 +266,7 @@ def main(args): opt.load_state_dict(opt_state) logger.log('resume opt_state') - ## Step 5: start training + ## Step 5: start training. if args.verify: eps_scheduler = FixedScheduler(args.eps) with torch.no_grad(): @@ -295,10 +274,12 @@ def main(args): else: timer = 0.0 best_loss = 1e10 - # with torch.autograd.detect_anomaly(): + # Main training loop for t in range(epoch + 1, args.num_epochs+1): logger.log("Epoch {}, learning rate {}".format(t, lr_scheduler.get_last_lr())) start_time = time.time() + + # Training one epoch Train(model_loss, t, train_data, eps_scheduler, norm, True, opt, args.bound_type, loss_fusion=True) lr_scheduler.step() epoch_time = time.time() - start_time @@ -308,21 +289,19 @@ def main(args): logger.log("Evaluating...") torch.cuda.empty_cache() - # remove 'model.' in state_dict + # remove 'model.' in state_dict (hack for saving models so far...) state_dict_loss = model_loss.state_dict() state_dict = {} for name in state_dict_loss: assert (name.startswith('model.')) state_dict[name[6:]] = state_dict_loss[name] + # Test one epoch. with torch.no_grad(): - if t > int(eps_scheduler.params['start']) + int(eps_scheduler.params['length']): - m = Train(model_loss, t, test_data, eps_scheduler, norm, False, None, args.bound_type, - loss_fusion=False, final_node_name=final_name2) - else: - m = Train(model_loss, t, test_data, eps_scheduler, norm, False, None, args.bound_type, - loss_fusion=False, final_node_name=final_name2) + m = Train(model_loss, t, test_data, eps_scheduler, norm, False, None, args.bound_type, + loss_fusion=False, final_node_name=final_name2) + # Save checkpoints. save_dict = {'state_dict': state_dict, 'epoch': t, 'optimizer': opt.state_dict()} if not os.path.exists('saved_models'): os.mkdir('saved_models') @@ -337,7 +316,6 @@ def main(args): torch.save(save_dict, 'saved_models/' + exp_name) else: torch.save(save_dict, 'saved_models/' + exp_name) - # del model torch.cuda.empty_cache() diff --git a/tests/data/.gitignore b/tests/data/.gitignore index 6c425f8..677d9db 100644 --- a/tests/data/.gitignore +++ b/tests/data/.gitignore @@ -1,2 +1,5 @@ ckpt_lstm ckpt_transformer +cifar-10-python.tar.gz +cifar-10-batches-py 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self.perturbed = perturbed self.default_shape = lower.shape -def equal(a, b): - return torch.allclose(a, b, eps) +class TestBoundOp(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName, + seed=1, ref_path='data/bound_ops_data', + generate=generate) + + def test(self): + device = 'cpu' + batch_size = 5 + dim_final = 7 + dim_output = 11 + dim_input = 11 + + # multiplication of [batch_size, dim_input] and [dim_output, dim_input]^T + weight = torch.randn(dim_output, dim_input, device=device) + bias = torch.randn(dim_output, device=device) + data_in = torch.randn(batch_size, dim_input, device=device) + data_in_delta = torch.randn(batch_size, dim_input, device=device) + dummy_in = Dummy(data_in - torch.abs(data_in_delta), data_in + torch.abs(data_in_delta), True) + dummy_weight = Dummy(weight) + dummy_bias = Dummy(bias) + + op = BoundLinear( + input_name=[None, None, None], + name=None, ori_name=None, attr=None, + inputs=[dummy_in, dummy_weight, dummy_bias], + output_index=0, options={}, device=device) -def test(): - torch.manual_seed(1) - torch.cuda.manual_seed_all(1) + # test `forward` + data_out = op(data_in, weight, bias) + self.assertEqual(data_out, data_in.matmul(weight.t()) + bias) - dim_input = 11 - - # multiplication of [batch_size, dim_input] and [dim_output, dim_input]^T - weight = torch.randn(dim_output, dim_input, device=device) - bias = torch.randn(dim_output, device=device) - data_in = torch.randn(batch_size, dim_input, device=device) - data_in_delta = torch.randn(batch_size, dim_input, device=device) - dummy_in = Dummy(data_in - torch.abs(data_in_delta), data_in + torch.abs(data_in_delta), True) - dummy_weight = Dummy(weight) - dummy_bias = Dummy(bias) + # test `bound_backward` + # The `transpose` here to make the randomization consistent with the previous reference. + # It can be removed once a new reference is generated. + last_lA = torch.randn(batch_size, dim_final, dim_output, device=device).transpose(0, 1) + last_uA = torch.randn(batch_size, dim_final, dim_output, device=device).transpose(0, 1) + A, lbias, ubias = op.bound_backward(last_lA, last_uA, *op.inputs) + self.assertEqual(A[0][0], last_lA.matmul(weight)) + self.assertEqual(A[0][1], last_uA.matmul(weight)) + self.assertEqual(lbias, last_lA.matmul(bias)) + self.assertEqual(ubias, last_uA.matmul(bias)) - op = BoundLinear( - input_name=[None, None, None], - name=None, ori_name=None, attr=None, - inputs=[dummy_in, dummy_weight, dummy_bias], - output_index=0, options={}, device=device) + # test `bound_forward` + # note that the upper bound may be actually smaller than the lower bound + # in these dummy linear bounds + bound_in = LinearBound( + lw=torch.randn(batch_size, dim_final, dim_input, device=device), + lb=torch.randn(batch_size, dim_input, device=device), + uw=torch.randn(batch_size, dim_final, dim_input, device=device), + ub=torch.randn(batch_size, dim_input, device=device), + lower=None, upper=None) + bound_weight = LinearBound(None, None, None, None, dummy_weight.lower, dummy_weight.upper) + bound_bias = LinearBound(None, None, None, None, dummy_bias.lower, dummy_bias.upper) + bound_out = op.bound_forward(dim_final, bound_in, bound_weight, bound_bias) + self.assertEqual(bound_out.lw, + bound_in.lw.matmul(weight.t().clamp(min=0)) + bound_in.uw.matmul(weight.t().clamp(max=0))) + self.assertEqual(bound_out.uw, + bound_in.uw.matmul(weight.t().clamp(min=0)) + bound_in.lw.matmul(weight.t().clamp(max=0))) + self.assertEqual(bound_out.lb, + bound_in.lb.matmul(weight.t().clamp(min=0)) + bound_in.ub.matmul(weight.t().clamp(max=0)) + bias) + self.assertEqual(bound_out.ub, + bound_in.ub.matmul(weight.t().clamp(min=0)) + bound_in.lb.matmul(weight.t().clamp(max=0)) + bias) - # test `forward` - data_out = op(data_in, weight, bias) - assert equal(data_out, data_in.matmul(weight.t()) + bias) + # test `interval_propagate` + bound_in = ( + torch.randn(*data_in.shape, device=device), + torch.randn(*data_in.shape, device=device)) + bound_weight = (bound_weight.lower, bound_weight.upper) + bound_bias = (bound_bias.lower, bound_bias.upper) + bound_out = op.interval_propagate(bound_in, bound_weight, bound_bias) + self.assertEqual(bound_out[0], + bound_in[0].matmul(weight.t().clamp(min=0)) + bound_in[1].matmul(weight.t().clamp(max=0)) + bias) + self.assertEqual(bound_out[1], + bound_in[1].matmul(weight.t().clamp(min=0)) + bound_in[0].matmul(weight.t().clamp(max=0)) + bias) - # test `bound_backward` - # The `transpose` here to make the randomization consistent with the previous reference. - # It can be removed once a new reference is generated. - last_lA = torch.randn(batch_size, dim_final, dim_output, device=device).transpose(0, 1) - last_uA = torch.randn(batch_size, dim_final, dim_output, device=device).transpose(0, 1) - A, lbias, ubias = op.bound_backward(last_lA, last_uA, *op.inputs) - assert equal(A[0][0], last_lA.matmul(weight)) - assert equal(A[0][1], last_uA.matmul(weight)) - assert equal(lbias, last_lA.matmul(bias)) - assert equal(ubias, last_uA.matmul(bias)) + # test weight perturbation + # `bound_backward` + ptb_weight = torch.randn(weight.shape) + op.inputs[1].upper += ptb_weight + op.inputs[1].perturbed = True + op.inputs[2].perturbation = None # no perturbation on bias + A, lbias, ubias = op.bound_backward(last_lA, last_uA, *op.inputs) + # `interval_propagate` + bound_weight = (op.inputs[1].lower, op.inputs[1].upper) + bound_out = op.interval_propagate(bound_in, bound_weight, bound_bias) - # test `bound_forward` - # note that the upper bound may be actually smaller than the lower bound - # in these dummy linear bounds - bound_in = LinearBound( - lw=torch.randn(batch_size, dim_final, dim_input, device=device), - lb=torch.randn(batch_size, dim_input, device=device), - uw=torch.randn(batch_size, dim_final, dim_input, device=device), - ub=torch.randn(batch_size, dim_input, device=device), - lower=None, upper=None) - bound_weight = LinearBound(None, None, None, None, dummy_weight.lower, dummy_weight.upper) - bound_bias = LinearBound(None, None, None, None, dummy_bias.lower, dummy_bias.upper) - bound_out = op.bound_forward(dim_final, bound_in, bound_weight, bound_bias) - assert equal(bound_out.lw, - bound_in.lw.matmul(weight.t().clamp(min=0)) + bound_in.uw.matmul(weight.t().clamp(max=0))) - assert equal(bound_out.uw, - bound_in.uw.matmul(weight.t().clamp(min=0)) + bound_in.lw.matmul(weight.t().clamp(max=0))) - assert equal(bound_out.lb, - bound_in.lb.matmul(weight.t().clamp(min=0)) + bound_in.ub.matmul(weight.t().clamp(max=0)) + bias) - assert equal(bound_out.ub, - bound_in.ub.matmul(weight.t().clamp(min=0)) + bound_in.lb.matmul(weight.t().clamp(max=0)) + bias) + self.result = (A, lbias, ubias, bound_out) - # test `interval_propagate` - bound_in = ( - torch.randn(*data_in.shape, device=device), - torch.randn(*data_in.shape, device=device)) - bound_weight = (bound_weight.lower, bound_weight.upper) - bound_bias = (bound_bias.lower, bound_bias.upper) - bound_out = op.interval_propagate(bound_in, bound_weight, bound_bias) - assert equal(bound_out[0], - bound_in[0].matmul(weight.t().clamp(min=0)) + bound_in[1].matmul(weight.t().clamp(max=0)) + bias) - assert equal(bound_out[1], - bound_in[1].matmul(weight.t().clamp(min=0)) + bound_in[0].matmul(weight.t().clamp(max=0)) + bias) + if self.generate: + self.save() - # test weight perturbation - # `bound_backward` - ptb_weight = torch.randn(weight.shape) - op.inputs[1].upper += ptb_weight - op.inputs[1].perturbed = True - op.inputs[2].perturbation = None # no perturbation on bias - A, lbias, ubias = op.bound_backward(last_lA, last_uA, *op.inputs) - # `interval_propagate` - bound_weight = (op.inputs[1].lower, op.inputs[1].upper) - bound_out = op.interval_propagate(bound_in, bound_weight, bound_bias) - if args.gen_ref: - if not os.path.exists('data/bound_ops'): - os.mkdir('data/bound_ops') - with open('data/bound_ops/weight_ptb.pkl', 'wb') as file: - pickle.dump((A, lbias, ubias, bound_out), file) - with open('data/bound_ops/weight_ptb.pkl', 'rb') as file: - A_ref, lbias_ref, ubias_ref, bound_out_ref = pickle.load(file) - for i in range(3): - for j in range(2): - if A_ref[i][j] is not None: - ref = A_ref[i][j] - # legacy reference - if ref.shape[0] == batch_size: - ref = ref.transpose(0, 1) - assert equal(A[i][j], ref) - lbias, ubias = lbias.transpose(0, 1), ubias.transpose(0, 1) - assert equal(lbias, lbias_ref) - assert equal(ubias, ubias_ref) - assert equal(bound_out[0], bound_out_ref[0]) and equal(bound_out[1], bound_out_ref[1]) + A_ref, lbias_ref, ubias_ref, bound_out_ref = self.reference + for i in range(3): + for j in range(2): + if A_ref[i][j] is not None: + ref = A_ref[i][j] + # legacy reference + if ref.shape[0] == batch_size: + ref = ref.transpose(0, 1) + self.assertEqual(A[i][j], ref) + lbias, ubias = lbias.transpose(0, 1), ubias.transpose(0, 1) + self.assertEqual(lbias, lbias_ref) + self.assertEqual(ubias, ubias_ref) + self.assertEqual(bound_out[0], bound_out_ref[0]) and equal(bound_out[1], bound_out_ref[1]) if __name__ == '__main__': - test() + # Change to generate=True when genearting reference results + testcase = TestBoundOp(generate=False) + testcase.test() \ No newline at end of file diff --git a/tests/test_constant.py b/tests/test_constant.py index f64a2fb..5f514ad 100644 --- a/tests/test_constant.py +++ b/tests/test_constant.py @@ -1,16 +1,12 @@ """Test classes in auto_LiRPA/bound_ops.py""" import torch -import argparse import os import torch.nn as nn import torch.nn.functional as F import torchvision from auto_LiRPA import BoundedModule, BoundedTensor from auto_LiRPA.perturbations import * - -parser = argparse.ArgumentParser() -parser.add_argument('--gen_ref', action='store_true', help='generate reference results') -args, unknown = parser.parse_known_args() +from testcase import TestCase class cnn_MNIST(nn.Module): def __init__(self): @@ -29,38 +25,36 @@ def forward(self, x): x = self.fc2(x) return 0.5 * x -def test(): - torch.manual_seed(1) - torch.cuda.manual_seed_all(1) +class TestConstant(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName, + seed=1, ref_path='data/constant_test_data', + generate=generate) - model = cnn_MNIST() - checkpoint = torch.load("../examples/vision/pretrain/mnist_cnn_small.pth", map_location="cpu") - model.load_state_dict(checkpoint) + def test(self): + model = cnn_MNIST() + checkpoint = torch.load("../examples/vision/pretrain/mnist_cnn_small.pth", map_location="cpu") + model.load_state_dict(checkpoint) - N = 2 - n_classes = 10 - image = torch.randn(N, 1, 28, 28) - image = image.to(torch.float32) / 255.0 + N = 2 + n_classes = 10 + image = torch.randn(N, 1, 28, 28) + image = image.to(torch.float32) / 255.0 - model = BoundedModule(model, torch.empty_like(image), device="cpu") - eps = 0.3 - norm = np.inf - ptb = PerturbationLpNorm(norm=norm, eps=eps) - image = BoundedTensor(image, ptb) - pred = model(image) - lb, ub = model.compute_bounds() + model = BoundedModule(model, torch.empty_like(image), device="cpu") + eps = 0.3 + norm = np.inf + ptb = PerturbationLpNorm(norm=norm, eps=eps) + image = BoundedTensor(image, ptb) + pred = model(image) + lb, ub = model.compute_bounds() - assert lb.shape == ub.shape == torch.Size((2, 10)) + assert lb.shape == ub.shape == torch.Size((2, 10)) - path = 'data/constant_test_data' - if args.gen_ref: - torch.save((lb, ub), path) - else: - lb_ref, ub_ref = torch.load(path) - print(lb, lb_ref, (lb - lb_ref).abs().max()) - print(ub, ub_ref, (ub - ub_ref).abs().max()) - assert torch.allclose(lb, lb_ref) - assert torch.allclose(ub, ub_ref) + self.result = (lb, ub) + self.check() if __name__ == '__main__': - test() + # Change to generate=True when genearting reference results + testcase = TestConstant(generate=False) + testcase.test() diff --git a/tests/test_conv.py b/tests/test_conv.py index 233afab..9390384 100644 --- a/tests/test_conv.py +++ b/tests/test_conv.py @@ -1,14 +1,11 @@ import torch -import argparse import os import torch.nn as nn import torch.nn.functional as F import torchvision from auto_LiRPA import BoundedModule, BoundedTensor -from auto_LiRPA.perturbations import * - -parser = argparse.ArgumentParser() -args, unknown = parser.parse_known_args() +from auto_LiRPA.perturbations import * +from testcase import TestCase class Flatten(nn.Module): def __init__(self): @@ -17,7 +14,6 @@ def __init__(self): def forward(self, x): return x.view((x.shape[0], -1)) - class cnn_model(nn.Module): def __init__(self, layers, padding, stride): super(cnn_model, self).__init__() @@ -33,52 +29,52 @@ def __init__(self, layers, padding, stride): self.module_list.append(Flatten()) self.module_list.append(nn.Linear(3 * length * length, 256)) self.module_list.append(nn.Linear(256, 10)) - self.model = nn.Sequential(*self.module_list) def forward(self, x): x = self.model(x) - return x +class TestConv(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName, + seed=1, ref_path=None, + generate=generate) -def test(): - torch.manual_seed(1) - torch.cuda.manual_seed_all(1) - - models = [2, 3] - paddings = [1, 2] - strides = [1, 3] + def test(self): + models = [2, 3] + paddings = [1, 2] + strides = [1, 3] - N = 2 - n_classes = 10 - image = torch.randn(N, 1, 28, 28) - image = image.to(torch.float32) / 255.0 + N = 2 + n_classes = 10 + image = torch.randn(N, 1, 28, 28) + image = image.to(torch.float32) / 255.0 - for layer_num in models: - for padding in paddings: - for stride in strides: - # print(layer_num, padding, stride) - try: - model_ori = cnn_model(layer_num, padding, stride) - except: - continue + for layer_num in models: + for padding in paddings: + for stride in strides: + try: + model_ori = cnn_model(layer_num, padding, stride) + except: + continue - model = BoundedModule(model_ori, image, device="cpu", bound_opts={"conv_mode": "patches"}) - eps = 0.3 - norm = np.inf - ptb = PerturbationLpNorm(norm=norm, eps=eps) - image = BoundedTensor(image, ptb) - pred = model(image) - lb, ub = model.compute_bounds() + model = BoundedModule(model_ori, image, device="cpu", bound_opts={"conv_mode": "patches"}) + eps = 0.3 + norm = np.inf + ptb = PerturbationLpNorm(norm=norm, eps=eps) + image = BoundedTensor(image, ptb) + pred = model(image) + lb, ub = model.compute_bounds() - model = BoundedModule(model_ori, image, device="cpu", bound_opts={"conv_mode": "matrix"}) - pred = model(image) - lb_ref, ub_ref = model.compute_bounds() + model = BoundedModule(model_ori, image, device="cpu", bound_opts={"conv_mode": "matrix"}) + pred = model(image) + lb_ref, ub_ref = model.compute_bounds() - assert lb.shape == ub.shape == torch.Size((N, n_classes)) - assert torch.allclose(lb, lb_ref) - assert torch.allclose(ub, ub_ref) + assert lb.shape == ub.shape == torch.Size((N, n_classes)) + self.assertEqual(lb, lb_ref) + self.assertEqual(ub, ub_ref) if __name__ == '__main__': - test() \ No newline at end of file + testcase = TestConv() + testcase.test() diff --git a/tests/test_linear_cnn_model.py b/tests/test_linear_cnn_model.py index 1b5f2a7..975336b 100644 --- a/tests/test_linear_cnn_model.py +++ b/tests/test_linear_cnn_model.py @@ -3,12 +3,11 @@ import torch.nn as nn from auto_LiRPA import BoundedModule, BoundedTensor from auto_LiRPA.perturbations import * -import pytest +from test_linear_model import TestLinearModel input_dim = 8 out_channel = 2 N = 10 -torch.manual_seed(0) class LinearCNNModel(nn.Module): def __init__(self): @@ -20,105 +19,43 @@ def forward(self, x): x = x.view(-1, input_dim //2 * input_dim // 2 * out_channel) return x -original_model = LinearCNNModel() - -input_data = torch.randn((N, 1, input_dim, input_dim)) - -def compute_and_compare_bounds(eps, norm, IBP, method): - model = BoundedModule(original_model, torch.empty_like(input_data)) - ptb = PerturbationLpNorm(norm=norm, eps=eps) - ptb_data = BoundedTensor(input_data, ptb) - pred = model(ptb_data) - label = torch.argmax(pred, dim=1).cpu().detach().numpy() - # Compute bounds. - lb, ub = model.compute_bounds(IBP=IBP, method=method) - # Compute reference. - conv_weight, conv_bias = list(model.parameters()) - conv_bias = conv_bias.view(1, out_channel, 1, 1) - matrix_eye = torch.eye(input_dim * input_dim).view(input_dim * input_dim, 1, input_dim, input_dim) - # Obtain equivalent weight and bias for convolution. - weight = original_model.conv(matrix_eye) - conv_bias # Output is (batch, channel, weight, height). - weight = weight.view(input_dim * input_dim, -1) # Dimension is (flattened_input, flattened_output). - bias = conv_bias.repeat(1, 1, input_dim //2, input_dim //2).view(-1) - flattend_data = input_data.view(N, -1) - # Compute dual norm. - if norm == 1: - q = np.inf - elif norm == np.inf: - q = 1.0 - else: - q = 1.0 / (1.0 - (1.0 / norm)) - # Manually compute bounds. - norm = weight.t().norm(p=q, dim=1) - expected_pred = flattend_data.matmul(weight) + bias - expected_ub = eps * norm + expected_pred - expected_lb = -eps * norm + expected_pred - # Check equivalence. - if method == 'backward' or method == 'forward': - assert torch.allclose(expected_pred, pred, 1e-4) - assert torch.allclose(expected_ub, ub, 1e-4) - assert torch.allclose(expected_lb, lb, 1e-4) - -def test_Linf_forward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=False, method='forward') - -def test_Linf_backward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=False, method='backward') - -def test_Linf_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=True, method=None) - -def test_Linf_backward_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=True, method='backward') - -def test_L2_forward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=False, method='forward') - -def test_L2_backward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=False, method='backward') - -def test_L2_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=True, method=None) - -def test_L2_backward_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=True, method='backward') - -def test_L1_forward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=False, method='forward') - -def test_L1_backward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=False, method='backward') - -def test_L1_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=True, method=None) - -def test_L1_backward_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=True, method='backward') - - -if __name__ == '__main__': - test_Linf_forward() - test_Linf_backward() - test_Linf_IBP() - test_Linf_backward_IBP() - test_L2_forward() - test_L2_backward() - test_L2_IBP() - test_L2_backward_IBP() - test_L1_forward() - test_L1_backward() - test_L1_IBP() - test_L1_backward_IBP() - +class TestLinearCNNModel(TestLinearModel): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName) + self.original_model = LinearCNNModel() + + def compute_and_compare_bounds(self, eps, norm, IBP, method): + input_data = torch.randn((N, 1, input_dim, input_dim)) + model = BoundedModule(self.original_model, torch.empty_like(input_data)) + ptb = PerturbationLpNorm(norm=norm, eps=eps) + ptb_data = BoundedTensor(input_data, ptb) + pred = model(ptb_data) + label = torch.argmax(pred, dim=1).cpu().detach().numpy() + # Compute bounds. + lb, ub = model.compute_bounds(IBP=IBP, method=method) + # Compute reference. + conv_weight, conv_bias = list(model.parameters()) + conv_bias = conv_bias.view(1, out_channel, 1, 1) + matrix_eye = torch.eye(input_dim * input_dim).view(input_dim * input_dim, 1, input_dim, input_dim) + # Obtain equivalent weight and bias for convolution. + weight = self.original_model.conv(matrix_eye) - conv_bias # Output is (batch, channel, weight, height). + weight = weight.view(input_dim * input_dim, -1) # Dimension is (flattened_input, flattened_output). + bias = conv_bias.repeat(1, 1, input_dim //2, input_dim //2).view(-1) + flattend_data = input_data.view(N, -1) + # Compute dual norm. + if norm == 1: + q = np.inf + elif norm == np.inf: + q = 1.0 + else: + q = 1.0 / (1.0 - (1.0 / norm)) + # Manually compute bounds. + norm = weight.t().norm(p=q, dim=1) + expected_pred = flattend_data.matmul(weight) + bias + expected_ub = eps * norm + expected_pred + expected_lb = -eps * norm + expected_pred + # Check equivalence. + if method == 'backward' or method == 'forward': + self.assertEqual(expected_pred, pred) + self.assertEqual(expected_ub, ub) + self.assertEqual(expected_lb, lb) diff --git a/tests/test_linear_model.py b/tests/test_linear_model.py index c6317b2..305d661 100644 --- a/tests/test_linear_model.py +++ b/tests/test_linear_model.py @@ -3,10 +3,10 @@ import torch.nn as nn from auto_LiRPA import BoundedModule, BoundedTensor from auto_LiRPA.perturbations import * +from testcase import TestCase n_classes = 3 N = 10 -torch.manual_seed(0) class LinearModel(nn.Module): def __init__(self): @@ -17,113 +17,84 @@ def forward(self, x): x = self.fc(x) return x -original_model = LinearModel() - -input_data = torch.randn((N, 256)) - -def compute_and_compare_bounds(eps, norm, IBP, method): - model = BoundedModule(original_model, torch.empty_like(input_data)) - ptb = PerturbationLpNorm(norm=norm, eps=eps) - ptb_data = BoundedTensor(input_data, ptb) - pred = model(ptb_data) - label = torch.argmax(pred, dim=1).cpu().detach().numpy() - # Compute bounds. - lb, ub = model.compute_bounds(IBP=IBP, method=method) - # Compute dual norm. - if norm == 1: - q = np.inf - elif norm == np.inf: - q = 1.0 - else: - q = 1.0 / (1.0 - (1.0 / norm)) - # Compute reference manually. - weight, bias = list(model.parameters()) - norm = weight.norm(p=q, dim=1) - expected_pred = input_data.matmul(weight.t()) + bias - expected_ub = eps * norm + expected_pred - expected_lb = -eps * norm + expected_pred - # Check equivalence. - torch.allclose(expected_pred, pred) - torch.allclose(expected_ub, ub) - torch.allclose(expected_lb, lb) - - """ - pred = pred.detach().cpu().numpy() - lb = lb.detach().cpu().numpy() - ub = ub.detach().cpu().numpy() - - print("Computed:") - for i in range(N): - for j in range(n_classes): - print("f_{j}(x_0) = {fx0:8.3f}, {l:8.3f} <= f_{j}(x_0+delta) <= {u:8.3f}".format(j=j, fx0=pred[i][j], l=lb[i][j], u=ub[i][j])) - - # Expected output: - print("Expected:") - for i in range(N): - for j in range(n_classes): - print("f_{j}(x_0) = {fx0:8.3f}, {l:8.3f} <= f_{j}(x_0+delta) <= {u:8.3f}".format(j=j, fx0=expected_pred[i][j], l=expected_lb[i][j], u=expected_ub[i][j])) - """ - -def test_Linf_forward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=False, method='forward') - -def test_Linf_backward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=False, method='backward') - -def test_Linf_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=True, method=None) - -def test_Linf_backward_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=True, method='backward') - -def test_L2_forward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=False, method='forward') - -def test_L2_backward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=False, method='backward') - -def test_L2_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=True, method=None) - -def test_L2_backward_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=1.0, norm=2, IBP=True, method='backward') - -def test_L1_forward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=False, method='forward') - -def test_L1_backward(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=False, method='backward') - -def test_L1_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=True, method=None) - -def test_L1_backward_IBP(): - with np.errstate(divide='ignore'): - compute_and_compare_bounds(eps=3.0, norm=1, IBP=True, method='backward') - - -if __name__ == '__main__': - test_Linf_forward() - test_Linf_backward() - test_Linf_IBP() - test_Linf_backward_IBP() - test_L2_forward() - test_L2_backward() - test_L2_IBP() - test_L2_backward_IBP() - test_L1_forward() - test_L1_backward() - test_L1_IBP() - test_L1_backward_IBP() +class TestLinearModel(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName, seed=0) + self.original_model = LinearModel() + + def compute_and_compare_bounds(self, eps, norm, IBP, method): + input_data = torch.randn((N, 256)) + model = BoundedModule(self.original_model, torch.empty_like(input_data)) + ptb = PerturbationLpNorm(norm=norm, eps=eps) + ptb_data = BoundedTensor(input_data, ptb) + pred = model(ptb_data) + label = torch.argmax(pred, dim=1).cpu().detach().numpy() + # Compute bounds. + lb, ub = model.compute_bounds(IBP=IBP, method=method) + # Compute dual norm. + if norm == 1: + q = np.inf + elif norm == np.inf: + q = 1.0 + else: + q = 1.0 / (1.0 - (1.0 / norm)) + # Compute reference manually. + weight, bias = list(model.parameters()) + norm = weight.norm(p=q, dim=1) + expected_pred = input_data.matmul(weight.t()) + bias + expected_ub = eps * norm + expected_pred + expected_lb = -eps * norm + expected_pred + + # Check equivalence. + self.assertEqual(expected_pred, pred) + self.assertEqual(expected_ub, ub) + self.assertEqual(expected_lb, lb) + + def test_Linf_forward(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=False, method='forward') + + def test_Linf_backward(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=False, method='backward') + + def test_Linf_IBP(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=True, method=None) + + def test_Linf_backward_IBP(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=0.3, norm=np.inf, IBP=True, method='backward') + + def test_L2_forward(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=1.0, norm=2, IBP=False, method='forward') + + def test_L2_backward(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=1.0, norm=2, IBP=False, method='backward') + + def test_L2_IBP(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=1.0, norm=2, IBP=True, method=None) + + def test_L2_backward_IBP(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=1.0, norm=2, IBP=True, method='backward') + + def test_L1_forward(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=3.0, norm=1, IBP=False, method='forward') + + def test_L1_backward(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=3.0, norm=1, IBP=False, method='backward') + + def test_L1_IBP(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=3.0, norm=1, IBP=True, method=None) + + def test_L1_backward_IBP(self): + with np.errstate(divide='ignore'): + self.compute_and_compare_bounds(eps=3.0, norm=1, IBP=True, method='backward') diff --git a/tests/test_resnet_patches.py b/tests/test_resnet_patches.py index e660915..c40478f 100644 --- a/tests/test_resnet_patches.py +++ b/tests/test_resnet_patches.py @@ -1,145 +1,52 @@ -'''ResNet in PyTorch. - -For Pre-activation ResNet, see 'preact_resnet.py'. - -Reference: -[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun - Deep Residual Learning for Image Recognition. arXiv:1512.03385 -''' import torch +import random +import numpy as np import torch.nn as nn import torch.nn.functional as F -import argparse -import os import torchvision from auto_LiRPA import BoundedModule, BoundedTensor from auto_LiRPA.perturbations import * - - -class BasicBlock(nn.Module): - expansion = 1 - - def __init__(self, in_planes, planes, stride=1): - 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 != self.expansion*planes: - 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 Bottleneck(nn.Module): - expansion = 4 - - def __init__(self, in_planes, planes, stride=1): - super(Bottleneck, self).__init__() - self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False) - self.bn1 = nn.BatchNorm2d(planes) - self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, - stride=stride, padding=1, bias=False) - self.bn2 = nn.BatchNorm2d(planes) - self.conv3 = nn.Conv2d(planes, self.expansion * - planes, kernel_size=1, bias=False) - self.bn3 = nn.BatchNorm2d(self.expansion*planes) - - self.shortcut = nn.Sequential() - if stride != 1 or in_planes != self.expansion*planes: - 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 = F.relu(self.bn2(self.conv2(out))) - out = self.bn3(self.conv3(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 = 2 - - self.conv1 = nn.Conv2d(3, 2, kernel_size=3, - stride=1, padding=1, bias=False) - self.bn1 = nn.BatchNorm2d(2) - self.layer1 = self._make_layer(block, 2, num_blocks[0], stride=1) - self.linear = nn.Linear(2*block.expansion, num_classes) - - 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 = F.avg_pool2d(out, 32) - out = out.view(out.size(0), -1) - out = self.linear(out) - return out - - -def ResNet18(): - return ResNet(BasicBlock, [2]) - - -def test(): - net = ResNet18() - N = 2 - n_classes = 10 - x = torch.randn(N,3,32,32) - y = net(x) - - device = 'cpu' - if device == 'cuda': - x = x.cuda() - y = y.cuda() - - model = BoundedModule(net, torch.empty_like(x), bound_opts={"conv_mode": "patches"}, device=device) - print("Model structure: \n", str(net)) - eps = 0.3 - norm = np.inf - ptb = PerturbationLpNorm(norm=norm, eps=eps) - image = BoundedTensor(x, ptb) - pred = model(image) - lb, ub = model.compute_bounds() - - model = BoundedModule(net, torch.empty_like(x), bound_opts={"conv_mode": "matrix"}, device=device) - eps = 0.3 - norm = np.inf - ptb = PerturbationLpNorm(norm=norm, eps=eps) - image = BoundedTensor(x, ptb) - pred = model(image) - lb_ref, ub_ref = model.compute_bounds() - - # assert lb.shape == ub.shape == torch.Size((N, n_classes)) - print((lb - lb_ref).sum(), (ub - ub_ref).sum()) - assert torch.allclose(lb, lb_ref) - assert torch.allclose(ub, ub_ref) - -if __name__ == "__main__": - test() \ No newline at end of file +import sys +sys.path.append('../examples/vision') +import models +from testcase import TestCase + +class TestResnetPatches(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName, + seed=1234, ref_path='data/resnet_patches_test_data', + generate=generate) + + def test(self): + model_oris = [ + models.model_resnet(width=1, mult=2), + models.ResNet18(in_planes=2) + ] + self.result = [] + + for model_ori in model_oris: + conv_mode = 'patches' # conv_mode can be set as 'matrix' or 'patches' + + normalize = torchvision.transforms.Normalize(mean = [0.4914, 0.4822, 0.4465], std = [0.2023, 0.1994, 0.2010]) + test_data = torchvision.datasets.CIFAR10("./data", train=False, download=True, + transform=torchvision.transforms.Compose([torchvision.transforms.ToTensor(), normalize])) + N = 1 + n_classes = 10 + + image = torch.Tensor(test_data.data[:N]).reshape(N,3,32,32) + image = image.to(torch.float32) / 255.0 + + model = BoundedModule(model_ori, image, bound_opts={"conv_mode": conv_mode}) + + ptb = PerturbationLpNorm(norm = np.inf, eps = 0.03) + image = BoundedTensor(image, ptb) + pred = model(image) + lb, ub = model.compute_bounds(IBP=False, C=None, method='backward') + self.result += [lb, ub] + + self.check() + +if __name__ == '__main__': + # Change to generate=True when genearting reference results + testcase = TestResnetPatches(generate=True) + testcase.test() \ No newline at end of file diff --git a/tests/test_state_dict_name.py b/tests/test_state_dict_name.py index 745db56..03473d6 100644 --- a/tests/test_state_dict_name.py +++ b/tests/test_state_dict_name.py @@ -1,8 +1,8 @@ import torch import torch.nn as nn import torch.nn.functional as F - from auto_LiRPA import BoundedModule +from testcase import TestCase class FeatureExtraction(nn.Module): @@ -42,25 +42,30 @@ def forward(self, x): return self.fc(x) -def test(): - nobound_model = cnn_MNIST_nobound() - print('expected', nobound_model.state_dict().keys()) - - model = cnn_MNIST() - state_dict = model.state_dict() - dummy = torch.randn((1, 1, 28, 28)) - ret1 = model(dummy) +class TestStateDictName(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName) - # create second model and load state_dict to test load_state_dict() whether works proper - model = cnn_MNIST() - model.load_state_dict(state_dict, strict=False) - ret2 = model(dummy) - assert (ret1 == ret2).all() + def test(self): + nobound_model = cnn_MNIST_nobound() - print('got', model.state_dict().keys()) + model = cnn_MNIST() + state_dict = model.state_dict() + dummy = torch.randn((1, 1, 28, 28)) + ret1 = model(dummy) - assert (nobound_model.state_dict().keys() == model.state_dict().keys()) + # create second model and load state_dict to test load_state_dict() whether works proper + model = cnn_MNIST() + model.load_state_dict(state_dict, strict=False) + ret2 = model(dummy) + self.assertEqual(ret1, ret2) + self.assertEqual(nobound_model.state_dict().keys(), model.state_dict().keys()) + print('expected', nobound_model.state_dict().keys()) + print('got', model.state_dict().keys()) + if __name__ == '__main__': - test() + # Change to generate=True when genearting reference results + testcase = TestStateDictName(generate=False) + testcase.test() diff --git a/tests/test_vision_models.py b/tests/test_vision_models.py index e9103c7..3adecf4 100644 --- a/tests/test_vision_models.py +++ b/tests/test_vision_models.py @@ -5,11 +5,8 @@ from auto_LiRPA import BoundedModule, BoundedTensor from auto_LiRPA.perturbations import * -# with open('data/vision_test_data.pickle', 'rb') as handle: -# data = pickle.load(handle) data = torch.load('data/vision_test_data') - class cnn_4layer_test(nn.Module): def __init__(self): super(cnn_4layer_test, self).__init__() diff --git a/tests/test_weight_perturbation.py b/tests/test_weight_perturbation.py index 09f8d56..60fbdc1 100644 --- a/tests/test_weight_perturbation.py +++ b/tests/test_weight_perturbation.py @@ -2,115 +2,90 @@ import random import argparse import torch.nn.functional as F - +import subprocess +import numpy as np +from testcase import TestCase +import sys +sys.path.append('../examples/vision') +import models from auto_LiRPA import BoundedModule, BoundedParameter from auto_LiRPA.perturbations import * -parser = argparse.ArgumentParser() -parser.add_argument('--gen_ref', action='store_true', help='generate reference results') -args, unknown = parser.parse_known_args() - -if args.gen_ref: - data = {} -else: - data = torch.load('data/weight_perturbation_test_data') - -class mlp_3layer(nn.Module): - def __init__(self, in_ch=1, in_dim=28, width=1, pert_weight=True, pert_bias=True, norm=2): - super(mlp_3layer, self).__init__() - self.fc1 = nn.Linear(in_ch * in_dim * in_dim, 64 * width) - self.fc2 = nn.Linear(64 * width, 64 * width) - self.fc3 = nn.Linear(64 * width, 10) - - eps = 0.01 - global ptb - ptb = PerturbationLpNorm(norm=norm, eps=eps) - if pert_weight: - self.fc1.weight = BoundedParameter(self.fc1.weight.data, ptb) - self.fc2.weight = BoundedParameter(self.fc2.weight.data, ptb) - self.fc3.weight = BoundedParameter(self.fc3.weight.data, ptb) - - if pert_bias: - self.fc1.bias = BoundedParameter(self.fc1.bias.data, ptb) - self.fc2.bias = BoundedParameter(self.fc2.bias.data, ptb) - self.fc3.bias = BoundedParameter(self.fc3.bias.data, ptb) - - def forward(self, x): - x = x.view(-1, 784) - x = F.relu(self.fc1(x)) - x = F.relu(self.fc2(x)) - x = self.fc3(x) - return x - - -def verify_bounds(model, x, IBP, method, forward_ret, lb_name, ub_name): - lb, ub = model(method_opt="compute_bounds", x=(x,), IBP=IBP, method=method) - if args.gen_ref: - data[lb_name] = lb.detach().data.clone() - data[ub_name] = ub.detach().data.clone() - assert torch.allclose(lb, data[lb_name], 1e-4) - assert torch.allclose(ub, data[ub_name], 1e-4) - assert ((lb - data[lb_name]).pow(2).sum() < 1e-9) - assert ((ub - data[ub_name]).pow(2).sum() < 1e-9) - - # test gradient backward propagation - loss = (ub - lb).abs().sum() - loss.backward() - - # gradient w.r.t input only - grad = x.grad - if args.gen_ref: - data[lb_name+'_grad'] = grad.detach().data.clone() - assert torch.allclose(grad, data[lb_name + '_grad'], 1e-4) - assert ((grad - data[lb_name + '_grad']).pow(2).sum() < 1e-9) - - -def test(): - torch.manual_seed(1234) - torch.cuda.manual_seed_all(1234) - random.seed(1234) - np.random.seed(123) - - model_ori = mlp_3layer(pert_weight=True, pert_bias=True).eval() - if args.gen_ref: - data['model'] = model_ori.state_dict() - data['data'] = torch.randn(8, 1, 28, 28) - model_ori.load_state_dict(data['model']) - state_dict = copy.deepcopy(model_ori.state_dict()) - dummy_input = data['data'].requires_grad_() - inputs = (dummy_input,) - - model = BoundedModule(model_ori, inputs) - forward_ret = model(dummy_input) - model_ori.eval() - - assert torch.isclose(model_ori(dummy_input), model_ori(dummy_input), 1e-8).all() - - def verify_model(pert_weight=True, pert_bias=True, norm=np.inf, lb_name='', ub_name=''): - model_ori_ = mlp_3layer(pert_weight=pert_weight, pert_bias=pert_bias, norm=norm).eval() - model_ori_.load_state_dict(state_dict) - model_ = BoundedModule(model_ori_, inputs) - - ptb.eps = 0.01 - - verify_bounds(model_, dummy_input, IBP=True, method='backward', forward_ret=forward_ret, - lb_name=lb_name + '_CROWN-IBP', ub_name=ub_name + '_CROWN-IBP') # CROWN-IBP - verify_bounds(model_, dummy_input, IBP=False, method='backward', forward_ret=forward_ret, - lb_name=lb_name + '_CROWN', ub_name=ub_name + '_CROWN') # CROWN - - # Linf - verify_model(pert_weight=True, pert_bias=True, norm=np.inf, lb_name='l_inf_weights_bias_lb', - ub_name='l_inf_weights_bias_ub') - verify_model(pert_weight=True, pert_bias=False, norm=np.inf, lb_name='l_inf_weights_lb', ub_name='l_inf_weights_ub') - verify_model(pert_weight=False, pert_bias=True, norm=np.inf, lb_name='l_inf_bias_lb', ub_name='l_inf_bias_ub') - - # L2 - verify_model(pert_weight=True, pert_bias=True, norm=2, lb_name='l_2_weights_bias_lb', ub_name='l_2_weights_bias_ub') - verify_model(pert_weight=True, pert_bias=False, norm=2, lb_name='l_2_weights_lb', ub_name='l_2_weights_ub') - verify_model(pert_weight=False, pert_bias=True, norm=2, lb_name='l_2_bias_lb', ub_name='l_2_bias_ub') - - if args.gen_ref: - torch.save(data, 'data/weight_perturbation_test_data') -if __name__ == "__main__": - test() +class TestWeightPerturbation(TestCase): + def __init__(self, methodName='runTest', generate=False): + super().__init__(methodName, seed=1234, ref_path='data/weight_perturbation_test_data') + self.result = {} + + def test_training(self): + ret = subprocess.run( + ['python', 'weights_training.py', + '--device', 'cpu', + '--scheduler_opts', 'start=1,length=100', + '--num_epochs', '1', + '--truncate_data', '5'], + cwd='../examples/vision', capture_output=True) + self.assertEqual(ret.returncode, 0, ret.stderr) + res_test = ret.stdout.decode().split('\n')[-2].split(' ') + assert abs(float(res_test[8].split('=')[1]) - 2.246) < 0.01 + + def verify_bounds(self, model, x, IBP, method, forward_ret, lb_name, ub_name): + lb, ub = model(method_opt="compute_bounds", x=(x,), IBP=IBP, method=method) + self.result[lb_name] = lb.detach().data.clone() + self.result[ub_name] = ub.detach().data.clone() + assert torch.allclose(lb, self.result[lb_name], 1e-4) + assert torch.allclose(ub, self.result[ub_name], 1e-4) + assert ((lb - self.result[lb_name]).pow(2).sum() < 1e-9) + assert ((ub - self.result[ub_name]).pow(2).sum() < 1e-9) + + # test gradient backward propagation + loss = (ub - lb).abs().sum() + loss.backward() + + # gradient w.r.t input only + grad = x.grad + self.result[lb_name+'_grad'] = grad.detach().data.clone() + assert torch.allclose(grad, self.result[lb_name + '_grad'], 1e-4) + assert ((grad - self.result[lb_name + '_grad']).pow(2).sum() < 1e-9) + + def test_perturbation(self): + np.random.seed(123) # FIXME This seed is inconsistent with other seeds (1234) + + model_ori = models.Models['mlp_3layer_weight_perturb'](pert_weight=True, pert_bias=True).eval() + self.result['model'] = model_ori.state_dict() + self.result['data'] = torch.randn(8, 1, 28, 28) + model_ori.load_state_dict(self.result['model']) + state_dict = copy.deepcopy(model_ori.state_dict()) + dummy_input = self.result['data'].requires_grad_() + inputs = (dummy_input,) + + model = BoundedModule(model_ori, inputs) + forward_ret = model(dummy_input) + model_ori.eval() + + assert torch.isclose(model_ori(dummy_input), model_ori(dummy_input), 1e-8).all() + + def verify_model(pert_weight=True, pert_bias=True, norm=np.inf, lb_name='', ub_name=''): + model_ori_ = models.Models['mlp_3layer_weight_perturb'](pert_weight=pert_weight, pert_bias=pert_bias, norm=norm).eval() + model_ori_.load_state_dict(state_dict) + model_ = BoundedModule(model_ori_, inputs) + model_.ptb = model_ori.ptb + + self.verify_bounds(model_, dummy_input, IBP=True, method='backward', forward_ret=forward_ret, + lb_name=lb_name + '_CROWN-IBP', ub_name=ub_name + '_CROWN-IBP') # CROWN-IBP + self.verify_bounds(model_, dummy_input, IBP=False, method='backward', forward_ret=forward_ret, + lb_name=lb_name + '_CROWN', ub_name=ub_name + '_CROWN') # CROWN + + # Linf + verify_model(pert_weight=True, pert_bias=True, norm=np.inf, lb_name='l_inf_weights_bias_lb', + ub_name='l_inf_weights_bias_ub') + verify_model(pert_weight=True, pert_bias=False, norm=np.inf, lb_name='l_inf_weights_lb', ub_name='l_inf_weights_ub') + verify_model(pert_weight=False, pert_bias=True, norm=np.inf, lb_name='l_inf_bias_lb', ub_name='l_inf_bias_ub') + + # L2 + verify_model(pert_weight=True, pert_bias=True, norm=2, lb_name='l_2_weights_bias_lb', ub_name='l_2_weights_bias_ub') + verify_model(pert_weight=True, pert_bias=False, norm=2, lb_name='l_2_weights_lb', ub_name='l_2_weights_ub') + verify_model(pert_weight=False, pert_bias=True, norm=2, lb_name='l_2_bias_lb', ub_name='l_2_bias_ub') + + if self.generate: + self.save() diff --git a/tests/testcase.py b/tests/testcase.py new file mode 100644 index 0000000..5620617 --- /dev/null +++ b/tests/testcase.py @@ -0,0 +1,61 @@ +import unittest +import random +import torch +import numpy as np + +""" +Superclass for unit test cases in auto_LiRPA. +""" +class TestCase(unittest.TestCase): + def __init__(self, methodName='runTest', seed=1, ref_path=None, generate=False): + super().__init__(methodName) + + self.addTypeEqualityFunc(np.ndarray, 'assertArrayEqual') + self.addTypeEqualityFunc(torch.Tensor, 'assertTensorEqual') + + self.set_seed(seed) + self.ref_path = ref_path + self.generate = generate + + def set_seed(self, seed): + torch.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + random.seed(seed) + np.random.seed(seed) + + """ + Load the reference result if it exists + """ + def setUp(self): + self.reference = torch.load(self.ref_path) if self.ref_path else None + + """ + Save result for future comparison + """ + def save(self): + print('Saving result to', self.ref_path) + torch.save(self.result, self.ref_path) + + """ + This function can be called at the end of each test. + If `self.generate == True`, save results for future comparison; otherwise, + compare the current results `self.result` with the loaded reference + `self.reference`. Results are expected to be a list or tuple of + `torch.Tensor` instances. + """ + def check(self): + if self.generate: + self.save() + else: + for i in range(len(self.result)): + self.assertEqual(self.result[i], self.reference[i]) + + def assertArrayEqual(self, a, b, msg=None): + self.assertIsInstance(a, np.ndarray, 'First argument is not an np.ndarray') + self.assertIsInstance(b, np.ndarray, 'Second argument is not an np.ndarray') + return np.allclose(a, b) + + def assertTensorEqual(self, a, b, msg=None): + self.assertIsInstance(a, torch.Tensor, 'First argument is not an torch.Tensor') + self.assertIsInstance(b, torch.Tensor, 'Second argument is not an torch.Tensor') + return torch.allclose(a, b)

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