def get_explanation(generated_data, discriminator, prediction, XAItype="shap", cuda=True, trained_data=None,
data_type="mnist") -> None:
"""
This function calculates the explanation for given generated images using the desired xAI systems and the
:param generated_data: data created by the generator
:type generated_data: torch.Tensor
:param discriminator: the discriminator model
:type discriminator: torch.nn.Module
:param prediction: tensor of predictions by the discriminator on the generated data
:type prediction: torch.Tensor
:param XAItype: the type of xAI system to use. One of ("shap", "lime", "saliency")
:type XAItype: str
:param cuda: whether to use gpu
:type cuda: bool
:param trained_data: a batch from the dataset
:type trained_data: torch.Tensor
:param data_type: the type of the dataset used. One of ("cifar", "mnist", "fmnist")
:type data_type: str
:return:
:rtype:
"""
# initialize temp values to all 1s
temp = values_target(size=generated_data.size(), value=1.0, cuda=cuda)
# mask values with low prediction
mask = (prediction < 0.5).view(-1)
indices = (mask.nonzero(as_tuple=False)).detach().cpu().numpy().flatten().tolist()
data = generated_data[mask, :]
if len(indices) > 1:
if XAItype == "saliency":
for i in range(len(indices)):
explainer = Saliency(discriminator)
temp[indices[i], :] = explainer.attribute(data[i, :].detach().unsqueeze(0))
elif XAItype == "shap":
for i in range(len(indices)):
explainer = DeepLiftShap(discriminator)
temp[indices[i], :] = explainer.attribute(data[i, :].detach().unsqueeze(0), trained_data, target=0)
elif XAItype == "lime":
explainer = lime_image.LimeImageExplainer()
global discriminatorLime
discriminatorLime = deepcopy(discriminator)
discriminatorLime.cpu()
discriminatorLime.eval()
for i in range(len(indices)):
if data_type == "cifar":
tmp = data[i, :].detach().cpu().numpy()
tmp = np.reshape(tmp, (32, 32, 3)).astype(np.double)
exp = explainer.explain_instance(tmp, batch_predict_cifar, num_samples=100)
else:
tmp = data[i, :].squeeze().detach().cpu().numpy().astype(np.double)
exp = explainer.explain_instance(tmp, batch_predict, num_samples=100)
_, mask = exp.get_image_and_mask(exp.top_labels[0], positive_only=False, negative_only=False)
temp[indices[i], :] = torch.tensor(mask.astype(np.float))
del discriminatorLime
else:
raise Exception("wrong xAI type given")
if cuda:
temp = temp.cuda()
set_values(normalize_vector(temp))
def main(args):
train_loader, test_loader = data_generator(args.data_dir,1)
for m in range(len(models)):
model_name = "model_{}_NumFeatures_{}".format(models[m],args.NumFeatures)
model_filename = args.model_dir + 'm_' + model_name + '.pt'
pretrained_model = torch.load(open(model_filename, "rb"),map_location=device)
pretrained_model.to(device)
if(args.GradFlag):
Grad = Saliency(pretrained_model)
if(args.IGFlag):
IG = IntegratedGradients(pretrained_model)
if(args.DLFlag):
DL = DeepLift(pretrained_model)
if(args.GSFlag):
GS = GradientShap(pretrained_model)
if(args.DLSFlag):
DLS = DeepLiftShap(pretrained_model)
if(args.SGFlag):
Grad_ = Saliency(pretrained_model)
SG = NoiseTunnel(Grad_)
if(args.ShapleySamplingFlag):
SS = ShapleyValueSampling(pretrained_model)
if(args.GSFlag):
FP = FeaturePermutation(pretrained_model)
if(args.FeatureAblationFlag):
FA = FeatureAblation(pretrained_model)
if(args.OcclusionFlag):
OS = Occlusion(pretrained_model)
timeMask=np.zeros((args.NumTimeSteps, args.NumFeatures),dtype=int)
featureMask=np.zeros((args.NumTimeSteps, args.NumFeatures),dtype=int)
for i in range (args.NumTimeSteps):
timeMask[i,:]=i
for i in range (args.NumTimeSteps):
featureMask[:,i]=i
indexes = [[] for i in range(5,10)]
for i ,(data, target) in enumerate(test_loader):
if(target==5 or target==6 or target==7 or target==8 or target==9):
index=target-5
if(len(indexes[index])<1):
indexes[index].append(i)
for j, index in enumerate(indexes):
print(index)
# indexes = [[21],[17],[84],[9]]
for j, index in enumerate(indexes):
print("Getting Saliency for number", j+1)
for i, (data, target) in enumerate(test_loader):
if(i in index):
labels = target.to(device)
input = data.reshape(-1, args.NumTimeSteps, args.NumFeatures).to(device)
input = Variable(input, volatile=False, requires_grad=True)
baseline_single=torch.Tensor(np.random.random(input.shape)).to(device)
baseline_multiple=torch.Tensor(np.random.random((input.shape[0]*5,input.shape[1],input.shape[2]))).to(device)
inputMask= np.zeros((input.shape))
inputMask[:,:,:]=timeMask
inputMask =torch.Tensor(inputMask).to(device)
mask_single= torch.Tensor(timeMask).to(device)
mask_single=mask_single.reshape(1,args.NumTimeSteps, args.NumFeatures).to(device)
Data=data.reshape(args.NumTimeSteps, args.NumFeatures).data.cpu().numpy()
target_=int(target.data.cpu().numpy()[0])
plotExampleBox(Data,args.Graph_dir+'Sample_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.GradFlag):
attributions = Grad.attribute(input, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_Grad_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(Grad,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=None)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_Grad_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.IGFlag):
attributions = IG.attribute(input, \
baselines=baseline_single, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_IG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(IG,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_IG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.DLFlag):
attributions = DL.attribute(input, \
baselines=baseline_single, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_DL_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(DL,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_DL_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.GSFlag):
attributions = GS.attribute(input, \
baselines=baseline_multiple, \
stdevs=0.09,\
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_GS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(GS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_multiple)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_GS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.DLSFlag):
attributions = DLS.attribute(input, \
baselines=baseline_multiple, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_DLS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(DLS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_multiple)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_DLS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.SGFlag):
attributions = SG.attribute(input, \
target=labels)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_SG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(SG,input, args.NumFeatures,args.NumTimeSteps, labels)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_SG_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.ShapleySamplingFlag):
attributions = SS.attribute(input, \
baselines=baseline_single, \
target=labels,\
feature_mask=inputMask)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_SVS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(SS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single,hasFeatureMask=inputMask)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_SVS_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
# if(args.FeaturePermutationFlag):
# attributions = FP.attribute(input, \
# target=labels),
# # perturbations_per_eval= 1,\
# # feature_mask=mask_single)
# saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
# plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_FP',greyScale=True)
if(args.FeatureAblationFlag):
attributions = FA.attribute(input, \
target=labels)
# perturbations_per_eval= input.shape[0],\
# feature_mask=mask_single)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_FA_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(FA,input, args.NumFeatures,args.NumTimeSteps, labels)
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_FA_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.OcclusionFlag):
attributions = OS.attribute(input, \
sliding_window_shapes=(1,int(args.NumFeatures/10)),
target=labels,
baselines=baseline_single)
saliency_=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,attributions)
plotExampleBox(saliency_[0],args.Graph_dir+models[m]+'_FO_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
if(args.TSRFlag):
TSR_attributions = getTwoStepRescaling(OS,input, args.NumFeatures,args.NumTimeSteps, labels,hasBaseline=baseline_single,hasSliding_window_shapes= (1,int(args.NumFeatures/10)))
TSR_saliency=Helper.givenAttGetRescaledSaliency(args.NumTimeSteps, args.NumFeatures,TSR_attributions,isTensor=False)
plotExampleBox(TSR_saliency,args.Graph_dir+models[m]+'_TSR_FO_MNIST_'+str(target_)+'_index_'+str(i+1),greyScale=True)
ax.set_xticks([])
ax.set_yticks([])
ax.set_yticklabels([])
ax.set_xticklabels([])
# plt.axis('off')
if i == 1:
ax.set_ylabel(r"$s'$",
rotation=0,
fontsize=10,
fontfamily="Times New Roman")
ax.yaxis.set_label_coords(-0.22, 0.4)
input = np.array((obs, next_obs)).astype(np.float32)
input = th.tensor(np.expand_dims(input, axis=0)).to('cpu').to(dtype)
input.requires_grad = True
attributions = sal.attribute(input)
attributions = np.abs(attributions.detach()[0, ...])
ax = plt.subplot(3, w, w + i)
ax.set_xticks([])
ax.set_yticks([])
ax.set_yticklabels([])
ax.set_xticklabels([])
ax.imshow(attributions[0, ...],
cmap='gray',
vmin=attributions.min(),
vmax=attributions.max())
if i == 1:
ax.set_ylabel(r"$\left\vert \frac{dR}{ds} \right\vert$",
rotation=0,
fontsize=10,
grads_sal = list()
grads_igrad = list()
grads_occ = list()
grads_gshap = list()
grads_dlift = list()
signal = list()
for idx in range(16):
x = signals[idx].float().unsqueeze(0)
x.requires_grad = True
model.eval()
# Saliency
saliency = Saliency(model)
grads = saliency.attribute(x, target=labels[idx].item())
grads_sal.append(grads.squeeze().cpu().detach().numpy())
# Occlusion
occlusion = Occlusion(model)
grads = occlusion.attribute(x,
strides=(1, int(FS / 100)),
target=labels[idx].item(),
sliding_window_shapes=(1, int(FS / 10)),
baselines=0)
grads_occ.append(grads.squeeze().cpu().detach().numpy())
# Integrated Gradients
integrated_gradients = IntegratedGradients(model)
grads = integrated_gradients.attribute(x,
target=labels[idx].item(),
def extract_Sa(self, X_test):
saliency = Saliency(self.net)
start = time.time()
saliency_attr_test = saliency.attribute(X_test.to(self.device))
print("temps train", time.time() - start)
return saliency_attr_test.detach().cpu().numpy()
def visualize_maps(
model: torch.nn.Module,
inputs: Union[Tuple[torch.Tensor, torch.Tensor]],
labels: torch.Tensor,
title: str,
second_occlusion: Tuple[int, int, int] = (1, 2, 2),
baselines: Tuple[int, int] = (0, 0),
closest: bool = False,
) -> None:
"""
Visualizes the average of the inputs, or the single input, using various different XAI approaches
"""
single = inputs[1].ndim == 2
model.zero_grad()
model.eval()
occ = Occlusion(model)
saliency = Saliency(model)
saliency = NoiseTunnel(saliency)
igrad = IntegratedGradients(model)
igrad_2 = NoiseTunnel(igrad)
# deep_lift = DeepLift(model)
grad_shap = ShapleyValueSampling(model)
output = model(inputs[0], inputs[1])
output = F.softmax(output, dim=-1).argmax(dim=1, keepdim=True)
labels = F.softmax(labels, dim=-1).argmax(dim=1, keepdim=True)
if np.all(labels.cpu().numpy() == 1) and not closest:
return
if True:
targets = labels
else:
targets = output
print(targets)
correct = targets.cpu().numpy() == labels.cpu().numpy()
# if correct:
# return
occ_out = occ.attribute(
inputs,
baselines=baselines,
sliding_window_shapes=((1, 5, 5), second_occlusion),
target=targets,
)
# occ_out2 = occ.attribute(inputs, sliding_window_shapes=((1,20,20), second_occlusion), strides=(8,1), target=targets)
saliency_out = saliency.attribute(inputs,
nt_type="smoothgrad_sq",
n_samples=5,
target=targets,
abs=False)
# igrad_out = igrad.attribute(inputs, target=targets, internal_batch_size=1)
igrad_out = igrad_2.attribute(
inputs,
baselines=baselines,
target=targets,
n_samples=5,
nt_type="smoothgrad_sq",
internal_batch_size=1,
)
# deep_lift_out = deep_lift.attribute(inputs, target=targets)
grad_shap_out = grad_shap.attribute(inputs,
baselines=baselines,
target=targets)
if single:
inputs = convert_to_image(inputs)
occ_out = convert_to_image(occ_out)
saliency_out = convert_to_image(saliency_out)
igrad_out = convert_to_image(igrad_out)
# grad_shap_out = convert_to_image(grad_shap_out)
else:
inputs = convert_to_image_multi(inputs)
occ_out = convert_to_image_multi(occ_out)
saliency_out = convert_to_image_multi(saliency_out)
igrad_out = convert_to_image_multi(igrad_out)
grad_shap_out = convert_to_image_multi(grad_shap_out)
fig, axes = plt.subplots(2, 5)
(fig, axes[0, 0]) = visualization.visualize_image_attr(
occ_out[0][0],
inputs[0][0],
title="Original Image",
method="original_image",
show_colorbar=True,
plt_fig_axis=(fig, axes[0, 0]),
use_pyplot=False,
)
(fig, axes[0, 1]) = visualization.visualize_image_attr(
occ_out[0][0],
None,
sign="all",
title="Occ (5x5)",
show_colorbar=True,
plt_fig_axis=(fig, axes[0, 1]),
use_pyplot=False,
)
(fig, axes[0, 2]) = visualization.visualize_image_attr(
saliency_out[0][0],
None,
sign="all",
title="Saliency",
show_colorbar=True,
plt_fig_axis=(fig, axes[0, 2]),
use_pyplot=False,
)
(fig, axes[0, 3]) = visualization.visualize_image_attr(
igrad_out[0][0],
None,
sign="all",
title="Integrated Grad",
show_colorbar=True,
plt_fig_axis=(fig, axes[0, 3]),
use_pyplot=False,
)
(fig, axes[0, 4]) = visualization.visualize_image_attr(
grad_shap_out[0],
None,
title="GradSHAP",
show_colorbar=True,
plt_fig_axis=(fig, axes[0, 4]),
use_pyplot=False,
)
##### Second Input Labels #########################################################################################
(fig, axes[1, 0]) = visualization.visualize_image_attr(
occ_out[1],
inputs[1],
title="Original Aux",
method="original_image",
show_colorbar=True,
plt_fig_axis=(fig, axes[1, 0]),
use_pyplot=False,
)
(fig, axes[1, 1]) = visualization.visualize_image_attr(
occ_out[1],
None,
sign="all",
title="Occ (1x1)",
show_colorbar=True,
plt_fig_axis=(fig, axes[1, 1]),
use_pyplot=False,
)
(fig, axes[1, 2]) = visualization.visualize_image_attr(
saliency_out[1],
None,
sign="all",
title="Saliency",
show_colorbar=True,
plt_fig_axis=(fig, axes[1, 2]),
use_pyplot=False,
)
(fig, axes[1, 3]) = visualization.visualize_image_attr(
igrad_out[1],
None,
sign="all",
title="Integrated Grad",
show_colorbar=True,
plt_fig_axis=(fig, axes[1, 3]),
use_pyplot=False,
)
(fig, axes[1, 4]) = visualization.visualize_image_attr(
grad_shap_out[1],
None,
title="GradSHAP",
show_colorbar=True,
plt_fig_axis=(fig, axes[1, 4]),
use_pyplot=False,
)
fig.suptitle(
title +
f" Label: {labels.cpu().numpy()} Pred: {targets.cpu().numpy()}")
plt.savefig(
f"{title}_{'single' if single else 'multi'}_{'Failed' if correct else 'Success'}_baseline{baselines[0]}.png",
dpi=300,
)
plt.clf()
plt.cla()
def run_saliency_methods(saliency_methods,
pretrained_model,
test_shape,
train_loader,
test_loader,
device,
model_type,
model_name,
saliency_dir,
tsr_graph_dir=None,
tsr_inputs_to_graph=()):
_, num_timesteps, num_features = test_shape
run_grad = "Grad" in saliency_methods
run_grad_tsr = "Grad_TSR" in saliency_methods
run_ig = "IG" in saliency_methods
run_ig_tsr = "IG_TSR" in saliency_methods
run_dl = "DL" in saliency_methods
run_gs = "GS" in saliency_methods
run_dls = "DLS" in saliency_methods
run_dls_tsr = "DLS_TSR" in saliency_methods
run_sg = "SG" in saliency_methods
run_shapley_sampling = "ShapleySampling" in saliency_methods
run_feature_permutation = "FeaturePermutation" in saliency_methods
run_feature_ablation = "FeatureAblation" in saliency_methods
run_occlusion = "Occlusion" in saliency_methods
run_fit = "FIT" in saliency_methods
run_ifit = "IFIT" in saliency_methods
run_wfit = "WFIT" in saliency_methods
run_iwfit = "IWFIT" in saliency_methods
if run_grad or run_grad_tsr:
Grad = Saliency(pretrained_model)
if run_grad:
rescaledGrad = np.zeros(test_shape)
if run_grad_tsr:
rescaledGrad_TSR = np.zeros(test_shape)
if run_ig or run_ig_tsr:
IG = IntegratedGradients(pretrained_model)
if run_ig:
rescaledIG = np.zeros(test_shape)
if run_ig_tsr:
rescaledIG_TSR = np.zeros(test_shape)
if run_dl:
rescaledDL = np.zeros(test_shape)
DL = DeepLift(pretrained_model)
if run_gs:
rescaledGS = np.zeros(test_shape)
GS = GradientShap(pretrained_model)
if run_dls or run_dls_tsr:
DLS = DeepLiftShap(pretrained_model)
if run_dls:
rescaledDLS = np.zeros(test_shape)
if run_dls_tsr:
rescaledDLS_TSR = np.zeros(test_shape)
if run_sg:
rescaledSG = np.zeros(test_shape)
Grad_ = Saliency(pretrained_model)
SG = NoiseTunnel(Grad_)
if run_shapley_sampling:
rescaledShapleySampling = np.zeros(test_shape)
SS = ShapleyValueSampling(pretrained_model)
if run_gs:
rescaledFeaturePermutation = np.zeros(test_shape)
FP = FeaturePermutation(pretrained_model)
if run_feature_ablation:
rescaledFeatureAblation = np.zeros(test_shape)
FA = FeatureAblation(pretrained_model)
if run_occlusion:
rescaledOcclusion = np.zeros(test_shape)
OS = Occlusion(pretrained_model)
if run_fit:
rescaledFIT = np.zeros(test_shape)
FIT = FITExplainer(pretrained_model, ft_dim_last=True)
generator = JointFeatureGenerator(num_features, data='none')
# TODO: Increase epochs
FIT.fit_generator(generator, train_loader, test_loader, n_epochs=300)
if run_ifit:
rescaledIFIT = np.zeros(test_shape)
if run_wfit:
rescaledWFIT = np.zeros(test_shape)
if run_iwfit:
rescaledIWFIT = np.zeros(test_shape)
idx = 0
mask = np.zeros((num_timesteps, num_features), dtype=int)
for i in range(num_timesteps):
mask[i, :] = i
for i, (samples, labels) in enumerate(test_loader):
input = samples.reshape(-1, num_timesteps, num_features).to(device)
input = Variable(input, volatile=False, requires_grad=True)
batch_size = input.shape[0]
baseline_single = torch.from_numpy(np.random.random(
input.shape)).to(device)
baseline_multiple = torch.from_numpy(
np.random.random((input.shape[0] * 5, input.shape[1],
input.shape[2]))).to(device)
inputMask = np.zeros((input.shape))
inputMask[:, :, :] = mask
inputMask = torch.from_numpy(inputMask).to(device)
mask_single = torch.from_numpy(mask).to(device)
mask_single = mask_single.reshape(1, num_timesteps,
num_features).to(device)
labels = torch.tensor(labels.int().tolist()).to(device)
if run_grad:
attributions = Grad.attribute(input, target=labels)
rescaledGrad[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_grad_tsr:
rescaledGrad_TSR[idx:idx + batch_size, :, :] = get_tsr_saliency(
Grad,
input,
labels,
graph_dir=tsr_graph_dir,
graph_name=f'{model_name}_{model_type}_Grad_TSR',
inputs_to_graph=tsr_inputs_to_graph,
cur_batch=i)
if run_ig:
attributions = IG.attribute(input,
baselines=baseline_single,
target=labels)
rescaledIG[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_ig_tsr:
rescaledIG_TSR[idx:idx + batch_size, :, :] = get_tsr_saliency(
IG,
input,
labels,
baseline=baseline_single,
graph_dir=tsr_graph_dir,
graph_name=f'{model_name}_{model_type}_IG_TSR',
inputs_to_graph=tsr_inputs_to_graph,
cur_batch=i)
if run_dl:
attributions = DL.attribute(input,
baselines=baseline_single,
target=labels)
rescaledDL[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_gs:
attributions = GS.attribute(input,
baselines=baseline_multiple,
stdevs=0.09,
target=labels)
rescaledGS[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_dls:
attributions = DLS.attribute(input,
baselines=baseline_multiple,
target=labels)
rescaledDLS[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_dls_tsr:
rescaledDLS_TSR[idx:idx + batch_size, :, :] = get_tsr_saliency(
DLS,
input,
labels,
baseline=baseline_multiple,
graph_dir=tsr_graph_dir,
graph_name=f'{model_name}_{model_type}_DLS_TSR',
inputs_to_graph=tsr_inputs_to_graph,
cur_batch=i)
if run_sg:
attributions = SG.attribute(input, target=labels)
rescaledSG[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_shapley_sampling:
attributions = SS.attribute(input,
baselines=baseline_single,
target=labels,
feature_mask=inputMask)
rescaledShapleySampling[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_feature_permutation:
attributions = FP.attribute(input,
target=labels,
perturbations_per_eval=input.shape[0],
feature_mask=mask_single)
rescaledFeaturePermutation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_feature_ablation:
attributions = FA.attribute(input, target=labels)
# perturbations_per_eval= input.shape[0],\
# feature_mask=mask_single)
rescaledFeatureAblation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_occlusion:
attributions = OS.attribute(input,
sliding_window_shapes=(1,
num_features),
target=labels,
baselines=baseline_single)
rescaledOcclusion[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_fit:
attributions = torch.from_numpy(FIT.attribute(input, labels))
rescaledFIT[idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
num_timesteps, num_features, attributions)
if run_ifit:
attributions = torch.from_numpy(
inverse_fit_attribute(input,
pretrained_model,
ft_dim_last=True))
rescaledIFIT[idx:idx + batch_size, :, :] = attributions
if run_wfit:
attributions = torch.from_numpy(
wfit_attribute(input,
pretrained_model,
N=test_shape[1],
ft_dim_last=True,
single_label=True))
rescaledWFIT[idx:idx + batch_size, :, :] = attributions
if run_iwfit:
attributions = torch.from_numpy(
wfit_attribute(input,
pretrained_model,
N=test_shape[1],
ft_dim_last=True,
single_label=True,
inverse=True))
rescaledIWFIT[idx:idx + batch_size, :, :] = attributions
idx += batch_size
if run_grad:
print("Saving Grad", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_Grad_rescaled",
rescaledGrad)
if run_grad_tsr:
print("Saving Grad_TSR", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_Grad_TSR_rescaled", rescaledGrad_TSR)
if run_ig:
print("Saving IG", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_IG_rescaled",
rescaledIG)
if run_ig_tsr:
print("Saving IG_TSR", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_IG_TSR_rescaled",
rescaledIG_TSR)
if run_dl:
print("Saving DL", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_DL_rescaled",
rescaledDL)
if run_gs:
print("Saving GS", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_GS_rescaled",
rescaledGS)
if run_dls:
print("Saving DLS", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_DLS_rescaled",
rescaledDLS)
if run_dls_tsr:
print("Saving DLS_TSR", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_DLS_TSR_rescaled",
rescaledDLS_TSR)
if run_sg:
print("Saving SG", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_SG_rescaled",
rescaledSG)
if run_shapley_sampling:
print("Saving ShapleySampling", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_ShapleySampling_rescaled", rescaledShapleySampling)
if run_feature_permutation:
print("Saving FeaturePermutation", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_FeaturePermutation_rescaled", rescaledFeaturePermutation)
if run_feature_ablation:
print("Saving FeatureAblation", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_FeatureAblation_rescaled", rescaledFeatureAblation)
if run_occlusion:
print("Saving Occlusion", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type +
"_Occlusion_rescaled", rescaledOcclusion)
if run_fit:
print("Saving FIT", model_name + "_" + model_type)
np.save(saliency_dir + model_name + "_" + model_type + "_FIT_rescaled",
rescaledFIT)
if run_ifit:
print("Saving IFIT", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_IFIT_rescaled",
rescaledIFIT)
if run_wfit:
print("Saving WFIT", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_WFIT_rescaled",
rescaledWFIT)
if run_iwfit:
print("Saving IWFIT", model_name + "_" + model_type)
np.save(
saliency_dir + model_name + "_" + model_type + "_IWFIT_rescaled",
rescaledIWFIT)
def saliency(classifier_model,
config,
dataset_features,
GNNgraph_list,
current_fold=None,
cuda=0):
'''
:param classifier_model: trained classifier model
:param config: parsed configuration file of config.yml
:param dataset_features: a dictionary of dataset features obtained from load_data.py
:param GNNgraph_list: a list of GNNgraphs obtained from the dataset
:param current_fold: has no use in this method
:param cuda: whether to use GPU to perform conversion to tensor
'''
# Initialise settings
config = config
interpretability_config = config["interpretability_methods"]["saliency"]
dataset_features = dataset_features
# Perform Saliency on the classifier model
sl = Saliency(classifier_model)
output_for_metrics_calculation = []
output_for_generating_saliency_map = {}
# Obtain attribution score for use in qualitative metrics
tmp_timing_list = []
for GNNgraph in GNNgraph_list:
output = {'graph': GNNgraph}
for _, label in dataset_features["label_dict"].items():
# Relabel all just in case, may only relabel those that need relabelling
# if performance is poor
original_label = GNNgraph.label
GNNgraph.label = label
node_feat, n2n, subg = graph_to_tensor(
[GNNgraph], dataset_features["feat_dim"],
dataset_features["edge_feat_dim"], cuda)
start_generation = perf_counter()
attribution = sl.attribute(node_feat,
additional_forward_args=(n2n, subg,
[GNNgraph]),
target=label)
tmp_timing_list.append(perf_counter() - start_generation)
attribution_score = torch.sum(attribution, dim=1).tolist()
attribution_score = standardize_scores(attribution_score)
GNNgraph.label = original_label
output[label] = attribution_score
output_for_metrics_calculation.append(output)
execution_time = sum(tmp_timing_list) / (len(tmp_timing_list))
# Obtain attribution score for use in generating saliency map for comparison with zero tensors
if interpretability_config["sample_ids"] is not None:
if ',' in str(interpretability_config["sample_ids"]):
sample_graph_id_list = list(
map(int, interpretability_config["sample_ids"].split(',')))
else:
sample_graph_id_list = [int(interpretability_config["sample_ids"])]
output_for_generating_saliency_map.update({
"layergradcam_%s_%s" % (str(assign_type), str(label)): []
for _, label in dataset_features["label_dict"].items()
})
for index in range(len(output_for_metrics_calculation)):
tmp_output = output_for_metrics_calculation[index]
tmp_label = tmp_output['graph'].label
if tmp_output['graph'].graph_id in sample_graph_id_list:
element_name = "layergradcam_%s_%s" % (str(assign_type),
str(tmp_label))
output_for_generating_saliency_map[element_name].append(
(tmp_output['graph'], tmp_output[tmp_label]))
elif interpretability_config["number_of_samples"] > 0:
# Randomly sample from existing list:
graph_idxes = list(range(len(output_for_metrics_calculation)))
random.shuffle(graph_idxes)
output_for_generating_saliency_map.update({
"saliency_class_%s" % str(label): []
for _, label in dataset_features["label_dict"].items()
})
# Begin appending found samples
for index in graph_idxes:
tmp_label = output_for_metrics_calculation[index]['graph'].label
if len(output_for_generating_saliency_map["saliency_class_%s" % str(tmp_label)]) < \
interpretability_config["number_of_samples"]:
output_for_generating_saliency_map[
"saliency_class_%s" % str(tmp_label)].append(
(output_for_metrics_calculation[index]['graph'],
output_for_metrics_calculation[index][tmp_label]))
return output_for_metrics_calculation, output_for_generating_saliency_map, execution_time
def captum_routine(model, dataloader, radiusneigh):
figdist, axdist = plt.subplots()
model.eval()
atrs_feat = []
nhalos, nhalos_compl = 0, 0
for i, data in enumerate(dataloader):
data.to(device)
data.x.requires_grad_(True)
# Apply the method and get attributions
if method == "saliency":
atrmethod = Saliency(
lambda datax: model_forward(datax, model, data))
elif method == "intgradients":
atrmethod = IntegratedGradients(
lambda datax: model_forward(datax, model, data)) # not working
attributions = atrmethod.attribute(data.x, target=0)
atrs_feat.append(attributions.detach().cpu().numpy().mean(axis=0))
atr_col = np.abs(attributions.detach().cpu().numpy()).mean(axis=1)
dists = np.sqrt(np.sum(data.pos.detach().cpu().numpy()**2.,
axis=1)) * boxsize
# Scatter plot of saliency vs distance to the center
sizes = 10.**(data.x[:, 3].detach().cpu().numpy() + 2.)
indxs = np.argwhere(dists > 0.).reshape(-1)
axdist.scatter(dists[indxs],
atr_col[indxs],
s=sizes[indxs] * 0.2,
c="blue",
alpha=0.3)
#axdist.scatter(dists[indxs], sizes[indxs], c=atr_col[indxs], alpha=0.5, vmin=0., vmax=0.3)
out = model(data)
y_out = out[:, 0]
err = (y_out.reshape(-1) - data.y) / data.y
err = np.abs(err.detach().cpu().numpy())
# Plot saliency graph
for ibatch in range(batch_size):
data.x.requires_grad_(False)
# Choose only the subhalos of the graph within the batch
indexes = np.argwhere(ibatch == data.batch).reshape(-1)
datagraph = data.x[indexes]
if indexes.shape[0] > 0: # Avoid possible segmentation fault
edge_index = radius_graph(datagraph[:, :3], r=radiusneigh)
num_nodes, num_edges = datagraph.shape[0], edge_index.shape[
1] // 2 # Divide by 2 since edges are counted doubled if not directed
if num_edges == num_nodes * (num_nodes - 1) // 2:
nhalos_compl += 1
nhalos += 1
if err[ibatch] < 0.015 and datagraph.shape[0] >= 10:
#if datagraph.shape[0]>=10 and datagraph.shape[0]<15:
#print("Ind", ibatch, "Error",err[ibatch])
visualize_points_3D(datagraph, ibatch, atr_col[indexes],
edge_index)
print("Number of graphs:", nhalos, "out of which ", nhalos_compl,
"are complete. Fraction:",
float(nhalos_compl) / float(nhalos))
axdist.set_ylabel("Saliency")
axdist.set_xlabel("Distance [kpc/h]")
#axdist.set_xscale("log")
#axdist.set_yscale("log")
figdist.savefig("Plots/distance_attribute_" + method + ".pdf")
plt.close(figdist)
# Feature importance plot
importances = np.abs(np.array(atrs_feat).mean(0))
feature_names = [r"$x$", r"$y$", r"$z$", r"$M_*$", r"$v$", r"$R_*$"]
np.savetxt("Outputs/feature_importance.txt", importances)
feature_importance_plot(importances, feature_names, method)
mode = 'L'
# load each image to PIL
imgs = [Image.fromarray(img, mode=mode) for img in img_list]
# apply the stored transform to the input
imgs = [transform(img) for img in imgs]
im_tensor = torch.stack(imgs)
print(im_tensor.shape)
n_img = torch.tensor([im_tensor.shape[0]], requires_grad=False)
# wrap in a class that allows saliency readout
wrapped_model = ModelReadoutWrapper(model)
saliency = Saliency(wrapped_model)
grads = saliency.attribute(inputs=im_tensor, target=0, additional_forward_args=n_img)
# now the model has been invoked and the results are available
results = wrapped_model.format_readout()
norm_grads = []
for i in range(len(grads)):
# get the original image
original_image = np.transpose((imgs[i].cpu().detach().numpy() / 2) + 0.5, (1, 2, 0))
# get the gradients for this image
img_grad = grads[i, :, :, :]
# shape must be h,w,c, move first dim to the end
img_grad = img_grad.permute((1, 2, 0))
norm_grad = _normalize_image_attr(img_grad.numpy(), sign="all", outlier_perc=2)
norm_grads.append(norm_grad)
if save_png:
att_score = results['attention_outputs'][0][i]
torch.save(seq_state,
options.out + "/all_mammal_20S_digestion_sequence_mod.pt")
torch.save(motif_state,
options.out + "/all_mammal_20S_digestion_motif_mod.pt")
## identify feature importance
# define also
## identify feature importance
saliency = Saliency(motif_model)
pos_saliency_loader = torch.utils.data.DataLoader(pos_train,
batch_size=len(pos_train))
pos_saliency = next(iter(pos_saliency_loader))
pos_grads = saliency.attribute(
(pos_saliency[0][:, :, 22:].type(dtype), pos_saliency[1].type(dtype),
pos_saliency[2].type(dtype)),
target=1,
abs=False)
neg_saliency_loader = torch.utils.data.DataLoader(neg_train,
batch_size=len(neg_train))
neg_saliency = next(iter(neg_saliency_loader))
neg_grads = saliency.attribute(
(neg_saliency[0][:, :, 22:].type(dtype), neg_saliency[1].type(dtype),
neg_saliency[2].type(dtype)),
target=0,
abs=False)
pos_c_flag = [i == 1 for i in pos_saliency[1]]
pos_i_flag = [i == 0 for i in pos_saliency[1]]
def get_attribution(real_img,
fake_img,
real_class,
fake_class,
net_module,
checkpoint_path,
input_shape,
channels,
methods=["ig", "grads", "gc", "ggc", "dl", "ingrad", "random", "residual"],
output_classes=6,
downsample_factors=[(2,2), (2,2), (2,2), (2,2)]):
imgs = [image_to_tensor(normalize_image(real_img).astype(np.float32)),
image_to_tensor(normalize_image(fake_img).astype(np.float32))]
classes = [real_class, fake_class]
net = init_network(checkpoint_path, input_shape, net_module, channels, output_classes=output_classes,eval_net=True, require_grad=False,
downsample_factors=downsample_factors)
attrs = []
attrs_names = []
if "residual" in methods:
res = np.abs(real_img - fake_img)
res = res - np.min(res)
attrs.append(torch.tensor(res/np.max(res)))
attrs_names.append("residual")
if "random" in methods:
rand = np.abs(np.random.randn(*np.shape(real_img)))
rand = np.abs(scipy.ndimage.filters.gaussian_filter(rand, 4))
rand = rand - np.min(rand)
rand = rand/np.max(np.abs(rand))
attrs.append(torch.tensor(rand))
attrs_names.append("random")
if "gc" in methods:
net.zero_grad()
last_conv_layer = [(name,module) for name, module in net.named_modules() if type(module) == torch.nn.Conv2d][-1]
layer_name = last_conv_layer[0]
layer = last_conv_layer[1]
layer_gc = LayerGradCam(net, layer)
gc_real = layer_gc.attribute(imgs[0], target=classes[0])
gc_fake = layer_gc.attribute(imgs[1], target=classes[1])
gc_real = project_layer_activations_to_input_rescale(gc_real.cpu().detach().numpy(), (input_shape[0], input_shape[1]))
gc_fake = project_layer_activations_to_input_rescale(gc_fake.cpu().detach().numpy(), (input_shape[0], input_shape[1]))
attrs.append(torch.tensor(gc_real[0,0,:,:]))
attrs_names.append("gc_real")
attrs.append(torch.tensor(gc_fake[0,0,:,:]))
attrs_names.append("gc_fake")
# SCAM
gc_diff_0, gc_diff_1 = get_sgc(real_img, fake_img, real_class,
fake_class, net_module, checkpoint_path,
input_shape, channels, None, output_classes=output_classes,
downsample_factors=downsample_factors)
attrs.append(gc_diff_0)
attrs_names.append("gc_diff_0")
attrs.append(gc_diff_1)
attrs_names.append("gc_diff_1")
if "ggc" in methods:
net.zero_grad()
last_conv = [module for module in net.modules() if type(module) == torch.nn.Conv2d][-1]
guided_gc = GuidedGradCam(net, last_conv)
ggc_real = guided_gc.attribute(imgs[0], target=classes[0])
ggc_fake = guided_gc.attribute(imgs[1], target=classes[1])
attrs.append(ggc_real[0,0,:,:])
attrs_names.append("ggc_real")
attrs.append(ggc_fake[0,0,:,:])
attrs_names.append("ggc_fake")
net.zero_grad()
gbp = GuidedBackprop(net)
gbp_real = gbp.attribute(imgs[0], target=classes[0])
gbp_fake = gbp.attribute(imgs[1], target=classes[1])
attrs.append(gbp_real[0,0,:,:])
attrs_names.append("gbp_real")
attrs.append(gbp_fake[0,0,:,:])
attrs_names.append("gbp_fake")
ggc_diff_0 = gbp_real[0,0,:,:] * gc_diff_0
ggc_diff_1 = gbp_fake[0,0,:,:] * gc_diff_1
attrs.append(ggc_diff_0)
attrs_names.append("ggc_diff_0")
attrs.append(ggc_diff_1)
attrs_names.append("ggc_diff_1")
# IG
if "ig" in methods:
baseline = image_to_tensor(np.zeros(input_shape, dtype=np.float32))
net.zero_grad()
ig = IntegratedGradients(net)
ig_real, delta_real = ig.attribute(imgs[0], baseline, target=classes[0], return_convergence_delta=True)
ig_fake, delta_fake = ig.attribute(imgs[1], baseline, target=classes[1], return_convergence_delta=True)
ig_diff_0, delta_diff = ig.attribute(imgs[0], imgs[1], target=classes[0], return_convergence_delta=True)
ig_diff_1, delta_diff = ig.attribute(imgs[1], imgs[0], target=classes[1], return_convergence_delta=True)
attrs.append(ig_real[0,0,:,:])
attrs_names.append("ig_real")
attrs.append(ig_fake[0,0,:,:])
attrs_names.append("ig_fake")
attrs.append(ig_diff_0[0,0,:,:])
attrs_names.append("ig_diff_0")
attrs.append(ig_diff_1[0,0,:,:])
attrs_names.append("ig_diff_1")
# DL
if "dl" in methods:
net.zero_grad()
dl = DeepLift(net)
dl_real = dl.attribute(imgs[0], target=classes[0])
dl_fake = dl.attribute(imgs[1], target=classes[1])
dl_diff_0 = dl.attribute(imgs[0], baselines=imgs[1], target=classes[0])
dl_diff_1 = dl.attribute(imgs[1], baselines=imgs[0], target=classes[1])
attrs.append(dl_real[0,0,:,:])
attrs_names.append("dl_real")
attrs.append(dl_fake[0,0,:,:])
attrs_names.append("dl_fake")
attrs.append(dl_diff_0[0,0,:,:])
attrs_names.append("dl_diff_0")
attrs.append(dl_diff_1[0,0,:,:])
attrs_names.append("dl_diff_1")
# INGRAD
if "ingrad" in methods:
net.zero_grad()
saliency = Saliency(net)
grads_real = saliency.attribute(imgs[0],
target=classes[0])
grads_fake = saliency.attribute(imgs[1],
target=classes[1])
attrs.append(grads_real[0,0,:,:])
attrs_names.append("grads_real")
attrs.append(grads_fake[0,0,:,:])
attrs_names.append("grads_fake")
net.zero_grad()
input_x_gradient = InputXGradient(net)
ingrad_real = input_x_gradient.attribute(imgs[0], target=classes[0])
ingrad_fake = input_x_gradient.attribute(imgs[1], target=classes[1])
ingrad_diff_0 = grads_fake * (imgs[0] - imgs[1])
ingrad_diff_1 = grads_real * (imgs[1] - imgs[0])
attrs.append(torch.abs(ingrad_real[0,0,:,:]))
attrs_names.append("ingrad_real")
attrs.append(torch.abs(ingrad_fake[0,0,:,:]))
attrs_names.append("ingrad_fake")
attrs.append(torch.abs(ingrad_diff_0[0,0,:,:]))
attrs_names.append("ingrad_diff_0")
attrs.append(torch.abs(ingrad_diff_1[0,0,:,:]))
attrs_names.append("ingrad_diff_1")
attrs = [a.detach().cpu().numpy() for a in attrs]
attrs_norm = [a/np.max(np.abs(a)) for a in attrs]
return attrs_norm, attrs_names
def main(args, DatasetsTypes, DataGenerationTypes, models, device):
for m in range(len(models)):
for x in range(len(DatasetsTypes)):
for y in range(len(DataGenerationTypes)):
if (DataGenerationTypes[y] == None):
args.DataName = DatasetsTypes[x] + "_Box"
else:
args.DataName = DatasetsTypes[
x] + "_" + DataGenerationTypes[y]
Training = np.load(args.data_dir + "SimulatedTrainingData_" +
args.DataName + "_F_" +
str(args.NumFeatures) + "_TS_" +
str(args.NumTimeSteps) + ".npy")
TrainingMetaDataset = np.load(args.data_dir +
"SimulatedTrainingMetaData_" +
args.DataName + "_F_" +
str(args.NumFeatures) + "_TS_" +
str(args.NumTimeSteps) + ".npy")
TrainingLabel = TrainingMetaDataset[:, 0]
Testing = np.load(args.data_dir + "SimulatedTestingData_" +
args.DataName + "_F_" +
str(args.NumFeatures) + "_TS_" +
str(args.NumTimeSteps) + ".npy")
TestingDataset_MetaData = np.load(args.data_dir +
"SimulatedTestingMetaData_" +
args.DataName + "_F_" +
str(args.NumFeatures) +
"_TS_" +
str(args.NumTimeSteps) +
".npy")
TestingLabel = TestingDataset_MetaData[:, 0]
Training = Training.reshape(
Training.shape[0], Training.shape[1] * Training.shape[2])
Testing = Testing.reshape(Testing.shape[0],
Testing.shape[1] * Testing.shape[2])
scaler = MinMaxScaler()
scaler.fit(Training)
Training = scaler.transform(Training)
Testing = scaler.transform(Testing)
TrainingRNN = Training.reshape(Training.shape[0],
args.NumTimeSteps,
args.NumFeatures)
TestingRNN = Testing.reshape(Testing.shape[0],
args.NumTimeSteps,
args.NumFeatures)
train_dataRNN = data_utils.TensorDataset(
torch.from_numpy(TrainingRNN),
torch.from_numpy(TrainingLabel))
train_loaderRNN = data_utils.DataLoader(
train_dataRNN, batch_size=args.batch_size, shuffle=True)
test_dataRNN = data_utils.TensorDataset(
torch.from_numpy(TestingRNN),
torch.from_numpy(TestingLabel))
test_loaderRNN = data_utils.DataLoader(
test_dataRNN, batch_size=args.batch_size, shuffle=False)
modelName = "Simulated"
modelName += args.DataName
saveModelName = "../Models/" + models[m] + "/" + modelName
saveModelBestName = saveModelName + "_BEST.pkl"
pretrained_model = torch.load(saveModelBestName,
map_location=device)
Test_Acc = checkAccuracy(test_loaderRNN, pretrained_model,
args)
print('{} {} model BestAcc {:.4f}'.format(
args.DataName, models[m], Test_Acc))
if (Test_Acc >= 90):
if (args.GradFlag):
rescaledGrad = np.zeros((TestingRNN.shape))
Grad = Saliency(pretrained_model)
if (args.IGFlag):
rescaledIG = np.zeros((TestingRNN.shape))
IG = IntegratedGradients(pretrained_model)
if (args.DLFlag):
rescaledDL = np.zeros((TestingRNN.shape))
DL = DeepLift(pretrained_model)
if (args.GSFlag):
rescaledGS = np.zeros((TestingRNN.shape))
GS = GradientShap(pretrained_model)
if (args.DLSFlag):
rescaledDLS = np.zeros((TestingRNN.shape))
DLS = DeepLiftShap(pretrained_model)
if (args.SGFlag):
rescaledSG = np.zeros((TestingRNN.shape))
Grad_ = Saliency(pretrained_model)
SG = NoiseTunnel(Grad_)
if (args.ShapleySamplingFlag):
rescaledShapleySampling = np.zeros((TestingRNN.shape))
SS = ShapleyValueSampling(pretrained_model)
if (args.GSFlag):
rescaledFeaturePermutation = np.zeros(
(TestingRNN.shape))
FP = FeaturePermutation(pretrained_model)
if (args.FeatureAblationFlag):
rescaledFeatureAblation = np.zeros((TestingRNN.shape))
FA = FeatureAblation(pretrained_model)
if (args.OcclusionFlag):
rescaledOcclusion = np.zeros((TestingRNN.shape))
OS = Occlusion(pretrained_model)
idx = 0
mask = np.zeros((args.NumTimeSteps, args.NumFeatures),
dtype=int)
for i in range(args.NumTimeSteps):
mask[i, :] = i
for i, (samples, labels) in enumerate(test_loaderRNN):
print('[{}/{}] {} {} model accuracy {:.2f}'\
.format(i,len(test_loaderRNN), models[m], args.DataName, Test_Acc))
input = samples.reshape(-1, args.NumTimeSteps,
args.NumFeatures).to(device)
input = Variable(input,
volatile=False,
requires_grad=True)
batch_size = input.shape[0]
baseline_single = torch.from_numpy(
np.random.random(input.shape)).to(device)
baseline_multiple = torch.from_numpy(
np.random.random(
(input.shape[0] * 5, input.shape[1],
input.shape[2]))).to(device)
inputMask = np.zeros((input.shape))
inputMask[:, :, :] = mask
inputMask = torch.from_numpy(inputMask).to(device)
mask_single = torch.from_numpy(mask).to(device)
mask_single = mask_single.reshape(
1, args.NumTimeSteps, args.NumFeatures).to(device)
labels = torch.tensor(labels.int().tolist()).to(device)
if (args.GradFlag):
attributions = Grad.attribute(input, \
target=labels)
rescaledGrad[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.IGFlag):
attributions = IG.attribute(input, \
baselines=baseline_single, \
target=labels)
rescaledIG[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.DLFlag):
attributions = DL.attribute(input, \
baselines=baseline_single, \
target=labels)
rescaledDL[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.GSFlag):
attributions = GS.attribute(input, \
baselines=baseline_multiple, \
stdevs=0.09,\
target=labels)
rescaledGS[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.DLSFlag):
attributions = DLS.attribute(input, \
baselines=baseline_multiple, \
target=labels)
rescaledDLS[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.SGFlag):
attributions = SG.attribute(input, \
target=labels)
rescaledSG[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.ShapleySamplingFlag):
attributions = SS.attribute(input, \
baselines=baseline_single, \
target=labels,\
feature_mask=inputMask)
rescaledShapleySampling[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.FeaturePermutationFlag):
attributions = FP.attribute(input, \
target=labels,
perturbations_per_eval= input.shape[0],\
feature_mask=mask_single)
rescaledFeaturePermutation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.FeatureAblationFlag):
attributions = FA.attribute(input, \
target=labels)
# perturbations_per_eval= input.shape[0],\
# feature_mask=mask_single)
rescaledFeatureAblation[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
if (args.OcclusionFlag):
attributions = OS.attribute(input, \
sliding_window_shapes=(1,args.NumFeatures),
target=labels,
baselines=baseline_single)
rescaledOcclusion[
idx:idx +
batch_size, :, :] = Helper.givenAttGetRescaledSaliency(
args, attributions)
idx += batch_size
if (args.plot):
index = random.randint(0, TestingRNN.shape[0] - 1)
plotExampleBox(TestingRNN[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_sample',
flip=True)
print("Plotting sample", index)
if (args.GradFlag):
plotExampleBox(rescaledGrad[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_Grad',
greyScale=True,
flip=True)
if (args.IGFlag):
plotExampleBox(rescaledIG[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_IG',
greyScale=True,
flip=True)
if (args.DLFlag):
plotExampleBox(rescaledDL[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_DL',
greyScale=True,
flip=True)
if (args.GSFlag):
plotExampleBox(rescaledGS[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_GS',
greyScale=True,
flip=True)
if (args.DLSFlag):
plotExampleBox(rescaledDLS[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_DLS',
greyScale=True,
flip=True)
if (args.SGFlag):
plotExampleBox(rescaledSG[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_SG',
greyScale=True,
flip=True)
if (args.ShapleySamplingFlag):
plotExampleBox(
rescaledShapleySampling[index, :, :],
args.Saliency_Maps_graphs_dir + args.DataName +
"_" + models[m] + '_ShapleySampling',
greyScale=True,
flip=True)
if (args.FeaturePermutationFlag):
plotExampleBox(
rescaledFeaturePermutation[index, :, :],
args.Saliency_Maps_graphs_dir + args.DataName +
"_" + models[m] + '_FeaturePermutation',
greyScale=True,
flip=True)
if (args.FeatureAblationFlag):
plotExampleBox(
rescaledFeatureAblation[index, :, :],
args.Saliency_Maps_graphs_dir + args.DataName +
"_" + models[m] + '_FeatureAblation',
greyScale=True,
flip=True)
if (args.OcclusionFlag):
plotExampleBox(rescaledOcclusion[index, :, :],
args.Saliency_Maps_graphs_dir +
args.DataName + "_" + models[m] +
'_Occlusion',
greyScale=True,
flip=True)
if (args.save):
if (args.GradFlag):
print("Saving Grad", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_Grad_rescaled", rescaledGrad)
if (args.IGFlag):
print("Saving IG", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_IG_rescaled", rescaledIG)
if (args.DLFlag):
print("Saving DL", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_DL_rescaled", rescaledDL)
if (args.GSFlag):
print("Saving GS", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_GS_rescaled", rescaledGS)
if (args.DLSFlag):
print("Saving DLS", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_DLS_rescaled", rescaledDLS)
if (args.SGFlag):
print("Saving SG", modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_SG_rescaled", rescaledSG)
if (args.ShapleySamplingFlag):
print("Saving ShapleySampling",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_ShapleySampling_rescaled",
rescaledShapleySampling)
if (args.FeaturePermutationFlag):
print("Saving FeaturePermutation",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_FeaturePermutation_rescaled",
rescaledFeaturePermutation)
if (args.FeatureAblationFlag):
print("Saving FeatureAblation",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_FeatureAblation_rescaled",
rescaledFeatureAblation)
if (args.OcclusionFlag):
print("Saving Occlusion",
modelName + "_" + models[m])
np.save(
args.Saliency_dir + modelName + "_" +
models[m] + "_Occlusion_rescaled",
rescaledOcclusion)
else:
logging.basicConfig(filename=args.log_file,
level=logging.DEBUG)
logging.debug('{} {} model BestAcc {:.4f}'.format(
args.DataName, models[m], Test_Acc))
if not os.path.exists(args.ignore_list):
with open(args.ignore_list, 'w') as fp:
fp.write(args.DataName + '_' + models[m] + '\n')
else:
with open(args.ignore_list, "a") as fp:
fp.write(args.DataName + '_' + models[m] + '\n')
scores = gcd.attribute(input,
target=labels[ind].item(),
kernel_type='gaussian',
kernel_size=7,
kernel_sigma=1.0,
method='fro',
sampling_method='min',
num_samples=1000,
sample_std=1.0)
# %% [markdown]
# Computes gradients with respect to class `ind` and transposes them for visualization purposes.
# %%
saliency = Saliency(net)
grads = saliency.attribute(input, target=labels[ind].item())
grads = np.transpose(grads.squeeze().cpu().detach().numpy(), (1, 2, 0))
# %% [markdown]
# Applies integrated gradients attribution algorithm on test image. Integrated Gradients computes the integral of the gradients of the output prediction for the class index `ind` with respect to the input image pixels. More details about integrated gradients can be found in the original paper: https://arxiv.org/abs/1703.01365
# %%
ig = IntegratedBlurredGradients(net)
attr_ig, delta = attribute_image_features(ig,
input,
n_steps=50,
return_convergence_delta=True)
print('Approximation delta: ', abs(delta))
from captum.attr._core.integrated_blurred_gradients import GaussianFilter
gf = GaussianFilter(2.0)
ig = IntegratedGradients(net)
