def extract_GS(self, X_train, X_test):
gs = GradientShap(self.net)
start = time.time()
gs_attr_test = gs.attribute(X_test.to(self.device),
X_train.to(self.device))
print("temps train", time.time() - start)
return gs_attr_test.detach().cpu().numpy()
class GradientShapExplainer:
def __init__(self, model, activation=torch.nn.Softmax(-1)):
self.device = 'cuda' #'cuda' if torch.cuda.is_available() else 'cpu'
self.base_model = model.to(self.device)
self.base_model.device = self.device
self.explainer = GradientShap(self.base_model)
self.activation = activation
def attribute(self, x, y, retrospective=False):
x, y = x.to(self.device), y.to(self.device)
if retrospective:
score = self.explainer.attribute(x,
target=y.long(),
n_samples=50,
stdevs=0.0001,
baselines=torch.cat(
[x * 0, x * 1]))
score = abs(score.cpu().numpy())
else:
score = np.zeros(x.shape)
for t in range(x.shape[-1]):
x_in = x[:, :, :t + 1]
pred = self.activation(self.base_model(x_in))
if type(self.activation).__name__ == type(
torch.nn.Softmax(-1)).__name__:
target = torch.argmax(pred, -1)
imp = self.explainer.attribute(x_in,
target=target.long(),
n_samples=50,
stdevs=0.0001,
baselines=torch.cat([
x[:, :, :t + 1] * 0,
x[:, :, :t + 1] * 1
]))
score[:, :, t] = imp.cpu().numpy()[:, :, -1]
else:
n_labels = pred.shape[1]
if n_labels > 1:
imp = torch.zeros(list(x_in.shape) + [n_labels])
for l in range(n_labels):
target = (pred[:, l] > 0.5).float() #[:,0]
imp[:, :, :, l] = self.explainer.attribute(
x_in,
target=target.long(),
baselines=(x_in * 0))
score[:, :,
t] = (imp.detach().cpu().numpy()).max(3)[:, :,
-1]
else:
#this is for spike with just one label. and we will explain one cla
target = (pred > 0.5).float()[:, 0]
imp = self.explainer.attribute(
x_in,
target=target.long(),
baselines=(x[:, :, :t + 1] * 0))
score[:, :, t] = abs(imp.detach().cpu().numpy()[:, :,
-1])
return score
def compute_gradient_shap(self, img_path, target):
# open image
img, transformed_img, input = self.open_image(img_path)
rand_img_dist = torch.cat([input * 0, input * 1])
gradient_shap = GradientShap(self.model)
attributions_gs = gradient_shap.attribute(input,
n_samples=50,
stdevs=0.0001,
baselines=rand_img_dist,
target=target)
attributions_gs = np.transpose(
attributions_gs.squeeze().cpu().detach().numpy(), (1, 2, 0))
return attributions_gs
def test_basic_sensitivity_max_multiple_gradshap(self) -> None:
model = BasicModel2()
gs = GradientShap(model)
input1 = torch.tensor([0.0] * 5)
input2 = torch.tensor([0.0] * 5)
baseline1 = torch.arange(0, 2).float() / 1000
baseline2 = torch.arange(0, 2).float() / 1000
self.sensitivity_max_assert(
gs.attribute,
(input1, input2),
torch.zeros(5),
baselines=(baseline1, baseline2),
max_examples_per_batch=2,
)
self.sensitivity_max_assert(
gs.attribute,
(input1, input2),
torch.zeros(5),
baselines=(baseline1, baseline2),
max_examples_per_batch=20,
)
class gradientshap_explainer:
def __init__(self, model, train_data):
model.eval()
self.explainer = GradientShap(model)
self.model = model
def get_feature_importance(self, data):
data.requires_grad = True
baseline_dist = torch.randn(data.shape) * 0.001
return torch.stack([self.explainer.attribute(data, stdevs=0.09, n_samples=4, baselines=baseline_dist,
target=i, return_convergence_delta=True)[0] for i in
range(global_vars.get('n_classes'))], axis=0).detach()
def __init__(self,
normal_path,
adv_path,
collate_path,
model_path,
normal_only=False,
large_normal=False):
self.normal_only = normal_only
self.model = torch.load(model_path)
self.normal_dataset = XrayDataset(normal_path,
collate_path,
'Cardiomegaly',
1,
normalise=False,
only=None)
self.adv_dataset = XrayDataset(adv_path,
collate_path,
'Cardiomegaly',
1,
normalise=False,
adv=adv_path)
self.num_normal = len(self.adv_dataset)
if large_normal:
# For training anomaly detection AEs
self.num_normal = self.num_normal * 5
self.labels = [0 for i in range(self.num_normal)]
if not self.normal_only:
self.labels += [1 for j in range(len(self.adv_dataset))]
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.device = device
self.model = self.model.to(device)
self.model.eval()
self.shap = GradientShap(self.model)
self.baseline = torch.randn(1,
3,
224,
224,
requires_grad=True,
device=device)
class Explainer():
def __init__(self, model, stdevs=0.09, n_samples=4):
self.model = model
self.explain = GradientShap(model)
self.stdevs = stdevs
self.n_samples = n_samples
def get_attribution_map(self, img, target=None):
if target is None:
target = torch.argmax(self.model(img), 1)
baseline_dist = torch.randn_like(img) * 0.001
attributions, delta = self.explain.attribute(
img,
stdevs=self.stdevs,
n_samples=self.n_samples,
baselines=baseline_dist,
target=target,
return_convergence_delta=True)
return attributions
def find_genes_GSClass(drug, ens, meta, test_tcga_expr):
from captum.attr import GradientShap
gs = GradientShap(ens)
# find genes for Sensitive Class
sensitive = ['Complete Response', 'Partial Response']
sen_idx = meta.loc[meta['label'].isin(sensitive)].index
sen = torch.FloatTensor(test_tcga_expr.loc[sen_idx].values)
res_idx = meta.loc[~meta['label'].isin(sensitive)].index
res = torch.FloatTensor(test_tcga_expr.loc[res_idx].values)
sen_attr = attribute(gs, sen, res, sen_idx)
res_attr = attribute(gs, res, sen, res_idx)
sen_genes = get_ranked_list(sen_attr)
res_genes = get_ranked_list(res_attr)
out = pd.DataFrame(columns=['sensitive', 'resistant'])
out['sensitive'] = sen_genes
out['resistant'] = res_genes
out.to_csv(res_dir + drug + '/genes.csv', index=False)
def return_spw_importances_(train_methyl_array,
val_methyl_array,
interest_col,
select_subtypes,
capsules_pickle,
include_last,
n_bins,
spw_config,
model_state_dict_pkl,
batch_size,
by_subtype=False):
ma = MethylationArray.from_pickle(train_methyl_array)
ma_v = MethylationArray.from_pickle(val_methyl_array)
try:
ma.remove_na_samples(interest_col)
ma_v.remove_na_samples(interest_col)
except:
pass
if select_subtypes:
ma.pheno = ma.pheno.loc[ma.pheno[interest_col].isin(select_subtypes)]
ma.beta = ma.beta.loc[ma.pheno.index]
ma_v.pheno = ma_v.pheno.loc[ma_v.pheno[interest_col].isin(
select_subtypes)]
ma_v.beta = ma_v.beta.loc[ma_v.pheno.index]
capsules_dict = torch.load(capsules_pickle)
final_modules, modulecpgs, module_names = capsules_dict[
'final_modules'], capsules_dict['modulecpgs'], capsules_dict[
'module_names']
if not include_last:
ma.beta = ma.beta.loc[:, modulecpgs]
ma_v.beta = ma_v.beta.loc[:, modulecpgs]
original_interest_col = interest_col
if n_bins:
new_interest_col = interest_col + '_binned'
ma.pheno.loc[:,
new_interest_col], bins = pd.cut(ma.pheno[interest_col],
bins=n_bins,
retbins=True)
ma_v.pheno.loc[:,
new_interest_col], _ = pd.cut(ma_v.pheno[interest_col],
bins=bins,
retbins=True)
interest_col = new_interest_col
datasets = dict()
datasets['train'] = MethylationDataset(
ma,
interest_col,
modules=final_modules,
module_names=module_names,
original_interest_col=original_interest_col,
run_spw=True)
datasets['val'] = MethylationDataset(
ma_v,
interest_col,
modules=final_modules,
module_names=module_names,
original_interest_col=original_interest_col,
run_spw=True)
y_val = datasets['val'].y_label
y_val_uniq = np.unique(y_val)
dataloaders = dict()
dataloaders['train'] = DataLoader(datasets['train'],
batch_size=batch_size,
shuffle=True,
num_workers=8,
pin_memory=True,
drop_last=True)
dataloaders['val'] = DataLoader(datasets['val'],
batch_size=batch_size,
shuffle=False,
num_workers=8,
pin_memory=True,
drop_last=False)
n_primary = len(final_modules)
spw_config = torch.load(spw_config)
spw_config.pop('module_names')
model = MethylSPWNet(**spw_config)
model.load_state_dict(torch.load(model_state_dict_pkl))
if torch.cuda.is_available():
model = model.cuda()
model.eval()
pathway_extractor = model.pathways
#extract_pathways = lambda modules_x:torch.cat([pathway_extractor[i](module_x) for i,module_x in enumerate(modules_x)],dim=1)
tensor_data = dict(train=dict(X=[], y=[]), val=dict(X=[], y=[]))
for k in tensor_data:
for i, (batch) in enumerate(dataloaders[k]):
x = batch[0]
y_true = batch[-1].argmax(1) #[-2]
modules_x = batch[1:-1] #2]
if torch.cuda.is_available():
x = x.cuda()
modules_x = modules_x[0].cuda(
) #[module.cuda() for module in modules_x]
tensor_data[k]['X'].append(
pathway_extractor(x, modules_x).detach().cpu()
) #extract_pathways(modules_x).detach().cpu())
tensor_data[k]['y'].append(y_true.flatten().view(-1, 1))
tensor_data[k]['X'] = torch.cat(tensor_data[k]['X'], dim=0)
tensor_data[k]['y'] = torch.cat(tensor_data[k]['y'], dim=0)
print(tensor_data[k]['X'].size(), tensor_data[k]['y'].size())
tensor_data[k] = TensorDataset(tensor_data[k]['X'],
tensor_data[k]['y'])
dataloaders[k] = DataLoader(tensor_data[k],
batch_size=32,
sampler=ImbalancedDatasetSampler(
tensor_data[k]))
model = model.output_net
to_cuda = lambda x: x.cuda() if torch.cuda.is_available() else x
y = np.unique(tensor_data['train'].tensors[1].numpy().flatten())
gs = GradientShap(model)
X_train = torch.cat(
[next(iter(dataloaders['train']))[0] for i in range(2)], dim=0)
if torch.cuda.is_available():
X_train = X_train.cuda()
#val_loader=iter(dataloaders['val'])
def return_importances(dataloaders, X_train):
attributions = []
for i in range(20):
batch = next(iter(dataloaders['val']))
X_test = to_cuda(batch[0])
y_test = to_cuda(batch[1].flatten())
attributions.append(
torch.abs(
gs.attribute(
X_test,
stdevs=0.03,
n_samples=200,
baselines=X_train,
target=y_test,
return_convergence_delta=False))) #torch.tensor(y_i)
attributions = torch.sum(torch.cat(attributions, dim=0), dim=0)
importances = pd.DataFrame(
pd.Series(attributions.detach().cpu().numpy(),
index=module_names).sort_values(ascending=False),
columns=['importances'])
return importances
if by_subtype:
importances = []
for k in y_val_uniq:
idx = np.where(y_val == k)[0]
if len(idx) > 2:
val_dataset = Subset(tensor_data['val'], idx)
n_concat = int(np.ceil(64. / len(idx)))
if n_concat > 1:
val_dataset = ConcatDataset([val_dataset] * n_concat)
#sampler=SubsetRandomSampler(idx)
dataloaders['val'] = DataLoader(val_dataset,
batch_size=32,
shuffle=True)
df = return_importances(dataloaders, X_train)
df['subtype'] = k
importances.append(df)
importances = pd.concat(importances)
else:
importances = return_importances(dataloaders, X_train)
return importances
def generate_heatmap(self, img):
if self.is_cpu:
model = torch.load(self.model_path,
map_location=torch.device("cpu"))
else:
model = torch.load(self.model_path)
model = model["model"]
model.eval()
transform = transforms.Compose(
[transforms.Resize((64, 64)),
transforms.ToTensor()])
transform_normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
img_t = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = Image.fromarray(np.uint8(img_t))
transformed_img = transform(img)
input = transform_normalize(transformed_img)
input = input.unsqueeze(0).to(device)
output = model(input)
output = F.softmax(output, dim=1)
prediction_score, pred_label_idx = torch.topk(output, 1)
pred_label_idx.squeeze_()
default_cmap = LinearSegmentedColormap.from_list("custom blue",
[(0, "#ffffff"),
(0.25, "#000000"),
(1, "#000000")],
N=256)
gradient_shap = GradientShap(model)
rand_img_dist = torch.cat([input * 0, input * 1])
attributions_gs = gradient_shap.attribute(
input,
n_samples=50,
stdevs=0.0001,
baselines=rand_img_dist,
target=pred_label_idx,
)
out = viz.visualize_image_attr_multiple(
np.transpose(attributions_gs.squeeze().cpu().detach().numpy(),
(1, 2, 0)),
np.transpose(transformed_img.squeeze().cpu().detach().numpy(),
(1, 2, 0)),
["original_image", "heat_map"],
["all", "absolute_value"],
cmap=default_cmap,
show_colorbar=True,
)
path = "model_output/" + self.model_path_name + "_heat_map.png"
out[1][0].get_figure().savefig(path)
plt.clf()
plt.close()
return path
def measure_model(
model_version,
dataset,
out_folder,
weights_dir,
device,
method=METHODS["gradcam"],
sample_images=50,
step=1,
):
invTrans = get_inverse_normalization_transformation()
data_dir = os.path.join("data")
if model_version == "resnet18":
model = create_resnet18_model(num_of_classes=NUM_OF_CLASSES[dataset])
elif model_version == "resnet50":
model = create_resnet50_model(num_of_classes=NUM_OF_CLASSES[dataset])
elif model_version == "densenet":
model = create_densenet121_model(
num_of_classes=NUM_OF_CLASSES[dataset])
else:
model = create_efficientnetb0_model(
num_of_classes=NUM_OF_CLASSES[dataset])
model.load_state_dict(torch.load(weights_dir))
# print(model)
model.eval()
model.to(device)
test_dataset = CustomDataset(
dataset=dataset,
transformer=get_default_transformation(),
data_type="test",
root_dir=data_dir,
step=step,
)
data_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
try:
image_ids = random.sample(range(0, test_dataset.__len__()),
sample_images)
except ValueError:
raise ValueError(
f"Image sample number ({sample_images}) exceeded dataset size ({test_dataset.__len__()})."
)
classes_map = test_dataset.classes_map
print(f"Measuring {model_version} on {dataset} dataset, with {method}")
print("-" * 10)
pbar = tqdm(total=test_dataset.__len__(), desc="Model test completion")
multipy_by_inputs = False
if method == METHODS["ig"]:
attr_method = IntegratedGradients(model)
nt_samples = 8
n_perturb_samples = 3
if method == METHODS["saliency"]:
attr_method = Saliency(model)
nt_samples = 8
n_perturb_samples = 10
if method == METHODS["gradcam"]:
if model_version == "efficientnet":
attr_method = GuidedGradCam(model, model._conv_stem)
elif model_version == "densenet":
attr_method = GuidedGradCam(model, model.features.conv0)
else:
attr_method = GuidedGradCam(model, model.conv1)
nt_samples = 8
n_perturb_samples = 10
if method == METHODS["deconv"]:
attr_method = Deconvolution(model)
nt_samples = 8
n_perturb_samples = 10
if method == METHODS["gradshap"]:
attr_method = GradientShap(model)
nt_samples = 8
if model_version == "efficientnet":
n_perturb_samples = 3
elif model_version == "densenet":
n_perturb_samples = 2
else:
n_perturb_samples = 10
if method == METHODS["gbp"]:
attr_method = GuidedBackprop(model)
nt_samples = 8
n_perturb_samples = 10
if method == "lime":
attr_method = Lime(model)
nt_samples = 8
n_perturb_samples = 10
feature_mask = torch.tensor(lime_mask).to(device)
multipy_by_inputs = True
if method == METHODS['ig']:
nt = attr_method
else:
nt = NoiseTunnel(attr_method)
scores = []
@infidelity_perturb_func_decorator(multipy_by_inputs=multipy_by_inputs)
def perturb_fn(inputs):
noise = torch.tensor(np.random.normal(0, 0.003, inputs.shape)).float()
noise = noise.to(device)
return inputs - noise
for input, label in data_loader:
pbar.update(1)
inv_input = invTrans(input)
input = input.to(device)
input.requires_grad = True
output = model(input)
output = F.softmax(output, dim=1)
prediction_score, pred_label_idx = torch.topk(output, 1)
prediction_score = prediction_score.cpu().detach().numpy()[0][0]
pred_label_idx.squeeze_()
if method == METHODS['gradshap']:
baseline = torch.randn(input.shape)
baseline = baseline.to(device)
if method == "lime":
attributions = attr_method.attribute(input, target=1, n_samples=50)
elif method == METHODS['ig']:
attributions = nt.attribute(
input,
target=pred_label_idx,
n_steps=25,
)
elif method == METHODS['gradshap']:
attributions = nt.attribute(input,
target=pred_label_idx,
baselines=baseline)
else:
attributions = nt.attribute(
input,
nt_type="smoothgrad",
nt_samples=nt_samples,
target=pred_label_idx,
)
infid = infidelity(model,
perturb_fn,
input,
attributions,
target=pred_label_idx)
if method == "lime":
sens = sensitivity_max(
attr_method.attribute,
input,
target=pred_label_idx,
n_perturb_samples=1,
n_samples=200,
feature_mask=feature_mask,
)
elif method == METHODS['ig']:
sens = sensitivity_max(
nt.attribute,
input,
target=pred_label_idx,
n_perturb_samples=n_perturb_samples,
n_steps=25,
)
elif method == METHODS['gradshap']:
sens = sensitivity_max(nt.attribute,
input,
target=pred_label_idx,
n_perturb_samples=n_perturb_samples,
baselines=baseline)
else:
sens = sensitivity_max(
nt.attribute,
input,
target=pred_label_idx,
n_perturb_samples=n_perturb_samples,
)
inf_value = infid.cpu().detach().numpy()[0]
sens_value = sens.cpu().detach().numpy()[0]
if pbar.n in image_ids:
attr_data = attributions.squeeze().cpu().detach().numpy()
fig, ax = viz.visualize_image_attr_multiple(
np.transpose(attr_data, (1, 2, 0)),
np.transpose(inv_input.squeeze().cpu().detach().numpy(),
(1, 2, 0)),
["original_image", "heat_map"],
["all", "positive"],
titles=["original_image", "heat_map"],
cmap=default_cmap,
show_colorbar=True,
use_pyplot=False,
fig_size=(8, 6),
)
ax[0].set_xlabel(
f"Infidelity: {'{0:.6f}'.format(inf_value)}\n Sensitivity: {'{0:.6f}'.format(sens_value)}"
)
fig.suptitle(
f"True: {classes_map[str(label.numpy()[0])][0]}, Pred: {classes_map[str(pred_label_idx.item())][0]}\nScore: {'{0:.4f}'.format(prediction_score)}",
fontsize=16,
)
fig.savefig(
os.path.join(
out_folder,
f"{str(pbar.n)}-{classes_map[str(label.numpy()[0])][0]}-{classes_map[str(pred_label_idx.item())][0]}.png",
))
plt.close(fig)
# if pbar.n > 25:
# break
scores.append([inf_value, sens_value])
pbar.close()
np.savetxt(
os.path.join(out_folder, f"{model_version}-{dataset}-{method}.csv"),
np.array(scores),
delimiter=",",
header="infidelity,sensitivity",
)
print(f"Artifacts stored at {out_folder}")
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)
def __init__(self, model, activation=torch.nn.Softmax(-1)):
self.device = 'cuda' #'cuda' if torch.cuda.is_available() else 'cpu'
self.base_model = model.to(self.device)
self.base_model.device = self.device
self.explainer = GradientShap(self.base_model)
self.activation = activation
def evaluation_ten_classes(initiate_or_load_model,
config_data,
singleton_scope=False,
reshape_size=None,
FIND_OPTIM_BRANCH_MODEL=False,
realtime_update=False,
ALLOW_ADHOC_NOPTIM=False):
from pipeline.training.training_utils import prepare_save_dirs
xai_mode = config_data['xai_mode']
MODEL_DIR, INFO_DIR, CACHE_FOLDER_DIR = prepare_save_dirs(config_data)
############################
VERBOSE = 0
############################
if not FIND_OPTIM_BRANCH_MODEL:
print(
'Using the following the model from (only) continuous training for xai evaluation [%s]'
% (str(xai_mode)))
net, evaluator = initiate_or_load_model(MODEL_DIR,
INFO_DIR,
config_data,
verbose=VERBOSE)
else:
BRANCH_FOLDER_DIR = MODEL_DIR[:MODEL_DIR.find('.model')] + '.%s' % (
str(config_data['branch_name_label']))
BRANCH_MODEL_DIR = os.path.join(
BRANCH_FOLDER_DIR,
'%s.%s.model' % (str(config_data['model_name']),
str(config_data['branch_name_label'])))
# BRANCH_MODEL_DIR = MODEL_DIR[:MODEL_DIR.find('.model')] + '.%s.model'%(str(config_data['branch_name_label']))
if ALLOW_ADHOC_NOPTIM: # this is intended only for debug runs
print('<< [EXY1] ALLOWING ADHOC NOPTIM >>')
import shutil
shutil.copyfile(BRANCH_MODEL_DIR, BRANCH_MODEL_DIR + '.noptim')
if os.path.exists(BRANCH_MODEL_DIR + '.optim'):
BRANCH_MODEL_DIR = BRANCH_MODEL_DIR + '.optim'
print(
' Using the OPTIMIZED branch model for [%s] xai evaluation: %s'
% (str(xai_mode), str(BRANCH_MODEL_DIR)))
elif os.path.exists(BRANCH_MODEL_DIR + '.noptim'):
BRANCH_MODEL_DIR = BRANCH_MODEL_DIR + '.noptim'
print(
' Using the partially optimized branch model for [%s] xai evaluation: %s'
% (str(xai_mode), str(BRANCH_MODEL_DIR)))
else:
raise RuntimeError(
'Attempting to find .optim or .noptim model, but not found.')
if VERBOSE >= 250:
print(
' """You may see a warning by pytorch for ReLu backward hook. It has been fixed externally, so you can ignore it."""'
)
net, evaluator = initiate_or_load_model(BRANCH_MODEL_DIR,
INFO_DIR,
config_data,
verbose=VERBOSE)
if xai_mode == 'Saliency': attrmodel = Saliency(net)
elif xai_mode == 'IntegratedGradients':
attrmodel = IntegratedGradients(net)
elif xai_mode == 'InputXGradient':
attrmodel = InputXGradient(net)
elif xai_mode == 'DeepLift':
attrmodel = DeepLift(net)
elif xai_mode == 'GuidedBackprop':
attrmodel = GuidedBackprop(net)
elif xai_mode == 'GuidedGradCam':
attrmodel = GuidedGradCam(net, net.select_first_layer()) # first layer
elif xai_mode == 'Deconvolution':
attrmodel = Deconvolution(net)
elif xai_mode == 'GradientShap':
attrmodel = GradientShap(net)
elif xai_mode == 'DeepLiftShap':
attrmodel = DeepLiftShap(net)
else:
raise RuntimeError('No valid attribution selected.')
if singleton_scope: # just to observe a single datapoint, mostly for debugging
singleton_scope_oberservation(net, attrmodel, config_data,
CACHE_FOLDER_DIR)
else:
aggregate_evaluation(net,
attrmodel,
config_data,
CACHE_FOLDER_DIR,
reshape_size=reshape_size,
realtime_update=realtime_update,
EVALUATE_BRANCH=FIND_OPTIM_BRANCH_MODEL)
avg_ig_vals_dict = {i: avg_ig_vals[i] for i in range(len(avg_ig_vals))}
# Now we can get only the top X many results. ordered stores the keys in order
ordered = sorted(avg_ig_vals_dict,
key=lambda i: abs(avg_ig_vals_dict[i]),
reverse=True)
top = {i: avg_ig_vals_dict[i] for i in ordered[:args.num_ig]}
vis_importance(list(top),
list(top.values()),
plotter_instance=plotter,
title="Integrated Gradients (Top {})".format(args.num_ig))
print('======================= Done!')
# Try looking at SHAP values now
shap = GradientShap(model)
print(
"======================= Calculating SHAP Values (DeepLift Approximation)..."
)
# We calculate it using only one sample
batch_size = 1
# Reset our dataloader
test_loader = DataLoader(dataset=test_set,
batch_size=batch_size,
shuffle=False,
collate_fn=visit_collate_fn,
num_workers=0)
if args.mean:
def __init__(self, predictor: Predictor):
CaptumAttribution.__init__(self, 'gradshap', predictor)
self.submodel = self.predictor._model.captum_sub_model()
GradientShap.__init__(self, self.submodel)
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 __init__(self, model, stdevs=0.09, n_samples=4):
self.model = model
self.explain = GradientShap(model)
self.stdevs = stdevs
self.n_samples = n_samples
def __init__(self, model, train_data):
model.eval()
self.explainer = GradientShap(model)
self.model = model
def main(args):
warnings.filterwarnings("ignore")
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Executing on device:', device)
# --- Get Threshold ---
model = './model/base' + str(args.model_index) + '.pkl'
threshold, _ = get_threshold(model_dir=model,
use_cached=args.use_cached,
search_space=np.linspace(
0, 1, args.search_num))
# --- Load Model ---
model = torch.load(model, map_location=device).to(device)
model = model.eval()
# --- Fix Seed ---
torch.manual_seed(123)
np.random.seed(123)
# --- Label ---
label_dir = '../CheXpert-v1.0-small/valid.csv'
label = pd.read_csv(label_dir)
target_label = np.array(list(label.keys())[5:])
target_obsrv = np.array([8, 2, 6, 5, 10])
label = label.values
label_gd = label[:, 5:]
# --- Image ---
img_index = args.image_index
img_dir = '../'
transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
])
print('Patient:', label[img_index][0])
img = Image.open(os.path.join(img_dir, label[img_index][0])).convert('RGB')
img = transform(img)
img_transformed = img / 255
img_transformed = img_transformed.unsqueeze(0).to(device)
# --- Predict ---
pred = model(img_transformed)
print()
print('[Prediction]')
print('{:17s}|{:4s}|{:4s}|{:4s}|{:3s}'.format('Pathology', 'Prob', 'Thrs',
'Pred', 'Ans'))
for lbl, prd, thrsh, gd in zip(target_label[target_obsrv],
pred[0][target_obsrv],
threshold[target_obsrv],
label_gd[img_index][target_obsrv]):
print('{:17s}:{:4.2f} {:4.2f} {:4d} {:3d}'.format(
lbl, prd.item(), thrsh, int(prd.item() > thrsh), int(gd)))
print()
del pred
torch.cuda.empty_cache()
gc.collect()
model = model.to(torch.device('cpu'))
img_transformed = img_transformed.to(torch.device('cpu'))
# --- Visualization ---
pathology_input = input(
'Please enter which pathology to visualize:\n[0]Atelectasis\n[1]Cardiomegaly\n[2]Consolidation\n[3]Edema\n[4]Pleural Effusion\n[5]Exit\n'
)
if pathology_input == '0':
pathology = 8
print('Diagnosis on Atelectasis')
elif pathology_input == '1':
pathology = 2
print('Diagnosis on Cardiomegaly')
elif pathology_input == '2':
pathology = 6
print('Diagnosis on Consolidation')
elif pathology_input == '3':
pathology = 5
print('Diagnosis on Edema')
elif pathology_input == '4':
pathology = 10
print('Diagnosis on Pleural Effusion')
elif pathology_input == '5':
print('Exiting...')
return
else:
raise NotImplementedError('Only 0-5 are valid input values')
default_cmap = LinearSegmentedColormap.from_list('custom blue',
[(0, '#ffffff'),
(0.25, '#000000'),
(1, '#000000')],
N=256)
print()
method_input = input(
'Please enter which method to visualize:\n[0]GradientShap\n[1]DeepLift\n[2]Exit\n'
)
if method_input == '0':
print('Using GradientShap')
# --- Gradient Shap ---
gradient_shap = GradientShap(model)
# === baseline distribution ===
rand_img_dist = torch.cat([img_transformed * 0, img_transformed * 1])
attributions_gs = gradient_shap.attribute(img_transformed,
n_samples=50,
stdevs=0.0001,
baselines=rand_img_dist,
target=pathology)
_ = viz.visualize_image_attr_multiple(
np.transpose(attributions_gs.squeeze().cpu().detach().numpy(),
(1, 2, 0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1, 2, 0)),
["original_image", "heat_map"], ["all", "absolute_value"],
cmap=default_cmap,
show_colorbar=True)
del attributions_gs
elif method_input == '1':
print('Using DeepLIFT')
# --- Deep Lift ---
model = model_transform(model)
dl = DeepLift(model)
attr_dl = dl.attribute(img_transformed,
target=pathology,
baselines=img_transformed * 0)
_ = viz.visualize_image_attr_multiple(
np.transpose(attr_dl.squeeze().cpu().detach().numpy(), (1, 2, 0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1, 2, 0)),
["original_image", "heat_map"], ["all", "positive"],
cmap=default_cmap,
show_colorbar=True)
del attr_dl
elif method_input == '2':
print('Exiting...')
return
else:
raise NotImplementedError('Only 0-2 are valid input values')
"""
elif method_input == '2':
print('Using Integrated Gradients')
# --- Integrated Gradients ---
integrated_gradients = IntegratedGradients(model)
attributions_ig = integrated_gradients.attribute(img_transformed, target=pathology, n_steps=200)
_ = viz.visualize_image_attr_multiple(np.transpose(attributions_ig.squeeze().cpu().detach().numpy(), (1,2,0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1,2,0)),
method=["original_image", "heat_map"],
cmap=default_cmap,
show_colorbar=True,
sign=["all", "positive"])
del attributions_ig
elif method_input == '3':
print('Using Noise Tunnel')
# --- Noise Tunnel ---
integrated_gradients = IntegratedGradients(model)
noise_tunnel = NoiseTunnel(integrated_gradients)
attributions_ig_nt = noise_tunnel.attribute(img_transformed, n_samples=10, nt_type='smoothgrad_sq', target=pathology)
_ = viz.visualize_image_attr_multiple(np.transpose(attributions_ig_nt.squeeze().cpu().detach().numpy(), (1,2,0)),
np.transpose(img.squeeze().cpu().detach().numpy(), (1,2,0)),
["original_image", "heat_map"],
["all", "positive"],
cmap=default_cmap,
show_colorbar=True)
del attributions_ig_nt
"""
gc.collect()
return
def measure_filter_model(
model_version,
dataset,
out_folder,
weights_dir,
device,
method=METHODS["gradcam"],
sample_images=50,
step=1,
use_infidelity=False,
use_sensitivity=False,
render=False,
ids=None,
):
invTrans = get_inverse_normalization_transformation()
data_dir = os.path.join("data")
if model_version == "resnet18":
model = create_resnet18_model(num_of_classes=NUM_OF_CLASSES[dataset])
elif model_version == "resnet50":
model = create_resnet50_model(num_of_classes=NUM_OF_CLASSES[dataset])
elif model_version == "densenet":
model = create_densenet121_model(
num_of_classes=NUM_OF_CLASSES[dataset])
else:
model = create_efficientnetb0_model(
num_of_classes=NUM_OF_CLASSES[dataset])
model.load_state_dict(torch.load(weights_dir))
# print(model)
model.eval()
model.to(device)
test_dataset = CustomDataset(
dataset=dataset,
transformer=get_default_transformation(),
data_type="test",
root_dir=data_dir,
step=step,
add_filters=True,
ids=ids,
)
data_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=1,
shuffle=False,
num_workers=4)
try:
image_ids = random.sample(range(0, test_dataset.__len__()),
test_dataset.__len__())
except ValueError:
raise ValueError(
f"Image sample number ({test_dataset.__len__()}) exceeded dataset size ({test_dataset.__len__()})."
)
classes_map = test_dataset.classes_map
print(f"Measuring {model_version} on {dataset} dataset, with {method}")
print("-" * 10)
pbar = tqdm(total=test_dataset.__len__(), desc="Model test completion")
multipy_by_inputs = False
if method == METHODS["ig"]:
attr_method = IntegratedGradients(model)
nt_samples = 1
n_perturb_samples = 1
if method == METHODS["saliency"]:
attr_method = Saliency(model)
nt_samples = 8
n_perturb_samples = 2
if method == METHODS["gradcam"]:
if model_version == "efficientnet":
attr_method = GuidedGradCam(model, model._conv_stem)
elif model_version == "densenet":
attr_method = GuidedGradCam(model, model.features.conv0)
else:
attr_method = GuidedGradCam(model, model.conv1)
nt_samples = 8
n_perturb_samples = 2
if method == METHODS["deconv"]:
attr_method = Deconvolution(model)
nt_samples = 8
n_perturb_samples = 2
if method == METHODS["gradshap"]:
attr_method = GradientShap(model)
nt_samples = 8
n_perturb_samples = 2
if method == METHODS["gbp"]:
attr_method = GuidedBackprop(model)
nt_samples = 8
n_perturb_samples = 2
if method == "lime":
attr_method = Lime(model)
nt_samples = 8
n_perturb_samples = 2
feature_mask = torch.tensor(lime_mask).to(device)
multipy_by_inputs = True
if method == METHODS["ig"]:
nt = attr_method
else:
nt = NoiseTunnel(attr_method)
scores = []
@infidelity_perturb_func_decorator(multipy_by_inputs=multipy_by_inputs)
def perturb_fn(inputs):
noise = torch.tensor(np.random.normal(0, 0.003, inputs.shape)).float()
noise = noise.to(device)
return inputs - noise
OUR_FILTERS = [
"none",
"fx_freaky_details 2,10,1,11,0,32,0",
"normalize_local 8,10",
"fx_boost_chroma 90,0,0",
"fx_mighty_details 25,1,25,1,11,0",
"sharpen 300",
]
idx = 0
filter_count = 0
filter_attrs = {filter_name: [] for filter_name in OUR_FILTERS}
predicted_main_class = 0
for input, label in data_loader:
pbar.update(1)
inv_input = invTrans(input)
input = input.to(device)
input.requires_grad = True
output = model(input)
output = F.softmax(output, dim=1)
prediction_score, pred_label_idx = torch.topk(output, 1)
prediction_score = prediction_score.cpu().detach().numpy()[0][0]
pred_label_idx.squeeze_()
if OUR_FILTERS[filter_count] == 'none':
predicted_main_class = pred_label_idx.item()
if method == METHODS["gradshap"]:
baseline = torch.randn(input.shape)
baseline = baseline.to(device)
if method == "lime":
attributions = attr_method.attribute(input, target=1, n_samples=50)
elif method == METHODS["ig"]:
attributions = nt.attribute(
input,
target=predicted_main_class,
n_steps=25,
)
elif method == METHODS["gradshap"]:
attributions = nt.attribute(input,
target=predicted_main_class,
baselines=baseline)
else:
attributions = nt.attribute(
input,
nt_type="smoothgrad",
nt_samples=nt_samples,
target=predicted_main_class,
)
if use_infidelity:
infid = infidelity(model,
perturb_fn,
input,
attributions,
target=predicted_main_class)
inf_value = infid.cpu().detach().numpy()[0]
else:
inf_value = 0
if use_sensitivity:
if method == "lime":
sens = sensitivity_max(
attr_method.attribute,
input,
target=predicted_main_class,
n_perturb_samples=1,
n_samples=200,
feature_mask=feature_mask,
)
elif method == METHODS["ig"]:
sens = sensitivity_max(
nt.attribute,
input,
target=predicted_main_class,
n_perturb_samples=n_perturb_samples,
n_steps=25,
)
elif method == METHODS["gradshap"]:
sens = sensitivity_max(
nt.attribute,
input,
target=predicted_main_class,
n_perturb_samples=n_perturb_samples,
baselines=baseline,
)
else:
sens = sensitivity_max(
nt.attribute,
input,
target=predicted_main_class,
n_perturb_samples=n_perturb_samples,
)
sens_value = sens.cpu().detach().numpy()[0]
else:
sens_value = 0
# filter_name = test_dataset.data.iloc[pbar.n]["filter"].split(" ")[0]
attr_data = attributions.squeeze().cpu().detach().numpy()
if render:
fig, ax = viz.visualize_image_attr_multiple(
np.transpose(attr_data, (1, 2, 0)),
np.transpose(inv_input.squeeze().cpu().detach().numpy(),
(1, 2, 0)),
["original_image", "heat_map"],
["all", "positive"],
titles=["original_image", "heat_map"],
cmap=default_cmap,
show_colorbar=True,
use_pyplot=False,
fig_size=(8, 6),
)
if use_sensitivity or use_infidelity:
ax[0].set_xlabel(
f"Infidelity: {'{0:.6f}'.format(inf_value)}\n Sensitivity: {'{0:.6f}'.format(sens_value)}"
)
fig.suptitle(
f"True: {classes_map[str(label.numpy()[0])][0]}, Pred: {classes_map[str(pred_label_idx.item())][0]}\nScore: {'{0:.4f}'.format(prediction_score)}",
fontsize=16,
)
fig.savefig(
os.path.join(
out_folder,
f"{str(idx)}-{str(filter_count)}-{str(label.numpy()[0])}-{str(OUR_FILTERS[filter_count])}-{classes_map[str(label.numpy()[0])][0]}-{classes_map[str(pred_label_idx.item())][0]}.png",
))
plt.close(fig)
# if pbar.n > 25:
# break
score_for_true_label = output.cpu().detach().numpy(
)[0][predicted_main_class]
filter_attrs[OUR_FILTERS[filter_count]] = [
np.moveaxis(attr_data, 0, -1),
"{0:.8f}".format(score_for_true_label),
]
data_range_for_current_set = MAX_ATT_VALUES[model_version][method][
dataset]
filter_count += 1
if filter_count >= len(OUR_FILTERS):
ssims = []
for rot in OUR_FILTERS:
ssims.append("{0:.8f}".format(
ssim(
filter_attrs["none"][0],
filter_attrs[rot][0],
win_size=11,
data_range=data_range_for_current_set,
multichannel=True,
)))
ssims.append(filter_attrs[rot][1])
scores.append(ssims)
filter_count = 0
predicted_main_class = 0
idx += 1
pbar.close()
indexes = []
for filter_name in OUR_FILTERS:
indexes.append(str(filter_name) + "-ssim")
indexes.append(str(filter_name) + "-score")
np.savetxt(
os.path.join(
out_folder,
f"{model_version}-{dataset}-{method}-ssim-with-range.csv"),
np.array(scores),
delimiter=";",
fmt="%s",
header=";".join([str(rot) for rot in indexes]),
)
print(f"Artifacts stored at {out_folder}")
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(), n_steps=1000)
grads_igrad.append(grads.squeeze().cpu().detach().numpy())
# Gradient SHAP
gshap = GradientShap(model)
baseline_dist = torch.cat([x*0, x*1])
grads = gshap.attribute(x, n_samples=10, stdevs=0.1, baselines=baseline_dist, target=labels[idx].item())
grads_gshap.append(grads.squeeze().cpu().detach().numpy())
# DeepLIFT
dlift = DeepLift(model)
grads = dlift.attribute(x, x*0, target=labels[idx].item())
grads_dlift.append(grads.squeeze().cpu().detach().numpy())
signal.append(x.squeeze().cpu().detach().numpy())
with open(os.path.join('results', 'interp_' + os.path.basename(MODEL) + '.pk'), 'wb') as hf:
pk.dump({'x': signal, 'sal': grads_sal, 'occ': grads_occ, 'igrad': grads_igrad, 'gshap': grads_gshap, 'dlift': grads_dlift}, hf)
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')