'classifier_path': classifier_path,
'K': K,
'L': L,
'train_step': train_steps,
'Nalpha': Nalpha,
'Nbeta': Nbeta,
'lam': lam,
'batch_size': batch_size,
'lr': lr,
'randseed': randseed,
'traininfo': traininfo
})
else: # load pretrained model
gce = torch.load(os.path.join(gce_path, 'model.pt'), map_location=device)
# --- compute final information flow ---
I = gce.informationFlow()
Is = gce.informationFlow_singledim(range(0, K + L))
print('Information flow of K=%d causal factors on classifier output:' % K)
print(Is[:K])
print('Information flow of L=%d noncausal factors on classifier output:' % L)
print(Is[K:])
# --- generate explanation and create figure ---
sample_ind = np.concatenate(
(np.where(vaY == 0)[0][:4], np.where(vaY == 1)[0][:4]))
x = torch.from_numpy(vaX[sample_ind])
zs_sweep = [-3., -2., -1., 0., 1., 2., 3.]
Xhats, yhats = gce.explain(x, zs_sweep)
plotting.plotExplanation(1. - Xhats, yhats, save_path='figs/fig3')
os.makedirs(gce_path)
torch.save((gce, traininfo), os.path.join(gce_path, filename))
else: # load pretrained model
gce, traininfo = torch.load(os.path.join(gce_path, filename))
# get data
gce.encoder.eval()
gce.decoder.eval()
torch.cuda.empty_cache()
data['loss'][i_f, i_l, :] = traininfo['loss']
data['loss_ce'][i_f, i_l, :] = traininfo['loss_ce']
data['loss_nll'][i_f, i_l, :] = traininfo['loss_nll']
data['Ijoint'][i_f, i_l] = gce.informationFlow()
data['Is'][i_f,
i_l, :] = gce.informationFlow_singledim(dims=range(K + L))
# save figures for explanation
sample_ind = np.concatenate(
(np.where(vaY == 0)[0][:3], np.where(vaY == 1)[0][:3],
np.where(vaY == 2)[0][:2]))
x = torch.from_numpy(vaX[sample_ind])
zs_sweep = [-3., -2., -1., 0., 1., 2., 3.]
Xhats, yhats = gce.explain(x, zs_sweep)
if not os.path.exists('./figs/fig19/'):
os.makedirs('./figs/fig19/')
plotting.plotExplanation(1. - Xhats,
yhats,
save_path='./figs/fig19/%dfilters_lambda%g' %
(nfilt, lam))
plt.close('all')
# save all results to file
from scipy.io import savemat
savemat('./results/fig18.mat', {'data': data})
decoder = Decoder(K+L, c_dim, x_dim).to(device)
encoder.apply(util.weights_init_normal)
decoder.apply(util.weights_init_normal)
# %% train GCE
gce = GenerativeCausalExplainer(classifier, decoder, encoder, device)
traininfo = gce.train(X, K, L,
steps=train_steps,
Nalpha=Nalpha,
Nbeta=Nbeta,
lam=lam,
batch_size=batch_size,
lr=lr)
torch.save(gce, 'results/gce_fmnist.pth')
#gce = torch.load('results/gce_fmnist.pth', map_location=device)
# %%
I = gce.informationFlow()
Is = gce.informationFlow_singledim(range(0,K+L))
# %% generate explanation and create figure
sample_ind = np.concatenate((np.where(vaY == 0)[0][:3],
np.where(vaY == 1)[0][:3],
np.where(vaY == 2)[0][:2]))
x = torch.from_numpy(vaX[sample_ind])
zs_sweep = [-3., -2., -1., 0., 1., 2., 3.]
Xhats, yhats = gce.explain(x, zs_sweep)
plotting.plotExplanation(1.-Xhats, yhats, save_path='figs/fig_fmnist_qual')
# %%
def train_explainer(dataset, classes_used, K, L, lam, print_train_losses=True):
# --- parameters ---
# dataset
data_classes_lst = [int(i) for i in str(classes_used)]
dataset_name_full = dataset + '_' + str(classes_used)
# classifier
classifier_path = './pretrained_models/{}_classifier'.format(
dataset_name_full)
# GCE params
randseed = 0
gce_path = os.path.join(
'outputs', dataset_name_full +
'_gce_K{}_L{}_lambda{}'.format(K, L,
str(lam).replace('.', "")))
retrain_gce = True # train explanatory VAE from scratch
save_gce = True # save/overwrite pretrained explanatory VAE at gce_path
# other train params
train_steps = 3000 #8000
Nalpha = 25
Nbeta = 70
batch_size = 64
lr = 5e-4
# --- initialize ---
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if randseed is not None:
np.random.seed(randseed)
torch.manual_seed(randseed)
ylabels = range(0, len(data_classes_lst))
# --- load data ---
from load_mnist import load_mnist_classSelect, load_fashion_mnist_classSelect
if dataset == 'mnist':
fn = load_mnist_classSelect
elif dataset == 'fmnist':
fn = load_fashion_mnist_classSelect
elif dataset == 'cifar':
fn = load_cifar_classSelect
else:
print('dataset not correctly specified')
X, Y, tridx = fn('train', data_classes_lst, ylabels)
vaX, vaY, vaidx = fn('val', data_classes_lst, ylabels)
if dataset == "cifar":
X, vaX = X / 255, vaX / 255
ntrain, nrow, ncol, c_dim = X.shape
x_dim = nrow * ncol
# --- load classifier ---
from models.CNN_classifier import CNN
classifier = CNN(len(data_classes_lst), c_dim).to(device)
checkpoint = torch.load('%s/model.pt' % classifier_path,
map_location=device)
classifier.load_state_dict(checkpoint['model_state_dict_classifier'])
# --- train/load GCE ---
from models.CVAEImageNet import Decoder, Encoder
if retrain_gce:
encoder = Encoder(K + L, c_dim, x_dim).to(device)
decoder = Decoder(K + L, c_dim, x_dim).to(device)
encoder.apply(util.weights_init_normal)
decoder.apply(util.weights_init_normal)
gce = GenerativeCausalExplainer(classifier,
decoder,
encoder,
device,
save_output=True,
save_model_params=False,
save_dir=gce_path,
debug_print=print_train_losses)
traininfo = gce.train(X,
K,
L,
steps=train_steps,
Nalpha=Nalpha,
Nbeta=Nbeta,
lam=lam,
batch_size=batch_size,
lr=lr)
if save_gce:
if not os.path.exists(gce_path):
os.makedirs(gce_path)
torch.save(gce, os.path.join(gce_path, 'model.pt'))
sio.savemat(
os.path.join(gce_path, 'training-info.mat'), {
'data_classes_lst': data_classes_lst,
'classifier_path': classifier_path,
'K': K,
'L': L,
'train_step': train_steps,
'Nalpha': Nalpha,
'Nbeta': Nbeta,
'lam': lam,
'batch_size': batch_size,
'lr': lr,
'randseed': randseed,
'traininfo': traininfo
})
else: # load pretrained model
gce = torch.load(os.path.join(gce_path, 'model.pt'),
map_location=device)
traininfo = None
# --- compute final information flow ---
I = gce.informationFlow()
Is = gce.informationFlow_singledim(range(0, K + L))
print('Information flow of K=%d causal factors on classifier output:' % K)
print(Is[:K])
print('Information flow of L=%d noncausal factors on classifier output:' %
L)
print(Is[K:])
# --- generate explanation and create figure ---
nr_labels = len(data_classes_lst)
nr_samples_fig = 8
sample_ind = np.empty(0, dtype=int)
# retrieve samples from each class
samples_per_class = math.ceil(nr_samples_fig / nr_labels)
for i in range(nr_labels):
samples_per_class = math.ceil(
(nr_samples_fig - i * samples_per_class) / (nr_labels - i))
sample_ind = np.int_(
np.concatenate(
[sample_ind,
np.where(vaY == i)[0][:samples_per_class]]))
x = torch.from_numpy(vaX[sample_ind])
zs_sweep = [-3., -2., -1., 0., 1., 2., 3.]
Xhats, yhats = gce.explain(x, zs_sweep)
plot_save_dir = os.path.join(gce_path, 'figs/')
if not os.path.exists(plot_save_dir):
os.makedirs(plot_save_dir)
plotting.plotExplanation(1. - Xhats, yhats, save_path=plot_save_dir)
return traininfo
batch_size=batch_size,
lr=lr)
if save_gce:
if not os.path.exists(gce_path):
os.makedirs(gce_path)
torch.save(gce, os.path.join(gce_path,'model.pt'))
sio.savemat(os.path.join(gce_path, 'training-info.mat'), {
'data_classes' : data_classes, 'classifier_path' : classifier_path,
'K' : K, 'L' : L, 'train_step' : train_steps, 'Nalpha' : Nalpha,
'Nbeta' : Nbeta, 'lam' : lam, 'batch_size' : batch_size, 'lr' : lr,
'randseed' : randseed, 'traininfo' : traininfo})
else: # load pretrained model
gce = torch.load(os.path.join(gce_path, 'model.pt'), map_location=device)
# --- compute final information flow ---
I = gce.informationFlow()
Is = gce.informationFlow_singledim(range(0, K+L))
print('Information flow of K=%d causal factors on classifier output:' % K)
print(Is[:K])
print('Information flow of L=%d noncausal factors on classifier output:' % L)
print(Is[K:])
# --- generate explanation and create figure ---
sample_ind = np.concatenate((np.where(vaY == 0)[0][:4],
np.where(vaY == 1)[0][:4]))
x = torch.from_numpy(vaX[sample_ind])
zs_sweep = [-3., -2., -1., 0., 1., 2., 3.]
Xhats, yhats = gce.explain(x, zs_sweep)
plotting.plotExplanation(1. - Xhats, yhats, save_path='/Fig3CIFAR')
lam=lam,
batch_size=batch_size,
lr=lr)
# get data
gce.encoder.eval()
gce.decoder.eval()
torch.cuda.empty_cache()
torch.save(gce, 'results/gce_vae_capacity_%dfilters_lambda%g.pth' \
% (nfilt, lam))
data['loss'][i_f, i_l, :] = traininfo['loss']
data['loss_ce'][i_f, i_l, :] = traininfo['loss_ce']
data['loss_nll'][i_f, i_l, :] = traininfo['loss_nll']
data['Ijoint'][i_f, i_l] = gce.informationFlow()
data['Is'][i_f,
i_l, :] = gce.informationFlow_singledim(dims=range(K + L))
# save figures for explanation
sample_ind = np.concatenate(
(np.where(vaY == 0)[0][:3], np.where(vaY == 1)[0][:3],
np.where(vaY == 2)[0][:2]))
x = torch.from_numpy(vaX[sample_ind])
zs_sweep = [-3., -2., -1., 0., 1., 2., 3.]
Xhats, yhats = gce.explain(x, zs_sweep)
plotting.plotExplanation(
1. - Xhats,
yhats,
save_path='figs/fig_vae_capacity_%dfilters_lambda%g' %
(nfilt, lam))
# save all results to file
from scipy.io import savemat
savemat('results/vae_capacity_data.mat', {'data': data})
