training_epochs=train_params['training_epochs'],
batch_size=train_params['batch_size'],
denoising=train_params['denoising'],
learning_rate=train_params['learning_rate'],
train_dir=train_dir,
loss_display_step=train_params['loss_display_step'],
saver_step=train_params['saver_step'],
z_rotate=train_params['z_rotate'],
encoder=encoder,
decoder=decoder,
encoder_args=enc_args,
decoder_args=dec_args,
n_output=[2048, 3])
conf.experiment_name = experiment_name
conf.held_out_step = 5 # How often to evaluate/print out loss on
# held_out data (if they are provided in ae.train() ).
conf.save(osp.join(train_dir, 'configuration'))
# If you ran the above lines, you can reload a saved model like this:
# In[9]:
load_pre_trained_ae = True
restore_epoch = 400
if load_pre_trained_ae:
conf = Conf.load(train_dir + '/configuration')
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(conf.train_dir, epoch=restore_epoch)
def evaluate(setup, results, models, targets_list, victims_list):
top_out_dir = osp.join(BASE_DIR, "latent_3d_points", "data")
# print(BASE_DIR)
# Top-dir of where point-clouds are stored.
top_in_dir = osp.join(BASE_DIR, "latent_3d_points", "data",
"shape_net_core_uniform_samples_2048")
# experiment_name = 'single_class_ae'
experiment_name = 'new_ae'
n_pc_ppoints = 1024 # 2048 # Number of points per model.
bneck_size = 128 # Bottleneck-AE size
# Loss to optimize: 'emd' or 'chamfer' # Bottleneck-AE size
ae_loss = 'chamfer'
train_params = default_train_params()
encoder, decoder, enc_args, dec_args = mlp_architecture_ala_iclr_18(
n_pc_ppoints, bneck_size)
train_dir = create_dir(osp.join(top_out_dir, experiment_name))
conf = Conf(n_input=[n_pc_ppoints, 3],
loss=ae_loss,
training_epochs=train_params['training_epochs'],
batch_size=train_params['batch_size'],
denoising=train_params['denoising'],
learning_rate=train_params['learning_rate'],
train_dir=train_dir,
loss_display_step=train_params['loss_display_step'],
saver_step=train_params['saver_step'],
z_rotate=train_params['z_rotate'],
encoder=encoder,
decoder=decoder,
encoder_args=enc_args,
decoder_args=dec_args)
conf.experiment_name = experiment_name
conf.held_out_step = 5 # How often to evaluate/print out loss on
# held_out data (if they are provided in ae.train() ).
conf.save(osp.join(train_dir, 'configuration'))
load_pre_trained_ae = True
restore_epoch = 500
if load_pre_trained_ae:
conf = Conf.load(train_dir + '/configuration')
reset_tf_graph()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(conf.train_dir, epoch=restore_epoch, verbose=True)
models["ae"] = ae
# all_resulting_corrects = []
# natural_L_2_norm_orig = []
# natural_L_2_norm_adv = []
# natural_L_2_norm_nat = []
# natural_L_infty_norm_orig = []
# natural_L_infty_norm_adv = []
# natural_L_infty_norm_nat = []
# L_2_norm_adv = []
# L_2_norm_nat = []
# L_infty_norm_adv = []
# L_infty_norm_nat = []
# L_cham_norm_adv = []
# L_cham_norm_nat = []
# L_emd_norm_adv = []
# L_emd_norm_nat = []
# natural_L_cham_norm_orig = []
# natural_L_cham_norm_adv = []
# natural_L_cham_norm_nat = []
accuracies_disc = {
"orig_acc": "original accuracy on PointNet ",
"adv_suc": "natural adverserial sucess rate on PointNet ",
"adv_acc": "natural adverserial accuracy on PointNet ",
"proj_acc": "projected accuracy on PointNet ",
"rec_suc": "defended natural adverserial sucess rate on PointNet ",
"rec_acc": "reconstructed defense accuracy on PointNet ",
"orig_acc_pp": "original accuracy on PointNet_++ ",
"orig_acc_gcn": "original accuracy on DGCN",
"orig_acc_p": "original accuracy on PointNet_+ ",
"adv_suc_pp": "natural adverserial sucess rate on PointNet_++ ",
"adv_suc_gcn": "natural adverserial sucess rate on DGCN",
"adv_suc_p": "natural adverserial sucess rate on PointNet_+ ",
"adv_acc_pp": "natural adverserial accuracy on PointNet_++ ",
"adv_acc_gcn": "natural adverserial accuracy on DGCN",
"adv_acc_p": "natural adverserial accuracy on PointNet_+ ",
"proj_acc_pp": "projected accuracy on PointNet_++ ",
"proj_acc_gcn": "projected accuracy on DGCN",
"proj_acc_p": "projected accuracy on PointNet_+ ",
"rec_suc_pp":
"defended natural adverserial sucess rate on PointNet_++ ",
"rec_suc_gcn": "defended natural adverserial sucess rate on DGCN",
"rec_suc_p": "defended natural adverserial sucess rate on PointNet_+ ",
"rec_acc_pp": "reconstructed defense accuracy on PointNet_++ ",
"rec_acc_gcn": "reconstructed defense accuracy on DGCN",
"rec_acc_p": "reconstructed defense accuracy on PointNet_+ ",
"b_adv_suc": "baseline adverserial sucess rate on PointNet ",
"b_adv_acc": "baseline adverserial accuracy on PointNet ",
"b_rec_suc":
"baseline defended natural adverserial sucess rate on PointNet ",
"b_rec_acc": "baselin ereconstructed defense accuracy on PointNet ",
"b_adv_suc_pp": "baseline adverserial sucess rate on PointNet_++ ",
"b_adv_suc_gcn": "baseline adverserial sucess rate on DGCN",
"b_adv_suc_p": "baseline adverserial sucess rate on PointNet_+ ",
"b_adv_acc_pp": "baseline adverserial accuracy on PointNet_++ ",
"b_adv_acc_gcn": "baseline adverserial accuracy on DGCN",
"b_adv_acc_p": "baseline adverserial accuracy on PointNet_+ ",
"b_rec_suc_pp":
"baseline defended natural adverserial sucess rate on PointNet_++ ",
"b_rec_suc_gcn":
"baseline defended natural adverserial sucess rate on DGCN",
"b_rec_suc_p":
"baseline defended natural adverserial sucess rate on PointNet_+ ",
"b_rec_acc_pp":
"baselin ereconstructed defense accuracy on PointNet_++ ",
"b_rec_acc_gcn": "baselin ereconstructed defense accuracy on DGCN",
"b_rec_acc_p":
"baselin ereconstructed defense accuracy on PointNet_+ ",
"orig_acc_r": "original accuracy under Random defense",
"adv_suc_r": "natural adverserial accuracy under Random defense",
"adv_acc_r": "natural adverserial sucess rate under Random defense",
"b_adv_suc_r": "baseline adverserial accuracy under Random defense",
"b_adv_acc_r":
"baseline adverserial sucess rate under Random defense",
"orig_acc_o": "original accuracy under Outlier defense",
"adv_suc_o": "natural adverserial accuracy under Outlier defense",
"adv_acc_o": "natural adverserial sucess rate under Outlier defense",
"b_adv_suc_o": "baseline adverserial accuracy under Outlier defense",
"b_adv_acc_o":
"baseline adverserial sucess rate under Outlier defense",
"orig_acc_bust": "original accuracy under Robust model",
"adv_acc_bust": "natural adverserial accuracy under Robust model"
}
# accuracies_names = [
# "orig_acc", "adv_acc", "proj_acc", "rec_acc", "orig_acc_pp", , "orig_acc_p"
# "adv_acc_pp", "proj_acc_pp", "rec_acc_pp", "adv_acc_p", "proj_acc_p", "rec_acc_p""orig_acc_r",
# "adv_acc_r","orig_acc_o","adv_acc_o"]
norms_names = ["natural_L_cham_norm_orig"]
ev_results = ListDict(accuracies_disc.keys() + norms_names)
# norms_results = ListDict(norms_names)
setups = ListDict(setup.keys())
save_results(setup["results_file"], ev_results + setups)
for target in targets_list:
setup["target"] = target
for victim in victims_list:
if victim == setup["target"]:
continue
setup["victim"] = victim
for batch_indx in range(int(setup["batch_size"])):
predictions, norms = evaluate_all_shapes_scale(
batch_indx=batch_indx, setup=setup, models=models)
[setups.append(setup) for ii in range(setup["batch_size"])]
# norms_results.remove(norms_results - ListDict(norms))
# norms_results.partial_extend(ListDict(norms))
ev_results.remove(ev_results - ListDict(predictions) -
ListDict(norms))
ev_results.partial_extend(
ListDict(predictions)).partial_extend(ListDict(norms))
save_results(setup["results_file"], ev_results + setups)
save_results(setup["results_file"], ev_results + setups + results)
return ev_results
conf = Conf(n_input=[n_pc_points, 3],
loss=ae_loss,
training_epochs=train_params['training_epochs'],
batch_size=train_params['batch_size'],
denoising=train_params['denoising'],
learning_rate=train_params['learning_rate'],
train_dir=train_dir,
loss_display_step=train_params['loss_display_step'],
saver_step=train_params['saver_step'],
z_rotate=train_params['z_rotate'],
encoder=encoder,
decoder=decoder,
encoder_args=enc_args,
decoder_args=dec_args)
conf.experiment_name = experiment_name
conf.held_out_step = 5 # How often to evaluate/print out loss on held_out data (if any).
reset_tf_graph()
ae = PointNetAutoEncoder(experiment_name, conf)
t18 = Tork18('/home/ceteke/Documents/datasets/tork18', 'pour')
X, y = t18.get_dataset()
if len(num_points) != len(train_sizes):
num_points.append(int(len(X) * t))
ae.restore_model(osp.join(top_out_dir, experiment_name), 350, True)
scores = 0.0
for _ in range(folds):
X_train, X_test, y_train, y_test = train_test_split(X,
loss = ae_loss,
training_epochs = train_params['training_epochs'],
batch_size = train_params['batch_size'],
denoising = train_params['denoising'],
learning_rate = train_params['learning_rate'],
loss_display_step = train_params['loss_display_step'],
saver_step = train_params['saver_step'],
z_rotate = train_params['z_rotate'],
train_dir = train_dir,
encoder = encoder,
decoder = decoder,
encoder_args = enc_args,
decoder_args = dec_args
)
conf.experiment_name = experiment_name
conf.held_out_step = 5
reset_tf_graph()
pdb.set_trace()
ae = PointNetAutoEncoder(conf.experiment_name, conf)
ae.restore_model(model_dir, 500)
model_path = '../data/lgan_plane/G_network_299.pth'
g = torch.load(model_path)
batch_size=50
for i in xrange(100):
noise=Variable(torch.cuda.FloatTensor(batch_size, 128))
generate_noise(noise)
fake_x = g(noise).data.cpu().numpy()
fake_pc = ae.decode(fake_x)