def main(args):
# =====================================
# Load config
# =====================================
with open(join(args.output_dir, 'config.json')) as f:
config = json.load(f)
args.__dict__.update(config)
# =====================================
# Dataset
# =====================================
celebA_loader = TFCelebALoader(root_dir=args.celebA_root_dir)
img_height, img_width = args.celebA_resize_size, args.celebA_resize_size
celebA_loader.build_transformation_flow_tf(
*celebA_loader.get_transform_fns("1Konny", resize_size=args.celebA_resize_size))
num_train = celebA_loader.num_train_data
# =====================================
# Instantiate model
# =====================================
if args.activation == "relu":
activation = tf.nn.relu
elif args.activation == "leaky_relu":
activation = tf.nn.leaky_relu
else:
raise ValueError("Do not support '{}' activation!".format(args.activation))
if args.enc_dec_model == "1Konny":
# assert args.z_dim == 65, "For 1Konny, z_dim must be 65. Found {}!".format(args.z_dim)
encoder = Encoder_1Konny(args.z_dim, stochastic=True, activation=activation)
decoder = Decoder_1Konny([img_height, img_width, 3], activation=activation,
output_activation=tf.nn.sigmoid)
disc_z = DiscriminatorZ_1Konny()
else:
raise ValueError("Do not support encoder/decoder model '{}'!".format(args.enc_dec_model))
model = AAE([img_height, img_width, 3], args.z_dim,
encoder=encoder, decoder=decoder,
discriminator_z=disc_z,
rec_x_mode=args.rec_x_mode,
stochastic_z=args.stochastic_z,
use_gp0_z=True, gp0_z_mode=args.gp0_z_mode)
loss_coeff_dict = {
'rec_x': args.rec_x_coeff,
'G_loss_z1_gen': args.G_loss_z1_gen_coeff,
'D_loss_z1_gen': args.D_loss_z1_gen_coeff,
'gp0_z': args.gp0_z_coeff,
}
model.build(loss_coeff_dict)
SimpleParamPrinter.print_all_params_tf_slim()
# =====================================
# Load model
# =====================================
config_proto = tf.ConfigProto(allow_soft_placement=True)
config_proto.gpu_options.allow_growth = True
config_proto.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=config_proto)
model_dir = make_dir_if_not_exist(join(args.output_dir, "model_tf"))
train_helper = SimpleTrainHelper(log_dir=None, save_dir=model_dir)
# Load model
train_helper.load(sess, load_step=args.load_step)
# =====================================
# Experiments
# save_dir = remove_dir_if_exist(join(args.save_dir, "AAE_{}".format(args.run)), ask_4_permission=True)
# save_dir = make_dir_if_not_exist(save_dir)
save_dir = make_dir_if_not_exist(join(args.save_dir, "AAE_{}".format(args.run)))
# =====================================
np.set_printoptions(threshold=np.nan, linewidth=1000, precision=3, suppress=True)
num_bins = args.num_bins
data_proportion = args.data_proportion
bin_limits = tuple([float(s) for s in args.bin_limits.split(";")])
top_k = args.top_k
f = open(join(save_dir, 'log[bins={},bin_limits={},data={}].txt'.
format(num_bins, bin_limits, data_proportion)), mode='w')
print_ = functools.partial(print_both, file=f)
result_file = join(args.informativeness_metrics_dir, "AAE_{}".format(args.run),
'results[bins={},bin_limits={},data={}].npz'.
format(num_bins, bin_limits, data_proportion))
results = np.load(result_file, "r")
print_("")
print_("num_bins: {}".format(num_bins))
print_("bin_limits: {}".format(bin_limits))
print_("data_proportion: {}".format(data_proportion))
print_("top_k: {}".format(top_k))
# Plotting
# =========================================== #
# seed = 389
# num_samples = 30
seed = 398
num_samples = 1
ids = list(range(seed, seed + num_samples))
print_("\nids: {}".format(ids))
data = celebA_loader.sample_images_from_dataset(sess, 'train', ids)
span = 3
points_one_side = 5
print_("sorted_MI: {}".format(results["sorted_MI_z_x"]))
print_("sorted_z_ids: {}".format(results["sorted_z_comps"]))
print_("sorted_norm_MI: {}".format(results["sorted_norm_MI_z_x"]))
print_("sorted_norm_z_ids: {}".format(results["sorted_norm_z_comps"]))
top_MI = results["sorted_MI_z_x"][:top_k]
top_z_ids = results["sorted_z_comps"][:top_k]
top_norm_MI = results["sorted_norm_MI_z_x"][:top_k]
top_norm_z_ids = results["sorted_norm_z_comps"][:top_k]
for n in range(len(ids)):
if top_k == 10:
print("Plot conditional all comps z traverse with train sample {}!".format(ids[n]))
img_file = join(save_dir, "x_train[{}][bins={},bin_limits={},data={}].png".
format(ids[n], num_bins, bin_limits, data_proportion))
model.cond_all_latents_traverse_v2(img_file, sess, data[n],
z_comps=top_z_ids,
z_comp_labels=["z[{}] ({:.2f})".format(comp, mi)
for comp, mi in zip(top_z_ids, top_MI)],
span=span, points_1_side=points_one_side,
hl_x=True,
font_size=9,
subplot_adjust={'left': 0.15, 'right': 0.99,
'bottom': 0.01, 'top': 0.99},
size_inches=(6.3, 4.9),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
img_file = join(save_dir, "x_train[{}][bins={},bin_limits={},data={},norm].png".
format(ids[n], num_bins, bin_limits, data_proportion))
model.cond_all_latents_traverse_v2(img_file, sess, data[n],
z_comps=top_norm_z_ids,
z_comp_labels=["z[{}] ({:.2f})".format(comp, mi)
for comp, mi in zip(top_norm_z_ids, top_norm_MI)],
span=span, points_1_side=points_one_side,
hl_x=True,
font_size=9,
subplot_adjust={'left': 0.15, 'right': 0.99,
'bottom': 0.01, 'top': 0.99},
size_inches=(6.3, 4.9),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
elif top_k == 45:
print("Plot conditional all comps z traverse with train sample {}!".format(ids[n]))
img_file = join(save_dir, "x_train[{}][bins={},bin_limits={},data={}].png".
format(ids[n], num_bins, bin_limits, data_proportion))
model.cond_all_latents_traverse_v2(img_file, sess, data[n],
z_comps=top_z_ids,
z_comp_labels=["z[{}] ({:.2f})".format(comp, mi)
for comp, mi in zip(top_z_ids, top_MI)],
span=span, points_1_side=points_one_side,
hl_x=True,
font_size=5,
subplot_adjust={'left': 0.19, 'right': 0.99,
'bottom': 0.01, 'top': 0.99},
size_inches=(2.98, 9.85),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
img_file = join(save_dir, "x_train[{}][bins={},bin_limits={},data={},norm].png".
format(ids[n], num_bins, bin_limits, data_proportion))
model.cond_all_latents_traverse_v2(img_file, sess, data[n],
z_comps=top_norm_z_ids,
z_comp_labels=["z[{}] ({:.2f})".format(comp, mi)
for comp, mi in zip(top_norm_z_ids, top_norm_MI)],
span=span, points_1_side=points_one_side,
hl_x=True,
font_size=5,
subplot_adjust={'left': 0.19, 'right': 0.99,
'bottom': 0.01, 'top': 0.99},
size_inches=(2.98, 9.85),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
# =========================================== #
f.close()
def main(args):
# =====================================
# Load config
# =====================================
with open(join(args.output_dir, 'config.json')) as f:
config = json.load(f)
args.__dict__.update(config)
# =====================================
# Dataset
# =====================================
celebA_loader = TFCelebAWithAttrLoader(root_dir=args.celebA_root_dir)
img_height, img_width = args.celebA_resize_size, args.celebA_resize_size
celebA_loader.build_transformation_flow_tf(
*celebA_loader.get_transform_fns("1Konny",
resize_size=args.celebA_resize_size))
num_train = celebA_loader.num_train_data
# =====================================
# Instantiate model
# =====================================
if args.activation == "relu":
activation = tf.nn.relu
elif args.activation == "leaky_relu":
activation = tf.nn.leaky_relu
else:
raise ValueError("Do not support '{}' activation!".format(
args.activation))
if args.enc_dec_model == "1Konny":
# assert args.z_dim == 65, "For 1Konny, z_dim must be 65. Found {}!".format(args.z_dim)
encoder = Encoder_1Konny(args.z_dim,
stochastic=True,
activation=activation)
decoder = Decoder_1Konny([img_height, img_width, 3],
activation=activation,
output_activation=tf.nn.sigmoid)
disc_z = DiscriminatorZ_1Konny()
else:
raise ValueError("Do not support encoder/decoder model '{}'!".format(
args.enc_dec_model))
model = AAE([img_height, img_width, 3],
args.z_dim,
encoder=encoder,
decoder=decoder,
discriminator_z=disc_z,
rec_x_mode=args.rec_x_mode,
stochastic_z=args.stochastic_z,
use_gp0_z=True,
gp0_z_mode=args.gp0_z_mode)
loss_coeff_dict = {
'rec_x': args.rec_x_coeff,
'G_loss_z1_gen': args.G_loss_z1_gen_coeff,
'D_loss_z1_gen': args.D_loss_z1_gen_coeff,
'gp0_z': args.gp0_z_coeff,
}
model.build(loss_coeff_dict)
SimpleParamPrinter.print_all_params_tf_slim()
# =====================================
# Load model
# =====================================
config_proto = tf.ConfigProto(allow_soft_placement=True)
config_proto.gpu_options.allow_growth = True
config_proto.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=config_proto)
model_dir = make_dir_if_not_exist(join(args.output_dir, "model_tf"))
train_helper = SimpleTrainHelper(log_dir=None, save_dir=model_dir)
# Load model
train_helper.load(sess, load_step=args.load_step)
# =====================================
# Experiments
# save_dir = remove_dir_if_exist(join(args.save_dir, "AAE_{}".format(args.run)), ask_4_permission=True)
# save_dir = make_dir_if_not_exist(save_dir)
save_dir = make_dir_if_not_exist(
join(args.save_dir, "AAE_{}".format(args.run)))
# =====================================
np.set_printoptions(threshold=np.nan,
linewidth=1000,
precision=3,
suppress=True)
num_bins = args.num_bins
bin_limits = tuple([float(s) for s in args.bin_limits.split(";")])
data_proportion = args.data_proportion
f = open(join(
save_dir, 'log[bins={},bin_limits={},data={}].txt'.format(
num_bins, bin_limits, data_proportion)),
mode='w')
print_ = functools.partial(print_both, file=f)
result_file = join(
args.interpretability_metrics_dir, "AAE_{}".format(args.run),
"results[bins={},bin_limits={},data={}].npz".format(
num_bins, bin_limits, data_proportion))
results = np.load(result_file, "r")
print_("")
print_("num_bins: {}".format(num_bins))
print_("bin_limits: {}".format(bin_limits))
print_("data_proportion: {}".format(data_proportion))
# Plotting
# =========================================== #
# seed = 389
# num_samples = 30
seed = 398
num_samples = 1
ids = list(range(seed, seed + num_samples))
print_("\nids: {}".format(ids))
data = celebA_loader.sample_images_from_dataset(sess, 'train', ids)
span = 3
points_one_side = 5
attr_names = celebA_loader.attributes
print_("attr_names: {}".format(attr_names))
print_("results.keys: {}".format(list(results.keys())))
# (z_dim, num_attrs)
MI_ids_sorted = results['MI_ids_sorted']
MI_sorted = results['MI_sorted']
MI_gap_y = results['MI_gap_y']
H_y = results['H_y_4_diff_z'][:, 0]
assert MI_ids_sorted.shape[1] == len(attr_names) == len(MI_gap_y) == len(H_y), \
"MI_ids_sorted.shape: {}, len(attr_names): {}, len(MI_gap_y): {}, len(H_y): {}".format(
MI_ids_sorted.shape, len(attr_names), len(MI_gap_y), len(H_y))
print_("\nShow RMIG!")
for i in range(len(attr_names)):
print("{}: RMIG: {:.4f}, RMIG (unnorm): {:.4f}, H: {:.4f}".format(
attr_names[i], MI_gap_y[i], MI_gap_y[i] * H_y[i], H_y[i]))
print_("\nShow JEMMI!")
H_z_y = results['H_z_y']
MI_z_y = results['MI_z_y']
ids_sorted_by_MI = np.argsort(MI_z_y, axis=0)[::-1]
MI_z_y_sorted = np.take_along_axis(MI_z_y, ids_sorted_by_MI, axis=0)
H_z_y_sorted = np.take_along_axis(H_z_y, ids_sorted_by_MI, axis=0)
H_diff = H_z_y_sorted[0, :] - MI_z_y_sorted[0, :]
JEMMI_unnorm = H_diff + MI_z_y_sorted[1, :]
JEMMI_norm = JEMMI_unnorm / (np.log(num_bins) + H_y)
for i in range(len(attr_names)):
print(
"{}: JEMMI: {:.4f}, JEMMI (unnorm): {:.4f}, H_diff: {:.4f}, I2: {:.4f}, top 2 latents: z{}, z{}"
.format(attr_names[i], JEMMI_norm[i], JEMMI_unnorm[i], H_diff[i],
MI_z_y_sorted[1, i], ids_sorted_by_MI[0, i],
ids_sorted_by_MI[1, i]))
# Uncomment if you want
'''
for n in range(len(ids)):
for k in range(len(attr_names)):
MI_ids_top10 = MI_ids_sorted[:10, k]
MI_top10 = MI_sorted[:10, k]
print("Plot top 10 latents for factor '{}'!".format(attr_names[k]))
img_file = join(save_dir, "x_train[{}][attr={}][bins={},bin_limits={},data={}].png".
format(ids[n], attr_names[k], num_bins, bin_limits, data_proportion))
model.cond_all_latents_traverse_v2(img_file, sess, data[n],
z_comps=MI_ids_top10,
z_comp_labels=["z[{}] ({:.4f})".format(comp, mi)
for comp, mi in zip(MI_ids_top10, MI_top10)],
span=span, points_1_side=points_one_side,
hl_x=True,
font_size=9,
title="{} (MI gap={:.4f}, H={:.4f})".format(
attr_names[k], MI_gap_y[k], H_y[k]),
title_font_scale=1.5,
subplot_adjust={'left': 0.16, 'right': 0.99,
'bottom': 0.01, 'top': 0.95},
size_inches=(6.5, 5.2),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
'''
# Top 5 only
for n in range(len(ids)):
for k in range(len(attr_names)):
MI_ids_top10 = MI_ids_sorted[:5, k]
MI_top10 = MI_sorted[:5, k]
print("Plot top 5 latents for factor '{}'!".format(attr_names[k]))
img_file = join(
save_dir, "train{}_attr={}_bins={}_data={}.png".format(
ids[n], attr_names[k], num_bins, data_proportion))
model.cond_all_latents_traverse_v2(
img_file,
sess,
data[n],
z_comps=MI_ids_top10,
z_comp_labels=[
"z[{}] ({:.4f})".format(comp, mi)
for comp, mi in zip(MI_ids_top10, MI_top10)
],
span=span,
points_1_side=points_one_side,
hl_x=True,
font_size=9,
title="{} (MI gap={:.4f}, H={:.4f})".format(
attr_names[k], MI_gap_y[k], H_y[k]),
title_font_scale=1.5,
subplot_adjust={
'left': 0.16,
'right': 0.99,
'bottom': 0.005,
'top': 0.93
},
size_inches=(6.5, 2.8),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
'''
def main(args):
# =====================================
# Load config
# =====================================
with open(join(args.output_dir, 'config.json')) as f:
config = json.load(f)
args.__dict__.update(config)
# =====================================
# Dataset
# =====================================
data_file = join(RAW_DATA_DIR, "ComputerVision", "dSprites",
"dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz")
# It is already in the range [0, 1]
with np.load(data_file, encoding="latin1") as f:
x_train = f['imgs']
x_train = np.reshape(x_train, [3, 6, 40, 32, 32, 64, 64, 1])
# =====================================
# Instantiate model
# =====================================
if args.enc_dec_model == "1Konny":
encoder = Encoder_1Konny(args.z_dim, stochastic=True)
decoder = Decoder_1Konny()
disc_z = DiscriminatorZ_1Konny()
else:
raise ValueError("Do not support enc_dec_model='{}'!".format(
args.enc_dec_model))
model = AAE([64, 64, 1],
args.z_dim,
encoder=encoder,
decoder=decoder,
discriminator_z=disc_z,
rec_x_mode=args.rec_x_mode,
stochastic_z=args.stochastic_z,
use_gp0_z=True,
gp0_z_mode=args.gp0_z_mode)
loss_coeff_dict = {
'rec_x': args.rec_x_coeff,
'G_loss_z1_gen': args.G_loss_z1_gen_coeff,
'D_loss_z1_gen': args.D_loss_z1_gen_coeff,
'gp0_z': args.gp0_z_coeff,
}
model.build(loss_coeff_dict)
SimpleParamPrinter.print_all_params_tf_slim()
# =====================================
# Load model
# =====================================
config_proto = tf.ConfigProto(allow_soft_placement=True)
config_proto.gpu_options.allow_growth = True
config_proto.gpu_options.per_process_gpu_memory_fraction = 0.9
sess = tf.Session(config=config_proto)
model_dir = make_dir_if_not_exist(join(args.output_dir, "model_tf"))
train_helper = SimpleTrainHelper(log_dir=None, save_dir=model_dir)
# Load model
train_helper.load(sess, load_step=args.load_step)
# =====================================
# Experiments
# save_dir = remove_dir_if_exist(join(args.save_dir, "{}_{}".format(args.enc_dec_model, args.run)), ask_4_permission=True)
# save_dir = make_dir_if_not_exist(save_dir)
save_dir = make_dir_if_not_exist(
join(args.save_dir, "{}_{}".format(args.enc_dec_model, args.run)))
# =====================================
np.set_printoptions(threshold=np.nan,
linewidth=1000,
precision=3,
suppress=True)
num_bins = args.num_bins
bin_limits = tuple([float(s) for s in args.bin_limits.split(";")])
data_proportion = args.data_proportion
# Logs
f = open(join(
save_dir, 'log[bins={},bin_limits={},data={}].txt'.format(
num_bins, bin_limits, data_proportion)),
mode='w')
print_ = functools.partial(print_both, file=f)
print_("")
print_("num_bins: {}".format(num_bins))
print_("bin_limits: {}".format(bin_limits))
print_("data_proportion: {}".format(data_proportion))
# Results
result_file = join(
args.interpretability_metrics_dir,
"{}_{}".format(args.enc_dec_model, args.run),
"results[bins={},bin_limits={},data={}].npz".format(
num_bins, bin_limits, data_proportion))
results = np.load(result_file, "r")
print_("results.keys: {}".format(list(results.keys())))
# Plotting
# =========================================== #
data = [
x_train[0, 3, 20, 16, 16], x_train[1, 3, 20, 16, 16], x_train[2, 3, 20,
16, 16]
]
gt_factors = ['Shape', 'Scale', 'Rotation', 'Pos_x', 'Pos_y']
ids_sorted = results['ids_sorted']
MI_z_y_sorted = results['MI_z_y_sorted']
H_z_y_sorted = results['H_z_y_sorted']
H_y = results['H_y']
RMIG = results['RMIG']
JEMMI = results['JEMMI']
print_("MI_z_y_sorted:\n{}".format(MI_z_y_sorted))
print_("\nShow RMIG!")
for k in range(len(gt_factors)):
print_(
"{}, RMIG: {:.4f}, RMIG (unnorm): {:.4f}, H: {:.4f}, I1: {:.4f}, I2: {:.4f}"
.format(gt_factors[k], RMIG[k], RMIG[k] * H_y[k], H_y[k],
MI_z_y_sorted[0, k], MI_z_y_sorted[1, k]))
print_("\nShow JEMMI!")
for k in range(len(gt_factors)):
print_(
"{}, JEMMI: {:.4f}, JEMMI (unnorm): {:.4f}, H1: {:.4f}, H1-I1: {:.4f}, I2: {:.4f}, "
"top2 ids: z{}, z{}".format(
gt_factors[k], JEMMI[k],
JEMMI[k] * (H_y[k] + np.log(num_bins)), H_z_y_sorted[0, k],
H_z_y_sorted[0, k] - MI_z_y_sorted[0, k], MI_z_y_sorted[1, k],
ids_sorted[0, k], ids_sorted[1, k]))
span = 3
points_one_side = 5
for n in range(len(data)):
for k in range(len(gt_factors)):
print("x={}, y={}!".format(n, gt_factors[k]))
img_file = join(
save_dir, "{}[x={},bins={},bin_limits={},data={}].png".format(
gt_factors[k], n, num_bins, bin_limits, data_proportion))
'''
ids_top10 = ids_sorted[:10, k]
MI_top10 = MI_z_y_sorted[:10, k]
model.cond_all_latents_traverse_v2(img_file, sess, data[n],
z_comps=ids_top10,
z_comp_labels=["z[{}] ({:.4f})".format(comp, mi)
for comp, mi in zip(ids_top10, MI_top10)],
span=span, points_1_side=points_one_side,
hl_x=True,
font_size=9,
title="{} (RMIG={:.4f}, JEMMI={:.4f}, H={:.4f})".format(
gt_factors[k], RMIG[k], JEMMI[k], H_y[k]),
title_font_scale=1.5,
subplot_adjust={'left': 0.16, 'right': 0.99,
'bottom': 0.01, 'top': 0.95},
size_inches=(6.5, 5.2),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
'''
ids_top3 = ids_sorted[:3, k]
MI_top3 = MI_z_y_sorted[:3, k]
model.cond_all_latents_traverse_v2(
img_file,
sess,
data[n],
z_comps=ids_top3,
z_comp_labels=[
"z[{}] ({:.4f})".format(comp, mi)
for comp, mi in zip(ids_top3, MI_top3)
],
span=span,
points_1_side=points_one_side,
hl_x=True,
font_size=9,
title="{} (RMIG={:.4f}, JEMMI={:.4f}, H={:.4f})".format(
gt_factors[k], RMIG[k], JEMMI[k], H_y[k]),
title_font_scale=1.5,
subplot_adjust={
'left': 0.16,
'right': 0.99,
'bottom': 0.01,
'top': 0.88
},
size_inches=(6.2, 1.7),
batch_size=args.batch_size,
dec_output_2_img_func=binary_float_to_uint8)
f.close()