def load_displacements_and_energy(model_root,
use_reencoded=False,
use_extra_samples=False):
from keras.models import Model, load_model
encoder = load_model(os.path.join(model_root, 'keras_models/encoder.hdf5'))
U = igl.eigen.MatrixXd()
igl.readDMAT(
os.path.join(model_root, 'pca_results/ae_pca_components.dmat'), U)
if not use_reencoded and not use_extra_samples:
training_data_path = os.path.join(model_root, 'training_data/training')
displacements = my_utils.load_displacement_dmats_to_numpy(
training_data_path)
flatten_displ, unflatten_displ = my_utils.get_flattners(displacements)
encoded_displacements = encoder.predict(
flatten_displ(displacements) @ U)
energies = my_utils.load_energy_dmats_to_numpy(training_data_path)
if use_reencoded:
reencoded_training_data_path = os.path.join(
model_root, 'augmented_training_data/reencoded/')
displacements_path = os.path.join(reencoded_training_data_path,
'displacements.dmat')
enc_displacements_path = os.path.join(reencoded_training_data_path,
'enc_displacements.dmat')
energies_path = os.path.join(reencoded_training_data_path,
'energies.dmat')
displacements = my_utils.read_double_dmat_to_numpy(displacements_path)
encoded_displacements = my_utils.read_double_dmat_to_numpy(
enc_displacements_path)
energies = my_utils.read_double_dmat_to_numpy(energies_path)
if use_extra_samples:
sampled_training_data_path = os.path.join(
model_root, 'augmented_training_data/sampled/')
displacements_path = os.path.join(sampled_training_data_path,
'displacements.dmat')
enc_displacements_path = os.path.join(sampled_training_data_path,
'enc_displacements.dmat')
energies_path = os.path.join(sampled_training_data_path,
'energies.dmat')
displacements = numpy.append(
displacements,
my_utils.read_double_dmat_to_numpy(displacements_path),
axis=0)
encoded_displacements = numpy.append(
encoded_displacements,
my_utils.read_double_dmat_to_numpy(enc_displacements_path),
axis=0)
energies = numpy.append(
energies,
my_utils.read_double_dmat_to_numpy(energies_path),
axis=0)
return displacements, encoded_displacements, energies
def build_energy_model(model_root, energy_model_config):
"""energy basis"""
# TODO SAVE ENERGY MODEL
base_path = model_root
training_data_path = os.path.join(base_path, 'training_data/training')
validation_data_path = os.path.join(base_path, 'training_data/validation')
# Energies
energies = my_utils.load_energy_dmats_to_numpy(training_data_path)
if os.path.exists(validation_data_path):
energies_test = my_utils.load_energy_dmats_to_numpy(
validation_data_path)
else:
energies_test = None
flatten_data, unflatten_data = my_utils.get_flattners(energies)
energies = flatten_data(energies) #/10000
# print(energies[100])
energies_test = flatten_data(energies_test)
if not os.path.exists(os.path.join(base_path, 'pca_results/')):
os.makedirs(os.path.join(base_path, 'pca_results/'))
basis_path = os.path.join(base_path,
'pca_results/energy_pca_components.dmat')
tet_path = os.path.join(base_path, 'pca_results/energy_indices.dmat')
start = time.time()
basis_opt(energy_model_config,
model_root,
basis_path,
tet_path,
energies,
energies_test,
energy_model_config['pca_dim'],
energy_model_config['num_sample_tets'],
True,
target_mins=energy_model_config['target_anneal_mins'],
brute_force_its=energy_model_config['brute_force_iterations'])
print("Energy optimization took", time.time() - start)
def pca_analysis(data, n_components, plot_explained_variance=False):
""" Returns and encoder and decoder for projecting into and out of the PCA reduced space """
print("Doing PCA with", n_components, "components...")
# numpy.random.shuffle(numpy_displacements_sample) # Necessary?
pca = PCA(n_components=n_components)
flatten_data, unflatten_data = my_utils.get_flattners(data)
flattened_data = flatten_data(data)
pca.fit(flattened_data)
if plot_explained_variance:
explained_var = pca.explained_variance_ratio_
plt.xlabel('Component')
plt.ylabel('Explained Variance')
plt.plot(list(range(1, n_components + 1)), explained_var)
plt.show(block=False)
print("Total explained variance =", sum(explained_var))
mse = mean_squared_error(
flattened_data, pca.inverse_transform(pca.transform(flattened_data)))
def encode(decoded_data):
#return pca.transform(flatten_data(decoded_data))
#return pca.components_ @ flatten_data(decoded_data).T # We don't need to subtract the mean before going to/from reduced space?
X = flatten_data(decoded_data) - pca.mean_
return numpy.dot(X, pca.components_.T)
def decode(encoded_data):
# return unflatten_data(pca.inverse_transform(encoded_data))
# return unflatten_data((pca.components_.T @ encoded_data).T)
X_original = numpy.dot(encoded_data, pca.components_)
X_original = X_original + pca.mean_
return unflatten_data(X_original)
return pca, encode, decode, pca.explained_variance_ratio_, mse
def sparse_learn_weights_l1(model_root,
use_reencoded=False,
use_extra_samples=False):
"""use_reencoded will use the displacements that have been encoded and then decoded after
training the model. use_extra_samples uses the gaussian sampled poses around the training data"""
V, T, F = my_utils.read_MESH_to_numpy(os.path.join(model_root,
'tets.mesh'))
print("Computing volumes...")
volumes = my_utils.compute_element_volumes(V, T)
# Scale to [0, 1]
max_vol = numpy.max(volumes)
volumes /= max_vol
# numpy.set_printoptions(threshold=numpy.inf)
# print(volumes)
displacements, encoded_displacements, energies = load_displacements_and_energy(
model_root, use_reencoded, use_extra_samples)
# Normalize energy
energies = energies / numpy.apply_along_axis(numpy.linalg.norm, 1,
energies)[:, None]
flatten_data, unflatten_data = my_utils.get_flattners(energies)
use_binary = False
if use_binary:
ae_decode, hist = binary_discrete_nn_analysis(
encoded_displacements,
flatten_data(energies),
[],
[],
activation='elu',
epochs=70,
batch_size=512, #len(energies),#100,
layers=[
200, 50
], # [200, 200, 50] First two layers being wide seems best so far. maybe an additional narrow third 0.0055 see
do_fine_tuning=False,
model_root=model_root,
autoencoder_config=None,
energy_model_config={'enabled': False},
)
else:
ae_decode, hist = discrete_nn_analysis(
encoded_displacements,
flatten_data(energies),
[],
[],
volumes,
activation='elu',
epochs=400,
batch_size=256, #len(energies),#100,
layers=[
50, 50, 3
], # [200, 200, 50] First two layers being wide seems best so far. maybe an additional narrow third 0.0055 see
do_fine_tuning=False,
model_root=model_root,
autoencoder_config=None,
energy_model_config={'enabled': False},
)
def fit_nn_direct_energy(
reduced_space_samples,
energy_samples,
reduced_space_validation_samples,
energy_validation_samples,
activation='relu',
epochs=100,
batch_size=100,
layers=[32, 16],
do_fine_tuning=False,
model_root=None,
autoencoder_config=None,
energy_model_config=None
): # TODO should probably just pass in both configs here
"""
Returns and encoder and decoder for going into and out of the reduced latent space.
If pca_weights is given, then do a weighted mse.
If pca_object is given, then the first and final layers will do a pca transformation of the reduced_space_samples.
"""
import keras
import keras.backend as K
from keras.layers import Input, Dense, Lambda
from keras.models import Model, load_model
from keras.engine.topology import Layer
from keras.callbacks import History
flatten_data, unflatten_data = my_utils.get_flattners(
reduced_space_samples)
# TODO: Do I need to shuffle?
reduced_space_samples = flatten_data(reduced_space_samples)
input = Input(shape=(len(reduced_space_samples[0]), ),
name="energy_model_input")
output = input
for i, layer_width in enumerate(layers):
output = Dense(layer_width,
activation=activation,
name="dense_decode_layer_" + str(i))(output)
output = Dense(
len(energy_samples[0]),
activation='relu',
name="output_layer" + str(i),
)(output)
model = Model(input, output)
optimizer = keras.optimizers.Adam(lr=0.1,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0)
model.compile(
optimizer=optimizer,
loss='mean_absolute_percentage_error',
)
def mean_absolute_percentage_error(y_true, y_pred):
## Note: does not handle mix 1d representation
#if _is_1d(y_true):
# y_true, y_pred = _check_1d_array(y_true, y_pred)
return numpy.mean(numpy.abs((y_true - y_pred) / y_true)) * 100
hist = History()
model_start_time = time.time()
from keras.callbacks import Callback
idx = numpy.random.randint(len(reduced_space_samples), size=10)
class MyHistoryCallback(Callback):
"""Callback that records events into a `History` object.
This callback is automatically applied to
every Keras model. The `History` object
gets returned by the `fit` method of models.
"""
def on_train_begin(self, logs=None):
self.epoch = []
self.history = {}
def on_epoch_end(self, epoch, logs=None):
actual = energy_samples[idx, :]
pred = self.model.predict(reduced_space_samples[idx, :])
mse = mean_absolute_percentage_error(actual, pred)
print("Actual energy: ", actual)
print("Predicted energy: ", pred)
self.history.setdefault('mse', []).append(mse)
# self.history.setdefault('val_mse', []).append(val_mse)
print()
print("Percentage error: ", mse)
# print("Val Mean squared error: ", val_mse)
logs = logs or {}
self.epoch.append(epoch)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
my_hist_callback = MyHistoryCallback()
model.fit(
reduced_space_samples,
energy_samples,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
# validation_data=(reduced_space_validation_samples, energy_validation_samples),
callbacks=[my_hist_callback])
if model_root:
print("Saving model...")
models_with_names = [
(model, "direct_energy_model"),
]
keras_models_dir = os.path.join(model_root, 'keras_models')
my_utils.create_dir_if_not_exist(keras_models_dir)
for keras_model, name in models_with_names:
keras_model_file = os.path.join(keras_models_dir, name + ".hdf5")
keras_model.save(keras_model_file) # Save the keras model
print("Finished saving model.")
print("Total training time: ", time.time() - model_start_time)
# model.save('discrete_energy_model.hdf5')
# if energy_model_confiredict(normalize(flatten_data(decoded_data)))
def decode(encoded_data):
return unflatten_data(model.predict(encoded_data))
return decode, hist
def binary_discrete_nn_analysis(
reduced_space_samples,
energy_samples,
reduced_space_validation_samples,
energy_validation_samples,
activation='relu',
epochs=100,
batch_size=100,
layers=[32, 16],
do_fine_tuning=False,
model_root=None,
autoencoder_config=None,
energy_model_config=None
): # TODO should probably just pass in both configs here
"""
Returns and encoder and decoder for going into and out of the reduced latent space.
If pca_weights is given, then do a weighted mse.
If pca_object is given, then the first and final layers will do a pca transformation of the reduced_space_samples.
"""
import keras
import keras.backend as K
from keras.layers import Input, Dense, Lambda, multiply, ActivityRegularization
from keras.models import Model, load_model
from keras.engine.topology import Layer
from keras.callbacks import History
flatten_data, unflatten_data = my_utils.get_flattners(
reduced_space_samples)
# TODO: Do I need to shuffle?
reduced_space_samples = flatten_data(reduced_space_samples)
energy_samples = flatten_data(energy_samples)
reduced_space_validation_samples = flatten_data(
reduced_space_samples[:10] if reduced_space_validation_samples is None
else reduced_space_validation_samples)
energy_validation_samples = flatten_data(
energy_samples[:10]
if energy_validation_samples is None else energy_validation_samples)
input = Input(shape=(len(reduced_space_samples[0]), ),
name="energy_model_input")
output = input
for i, layer_width in enumerate(layers):
output = Dense(layer_width,
activation=activation,
name="dense_decode_layer_" + str(i))(output)
fixed_input = Input(tensor=K.variable([[1.0]]))
weights = Dense(len(energy_samples[0]), activation=None,
use_bias=False)(fixed_input)
# hard_sigmoid?
output = Dense(
len(energy_samples[0]),
activation='hard_sigmoid',
name="output_layer" + str(i),
# activity_regularizer=keras.regularizers.l1(0.0001), # TODO what val? default was 0.01
)(output)
output = multiply([output, weights])
output = ActivityRegularization(l1=0.001, l2=0.0)(output)
model = Model([input, fixed_input], output)
def energy_loss(y_true, y_pred):
return K.mean(
K.square(
K.sum(y_pred * y_true, axis=-1) -
K.sum(y_true, axis=-1))) # TODO should mean be over an axis?
def energy_loss_numpy(y_true, y_pred):
return numpy.mean(
numpy.square(
numpy.sum(y_pred * y_true, axis=-1) -
numpy.sum(y_true, axis=-1)))
optimizer = keras.optimizers.Adam(lr=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0)
model.compile(
optimizer=optimizer,
loss=energy_loss #'mean_squared_error',
)
hist = History()
model_start_time = time.time()
from keras.callbacks import Callback
idx = numpy.random.randint(len(reduced_space_samples), size=10)
class MyHistoryCallback(Callback):
"""Callback that records events into a `History` object.
This callback is automatically applied to
every Keras model. The `History` object
gets returned by the `fit` method of models.
"""
def on_train_begin(self, logs=None):
self.epoch = []
self.history = {}
def on_epoch_end(self, epoch, logs=None):
actual = energy_samples[idx, :]
pred = self.model.predict(reduced_space_samples[idx, :])
mse = energy_loss_numpy(actual, pred)
print("Actual energy: ", (actual).sum(axis=1))
print("Predicted energy: ", numpy.sum(pred * actual, axis=-1))
nonzeros = [p[numpy.nonzero(p)] for p in pred]
# print("non zero weights: ", nonzeros)
print("len(nonzero):", [len(nz) for nz in nonzeros])
#val_mse = mean_squared_error(high_dim_pca_decode(self.model.predict(encoded_high_dim_pca_test_displacements)), test_displacements)
self.history.setdefault('mse', []).append(mse)
# self.history.setdefault('val_mse', []).append(val_mse)
print()
print("Mean squared error: ", mse)
# print("Val Mean squared error: ", val_mse)
logs = logs or {}
self.epoch.append(epoch)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
my_hist_callback = MyHistoryCallback()
model.fit(
reduced_space_samples,
energy_samples,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
# validation_data=(reduced_space_validation_samples, energy_validation_samples),
callbacks=[my_hist_callback])
if model_root:
print("Saving model...")
models_with_names = [
(model, "l1_discrete_energy_model"),
]
keras_models_dir = os.path.join(model_root, 'keras_models')
my_utils.create_dir_if_not_exist(keras_models_dir)
for keras_model, name in models_with_names:
keras_model_file = os.path.join(keras_models_dir, name + ".hdf5")
keras_model.save(keras_model_file) # Save the keras model
print("Finished saving model.")
print("Total training time: ", time.time() - model_start_time)
# model.save('discrete_energy_model.hdf5')
# if energy_model_confiredict(normalize(flatten_data(decoded_data)))
def decode(encoded_data):
return unflatten_data(model.predict(encoded_data))
return decode, hist
def autoencoder_analysis(
data,
test_data,
activation='relu',
latent_dim=3,
epochs=100,
batch_size=100,
learning_rate=0.001,
layers=[32, 16],
pca_weights=None,
pca_basis=None,
do_fine_tuning=False,
model_root=None,
autoencoder_config=None,
callback=None,
): # TODO should probably just pass in both configs here
"""
Returns and encoder and decoder for going into and out of the reduced latent space.
If pca_weights is given, then do a weighted mse.
"""
assert not (
(pca_weights is not None) and
(pca_basis is not None)) # pca_weights incompatible with pca_object
import keras
import keras.backend as K
from keras.layers import Input, Dense, Lambda
from keras.models import Model, load_model
from keras.engine.topology import Layer
from keras.callbacks import History
flatten_data, unflatten_data = my_utils.get_flattners(data)
# TODO: Do I need to shuffle?
train_data = data
test_data = test_data if test_data is not None else data[:10]
## Preprocess the data
# mean = numpy.mean(train_data, axis=0)
# std = numpy.std(train_data, axis=0)
mean = numpy.mean(train_data)
std = numpy.std(train_data)
s_min = numpy.min(train_data)
s_max = numpy.max(train_data)
print(mean)
print(std)
# TODO dig into this. Why does it mess up?
def normalize(data):
return data
#return (data - mean) / std
# return numpy.nan_to_num((train_data - s_min) / (s_max - s_min))
def denormalize(data):
return data
#return data * std + mean
# return data * (s_max - s_min) + s_mi
# My elu fixes the jvp problem
if activation == "my_elu":
activation = my_utils.create_my_elu()
## Set up the network
input = Input(shape=(len(train_data[0]), ), name="encoder_input")
output = input
if pca_basis is not None:
# output = Lambda(pca_transform_layer)(output)
# output = PCALayer(pca_object, is_inv=False, fine_tune=do_fine_tuning)(output)
W = pca_basis
b = numpy.zeros(pca_basis.shape[1])
output = Dense(pca_basis.shape[1],
activation='linear',
weights=[W, b],
trainable=do_fine_tuning,
name="pca_encode_layer")(output)
for i, layer_width in enumerate(layers):
act = activation
# if i == len(layers) - 1:
# act = 'sigmoid'
output = Dense(layer_width,
activation=act,
name="dense_encode_layer_" + str(i))(output)
# -- Encoded layer
output = Dense(latent_dim, activation=activation, name="encoded_layer")(
output) # TODO Tanh into encoded layer to bound vars?
for i, layer_width in enumerate(reversed(layers)):
output = Dense(layer_width,
activation=activation,
name="dense_decode_layer_" + str(i))(output)
if pca_basis is not None:
output = Dense(
pca_basis.shape[1],
activation='linear',
name="to_pca_decode_layer"
)(
output
) ## TODO is this right?? Possibly should just change earlier layer width
# output = Lambda(pca_inv_transform_layer)(output)
# output = PCALayer(pca_object, is_inv=True, fine_tune=do_fine_tuning)(output)
W = pca_basis.T
b = numpy.zeros(len(train_data[0]))
output = Dense(len(train_data[0]),
activation='linear',
weights=[W, b],
trainable=do_fine_tuning,
name="pca_decode_layer")(output)
else:
output = Dense(
len(train_data[0]),
activation='linear',
name="decoder_output_layer"
)(
output
) #'linear',)(output) # First test seems to indicate no change on output with linear
autoencoder = Model(input, output)
## Set the optimization parameters
optimizer = keras.optimizers.Adam(lr=learning_rate,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0)
autoencoder.compile(
optimizer=optimizer,
loss='mean_squared_error',
)
hist = History()
model_start_time = time.time()
autoencoder.fit(train_data,
train_data,
epochs=epochs,
batch_size=batch_size,
shuffle=True,
validation_data=(test_data, test_data),
callbacks=[hist, callback])
training_time = time.time() - model_start_time
print("Total training time: ", training_time)
autoencoder, encoder, decoder = my_utils.decompose_ae(autoencoder,
do_energy=False)
# Save the models in both tensorflow and keras formats
if model_root:
print("Saving model...")
models_with_names = [
(autoencoder, "autoencoder"),
(encoder, "encoder"),
(decoder, "decoder"),
]
keras_models_dir = os.path.join(model_root, 'keras_models')
my_utils.create_dir_if_not_exist(keras_models_dir)
for keras_model, name in models_with_names:
keras_model_file = os.path.join(keras_models_dir, name + ".hdf5")
keras_model.save(keras_model_file) # Save the keras model
print("Finished saving model.")
def encode(decoded_data):
return encoder.predict(normalize(flatten_data(decoded_data)))
def decode(encoded_data):
return unflatten_data(denormalize(decoder.predict(encoded_data)))
return encode, decode, training_time
def generate_model(
model_root, # This is the root of the standard formatted directory created by build-model.py
learning_config, # Comes from the training config file
):
autoencoder_config = learning_config['autoencoder_config']
energy_model_config = learning_config['energy_model_config']
save_objs = learning_config['save_objs']
train_in_full_space = False
record_full_mse_each_epoch = False
training_data_path = os.path.join(model_root, 'training_data/training')
validation_data_path = os.path.join(model_root, 'training_data/validation')
# Loading the rest pose
base_verts, face_indices = my_utils.load_base_vert_and_face_dmat_to_numpy(
training_data_path)
base_verts_eig = p2e(base_verts)
face_indices_eig = p2e(face_indices)
# Loading displacements for training data
displacements = my_utils.load_displacement_dmats_to_numpy(
training_data_path)
if os.path.exists(validation_data_path):
test_displacements = my_utils.load_displacement_dmats_to_numpy(
validation_data_path)
else:
test_displacements = None
flatten_data, unflatten_data = my_utils.get_flattners(displacements)
displacements = flatten_data(displacements)
test_displacements = flatten_data(test_displacements)
# Do the training
pca_ae_train_dim = autoencoder_config['pca_layer_dim']
pca_compare_dims = autoencoder_config['pca_compare_dims']
ae_dim = autoencoder_config['ae_encoded_dim']
ae_epochs = autoencoder_config['training_epochs']
batch_size = autoencoder_config['batch_size'] if autoencoder_config[
'batch_size'] > 0 else len(displacements)
learning_rate = autoencoder_config['learning_rate']
layers = autoencoder_config['non_pca_layer_sizes']
do_fine_tuning = autoencoder_config['do_fine_tuning']
activation = autoencoder_config['activation']
save_pca_components = True
save_autoencoder = True
training_results = {
'autoencoder': {},
'pca': {},
}
# Normal low dim pca first
for pca_dim in pca_compare_dims:
U, explained_var, pca_encode, pca_decode = pca_no_centering(
displacements, pca_dim)
encoded_pca_displacements = pca_encode(displacements)
decoded_pca_displacements = pca_decode(encoded_pca_displacements)
if test_displacements is not None:
encoded_pca_test_displacements = pca_encode(test_displacements)
decoded_pca_test_displacements = pca_decode(
encoded_pca_test_displacements)
if save_pca_components:
pca_results_filename = os.path.join(
model_root,
'pca_results/pca_components_' + str(pca_dim) + '.dmat')
print('Saving pca results to', pca_results_filename)
my_utils.create_dir_if_not_exist(
os.path.dirname(pca_results_filename))
my_utils.save_numpy_mat_to_dmat(pca_results_filename,
numpy.ascontiguousarray(U))
training_mse = mean_squared_error(
flatten_data(decoded_pca_displacements),
flatten_data(displacements))
if test_displacements is not None:
validation_mse = mean_squared_error(
flatten_data(decoded_pca_test_displacements),
flatten_data(test_displacements))
training_results['pca'][str(pca_dim) + '-components'] = {}
training_results['pca'][
str(pca_dim) + '-components']['training-mse'] = training_mse
training_results['pca'][
str(pca_dim) +
'-components']['explained_var'] = numpy.sum(explained_var)
if test_displacements is not None:
training_results['pca'][
str(pca_dim) +
'-components']['validation-mse'] = validation_mse
print(str(pca_dim) + ' training MSE: ', training_mse)
print(
str(pca_dim) + ' explained variance: ',
numpy.sum(explained_var))
print()
# High dim pca to train autoencoder
pca_start_time = time.time()
U_ae, explained_var, high_dim_pca_encode, high_dim_pca_decode = pca_no_centering(
displacements, pca_ae_train_dim)
pca_train_time = time.time() - pca_start_time
if train_in_full_space:
high_dim_pca_encode = lambda x: x
high_dim_pca_decode = lambda x: x
encoded_high_dim_pca_displacements = high_dim_pca_encode(displacements)
if test_displacements is not None:
encoded_high_dim_pca_test_displacements = high_dim_pca_encode(
test_displacements)
else:
encoded_high_dim_pca_test_displacements = None
from keras.callbacks import Callback
class MyHistoryCallback(Callback):
"""Callback that records events into a `History` object.
This callback is automatically applied to
every Keras model. The `History` object
gets returned by the `fit` method of models.
"""
def on_train_begin(self, logs=None):
self.epoch = []
self.history = {}
def on_epoch_end(self, epoch, logs=None):
if record_full_mse_each_epoch:
mse = mean_squared_error(
high_dim_pca_decode(
self.model.predict(
encoded_high_dim_pca_displacements)),
displacements)
val_mse = mean_squared_error(
high_dim_pca_decode(
self.model.predict(
encoded_high_dim_pca_test_displacements)),
test_displacements)
self.history.setdefault('mse', []).append(mse)
self.history.setdefault('val_mse', []).append(val_mse)
print()
print("Mean squared error: ", mse)
print("Val Mean squared error: ", val_mse)
logs = logs or {}
self.epoch.append(epoch)
for k, v in logs.items():
self.history.setdefault(k, []).append(v)
my_hist_callback = MyHistoryCallback()
ae_pca_basis_path = os.path.join(model_root,
'pca_results/ae_pca_components.dmat')
print('Saving pca results to', ae_pca_basis_path)
my_utils.save_numpy_mat_to_dmat(ae_pca_basis_path,
numpy.ascontiguousarray(U_ae))
ae_encode, ae_decode, ae_train_time = autoencoder_analysis(
# displacements, # Uncomment to train in full space
# test_displacements,
encoded_high_dim_pca_displacements,
encoded_high_dim_pca_test_displacements,
activation=activation,
latent_dim=ae_dim,
epochs=ae_epochs,
batch_size=batch_size,
learning_rate=learning_rate,
layers=
layers, # [200, 200, 50] First two layers being wide seems best so far. maybe an additional narrow third 0.0055 see
# pca_basis=U_ae,
do_fine_tuning=do_fine_tuning,
model_root=model_root,
autoencoder_config=autoencoder_config,
callback=my_hist_callback,
)
# decoded_autoencoder_displacements = ae_decode(ae_encode(displacements))
# decoded_autoencoder_test_displacements = ae_decode(ae_encode(test_displacements))
decoded_autoencoder_displacements = high_dim_pca_decode(
ae_decode(ae_encode(high_dim_pca_encode(displacements))))
if test_displacements is not None:
decoded_autoencoder_test_displacements = high_dim_pca_decode(
ae_decode(ae_encode(high_dim_pca_encode(test_displacements))))
ae_training_mse = mean_squared_error(
flatten_data(decoded_autoencoder_displacements),
flatten_data(displacements))
training_results['autoencoder']['training-mse'] = ae_training_mse
if test_displacements is not None:
training_results['autoencoder']['validation-mse'] = mean_squared_error(
flatten_data(decoded_autoencoder_test_displacements),
flatten_data(test_displacements))
print('Finale ae training MSE:', ae_training_mse)
training_results['autoencoder']['ae_training_time_s'] = ae_train_time
training_results['autoencoder']['pca_training_time_s'] = pca_train_time
# TODO output energy loss as well
with open(os.path.join(model_root, 'training_results.json'), 'w') as f:
json.dump(training_results, f, indent=2)
history_path = os.path.join(model_root, "training_history.json")
with open(history_path, 'w') as f:
json.dump(my_hist_callback.history, f, indent=2)
if save_objs:
print("Saving objs of decoded training data...")
obj_dir = os.path.join(model_root, 'decoded_training_objs/')
if not os.path.exists(obj_dir):
os.makedirs(obj_dir)
for i, dec_displ in enumerate(decoded_autoencoder_displacements):
decoded_verts = base_verts + dec_displ.reshape(
(len(base_verts), 3))
decoded_verts_eig = p2e(decoded_verts)
obj_path = os.path.join(obj_dir, "decoded_" + str(i) + ".obj")
igl.writeOBJ(obj_path, decoded_verts_eig, face_indices_eig)
def reencode_and_augment_training_data(model_root, num_extra_per_poses=0):
"""
Loads existing traing data and generates new encoding / energy vector pairs for
1. Energy evalutated on decoded displacements of training data.
2. Energy evaluated on poses sampled around the encoded training poses.
"""
training_data_path = os.path.join(model_root, 'training_data/training')
U = igl.eigen.MatrixXd()
igl.readDMAT(
os.path.join(model_root, 'pca_results/ae_pca_components.dmat'), U)
displacements = my_utils.load_displacement_dmats_to_numpy(
training_data_path)
flatten_displ, unflatten_displ = my_utils.get_flattners(displacements)
from keras.models import Model, load_model
encoder = load_model(os.path.join(model_root, 'keras_models/encoder.hdf5'))
decoder = load_model(os.path.join(model_root, 'keras_models/decoder.hdf5'))
encoded_displacements = encoder.predict(flatten_displ(displacements) @ U)
decoded_displacements = decoder.predict(
encoded_displacements) @ U.transpose()
print('Generating extra samples...')
extra_encoded_displacements = sample_more_encoded_displacements(
encoded_displacements, num_extra_per_poses)
extra_decoded_displacements = decoder.predict(
extra_encoded_displacements) @ U.transpose()
sampled_training_data_path = os.path.join(
model_root, 'augmented_training_data/sampled/')
reencoded_training_data_path = os.path.join(
model_root, 'augmented_training_data/reencoded/')
my_utils.create_dir_if_not_exist(sampled_training_data_path)
my_utils.create_dir_if_not_exist(reencoded_training_data_path)
extra_displacements_path = save_mat_with_prefix(
sampled_training_data_path, 'displacements',
extra_decoded_displacements)
save_mat_with_prefix(sampled_training_data_path, 'enc_displacements',
extra_encoded_displacements)
reencoded_displacements_path = save_mat_with_prefix(
reencoded_training_data_path, 'displacements', decoded_displacements)
save_mat_with_prefix(reencoded_training_data_path, 'enc_displacements',
encoded_displacements)
tet_mesh_path = os.path.join(model_root, 'tets.mesh')
parameters_path = os.path.join(model_root,
'training_data/training/parameters.json')
print('Computing energies for reencoded poses...')
subprocess.call([
'./generate_data_for_pose/build/bin/GenerateDataForPose',
reencoded_displacements_path, tet_mesh_path, parameters_path
])
print('Computing energies for samples...')
subprocess.call([
'./generate_data_for_pose/build/bin/GenerateDataForPose',
extra_displacements_path, tet_mesh_path, parameters_path
])
