def DeepConv1DOptLearnerStaticArchitecture(param_trainable,
init_wrapper,
smpl_params,
input_info,
faces,
emb_size=1000,
input_type="3D_POINTS"):
""" Optimised learner network architecture """
# An embedding layer is required to optimise the parameters
optlearner_input = Input(shape=(1, ), name="embedding_index")
# Initialise the embedding layers
emb_layers = init_emb_layers(optlearner_input, emb_size, param_trainable,
init_wrapper)
optlearner_params = Concatenate(name="parameter_embedding")(emb_layers)
optlearner_params = Reshape(target_shape=(85, ),
name="learned_params")(optlearner_params)
print("optlearner parameters shape: " + str(optlearner_params.shape))
#exit(1)
# Ground truth parameters and point cloud are inputs to the model as well
gt_params = Input(shape=(85, ), name="gt_params")
gt_pc = Input(shape=(6890, 3), name="gt_pc")
print("gt parameters shape: " + str(gt_params.shape))
print("gt point cloud shape: " + str(gt_pc.shape))
# Compute the true offset (i.e. difference) between the ground truth and learned parameters
pi = K.constant(np.pi)
delta_d = Lambda(lambda x: x[0] - x[1],
name="delta_d")([gt_params, optlearner_params])
#delta_d = Lambda(lambda x: x[0] - x[1], name="delta_d_no_mod")([gt_params, optlearner_params])
#delta_d = Lambda(lambda x: K.tf.math.floormod(x - pi, 2*pi) - pi, name="delta_d")(delta_d) # custom modulo 2pi of delta_d
#delta_d = custom_mod(delta_d, pi, name="delta_d") # custom modulo 2pi of delta_d
print("delta_d shape: " + str(delta_d.shape))
#exit(1)
# Calculate the (batched) MSE between the learned parameters and the ground truth parameters
false_loss_delta_d = Lambda(lambda x: K.mean(K.square(x), axis=1))(delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
#exit(1)
false_loss_delta_d = Reshape(target_shape=(1, ),
name="delta_d_mse")(false_loss_delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
# Load SMPL model and get necessary parameters
optlearner_pc = get_pc(optlearner_params, smpl_params, input_info,
faces) # UNCOMMENT
print("optlearner_pc shape: " + str(optlearner_pc.shape))
#exit(1)
#optlearner_pc = Dense(6890*3)(delta_d)
#optlearner_pc = Reshape((6890, 3))(optlearner_pc)
# Get the (batched) Euclidean loss between the learned and ground truth point clouds
pc_euclidean_diff = Lambda(lambda x: x[0] - x[1])([gt_pc, optlearner_pc])
pc_euclidean_dist = Lambda(lambda x: K.sum(K.square(x), axis=-1))(
pc_euclidean_diff)
print('pc euclidean dist ' + str(pc_euclidean_dist.shape))
#exit(1)
false_loss_pc = Lambda(lambda x: K.mean(x, axis=1))(pc_euclidean_dist)
false_loss_pc = Reshape(target_shape=(1, ),
name="pc_mean_euc_dist")(false_loss_pc)
print("point cloud loss shape: " + str(false_loss_pc.shape))
#exit(1)
# Gather sets of points and compute their cross product to get mesh normals
# In order of: right hand, right wrist, right forearm, right bicep end, right bicep, right shoulder, top of cranium, left shoulder, left bicep, left bicep end, left forearm, left wrist, left hand,
# chest, belly/belly button, back of neck, upper back, central back, lower back/tailbone,
# left foot, left over-ankle, left shin, left over-knee, left quadricep, left hip, right, hip, right, quadricep, right over-knee, right shin, right, over-ankle, right foot
vertex_list = [
5674, 5705, 5039, 5151, 4977, 4198, 411, 606, 1506, 1682, 1571, 2244,
2212, 3074, 3500, 460, 2878, 3014, 3021, 3365, 4606, 4588, 4671, 6877,
1799, 5262, 3479, 1187, 1102, 1120, 6740
]
#face_array = np.array([11396, 8620, 7866, 5431, 6460, 1732, 4507])
pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
pc_euclidean_diff
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
vertex_diff_NOGRAD = Lambda(lambda x: K.tf.gather(
x, np.array(vertex_list).astype(np.int32), axis=-2))(
pc_euclidean_diff_NOGRAD)
print("vertex_diff_NOGRAD shape: " + str(vertex_diff_NOGRAD.shape))
vertex_diff_NOGRAD = Flatten()(vertex_diff_NOGRAD)
#exit(1)
face_array = np.array([[face for face in faces if vertex in face][0]
for vertex in vertex_list
]) # only take a single face for each vertex
print("face_array shape: " + str(face_array.shape))
gt_normals = get_mesh_normals(gt_pc,
face_array,
layer_name="gt_cross_product")
print("gt_normals shape: " + str(gt_normals.shape))
opt_normals = get_mesh_normals(optlearner_pc,
face_array,
layer_name="opt_cross_product")
print("opt_normals shape: " + str(opt_normals.shape))
#exit(1)
# Learn the offset in parameters from the difference between the ground truth and learned mesh normals
diff_normals = Lambda(lambda x: K.tf.cross(x[0], x[1]),
name="diff_cross_product")([gt_normals, opt_normals])
diff_normals_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
diff_normals
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
diff_angles = Lambda(lambda x: K.tf.subtract(x[0], x[1]),
name="diff_angle")([gt_normals, opt_normals])
diff_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(diff_angles)
diff_angles_norm_NOGRAD = Lambda(
lambda x: K.tf.norm(x, axis=-1),
name="diff_angle_norm")(diff_angles_NOGRAD)
dist_angles = Lambda(lambda x: K.mean(K.square(x), axis=-1),
name="diff_angle_mse")(diff_angles)
dist_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(dist_angles)
print("diff_angles shape: " + str(diff_angles.shape))
print("dist_angles shape: " + str(dist_angles.shape))
#pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(pc_euclidean_diff) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
#print("diff_normals_NOGRAD shape: " + str(diff_normals_NOGRAD.shape))
diff_normals_NOGRAD = Flatten()(diff_normals_NOGRAD)
diff_angles_NOGRAD = Flatten()(diff_angles_NOGRAD)
mesh_diff_NOGRAD = Concatenate()([diff_normals_NOGRAD, dist_angles_NOGRAD])
if input_type == "3D_POINTS":
#optlearner_architecture = Dense(2**9, activation="relu")(vertex_diff_NOGRAD)
optlearner_architecture = Dense(2**7,
activation="relu")(vertex_diff_NOGRAD)
if input_type == "MESH_NORMALS":
#optlearner_architecture = Dense(2**11, activation="relu")(diff_angles_norm_NOGRAD)
#optlearner_architecture = Dense(2**11, activation="relu")(diff_angles_NOGRAD)
#optlearner_architecture = Dense(2**9, activation="relu")(mesh_diff_NOGRAD)
optlearner_architecture = Dense(2**7,
activation="relu")(mesh_diff_NOGRAD)
#optlearner_architecture = BatchNormalization()(optlearner_architecture)
#optlearner_architecture = Dropout(0.5)(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Reshape(
(optlearner_architecture.shape[1].value, 1))(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Conv1D(
64, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
64, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = BatchNormalization()(optlearner_architecture)
optlearner_architecture = MaxPooling1D(2)(optlearner_architecture)
#optlearner_architecture = MaxPooling1D(3)(optlearner_architecture)
optlearner_architecture = Conv1D(
128, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
128, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = BatchNormalization()(optlearner_architecture)
optlearner_architecture = MaxPooling1D(3)(optlearner_architecture)
optlearner_architecture = Conv1D(
256, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
256, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = BatchNormalization()(optlearner_architecture)
optlearner_architecture = MaxPooling1D(3)(optlearner_architecture)
optlearner_architecture = Conv1D(
512, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
512, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = BatchNormalization()(optlearner_architecture)
#optlearner_architecture = AveragePooling1D(2)(optlearner_architecture)
optlearner_architecture = GlobalAveragePooling1D()(optlearner_architecture)
#print('optlearner_architecture shape: '+str(optlearner_architecture.shape))
#optlearner_architecture = Flatten()(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
#optlearner_architecture = Dropout(0.5)(optlearner_architecture)
optlearner_architecture = Dense(2**7,
activation="relu")(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
#delta_d_hat = Dense(85, activation=pos_scaled_tanh, name="delta_d_hat")(optlearner_architecture)
delta_d_hat = Dense(85, activation="linear",
name="delta_d_hat")(optlearner_architecture)
#delta_d_hat = Dense(85, activation=centred_linear, name="delta_d_hat")(optlearner_architecture)
print('delta_d_hat shape: ' + str(delta_d_hat.shape))
#exit(1)
# Calculate the (batched) MSE between the learned and ground truth offset in the parameters
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d)
false_loss_delta_d_hat = Lambda(
lambda x: K.mean(K.square(x[0] - x[1]), axis=1))(
[delta_d_NOGRAD, delta_d_hat])
#false_loss_delta_d_hat = Lambda(lambda x: K.sum(K.square(x[0] - x[1]), axis=1))([delta_d_NOGRAD, delta_d_hat])
#false_loss_delta_d_hat = Lambda(lambda x: mape(x[0], x[1]))([delta_d_NOGRAD, delta_d_hat])
false_loss_delta_d_hat = Reshape(
target_shape=(1, ), name="delta_d_hat_mse")(false_loss_delta_d_hat)
print("delta_d_hat loss shape: " + str(false_loss_delta_d_hat.shape))
#false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD, delta_d_hat)
false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD,
delta_d_hat,
average=False)
false_sin_loss_delta_d_hat = Lambda(
lambda x: x, name="delta_d_hat_sin_output")(false_sin_loss_delta_d_hat)
print("delta_d_hat sin loss shape: " +
str(false_sin_loss_delta_d_hat.shape))
# Prevent model from using the delta_d_hat gradient in final loss
delta_d_hat_NOGRAD = Lambda(lambda x: K.stop_gradient(x),
name='optlearner_output_NOGRAD')(delta_d_hat)
# False loss designed to pass the learned offset as a gradient to the embedding layer
false_loss_smpl = Multiply(name="smpl_diff")(
[optlearner_params, delta_d_hat_NOGRAD])
print("smpl loss shape: " + str(false_loss_smpl.shape))
#return [optlearner_input, gt_params, gt_pc], [optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc, false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl, delta_d, delta_d_hat, delta_d_hat_NOGRAD]
return [optlearner_input, gt_params, gt_pc], [
optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc,
false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl,
delta_d, delta_d_hat, dist_angles
]
def OptLearnerMeshNormalStaticModArchitecture(param_trainable,
init_wrapper,
smpl_params,
input_info,
faces,
emb_size=1000):
""" Optimised learner network architecture """
# An embedding layer is required to optimise the parameters
optlearner_input = Input(shape=(1, ), name="embedding_index")
# Initialise the embedding layers
emb_layers = init_emb_layers(optlearner_input, emb_size, param_trainable,
init_wrapper)
optlearner_params = Concatenate(name="parameter_embedding")(emb_layers)
optlearner_params = Reshape(target_shape=(85, ),
name="learned_params")(optlearner_params)
print("optlearner parameters shape: " + str(optlearner_params.shape))
#exit(1)
# Ground truth parameters and point cloud are inputs to the model as well
gt_params = Input(shape=(85, ), name="gt_params")
gt_pc = Input(shape=(6890, 3), name="gt_pc")
print("gt parameters shape: " + str(gt_params.shape))
print("gt point cloud shape: " + str(gt_pc.shape))
# Compute the true offset (i.e. difference) between the ground truth and learned parameters
pi = K.constant(np.pi)
delta_d = Lambda(lambda x: x[0] - x[1],
name="delta_d")([gt_params, optlearner_params])
#delta_d = Lambda(lambda x: x[0] - x[1], name="delta_d_no_mod")([gt_params, optlearner_params])
#delta_d = Lambda(lambda x: K.tf.math.floormod(x - pi, 2*pi) - pi, name="delta_d")(delta_d) # custom modulo 2pi of delta_d
#delta_d = custom_mod(delta_d, pi, name="delta_d") # custom modulo 2pi of delta_d
print("delta_d shape: " + str(delta_d.shape))
#exit(1)
# Calculate the (batched) MSE between the learned parameters and the ground truth parameters
false_loss_delta_d = Lambda(lambda x: K.mean(K.square(x), axis=1))(delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
#exit(1)
false_loss_delta_d = Reshape(target_shape=(1, ),
name="delta_d_mse")(false_loss_delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
# Load SMPL model and get necessary parameters
optlearner_pc = get_pc(optlearner_params, smpl_params, input_info, faces)
# Get the (batched) Euclidean loss between the learned and ground truth point clouds
pc_euclidean_diff = Lambda(lambda x: x[0] - x[1])([gt_pc, optlearner_pc])
pc_euclidean_dist = Lambda(lambda x: K.sum(K.square(x), axis=-1))(
pc_euclidean_diff)
print('pc euclidean dist ' + str(pc_euclidean_dist.shape))
#exit(1)
false_loss_pc = Lambda(lambda x: K.mean(x, axis=1))(pc_euclidean_dist)
false_loss_pc = Reshape(target_shape=(1, ),
name="pc_mean_euc_dist")(false_loss_pc)
print("point cloud loss shape: " + str(false_loss_pc.shape))
#exit(1)
# Gather sets of points and compute their cross product to get mesh normals
#vertex_list=[1850, 1600, 2050, 1300, 5350, 5050, 5500]
#vertex_list=[1850, 1600, 2050, 5350, 5050, 5500]
# In order of right wrist, right, bicep, right shoulder, cranium top, left shoulder, left bicep, left wrist
vertex_list = [5705, 4977, 4198, 411, 606, 1506, 2244]
#face_array = np.array([11396, 8620, 7866, 5431, 6460, 1732, 4507])
pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
pc_euclidean_diff
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
vertex_diff_NOGRAD = Lambda(lambda x: K.tf.gather(
x, np.array(vertex_list).astype(np.int32), axis=-2))(
pc_euclidean_diff_NOGRAD)
print("vertex_diff_NOGRAD shape: " + str(vertex_diff_NOGRAD.shape))
vertex_diff_NOGRAD = Flatten()(vertex_diff_NOGRAD)
#exit(1)
face_array = np.array([[face for face in faces if vertex in face][0]
for vertex in vertex_list
]) # only take a single face for each vertex
print("face_array shape: " + str(face_array.shape))
gt_normals = get_mesh_normals(gt_pc,
face_array,
layer_name="gt_cross_product")
print("gt_normals shape: " + str(gt_normals.shape))
opt_normals = get_mesh_normals(optlearner_pc,
face_array,
layer_name="opt_cross_product")
print("opt_normals shape: " + str(opt_normals.shape))
#exit(1)
# Learn the offset in parameters from the difference between the ground truth and learned mesh normals
diff_normals = Lambda(lambda x: K.tf.cross(x[0], x[1]),
name="diff_cross_product")([gt_normals, opt_normals])
diff_normals_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
diff_normals
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
diff_angles = Lambda(lambda x: K.tf.subtract(x[0], x[1]),
name="diff_angle")([gt_normals, opt_normals])
diff_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(diff_angles)
diff_angles_norm_NOGRAD = Lambda(
lambda x: K.tf.norm(x, axis=-1),
name="diff_angle_norm")(diff_angles_NOGRAD)
dist_angles = Lambda(lambda x: K.mean(K.square(x), axis=-1),
name="diff_angle_mse")(diff_angles)
dist_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(dist_angles)
print("diff_angles shape: " + str(diff_angles.shape))
print("dist_angles shape: " + str(dist_angles.shape))
#pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(pc_euclidean_diff) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
#print("diff_normals_NOGRAD shape: " + str(diff_normals_NOGRAD.shape))
diff_normals_NOGRAD = Flatten()(diff_normals_NOGRAD)
diff_angles_NOGRAD = Flatten()(diff_angles_NOGRAD)
mesh_diff_NOGRAD = Concatenate()([diff_normals_NOGRAD, dist_angles_NOGRAD])
optlearner_architecture = Dense(2**11,
activation="relu")(vertex_diff_NOGRAD)
#optlearner_architecture = Dense(2**11, activation="relu")(diff_angles_norm_NOGRAD)
#optlearner_architecture = Dense(2**11, activation="relu")(diff_angles_NOGRAD)
#optlearner_architecture = Dense(2**11, activation="relu")(mesh_diff_NOGRAD)
optlearner_architecture = BatchNormalization()(optlearner_architecture)
optlearner_architecture = Dropout(0.5)(optlearner_architecture)
#optlearner_architecture = Dropout(0.2)(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
#delta_d_hat = Dense(85, activation=pos_scaled_tanh, name="delta_d_hat")(optlearner_architecture)
delta_d_hat = Dense(85, activation="linear",
name="delta_d_hat")(optlearner_architecture)
#delta_d_hat = Dense(85, activation=centred_linear, name="delta_d_hat")(optlearner_architecture)
print('delta_d_hat shape: ' + str(delta_d_hat.shape))
#exit(1)
# Calculate the (batched) MSE between the learned and ground truth offset in the parameters
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d)
false_loss_delta_d_hat = Lambda(
lambda x: K.mean(K.square(x[0] - x[1]), axis=1))(
[delta_d_NOGRAD, delta_d_hat])
#false_loss_delta_d_hat = Lambda(lambda x: K.sum(K.square(x[0] - x[1]), axis=1))([delta_d_NOGRAD, delta_d_hat])
#false_loss_delta_d_hat = Lambda(lambda x: mape(x[0], x[1]))([delta_d_NOGRAD, delta_d_hat])
false_loss_delta_d_hat = Reshape(
target_shape=(1, ), name="delta_d_hat_mse")(false_loss_delta_d_hat)
print("delta_d_hat loss shape: " + str(false_loss_delta_d_hat.shape))
false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD, delta_d_hat)
false_sin_loss_delta_d_hat = Lambda(
lambda x: x, name="delta_d_hat_sin_output")(false_sin_loss_delta_d_hat)
print("delta_d_hat sin loss shape: " +
str(false_sin_loss_delta_d_hat.shape))
# Prevent model from using the delta_d_hat gradient in final loss
delta_d_hat_NOGRAD = Lambda(lambda x: K.stop_gradient(x),
name='optlearner_output_NOGRAD')(delta_d_hat)
# False loss designed to pass the learned offset as a gradient to the embedding layer
false_loss_smpl = Multiply(name="smpl_diff")(
[optlearner_params, delta_d_hat_NOGRAD])
print("smpl loss shape: " + str(false_loss_smpl.shape))
#return [optlearner_input, gt_params, gt_pc], [optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc, false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl, delta_d, delta_d_hat, delta_d_hat_NOGRAD]
return [optlearner_input, gt_params, gt_pc], [
optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc,
false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl,
delta_d, delta_d_hat, dist_angles
]
def ProbCNNOptLearnerStaticArchitecture(param_trainable,
init_wrapper,
smpl_params,
input_info,
faces,
emb_size=1000,
input_type="3D_POINTS"):
""" Optimised learner network architecture that outputs samples from a distribution instead of deterministic estimates """
# An embedding layer is required to optimise the parameters
optlearner_input = Input(shape=(1, ), name="embedding_index")
# Initialise the embedding layers
emb_layers = init_emb_layers(optlearner_input, emb_size, param_trainable,
init_wrapper)
optlearner_params = Concatenate(name="parameter_embedding")(emb_layers)
optlearner_params = Reshape(target_shape=(85, ),
name="learned_params")(optlearner_params)
print("optlearner parameters shape: " + str(optlearner_params.shape))
#exit(1)
# Ground truth parameters and point cloud are inputs to the model as well
gt_params = Input(shape=(85, ), name="gt_params")
gt_pc = Input(shape=(6890, 3), name="gt_pc")
print("gt parameters shape: " + str(gt_params.shape))
print("gt point cloud shape: " + str(gt_pc.shape))
# Compute the true offset (i.e. difference) between the ground truth and learned parameters
pi = K.constant(np.pi)
delta_d = Lambda(lambda x: x[0] - x[1],
name="delta_d")([gt_params, optlearner_params])
#delta_d = Lambda(lambda x: x[0] - x[1], name="delta_d_no_mod")([gt_params, optlearner_params])
#delta_d = Lambda(lambda x: K.tf.math.floormod(x - pi, 2*pi) - pi, name="delta_d")(delta_d) # custom modulo 2pi of delta_d
#delta_d = custom_mod(delta_d, pi, name="delta_d") # custom modulo 2pi of delta_d
print("delta_d shape: " + str(delta_d.shape))
#exit(1)
# Calculate the (batched) MSE between the learned parameters and the ground truth parameters
false_loss_delta_d = Lambda(lambda x: K.mean(K.square(x), axis=1))(delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
#exit(1)
false_loss_delta_d = Reshape(target_shape=(1, ),
name="delta_d_mse")(false_loss_delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
# Load SMPL model and get necessary parameters
optlearner_pc = get_pc(optlearner_params, smpl_params, input_info,
faces) # UNCOMMENT
print("optlearner_pc shape: " + str(optlearner_pc.shape))
#exit(1)
#optlearner_pc = Dense(6890*3)(delta_d)
#optlearner_pc = Reshape((6890, 3))(optlearner_pc)
# Get the (batched) Euclidean loss between the learned and ground truth point clouds
pc_euclidean_diff = Lambda(lambda x: x[0] - x[1])([gt_pc, optlearner_pc])
pc_euclidean_dist = Lambda(lambda x: K.sum(K.square(x), axis=-1))(
pc_euclidean_diff)
print('pc euclidean dist ' + str(pc_euclidean_dist.shape))
#exit(1)
false_loss_pc = Lambda(lambda x: K.mean(x, axis=1))(pc_euclidean_dist)
false_loss_pc = Reshape(target_shape=(1, ),
name="pc_mean_euc_dist")(false_loss_pc)
print("point cloud loss shape: " + str(false_loss_pc.shape))
#exit(1)
# Gather sets of points and compute their cross product to get mesh normals
# In order of: right hand, right wrist, right forearm, right bicep end, right bicep, right shoulder, top of cranium, left shoulder, left bicep, left bicep end, left forearm, left wrist, left hand,
# chest, belly/belly button, back of neck, upper back, central back, lower back/tailbone,
# left foot, left over-ankle, left shin, left over-knee, left quadricep, left hip, right, hip, right, quadricep, right over-knee, right shin, right, over-ankle, right foot
vertex_list = [
5674, 5705, 5039, 5151, 4977, 4198, 411, 606, 1506, 1682, 1571, 2244,
2212, 3074, 3500, 460, 2878, 3014, 3021, 3365, 4606, 4588, 4671, 6877,
1799, 5262, 3479, 1187, 1102, 1120, 6740
]
#face_array = np.array([11396, 8620, 7866, 5431, 6460, 1732, 4507])
pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
pc_euclidean_diff
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
vertex_diff_NOGRAD = Lambda(lambda x: K.tf.gather(
x, np.array(vertex_list).astype(np.int32), axis=-2))(
pc_euclidean_diff_NOGRAD)
print("vertex_diff_NOGRAD shape: " + str(vertex_diff_NOGRAD.shape))
vertex_diff_NOGRAD = Flatten()(vertex_diff_NOGRAD)
#exit(1)
face_array = np.array([[face for face in faces if vertex in face][0]
for vertex in vertex_list
]) # only take a single face for each vertex
print("face_array shape: " + str(face_array.shape))
gt_normals = get_mesh_normals(gt_pc,
face_array,
layer_name="gt_cross_product")
print("gt_normals shape: " + str(gt_normals.shape))
opt_normals = get_mesh_normals(optlearner_pc,
face_array,
layer_name="opt_cross_product")
print("opt_normals shape: " + str(opt_normals.shape))
#exit(1)
# Learn the offset in parameters from the difference between the ground truth and learned mesh normals
diff_normals = Lambda(lambda x: K.tf.cross(x[0], x[1]),
name="diff_cross_product")([gt_normals, opt_normals])
diff_normals_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
diff_normals
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
diff_angles = Lambda(lambda x: K.tf.subtract(x[0], x[1]),
name="diff_angle")([gt_normals, opt_normals])
diff_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(diff_angles)
diff_angles_norm_NOGRAD = Lambda(
lambda x: K.tf.norm(x, axis=-1),
name="diff_angle_norm")(diff_angles_NOGRAD)
dist_angles = Lambda(lambda x: K.mean(K.square(x), axis=-1),
name="diff_angle_mse")(diff_angles)
dist_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(dist_angles)
print("diff_angles shape: " + str(diff_angles.shape))
print("dist_angles shape: " + str(dist_angles.shape))
#pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(pc_euclidean_diff) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
#print("diff_normals_NOGRAD shape: " + str(diff_normals_NOGRAD.shape))
diff_normals_NOGRAD = Flatten()(diff_normals_NOGRAD)
diff_angles_NOGRAD = Flatten()(diff_angles_NOGRAD)
mesh_diff_NOGRAD = Concatenate()([diff_normals_NOGRAD, dist_angles_NOGRAD])
if input_type == "3D_POINTS":
optlearner_architecture = Dense(2**9,
activation="relu")(vertex_diff_NOGRAD)
if input_type == "MESH_NORMALS":
optlearner_architecture = Dense(2**9,
activation="relu")(mesh_diff_NOGRAD)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Reshape(
(optlearner_architecture.shape[1].value, 1))(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Conv1D(
64, 5, activation="relu")(optlearner_architecture)
optlearner_architecture = MaxPooling1D(3)(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Conv1D(
128, 3, activation="relu")(optlearner_architecture)
optlearner_architecture = AveragePooling1D(2)(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Flatten()(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Dropout(0.5)(optlearner_architecture)
# Learn the parameters of a univariate Gausssian distribution for each SMPL parameter
mu_architecture = Dense(2**7, activation="relu")(optlearner_architecture)
delta_d_hat_mu = Dense(85, activation="linear",
name="delta_d_hat_mu")(mu_architecture)
print('delta_d_hat_mu shape: ' + str(delta_d_hat_mu.shape))
sigma_architecture = Dense(2**7,
activation="relu")(optlearner_architecture)
delta_d_hat_sigma = Dense(
85, activation="softplus",
name="delta_d_hat_sigma_uncorrected")(sigma_architecture)
delta_d_hat_sigma = Lambda(lambda x: x + 1e-5,
name="delta_d_hat_sigma")(delta_d_hat_sigma)
print('delta_d_hat_sigma shape: ' + str(delta_d_hat_sigma.shape))
#exit(1)
# Draw a sample from the distribution
delta_d_hat = normal_sample(delta_d_hat_mu, delta_d_hat_sigma)
delta_d_hat = Lambda(lambda x: x, name="delta_d_hat")(delta_d_hat)
# Calculate the log probability of the sample and the entropy of the distribution
#log_prob = normal_log_prob(delta_d_hat, delta_d_hat_mu, delta_d_hat_sigma)
log_prob = normal_log_prob_pos(delta_d_hat, delta_d_hat_mu,
delta_d_hat_sigma)
#entropy = normal_entropy(delta_d_hat_sigma)
#entropy = normal_entropy_pos(delta_d_hat_sigma)
false_entropy = Lambda(lambda x: 1. / (1. + x),
name="false_entropy")(delta_d_hat_sigma)
# Calculate the (batched) MSE between the learned and ground truth offset in the parameters
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d)
delta_d_hat_square_diff = Lambda(lambda x: K.square(x[0] - x[1]),
name="delta_d_hat_square_diff")(
[delta_d_NOGRAD, delta_d_hat])
delta_d_hat_mse = Lambda(lambda x: K.mean(K.square(x[0] - x[1]), axis=1))(
[delta_d_NOGRAD, delta_d_hat])
delta_d_hat_mse = Reshape(target_shape=(1, ),
name="delta_d_hat_mse")(delta_d_hat_mse)
print("delta_d_hat loss shape: " + str(delta_d_hat_mse.shape))
# Construct the loss function
#advantage = Lambda(lambda x: K.abs(x[0]) - K.abs(x[0] - x[1]), name="adv_1_norm")([delta_d_NOGRAD, delta_d_hat]) # 1-norm advantage
advantage = Lambda(lambda x: K.square(x[0]) - K.square(x[0] - x[1]),
name="adv_2_norm")([delta_d_NOGRAD,
delta_d_hat]) # 2-norm advantage
#advantage = Lambda(lambda x: K.mean(K.square(x[0]), axis=-1) - K.mean(K.square(x[0] - x[1]), axis=-1), name="adv_2_norm")([delta_d_NOGRAD, delta_d_hat]) # 2-norm advantage
advantage_NOGRAD = Lambda(lambda x: K.stop_gradient(x),
name="adv_NOGRAD")(advantage)
#eligibility = Multiply(name="eligibility")([log_prob, advantage_NOGRAD])
eligibility = Multiply(name="eligibility")([log_prob, advantage])
#false_loss_delta_d_hat = distributional_model_loss(eligibility, entropy, weighting=1.)
weighting = 1.
false_loss_delta_d_hat = Lambda(lambda x: -x[0] + weighting * x[1],
name="dist_perf")(
[eligibility, false_entropy])
#penalty_func_mu = Lambda(lambda x: 0.1*K.exp(K.square(x) - np.pi**2))(delta_d_hat_mu)
penalty_func_sigma = Lambda(lambda x: K.square(x))(delta_d_hat_sigma)
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(x[0] + x[1] + x[2], axis=1), name="reg_dist_goodness")([false_loss_delta_d_hat, penalty_func_mu, penalty_func_sigma])
false_loss_delta_d_hat = Lambda(
lambda x: K.mean(x[0] + x[1], axis=1),
name="reg_dist_goodness")([false_loss_delta_d_hat, penalty_func_sigma])
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(x[0] + x[1], axis=1), name="reg_dist_goodness")([false_loss_delta_d_hat, penalty_func_mu])
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(x[0] + x[1], axis=1), name="reg_dist_goodness")([false_loss_delta_d_hat, delta_d_hat_square_diff])
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(x, axis=1), name="mean_dist_perf_unshaped")(false_loss_delta_d_hat)
false_loss_delta_d_hat = Reshape(
target_shape=(1, ), name="mean_dist_perf")(false_loss_delta_d_hat)
# Get metrics
#false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD, delta_d_hat)
false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD,
delta_d_hat,
average=False)
false_sin_loss_delta_d_hat = Lambda(
lambda x: x, name="delta_d_hat_sin_output")(false_sin_loss_delta_d_hat)
print("delta_d_hat sin loss shape: " +
str(false_sin_loss_delta_d_hat.shape))
# Prevent model from using the delta_d_hat gradient in final loss
delta_d_hat_NOGRAD = Lambda(lambda x: K.stop_gradient(x),
name='optlearner_output_NOGRAD')(delta_d_hat)
# False loss designed to pass the learned offset as a gradient to the embedding layer
false_loss_smpl = Multiply(name="smpl_diff")(
[optlearner_params, delta_d_hat_NOGRAD])
print("smpl loss shape: " + str(false_loss_smpl.shape))
return [optlearner_input, gt_params, gt_pc], [
optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc,
false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl,
delta_d, delta_d_hat, dist_angles, delta_d_hat_mu, delta_d_hat_sigma,
delta_d_hat_mse
]
def ConditionalOptLearnerArchitecture(param_trainable,
init_wrapper,
smpl_params,
input_info,
faces,
emb_size=1000,
input_type="3D_POINTS"):
""" Optimised learner network architecture """
# An embedding layer is required to optimise the parameters
optlearner_input = Input(shape=(1, ), name="embedding_index")
# Initialise the embedding layers
emb_layers = init_emb_layers(optlearner_input, emb_size, param_trainable,
init_wrapper)
optlearner_params = Concatenate(name="parameter_embedding")(emb_layers)
optlearner_params = Reshape(target_shape=(85, ),
name="learned_params")(optlearner_params)
print("optlearner parameters shape: " + str(optlearner_params.shape))
#exit(1)
# Ground truth parameters and point cloud are inputs to the model as well
gt_params = Input(shape=(85, ), name="gt_params")
gt_pc = Input(shape=(6890, 3), name="gt_pc")
print("gt parameters shape: " + str(gt_params.shape))
print("gt point cloud shape: " + str(gt_pc.shape))
# Compute the true offset (i.e. difference) between the ground truth and learned parameters
pi = K.constant(np.pi)
delta_d = Lambda(lambda x: x[0] - x[1],
name="delta_d")([gt_params, optlearner_params])
print("delta_d shape: " + str(delta_d.shape))
#exit(1)
# Calculate the (batched) MSE between the learned parameters and the ground truth parameters
false_loss_delta_d = Lambda(lambda x: K.mean(K.square(x), axis=1))(delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
#exit(1)
false_loss_delta_d = Reshape(target_shape=(1, ),
name="delta_d_mse")(false_loss_delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
# Load SMPL model and get necessary parameters
optlearner_pc = get_pc(optlearner_params, smpl_params, input_info,
faces) # UNCOMMENT
print("optlearner_pc shape: " + str(optlearner_pc.shape))
#exit(1)
# Get the (batched) Euclidean loss between the learned and ground truth point clouds
pc_euclidean_diff = Lambda(lambda x: x[0] - x[1])([gt_pc, optlearner_pc])
pc_euclidean_dist = Lambda(lambda x: K.sum(K.square(x), axis=-1))(
pc_euclidean_diff)
print('pc euclidean dist ' + str(pc_euclidean_dist.shape))
#exit(1)
false_loss_pc = Lambda(lambda x: K.mean(x, axis=1))(pc_euclidean_dist)
false_loss_pc = Reshape(target_shape=(1, ),
name="pc_mean_euc_dist")(false_loss_pc)
print("point cloud loss shape: " + str(false_loss_pc.shape))
#exit(1)
# Gather sets of points and compute their cross product to get mesh normals
# In order of: right hand, right wrist, right forearm, right bicep end, right bicep, right shoulder, top of cranium, left shoulder, left bicep, left bicep end, left forearm, left wrist, left hand,
# chest, belly/belly button, back of neck, upper back, central back, lower back/tailbone,
# left foot, left over-ankle, left shin, left over-knee, left quadricep, left hip, right, hip, right, quadricep, right over-knee, right shin, right, over-ankle, right foot
vertex_list = [
5674, 5705, 5039, 5151, 4977, 4198, 411, 606, 1506, 1682, 1571, 2244,
2212, 3074, 3500, 460, 2878, 3014, 3021, 3365, 4606, 4588, 4671, 6877,
1799, 5262, 3479, 1187, 1102, 1120, 6740
]
#vertex_list = [5674, 5512, 5474, 5705, 6335, 5438, 5039, 5047, 5074, 5151, 5147, 5188, 4977, 4870, 4744, 4198, 4195, 5324, 411, 164, 3676, 606, 1826, 1863, 1506, 1382, 1389, 1682, 1675, 1714, 1571, 1579, 1603, 2244, 1923, 1936, 2212, 2007, 2171, 3074, 539, 4081, 3500, 6875, 3477, 460, 426, 3921, 2878, 2969, 6428, 3014, 892, 4380, 3021, 1188, 4675, 3365, 3336, 3338, 1120, 1136, 1158, 1102, 1110, 1114, 1187, 1144, 977, 3479, 872, 1161, 1799, 3150, 1804, 5262, 6567, 5268, 6877, 4359, 4647, 4671, 4630, 4462, 4588, 4641, 4600, 4606, 4622, 4644, 6740, 6744, 6737]
#face_array = np.array([11396, 8620, 7866, 5431, 6460, 1732, 4507])
pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
pc_euclidean_diff
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
vertex_diff_NOGRAD = Lambda(lambda x: K.tf.gather(
x, np.array(vertex_list).astype(np.int32), axis=-2))(
pc_euclidean_diff_NOGRAD)
print("vertex_diff_NOGRAD shape: " + str(vertex_diff_NOGRAD.shape))
vertex_diff_NOGRAD = Flatten()(vertex_diff_NOGRAD)
#exit(1)
face_array = np.array([[face for face in faces if vertex in face][0]
for vertex in vertex_list
]) # only take a single face for each vertex
print("face_array shape: " + str(face_array.shape))
gt_normals = get_mesh_normals(gt_pc,
face_array,
layer_name="gt_cross_product")
print("gt_normals shape: " + str(gt_normals.shape))
opt_normals = get_mesh_normals(optlearner_pc,
face_array,
layer_name="opt_cross_product")
print("opt_normals shape: " + str(opt_normals.shape))
#exit(1)
# Learn the offset in parameters from the difference between the ground truth and learned mesh normals
diff_normals = Lambda(lambda x: K.tf.cross(x[0], x[1]),
name="diff_cross_product")([gt_normals, opt_normals])
diff_normals_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
diff_normals
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
diff_angles = Lambda(lambda x: K.tf.subtract(x[0], x[1]),
name="diff_angle")([gt_normals, opt_normals])
diff_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(diff_angles)
diff_angles_norm_NOGRAD = Lambda(
lambda x: K.tf.norm(x, axis=-1),
name="diff_angle_norm")(diff_angles_NOGRAD)
dist_angles = Lambda(lambda x: K.mean(K.square(x), axis=-1),
name="diff_angle_mse")(diff_angles)
dist_angles_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(dist_angles)
print("diff_angles shape: " + str(diff_angles.shape))
print("dist_angles shape: " + str(dist_angles.shape))
diff_normals_NOGRAD = Flatten()(diff_normals_NOGRAD)
diff_angles_NOGRAD = Flatten()(diff_angles_NOGRAD)
mesh_diff_NOGRAD = Concatenate()([diff_normals_NOGRAD, dist_angles_NOGRAD])
if input_type == "3D_POINTS":
optlearner_architecture = Dense(2**9,
activation="relu")(vertex_diff_NOGRAD)
if input_type == "MESH_NORMALS":
optlearner_architecture = Dense(2**9,
activation="relu")(mesh_diff_NOGRAD)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Reshape(
(optlearner_architecture.shape[1].value, 1))(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Conv1D(
64, 5, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
128, 5, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
256, 3, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Conv1D(
512, 3, strides=2, activation="relu")(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
optlearner_architecture = Reshape((-1, ))(optlearner_architecture)
print('optlearner_architecture shape: ' +
str(optlearner_architecture.shape))
# Learn a 6D representation of the parameters
trainable_params = [
int(param[6:8]) for param, trainable in param_trainable.items()
if trainable
]
trainable_joints = 3 * np.unique(
[param // 3 for param in trainable_params])
trainable_params = np.ravel([[param, param + 1, param + 2]
for param in trainable_joints])
print("trainable_params in conditional network: " + str(trainable_params))
num_trainable_joints = len(trainable_joints)
num_trainable_params = len(trainable_params)
mapped_pose = Dense(num_trainable_joints * 6,
activation="linear",
name="mapped_pose")(optlearner_architecture)
# Reshape mapped predictions into vectors
mapped_pose_vec = Reshape((num_trainable_joints, 3, 2),
name="mapped_pose_mat")(mapped_pose)
print("mapped_pose shape: " + str(mapped_pose_vec.shape))
# Convert 6D representation to rotation matrix in SO(3)
rot3d_pose = rot3d_from_ortho6d(mapped_pose_vec)
rot3d_pose = Lambda(lambda x: x, name="rot3d_pose")(rot3d_pose)
print("rot3d_pose shape: " + str(rot3d_pose.shape))
# Cast GT difference SO(3) representation to 6D for loss calculation
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d)
delta_d_pose = Lambda(lambda x: K.tf.gather(
x, np.array(trainable_params), axis=1))(delta_d_NOGRAD)
print("delta_d_pose shape: " + str(delta_d_pose.shape))
delta_d_pose_vec = Reshape((num_trainable_joints, 3),
name="delta_d_pose_vec")(delta_d_pose)
print("delta_d_pose_vec shape: " + str(delta_d_pose_vec.shape))
#rot3d_delta_d_pose = rot3d_from_euler(delta_d_pose_vec)
#rot3d_delta_d_trans = rot3d_from_euler(delta_d_trans_vec)
rot3d_delta_d_pose = rot3d_from_rodrigues(delta_d_pose_vec)
rot3d_delta_d_pose = Lambda(lambda x: x,
name="rot3d_delta_d_pose")(rot3d_delta_d_pose)
print("rot3d_delta_d_pose shape: " + str(rot3d_delta_d_pose.shape))
mapped_delta_d_pose_vec = ortho6d_from_rot3d(rot3d_delta_d_pose)
print("mapped_delta_d_pose_vec shape: " +
str(mapped_delta_d_pose_vec.shape))
mapped_delta_d_pose = Reshape(
(num_trainable_joints * 3 * 2, ),
name="mapped_delta_d_pose")(mapped_delta_d_pose_vec)
print("mapped_delta_d_pose shape: " + str(mapped_delta_d_pose.shape))
mapped_delta_d = Lambda(lambda x: x,
name="mapped_delta_d")(mapped_delta_d_pose)
print("mapped_delta_d shape: " + str(mapped_delta_d.shape))
#exit(1)
# Calculate L2-norm on 6D orthogonal representation or geodesic loss on SO(3) representation
mapped_delta_d_hat = Lambda(lambda x: x,
name="mapped_delta_d_hat")(mapped_pose)
print("mapped_delta_d_hat shape: " + str(mapped_delta_d_hat.shape))
#exit(1)
mapped_delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
mapped_delta_d)
# L2-norm loss on 6D representation
false_loss_delta_d_hat = Lambda(
lambda x: K.mean(K.square(x[0] - x[1]), axis=-1))(
[mapped_delta_d_NOGRAD, mapped_delta_d_hat])
false_loss_delta_d_hat = Reshape(
(1, ), name="delta_d_hat_mse")(false_loss_delta_d_hat)
# Apply rotation in Rodrigues angles
rodrigues_pose = rodrigues_from_rot3d(rot3d_pose)
print("rodrigues_pose shape: " + str(rodrigues_pose.shape))
delta_d_hat_pose = Reshape(
(num_trainable_params, ),
name="delta_d_hat_pose_reshaped")(rodrigues_pose)
print("delta_d_hat_pose shape: " + str(delta_d_hat_pose.shape))
#exit(1)
# Concatenate to form final update vector
# indices = K.constant(trainable_params, shape=(1, num_trainable_params), dtype="int32")
# print("indices shape: " + str(indices.shape))
# indices = Lambda(lambda x: K.tf.placeholder_with_default(x, [None, num_trainable_params]))(indices)
# print("indices shape: " + str(indices.shape))
# zeros = Lambda(lambda x: K.zeros_like(x))(delta_d_NOGRAD)
# scatter = Lambda(lambda x: tf.scatter_nd_add(x[0], x[1], x[2]))([zeros, indices, delta_d_hat_pose])
# print("scatter shape: " +str(scatter.shape))
# exit(1)
delta_d_hat = Lambda(lambda x: x, name="delta_d_hat")(delta_d_hat_pose)
print('delta_d_hat shape: ' + str(delta_d_hat.shape))
#exit(1)
#false_sin_loss_delta_d_hat = get_angular_distance_metric(delta_d_NOGRAD, delta_d_hat)
false_sin_loss_delta_d_hat = get_sin_metric(delta_d_pose, delta_d_hat)
#false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD, delta_d_hat, average=False)
false_sin_loss_delta_d_hat = Lambda(
lambda x: x, name="delta_d_hat_sin_output")(false_sin_loss_delta_d_hat)
#false_sin_loss_delta_d_hat = Lambda(lambda x: x, name="delta_d_hat_sin_output")(false_loss_new_pc)
print("delta_d_hat sin loss shape: " +
str(false_sin_loss_delta_d_hat.shape))
# Prevent model from using the delta_d_hat gradient in final loss
delta_d_hat_NOGRAD = Lambda(lambda x: K.stop_gradient(x),
name='optlearner_output_NOGRAD')(delta_d_hat)
# False loss designed to pass the learned offset as a gradient to the embedding layer
#false_loss_smpl = Multiply(name="smpl_diff")([optlearner_params, delta_d_hat_NOGRAD])
false_loss_smpl = Lambda(lambda x: x, name="smpl_diff")(optlearner_params)
print("smpl loss shape: " + str(false_loss_smpl.shape))
return [optlearner_input, gt_params, gt_pc], [
optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc,
false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl,
delta_d, delta_d_hat, dist_angles, rot3d_delta_d_pose, rot3d_pose,
mapped_pose, mapped_delta_d_pose
]
def OptLearnerStaticArchitecture(param_trainable, init_wrapper, emb_size=1000):
""" Optimised learner network architecture """
# An embedding layer is required to optimise the parameters
optlearner_input = Input(shape=(1, ), name="embedding_index")
# Initialise the embedding layers
emb_layers = init_emb_layers(optlearner_input, emb_size, param_trainable,
init_wrapper)
optlearner_params = Concatenate(name="parameter_embedding")(emb_layers)
optlearner_params = Reshape(target_shape=(85, ),
name="learned_params")(optlearner_params)
print("optlearner parameters shape: " + str(optlearner_params.shape))
#exit(1)
# Ground truth parameters and point cloud are inputs to the model as well
gt_params = Input(shape=(85, ), name="gt_params")
gt_pc = Input(shape=(6890, 3), name="gt_pc")
print("gt parameters shape: " + str(gt_params.shape))
print("gt point cloud shape: " + str(gt_pc.shape))
# Compute the true offset (i.e. difference) between the ground truth and learned parameters
delta_d = Lambda(lambda x: x[0] - x[1],
name="delta_d")([gt_params, optlearner_params])
print("delta_d shape: " + str(delta_d.shape))
#exit(1)
# Calculate the (batched) MSE between the learned parameters and the ground truth parameters
false_loss_delta_d = Lambda(lambda x: K.mean(K.square(x), axis=1))(delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
#exit(1)
false_loss_delta_d = Reshape(target_shape=(1, ),
name="delta_d_mse")(false_loss_delta_d)
print("delta_d loss shape: " + str(false_loss_delta_d.shape))
# Load SMPL model and get necessary parameters
smpl_params = load_params(
'./keras_rotationnet_v2_demo_for_hidde/basicModel_f_lbs_10_207_0_v1.0.0.pkl'
)
_, _, input_info = get_parameters()
input_betas = Lambda(lambda x: x[:, 72:82])(optlearner_params)
input_pose_rodrigues = Lambda(lambda x: x[:, 0:72])(optlearner_params)
input_trans = Lambda(lambda x: x[:, 82:85])(optlearner_params)
# Get the point cloud corresponding to these parameters
optlearner_pc = Points3DFromSMPLParams(input_betas, input_pose_rodrigues,
input_trans, smpl_params,
input_info)
print("optlearner point cloud shape: " + str(optlearner_pc.shape))
#exit(1)
# Get the (batched) Euclidean loss between the learned and ground truth point clouds
pc_euclidean_diff = Lambda(lambda x: x[0] - x[1])([gt_pc, optlearner_pc])
pc_euclidean_dist = Lambda(lambda x: K.sum(K.square(x), axis=-1))(
pc_euclidean_diff)
print('pc euclidean dist ' + str(pc_euclidean_dist.shape))
#exit(1)
false_loss_pc = Lambda(lambda x: K.mean(x, axis=1))(pc_euclidean_dist)
false_loss_pc = Reshape(target_shape=(1, ),
name="pc_mean_euc_dist")(false_loss_pc)
print("point cloud loss shape: " + str(false_loss_pc.shape))
# Learn the offset in parameters from the difference between the ground truth and learned point clouds
pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(
pc_euclidean_diff
) # This is added to avoid influencing embedding layer parameters by a "bad" gradient network
print("shape of output: " + str(pc_euclidean_diff_NOGRAD.shape))
vertices = [1850, 1600, 2050, 1300, 5350, 5050, 5500]
pc_euclidean_diff_NOGRAD = Lambda(lambda x: K.tf.gather(
x, np.array(vertices).astype(np.int32), axis=-2))(
pc_euclidean_diff_NOGRAD)
print("shape of output: " + str(pc_euclidean_diff_NOGRAD.shape))
#exit(1)
pc_euclidean_diff_NOGRAD = Flatten()(pc_euclidean_diff_NOGRAD)
optlearner_architecture = Dense(
2**11, activation="relu")(pc_euclidean_diff_NOGRAD)
optlearner_architecture = BatchNormalization()(optlearner_architecture)
optlearner_architecture = Dropout(0.5)(optlearner_architecture)
print('optlearner_architecture ' + str(optlearner_architecture.shape))
#exit(1)
delta_d_hat = Dense(85, activation="linear",
name="delta_d_hat")(optlearner_architecture)
print('delta_d_hat shape ' + str(delta_d_hat.shape))
#exit(1)
# Calculate the (batched) MSE between the learned and ground truth offset in the parameters
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d)
false_loss_delta_d_hat = Lambda(
lambda x: K.sum(K.square(x[0] - x[1]), axis=1))(
[delta_d_NOGRAD, delta_d_hat])
false_loss_delta_d_hat = Reshape(
target_shape=(1, ), name="delta_d_hat_mse")(false_loss_delta_d_hat)
print("delta_d_hat loss shape: " + str(false_loss_delta_d_hat.shape))
false_sin_loss_delta_d_hat = get_sin_metric(delta_d_NOGRAD, delta_d_hat)
print("delta_d_hat sin loss shape: " +
str(false_sin_loss_delta_d_hat.shape))
# Prevent model from using the delta_d_hat gradient in final loss
delta_d_hat_NOGRAD = Lambda(lambda x: K.stop_gradient(x),
name='optlearner_output_NOGRAD')(delta_d_hat)
# False loss designed to pass the learned offset as a gradient to the embedding layer
false_loss_smpl = Lambda(lambda x: x[1] * x[0], name="smpl_diff")(
[optlearner_params, delta_d_hat_NOGRAD])
print("smpl loss shape: " + str(false_loss_smpl.shape))
return [optlearner_input, gt_params, gt_pc], [
optlearner_params, false_loss_delta_d, optlearner_pc, false_loss_pc,
false_loss_delta_d_hat, false_sin_loss_delta_d_hat, false_loss_smpl,
delta_d, delta_d_hat, delta_d_hat_NOGRAD
]