def test_softsign(self):
def softsign(x):
return np.divide(x, np.ones_like(x) + np.absolute(x))
x = backend.placeholder(ndim=2)
f = backend.function([x], [activations.softsign(x)])
test_values = np.random.random((2, 5))
result = f([test_values])[0]
expected = softsign(test_values)
self.assertAllClose(result, expected, rtol=1e-05)
def test_softsign():
"""Test using a reference softsign implementation.
"""
def softsign(x):
return np.divide(x, np.ones_like(x) + np.absolute(x))
x = K.placeholder(ndim=2)
f = K.function([x], [activations.softsign(x)])
test_values = get_standard_values()
result = f([test_values])[0]
expected = softsign(test_values)
assert_allclose(result, expected, rtol=1e-05)
def test_softsign():
"""Test using a reference softsign implementation.
"""
def softsign(x):
return np.divide(x, np.ones_like(x) + np.absolute(x))
x = K.placeholder(ndim=2)
f = K.function([x], [activations.softsign(x)])
test_values = get_standard_values()
result = f([test_values])[0]
expected = softsign(test_values)
assert_allclose(result, expected, rtol=1e-05)
def get_initial_state(self, inputs):
print('inputs shape:', inputs.get_shape())
# apply the matrix on the first time step to get the initial s0.
s0 = activations.softsign(K.dot(inputs[:, 0], self.W_s))
# from keras.layers.recurrent to initialize a vector of (batchsize,
# output_dim)
y0 = K.zeros_like(inputs) # (samples, timesteps, input_dims)
y0 = K.sum(y0, axis=(1, 2)) # (samples, )
y0 = K.expand_dims(y0) # (samples, 1)
y0 = K.tile(y0, [1, self.output_dim])
return [y0, s0]
acttf = kact.linear(nettf)
# need to convert from TensorFlow tensors to numpy arrays before plotting
# eval() is called because TensorFlow tensors have no values until they are "run"
plt_act(nettf.eval(), acttf.eval(), 'linear activation function')
# relu activation function
acttf = kact.relu(nettf)
plt_act(nettf.eval(), acttf.eval(), 'rectified linear (relu)')
# sigmoid activation function
acttf = kact.sigmoid(nettf)
plt_act(nettf.eval(), acttf.eval(), 'sigmoid')
# hard sigmoid activation function
acttf = kact.hard_sigmoid(nettf)
plt_act(nettf.eval(), acttf.eval(), 'hard sigmoid')
# tanh activation function
acttf = kact.tanh(nettf)
plt_act(nettf.eval(), acttf.eval(), 'tanh')
# softsign activation function
acttf = kact.softsign(nettf)
plt_act(nettf.eval(), acttf.eval(), 'softsign')
# close the TensorFlow session
session.close()
# done
print('Done!')
def NewDeepConv1DOptLearnerArchitecture(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))
# Get trainable parameters
trainable_params = Input(shape=(85, ), name="trainable_params")
print("trainable_params shape: " + str(trainable_params.shape))
#exit(1)
# 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
]
# with added vertices in the feet
#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, 3392, 3545, 3438, 6838, 6781, 6792]
#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)
if input_type == "ONLY_NORMALS":
optlearner_architecture = Dense(2**9,
activation="relu")(diff_normals_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))
#DROPOUT = 0.1
DROPOUT = 0.0
optlearner_architecture = Conv1D(
64, 5, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Dropout(DROPOUT)(optlearner_architecture)
optlearner_architecture = Conv1D(
128, 5, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Dropout(DROPOUT)(optlearner_architecture)
optlearner_architecture = Conv1D(
256, 3, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Dropout(DROPOUT)(optlearner_architecture)
optlearner_architecture = Conv1D(
512, 3, strides=2, activation="relu")(optlearner_architecture)
optlearner_architecture = Dropout(DROPOUT)(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 = Reshape((-1, ))(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="linear",
name="delta_d_hat")(optlearner_architecture)
print('delta_d_hat shape: ' + str(delta_d_hat.shape))
#exit(1)
# Filter parameters such that the model is only evaluated on trainable parameters
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d)
#delta_d_NOGRAD_filtered = Lambda(lambda x: K.switch())()
#delta_d_NOGRAD_FILTERED = Multiply()([delta_d_NOGRAD, trainable_params])
delta_d_NOGRAD_FILTERED = Lambda(lambda x: x[0] * x[1])(
[delta_d_NOGRAD, trainable_params])
print("delta_d_NOGRAD_FILTERED shape: " +
str(delta_d_NOGRAD_FILTERED.shape))
delta_d_NOGRAD = Lambda(lambda x: x)(delta_d_NOGRAD_FILTERED)
delta_d_NOGRAD = Lambda(lambda x: K.stop_gradient(x))(delta_d_NOGRAD)
#exit(1)
# Split parameters by type
delta_d_pose, delta_d_shape, delta_d_trans = split_and_reshape_euler_angles(
delta_d_NOGRAD)
delta_d_hat_pose, delta_d_hat_shape, delta_d_hat_trans = split_and_reshape_euler_angles(
delta_d_hat)
# Calculate the angular loss between parameters (and MSE for shape)
pose_thetas = angle_between_vectors(delta_d_pose,
delta_d_hat_pose,
with_norms=True)
pose_loss = Lambda(lambda x: K.mean(x, axis=1))(pose_thetas)
shape_loss = Lambda(
lambda x: K.mean(K.square(x[0] - x[1]), axis=-1, keepdims=True))(
[delta_d_shape, delta_d_hat_shape])
trans_thetas = angle_between_vectors(delta_d_trans,
delta_d_hat_trans,
with_norms=True)
trans_loss = Lambda(lambda x: K.mean(x, axis=1))(trans_thetas)
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(K.tf.concat(x, axis=-1), axis=-1))([pose_loss, shape_loss, trans_loss])
# Calculate the (batched) MSE between the learned and ground truth offset in the parameters
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])
# Calculate the loss for direction and magnitude separately
sign_loss = Lambda(lambda x: 0.5 * (1. - softsign(10 * x[0] * x[1])))(
[delta_d_NOGRAD, delta_d_hat])
#magnitude_loss = Lambda(lambda x: K.abs(K.abs(x[0]) - K.abs(x[1])))([delta_d_NOGRAD, delta_d_hat])
magnitude_loss = Lambda(lambda x: K.exp(K.abs(x[1]) - K.abs(x[0])))(
[delta_d_NOGRAD, delta_d_hat])
weighting = 0.1
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(x[0] + weighting*x[1], axis=-1))([sign_loss, magnitude_loss])
# Calculate the loss on rendered updated parameters
new_params = Lambda(lambda x: x[0] + x[1],
name="new_params")([optlearner_params, delta_d_hat])
new_pc = Lambda(lambda x: get_pc(x, smpl_params, input_info, faces))(
new_params)
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(K.sum(K.square(x[0] - x[1]), axis=-1), axis=1), name="new_params_euc_dist")([gt_pc, new_pc])
#false_loss_delta_d_hat = Lambda(lambda x: K.mean(K.square(x[0] - x[1]), axis=1) + x[2])([delta_d_NOGRAD, delta_d_hat, false_loss_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))
# Metrics
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_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))
#per_param_mse = Lambda(lambda x: K.square(x[0] - x[1]))([delta_d_NOGRAD, delta_d_hat])
per_param_mse = Lambda(lambda x: K.square(K.sin(x[0] - x[1])))(
[delta_d_NOGRAD, delta_d_hat])
per_param_mse = Reshape((85, ), name="params_mse")(per_param_mse)
# 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]
return [optlearner_input, gt_params, gt_pc, trainable_params], [
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, per_param_mse
]
