def __init__(self,
TARGET_OFFSET,
TARGET_PARAMS_TO_OFFSET,
PARAM_OFFSET,
PARAMS_TO_OFFSET,
SMPL,
BATCH_SIZE=1,
opt_lr=0.05,
opt_iter=5,
reward_factor=100,
reward_scale=0.2,
step_limit=100,
epsilon=1e-3):
# Environment configuration parameters
self.POSE_OFFSET = A2CEnv.format_offset_dict(TARGET_OFFSET,
TARGET_PARAMS_TO_OFFSET)
self.PARAM_OFFSET = A2CEnv.format_offset_dict(PARAM_OFFSET,
PARAMS_TO_OFFSET)
self.smpl = SMPL
self.BATCH_SIZE = BATCH_SIZE
# Parameters for batched TF implementation of SMPL model
self.smpl_params, self.input_info, self.faces = load_smpl_params()
# Reward configuration parameters
self.opt_lr = opt_lr
self.opt_iter = opt_iter
self.reward_factor = reward_factor
self.reward_scale = reward_scale
self.step_limit = step_limit
self.epsilon = epsilon
# Store environment states etc.
self.target_params, self.target_pcs = self.generate_targets()
self.pred_params, self.pred_pcs = self.generate_preds()
self.steps_taken = np.zeros((self.BATCH_SIZE))
self.done = np.array([False for i in range(self.BATCH_SIZE)])
self.reward_sum = np.zeros((self.BATCH_SIZE))
# Initialise gradient descent model
self.GD_model = self.init_GD_model()
def construct_optlearner_model(ARCHITECTURE,
param_trainable,
emb_initialiser,
data_samples,
INPUT_TYPE,
GROUPS=[],
update_weight=1.0,
DROPOUT=0.0,
PARAM_2D_TRAINABLE=False,
INCLUDE_SHAPE=True):
smpl_params, input_info, faces = load_smpl_params()
print("Optimiser architecture: " + str(ARCHITECTURE))
optlearner_inputs, optlearner_outputs = architecture_inputs_and_outputs(
ARCHITECTURE, param_trainable, emb_initialiser, smpl_params,
input_info, faces, data_samples, INPUT_TYPE, GROUPS, update_weight,
DROPOUT, PARAM_2D_TRAINABLE, INCLUDE_SHAPE)
print("optlearner inputs " + str(optlearner_inputs))
print("optlearner outputs " + str(optlearner_outputs))
optlearner_model = Model(inputs=optlearner_inputs,
outputs=optlearner_outputs)
return optlearner_model
#opt_lr = 0.5
#opt_epochs = 20
#opt_cb = OptimisationCallback(OPTIMISATION_PERIOD, opt_epochs, opt_lr, opt_logs_dir, smpl, X_data, train_inputs=X_cb, train_silh=silh_cb, pred_path=opt_vis_dir, period=1, trainable_params=trainable_params, visualise=False)
def print_summary_wrapper(path):
def print_summary(s):
with open(path + "model_summary.txt", 'a') as f:
f.write(s)
print(s)
return print_summary
# Build and compile the model
smpl_params, input_info, faces = load_smpl_params()
print("Optimiser architecture: " + str(ARCHITECTURE))
if ARCHITECTURE == "OptLearnerMeshNormalStaticModArchitecture":
optlearner_inputs, optlearner_outputs = OptLearnerMeshNormalStaticModArchitecture(
param_trainable=param_trainable,
init_wrapper=emb_initialiser,
smpl_params=smpl_params,
input_info=input_info,
faces=faces,
emb_size=data_samples,
input_type=INPUT_TYPE)
elif ARCHITECTURE == "BasicFCOptLearnerStaticArchitecture":
optlearner_inputs, optlearner_outputs = BasicFCOptLearnerStaticArchitecture(
param_trainable=param_trainable,
init_wrapper=emb_initialiser,
smpl_params=smpl_params,
