def setup_test_data(trainable_params, DISTRACTOR, PARAMS_TO_OFFSET, POSE_OFFSET, ARCHITECTURE, JOINT_LEVELS, data_samples, num_test_samples=5, num_cb_samples=5, MODE="RODRIGUES", LOAD_DATA_DIR=None):
# Gather trainable params
param_ids = ["param_{:02d}".format(i) for i in range(85)]
trainable_params = format_offsetable_params(trainable_params)
param_trainable = { param: (param in trainable_params) for param in param_ids }
DISTRACTOR = format_distractor_dict(DISTRACTOR, trainable_params)
# Gather offsetable params
if PARAMS_TO_OFFSET == "trainable_params":
PARAMS_TO_OFFSET = trainable_params
PARAMS_TO_OFFSET = format_offsetable_params(PARAMS_TO_OFFSET)
POSE_OFFSET = format_distractor_dict(POSE_OFFSET, PARAMS_TO_OFFSET)
# Define the kinematic tree
kin_tree = format_joint_levels(JOINT_LEVELS)
# Generate the data from the SMPL parameters
print("loading SMPL...")
smpl = SMPLModel('/data/cvfs/hjhb2/projects/deep_optimiser/code/keras_rotationnet_v2_demo_for_hidde/basicModel_f_lbs_10_207_0_v1.0.0.pkl')
# Generate and format the data
print("Gather input data...")
X_test, Y_test = gather_input_data(data_samples, smpl, PARAMS_TO_OFFSET, POSE_OFFSET, ARCHITECTURE, param_trainable, num_test_samples=num_test_samples, MODE=MODE, LOAD_DATA_DIR=LOAD_DATA_DIR)
# Render silhouettes for the callback data
X_cb, Y_cb, silh_cb = gather_cb_data(X_test, Y_test, data_samples, num_cb_samples, where="front")
return X_test, Y_test, X_cb, Y_cb, silh_cb, smpl, kin_tree, trainable_params, param_trainable, DISTRACTOR
else:
dirs = ["../"]
dir_paths = dirs
dirs = [dir_name + "logs/delta_d_magnitude.txt" for dir_name in dirs]
dirs_trainable_params = []
for dir_name in dir_paths:
with open(dir_name + "code/config.yaml", 'r') as f:
try:
setup_params = yaml.safe_load(f)
#print(setup_params)
except yaml.YAMLError as exc:
print(exc)
trainable_params_ = setup_params["PARAMS"]["TRAINABLE"]
trainable_params_ = format_offsetable_params(trainable_params_)
dirs_trainable_params.append(trainable_params_)
trainable_params = dirs_trainable_params[0]
#kinematic_levels = None
kinematic_levels = [[0], [3], [1, 2, 6], [4, 5, 9], [7, 8, 12, 13, 14],
[15, 16, 17], [18, 19], [20, 21], [22, 23]]
SUBSAMPLE_PERIOD = 20
#SUBSAMPLE_PERIOD = 10
#SUBSAMPLE_PERIOD = 2
def add_losses(dir_losses, epoch_array, curr_epoch):
epoch_str = "".join(epoch_array)
def setup_train_data(trainable_params,
DISTRACTOR,
PARAMS_TO_OFFSET,
POSE_OFFSET,
ARCHITECTURE,
data_samples,
BATCH_SIZE,
train_vis_dir,
num_cb_samples=5,
MODE="RODRIGUES",
LOAD_DATA_DIR=None,
use_generator=False,
RESET_PERIOD=None,
groups=None,
TRAIN_PERIOD=5):
# Gather trainable params
param_ids = ["param_{:02d}".format(i) for i in range(85)]
trainable_params = format_offsetable_params(trainable_params)
param_trainable = {
param: (param in trainable_params)
for param in param_ids
}
DISTRACTOR = format_distractor_dict(DISTRACTOR, trainable_params)
# Gather offsetable params
if PARAMS_TO_OFFSET == "trainable_params":
PARAMS_TO_OFFSET = trainable_params
PARAMS_TO_OFFSET = format_offsetable_params(PARAMS_TO_OFFSET)
POSE_OFFSET = format_distractor_dict(POSE_OFFSET, PARAMS_TO_OFFSET)
# Define the kinematic tree
kin_tree = format_joint_levels(groups)
#print(kin_tree)
efficient_kin_tree = [[
param for param in level if param in trainable_params
] for level in kin_tree]
efficient_kin_tree = [entry for entry in efficient_kin_tree
if entry] # filter empty entries
#print(efficient_kin_tree)
# Generate the data from the SMPL parameters
print("loading SMPL...")
smpl = SMPLModel(
'/data/cvfs/hjhb2/projects/deep_optimiser/code/keras_rotationnet_v2_demo_for_hidde/basicModel_f_lbs_10_207_0_v1.0.0.pkl'
)
# Generate and format the data
print("Gather input data...")
if use_generator:
trainable_params_mask = [
int(param_trainable[key])
for key in sorted(param_trainable.keys(),
key=lambda x: int(x[6:8]))
]
update_generator = OptLearnerUpdateGenerator(
data_samples,
RESET_PERIOD,
POSE_OFFSET,
PARAMS_TO_OFFSET,
ARCHITECTURE,
batch_size=BATCH_SIZE,
smpl=smpl,
shuffle=True,
save_path=train_vis_dir,
trainable_params_mask=trainable_params_mask,
kin_tree=efficient_kin_tree,
train_period=TRAIN_PERIOD)
X_train, Y_train = update_generator.yield_data()
print("Y_train shapes: " + str([datum.shape for datum in Y_train]))
else:
update_generator = None
X_train, Y_train = gather_input_data(data_samples,
smpl,
PARAMS_TO_OFFSET,
POSE_OFFSET,
ARCHITECTURE,
param_trainable,
num_test_samples=-1,
MODE=MODE,
LOAD_DATA_DIR=LOAD_DATA_DIR,
use_generator=use_generator,
kin_tree=efficient_kin_tree)
# Render silhouettes for the callback data
X_cb, Y_cb, silh_cb = gather_cb_data(X_train,
Y_train,
data_samples,
num_cb_samples,
where="spread")
return X_train, Y_train, X_cb, Y_cb, silh_cb, smpl, trainable_params, param_trainable, DISTRACTOR, POSE_OFFSET, update_generator
control_logs_dirs = []
test_vis_dirs = []
control_dirs = []
for lr in LEARNING_RATES:
sub_dir = "lr_{:02f}/".format(lr)
logs_dir, control_logs_dir, test_vis_dir, control_dir = create_subdir(
exp_dir, model_name, sub_dir, save_suffix)
logs_dirs.append(logs_dir)
control_logs_dirs.append(control_logs_dir)
test_vis_dirs.append(test_vis_dir)
control_dirs.append(control_dir)
# Generate the data from the SMPL parameters
# Gather trainable params
param_ids = ["param_{:02d}".format(i) for i in range(85)]
trainable_params = format_offsetable_params(trainable_params)
param_trainable = {param: (param in trainable_params) for param in param_ids}
DISTRACTOR = format_distractor_dict(DISTRACTOR, trainable_params)
# Gather offsetable params
if PARAMS_TO_OFFSET == "trainable_params":
PARAMS_TO_OFFSET = trainable_params
PARAMS_TO_OFFSET = format_offsetable_params(PARAMS_TO_OFFSET)
POSE_OFFSET = format_distractor_dict(POSE_OFFSET, PARAMS_TO_OFFSET)
# Define the kinematic tree
kin_tree = format_joint_levels(JOINT_LEVELS)
# Generate the data from the SMPL parameters
print("loading SMPL...")
smpl = SMPLModel(