name=params['name'],
data_stream_train=input_data,
data_stream_valid=valid_data,
data_stream_test=None,
logdir_train=params['log_dir_train'],
logdir_test=params['log_dir_test'],
saver_directory=params['saver_directory'],
tiny=params['tiny'],
w_loss=params['weighted_loss'],
w_summary=True,
joints=params['joint_list'],
modif=False)
model.generate_model()
load_file = None
model.training_init(nEpochs=params['nepochs'],
epochSize=epochSize,
saveStep=summary_steps,
load=load_file)
heatmaps = model.get_heatmaps(load=load_file)
print("Output heatmaps result. Shape: " + str(heatmaps.shape))
df_file_names = valid_data.df[valid_data.file_name_col]
gt_coords = valid_data.df[valid_data.coords_cols].as_matrix()
lm_cnt = valid_data.lm_cnt
pred_coord = heatmap_to_coord(heatmaps, valid_data.img_width,
valid_data.img_height)
write_point_result(pred_coord, gt_coords, lm_cnt, params,
params['valid_result_dir'])
print('--Creating Dataset')
dataset = DataGenerator(params['joint_list'], params['img_directory'],
params['training_txt_file'])
dataset._create_train_table()
dataset._randomize()
dataset._create_sets()
model = HourglassModel(nFeat=params['nfeats'],
nStack=params['nstacks'],
nModules=params['nmodules'],
nLow=params['nlow'],
outputDim=params['num_joints'],
batch_size=params['batch_size'],
training=True,
drop_rate=params['dropout_rate'],
lear_rate=params['learning_rate'],
decay=params['learning_rate_decay'],
decay_step=params['decay_step'],
dataset=dataset,
name=params['name'],
logdir_train=params['log_dir_train'],
logdir_test=params['log_dir_test'],
tiny=params['tiny'],
modif=False)
model.generate_model()
model.training_init(nEpochs=params['nepochs'],
epochSize=params['epoch_size'],
saveStep=params['saver_step'],
dataset=None)
# add some parameters from the terminal
parser = argparse.ArgumentParser(description='Launch the training of the Hourglass model.', add_help=True, epilog='Just a test for this parameter')
parser.add_argument('--version', action='version', version='Version 1.0')
parser.add_argument('--cfg', required=False, default = './config.cfg', help='The path for your config file')
args = parser.parse_args()
print('>>>>> Parsing Config File From %s' %(args.cfg))
params = process_config(args.cfg)
print('>>>>> Creating Dataset Now')
# dataset.train_set is the table of the training set's names
dataset = DataGenerator(joints_name = params['joint_list'],img_dir = params['img_directory'], train_data_file = params['training_txt_file'],
camera_extrinsic = params['camera_extrinsic'], camera_intrinsic = params['camera_intrinsic'])
dataset._create_train_table()
# nfeats:256, nstacks:4 nmodules:1(not used)
# nlow:4 (Number of downsampling in one stack)
# mcam:false (attention system(not needed))
# name:pretrained model
# tiny:false weighted_loss:false
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
model = HourglassModel(nFeat=params['nfeats'], nStack=params['nstacks'], nModules=params['nmodules'],
nLow=params['nlow'], outputDim=params['num_joints'], batch_size=params['batch_size'], training=True,
drop_rate= params['dropout_rate'], lear_rate=params['learning_rate'], decay=params['learning_rate_decay'], decay_step=params['decay_step'],
dataset=dataset, name=params['name'], w_summary = True, logdir_train=params['log_dir_train'], logdir_test=params['log_dir_test'], tiny= params['tiny'],
w_loss=params['weighted_loss'] , joints= params['joint_list'], gpu_frac=params['gpu_frac'], model_save_dir=params['model_save_dir'])
print('>>>>> Creating Hourglass Model')
model.generate_model()
model.training_init(nEpochs=params['nepochs'], epochSize=params['epoch_size'], saveStep=params['saver_step'],valid_iter=params['valid_iteration'], pre_trained = params['pretrained_model'], human_pretrained_model = params['human_pretrained_model'])
outputDim=3,
batch_size=16,
training=True,
drop_rate=0.2,
lear_rate=2.5 * 1e-4,
decay=0.96,
decay_step=1000,
logdir_train='./logdir_train',
logdir_test='./logdir_test',
tiny=True,
w_loss=False,
modif=False)
model.generate_model()
model.training_init(data_gen,
nEpochs=50,
epochSize=1000,
batchSize=16,
saveStep=500,
load=None)
else:
data_gen = SFSTestDataProvider(str(data_dir + "test/"))
model_test = HourglassModel(nFeat=256,
nStack=4,
nLow=4,
outputDim=3,
batch_size=16,
training=False,
drop_rate=0.2,
lear_rate=2.5 * 1e-4,
decay=0.96,
decay_step=1000,
logdir_train='./logdir_train',
params['train_img_directory'],
params['training_data_file'])
dataset.creator()
model = HourglassModel(nFeat=params['nfeats'],
nStack=params['nstacks'],
nModules=params['nmodules'],
nLow=params['nlow'],
outputDim=params['num_joints'],
batch_size=params['batch_size'],
attention=params['mcam'],
training=True,
drop_rate=params['dropout_rate'],
lear_rate=params['learning_rate'],
decay=params['learning_rate_decay'],
decay_step=params['decay_step'],
dataset=dataset,
name=params['name'],
logdir_train=params['log_dir_train'],
logdir_test=params['log_dir_test'],
tiny=params['tiny'],
w_loss=params['weighted_loss'],
joints=params['joint_list'],
modif=False)
model.generate_model()
model.training_init(nEpochs=params['nepochs'],
epochSize=params['epoch_size'],
saveStep=params['saver_step'],
saver_dir=params['model_file'],
dataset=None,
load=params['model_file'] + params['load_file'])
model = HourglassModel(f_loss=f_loss,
nFeat=params['nfeats'],
nStack=params['nstacks'],
nModules=params['nmodules'],
nLow=params['nlow'],
outputDim=params['num_joints'],
batch_size=params['batch_size'],
attention=params['mcam'],
training=True,
drop_rate=params['dropout_rate'],
lear_rate=params['learning_rate'],
decay=params['learning_rate_decay'],
decay_step=params['decay_step'],
dataset=dataset,
name=params['name'],
logdir_train=params['log_dir_train'],
logdir_test=params['log_dir_test'],
saver_dir=params['saver_directory'],
tiny=params['tiny'],
w_loss=params['weighted_loss'],
joints=params['joint_list'],
modif=modif)
model.generate_model()
model.training_init(nEpochs=params['nepochs'],
epochSize=params['epoch_size'],
saveStep=params['saver_step'],
config=config,
dataset=dataset,
load=model_n,
reset=reset) ##load need change
params[option] = eval(config.get(section, option))
if section == 'Network':
for option in config.options(section):
params[option] = eval(config.get(section, option))
if section == 'Train':
for option in config.options(section):
params[option] = eval(config.get(section, option))
if section == 'Validation':
for option in config.options(section):
params[option] = eval(config.get(section, option))
if section == 'Saver':
for option in config.options(section):
params[option] = eval(config.get(section, option))
return params
if __name__ == '__main__':
print('--Parsing Config File')
params = process_config('config.cfg')
print('--Creating Dataset')
dataset = DataGenerator(params['joint_list'], params['img_directory'], params['training_txt_file'], remove_joints=params['remove_joints'])
dataset._create_train_table()
dataset._randomize()
dataset._create_sets()
model = HourglassModel(nFeat=params['nfeats'], nStack=params['nstacks'], nModules=params['nmodules'], nLow=params['nlow'], outputDim=params['num_joints'], batch_size=params['batch_size'], attention = params['mcam'],training=True, drop_rate= params['dropout_rate'], lear_rate=params['learning_rate'], decay=params['learning_rate_decay'], decay_step=params['decay_step'], dataset=dataset, name=params['name'], logdir_train=params['log_dir_train'], logdir_test=params['log_dir_test'], tiny= params['tiny'], w_loss=params['weighted_loss'] , joints= params['joint_list'],modif=False)
model.generate_model()
model.training_init(nEpochs=params['nepochs'], epochSize=params['epoch_size'], saveStep=params['saver_step'], dataset = None)