dataset._create_test_table(
) #creates the lists with dicts of the coord. of boxes, joints and the corresp. weights
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=False,
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'],
modif=False,
save_photos=params['save_photos'],
number_save=params['number_save'],
where_save=params['where_save'],
headed=params['headed'],
resolutions=params['resolutions'],
head_stacks=params['head_stacks'])
model.generate_test_model()
model.test_init(dataset=dataset, load="./logs/test/model_hour_150")
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',
logdir_test='./logdir_test',
tiny=True,
w_loss=False,
modif=False)
model_test.generate_model()
model_test.test_init(data_gen, load=model_load_dir, save=save_dir)
