def load_sample_dict():
# load data
os.chdir('/home/jonfrey/PLR2')
sys.path.append('src')
sys.path.append('src/dense_fusion')
from loaders_v2 import ConfigLoader, GenericDataset
exp_cfg = ConfigLoader().from_file(
'/home/jonfrey/PLR2/yaml/exp/exp_ws_deepim.yml')
env_cfg = ConfigLoader().from_file(
'/home/jonfrey/PLR2/yaml/env/env_natrix_jonas.yml')
generic = GenericDataset(cfg_d=exp_cfg['d_train'], cfg_env=env_cfg)
img = Image.open(
'/media/scratch1/jonfrey/datasets/YCB_Video_Dataset/data/0000/000001-color.png'
)
out = generic[0]
generic.visu = True
names = [
'cloud', 'choose', 'img_masked', 'target', 'model_points', 'idx',
'add_depth', 'add_mask', 'img', 'cam', 'rot', 'trans', 'desig'
]
sample = {}
print(len(out[0]))
for i, o in enumerate(out[0]):
sample[names[i]] = o
return sample
doc['visu']['on_pred_fail'] = 0.03
doc['d_test']['noise_translation'] = 0.001
doc['d_test']['noise_rotation'] = 1
new_file = p + f'/model{j}.yml'
new_exps.append(new_file)
try:
os.makedirs(p)
except:
pass
with open(new_file, 'w') as f:
print(f'Created exp {j} at {new_file}')
yaml.dump(doc, f, default_flow_style=False, sort_keys=False)
env = ConfigLoader().from_file(env_cfg_path).get_FullLoader()
env_cfg_path = args.env
for exp_cfg_path in new_exps:
seed_everything(42)
# for exp_cfg_path in exp_cfg_paths:
exp = ConfigLoader().from_file(exp_cfg_path).get_FullLoader()
model_path = exp['model_path']
# copy config files to model path
if not os.path.exists(model_path):
os.makedirs(model_path)
print((pad("Generating network run folder")))
else:
print((pad("Network run folder already exits")))
exp_cfg_fn = os.path.split(exp_cfg_path)[-1]
def get_flags():
parser = argparse.ArgumentParser()
parser.add_argument(
'--exp',
type=file_path,
default='yaml/exp/exp_ws_motion_train.yml', # required=True,
help='The main experiment yaml file.')
parser.add_argument('--env',
type=file_path,
default='yaml/env/env_natrix_jonas.yml',
help='The environment yaml file.')
return parser.parse_args()
if __name__ == "__main__":
args = get_flags()
exp = ConfigLoader().from_file(args.exp).get_FullLoader()
env = ConfigLoader().from_file(args.env).get_FullLoader()
dataset_train = GenericDataset(cfg_d=exp['d_train'], cfg_env=env)
K1 = np.array([[1066.778, 0, 312.9869], [0, 1067.487, 241.3109], [0, 0,
1]])
K2 = np.array([[1066.778, 0, 312.9869], [0, 1067.487, 241.3109], [0, 0,
1]])
obj_name_2_idx = copy.deepcopy(dataset_train._backend._name_to_idx)
RendererYCB('/media/scratch1/jonfrey/datasets/YCB_Video_Dataset',
obj_name_2_idx=obj_name_2_idx,
K1=K1,
K2=K2)
tarls[j, :, :] = copy.deepcopy(
self.pose_dict[i[0]][best_match, :4, :4])
# print(f'loading time is {time.time()-st}')
return imgls, depls, tarls
if __name__ == "__main__":
import sys
import os
sys.path.insert(0, os.getcwd())
sys.path.append(os.path.join(os.getcwd() + '/src'))
sys.path.append(os.path.join(os.getcwd() + '/lib'))
from loaders_v2 import ConfigLoader
from loaders_v2 import GenericDataset
exp_cfg_path = 'yaml/exp/exp_ws_deepim.yml'
env_cfg_path = 'yaml/env/env_natrix_jonas.yml'
exp = ConfigLoader().from_file(exp_cfg_path).get_FullLoader()
env = ConfigLoader().from_file(env_cfg_path).get_FullLoader()
dataset_train = GenericDataset(cfg_d=exp['d_train'], cfg_env=env)
store = env['p_ycb'] + '/viewpoints_renderings'
vm = ViewpointManager(store=store,
name_to_idx=dataset_train._backend._name_to_idx,
nr_of_images_per_object=10,
device='cuda:0',
load_images=True)
i = 19
import os
import sys
import yaml
sys.path.insert(0, os.getcwd())
sys.path.append(os.path.join(os.getcwd() + '/src'))
sys.path.append(os.path.join(os.getcwd() + '/lib'))
from loaders_v2 import ConfigLoader
from loaders_v2 import GenericDataset
import torch
import time
import datetime
exp = ConfigLoader().from_file('yaml/exp/exp_natrix.yml').get_FullLoader()
env = ConfigLoader().from_file('yaml/env/env_natrix_jonas.yml').get_FullLoader()
dataset_test = GenericDataset(
cfg_d=exp['d_test'],
cfg_env=env)
store = env['p_ycb'] + '/viewpoints_renderings'
dataloader_test = torch.utils.data.DataLoader(dataset_test,
batch_size = 1,
num_workers = 15,
pin_memory= False,
shuffle= False)
print(len(dataloader_test))
st = time.time()
for j, b in enumerate( dataloader_test ):
if j % 50 == 0 and j != 0:
ti = (time.time()-st)/j *len(dataset_test)
t1 = str(datetime.timedelta(seconds=(time.time()-st) ))