def _set_filter(self):
# TODO : add default Kalman filters for each dataset, use them to evaluate velocity
prev_load_name = self.args.load_name
if self.args.dataset == 'NGSIM':
self.args.load_name = 'CV_NGSIM_143_bis'
self.filter = get_net()
elif self.args.dataset in ['Argoverse', 'Fusion']:
print('No default filter for ' + self.args.dataset)
self.filter = None
self.args.load_name = prev_load_name
def _set_data_getter(self, change_index=False):
if self.controls['net'].value is not None:
self.args.load_name = self.controls['net'].value
is_new_dataset = False
if self.args.dataset != self.controls['dataset'].value:
self.args.dataset = self.controls['dataset'].value
is_new_dataset = True
if self._model_type == 'multi_obj':
self.data_getter = MemoryData(get_multi_object_net(), get_multi_object_test_set(), self.args)
elif self._model_type == 'multi_pred':
self.args.model_type = 'nn_attention'
self.data_getter = MemoryData(get_multi_object_net(), get_multi_object_test_set(), self.args)
else:
self.data_getter = MemoryData(get_net(), get_multi_object_test_set(), self.args)
if change_index:
self._set_index()
self._set_data()
import torch
from torch.utils.data import DataLoader
import numpy as np
from utils.losses import maskedMSE
from utils.utils import Settings, get_net, get_test_set
if __name__ == '__main__':
args = Settings()
net = get_net()
testSet = get_test_set()
testDataloader = DataLoader(testSet,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
collate_fn=testSet.collate_fn)
net.train_flag = False
it_testDataloader = iter(testDataloader)
len_test = len(it_testDataloader)
avg_loss = 0
hist_test = []
fut_test = []
pred_test = []
proba_man_test = []
mask_test = []
# path_list = testSet.dataset['path']
def _get_dataset(self):
dataset_list = ['NGSIM', 'Argoverse', 'Fusion']
self.args.dataset = st.sidebar.selectbox('Dataset:', dataset_list)
self.data_getter = MemoryData(get_net(), get_multi_object_test_set(),
self.args)
