def get_multi_object_dataset():
args = Settings()
if args.dataset == 'NGSIM':
trSet = MultiObjectNGSIMDataset(
args.NGSIM_data_directory + 'TrainSet_traj_v2.mat',
args.NGSIM_data_directory + 'TrainSet_tracks_v2.mat',
args=args)
valSet = MultiObjectNGSIMDataset(
args.NGSIM_data_directory + 'ValSet_traj_v2.mat',
args.NGSIM_data_directory + 'ValSet_tracks_v2.mat',
args=args)
elif args.dataset == 'Argoverse':
trSet = MultiObjectArgoverseDataset(args.argoverse_data_directory +
'train/data',
args=args)
valSet = MultiObjectArgoverseDataset(args.argoverse_data_directory +
'val/data',
args=args)
elif args.dataset == 'Fusion':
trSet = MultiObjectFusionDataset(args.fusion_data_directory +
'train_sequenced_data.tar',
args=args)
valSet = MultiObjectFusionDataset(args.fusion_data_directory +
'val_sequenced_data.tar',
args=args)
return trSet, valSet
def __init__(self):
self.args = Settings()
self.field_height = self.args.field_height
self.field_width = self.args.field_width
self.pixel_per_meters = self.args.pixel_per_meters
self.image_height = int(self.pixel_per_meters * self.field_height)
self.image_width = int(self.pixel_per_meters * self.field_width)
self.image_center = np.array(
[self.image_width // 2, self.image_height // 2])
def __init__(self, head_selection):
self.head_selection = head_selection
self.color_names = ['1 Blue', '2 Orange', '3 Green', '4 Red', '5 Purple', '6 Brown', '7 Pink', '8 Gray', '9 Olive', '10 Cyan']
self.active_heads = []
self.args = Settings()
self.field_height = self.args.field_height
self.field_width = self.args.field_width
self.pixel_per_meters = 1#self.args.pixel_per_meters
self.source_object = ColumnDataSource(data={'x': [], 'y': []})
self.object_glyph = Circle(x='x', y='y', radius=0.5, fill_alpha=0.8, fill_color='red')
self.source_vehicle = ColumnDataSource(data={'x': [], 'y': [], 'angle': [], 'width': [], 'height': []})
self.source_vehicle.selected.on_change('indices', self._tap_on_veh)
self.vehicle_glyph = Rect(x='x', y='y', angle='angle', width='width', height='height',
fill_color='red', fill_alpha=0.8)
self.image = None
self.get_image()
self.image_height = int(self.pixel_per_meters*self.field_height)
self.image_width = int(self.pixel_per_meters*self.field_width)
self.image_center = np.array([self.image_width//2, self.image_height//2])
self.source_ellipse = []
self.ellipse_glyph = Ellipse(x='x', y='y', width='width', height='height', angle='angle',
fill_alpha='alpha', line_color=None, fill_color='blue')
self.n_ellipses = 0
self.source_lane = []
self.lane_glyph = Line(x='x', y='y', line_color='gray', line_dash='dashed', line_width=3)
self.n_lanes = 0
self.source_arrow = [[], []]
self.arrow_glyph = Scatter(x='x', y='y', angle='angle', size='size', marker='triangle', line_color=None)
self.arrow_glyph_list = []
self.source_path_fut = []
self.source_path_pred = []
self.source_path_past = []
self.fut_path_glyph = Line(x='x', y='y', line_color='green', line_width=2)
self.pred_path_glyph = Line(x='x', y='y', line_color='red', line_width=2)
self.past_path_glyph = Line(x='x', y='y', line_color='gray', line_width=2)
self.n_past = 0
self.n_fut = 0
self.n_pred = 0
self.attention_matrix = None
def __init__(self):
st.title('Plot sample trajectories')
self.args = Settings()
self._get_net()
self._get_dataset()
self._get_filter()
self.index = self._select_data()
self.scene_plotter = ScenePlotter()
self.draw_lanes = True
self.draw_past = True
self.draw_fut = True
self.draw_pred = True
self.draw_cov = True
self._set_what_to_draw()
self._draw_image(
"Road scene",
"This represents the road scene, input past observation and forecasting"
)
def get_multi_object_test_set():
args = Settings()
if args.dataset == 'NGSIM':
testSet = MultiObjectNGSIMDataset(
args.NGSIM_test_data_directory + 'TestSet_traj_v2.mat',
args.NGSIM_test_data_directory + 'TestSet_tracks_v2.mat', args)
elif args.dataset == 'Fusion':
testSet = MultiObjectFusionDataset(
args.fusion_data_directory + 'test_sequenced_data.tar', args)
elif args.dataset == 'Argoverse':
testSet = MultiObjectArgoverseDataset(
args.argoverse_data_directory + '/val/dataset2/', False, False,
True)
else:
raise RuntimeError(
'Multi object loader does not support other datasets than NGSIM and Fusion.'
)
return testSet
import pickle
import matplotlib.pyplot as plt
from matplotlib.legend_handler import HandlerPatch
from matplotlib.patches import Ellipse
from matplotlib.lines import Line2D
from matplotlib_scalebar.scalebar import ScaleBar
from utils.utils import Settings
def latex_string(data):
out = ''
for value in data:
out += "& {:2.2f} ".format(value)
return out
args = Settings()
# if args.dataset == 'NGSIM':
# x_axis = 1
# y_axis = 0
# else:
x_axis = 1
y_axis = 0
try:
results = np.load('./results/' + args.load_name + '.npz')
is_pickle = False
print('loaded %s' % args.load_name)
except FileNotFoundError as err:
try:
results = pickle.load(open('./results/' + args.load_name + '.pickle', 'rb'))
is_pickle = True
def __init__(self):
### Methods
self.args = Settings()
self.index = None
self.data_getter = None
self.filter = None
self._data = None
self._model_type = None
self._model_dir = self.args.models_path + 'unique_object/'
self.controls = {}
self.scene_plotter = ScenePlotter(self._set_head_selection)
### initialization of the interface
## Model type selector
def update_select_net():
if self._model_dir is not None:
file_list = [fn for fn in os.listdir(self._model_dir) if os.path.isfile(os.path.join(self._model_dir, fn))]
file_list.sort(key=lambda fn: os.stat(os.path.join(self._model_dir, fn)).st_mtime, reverse=True)
file_list = [os.path.splitext(fn)[0] for fn in file_list]
self.controls['net'].options = file_list
# print(self._model_dir)
# print('file_list')
# print(file_list)
if len(file_list) > 0:
self.controls['net'].value = file_list[0]
else:
self.controls['net'].value = None
def update_model_type():
if self.controls['multi_mono_object'].active == 0:
self._model_type = 'mono'
self._model_dir = self.args.models_path + 'unique_object/'
elif self.controls['multi_mono_object'].active == 1:
self._model_type = 'multi_obj'
self._model_dir = self.args.models_path + 'multi_objects/'
elif self.controls['multi_mono_object'].active == 2:
self._model_type = 'multi_pred'
self._model_dir = self.args.models_path + 'multi_pred/'
model_types = ['CV', 'CA', 'Bicycle', 'CV_LSTM', 'CA_LSTM', 'Bicycle_LSTM', 'nn_attention']
existing_types = [type for type in model_types if os.path.isdir(self._model_dir + type)]
self.controls['model_sub_type'].options = existing_types
print('existing types')
print(existing_types)
if len(existing_types) > 0 and not self.controls['model_sub_type'].value in existing_types:
self.controls['model_sub_type'].value = existing_types[0]
return
set_model_sub_type()
update_select_net()
def set_model_sub_type():
if self.controls['model_sub_type'].value is not None:
self._model_dir = self._model_dir + self.controls['model_sub_type'].value + '/'
self.args.model_type = self.controls['model_sub_type'].value
else:
self._model_dir = None
def update_multi_mono_object(attr, old, new):
update_model_type()
print(self._model_dir)
self._set_data_getter()
print('___')
multi_mono_object = RadioButtonGroup(labels=["Mono-object", "Multi-objects", "Multi-pred"], active=1)
self.controls['multi_mono_object'] = multi_mono_object
multi_mono_object.on_change('active', update_multi_mono_object)
## Model sub type selector
model_types = ['CV', 'CA', 'Bicycle', 'CV_LSTM', 'CA_LSTM', 'Bicycle_LSTM', 'nn_attention']
model_sub_type = Select(title='Select model type:', value=model_types[3], options=model_types)
self.controls['model_sub_type'] = model_sub_type
model_sub_type.on_change('value', lambda att, old, new: update_model_type())
## Model selector
select = Select(title="Select parameter file:", value=None, options=[])
self.controls['net'] = select
select.on_change('value', lambda att, old, new: self._set_data_getter())
## Select dataset to use
dataset_list = ['Argoverse', 'Fusion', 'NGSIM']
select = Select(title='Dataset:', value=dataset_list[0], options=dataset_list)
self.controls['dataset'] = select
select.on_change('value', lambda att, old, new: self._set_data_getter(change_index=True))
## Set what to draw
checkbox_group = CheckboxGroup(
labels=['Draw lanes', 'Draw history', 'Draw true future', 'Draw forecast', 'Draw forecast covariance'],
active=[0, 1, 2, 3, 4])
self.controls['check_box'] = checkbox_group
checkbox_group.on_change('active',
lambda att, old, new: (self._update_cov(), self._update_lanes(), self._update_path()))
## Set the number of pred
n_pred = Slider(start=1, end=6, step=1, value=1, title='Number of prediction hypothesis')
self.controls['n_pred'] = n_pred
n_pred.on_change('value', lambda att, old, new: (self._update_cov(), self._update_path()))
## Sequence ID input
text_input = TextInput(title="Sequence ID to plot:", value="Random")
self.controls['sequence_id'] = text_input
## Head selection input
multi_select_head = MultiSelect(title='Attention head multiple selection:',
value=[], options=[])
self.controls['Head_selection'] = multi_select_head
multi_select_head.on_change('value', self.scene_plotter.set_active_heads)
## Refresh all sample
button = Button(label="Refresh", button_type="success")
self.controls['refresh'] = button
button.on_click(
lambda event: (self._set_index(), self._set_data()))
# button.js_on_click(CustomJS(args=dict(p=self.image), code="""p.reset.emit()"""))
update_multi_mono_object(None, None, None)
## Set the interface layout
inputs = column(*(self.controls.values()), width=320, height=1000)
inputs.sizing_mode = "fixed"
lay = layout([[inputs, self.scene_plotter.get_image()]], sizing_mode="scale_both")
curdoc().add_root(lay)
self.scene_plotter._tap_on_veh('selected', [], [0])
def __init__(self):
self.args = Settings()
self.field_height = self.args.field_height
self.field_width = self.args.field_width
print('nll', self.nll_test[object_class, indices])
print('miss rate', self.miss_rate[object_class, indices])
print(' ==========================')
else:
self._translate_object_class(object_class, self.print_stats,
spacing)
def plot_hist(self, object_class='all', spacing=1):
self._compute_stats()
if object_class in self.class_dict:
indices = self._get_indices_at_spacing(spacing)
# hist, bins = np.histogram(self.self.dist_error[object_class][indices, :].transpose(), bins=20)
logbins = np.logspace(np.log10(1.e-2), np.log10(1000), 12)
print('indices', indices)
plt.hist(np.array(self.dist_error[object_class])[indices, :],
bins=logbins,
label=[str(int((i + 1) / 5)) for i in indices])
plt.xscale('log')
plt.legend()
plt.show()
else:
self._translate_object_class(object_class, self.plot_hist, spacing)
stats = StatMultiObject(Settings())
# stats.print_stats('ego', 1)
# stats.print_stats('car')
# stats.print_stats('bicycle', 1)
stats.plot_hist('ego')
def get_multi_object_net():
args = Settings()
if args.model_type[-3:] == 'GRU':
raise RuntimeError(
'The action prediction using GRU have not been implemented for multi object data.'
)
if args.model_type == 'CV':
net = MultiObjectKalman(args, CV_model)
elif args.model_type == 'Bicycle':
net = MultiObjectKalman(args, Bicycle_model)
elif args.model_type == 'CA':
net = MultiObjectKalman(args, CA_model)
elif args.model_type == 'CV_LSTM':
net = MultiObjectKalman(args, CV_LSTM_model)
elif args.model_type == 'CA_LSTM':
net = MultiObjectKalman(args, CA_LSTM_model)
elif args.model_type == 'Bicycle_LSTM':
net = MultiObjectKalman(args, Bicycle_LSTM_model)
elif args.model_type == 'nn_attention':
# currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
currentdir = os.getcwd()
module_dir = os.path.join(os.path.dirname(currentdir), 'Argoverse')
sys.path.insert(0, module_dir)
from attention_predictor import AttentionPredictor
net = AttentionPredictor()
# elif args.model_type == 'CV_GRU':
# net = CV_GRU_model(args)
# elif args.model_type == 'CA_GRU':
# net = CA_GRU_model(args)
# elif args.model_type == 'Bicycle_GRU':
# net = Bicycle_GRU_model(args)
else:
print('Model type ' + args.model_type + ' is not known.')
net = net.to(args.device)
if args.load_name != '':
try:
if args.model_type == 'nn_attention':
net.load_state_dict(
torch.load('./trained_models/multi_pred/' +
args.model_type + '/' + args.load_name + '.tar',
map_location=args.device))
else:
net.load_state_dict(
torch.load('./trained_models/multi_objects/' +
args.model_type + '/' + args.load_name + '.tar',
map_location=args.device))
except RuntimeError as err:
print(err)
print('Loading what can be loaded with option strict=False.')
if args.model_type == 'nn_attention':
net.load_state_dict(
torch.load('./trained_models/multi_pred/' +
args.model_type + '/' + args.load_name + '.tar',
map_location=args.device),
strict=False)
else:
net.load_state_dict(
torch.load('./trained_models/multi_objects/' +
args.model_type + '/' + args.load_name + '.tar',
map_location=args.device),
strict=False)
return net
