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visualize_data.py
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visualize_data.py
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from data import Argoverse_Data
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
def data_visualization(dataloader,social=False):
for i_batch, traj_dict in enumerate(dataloader):
print(f"{i_batch} batch")
if social:
input_traj=traj_dict['train_agent']
gt_traj=traj_dict['gt_agent']
neighbour_traj=traj_dict['neighbour']
# import pdb; pdb.set_trace()
print(f"Shape of neighbour trajectory in batch are",end=' ')
for i in range(len(neighbour_traj)):
print(f"{len(neighbour_traj[i])}",end=" ")
print()# and {len(neighbour_traj[1])}")
else:
input_traj=traj_dict['train_agent']
gt_traj=traj_dict['gt_agent']
plt.grid(True)
plt.plot(input_traj[0,:,0].numpy(),input_traj[0,:,1].numpy(),'g-o',gt_traj[0,:,0].numpy(),gt_traj[0,:,1].numpy(),'r-o')
plt.show(block=False)
plt.pause(5)
plt.clf()
if i_batch==5:
exit()
if __name__=="__main__":
social=False
argoverse_sample=Argoverse_Data('data/forecasting_sample/data/',social=social)
if social:
train_loader = DataLoader(argoverse_sample, batch_size=2,collate_fn=collate_traj)
else:
train_loader = DataLoader(argoverse_sample, batch_size=2)
data_visualization(train_loader,social=social)