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Rectified the empty value error in the result #20

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Rectified the empty value error in the result #20

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26 changes: 13 additions & 13 deletions train_individualTF.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,50 +234,50 @@ def main():
frames = []
dt = []

for id_b,batch in enumerate(test_dl):
inp_.append(batch['src'])
gt.append(batch['trg'][:,:,0:2])
for id_b, batch in enumerate(test_dl):
inp_.append(batch['src'][:, :, 0:2])
gt.append(batch['trg'][:, :, 0:2])
frames.append(batch['frames'])
peds.append(batch['peds'])
dt.append(batch['dataset'])

inp = (batch['src'][:, 1:, 2:4].to(device) - mean.to(device)) / std.to(device)
src_att = torch.ones((inp.shape[0], 1, inp.shape[1])).to(device)
start_of_seq = torch.Tensor([0, 0, 1]).unsqueeze(0).unsqueeze(1).repeat(inp.shape[0], 1, 1).to(
device)
dec_inp=start_of_seq
dec_inp = start_of_seq

for i in range(args.preds):
trg_att = subsequent_mask(dec_inp.shape[1]).repeat(dec_inp.shape[0], 1, 1).to(device)
out = model(inp, dec_inp, src_att, trg_att)
dec_inp=torch.cat((dec_inp,out[:,-1:,:]),1)

dec_inp = torch.cat((dec_inp, out[:, -1:, :]), 1)

preds_tr_b=(dec_inp[:,1:,0:2]*std.to(device)+mean.to(device)).cpu().numpy().cumsum(1)+batch['src'][:,-1:,0:2].cpu().numpy()
preds_tr_b = (dec_inp[:, 1:, 0:2] * std.to(device) + mean.to(device)).cpu().numpy().cumsum(1) + \
batch['src'][:, -1:, 0:2].cpu().numpy()
pr.append(preds_tr_b)
print("test epoch %03i/%03i batch %04i / %04i" % (
epoch, args.max_epoch, id_b, len(test_dl)))
epoch, args.max_epoch, id_b, len(test_dl)))

peds = np.concatenate(peds, 0)
frames = np.concatenate(frames, 0)
dt = np.concatenate(dt, 0)
gt = np.concatenate(gt, 0)
inp_ = np.concatenate(inp_, 0)
dt_names = test_dataset.data['dataset_name']
pr = np.concatenate(pr, 0)
mad, fad, errs = baselineUtils.distance_metrics(gt, pr)

mad, fad, errs = baselineUtils.distance_metrics(gt, pr) # In this method, we take euclidean dist bw all true trajectory points and pred trajs points, and then divide by total number of trajs points

log.add_scalar('eval/DET_mad', mad, epoch)
log.add_scalar('eval/DET_fad', fad, epoch)


# print(gt.shape, inp_.shape, pr.shape)
# log.add_scalar('eval/DET_mad', mad, epoch)
# log.add_scalar('eval/DET_fad', fad, epoch)

scipy.io.savemat(f"output/Individual/{args.name}/det_{epoch}.mat",
{'input': inp, 'gt': gt, 'pr': pr, 'peds': peds, 'frames': frames, 'dt': dt,
{'input': inp_, 'gt': gt, 'pr': pr, 'peds': peds, 'frames': frames, 'dt': dt,
'dt_names': dt_names})


if epoch%args.save_step==0:

torch.save(model.state_dict(),f'models/Individual/{args.name}/{epoch:05d}.pth')
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