Input sequences are empty in the output result.

[Akhil-Raj]

Trajectory-Transformer/train_individualTF.py

Lines 237 to 279 in baec1a1

	for id_b,batch in enumerate(test_dl):
	inp_.append(batch['src'])
	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
	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)
	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)))
	peds = np.concatenate(peds, 0)
	frames = np.concatenate(frames, 0)
	dt = np.concatenate(dt, 0)
	gt = np.concatenate(gt, 0)
	dt_names = test_dataset.data['dataset_name']
	pr = np.concatenate(pr, 0)
	mad, fad, errs = baselineUtils.distance_metrics(gt, pr)
	log.add_scalar('eval/DET_mad', mad, epoch)
	log.add_scalar('eval/DET_fad', fad, epoch)
	# 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,
	'dt_names': dt_names})

.mat files saved by the model(Line 277) has the value of 'input' key empty. This code should fix the problem :

                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

                    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)

                    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)))

                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)  # 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,
                                  'dt_names': dt_names})

[Akhil-Raj]

#20