SGNet.pytorch
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ChuhuaW
Cannot reproduce deterministic results
Hi, I have tried to reproduce your results by running SGNet.pytorch/tools/ethucy/train_deterministic.py and SGNet.pytorch/tools/ethucy/eval_deterministic.py. I didn't change anything except adding some model saving code from your previous commits in train_deterministic.py.
Can you help have a look at whether anything is wrong here?
Here are my training arguments:
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ${dset_name} --model SGNet ${args}
where
dset_name=ETH,args='--lr=0.0005 --dropout=0.5 --sigma=1.5'
dset_name=HOTEL,args='--lr=0.0001 --dropout=0.3'
dset_name=UNIV,args='--lr=0.0001'
dset_name=ZARA1,args='--lr=0.0001'
dset_name=ZARA2,args='--lr=0.0001'
Here are are training results:
ETH: ADE_08: 0.543764; FDE_08: 0.981109; ADE_12: 0.816298; FDE_12: 1.603263; HOTEL: ADE_08: 0.251949; FDE_08: 0.487048; ADE_12: 0.406558; FDE_12: 0.865508; UNIV: ADE_08: 0.405024; FDE_08: 0.795781; ADE_12: 0.647388; FDE_12: 1.345341; ZARA1: ADE_08: 0.235853; FDE_08: 0.470461; ADE_12: 0.381671; FDE_12: 0.803334; ZARA2: ADE_08: 0.188649; FDE_08: 0.383418; ADE_12: 0.311853; FDE_12: 0.669926;
Here are the training outputs from my terminal (e.g. Zara1):
ZARA1
ZARA1
ZARA1
Number of validation samples: 41
Number of test samples: 19
Train Epoch: 1 Goal loss: 13.3936 Decoder loss: 11.0987 Total: 24.4923
ADE_08: 0.294679; FDE_08: 0.551690; ADE_12: 0.447580; FDE_12: 0.887748
Saving checkpoints: metric_epoch_001_loss_0.4476.pth
Train Epoch: 2 Goal loss: 8.3851 Decoder loss: 7.7700 Total: 16.1551
ADE_08: 0.380591; FDE_08: 0.738910; ADE_12: 0.574689; FDE_12: 1.084587
Train Epoch: 3 Goal loss: 7.4418 Decoder loss: 7.2957 Total: 14.7374
ADE_08: 0.273209; FDE_08: 0.530974; ADE_12: 0.424851; FDE_12: 0.850812
Saving checkpoints: metric_epoch_003_loss_0.4249.pth
Train Epoch: 4 Goal loss: 7.1369 Decoder loss: 7.0551 Total: 14.1920
ADE_08: 0.286998; FDE_08: 0.565734; ADE_12: 0.450437; FDE_12: 0.906875
Train Epoch: 5 Goal loss: 6.9545 Decoder loss: 6.9175 Total: 13.8720
ADE_08: 0.260329; FDE_08: 0.512583; ADE_12: 0.414379; FDE_12: 0.856634
Saving checkpoints: metric_epoch_005_loss_0.4144.pth
Train Epoch: 6 Goal loss: 6.7755 Decoder loss: 6.7289 Total: 13.5045
ADE_08: 0.290368; FDE_08: 0.585993; ADE_12: 0.480131; FDE_12: 1.037864
Train Epoch: 7 Goal loss: 6.6577 Decoder loss: 6.6200 Total: 13.2777
ADE_08: 0.258839; FDE_08: 0.510076; ADE_12: 0.415547; FDE_12: 0.869290
Train Epoch: 8 Goal loss: 6.5346 Decoder loss: 6.4946 Total: 13.0292
ADE_08: 0.246034; FDE_08: 0.491056; ADE_12: 0.397619; FDE_12: 0.835792
Saving checkpoints: metric_epoch_008_loss_0.3976.pth
Train Epoch: 9 Goal loss: 6.4372 Decoder loss: 6.4017 Total: 12.8388
ADE_08: 0.251146; FDE_08: 0.509857; ADE_12: 0.417938; FDE_12: 0.909648
Train Epoch: 10 Goal loss: 6.3593 Decoder loss: 6.3158 Total: 12.6751
ADE_08: 0.253486; FDE_08: 0.506422; ADE_12: 0.410926; FDE_12: 0.867227
Train Epoch: 11 Goal loss: 6.2701 Decoder loss: 6.2177 Total: 12.4878
ADE_08: 0.278813; FDE_08: 0.553365; ADE_12: 0.449940; FDE_12: 0.942615
Train Epoch: 12 Goal loss: 6.2296 Decoder loss: 6.2038 Total: 12.4334
ADE_08: 0.245076; FDE_08: 0.489589; ADE_12: 0.398719; FDE_12: 0.846358
Train Epoch: 13 Goal loss: 6.1766 Decoder loss: 6.1378 Total: 12.3144
ADE_08: 0.261642; FDE_08: 0.502992; ADE_12: 0.413555; FDE_12: 0.856336
Train Epoch: 14 Goal loss: 6.1079 Decoder loss: 6.0661 Total: 12.1739
ADE_08: 0.253986; FDE_08: 0.501157; ADE_12: 0.408927; FDE_12: 0.858842
Train Epoch: 15 Goal loss: 6.0482 Decoder loss: 6.0074 Total: 12.0556
ADE_08: 0.237412; FDE_08: 0.472600; ADE_12: 0.385304; FDE_12: 0.816462
Saving checkpoints: metric_epoch_015_loss_0.3853.pth
Train Epoch: 16 Goal loss: 5.9789 Decoder loss: 5.9207 Total: 11.8996
ADE_08: 0.248604; FDE_08: 0.494847; ADE_12: 0.403149; FDE_12: 0.851186
Train Epoch: 17 Goal loss: 5.9886 Decoder loss: 5.9620 Total: 11.9507
ADE_08: 0.264590; FDE_08: 0.521281; ADE_12: 0.425393; FDE_12: 0.888515
Train Epoch: 18 Goal loss: 5.8995 Decoder loss: 5.8390 Total: 11.7384
ADE_08: 0.249374; FDE_08: 0.483068; ADE_12: 0.396050; FDE_12: 0.820998
Train Epoch: 19 Goal loss: 5.8871 Decoder loss: 5.8269 Total: 11.7140
ADE_08: 0.241822; FDE_08: 0.488735; ADE_12: 0.399218; FDE_12: 0.861516
Train Epoch: 20 Goal loss: 5.8296 Decoder loss: 5.7692 Total: 11.5988
ADE_08: 0.238986; FDE_08: 0.467400; ADE_12: 0.382569; FDE_12: 0.800729
Saving checkpoints: metric_epoch_020_loss_0.3826.pth
Train Epoch: 21 Goal loss: 5.8035 Decoder loss: 5.7227 Total: 11.5262
ADE_08: 0.246278; FDE_08: 0.498420; ADE_12: 0.406117; FDE_12: 0.873861
Train Epoch: 22 Goal loss: 5.7840 Decoder loss: 5.7143 Total: 11.4984
ADE_08: 0.245556; FDE_08: 0.489192; ADE_12: 0.399299; FDE_12: 0.848396
Train Epoch: 23 Goal loss: 5.7622 Decoder loss: 5.6884 Total: 11.4506
ADE_08: 0.241803; FDE_08: 0.473674; ADE_12: 0.386678; FDE_12: 0.807247
Train Epoch: 24 Goal loss: 5.7139 Decoder loss: 5.6287 Total: 11.3426
ADE_08: 0.237854; FDE_08: 0.473140; ADE_12: 0.384558; FDE_12: 0.809680
Train Epoch: 25 Goal loss: 5.6809 Decoder loss: 5.5965 Total: 11.2774
ADE_08: 0.243205; FDE_08: 0.483125; ADE_12: 0.392962; FDE_12: 0.827676
Train Epoch: 26 Goal loss: 5.6746 Decoder loss: 5.5734 Total: 11.2479
ADE_08: 0.241132; FDE_08: 0.483847; ADE_12: 0.393726; FDE_12: 0.837666
Train Epoch: 27 Goal loss: 5.6426 Decoder loss: 5.5449 Total: 11.1875
ADE_08: 0.238708; FDE_08: 0.473718; ADE_12: 0.385182; FDE_12: 0.809509
Train Epoch: 28 Goal loss: 5.5965 Decoder loss: 5.4837 Total: 11.0802
ADE_08: 0.245108; FDE_08: 0.486398; ADE_12: 0.396431; FDE_12: 0.837428
Train Epoch: 29 Goal loss: 5.6095 Decoder loss: 5.5072 Total: 11.1167
ADE_08: 0.244368; FDE_08: 0.483016; ADE_12: 0.391393; FDE_12: 0.814580
Train Epoch: 30 Goal loss: 5.5618 Decoder loss: 5.4448 Total: 11.0066
ADE_08: 0.242074; FDE_08: 0.474495; ADE_12: 0.387724; FDE_12: 0.810403
Train Epoch: 31 Goal loss: 5.5415 Decoder loss: 5.4163 Total: 10.9578
ADE_08: 0.235853; FDE_08: 0.470461; ADE_12: 0.381671; FDE_12: 0.803334
Saving checkpoints: metric_epoch_031_loss_0.3817.pth
Train Epoch: 32 Goal loss: 5.5443 Decoder loss: 5.4107 Total: 10.9550
ADE_08: 0.244977; FDE_08: 0.486834; ADE_12: 0.394192; FDE_12: 0.823241
Train Epoch: 33 Goal loss: 5.4947 Decoder loss: 5.3558 Total: 10.8505
ADE_08: 0.240755; FDE_08: 0.478803; ADE_12: 0.389154; FDE_12: 0.818785
Train Epoch: 34 Goal loss: 5.4762 Decoder loss: 5.3309 Total: 10.8071
ADE_08: 0.244053; FDE_08: 0.485707; ADE_12: 0.394861; FDE_12: 0.832431
Train Epoch: 35 Goal loss: 5.4659 Decoder loss: 5.3150 Total: 10.7809
ADE_08: 0.245577; FDE_08: 0.487264; ADE_12: 0.395676; FDE_12: 0.829373
Train Epoch: 36 Goal loss: 5.4495 Decoder loss: 5.2897 Total: 10.7392
ADE_08: 0.239127; FDE_08: 0.477790; ADE_12: 0.386703; FDE_12: 0.811449
Train Epoch: 37 Goal loss: 5.4346 Decoder loss: 5.2822 Total: 10.7167
ADE_08: 0.248550; FDE_08: 0.500743; ADE_12: 0.406487; FDE_12: 0.865554
Train Epoch: 38 Goal loss: 5.3894 Decoder loss: 5.2266 Total: 10.6161
ADE_08: 0.258613; FDE_08: 0.504668; ADE_12: 0.411209; FDE_12: 0.851276
Train Epoch: 39 Goal loss: 5.3827 Decoder loss: 5.2078 Total: 10.5905
ADE_08: 0.251402; FDE_08: 0.500842; ADE_12: 0.406572; FDE_12: 0.854904
Train Epoch: 40 Goal loss: 5.3578 Decoder loss: 5.1803 Total: 10.5381
ADE_08: 0.252681; FDE_08: 0.500086; ADE_12: 0.407249; FDE_12: 0.855631
Train Epoch: 41 Goal loss: 5.3440 Decoder loss: 5.1498 Total: 10.4937
ADE_08: 0.244587; FDE_08: 0.492789; ADE_12: 0.399140; FDE_12: 0.845556
Train Epoch: 42 Goal loss: 5.3255 Decoder loss: 5.1329 Total: 10.4584
ADE_08: 0.240022; FDE_08: 0.479255; ADE_12: 0.389297; FDE_12: 0.821116
Train Epoch: 43 Goal loss: 5.3073 Decoder loss: 5.1165 Total: 10.4238
ADE_08: 0.243913; FDE_08: 0.486718; ADE_12: 0.395276; FDE_12: 0.832791
Train Epoch: 44 Goal loss: 5.2892 Decoder loss: 5.0951 Total: 10.3842
ADE_08: 0.246676; FDE_08: 0.489490; ADE_12: 0.398171; FDE_12: 0.835557
Train Epoch: 45 Goal loss: 5.2664 Decoder loss: 5.0586 Total: 10.3250
ADE_08: 0.249650; FDE_08: 0.498006; ADE_12: 0.402356; FDE_12: 0.839605
Train Epoch: 46 Goal loss: 5.2504 Decoder loss: 5.0400 Total: 10.2904
ADE_08: 0.246008; FDE_08: 0.496606; ADE_12: 0.402466; FDE_12: 0.855231
Train Epoch: 47 Goal loss: 5.2392 Decoder loss: 5.0044 Total: 10.2437
ADE_08: 0.248422; FDE_08: 0.500811; ADE_12: 0.406097; FDE_12: 0.862268
Train Epoch: 48 Goal loss: 5.2152 Decoder loss: 4.9837 Total: 10.1989
ADE_08: 0.247908; FDE_08: 0.505135; ADE_12: 0.409776; FDE_12: 0.880500
Train Epoch: 49 Goal loss: 5.1839 Decoder loss: 4.9462 Total: 10.1302
ADE_08: 0.243334; FDE_08: 0.492921; ADE_12: 0.399223; FDE_12: 0.851002
Train Epoch: 50 Goal loss: 5.1815 Decoder loss: 4.9328 Total: 10.1143
ADE_08: 0.247995; FDE_08: 0.501935; ADE_12: 0.406349; FDE_12: 0.864324
@HRHLALALA Thanks for interesting in our paper. The dropout rate was probably set to 0.5 for your experiment on UNIV, ZARA1, ZARA2, as it is the default setting. And for deterministic experiment, you don't have to set sigma -- this is the standard deviation, and only apply to CVAE model. I'm attaching the checkpoints for ETH/UCY dataset here, and let me know if you can get similar results as in the paper. I also trained again on ZARA1 and attach my log here for reference.
Train Epoch: 1 Goal loss: 7.8752 Decoder loss: 7.4240 Total: 15.2992
ADE_08: 0.205366; FDE_08: 0.427921; ADE_12: 0.338283; FDE_12: 0.712461
Train Epoch: 2 Goal loss: 4.6825 Decoder loss: 4.8405 Total: 9.5230
ADE_08: 0.288085; FDE_08: 0.559531; ADE_12: 0.463402; FDE_12: 0.981222
Train Epoch: 3 Goal loss: 4.4858 Decoder loss: 4.6523 Total: 9.1381
ADE_08: 0.181439; FDE_08: 0.392425; ADE_12: 0.313678; FDE_12: 0.704375
Train Epoch: 4 Goal loss: 4.2913 Decoder loss: 4.4210 Total: 8.7124
ADE_08: 0.230115; FDE_08: 0.487011; ADE_12: 0.389181; FDE_12: 0.845964
Train Epoch: 5 Goal loss: 4.1755 Decoder loss: 4.2758 Total: 8.4513
ADE_08: 0.167939; FDE_08: 0.377876; ADE_12: 0.317006; FDE_12: 0.785227
Train Epoch: 6 Goal loss: 4.1162 Decoder loss: 4.1888 Total: 8.3050
ADE_08: 0.166464; FDE_08: 0.354999; ADE_12: 0.301010; FDE_12: 0.718141
Train Epoch: 7 Goal loss: 4.0531 Decoder loss: 4.1317 Total: 8.1847
ADE_08: 0.184430; FDE_08: 0.414347; ADE_12: 0.343615; FDE_12: 0.837005
Train Epoch: 8 Goal loss: 3.9779 Decoder loss: 4.0690 Total: 8.0469
ADE_08: 0.138170; FDE_08: 0.331704; ADE_12: 0.265631; FDE_12: 0.642644
Train Epoch: 9 Goal loss: 3.9018 Decoder loss: 3.9493 Total: 7.8510
ADE_08: 0.212174; FDE_08: 0.439908; ADE_12: 0.367158; FDE_12: 0.835217
Train Epoch: 10 Goal loss: 3.7895 Decoder loss: 3.7699 Total: 7.5594
ADE_08: 0.165783; FDE_08: 0.377216; ADE_12: 0.311176; FDE_12: 0.754264
Hi, thanks for your reply. It is really helpful to have the checkpoints. I can see a similar performance as in your paper.
I think the reason why I got worse results is that I used the data generated from the latest Trajectron++ version. As mentioned in https://github.com/ChuhuaW/SGNet.pytorch/issues/19#issue-1246418464 , they have fixed the gradient calculations.
I post my reproduced results below. Here are my training arguments:
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ${dset_name} --model SGNet ${args}
where
dset_name=ETH,args='--lr=0.0005 --dropout=0.5 '
dset_name=HOTEL,args='--lr=0.0001 --dropout=0.3'
dset_name=UNIV,args='--lr=0.0001 --dropout=0.0'
dset_name=ZARA1,args='--lr=0.0001 --dropout=0.0'
dset_name=ZARA2,args='--lr=0.0001 --dropout=0.0'
| Results from | ETH | HOTEL | UNIV | ZARA1 | ZARA2 |
|---|---|---|---|---|---|
| Paper | 0.63/1.38 | 0.27/0.63 | 0.40/0.96 | 0.26/0.64 | 0.21/0.53 |
| Provided Checkpoints on data A | 0.63/1.38 | 0.28/0.64 | 0.42/0.99 | 0.26/0.64 | 0.21/0.53 |
| Provided Checkpoints on data B | 0.97/1.81 | 0.55/1.07 | 0.66/1.37 | 0.69/1.36 | 0.50/1.02 |
| Weights trained on data A | 0.63/1.38 | 0.30/0.70 | 0.43/1.01 | 0.28/0.65 | 0.22/0.55 |
| Weights trained on data B | 0.81/1.60 | 0.41/0.87 | 0.58/1.24 | 0.37/0.79 | 0.31/0.68 |
where data A and data B are data preprocessed from Trajectron++ without and with fixed gradient.
Could you confirm these new results are reasonable? Please let me know if my hyperparameters are not consistent with yours. Thanks!
Thank you so much for conducting new experiments! I don't have any progress on the fixed Trajectron++ dataset yet. I will also post some numbers and hopefully they are close to yours.
Hi, thanks for your reply. It is really helpful to have the checkpoints. I can see a similar performance as in your paper.
I think the reason why I got worse results is that I used the data generated from the latest Trajectron++ version. As mentioned in #19 (comment) , they have fixed the gradient calculations.
I post my reproduced results below. Here are my training arguments:
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ${dset_name} --model SGNet ${args}where dset_name=ETH,args='--lr=0.0005 --dropout=0.5 ' dset_name=HOTEL,args='--lr=0.0001 --dropout=0.3' dset_name=UNIV,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA1,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA2,args='--lr=0.0001 --dropout=0.0'Results from ETH HOTEL UNIV ZARA1 ZARA2 Paper 0.63/1.38 0.27/0.63 0.40/0.96 0.26/0.64 0.21/0.53 Provided Checkpoints on data A 0.63/1.38 0.28/0.64 0.42/0.99 0.26/0.64 0.21/0.53 Provided Checkpoints on data B 0.97/1.81 0.55/1.07 0.66/1.37 0.69/1.36 0.50/1.02 Weights trained on data A 0.63/1.38 0.30/0.70 0.43/1.01 0.28/0.65 0.22/0.55 Weights trained on data B 0.81/1.60 0.41/0.87 0.58/1.24 0.37/0.79 0.31/0.68 where data A and data B are data preprocessed from Trajectron++ without and with fixed gradient.
Could you confirm these new results are reasonable? Please let me know if my hyperparameters are not consistent with yours. Thanks!
Hi @HRHLALALA, the results I reproduce are very similar to your last row 'Weights trained on data B' and I think it is a valid updated result.
Hi, thanks for your reply. It is really helpful to have the checkpoints. I can see a similar performance as in your paper.
I think the reason why I got worse results is that I used the data generated from the latest Trajectron++ version. As mentioned in #19 (comment) , they have fixed the gradient calculations.
I post my reproduced results below. Here are my training arguments:
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ${dset_name} --model SGNet ${args}where dset_name=ETH,args='--lr=0.0005 --dropout=0.5 ' dset_name=HOTEL,args='--lr=0.0001 --dropout=0.3' dset_name=UNIV,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA1,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA2,args='--lr=0.0001 --dropout=0.0'Results from ETH HOTEL UNIV ZARA1 ZARA2 Paper 0.63/1.38 0.27/0.63 0.40/0.96 0.26/0.64 0.21/0.53 Provided Checkpoints on data A 0.63/1.38 0.28/0.64 0.42/0.99 0.26/0.64 0.21/0.53 Provided Checkpoints on data B 0.97/1.81 0.55/1.07 0.66/1.37 0.69/1.36 0.50/1.02 Weights trained on data A 0.63/1.38 0.30/0.70 0.43/1.01 0.28/0.65 0.22/0.55 Weights trained on data B 0.81/1.60 0.41/0.87 0.58/1.24 0.37/0.79 0.31/0.68 where data A and data B are data preprocessed from Trajectron++ without and with fixed gradient.
Could you confirm these new results are reasonable? Please let me know if my hyperparameters are not consistent with yours. Thanks!
Hi, thanks for providing a comparing experiment. It looks like all of the results related to data B are actually correct cause they don't use future trajectory after np.gradient bug was fixed.
And that is, all of the results whose model based on trajectron++ have to be updated to a lower value ...
Do I get the right understanding?
Hi, thanks for your reply. It is really helpful to have the checkpoints. I can see a similar performance as in your paper. I think the reason why I got worse results is that I used the data generated from the latest Trajectron++ version. As mentioned in #19 (comment) , they have fixed the gradient calculations. I post my reproduced results below. Here are my training arguments:
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ${dset_name} --model SGNet ${args}where dset_name=ETH,args='--lr=0.0005 --dropout=0.5 ' dset_name=HOTEL,args='--lr=0.0001 --dropout=0.3' dset_name=UNIV,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA1,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA2,args='--lr=0.0001 --dropout=0.0' Results from ETH HOTEL UNIV ZARA1 ZARA2 Paper 0.63/1.38 0.27/0.63 0.40/0.96 0.26/0.64 0.21/0.53 Provided Checkpoints on data A 0.63/1.38 0.28/0.64 0.42/0.99 0.26/0.64 0.21/0.53 Provided Checkpoints on data B 0.97/1.81 0.55/1.07 0.66/1.37 0.69/1.36 0.50/1.02 Weights trained on data A 0.63/1.38 0.30/0.70 0.43/1.01 0.28/0.65 0.22/0.55 Weights trained on data B 0.81/1.60 0.41/0.87 0.58/1.24 0.37/0.79 0.31/0.68 where data A and data B are data preprocessed from Trajectron++ without and with fixed gradient. Could you confirm these new results are reasonable? Please let me know if my hyperparameters are not consistent with yours. Thanks!Hi, thanks for providing a comparing experiment. It looks like all of the results related to data B are actually correct cause they don't use future trajectory after
np.gradientbug was fixed. And that is, all of the results whose model based on trajectron++ have to be updated to a lower value ... Do I get the right understanding?
Yes
Hi, thanks for your reply. It is really helpful to have the checkpoints. I can see a similar performance as in your paper.
I think the reason why I got worse results is that I used the data generated from the latest Trajectron++ version. As mentioned in #19 (comment) , they have fixed the gradient calculations.
I post my reproduced results below. Here are my training arguments:
python tools/ethucy/train_deterministic.py --gpu $CUDA_VISIBLE_DEVICES --dataset ${dset_name} --model SGNet ${args}where dset_name=ETH,args='--lr=0.0005 --dropout=0.5 ' dset_name=HOTEL,args='--lr=0.0001 --dropout=0.3' dset_name=UNIV,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA1,args='--lr=0.0001 --dropout=0.0' dset_name=ZARA2,args='--lr=0.0001 --dropout=0.0'Results from ETH HOTEL UNIV ZARA1 ZARA2 Paper 0.63/1.38 0.27/0.63 0.40/0.96 0.26/0.64 0.21/0.53 Provided Checkpoints on data A 0.63/1.38 0.28/0.64 0.42/0.99 0.26/0.64 0.21/0.53 Provided Checkpoints on data B 0.97/1.81 0.55/1.07 0.66/1.37 0.69/1.36 0.50/1.02 Weights trained on data A 0.63/1.38 0.30/0.70 0.43/1.01 0.28/0.65 0.22/0.55 Weights trained on data B 0.81/1.60 0.41/0.87 0.58/1.24 0.37/0.79 0.31/0.68 where data A and data B are data preprocessed from Trajectron++ without and with fixed gradient.
Could you confirm these new results are reasonable? Please let me know if my hyperparameters are not consistent with yours. Thanks!
Hi HRHLALALA, I've tried several times but I couldn't reproduce similar results like you do. My results are always a little worse: (data A)
ETH: ADE_12: 0.643965; FDE_12: 1.457909;
HOTEL: ADE_12: 0.298510; FDE_12: 0.632796;
UNIV: ADE_12: 0.428731; FDE_12: 1.014463;
ZARA1: ADE_12: 0.285387; FDE_12: 0.700799;
I wonder if you have changed some of the original settings?
optimizer: Adam
scheduler: no
batch_size=128, lr=5e-4(ETH), 1e-4(HOTEL~ZARA02)
dropout=0.5(ETH), 0.3(HOTEL) 0（others）
Thanks a lot :D