Addresses #10 by setting max_history_length = min_history_length = 7 in the evaluate.py script and re-running evaluation.

This commit is contained in:
BorisIvanovic 2020-05-20 23:04:40 -04:00
parent 6cad26fbc4
commit b05c7b2ba4
12 changed files with 89 additions and 85 deletions

View file

@ -804,17 +804,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"KDE NLL for ETH - Univ\n", "KDE NLL for ETH - Univ\n",
"Ours: 2.922618333137345\n", "Ours: 1.3068431681049446\n",
"KDE NLL for ETH - Hotel\n", "KDE NLL for ETH - Hotel\n",
"Ours: -1.4172003110091507\n", "Ours: -1.939345347471224\n",
"KDE NLL for UCY - Univ\n", "KDE NLL for UCY - Univ\n",
"Ours: -1.0312765478327917\n", "Ours: -1.1288059163920086\n",
"KDE NLL for UCY - Zara 1\n", "KDE NLL for UCY - Zara 1\n",
"Ours: -1.320554662223125\n", "Ours: -1.4119791272274707\n",
"KDE NLL for UCY - Zara 2\n", "KDE NLL for UCY - Zara 2\n",
"Ours: -2.265794496180711\n", "Ours: -2.525154634369542\n",
"KDE NLL for Average\n", "KDE NLL for Average\n",
"Ours: -1.1628978588526018\n" "Ours: -1.392358401395975\n"
] ]
} }
], ],
@ -890,17 +890,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"FDE Most Likely for ETH - Univ\n", "FDE Most Likely for ETH - Univ\n",
"Ours: 1.8771982385234873\n", "Ours: 1.6763703267322265\n",
"FDE Most Likely for ETH - Hotel\n", "FDE Most Likely for ETH - Hotel\n",
"Ours: 0.617328603355503\n", "Ours: 0.4614785391399073\n",
"FDE Most Likely for UCY - Univ\n", "FDE Most Likely for UCY - Univ\n",
"Ours: 1.1245144943461012\n", "Ours: 1.0747924515297491\n",
"FDE Most Likely for UCY - Zara 1\n", "FDE Most Likely for UCY - Zara 1\n",
"Ours: 0.7944446572286218\n", "Ours: 0.7704666189102252\n",
"FDE Most Likely for UCY - Zara 2\n", "FDE Most Likely for UCY - Zara 2\n",
"Ours: 0.660121689972624\n", "Ours: 0.5865659029486421\n",
"FDE Most Likely for Average\n", "FDE Most Likely for Average\n",
"Ours: 1.0173143626632004\n" "Ours: 0.9542581296327649\n"
] ]
} }
], ],
@ -926,7 +926,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"# Most Likely Evaluation ADE Attention Radius 3m" "# Most Likely ADE Attention Radius 3m"
] ]
}, },
{ {
@ -970,17 +970,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"ADE Most Likely for ETH - Univ\n", "ADE Most Likely for ETH - Univ\n",
"Ours: 0.7914500193014897\n", "Ours: 0.7052341524825474\n",
"ADE Most Likely for ETH - Hotel\n", "ADE Most Likely for ETH - Hotel\n",
"Ours: 0.27776006532871594\n", "Ours: 0.21620184785291033\n",
"ADE Most Likely for UCY - Univ\n", "ADE Most Likely for UCY - Univ\n",
"Ours: 0.4266315625403817\n", "Ours: 0.40926643885853664\n",
"ADE Most Likely for UCY - Zara 1\n", "ADE Most Likely for UCY - Zara 1\n",
"Ours: 0.3099620513771519\n", "Ours: 0.2972134362490682\n",
"ADE Most Likely for UCY - Zara 2\n", "ADE Most Likely for UCY - Zara 2\n",
"Ours: 0.25468354869237164\n", "Ours: 0.22585898118058487\n",
"ADE Most Likely for Average\n", "ADE Most Likely for Average\n",
"Ours: 0.3919718759746782\n" "Ours: 0.3661968268243691\n"
] ]
} }
], ],
@ -1048,17 +1048,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"FDE Best of 20 for ETH - Univ\n", "FDE Best of 20 for ETH - Univ\n",
"Trajectron++: 0.941833113317412\n", "Trajectron++: 0.8574431969131285\n",
"FDE Best of 20 for ETH - Hotel\n", "FDE Best of 20 for ETH - Hotel\n",
"Trajectron++: 0.25054278903414373\n", "Trajectron++: 0.19084707198210932\n",
"FDE Best of 20 for UCY - Univ\n", "FDE Best of 20 for UCY - Univ\n",
"Trajectron++: 0.44114303527050003\n", "Trajectron++: 0.4287221576716801\n",
"FDE Best of 20 for UCY - Zara 1\n", "FDE Best of 20 for UCY - Zara 1\n",
"Trajectron++: 0.3371937683488606\n", "Trajectron++: 0.3159092794417088\n",
"FDE Best of 20 for UCY - Zara 2\n", "FDE Best of 20 for UCY - Zara 2\n",
"Trajectron++: 0.27854739859128275\n", "Trajectron++: 0.25292433731989494\n",
"FDE Best of 20 for Average\n", "FDE Best of 20 for Average\n",
"Trajectron++: 0.4106989096039851\n" "Trajectron++: 0.38676102425417497\n"
] ]
} }
], ],
@ -1125,24 +1125,24 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"FDE Best of 20 for ETH - Univ\n", "ADE Best of 20 for ETH - Univ\n",
"Trajectron++: 0.4934326048690871\n", "Trajectron++: 0.4334745882075259\n",
"FDE Best of 20 for ETH - Hotel\n", "ADE Best of 20 for ETH - Hotel\n",
"Trajectron++: 0.15716624347036173\n", "Trajectron++: 0.12103208674836993\n",
"FDE Best of 20 for UCY - Univ\n", "ADE Best of 20 for UCY - Univ\n",
"Trajectron++: 0.2269283937135511\n", "Trajectron++: 0.2200696369591878\n",
"FDE Best of 20 for UCY - Zara 1\n", "ADE Best of 20 for UCY - Zara 1\n",
"Trajectron++: 0.1771332219260789\n", "Trajectron++: 0.16745747931164434\n",
"FDE Best of 20 for UCY - Zara 2\n", "ADE Best of 20 for UCY - Zara 2\n",
"Trajectron++: 0.13879135677920432\n", "Trajectron++: 0.12485689651978099\n",
"FDE Best of 20 for Average\n", "ADE Best of 20 for Average\n",
"Trajectron++: 0.21259031594000022\n" "Trajectron++: 0.1987725413014945\n"
] ]
} }
], ],
"source": [ "source": [
"for dataset in dataset_names:\n", "for dataset in dataset_names:\n",
" print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n", " print('ADE Best of 20 for ' + pretty_dataset_name(dataset))\n",
" if dataset != 'Average':\n", " if dataset != 'Average':\n",
" print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
" else:\n", " else:\n",
@ -1221,17 +1221,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"KDE NLL for ETH - Univ\n", "KDE NLL for ETH - Univ\n",
"Ours: 3.927605528941531\n", "Ours: 1.7987703559884305\n",
"KDE NLL for ETH - Hotel\n", "KDE NLL for ETH - Hotel\n",
"Ours: -0.6236621180717697\n", "Ours: -1.2864991518790894\n",
"KDE NLL for UCY - Univ\n", "KDE NLL for UCY - Univ\n",
"Ours: -0.7887800746973215\n", "Ours: -0.8897570611371921\n",
"KDE NLL for UCY - Zara 1\n", "KDE NLL for UCY - Zara 1\n",
"Ours: -1.0283870848449408\n", "Ours: -1.1275849983603234\n",
"KDE NLL for UCY - Zara 2\n", "KDE NLL for UCY - Zara 2\n",
"Ours: -1.9260379497727524\n", "Ours: -2.1946898640072234\n",
"KDE NLL for Average\n", "KDE NLL for Average\n",
"Ours: -0.8485584209581651\n" "Ours: -1.1171728799903844\n"
] ]
} }
], ],
@ -1307,17 +1307,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"FDE Most Likely for ETH - Univ\n", "FDE Most Likely for ETH - Univ\n",
"Ours: 1.8566004278950379\n", "Ours: 1.6595803163079856\n",
"FDE Most Likely for ETH - Hotel\n", "FDE Most Likely for ETH - Hotel\n",
"Ours: 0.6021892704369326\n", "Ours: 0.4577661486145857\n",
"FDE Most Likely for UCY - Univ\n", "FDE Most Likely for UCY - Univ\n",
"Ours: 1.2171244891615123\n", "Ours: 1.1657061102834114\n",
"FDE Most Likely for UCY - Zara 1\n", "FDE Most Likely for UCY - Zara 1\n",
"Ours: 0.8159963144196981\n", "Ours: 0.792450220754066\n",
"FDE Most Likely for UCY - Zara 2\n", "FDE Most Likely for UCY - Zara 2\n",
"Ours: 0.6650947374043866\n", "Ours: 0.5878910318410495\n",
"FDE Most Likely for Average\n", "FDE Most Likely for Average\n",
"Ours: 1.080938659044269\n" "Ours: 1.0204553297895693\n"
] ]
} }
], ],
@ -1387,17 +1387,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"ADE Most Likely for ETH - Univ\n", "ADE Most Likely for ETH - Univ\n",
"Ours: 0.7925476110083421\n", "Ours: 0.7081645037230024\n",
"ADE Most Likely for ETH - Hotel\n", "ADE Most Likely for ETH - Hotel\n",
"Ours: 0.27995871067462225\n", "Ours: 0.21800273126936145\n",
"ADE Most Likely for UCY - Univ\n", "ADE Most Likely for UCY - Univ\n",
"Ours: 0.46060802655655825\n", "Ours: 0.4429943056454118\n",
"ADE Most Likely for UCY - Zara 1\n", "ADE Most Likely for UCY - Zara 1\n",
"Ours: 0.3145776262029875\n", "Ours: 0.30200377722385563\n",
"ADE Most Likely for UCY - Zara 2\n", "ADE Most Likely for UCY - Zara 2\n",
"Ours: 0.25594473472397583\n", "Ours: 0.22635933788153614\n",
"ADE Most Likely for Average\n", "ADE Most Likely for Average\n",
"Ours: 0.41564361286805335\n" "Ours: 0.3907335607353219\n"
] ]
} }
], ],
@ -1465,17 +1465,17 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"FDE Best of 20 for ETH - Univ\n", "FDE Best of 20 for ETH - Univ\n",
"Trajectron++: 0.9436506373753673\n", "Trajectron++: 0.8281783496158873\n",
"FDE Best of 20 for ETH - Hotel\n", "FDE Best of 20 for ETH - Hotel\n",
"Trajectron++: 0.26871119754090905\n", "Trajectron++: 0.20567153121359805\n",
"FDE Best of 20 for UCY - Univ\n", "FDE Best of 20 for UCY - Univ\n",
"Trajectron++: 0.45632249026126165\n", "Trajectron++: 0.44246075609459684\n",
"FDE Best of 20 for UCY - Zara 1\n", "FDE Best of 20 for UCY - Zara 1\n",
"Trajectron++: 0.33819336402725053\n", "Trajectron++: 0.3307553133179889\n",
"FDE Best of 20 for UCY - Zara 2\n", "FDE Best of 20 for UCY - Zara 2\n",
"Trajectron++: 0.278890779270787\n", "Trajectron++: 0.24909920986736078\n",
"FDE Best of 20 for Average\n", "FDE Best of 20 for Average\n",
"Trajectron++: 0.42212505975114584\n" "Trajectron++: 0.39711722810872235\n"
] ]
} }
], ],
@ -1542,24 +1542,24 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"FDE Best of 20 for ETH - Univ\n", "ADE Best of 20 for ETH - Univ\n",
"Trajectron++: 0.46389447635823006\n", "Trajectron++: 0.39238413417612106\n",
"FDE Best of 20 for ETH - Hotel\n", "ADE Best of 20 for ETH - Hotel\n",
"Trajectron++: 0.14917384309686665\n", "Trajectron++: 0.11769507047457346\n",
"FDE Best of 20 for UCY - Univ\n", "ADE Best of 20 for UCY - Univ\n",
"Trajectron++: 0.20508554382763963\n", "Trajectron++: 0.1990357831124613\n",
"FDE Best of 20 for UCY - Zara 1\n", "ADE Best of 20 for UCY - Zara 1\n",
"Trajectron++: 0.15763210664999946\n", "Trajectron++: 0.15218487860383714\n",
"FDE Best of 20 for UCY - Zara 2\n", "ADE Best of 20 for UCY - Zara 2\n",
"Trajectron++: 0.12714387128413132\n", "Trajectron++: 0.11350252815738102\n",
"FDE Best of 20 for Average\n", "ADE Best of 20 for Average\n",
"Trajectron++: 0.19313473176031068\n" "Trajectron++: 0.1802170043575296\n"
] ]
} }
], ],
"source": [ "source": [
"for dataset in dataset_names:\n", "for dataset in dataset_names:\n",
" print('FDE Best of 20 for ' + pretty_dataset_name(dataset))\n", " print('ADE Best of 20 for ' + pretty_dataset_name(dataset))\n",
" if dataset != 'Average':\n", " if dataset != 'Average':\n",
" print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n", " print(f\"Trajectron++: {perf_df[(perf_df['method'] == alg_name) & (perf_df['dataset'] == dataset)]['value'].mean()}\")\n",
" else:\n", " else:\n",

View file

@ -77,6 +77,7 @@ if __name__ == "__main__":
timesteps, timesteps,
ph, ph,
num_samples=1, num_samples=1,
min_history_timesteps=7,
min_future_timesteps=12, min_future_timesteps=12,
z_mode=False, z_mode=False,
gmm_mode=True, gmm_mode=True,
@ -114,6 +115,7 @@ if __name__ == "__main__":
timesteps, timesteps,
ph, ph,
num_samples=2000, num_samples=2000,
min_history_timesteps=7,
min_future_timesteps=12, min_future_timesteps=12,
z_mode=True, z_mode=True,
full_dist=False) full_dist=False)
@ -153,6 +155,7 @@ if __name__ == "__main__":
timesteps, timesteps,
ph, ph,
num_samples=20, num_samples=20,
min_history_timesteps=7,
min_future_timesteps=12, min_future_timesteps=12,
z_mode=False, z_mode=False,
gmm_mode=False, gmm_mode=False,
@ -194,6 +197,7 @@ if __name__ == "__main__":
timesteps, timesteps,
ph, ph,
num_samples=2000, num_samples=2000,
min_history_timesteps=7,
min_future_timesteps=12, min_future_timesteps=12,
z_mode=False, z_mode=False,
gmm_mode=False, gmm_mode=False,

View file

@ -1 +1 @@
{"batch_size": 256, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 8, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 5], "strides": [1, 1, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}}, "pred_state": {"PEDESTRIAN": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": false, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []} {"batch_size": 257, "grad_clip": 1.0, "learning_rate_style": "exp", "learning_rate": 0.001, "min_learning_rate": 1e-05, "learning_decay_rate": 0.9999, "prediction_horizon": 12, "minimum_history_length": 1, "maximum_history_length": 7, "map_encoder": {"PEDESTRIAN": {"heading_state_index": 6, "patch_size": [50, 10, 50, 90], "map_channels": 3, "hidden_channels": [10, 20, 10, 1], "output_size": 32, "masks": [5, 5, 5, 5], "strides": [1, 1, 1, 1], "dropout": 0.5}}, "k": 1, "k_eval": 25, "kl_min": 0.07, "kl_weight": 100.0, "kl_weight_start": 0, "kl_decay_rate": 0.99995, "kl_crossover": 400, "kl_sigmoid_divisor": 4, "rnn_kwargs": {"dropout_keep_prob": 0.75}, "MLP_dropout_keep_prob": 0.9, "enc_rnn_dim_edge": 32, "enc_rnn_dim_edge_influence": 32, "enc_rnn_dim_history": 32, "enc_rnn_dim_future": 32, "dec_rnn_dim": 128, "q_z_xy_MLP_dims": null, "p_z_x_MLP_dims": 32, "GMM_components": 1, "log_p_yt_xz_max": 6, "N": 1, "K": 25, "tau_init": 2.0, "tau_final": 0.05, "tau_decay_rate": 0.997, "use_z_logit_clipping": true, "z_logit_clip_start": 0.05, "z_logit_clip_final": 5.0, "z_logit_clip_crossover": 300, "z_logit_clip_divisor": 5, "dynamic": {"PEDESTRIAN": {"name": "SingleIntegrator", "distribution": true, "limits": {}}}, "state": {"PEDESTRIAN": {"position": ["x", "y"], "velocity": ["x", "y"], "acceleration": ["x", "y"]}}, "pred_state": {"PEDESTRIAN": {"position": ["x", "y"]}}, "log_histograms": false, "dynamic_edges": "yes", "edge_state_combine_method": "sum", "edge_influence_combine_method": "attention", "edge_addition_filter": [0.25, 0.5, 0.75, 1.0], "edge_removal_filter": [1.0, 0.0], "offline_scene_graph": "yes", "incl_robot_node": false, "node_freq_mult_train": false, "node_freq_mult_eval": false, "scene_freq_mult_train": false, "scene_freq_mult_eval": false, "scene_freq_mult_viz": false, "edge_encoding": true, "use_map_encoding": false, "augment": true, "override_attention_radius": []}

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