diff --git a/notebooks/00_align_simulators_vbd.ipynb b/notebooks/00_align_simulators_vbd.ipynb
index 3068c36a..bc79a57e 100644
--- a/notebooks/00_align_simulators_vbd.ipynb
+++ b/notebooks/00_align_simulators_vbd.ipynb
@@ -45,13 +45,13 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2024-11-04 14:47:08.762921: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
- "2024-11-04 14:47:08.769846: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
- "2024-11-04 14:47:08.776862: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "2024-11-04 14:47:08.778953: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
- "2024-11-04 14:47:08.785416: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "2024-11-04 16:31:02.114407: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
+ "2024-11-04 16:31:02.121474: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2024-11-04 16:31:02.128507: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2024-11-04 16:31:02.130633: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2024-11-04 16:31:02.136910: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI AVX512_BF16 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
- "2024-11-04 14:47:09.216740: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
+ "2024-11-04 16:31:02.579468: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
]
}
],
@@ -114,7 +114,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -138,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
@@ -173,7 +173,7 @@
},
{
"cell_type": "code",
- "execution_count": 11,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -267,7 +267,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 30,
"metadata": {},
"outputs": [
{
@@ -283,7 +283,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Diffusion: 100%|██████████| 50/50 [00:01<00:00, 31.60it/s]\n"
+ "Diffusion: 100%|██████████| 50/50 [00:01<00:00, 31.02it/s]\n"
]
}
],
@@ -334,6 +334,26 @@
"vbd_waymax_imgs = [plot_state(state) for state in state_logs]"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "torch.Tensor"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "type(pred['denoised_trajs'])"
+ ]
+ },
{
"cell_type": "markdown",
"metadata": {},
@@ -389,9 +409,28 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Steps remaining: 79\n"
+ ]
+ },
+ {
+ "ename": "NameError",
+ "evalue": "name 'pred' is not defined",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[6], line 12\u001b[0m\n\u001b[1;32m 5\u001b[0m waymax_vbd_sample \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mprocess_scenario(\n\u001b[1;32m 6\u001b[0m init_state,\n\u001b[1;32m 7\u001b[0m current_index\u001b[38;5;241m=\u001b[39minit_state\u001b[38;5;241m.\u001b[39mtimestep,\n\u001b[1;32m 8\u001b[0m use_log\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 9\u001b[0m )\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Save predicted trajectories\u001b[39;00m\n\u001b[0;32m---> 12\u001b[0m waymax_vbd_sample[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_denoised_trajs\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mpred\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdenoised_trajs\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 14\u001b[0m \u001b[38;5;66;03m# Save dictionary for further inspection\u001b[39;00m\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwaymax_vbd_sample_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSCENARIO_ID\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.pkl\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n",
+ "\u001b[0;31mNameError\u001b[0m: name 'pred' is not defined"
+ ]
+ }
+ ],
"source": [
"init_state = waymax_env.reset(scenario)\n",
"\n",
@@ -403,6 +442,9 @@
" use_log=False\n",
")\n",
"\n",
+ "# Save predicted trajectories\n",
+ "waymax_vbd_sample['pred_denoised_trajs'] = pred['denoised_trajs']\n",
+ "\n",
"# Save dictionary for further inspection\n",
"with open(f'waymax_vbd_sample_{SCENARIO_ID}.pkl', 'wb') as f:\n",
" pickle.dump(waymax_vbd_sample, f)\n",
@@ -426,7 +468,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
@@ -446,7 +488,7 @@
" return_vbd_data=True, # Use VBD\n",
" dynamics_model=\"state\", # Use state-based dynamics model\n",
" dist_to_goal_threshold=1e-5, # Trick to make sure the agents don't disappear when they reach the goal\n",
- " collision_behavior=\"ignore\", # Ignore collisions\n",
+ " collision_behavior=\"ignore\", # Ignore collisions|\n",
")\n",
"\n",
"# Make env\n",
@@ -471,7 +513,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
@@ -534,7 +576,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 17,
"metadata": {},
"outputs": [
{
@@ -548,7 +590,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "Diffusion: 100%|██████████| 50/50 [00:01<00:00, 30.26it/s]\n"
+ "Diffusion: 100%|██████████| 50/50 [00:01<00:00, 30.09it/s]\n"
]
}
],
@@ -572,7 +614,7 @@
},
{
"cell_type": "code",
- "execution_count": 23,
+ "execution_count": 18,
"metadata": {},
"outputs": [
{
@@ -620,7 +662,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
@@ -628,7 +670,7 @@
"text/html": [
"
\n",
" \n",
- " GPUDrive with VBD-trajs |
"
+ " GPUDrive with VBD-trajs
"
],
"text/plain": [
""
@@ -644,7 +686,7 @@
},
{
"cell_type": "code",
- "execution_count": 38,
+ "execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
@@ -660,7 +702,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
@@ -668,6 +710,9 @@
"\n",
"gpudrive_sample_batch_np = to_numpy(gpudrive_sample_batch)\n",
"\n",
+ "# Save VBD predicted trajectories\n",
+ "gpudrive_sample_batch_np['pred_denoised_trajs'] = pred['denoised_trajs']\n",
+ "\n",
"# Save as pickle \n",
"with open(f'gpudrive_vbd_sample_{SCENARIO_ID}.pkl', 'wb') as f:\n",
" pickle.dump(gpudrive_sample_batch_np, f)"
@@ -675,7 +720,7 @@
},
{
"cell_type": "code",
- "execution_count": 41,
+ "execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
diff --git a/notebooks/01_features_deepdive.ipynb b/notebooks/01_features_deepdive.ipynb
index 96038a4a..915c712f 100644
--- a/notebooks/01_features_deepdive.ipynb
+++ b/notebooks/01_features_deepdive.ipynb
@@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
@@ -39,7 +39,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 134,
"metadata": {},
"outputs": [],
"source": [
@@ -60,7 +60,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
@@ -80,7 +80,7 @@
},
{
"cell_type": "code",
- "execution_count": 26,
+ "execution_count": 102,
"metadata": {},
"outputs": [],
"source": [
@@ -185,16 +185,16 @@
},
{
"cell_type": "code",
- "execution_count": 27,
+ "execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "dict_keys(['agents_history', 'agents_interested', 'agents_type', 'agents_future', 'traffic_light_points', 'polylines', 'polylines_valid', 'relations', 'agents_id', 'anchors'])"
+ "dict_keys(['agents_history', 'agents_interested', 'agents_type', 'agents_future', 'traffic_light_points', 'polylines', 'polylines_valid', 'relations', 'agents_id', 'anchors', 'pred_denoised_trajs'])"
]
},
- "execution_count": 27,
+ "execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
@@ -205,16 +205,16 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 93,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
- "dict_keys(['agents_history', 'agents_interested', 'agents_type', 'agents_future', 'traffic_light_points', 'polylines', 'polylines_valid', 'relations', 'agents_id', 'anchors'])"
+ "dict_keys(['agents_history', 'agents_interested', 'agents_type', 'agents_future', 'traffic_light_points', 'polylines', 'polylines_valid', 'relations', 'agents_id', 'anchors', 'pred_denoised_trajs'])"
]
},
- "execution_count": 28,
+ "execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
@@ -260,7 +260,7 @@
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": 74,
"metadata": {},
"outputs": [
{
@@ -269,7 +269,7 @@
"((32, 12, 8), (32, 12, 8))"
]
},
- "execution_count": 29,
+ "execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
@@ -280,7 +280,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 75,
"metadata": {},
"outputs": [],
"source": [
@@ -289,7 +289,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
@@ -1219,26 +1219,190 @@
"waymax_vbd_data['polylines'][:, :, 0], gpudrive_vbd_data['polylines'].squeeze(0)[:, :, 0]"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## **Outputs** (predicted trajectories)\n",
+ "\n",
+ "- What: The predicted actions for the future 80 time steps\n",
+ " - Features:\n",
+ " - `x`: x positions\n",
+ " - How can `x` be in a local coordinate frame?\n",
+ " - `y`: y positions\n",
+ " - How can `y` be in a local coordinate frame?\n",
+ " - `theta`: What is theta? Is this the yaw?\n",
+ " - `v_x`: Velocity x (is this used by the dynamics model?)\n",
+ " - `v_y`: Velocity y (is this used?)\n",
+ "- Notes:\n",
+ " - Tried setting `global_frame=False`, but that doesn't help\n",
+ " - ..."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 136,
"metadata": {},
"outputs": [],
- "source": []
+ "source": [
+ "waymax_vbd_data['pred_denoised_trajs'] = waymax_vbd_data['pred_denoised_trajs'].squeeze(0).cpu().numpy()\n",
+ "gpudrive_vbd_data['pred_denoised_trajs'] = gpudrive_vbd_data['pred_denoised_trajs'].cpu().numpy()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 137,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(32, 80, 5)"
+ ]
+ },
+ "execution_count": 137,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "waymax_vbd_data['pred_denoised_trajs'].shape"
+ ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "## **Outputs** (predicted trajectories)"
+ "#### $x$"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 128,
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
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+ "