1079 lines
488 KiB
Text
1079 lines
488 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from argparse import Namespace\n",
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"import logging\n",
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"import os\n",
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"from pathlib import Path\n",
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"import random\n",
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"\n",
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"logger = logging.getLogger('model_test')\n",
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"\n",
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"model_path = Path('EXPERIMENTS/models/models_20241203_15_18_45_hof3-2024-12-03/')\n",
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"eval_data = Path('EXPERIMENTS/trajectron-data/hof3-nostep-2024-12-03_test.pkl')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"from trap.frame_emitter import Camera\n",
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"\n",
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"maps = None\n",
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"\n",
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"path = Path(\"EXPERIMENTS/raw/hof3/\")\n",
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"calibration_path = Path(\"../DATASETS/hof3/calibration.json\")\n",
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"homography_path = Path(\"../DATASETS/hof3/homography.json\")\n",
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"camera = Camera.from_paths(calibration_path, homography_path, 12)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2682"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from trap.tracker import FinalDisplacementFilter, TrackReader\n",
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"\n",
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"\n",
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"reader = TrackReader(path, camera.fps, exclude_whitelisted = False, include_blacklisted=False)\n",
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"tracks = [t for t in reader]\n",
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"filter = FinalDisplacementFilter(2) # people don't just (disappear) out of nowhere\n",
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"tracks = filter.apply(tracks, camera)\n",
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"len(tracks)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Scene: Duration: 31556.25s, Nodes: 94, Map: No. 0.08333333333333333\n",
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"\n",
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"Loading from EXPERIMENTS/models/models_20241203_15_18_45_hof3-2024-12-03/model_registrar-100.pt\n",
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"Loaded!\n",
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"\n"
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]
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}
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],
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"source": [
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"# Choose one of the model directory names under the experiment/*/models folders.\n",
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"# Possibilities are 'vel_ee', 'int_ee', 'int_ee_me', or 'robot'\n",
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"# model_dir = os.path.join(self.config.log_dir, 'int_ee')\n",
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"# model_dir = 'models/models_04_Oct_2023_21_04_48_eth_vel_ar3'\n",
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"\n",
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"# Load hyperparameters from json\n",
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"import json\n",
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"import dill\n",
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"from trajectron.utils import prediction_output_to_trajectories\n",
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"from trajectron.model.online.online_trajectron import OnlineTrajectron\n",
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"from trajectron.model.model_registrar import ModelRegistrar\n",
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"from trap.prediction_server import create_online_env\n",
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"\n",
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"eval_device = \"cuda:0\"\n",
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"config_file = model_path / 'config.json'\n",
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"if not os.path.exists(config_file):\n",
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" raise ValueError('Config json not found!')\n",
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"with open(config_file, 'r') as conf_json:\n",
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" logger.info(f\"Load config from {config_file}\")\n",
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" hyperparams = json.load(conf_json)\n",
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"\n",
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"logger.info(f\"Use hyperparams: {hyperparams=}\")\n",
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"\n",
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"with open(eval_data, 'rb') as f:\n",
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" eval_env = dill.load(f, encoding='latin1')\n",
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"\n",
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"logger.info('Loaded data from %s' % (eval_data,))\n",
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"\n",
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"# Creating a dummy environment with a single scene that contains information about the world.\n",
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"# When using this code, feel free to use whichever scene index or initial timestep you wish.\n",
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"scene_idx = 0\n",
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"\n",
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"# You need to have at least acceleration, so you want 2 timesteps of prior data, e.g. [0, 1],\n",
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"# so that you can immediately start incremental inference from the 3rd timestep onwards.\n",
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"init_timestep = 2\n",
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"\n",
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"eval_scene = eval_env.scenes[scene_idx]\n",
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"print(eval_scene, eval_scene.dt)\n",
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"online_env = create_online_env(eval_env, hyperparams, scene_idx, init_timestep)\n",
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"\n",
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"# auto-find highest iteration\n",
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"model_registrar = ModelRegistrar(model_path, eval_device)\n",
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"model_iterations = model_path.glob('model_registrar-*.pt')\n",
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"highest_iter = max([int(p.stem.split('-')[-1]) for p in model_iterations])\n",
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"logger.info(f\"Loading model {highest_iter}\")\n",
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"\n",
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"model_registrar.load_models(iter_num=highest_iter)\n",
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"\n",
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"trajectron = OnlineTrajectron(model_registrar,\n",
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" hyperparams,\n",
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" eval_device)\n",
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"\n",
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"# If you want to see what different robot futures do to the predictions, uncomment this line as well as\n",
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"# related \"... += adjustment\" lines below.\n",
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"# adjustment = np.stack([np.arange(13)/float(i*2.0) for i in range(6, 12)], axis=1)\n",
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"\n",
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"# Here's how you'd incrementally run the model, e.g. with streaming data.\n",
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"trajectron.set_environment(online_env, init_timestep)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 36,
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"metadata": {},
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"outputs": [],
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"source": [
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"track = tracks[9]\n",
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"track = tracks[20]\n",
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"track = tracks[21]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 37,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"139"
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]
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},
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"execution_count": 37,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"len(track.history)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"from trap.tracker import Smoother\n",
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"\n",
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"\n",
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"t = track.get_with_interpolated_history()\n",
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"# node = t.to_trajectron_node(camera, online_env)\n",
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"\n",
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"smoother = Smoother()\n",
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"track_s = smoother.smooth_track(t)\n",
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"track_s.track_id += \"smooth\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"input_tracks =[\n",
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" t,\n",
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" track_s\n",
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"]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{PEDESTRIAN/97903: array([[ 2.1625, 7.7215, 0.894, 0.45824, -0.52067, -1.0788]])}"
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]
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},
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"execution_count": 40,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"eval_scene.get_clipped_input_dict(238525, hyperparams['state'])\n",
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"# eval_scene.nodes[0].first_timestep"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
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" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
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"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
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" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769: UserWarning: RNN module weights are not part of single contiguous chunk of memory. This means they need to be compacted at every call, possibly greatly increasing memory usage. To compact weights again call flatten_parameters(). (Triggered internally at ../aten/src/ATen/native/cudnn/RNN.cpp:968.)\n",
|
|
" result = _VF.lstm(input, hx, self._flat_weights, self.bias, self.num_layers,\n",
|
|
"/home/ruben/suspicion/trap/.venv/lib/python3.10/site-packages/torch/distributions/distribution.py:44: UserWarning: <class 'trajectron.model.components.gmm2d.GMM2D'> does not define `arg_constraints`. Please set `arg_constraints = {}` or initialize the distribution with `validate_args=False` to turn off validation.\n",
|
|
" warnings.warn(f'{self.__class__} does not define `arg_constraints`. ' +\n"
|
|
]
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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",
|
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"text/plain": [
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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|
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"text/plain": [
|
|
"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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fZsKE+Wlqmrjb1j97dnLddUIPgLFsxEOPV77ylfn4xz+e2bNn59BDD82tt96az3zmM3nb29420qsCAACoOXomYCxoaZma5ubjs3btH5I0ZMKEebtt3c96VrJoUXLggbttlQBUkRG/vNXq1atz3nnn5bLLLsvDDz+cmTNn5tRTT80HP/jBtLS07PDzTu0HAGCssQ88tuxqz5QYM0Bt2bixK2vW3J7x4w9Lc/Ok3bLO669P/uqvBm9wDkDtG87+74iHHrvKzjsAAGONfWCGy5gBatGaNX9MMpDx4+elMsppxJo1yX33Jc95zqiuBoDdZDj7vw3bfRcAAAAARsCECYelrW1OurquzcaNXaO8rmTDhmTjxlFdDQBVSOgBAAAAwG7R1NSZSZOelw0blmbNmttHdV2HH57ceuuorgKAKiT0AAAAAGC3mjBhXlpbZ2XVqt9k48bVo7KOlpakuTlZt25UFg9AlRJ6AAAAALDbNTdPSmfn87J+/b1Zu/aOUVnH/PnJ738/KosGoEoJPQAAAAAoRaVSycSJh6e5ec+sWvXb9PdvGOHlJ5MnJytXjuhiAahiQg8AAAAAStXSMjWdncdn7drbs379fSO67IMOSu6+e0QXCUAVE3oAAAAAULpKpZKOjuemUmlJV9f1KYr+EVv2nDnJ0qUjtjgAqpjQAwAAAICq0da2TyZOPDrd3Temt3fFiCxz772Tv/wlKYoRWRwAVUzoAQAAAEBVaWhoTmfncdm4cVVWr74txQikFYccktx55wgUB0BVE3oAAAAAUJXGjTso7e37pavrN+nvX7tLy5o0KenuTvpH7qpZAFQhoQcAAAAAVaupqSOdnc/P2rV37PJNzo84IrnttpGpC4DqJPQAAAAAoKoN3uT86FQqzenuvvEZX+6qpSVpbEzWrRvhAgGoGkIPAAAAAGpCW9usjB8/P11dv8nGjV3PaBnz5ye///0IFwZA1RB6AAAAAFAzGhvbMmnSC7Jhw/1Zt+6eYX++UkmmTk0efngUigOgdEIPAAAAAGrOhAnz09jY/owud3Xggck9w89LAKgBQg8AAAAAalJr696bXe5q9bA+e8ABgg+AeiT0AAAAAKBmNTa2pbPz+Vm//u5s2LBkpz+3116Dl7h6hvdEB6BKCT0AAAAAqGmVSiUTJx6VohjI6tULd/pzRxyR3HrrKBYGwG4n9AAAAACgLrS3z017+/5ZteqaDAz07sT8gzc2X79+NxQHwG4h9AAAAACgbjQ1daaz8/h0d/8uvb0rdzj/4Ycnt9026mUBsJsIPQAAAACoK5VKYyZNel56ex/M+vX37WDeZNq05KGHdlNxAIwqoQcAAAAAdWnChOekUmnc4X0+9tsvuW/72QgANULoAQAAAEDdamubk7a2/bJq1W8yMLBxm/Mdckjy5z/vxsIAGBVCDwAAAADqWnPzpHR0/FW6u6/Lxo1rtjrPHnska9cmfX27ubgkN96YXHPN7l8vQD0SegAAAABQ9xoamtPZ+fysW/fn9PQs3+o8RxyRLNz+lbBG1IYNyVe/Ohi0vOAFu2+9APWsqewCAAAAgLHpgQe+lDVr/pCk2GxqZbPnxWavi83eL9LQ0Jqmpj3S2Dg5LS1T0ty8Z1papqW5eWaamialqckhD56uUqmko+OYrF375/T3r8m4cQds8X5T0+AZHw8/nOy11+jWcuONye9/n7z5zUlb2+iuC2AssQcAAAAAlGLvvd/1jD/b17cqPT0PpK/v0fT2PpwNG+7N6tU3ZOPG7vT3b/3yRVu37ZCloWFcmpsnp7l5Spqb90pLy4y0tc1KU9N0oUqNGz/+kGzYsCRr1vwhEyY8Z4v3nvWs5NprRy/06O1NvvnN5KCDkne+c3TWATCW+Q0NAAAAjLp16+5NX9/DSZrS0NCYpCmVSmMqlabNHk993ZSGhpYnPrPlFbqbmyeluXnSqNW7cePGbNy4Kr29S9Pb+3D6+h5Od/d1efTRR7Nx46on5tr87JNNr7cVojz5ulJpTnPz5DQ1TU1r64y0ts5Ka+usNDVNFabsRm1tc9LbuzJdXTeko2NBKpUn/1sdckjypz8lhx46suv84x+T3/wm+du/TSZMGNllAzDIb1IAAABg1DU1TUxRbHzi0Zei6M3AQJFkY4qif7Ppg6+T/gwMbEzS/8T0YuigdFEMhg2bH6Te/P2tvd7atNWrb82GDUs2vTuMn6aSpCENDc1paGhPpdKexsZxaWxsT0PD+DQ2Tkhj4/gnXrekUmlNQ8OTj4GBZOPGR9PbuzI9Pcuzdu2f09f3eIpiXXYcmjy1jqShoSUtLdPT0jIzra37pL19vzQ37yNA2QktLVPT2Nierq7fpKPjuDQ0DG6zPfZI7r138J4bI3HpqYGB5Ac/SDo6kne/e9eXB8C2+e0HAAAAjLqWlr3S0jLKN0kYpmnTTnvGn+3v709/f1f6+lZm48ZVTzxflYGBtenvX50NG5amKHqeCHT6Ngt2Nm4W7PRlYGAw4CmKjdl+yNGYhoaWNDSMS2PjuDQ0TEhj47gnwqSmbNy4MqtX35rHHvt1+vsfz8DA2jz9niiVDJ410/zEmTTNTzxa09jY9sRZJ3PS3n5gxo9/9qieSVNNGhvHp6PjuHR3X5uJExeksXEw5TjyyOSGG5Ljjtu15a9aNXg5q5NPTmbP3vV6Adg+oQcAAADAMDU2Nj5xE/XJu2V9gyHLY+ntXZm+vkeeuHfJqmzc+HgGBh5PpZK0tEx5op7Bm70PejL0GBjYFMD0ZGCgNwMDPSmKvgwM9KanZ3V6ex9KV9cNKYre9Pf3pFJpeOLMh61dxitD66lUWtPcPC3jxj0rEyc+N+3tc9LUNCVNTZOedlmyatXQ0JTOzhekq+vaTJgwL01NnWloSGbNSpYufeZhxW23Jbfckpx5ZlIjmwKg5gk9AAAAAKrcYMiyZ1pa9kzy7BFffn//+vT2Lk9Pz4onzl5Zmb6+RzMwsP6JObZ+aa2kSH//2qxfvzyPP35FVqz4Xoqib+j+LA0NLWlsHPfEZcU2hSRNaWqanvb2fdPaOjeNjU1papqU1tbBy3M1NU0tJSypVCqZNOl56e6+KW1tc9LSsldmzUquuy7ZZ5/hhxa/+MXgv29/+8jXCsC2CT0AAAAAxrjGxva0t89Ne/vcXV7Wxo1rsmHDsvT2/iU9PQ+kt3dFiqI3m0KP/v716el5IKtX35DHHvvvFEVfBu/h0pdKpfLEfVAG75HS2jo748cf+kTYMxhMtLTsnfb2Z6WlZfqohCMdHc/NmjV/SFH0pbV17xx9dHLzzckxx+z8Mr73vWT//ZOjjx7x8gDYAaEHAAAAACOmqWlCJkx4doZ7Rkp/f3/Wr78769cvyvr196Sv79GsX39fVq68bOiMk6IoUhS9GRjYmKamcRk37pBMnHjM0P1HKpXmjBt3YMaPPywNDS3P+GeYMOE5Wbv2jgwM9Ka9fW722CNZvjyZPn3Hn/3GN5IFC5KDDnrGqwdgFwg9AAAAAChdY2NjJkx49hOByfb19CzPY49dm8cf/2kefvi7KYqeJIOhyGBA0pw99vgf6eiYnyRpaGjJhAlHpa1t7k6fHTJ+/LOzbt09WbduUQ488MBce20ybVpSqWz7M9/7nsADoGxCDwAAAABqSmvr9MyY8drMmPHap73X39+fxx+/Ig8++IV0d1+VJCmK/vT3r0t7+36ZPPnVaW5uT1PTpOyxx4lpbGzb5nrGjTsg69ffn7Vr78hRRz07t9yy7UtWXXVVsu++Ag+Asgk9AAAAgDFp7drkrruefF0Ug3/FXzxxz+5Nf9G/afrmWloGH62tT/676XlT0/bPBmB0NTY2ZurUl2Tq1JdsMb2npyfLln04K1b8e5IiAwO9KYr/k732OjVtbXtn/PhD0tHx9ESjvX3fbNiwLBs3/jGdnYdt9TJXa9YkS5Ykp58+ij8YADtF6AEAAACMSePHJ0ceOfzPFUXS15f09Aw+enuT7u4nX/f1bf1zm4coW5u+6b2nhi2bQpW2tifDlU0Byyjcx7tutba25oADPpHkE0PTHn3011my5CPp71+djRvXpK1t70ybdlrGjz8snZ0LhuZra5uVnp7GzJjxh/z+98/JnnsmjY1PLvs//iM59dTd+MMAsE1CDwAAAIBh2BREtLQkEyeO7ro2BSwbNgyGKz09yerVTz5fvDjp7x+sY/z4wTBk08H4rQUomy930+tKZctQpb39ydebH9ivR1Om/HWmTPnrJMnGjRuzdOnH88ADX8zGjWvT3Dw1M2e+PVOnnpKWlslpbZ2ZpJJDDrktN910eP7qrwaXMTAw+O+cOTOyfMPywReVJE+EW9Pbpuehhx7arT8XwFgm9AAAAACoUpsHLFuzxx7J8uXJqlXJihWD4Uiy5aW6Nr9k16ZlPvVsk97ewXClr2/wvebmwUdb22CYMn58Mm7c4Ott1fnU9ba0DAYo7e2Dnx03bnBatV76q6mpKfvtd3722+/8JMmSJZ/MX/7y+SxZ8pHssccrsu++56StbXYqlYZMnXpb7rvv8KxatTBHHX9U0p5k3yTHJpmSpDFJf5JHk+XXL09lciVZn9xy7S058pmcXgTATqsUxVN/zZWru7s7nZ2d6erqSkdHR9nlAADAqLMPzHAZM8Bo2nS5rtWrk66uwftVbHq96dJdTw1UthasbApRNl3yq6dn8KyUtrZkwoTBs1MmThx8PmHC4FklTz075ZFHkvXrnwxN2tu3PENl03zPRHv7YGg0Zcq2Q6Uk6elZmz/96dXp6bk7bW0H5dBDv5WkP2ed9fFc9K3PJ8cnOSLJdpaR3iS3Jrk2+fT5n87ZZ5/9zIoGGKOGs//rTA8AAAAAhrS0JFOnDoYBRTEYVGx6DAwMPvr7k40bn5y2+eunPoriyWCiKAZDlE1ByooVyb33Dt5UftN8m58J0t//5Fkom98vpVIZXG9r65PhSUfHYDDS2Dj478SJg6HGxIlPnnEyfvxg6FKpDIYpjz2W3Hnn0+/D0tiYzJkz+PnW1vE58sj/TpL8+c/vyM03H57/+q89c9G3b0ten2TWzmzUJAuSzEz+/iN/n4kTJ+aMM87Ytf9QAGyVMz0AAKBk9oEZLmMGRt+mA/CbH9Tf/PnmB/+f+nx703Z00/KRfG9HP9+OlrnpniGNjYM3TG9oGHxeqTz5b1PTltM2zbPp303vbevo01Nr3Vb9W6u3KAYv5/Xoo4OX93r88cEwpSgGa1+/fvCxKdDYVENTUzJtWjJjxuBjypTBQGSTSZOSPfccPMvk8cefnD5r1uDnkqTSWUlek8FLWg3X/Ul+mBRdVXVIDqCqOdMDAAAAIMn99w/+Nf8mwwkMNj/Qv/m/mx/Qb2x88sD/juav1ntZbM+xx5Zdwa7bPATZsCFZuTJZtChZsiS58cbBoGTzYKS3d/C/1+zZyWGHJTNnDr5eunTwM89/fnsyL88s8MgTnzswaW1tTU9Pz4j8jAA8SegBAAAA1K199x18MHZVKoOXtNp0E/YZM5J587Y9/8BAsmxZct11yc03D57tMTCQrFs3eEZI77gNyXG7WNRxSe+9vbu4EAC2RugBAAAAAE9oaBi8n8ecOVtOHxhIXvOadyQTk0zZxZVMSTIhecc73pH/9//+3y4uDIDNNZRdAAAAAABUu4aG5Mc/+Wpy5Agt8Kjkqxd/dYQWBsAmQg8AAAAA2BntSbZ//9ydNzFJ2wgtC4AhQg8AAAAA2BkNSRpHaFlNceF5gFEg9AAAAACAndGbZMMILWt9kp4RWhYAQ4QeAAAAALAzepPcO0LLuu+J5QEwooQeAAAAALATfvrTnyb3JOnbxQX1JbknufTSS0egKgA2J/QAAAAAgJ3wP//n/xy8vNUfd3FBf0yyITnttNNGoCoANjcqoccDDzyQv/mbv8mUKVPS3t6eefPm5eabbx6NVQEAANQcPRNA7Tp4zsHJtXnml6bqTXJtsu+0fUeuKACGNI30Ah9//PEcf/zxedGLXpRf/OIX2XPPPbNo0aLsscceI70qAACAmqNnAqhtd9xxRyotleR7Sf5XksZhfLg/g5/rShY/snhU6gMY60Y89PjkJz+ZWbNm5ZJLLhmaNnfu3JFeDQAAQE3SMwHUvrWr1mb8nuOTbyY5NUnrTnyoJ8m3kzySPPLgI6NaH8BYNuKXt/rJT36So48+Oq9//euz11575Ygjjsi///u/j/RqAAAAapKeCaD2jRs3Lo8seSRZluRLSX6ZZNU2Zl6V5BdPzPeX5JElj2Tq1Km7pU6AsWjEz/S47777ctFFF+Xss8/O//k//yc33XRT3ve+96WlpSWnn3760+bv6elJT0/P0Ovu7u6RLgkAAKBqDLdnSvRNANVo6tSpKTYWmTt3bu6/4f7kriQTkkxO0pbBG54/lmRNkseTWbNmZeljS8srGGCMqBRFUYzkAltaWnL00UfnuuuuG5r2vve9LzfddFOuv/76p83/oQ99KB/+8IefNr2rqysdHR0jWRoAAFSl7u7udHZ22gceI4bbMyX6JoBa8PWvfz1vectbnjb9C1/4Qs4888wkycqVK3P11VfnWc96VubNm7ebKwSoXcPpmUb88lYzZszIIYccssW0Zz/72Vm6dOtJ9rnnnpuurq6hx7Jly0a6JAAAgKox3J4p0TcB1ILTTz89RVE87bEp8EiS5z73uXnd616Xd7zjHSVWClDfRvzyVscff3zuuuuuLabdfffdmTNnzlbnb21tTWvrztztCQAAoPYNt2dK9E0A9eJ5z3te7r///tx000156KGHMmPGjLJLAqg7I36mxwc+8IHccMMN+cQnPpF77rkn3/72t/OVr3xli1QbAABgrNIzAYxd733ve5MkRVHkoosuKrkagPo04vf0SJKf/exnOffcc7No0aLMnTs3Z599ds4444yd+qzrGQMAMNbYBx57dqVnSowZgFpVFEWmT5+ehx9+OFOnTs2KFSvS0DDif5MMUHeGs/87KqHHrrDzDgDAWGMfmOEyZgBq1z//8z/n4x//eJLk17/+dV70oheVXBFA9Sv1RuYAAAAAwNa95S1vGXr+f//v/y2vEIA6JfQAAAAAgN3kgAMOyCGHHJIk+elPf5q1a9eWXBFAfRF6AAAAAMBu9M1vfjPnnntubr311owfP77scgDqSlPZBQAAAADAWHLkkUfmyCOPLLsMgLrkTA8AAAAAKMnAwEDZJQDUFaEHAAAAAOxmt99+e4499tjsvffeufDCC8suB6BuCD0AAAAAYDebMGFCbrjhhixfvjxf+cpXUhRF2SUB1AWhBwAAAADsZnPnzs38+fOTJIsWLcqtt95ackUA9UHoAQAAAAAleOMb3zj0/Jvf/GaJlQDUD6EHAAAAAJTgjW98YxoaBg/PXXrppW5qDjAChB4AAAAAUIL99tsv73rXu5IkK1euzG9/+9uSKwKofUIPAAAAACjJxz72sbS0tCRJLrzwwpKrAah9Qg8AAAAAKMkee+yRd73rXZkzZ05e8pKXlF0OQM1rKrsAAAAAABjLPvvZz+azn/1s2WUA1AVnegAAAAAAAHVB6AEAAAAAJSuKIu94xzsyZ86cPPTQQ2WXA1CzhB4AAAAAULKHHnooX/3qV7N06dJcccUVZZcDULOEHgAAAABQshkzZmT8+PFJkv/4j/8ouRqA2iX0AAAAAICSVSqVHHHEEUmSq6++OkVRlFwRQG0SegAAAABAFXj1q1+dJHn88cdz9913l1wNQG0SegAAAABAFXjBC14w9PynP/1piZUA1C6hBwAAAABUgcMOOywNDYOH6/7zP/+z5GoAapPQAwAAAACqQFtbW+bOnZskmTBhQsnVANSmprILAAAAAAAGnXfeedmwYUNe/vKXl10KQE0SegAAAABAlTj99NPLLgGgprm8FQAAAABUmf7+/qxevbrsMgBqjtADAAAAAKpEX19fzjrrrLS3t+cf//Efyy4HoOa4vBUAAAAAVIn169fns5/9bJLk1ltvLbkagNrjTA8AAAAAqBIdHR1pb29PkvzpT38quRqA2iP0AAAAAIAqMn369CTJmjVr0tXVVXI1ALVF6AEAAAAAVWRT6JEky5YtK7ESgNoj9AAAAACAKrLXXnsNPRd6AAyP0AMAAAAAqsiECROGngs9AIZH6AEAAAAAVWTcuHFJkkMOOST77LNPydUA1JamsgsAAAAAAJ502mmn5bnPfW6OPvroHHHEEWWXA1BThB4AAAAAUEVOOOGEHHjggWlpaSm7FICa4/JWAAAAAFBF/v7v/z577713Pv3pT+fRRx8tuxyAmiL0AAAAAIAqsXDhwnzmM59Jknzyk5/MBz7wgZIrAqgtQg8AAAAAqBLXXnvt0PN3vvOd+cY3vlFiNQC1R+gBAAAAAFViYGCg7BIAaprQAwAAAACqUH9/f9klANQcoQcAAAAAVInGxsah50IPgOETegAAAABAlZg5c2YmTZqUJOnp6Sm3GIAaJPQAAAAAgCrxmte8Jp/4xCeSJOvXry+5GoDaI/QAAAAAgCrS3t6eJNmwYUPJlQDUnqayCwAAAAAAnnTMMcfk05/+dA444ICySwGoOUIPAAAAAKgiH/jAB3L33Xfn8ssvL7sUgJrj8lYAAAAAUEUWL16c+++/Pw888EDZpQDUHKEHAAAAAFSROXPmJEl++tOfllwJQO0RegAAAABAFZk9e3aS5DOf+UyKoii5GoDaIvQAAAAAgCrynOc8J0lSFEW6urpKrgagtgg9AAAAAKCKHHbYYUPPV65cWWIlALVH6AEAAAAAVeSggw4aer58+fISKwGoPUIPAAAAAKgie++9d1pbW5MkixcvLrkagNoy6qHHv/zLv6RSqeSss84a7VUBAADUHD0TAE9VqVTy4he/OEnS19dXcjUAtWVUQ4+bbropX/7yl4duvgQAAMCT9EwAbMtee+2VJHn44YdLrgSgtoxa6LFmzZqcdtpp+fd///fsscceo7UaAACAmqRnAmB7Xv/61+fCCy/Mi170orJLAagpoxZ6nHnmmXnFK16RE088cbvz9fT0pLu7e4sHAABAvdvZninRNwGMRe3t7fnc5z6Xf/iHfyi7FICa0jQaC/3ud7+bhQsX5qabbtrhvBdccEE+/OEPj0YZAAAAVWk4PVOibwIYiyZNmpSlS5dm3bp1ZZcCUFNG/EyPZcuW5f3vf3++9a1vpa2tbYfzn3vuuenq6hp6LFu2bKRLAgAAqBrD7ZkSfRPAWDRz5swkycqVK9PV1VVyNQC1Y8TP9Ljlllvy8MMP58gjjxya1t/fn2uuuSZf+MIX0tPTk8bGxqH3Wltb09raOtJlAAAAVKXh9kyJvglgLFqyZMnQ88WLF+fwww8vrxiAGjLioceLX/zi3H777VtMe+tb35qDDz4455xzztN23gEAAMYSPRMAO+P+++8fer5o0SKhB8BOGvHQY+LEiTnssMO2mDZ+/PhMmTLladMBAADGGj0TADtj8zP8Jk+eXGIlALVlxO/pAQAAAADsmqOOOmro3k+b7u8BwI6N+JkeW3PVVVftjtUAAADUJD0TAE+1zz77ZM6cObnrrruyYsWKPPvZzy67JICa4EwPAAAAAKhCe+21V5JkxYoVJVcCUDuEHgAAAABQhSZMmJAkufPOO0uuBKB2CD0AAAAAoAo9+uijSZL169eXXAlA7RB6AAAAAEAVOuGEE5Ika9euLbkSgNoh9AAAAACAKnTIIYckSe64446SKwGoHUIPAAAAAKhCs2fPTpLcdttt5RYCUEOEHgAAAABQhbq6upIM3ttj03MAtk/oAQAAAABVaO+99x567hJXADtH6AEAAAAAVWjSpElDzxctWlReIQA1ROgBAAAAAFWos7Nz6PmCBQtKrASgdgg9AAAAAKAKjRs3buj5rFmzSqwEoHYIPQAAAACgCrW1tQ09X79+fYmVANQOoQcAAAAAVKHm5uY0NjYmSX7zm9+UXA1AbRB6AAAAAECVevWrX50k+dznPldyJQC1QegBAAAAAFXqzW9+c5Jk9erVJVcCUBuEHgAAAABQpTo6OpIka9asKbkSgNog9AAAAACAKrVkyZIkydq1a0uuBKA2CD0AAAAAoAoVRZG3ve1tSYQeADtL6AEAAAAAVWj9+vXp7+9PkmzYsKHkagBqg9ADAAAAAKpQd3f30PP169enKIoSqwGoDUIPAAAAAKhCm0KP9vb2fOlLXxJ6AOyEprILAAAAAACebuXKlUmS6dOn54wzzii5GoDa4EwPAAAAAKgy/f39+cQnPpFkMPQAYOcIPQAAAACgynznO9/Jz3/+8yTJlClTSq4GoHYIPQAAAACgihRFkY9//ONJkhNOOCGnnnpqyRUB1A739AAAAACAKnL33XfnzjvvTEtLS37yk5+ko6Oj7JIAaoYzPQAAAACgivziF79IMniWh8ADYHiEHgAAAABQRX75y18mSQ466KD8+c9/Tm9vb8kVAdQOoQcAAAAAVImNGzfm6quvTpJcfPHFOfTQQ3PfffeVXBVA7RB6AAAAAECVqFQqmTFjRo444oisW7cuSTJ79uySqwKoHW5kDgAAAABVorGxMffdd1/+8Ic/ZP78+ZkyZUrGjRtXdlkANcOZHgAAAABQZZYtW5YkmTVrVsmVANQWoQcAAAAAVJn7778/SbLvvvuWWgdArXF5KwAAAACoIuedd14+97nPJRF6AAyXMz0AAAAAoIp0d3enu7s7STJ37tySqwGoLUIPAAAAAKgiM2fOTJLMnz8/z3/+80uuBqC2CD0AAAAAoIrsvffeSZIpU6bkiCOOKLkagNoi9AAAAACAKrIp9HjggQdKrgSg9gg9AAAAAKCK9PT0JEmWLVtWciUAtUfoAQAAAABV5Nprr02SrFu3LqtXry65GoDaIvQAAAAAgCpy/fXXJ0nmzJkj9AAYJqEHAAAAAFSJ1atX55prrkmS/Pd//3dmzpxZckUAtUXoAQAAAABV4oorrkhfX1/233//HHjggWWXA1BzhB4AAAAAUCV+/vOfJ0le/vKXp1KppCiKkisCqC1CDwAAAACoAo899li+853vJEnGjx+fuXPn5owzzii5KoDaIvQAAAAAgCpw1VVXZf369Zk/f37mzZuX+++/P4sWLSq7LICa0lR2AQAAAABA8prXvCb33HNPHnnkkQwMDCRJ7rvvvpKrAqgtzvQAAAAAgCoxd+7cHHXUUfnWt76VJHnggQfS09NTclUAtUPoAQAAAAAlWrx4ca677rqh13/3d3+XL3zhC0mSadOmpbGxsazSAGqO0AMAAAAASlIURc4444w873nPy0UXXZTf/OY3+cpXvpJKpZLPf/7z+fOf/5ymJleoB9hZvjEBAAAAoCSXXHJJrrjiirS1teV//I//kblz5+bTn/50HnnkkbznPe8puzyAmiP0AAAAAIAS3HnnnXn/+9+fJPnIRz6SAw44IEly9tlnl1kWQE1zeSsAAAAA2M3Wrl2b173udVmzZk1OOOGEfOADHyi7JIC6MOKhxwUXXJDnPve5mThxYvbaa6+ccsopueuuu0Z6NQAAADVJzwRAURR517velT/96U+ZPn16vvvd77pvB8AIGfHQ4+qrr86ZZ56ZG264IZdffnn6+vrykpe8JGvXrh3pVQEAANQcPRMAV199dS699NI0Njbmu9/9bqZPn152SQB1o1IURTGaK3jkkUey11575eqrr84LXvCCHc7f3d2dzs7OdHV1paOjYzRLAwCAqmAfeGwbbs+UGDMA9eDiiy9OV1eXy1oB7ITh7P+O+nlzXV1dSZLJkydv9f2enp709PQMve7u7h7tkgAAAKrGjnqmRN8EUI/e9ra3lV0CQF0a1RuZDwwM5Kyzzsrxxx+fww47bKvzXHDBBens7Bx6zJo1azRLAgAAqBo70zMl+iYAANhZo3p5q3e/+935xS9+kd/+9rfZZ599tjrP1v5iadasWU7TBgBgzHCporFrZ3qmRN8EAMDYVhWXt3rPe96Tn/3sZ7nmmmu2u/Pe2tqa1tbW0SoDAACgKu1sz5TomwAAYGeNeOhRFEXe+9735rLLLstVV12VuXPnjvQqAAAAapaeCQAARs+Ihx5nnnlmvv3tb+fHP/5xJk6cmOXLlydJOjs7097ePtKrAwAAqCl6JgAAGD0jfk+PSqWy1emXXHJJ3vKWt+zw865nDADAWGMfeGzZ1Z4pMWYAABhbSr2nxyjeFx0AAKDm6ZkAAGD0NJRdAAAAAAAAwEgQegAAAAAAAHVB6AEAAAAAANQFoQcAAAAAAFAXhB4AAAAAAEBdEHoAAAAAAAB1QegBAAAAAADUBaEHAAAAAABQF4QeAAAAAABAXRB6AAAAAAAAdUHoAQAAAAAA1AWhBwAAAAAAUBeEHgAAAAAAQF0QegAAAAAAAHVB6AEAAAAAANQFoQcAAAAAAFAXhB4AAAAAAEBdEHoAAAAAAAB1QegBAAAAAADUBaEHAAAAAABQF4QeAAAAAABAXRB6AAAAAAAAdUHoAQAAAAAA1AWhBwAAAAAAUBeEHgAAAAAAQF0QegAAAAAAAHVB6AEAAAAAANQFoQcAAAAAAFAXhB4AAAAAAEBdEHoAAAAAAAB1QegBAAAAAADUBaEHAAAAAABQF4QeAAAAAABAXRB6AAAAAAAAdUHoAQAAAAAA1AWhBwAAAAAAUBeEHgAAAAAAQF0QegAAAAAAAHVB6AEAAAAAANQFoQcAAAAAAFAXhB4AAAAAAEBdEHoAAAAAAAB1QegBAAAAAADUBaEHAAAAAABQF4QeAAAAAABAXRB6AAAAAAAAdUHoAQAAAAAA1AWhBwAAAAAAUBeEHgAAAAAAQF0QegAAAAAAAHVB6AEAAAAAANQFoQcAAAAAAFAXhB4AAAAAAEBdGLXQ44tf/GL23XfftLW1ZcGCBfnd7343WqsCAACoOXomAAAYeaMSenzve9/L2WefnfPPPz8LFy7M/Pnz89KXvjQPP/zwaKwOAACgpuiZAABgdIxK6PGZz3wmZ5xxRt761rfmkEMOyZe+9KWMGzcuF1988WisDgAAoKbomQAAYHQ0jfQCe3t7c8stt+Tcc88dmtbQ0JATTzwx119//dPm7+npSU9Pz9Drrq6uJEl3d/dIlwYAAFVp075vURQlV8LuMNyeKdE3AQAwtg2nZxrx0GPlypXp7+/PtGnTtpg+bdq03HnnnU+b/4ILLsiHP/zhp02fNWvWSJcGAABV7dFHH01nZ2fZZTDKhtszJfomAABIktWrV++wZxrx0GO4zj333Jx99tlDr1etWpU5c+Zk6dKlGj52Snd3d2bNmpVly5alo6Oj7HKoAcYMw2XMMFzGDMPV1dWV2bNnZ/LkyWWXQpV6at80MDCQxx57LFOmTEmlUtmttfiO2zW2366zDXeN7bdrbL9dY/vtGttv19mGu6bM7VcURVavXp2ZM2fucN4RDz2mTp2axsbGrFixYovpK1asyPTp0582f2tra1pbW582vbOz08BjWDo6OowZhsWYYbiMGYbLmGG4GhpG5ZZ7VJnh9kzJ1vumSZMmjVaJO8V33K6x/XadbbhrbL9dY/vtGttv19h+u8423DVlbb+dPUlixLuqlpaWHHXUUbniiiuGpg0MDOSKK67IscceO9KrAwAAqCl6JgAAGD2jcnmrs88+O6effnqOPvroHHPMMfm3f/u3rF27Nm9961tHY3UAAAA1Rc8EAACjY1RCjze+8Y155JFH8sEPfjDLly/P4Ycfnl/+8pdPu1Hf1rS2tub888/f6iWvYGuMGYbLmGG4jBmGy5hhuIyZsWdXeqayGa+7xvbbdbbhrrH9do3tt2tsv11j++0623DX1Mr2qxRFUZRdBAAAAAAAwK5yp0QAAAAAAKAuCD0AAAAAAIC6IPQAAAAAAADqgtADAAAAAACoC1UXenzxi1/Mvvvum7a2tixYsCC/+93vyi6JKvWhD30olUpli8fBBx9cdllUkWuuuSavfOUrM3PmzFQqlfzoRz/a4v2iKPLBD34wM2bMSHt7e0488cQsWrSonGKpCjsaM295y1ue9r3zspe9rJxiKd0FF1yQ5z73uZk4cWL22muvnHLKKbnrrru2mGfDhg0588wzM2XKlEyYMCGvfe1rs2LFipIqpmw7M2Ze+MIXPu175l3veldJFTMW7cw4faqvfe1rTxu3bW1tu6ni6vNM+pQf/OAHOfjgg9PW1pZ58+blP//zP3dTtdVn3333fdr2q1QqOfPMM7c6/1gff6PV84yVYzPb2359fX0555xzMm/evIwfPz4zZ87Mm9/85jz44IPbXeZYOlYxWv3TWBl/yY634da+DyuVSv71X/91m8scK2NwtPqxsXSsaEfb8LHHHst73/veHHTQQWlvb8/s2bPzvve9L11dXdtdbjUcO6mq0ON73/tezj777Jx//vlZuHBh5s+fn5e+9KV5+OGHyy6NKnXooYfmoYceGnr89re/LbskqsjatWszf/78fPGLX9zq+5/61Kfyuc99Ll/60pdy4403Zvz48XnpS1+aDRs27OZKqRY7GjNJ8rKXvWyL753vfOc7u7FCqsnVV1+dM888MzfccEMuv/zy9PX15SUveUnWrl07NM8HPvCB/PSnP80PfvCDXH311XnwwQfzmte8psSqKdPOjJkkOeOMM7b4nvnUpz5VUsWMRTs7Tp+qo6Nji3G7ZMmS3VRxdRpOn3Ldddfl1FNPzdvf/vbceuutOeWUU3LKKafkj3/8426suHrcdNNNW2y7yy+/PEny+te/fpufGcvjbzR6nrF0bGZ722/dunVZuHBhzjvvvCxcuDA//OEPc9ddd+VVr3rVDpc7Vo5VjEb/NJbGX7Ljbbj5tnvooYdy8cUXp1Kp5LWvfe12lzsWxuBo9WNj6VjRjrbhgw8+mAcffDAXXnhh/vjHP+ZrX/tafvnLX+btb3/7Dpdd+rGTooocc8wxxZlnnjn0ur+/v5g5c2ZxwQUXlFgV1er8888v5s+fX3YZ1IgkxWWXXTb0emBgoJg+fXrxr//6r0PTVq1aVbS2thbf+c53SqiQavPUMVMURXH66acXJ598cin1UP0efvjhIklx9dVXF0Ux+J3S3Nxc/OAHPxia54477iiSFNdff31ZZVJFnjpmiqIoTjjhhOL9739/eUXBU2xtnD7VJZdcUnR2du6+oqrccPuUN7zhDcUrXvGKLaYtWLCg+N//+3+PcGW16f3vf3+x//77FwMDA1t93/h70kj1PGP12MzW9v+f6ne/+12RpFiyZMk25xmrxypGqn8aq+OvKHZuDJ588snFX//1X293nrE6BkeiHxvrx4p2Zr/v+9//ftHS0lL09fVtc55qOHZSNWd69Pb25pZbbsmJJ544NK2hoSEnnnhirr/++hIro5otWrQoM2fOzH777ZfTTjstS5cuLbskasTixYuzfPnyLb5zOjs7s2DBAt85bNdVV12VvfbaKwcddFDe/e5359FHHy27JKrEplN8J0+enCS55ZZb0tfXt8X3zMEHH5zZs2f7niHJ08fMJt/61rcyderUHHbYYTn33HOzbt26MsqDJNsep0+1Zs2azJkzJ7NmzcrJJ5+cP/3pT7ujvKo1nD7l+uuv3+J3RZK89KUv9bsig8cJLr300rztbW9LpVLZ5nzG39Y9k57HsZnt6+rqSqVSyaRJk7Y7n2MVTxpO/2T8bd+KFSvy85//fKf+yn4sjsGR6MfG+rGindnv6+rqSkdHR5qamra7rLKPnVRN6LFy5cr09/dn2rRpW0yfNm1ali9fXlJVVLMFCxYMnVZ10UUXZfHixXn+85+f1atXl10aNWDT94rvHIbjZS97Wb7xjW/kiiuuyCc/+clcffXVOemkk9Lf3192aZRsYGAgZ511Vo4//vgcdthhSQa/Z1paWp7WFPueIdn6mEmS//W//lcuvfTSXHnllTn33HPzzW9+M3/zN39TYqWMZdsap0910EEH5eKLL86Pf/zjXHrppRkYGMhxxx2Xv/zlL7ux2uox3D5l+fLl9km34Uc/+lFWrVqVt7zlLducx/jbtmfS8zg2s20bNmzIOeeck1NPPTUdHR3bnM+xiicNt38y/rbv61//eiZOnLjDyzONxTE4Uv3YWD5WtDP7fStXrsxHP/rRvPOd79zusqrh2Mn2IxmoYieddNLQ8+c85zlZsGBB5syZk+9///s7lXoDDNeb3vSmoefz5s3Lc57znOy///656qqr8uIXv7jEyijbmWeemT/+8Y91ea1cRse2xszmDcS8efMyY8aMvPjFL869996b/ffff3eXyRi3s99txx57bI499tih18cdd1ye/exn58tf/nI++tGPjnaZVUefMnK++tWv5qSTTsrMmTO3OY/xx+7Q19eXN7zhDSmKIhdddNF25/Ud8CT908i6+OKLc9ppp6WtrW27843FMagf23U72obd3d15xStekUMOOSQf+tCHtrusavh/v2rO9Jg6dWoaGxuzYsWKLaavWLEi06dPL6kqasmkSZPyrGc9K/fcc0/ZpVADNn2v+M5hV+y3336ZOnWq750x7j3veU9+9rOf5corr8w+++wzNH369Onp7e3NqlWrtpjf9wzbGjNbs2DBgiTxPcNuN5xx+lTNzc054ogjjNsn7KhPmT59un3SrViyZEl+9atf5R3veMewPmf8PemZ9DyOzTzdpsBjyZIlufzyy7d7lsfWOFbxpB31T8bftv3mN7/JXXfdNezvxKT+x+BI9mNj9VjRjvb7Vq9enZe97GWZOHFiLrvssjQ3Nw9r+WUcO6ma0KOlpSVHHXVUrrjiiqFpAwMDueKKK7b4qw3YljVr1uTee+/NjBkzyi6FGjB37txMnz59i++c7u7u3Hjjjb5z2Gl/+ctf8uijj/reGaOKosh73vOeXHbZZfn1r3+duXPnbvH+UUcdlebm5i2+Z+66664sXbrU98wYtaMxszW33XZbkvieYbd5JuP0qfr7+3P77bcbt0/YUZ9y7LHHbvG7Ikkuv/zyMf+74pJLLslee+2VV7ziFcP6nPH3pGfS8zg2s6VNgceiRYvyq1/9KlOmTBn2MhyreNKO+ifjb9u++tWv5qijjsr8+fOH/dl6HYOj0Y+NtWNFO7Pf193dnZe85CVpaWnJT37ykx2eabQ1pRw7KfMu6k/13e9+t2htbS2+9rWvFX/+85+Ld77zncWkSZOK5cuXl10aVejv//7vi6uuuqpYvHhxce211xYnnnhiMXXq1OLhhx8uuzSqxOrVq4tbb721uPXWW4skxWc+85ni1ltvLZYsWVIURVH8y7/8SzFp0qTixz/+cfGHP/yhOPnkk4u5c+cW69evL7lyyrK9MbN69eri//v//r/i+uuvLxYvXlz86le/Ko488sjiwAMPLDZs2FB26ZTg3e9+d9HZ2VlcddVVxUMPPTT0WLdu3dA873rXu4rZs2cXv/71r4ubb765OPbYY4tjjz22xKop047GzD333FN85CMfKW6++eZi8eLFxY9//ONiv/32K17wgheUXDljyc58t/3t3/5t8U//9E9Drz/84Q8X//Vf/1Xce++9xS233FK86U1vKtra2oo//elPZfwIpdtRn/LU7XfttdcWTU1NxYUXXljccccdxfnnn180NzcXt99+e1k/Qun6+/uL2bNnF+ecc87T3jP+tjQSPc9f//VfF5///OeHXo+lYzPb2369vb3Fq171qmKfffYpbrvtti2+E3t6eoaW8dTtN5aOVYxE/zSWx19R7Pj/4aIoiq6urmLcuHHFRRddtNVljNUxOFL92EEHHVT88Ic/HHo9lo4V7WgbdnV1FQsWLCjmzZtX3HPPPVvMs3HjxqHlbL4Nq+XYSVWFHkVRFJ///OeL2bNnFy0tLcUxxxxT3HDDDWWXRJV64xvfWMyYMaNoaWkp9t577+KNb3xjcc8995RdFlXkyiuvLJI87XH66acXRVEUAwMDxXnnnVdMmzataG1tLV784hcXd911V7lFU6rtjZl169YVL3nJS4o999yzaG5uLubMmVOcccYZdbvzzY5tbawkKS655JKhedavX1/83d/9XbHHHnsU48aNK1796lcXDz30UHlFU6odjZmlS5cWL3jBC4rJkycXra2txQEHHFD8wz/8Q9HV1VVu4YwpO/PddsIJJwztTxVFUZx11llDPdy0adOKl7/85cXChQt3f/FVYkd9ylO3X1EUxfe///3iWc96VtHS0lIceuihxc9//vPdXHV1+a//+q8iyVb3zY2/LY1EzzNnzpzi/PPP32LaWDk2s73tt3jx4m1+J1555ZVDy3jq9htLxypGon8ay+OvKHb8/3BRFMWXv/zlor29vVi1atVWlzFWx+BI9WNP/cxYOla0o224rfGZpFi8ePEWy9n0mWo5dlJ5ojAAAAAAAICaVjX39AAAAAAAANgVQg8AAAAAAKAuCD0AAAAAAIC6IPQAAAAAAADqgtADAAAAAACoC0IPAAAAAACgLgg9AAAAAACAuiD0AAAAAAAA6oLQAwAAAAAAqAtCDwAAAAAAoC4IPQAAAAAAgLog9AAAAAAAAOrC/w+srn6Vie8K9QAAAABJRU5ErkJggg==",
|
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"text/plain": [
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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|
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"text/plain": [
|
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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|
|
"text/plain": [
|
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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|
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"text/plain": [
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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|
|
"text/plain": [
|
|
"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
|
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"data": {
|
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"image/png": 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XstdeIzJ48J6pqanbZP+2tjVZvPjbWb9+cZJkwIDpGTXqLamtrc2KFcmf/1w+1uOPl6fKOuqoZL+/KiWvTzJl6zXslG8neTwp2nb+VsSaNWvyj//4j7nkkku2uQD3Zz7zmQwYMCDvf//7q37kRl/R3NycZ555JpMmTcoTTzyR7373u/nP//zPLFq0aJP99t133zz88MP+3gGqnNADAAB6EdfAdNaGNvP97zelpmZoXjD7yyZrO+woDNh8/62954XbNtyg798/GTAgaWjY+NiwrX//8j4DB268kd8XrFmTLFxYDiiWLEmeeSZZvrw8KiQp/9nSkqxdW/5zK2tod5znQYOSkSOT4cOTcePK4cWoUcmQIeUwpb19XdasmZf29lUveG9dGhompq5uj+3UOC9Llvx3iqItpVJtRo16YwYN2jtJ+e967tzyQumDByd/+lPS3JycdFIyYUL5/WeeeWZuvPvG5J0pDzzprMeS3JSc+opTN1mPY0fOOeecfPe7381hhx2WW2+9NXV1dbn00ktz7rnnZr/99nsRhdCb3XrrrfnOd76TZcuW5Ze//GVOPPHEnHfeeTn55JMzcODAvPvd787++++f448/Pvvtt59RIABVQugBAAC9iGtgOmtDm3nssaaMGzd0k5ECdM6GkRVPPJEsXlweXfHssxtHVGweCLW3l0dcrF+/cURLUg6EGhvLQcXIkcno0eWfNyylUF9fDi02POrrd66+9vbWrFv3RFpbn3tBHQ0ZMGBqamsH7fD9TU0zs2zZT5+vYWzGjj03/foN7nh9/frkoYfKv++kScmttyarVyenn17+PTbXMLQh60etT85O51YJfSrJ9cmw2mF5btlzO9z9hf7yl7/kqKOOyrJly3LiiSdm2bJlmTVrVg466KDce++96dfPcqV91erVq/PLX/4yZ555ZsaNG5d3v/vdueyyyzpeHzlyZF75ylfmla98ZV71qldl//33T2tra37961/nJz/5SWbPnp0FCxZkxowZ+fa3v92x2DsAPY/QAwAAehHXwHSWNrNRW1t5dMXjj5cDi0WLkhUrtj2C5YUjXoqifNO/f//y6IqhQ8s3+seO3RhWbD5yplQq7ztkSHn/gQO3nD5rV7S0LM/atY+nKFqf/xfqtenff1Lq6obv9Gc899xv8txzt6RUqsmAAXtl1Kg3bbKQdVIOdubMKY/SeclLkhtvLJ/LM88s/17bUzekLq3DWpM3JtlR8yuS/CnJL5KxA8duMTXRzrrrrrvy6le/umNR6+HDh+eb3/xmzjjjjBf1eVSPm2++Oe9617syf/78JMnQoUMzfvz4zJ8/f5N1QJLkrLPOyh133JElS5Zssn3cuHH57W9/m+nTp++2ugHoHKEHAAD0Iq6B6axqazNtbeWRFvPnJwsWlKeJ2jCCItkYQGxYFDzZGEaUSuWpn8aNS8aM2Ti6orl545oi2+r1bggwhg7tngBjR4qiyLp1C9LSsrRjW21tY/r3n/L8Quw7b/ny3+XZZ3+ZJBky5GUZPvy0LYKOpBwQLViQ7LFHed2P//3f8nk966zytFY7a+KUiVnQtCAZnuSYJFOz6QLna5LMSnJ/knXJ/37rf/P617++U7/T5m666aacc845Oeqoo3L11Vdnzz333KXPo3qsX78+V111VS6//PIsWLAgSTJq1Ki87GUvy9FHH53f/e53uf3229PS0pKiKDJ27Ngce+yxWbx4cY455pi8613vytSpUyv8WwCwPUIPAADoRVwD01k9tc2sWZM8+ujGAGPZsq0HDi8caZGUp4YaOTLZc8/yFEtTppRvwK9dWw4vmprKoze213stlTZOMdXYWH5/T5v1qCjas3btvLS2Lu/YVl6HY9SLWndg5co/ZunSHyRJBg06ICNHvn6rQUdSHgmzZEkyfnw5IPrBD8rBUmfDjs2deeaZufGWG5P6lNf5KCVpT7I+yarkvrvuy8EHH/ziD7CZlpaW1NW9mAVF6AvWrVuXb3zjG/n0pz+dhQsXZvr06Zk7d26SZObMmXnlK1+ZN7zhDfmv//qvfOpTn8qnP/3p1NXV5W//9m9zySWXZNy4cXnkkUey7777WgsEoIcRegAAQC/iGpjO6u4209ZWvkn+2GMbb5Zv3nPcfNqnoiivUzFuXDm0mDKlHGK88B78unXlAGNnQoykPO3UC0OM3nYPckPI0dJSXsOiVKpJ//5Tt7vY+I6sX/9sFi36etraVqd//wkZM+Yd2ww6kmTevGTp0mTy5PI6Iz/8Yfn8n3VWMmzYiy4DerR169blrrvuSlNTU04//fSO7bNnz87ee++dUqmUe+65Jx/72Mfyy1+WR0gNHz4873//+3PFFVfkoosuymc+8xnBB0APIvQAAIBexDUwndWZNrNyZTJ3bnkExpNPlsOGzb3wvl5RbBx5MWVK+Wb59OmbhhcbtLZuDDGam8thyeajOF6ooaF8o723hhg7UhRF1q6dn9bWZc9vKe1yyJEkbW1tWbLkqqxduyD9+g3K2LHvTn1943bf8+ST5amspkwpr1Hys5+V1zs59dRy+AGU3X777fnwhz+cWbNmbbL90ksv3WRRdAAqS+gBAAC9iGtgOmtDm/l//68p/ftv2WZeOApj4MDy6Itp05K99y6vebEt7e3lUKSpKVm+PGlp2XKfFwYVG6aTGjasHGJsZ8BB1Vq/fknWrn0ySVIqldLQMDn19SO75LObmmZm2bKfJklGj35DBg8+YIfvWbq0HHBNmlQeafO73yUPP5wcf3w5vAK2tG7duvz93/99vvzlL2+y/XOf+1z+4R/+oUJVAfBCQg8AAOhFXAPTWTvbZoqivC7G8uXlIGPVqk1ff+Fi4ElSU1MOLzaMxrB0wpZaW1dkzZq5SdpTFEXq68ekoWFCl02D09q6MgsXfi2trcszcOA+GTXqLdudvmqDVauSBx8sj+KYPj2ZMyf59a+TQw5JDj20S0qDqnf99dfnyiuvzKte9apcdtllKZVK+c1vfpNjjjmm0qUB9Hmd6TP1sGXdAAAA2FkPPpgMGLDx+dbuuw8YUA4wJk0q/1xtU0p1t/b29Vm9+i9pb1+bJOnXb0gGDz4wpVLXDmt57rnf5Lnnbk5NTX3GjXtXGhrG7tT72tqS++8vr6fyV39VHqnzjW+UR3m8+91dWiJUvTe+8Y15wxvekFKplMcffzzf+ta3cu655+bBBx/MkCFDKl0eADtJ6AEAANBL7b9/sseuLRfBZjasy9HS8kySIjU19RkwYO/U1g7Y4Xs7q61tfZ566ktpbV2eQYMOzLRpn+rU++fMSZYtSw4+uDzV2P/+b3lkz/nnl58Dnbdh1NZ//Md/5MYbb8zjjz+eL3/5y7n44osrXBkAO8tlEAAAQC/VF9fQ6A4tLc9l7drHOp43NEzOgAFTuu14K1Y8mKef/n5KpfqMHfu3GTBgz069/7nnkj/9Kdlrr/Lj3nuT++4rL1I+fnw3FQ19zJo1a7J2bXmE19y5c1MURZdNYwdA9xJ6AAAA0KdsmLKqKNYlSfr12yODBx/crTc029rasnTpd7JmzaPp339SJk/+5E6t1fFCRZHMmpX0758cfXTS3Jx87WvlET+msoKuNWbMmPzgBz/I6aefnquuuirTp0/P//t//6/SZQGwE4QeAAAAVLVNp6zKC6as6t/tx16//tksXPiVtLevz8iRZ2TcuPNf1Oc89VQyf355Kqv+/ZOf/Sx5+unkne80lRV0l9e97nX50pe+lAsvvDAf/ehHU1dXl3/4h3+odFkA7ECpKIqi0kW8UGdWYQcAgGrgGpjO0mZ2bMOUVRumpGlomJz6+pG77fhNTTOzbNlNqa0dnHHj3pv6+sYX9TmtreXpq8aMSaZOTZ54IvnpT5NXvzrZZ58uLhrYqre//e351re+lSSZOXNmDjvssMoWBNAHdeb6V+gBAAAV5hqYztJmtvTCKauKokhd3R7p33/abp+Df/Hia7N69SMZMGCvjB59bqensHqhJ54oj/A47LCkpib54Q+TUik588zyc2D3WLduXYYNG5a1a9dm1KhReeyxxzJ48OBKlwXQp3Tm+tcgWAAAAHqd8pRVjz8/ZVVpt05Ztbm2tjVZsOCLaWtrzvDhp2bs2Lfu4ueVR3eMHZsceWR5Wquf/jQ57bRkwoQuKhrYaQ0NDbn44otz6aWXpl+/fhk4cGClSwJgO4QeAAAA9AotLcuydu3jKYr2lEo1aWiYnAEDplasnjVr5mXRoqtSKvXL+PHvTkPD2F3+zEWLknnzkkMPTerrkxtuSNrbk/e+twsKBl60D3zgA/nsZz+bRYsW5dZbb83xxx9f6ZIA2AahBwAAAD1SW9varFnzl7S3r0+S1NUNz+DBB+/2Kas299xzt+W5525OXd2oTJ582S5NYbVBUSSzZiVDhyZHHZUsXZpcf31y6qnJ5MldUDSwS4YNG5Z3vOMd+fKXv5x/+7d/y6GHHpoHH3wwxx57bKVLA2Az1vQAAIAKcw1MZ1VrmymKtqxZ82haW5tSnrKqfwYO3Ds1NfWVLi1JsmjRt7NmzewMGvSyjBnzhi773Obm5A9/SA4+OBk0KLn55mTJkuTss63dAT3JnDlzss8++6QoikyZMiVPP/10fv/732f//fevdGkAVc+aHgAAAPR4RVFk/fpFWbfuqSRJqVSbAQOmZ+DAvStc2UZtbevz1FNfSmvrsxk+/NSMG/e2Lv38OXOSVauSY45J1q1Lvva18sLlJ57YpYcBusBee+2V173udfnxj3+cfv36ZdWqVTnjjDMyc+bM7LHHHpUuD4DnGekBAAAV5hqYzurNbaa1tSlr1jyaomhPkjQ0jEt9/fiKT1m1ufXrn86CBV9Okowb984MGDCpSz+/rS2ZOTOZMiUZNy55+OHkN79JzjsvsUYy9FwPPfRQWltbM3HixBxyyCF54okncvLJJ+fHP/5xl0x1B8DWGekBAABAj9Devi6rV8/uWJejX7+hGTz4oJRKPfPm4MqVf8zSpdeltnZoJk36h/TrN7jLj/HMM8mf/5wccURSV5fcdFNSW5tccEGXHwroYjNmzOj4+cYbb8wRRxyRn//857nkkkvy6U9/uoKVAbCB0AMAAIAus2Fdjra2FSmKIjU1DRkwYO/U1vavdGnbtWzZL9LU9Js0NEzK5Mmf7LZ/sf3nPyetreXprNauTb7+9eT445N99umWwwHdaOzYsal5fuGdK664InvvvXfOP//8yhYFgNADAACAF68oiqxbtyDr1y95foqqmgwYMC39+vWcdTm2Z9Gib2XNmjkZMuSITJt2ebcdp709ufvuZPr0ZMyYZPbs5JZbkne8I+nfs/MgYBsGDx6cD33oQ/nc5z6X9vb2vOc978mJJ56YPffcs9KlAfRp1vQAAIAKcw1MZ1W6zbS0LMvatY+nKIqUSqXU1++Zhoaxu72OF6utrS1PPfUfaW1dlhEjTk9j4+HderympuQPf0j+6q+S+vrkF79IVq9OzjyzWw8L7CYPPPBAjjnmmKxatSp77713Zs6cmcbGxkqXBVBVrOkBAABAl2ltXZk1a+akKNqSJHV1IzJ48ME9bvHxHVm//tksXPiVtLevz7hx78iAAVO7/ZiPPZY0NyeveEV5tMc3v5m8/OXJwQd3+6GB3eRlL3tZ/vCHP+S4447LX/7yl7zvfe/Ld77znUqXBdBnCT0AAADYrvb2VRk0aEZqanpnF3L16kezePFVqakZkAkTPtQti5NvriiSWbOSkSOTl70sWb48ueaa5JxzytuA6jJ9+vR8//vfzzHHHJNrr702AwYMyH/9139VuiyAPqmm0gUAAADQs9XXj+mVgcfy5Xfnscc+mmXLfpzJkz+ZKVM+tlsCj/Xrk9/+Ntl772Ty5OThh5P/+Z/k/e8XeEA1O/LII/P2t789SfLNb34zv/rVrypcEUDf1PuuWgEAAGA7nn76R1mx4vcZOPCl3bo4+dY880x5kfKjj05qa5Obby6v3/G3f7tbywAq5Bvf+EaKoshVV12Vs88+Ow888EDGjx9f6bIA+hQjPQAAAOj12trasnDh1/PYYx9Nbe2ATJt2ecaOfetureHRR5NFizYGHtdemwwblpx++m4tA6iwL3/5yznooIPy9NNP57TTTktra2ulSwLoU4QeAAAA9FptbevzxBOfy/z5l2XIkCMybdrlGT78xN1ex333JfX1yYwZ5emtvvzl8uLlhx2220sBKmzAgAH5/Oc/n5qamtx33325+OKLK10SQJ9ieisAAAB6nfXrn82CBf+RJNlzzwvS0DC2InW0tiZ33ZUceGDS2JgsXZp8//vJO9+ZDBxYkZKAHuDYY4/NmDFjsmjRovzrv/5rTjvttBx77LGVLgugTxB6AAAA0GusWvWXLFlyTWprB2fSpH/YLQuTb0tzc/KHPyRHHpn061desPzuu5MLL0xqzKsAfVpDQ0O+973v5ZWvfGWKosgXvvAFoQfAbtItl2FPPfVU3vrWt2bEiBEZMGBAZsyYkXvvvbc7DgUAANDr6DN13nPP/SaPPfbRPPfcLzN58iczefLFFQ08nnoqmTMnOfbYcuBxxx3Jn/9cHuEh8ACS5BWveEUuuuiiJMmNN96Y8ePHZ+bMmZUtCqAP6PKRHs8991yOPvroHHfccfn5z3+eUaNGZc6cOdljjz26+lAAAAC9jj5T5yxZ8oOsWvVABg7cP9OmXV7pcpIkjzxSDjoOOaT8/Ec/SgYPTs46q7J1AT3PF77whaxatSpf//rXs2jRopxxxhmZN29eGhoaKl0aQNXq8tDjs5/9bCZOnJirr766Y9vUqVO7+jAAAAC9kj7TjrW1tWXRov/K+vULMmzY8T0m7CiK5N57kwkTknHjytuuvTY54IDkZS+raGlAD1UqlXLllVdmwIAB+eIXv5jjjz8+dXV1lS4LoKp1+aDbH/3oRzn00EPzhje8IaNHj87LX/7yfP3rX+/qwwAAAPRK+kzb1ta2JvPnfzbz5388w4Ydm2nTLs/w4a+udFlJyguW//a3yUtfWg482tuT//zP8vRWAg9ge2pra3P55ZensbEx1157bX784x9XuiSAqtblocdjjz2WK6+8MnvttVd+8Ytf5IILLsgHPvCBXHPNNVvdf926dWlubt7kAQAAUK0622dKqr/ftG7d4jz22KV54onPZdy4d2TatMszePABlS6rw4oVyZ13JkcdVZ7Gau3a5ItfTM4+O5k8udLVAb3B4MGDc8EFFyRJPv3pT6coigpXBFC9SkUXf8vW19fn0EMPzZ133tmx7QMf+EDuueee3HXXXVvsf9lll+UTn/jEFtubmpoydOjQriwNAAB6pObm5jQ2NroG7iM622dKqrfftHLlH7N06XWprR2aCRM+kNraAZUuaQuLFiULFiSHHpqUSsmzzybf+U7y7ncn/ftXujqgN1myZEkmTpyYlpaW3HTTTTnttNMqXRJAr9GZPlOXj/QYN25c9ttvv022vfSlL80TTzyx1f0vvvjiNDU1dTyefPLJri4JAACgx+hsnympvn7Ts8/enMce+2iamn6XyZM/mcmTP9IjA4+5c5Ply5PDDisHHk88kXz/+8n73y/wADpvzJgxGTx4cJLk8st7xlpFANWoyxcyP/roozN79uxNtv3lL3/J5G2M+W1oaEhDQ0NXlwEAANAjdbbPlFRPv2nx4u9m9eqHM2TIwT1mcfJteeCBZPjw5CUvKT9/5JFk5szk+dlpAF6UU045Jddee23uueeeLFq0KOPGjat0SQBVp8tHenzoQx/K3XffnU9/+tOZO3duvvvd7+a//uu/cuGFF3b1oQAAAHqdvtZnamtry5NP/nsee+yj6d9/cqZNuzyjRp1V6bK2qSjK63dMnJhMmlTedu+9yUMPJeedV9nagN7v/e9/f5KkKIpceeWVFa4GoDp1+UiPww47LDfccEMuvvjifPKTn8zUqVPz7//+7znnnHO6+lAAAAC9Tl/pM61f35SFC7+Y9va1GTv2HRk4cHqlS9qhlpZy4HH44cmA52fbuuOO8hRXb3xjRUsDqsRhhx2W0aNHZ+nSpfnLX/5S6XIAqlKXhx5J8rrXvS6ve93ruuOjAQAAer1q7jOtWTMvixZdlZqa+owf/4HU1zdWuqSdsmJFeUqrY45JamvL226+ufxnlf5VARVQKpXy7ne/O5/61KeyfPnySpcDUJW6fHorAAAA+p7ly+/OY499NM88c1MmT74sU6Zc0msCj8WLy2t2vDDw+MlPyqM9TjyxsrUB1edtb3tbkuTmm2/O4sWLK1wNQPXplpEeAAAA9A1PP31DVqyYlYED9+nxi5NvzWOPJatXl6e02uB//ieZMiU59NCKlQVUsZe85CU54ogj0tzcnOuvvz7nn39+hg4dWumyAKqGkR4AAAB0SltbWxYsuDKPPfbR1NYOzrRpn8rYsW+tdFmd9sc/lv884ICN2667Ltl7b4EH0L1++ctfZsKECfngBz+Y//3f/610OQBVRegBAADATmltXZn58z+b+fM/nmHDjs20aZdn+PDeOf/TPfckI0cm06Zt3HbNNcnBBycHHli5uoC+YejQoXn1q1+dJPn2t79d4WoAqovQAwAAgO1at25xHnvs0jz55L9m3Lh3ZNq0yzN48AE7fmMP1N6e3HFHeTTH2LEbt3/zm+U1PfbZp3K1AX3LOeeck1KplNtuuy3z58+vdDkAVcOaHgAAAGxVc/N9eeaZH6Zfv+GZPPmjqa2tr3RJu2T9+uSuu5Ijj0zqn/9V2tuTr389OfnkZNKkytYH9C33339/SqVSiqLId77znXzsYx+rdEkAVcFIDwAAADbx9NM/yWOPfTSrVj2UyZM/mUmT/r9eH3g0NyczZybHHrtp4PG1ryWnnirwAHa/GTNmpL29PUl5iquiKCpcEUB1EHoAAACQtra2LFz49Tz22EdTKtVk2rTLM27c+amtra10abts6dJk9uzk6KOTmud7we3tyVe/mpx1VjJ+fGXrA/qmqVOn5qCDDkqSzJkzJ/fff3+FKwKoDqa3AgAA6MPa2tZkwYIvpq2tOaNGvSnjx7+r0iV1qccfT1auTA47bOO29vbkK19J3vKW8mLmAJXypje9KX/4wx+SJD/+8Y9z8MEHV7gigN7PSA8AAIA+rK1tZcaOPS/Tpl2eIUMOrHQ5XerPf05aW5MDXrDm+obA49xzBR5A5b3pTW9KzfND0I466qgKVwNQHYQeAAAAfVh9/ag0NIytdBld7oEHkiFDkpe8ZOO2FwYew4ZVqjKAjaZNm5b3ve99SZKLLroora2tFa4IoPcTegAAAFA1iiK5665k4sRkzz03bm9vT770JYEH0PNcdtllGTFiRObNm5dZs2ZVuhyAXs+aHgAAAFSFtrbkd79LDjkkGTRo4/YNgcd55wk8gJ5njz32yHXXXZd99tknkyZNqnQ5AL2e0AMAAIBeb+3a5O67k6OPTurqNm4XeAC9wYknnljpEgCqhtADAACAXq2pKXnooeSVr0xKpY3bBR4AAH2P0AMAAIBea8mS5IknkmOO2XT7hkXLBR4AAH2L0AMAAIBeaf78pLk5OeywTbdvCDwsWg4A0PcIPQAAAOh1Zs9OamqSGTM23d7envznfyZveYvAAwCgLxJ6AAAA0Ks8+GCyxx7JxImbbm9vT7761eSNb0xGjqxMbQAAVJbQAwAAgF6hKJJ77kmmTk1Gjdr0tfb25OtfT17/+mT06MrUBwBA5Qk9AAAA6PGKIrnjjuRlL0uGDNny9auuSk45JRk7dreXBgBADyL0AAAAoEdraUl+97vkyCOThoYtX7/66uTEE5MJE3Z/bQAA9CxCDwAAAHqsVauSe+9Njj02qa3d8vVvfzt5xSuSyZN3f20AAPQ8Qg8AAAB6pGefTWbPLocapdKWr193XXLEEcn06bu/NgAAeiahBwAAAD3OokXlx5FHbv31669PZsxI9tln99YFAEDPJvQAAACgR3n88WT16uTgg7f++g03JC95SXLAAbu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|
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"text/plain": [
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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|
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"text/plain": [
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
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"image/png": 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|
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"text/plain": [
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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|
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"text/plain": [
|
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
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"image/png": 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|
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"text/plain": [
|
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"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
},
|
|
{
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"data": {
|
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"image/png": 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",
|
|
"text/plain": [
|
|
"<Figure size 2000x500 with 2 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from matplotlib import pyplot as plt\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"import trajectron.visualization as vis\n",
|
|
"# print(node.data.data)\n",
|
|
"\n",
|
|
"\n",
|
|
"for i in range(len(t.history)):\n",
|
|
" timestep = i+1\n",
|
|
" input_dict = {}\n",
|
|
" # print(node.data.data[-1])\n",
|
|
" for t in input_tracks:\n",
|
|
" node = t.to_trajectron_node(camera, online_env)\n",
|
|
" node.first_timestep = 0 # reset loaded track timestep\n",
|
|
" input_dict[node] = np.array(object=[node.data.data[i]])\n",
|
|
" # print(node.data.data[i])\n",
|
|
"\n",
|
|
" dists, preds = trajectron.incremental_forward(input_dict,\n",
|
|
" maps,\n",
|
|
" prediction_horizon=50,\n",
|
|
" num_samples=10,\n",
|
|
" full_dist=False,\n",
|
|
" gmm_mode=True)\n",
|
|
" prediction_dict, histories_dict, futures_dict = prediction_output_to_trajectories({timestep: preds},\n",
|
|
" eval_scene.dt,\n",
|
|
" hyperparams['maximum_history_length'],\n",
|
|
" hyperparams['prediction_horizon']\n",
|
|
" )\n",
|
|
" # print(trajectron.node_data[node]._right_index%trajectron.node_data[node]._capacity)\n",
|
|
" # # print(len(preds[node][0][0]))\n",
|
|
" # print(\n",
|
|
" # len(prediction_dict[timestep][node]),\n",
|
|
" # len(histories_dict[timestep][node]),\n",
|
|
" # len(futures_dict[timestep][node])\n",
|
|
" # )\n",
|
|
"\n",
|
|
" if timestep > 2 and (timestep%10 == 0):\n",
|
|
" fig = plt.figure(figsize=(20,5))\n",
|
|
" (ax1, ax2) = fig.subplots(1,2)\n",
|
|
" vis.visualize_distribution(ax1,\n",
|
|
" dists)\n",
|
|
" \n",
|
|
" vis.visualize_prediction(ax2,\n",
|
|
" {timestep: preds},\n",
|
|
" eval_scene.dt,\n",
|
|
" hyperparams['maximum_history_length'],\n",
|
|
" hyperparams['prediction_horizon'])\n",
|
|
" [ax.set_xlim([0, 25]) for ax in [ax1, ax2]]\n",
|
|
" [ax.set_ylim([0, 12]) for ax in [ax1, ax2]]\n",
|
|
" fig.show()\n",
|
|
" \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"array([[ 3.5313, 7.4048],\n",
|
|
" [ 3.4009, 7.4108],\n",
|
|
" [ 3.283, 7.4148],\n",
|
|
" [ 3.1773, 7.4189],\n",
|
|
" [ 3.0799, 7.4246],\n",
|
|
" [ 2.9867, 7.4343],\n",
|
|
" [ 2.895, 7.4478],\n",
|
|
" [ 2.8033, 7.4638],\n",
|
|
" [ 2.7102, 7.4809],\n",
|
|
" [ 2.6148, 7.4974],\n",
|
|
" [ 2.5164, 7.5119],\n",
|
|
" [ 2.4143, 7.5233],\n",
|
|
" [ 2.308, 7.5314],\n",
|
|
" [ 2.1988, 7.5376],\n",
|
|
" [ 2.0887, 7.5438],\n",
|
|
" [ 1.9807, 7.5519],\n",
|
|
" [ 1.88, 7.5653],\n",
|
|
" [ 1.786, 7.5849],\n",
|
|
" [ 1.6948, 7.6096],\n",
|
|
" [ 1.6013, 7.6312],\n",
|
|
" [ 1.5074, 7.6483],\n",
|
|
" [ 1.4168, 7.6609],\n",
|
|
" [ 1.3341, 7.6697],\n",
|
|
" [ 1.2634, 7.6755],\n",
|
|
" [ 1.2049, 7.6787],\n",
|
|
" [ 1.1542, 7.6785],\n",
|
|
" [ 1.1072, 7.6745],\n",
|
|
" [ 1.061, 7.6669],\n",
|
|
" [ 1.0157, 7.658],\n",
|
|
" [ 0.9709, 7.6495]])"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"position_state = {'position': ['x', 'y']}\n",
|
|
"node.first_timestep = 0\n",
|
|
"history = node.get(np.array([timestep-30,timestep]), position_state)\n",
|
|
"history[~np.isnan(history.sum(axis=1))]"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": ".venv",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.10.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|