test_long.py

import pandas as pd
import yaml
import argparse
import torch
from model import TrajCoordAE

CONFIG_FILE_PATH = 'config/sdd_longterm.yaml'  # yaml config file containing all the hyperparameters
DATASET_NAME = 'sdd'
EXPERIMENT_NAME = f'{DATASET_NAME}_longterm'

TEST_DATA_PATH = 'data/SDD/test_longterm.pkl'
TEST_IMAGE_PATH = 'data/SDD_semantic_maps/test_masks'
OBS_LEN = 5  # in timesteps
PRED_LEN = 30  # in timesteps
# NUM_GOALS = 20  # K_e
NUM_GOALS = 20  # K_e
NUM_TRAJ = 1  # K_a

ROUNDS = 3  # Y-net is stochastic. How often to evaluate the whole dataset
BATCH_SIZE = 4

print(f"Now test the {DATASET_NAME} data")

with open(CONFIG_FILE_PATH) as file:
    params = yaml.load(file, Loader=yaml.FullLoader)
# experiment_name = CONFIG_FILE_PATH.split('.yaml')[0].split('config/')[1]

df_test = pd.read_pickle(TEST_DATA_PATH)


df_test.head()




model = TrajCoordAE(obs_len=OBS_LEN, pred_len=PRED_LEN, params=params)


model.load(f'save_model/{EXPERIMENT_NAME}_weights.pt')


model.evaluate(df_test, params, image_path=TEST_IMAGE_PATH,
               batch_size=BATCH_SIZE, rounds=ROUNDS,exp_name=EXPERIMENT_NAME,
               num_goals=NUM_GOALS, num_traj=NUM_TRAJ, device=None, dataset_name=DATASET_NAME)