Delete folder benchmark_scripts with huggingface_hub

benchmark_scripts/check_task_suites.py

@@ -1,117 +0,0 @@
-"""
-This script is to test if users can successfully load all the environments, the benchmark initial states in their machines
-"""
-import os
-from termcolor import colored
-import cv2
-import h5py
-import subprocess
-import shutil
-import numpy as np
-
-from pathlib import Path
-
-# import init_path
-from libero.libero import benchmark, get_libero_path
-
-
-# def render_task(task, bddl_file, init_states, demo_file):
-#     env_args = {
-#         "bddl_file_name": bddl_file,
-#         "camera_heights": 128,
-#         "camera_widths": 128
-#     }
-
-#     env = OffScreenRenderEnv(**env_args)
-#     env.reset()
-#     obs = env.set_init_state(init_states[0])
-#     for _ in range(5):
-#         obs, _, _, _ = env.step([0.] * 7)
-#     images = [obs["agentview_image"]]
-
-#     with h5py.File(demo_file, "r") as f:
-#         states = f["data/demo_0/states"][()]
-#         obs = env.set_init_state(states[-1])
-
-#     images.append(obs["agentview_image"])
-#     images = np.concatenate(images, axis=1)
-#     cv2.imwrite(f"benchmark_tasks/{task.problem}-{task.language}.png", images[::-1, :, ::-1])
-#     env.close()
-
-
-def main():
-
-    benchmark_root_path = get_libero_path("benchmark_root")
-    init_states_default_path = get_libero_path("init_states")
-    datasets_default_path = get_libero_path("datasets")
-    bddl_files_default_path = get_libero_path("bddl_files")
-
-    # Check all the files
-    task_tuples = []
-    demo_files = []
-    for benchmark_name in [
-        "libero_object",
-        "libero_goal",
-        "libero_spatial",
-        "libero_10",
-        "libero_90",
-    ]:
-        benchmark_instance = benchmark.get_benchmark_dict()[benchmark_name]()
-        num_tasks = benchmark_instance.get_num_tasks()
-        # see how many tasks involved in the benchmark
-        print(f"{num_tasks} tasks in the benchmark {benchmark_instance.name}: ")
-
-        # Check if all the task names and their bddl file names
-        task_names = benchmark_instance.get_task_names()
-        print("The benchmark contains the following tasks:")
-        for task_id in range(num_tasks):
-            task_name = task_names[task_id]
-            task = benchmark_instance.get_task(task_id)
-            bddl_file = os.path.join(
-                bddl_files_default_path, task.problem_folder, task.bddl_file
-            )
-            assert os.path.exists(bddl_file), f"{bddl_file} does not exist!"
-            init_states_path = os.path.join(
-                init_states_default_path, task.problem_folder, task.init_states_file
-            )
-            assert os.path.exists(
-                init_states_path
-            ), f"{init_states_path} does not exist!"
-            demo_file = os.path.join(
-                datasets_default_path,
-                benchmark_instance.get_task_demonstration(task_id),
-            )
-            assert os.path.exists(demo_file), f"{demo_file} does not exist!"
-            init_states = benchmark_instance.get_task_init_states(task_id)
-            task_tuples.append((benchmark_name, task_id, bddl_file, demo_file))
-            demo_files.append(demo_file)
-
-    print(colored("All the files exist!", "green"))
-    processes = []
-    if os.path.exists("benchmark_tasks"):
-        shutil.rmtree("benchmark_tasks")
-
-    for i in range(len(task_tuples)):
-        command = f"python benchmark_scripts/render_single_task.py --benchmark_name {task_tuples[i][0]} --task_id {task_tuples[i][1]} --bddl_file {task_tuples[i][2]} --demo_file {task_tuples[i][3]}"
-        p = subprocess.Popen(command, shell=True)
-        processes.append(p)
-        if i % 10 == 9:
-            for p in processes:
-                p.wait()
-            processes = []
-
-    count = len(list(Path("benchmark_tasks").glob("*.png")))
-    print(f"Expected 130 tasks, Rendered {count} tasks successfully.")
-    if count < 130:
-        print(colored("Some tasks failed to render!", "red"))
-        for demo_file in demo_files:
-            if not os.path.exists(
-                os.path.join(
-                    "benchmark_tasks", demo_file.split("/")[-1].replace(".hdf5", ".png")
-                )
-            ):
-                print(demo_file)
-
-
-if __name__ == "__main__":
-    main()

benchmark_scripts/download_libero_datasets.py

@@ -1,46 +0,0 @@
-import init_path
-import argparse
-import os
-
-import libero.libero.utils.download_utils as download_utils
-from libero.libero import get_libero_path
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--download-dir",
-        type=str,
-        default=get_libero_path("datasets"),
-    )
-    parser.add_argument(
-        "--datasets",
-        type=str,
-        choices=["all", "libero_goal", "libero_spatial", "libero_object", "libero_100"],
-        default="all",
-    )
-    return parser.parse_args()
-
-
-def main():
-
-    args = parse_args()
-
-    # Ask users to specify the download directory of datasets
-    os.makedirs(args.download_dir, exist_ok=True)
-    print(f"Datasets downloaded to {args.download_dir}")
-    print(f"Downloading {args.datasets} datasets")
-
-    # If not, download
-    download_utils.libero_dataset_download(
-        download_dir=args.download_dir, datasets=args.datasets
-    )
-
-    # (TODO) If datasets exist, check if datasets are the same as benchmark
-
-    # Check if datasets exist first
-    download_utils.check_libero_dataset(download_dir=args.download_dir)
-
-
-if __name__ == "__main__":
-    main()

benchmark_scripts/init_path.py

@@ -1,8 +0,0 @@
-import sys
-import os
-
-path = os.path.dirname(os.path.realpath(__file__))
-sys.path.insert(0, os.path.join(path, "../"))
-
-# import robosuite.utils.macros as macros
-# macros.IMAGE_CONVENTION = "opencv"

benchmark_scripts/render_single_task.py

@@ -1,82 +0,0 @@
-import os
-from termcolor import colored
-import cv2
-import h5py
-import argparse
-import numpy as np
-
-from libero.libero.envs import OffScreenRenderEnv
-from libero.libero import benchmark, get_libero_path
-
-
-def render_task(task, bddl_file, init_states, demo_file):
-    env_args = {
-        "bddl_file_name": bddl_file,
-        "camera_heights": 128,
-        "camera_widths": 128,
-    }
-
-    env = OffScreenRenderEnv(**env_args)
-    env.reset()
-    obs = env.set_init_state(init_states[0])
-    for _ in range(5):
-        obs, _, _, _ = env.step([0.0] * 7)
-    images = [obs["agentview_image"]]
-
-    with h5py.File(demo_file, "r") as f:
-        states = f["data/demo_0/states"][()]
-        obs = env.set_init_state(states[-1])
-
-    images.append(obs["agentview_image"])
-    images = np.concatenate(images, axis=1)
-    cv2.imwrite(
-        f"benchmark_tasks/{task.problem}-{task.language}.png", images[::-1, :, ::-1]
-    )
-    env.close()
-
-
-def main():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--benchmark_name", type=str)
-    parser.add_argument("--task_id", type=int, default=0)
-    parser.add_argument("--bddl_file", type=str)
-    parser.add_argument("--demo_file", type=str)
-    args = parser.parse_args()
-
-    benchmark_name = args.benchmark_name
-    task_id = args.task_id
-    bddl_file = args.bddl_file
-    demo_file = args.demo_file
-
-    benchmark_instance = benchmark.get_benchmark_dict()[benchmark_name]()
-    env_args = {
-        "bddl_file_name": bddl_file,
-        "camera_heights": 128,
-        "camera_widths": 128,
-    }
-
-    os.makedirs("benchmark_tasks", exist_ok=True)
-
-    task = benchmark_instance.get_task(task_id)
-    init_states = benchmark_instance.get_task_init_states(task_id)
-
-    env = OffScreenRenderEnv(**env_args)
-    env.reset()
-    obs = env.set_init_state(init_states[0])
-    for _ in range(5):
-        obs, _, _, _ = env.step([0.0] * 7)
-    images = [obs["agentview_image"]]
-
-    with h5py.File(demo_file, "r") as f:
-        states = f["data/demo_0/states"][()]
-        obs = env.set_init_state(states[-1])
-
-    images.append(obs["agentview_image"])
-    images = np.concatenate(images, axis=1)
-    image_name = demo_file.split("/")[-1].replace(".hdf5", ".png")
-    cv2.imwrite(f"benchmark_tasks/{image_name}", images[::-1, :, ::-1])
-    env.close()
-
-
-if __name__ == "__main__":
-    main()

benchmark_scripts/shasum_files.py

@@ -1,49 +0,0 @@
-import os
-import hashlib
-from pathlib import Path
-
-
-def shasum_file(file_path):
-    if os.path.exists(file_path):
-        with open(file_path, "rb") as f:
-            data = f.read()
-        sha_hash = hashlib.sha1(data).hexdigest()
-        file_name = file_path.split("/")[-1]
-        return {file_name: sha_hash}
-    else:
-        return None
-
-
-def shasum_datasets(download_dir="datasets"):
-    dataset_shasum = {}
-    for dataset_name in [
-        "libero_object",
-        "libero_goal",
-        "libero_spatial",
-        "libero_10",
-        "libero_90",
-    ]:
-        dataset_dir = os.path.join(download_dir, dataset_name)
-        if os.path.exists(dataset_dir):
-            count = 0
-            for path in Path(dataset_dir).glob("*.hdf5"):
-                count += 1
-            if not (
-                (count == 10 and dataset_name != "libero_90")
-                or (count == 90 and dataset_name == "libero_90")
-            ):
-                print("file count doesn't match")
-        else:
-            print("dataset not found")
-        for path in Path(dataset_dir).glob("*.hdf5"):
-            dataset_shasum.update(shasum_file(str(path)))
-        print(dataset_shasum)
-
-
-# def shalsum_pretrained_models():
-
-
-# def shalsum_pretrained_policies():
-
-
-shasum_datasets()