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#!/usr/bin/env python
# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import subprocess
import sys
from pathlib import Path
import pytest
from tests.fixtures.constants import DUMMY_REPO_ID
from tests.utils import require_package
def _find_and_replace(text: str, finds_and_replaces: list[tuple[str, str]]) -> str:
for f, r in finds_and_replaces:
assert f in text
text = text.replace(f, r)
return text
# TODO(aliberts): Remove usage of subprocess calls and patch code with fixtures
def _run_script(path):
subprocess.run([sys.executable, path], check=True)
def _read_file(path):
with open(path) as file:
return file.read()
@pytest.mark.skip("TODO Fix and remove subprocess / excec calls")
def test_example_1(tmp_path, lerobot_dataset_factory):
_ = lerobot_dataset_factory(root=tmp_path, repo_id=DUMMY_REPO_ID)
path = "examples/1_load_lerobot_dataset.py"
file_contents = _read_file(path)
file_contents = _find_and_replace(
file_contents,
[
('repo_id = "lerobot/pusht"', f'repo_id = "{DUMMY_REPO_ID}"'),
(
"LeRobotDataset(repo_id",
f"LeRobotDataset(repo_id, root='{str(tmp_path)}'",
),
],
)
exec(file_contents, {})
assert Path("outputs/examples/1_load_lerobot_dataset/episode_0.mp4").exists()
@pytest.mark.skip("TODO Fix and remove subprocess / excec calls")
@require_package("gym_pusht")
def test_examples_basic2_basic3_advanced1():
"""
Train a model with example 3, check the outputs.
Evaluate the trained model with example 2, check the outputs.
Calculate the validation loss with advanced example 1, check the outputs.
"""
### Test example 3
file_contents = _read_file("examples/3_train_policy.py")
# Do fewer steps, use smaller batch, use CPU, and don't complicate things with dataloader workers.
file_contents = _find_and_replace(
file_contents,
[
("training_steps = 5000", "training_steps = 1"),
("num_workers=4", "num_workers=0"),
('device = torch.device("cuda")', 'device = torch.device("cpu")'),
("batch_size=64", "batch_size=1"),
],
)
# Pass empty globals to allow dictionary comprehension https://stackoverflow.com/a/32897127/4391249.
exec(file_contents, {})
for file_name in ["model.safetensors", "config.json"]:
assert Path(f"outputs/train/example_pusht_diffusion/{file_name}").exists()
### Test example 2
file_contents = _read_file("examples/2_evaluate_pretrained_policy.py")
# Do fewer evals, use CPU, and use the local model.
file_contents = _find_and_replace(
file_contents,
[
(
'pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))',
"",
),
(
'# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',
'pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',
),
('device = torch.device("cuda")', 'device = torch.device("cpu")'),
("step += 1", "break"),
],
)
exec(file_contents, {})
assert Path("outputs/eval/example_pusht_diffusion/rollout.mp4").exists()
## Test example 4
file_contents = _read_file("examples/advanced/2_calculate_validation_loss.py")
# Run on a single example from the last episode, use CPU, and use the local model.
file_contents = _find_and_replace(
file_contents,
[
(
'pretrained_policy_path = Path(snapshot_download("lerobot/diffusion_pusht"))',
"",
),
(
'# pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',
'pretrained_policy_path = Path("outputs/train/example_pusht_diffusion")',
),
("train_episodes = episodes[:num_train_episodes]", "train_episodes = [0]"),
("val_episodes = episodes[num_train_episodes:]", "val_episodes = [1]"),
("num_workers=4", "num_workers=0"),
('device = torch.device("cuda")', 'device = torch.device("cpu")'),
("batch_size=64", "batch_size=1"),
],
)
# Capture the output of the script
output_buffer = io.StringIO()
sys.stdout = output_buffer
exec(file_contents, {})
printed_output = output_buffer.getvalue()
# Restore stdout to its original state
sys.stdout = sys.__stdout__
assert "Average loss on validation set" in printed_output
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