Tianshou/test/throughput/test_batch_profile.py

import copy
import pickle

import numpy as np
import pytest
import torch

from tianshou.data import Batch


@pytest.fixture(scope="module")
def data():
    print("Initializing data...")
    np.random.seed(0)
    batch_set = [
        Batch(
            a=list(np.arange(1e3)),
            b={"b1": (3.14, 3.14), "b2": np.arange(1e3)},
            c=i,
        )
        for i in np.arange(int(1e4))
    ]
    batch0 = Batch(
        a=np.ones((3, 4), dtype=np.float64),
        b=Batch(
            c=np.ones((1,), dtype=np.float64),
            d=torch.ones((3, 3, 3), dtype=torch.float32),
            e=list(range(3)),
        ),
    )
    batchs1 = [copy.deepcopy(batch0) for _ in np.arange(1e4)]
    batchs2 = [copy.deepcopy(batch0) for _ in np.arange(1e4)]
    batch_len = int(1e4)
    batch3 = Batch(obs=[np.arange(20) for _ in np.arange(batch_len)], reward=np.arange(batch_len))
    indexs = np.random.choice(batch_len, size=batch_len // 10, replace=False)
    slice_dict = {
        "obs": [np.arange(20) for _ in np.arange(batch_len // 10)],
        "reward": np.arange(batch_len // 10),
    }
    dict_set = [
        {
            "obs": np.arange(20),
            "info": "this is info",
            "reward": 0,
        }
        for _ in np.arange(1e2)
    ]
    batch4 = Batch(
        a=np.ones((10000, 4), dtype=np.float64),
        b=Batch(
            c=np.ones((1,), dtype=np.float64),
            d=torch.ones((1000, 1000), dtype=torch.float32),
            e=np.arange(1000),
        ),
    )

    print("Initialized")
    return {
        "batch_set": batch_set,
        "batch0": batch0,
        "batchs1": batchs1,
        "batchs2": batchs2,
        "batch3": batch3,
        "indexs": indexs,
        "dict_set": dict_set,
        "slice_dict": slice_dict,
        "batch4": batch4,
    }


def test_init(data):
    """Test Batch __init__()."""
    for _ in np.arange(10):
        _ = Batch(data["batch_set"])


def test_get_item(data):
    """Test get with item."""
    for _ in np.arange(1e5):
        _ = data["batch3"][data["indexs"]]


def test_get_attr(data):
    """Test get with attr."""
    for _ in np.arange(1e6):
        data["batch3"].get("obs")
        data["batch3"].get("reward")
        _, _ = data["batch3"].obs, data["batch3"].reward


def test_set_item(data):
    """Test set with item."""
    for _ in np.arange(1e4):
        data["batch3"][data["indexs"]] = data["slice_dict"]


def test_set_attr(data):
    """Test set with attr."""
    for _ in np.arange(1e4):
        data["batch3"].c = np.arange(1e3)
        data["batch3"].obs = data["dict_set"]


def test_numpy_torch_convert(data):
    """Test conversion between numpy and torch."""
    for _ in np.arange(1e4):  # not sure what's wrong in torch==1.10.0
        data["batch4"].to_torch()
        data["batch4"].to_numpy()


def test_pickle(data):
    for _ in np.arange(1e4):
        pickle.loads(pickle.dumps(data["batch4"]))


def test_cat(data):
    """Test cat."""
    for i in range(10000):
        Batch.cat((data["batch0"], data["batch0"]))
        data["batchs1"][i].cat_(data["batch0"])


def test_stack(data):
    """Test stack."""
    for i in range(10000):
        Batch.stack((data["batch0"], data["batch0"]))
        data["batchs2"][i].stack_([data["batch0"]])


if __name__ == "__main__":
    pytest.main(["-s", "-k batch_profile", "--durations=0", "-v"])