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import unittest
import pytest
import numpy as np
import torch
import treetensor.torch as ttorch
from ding.policy.common_utils import default_preprocess_learn
shape_test = [
[2],
[1],
]
dtype_test = [
"int64",
"float32",
]
data_type_test = [
"numpy",
"torch",
"treetensor",
]
def get_action(shape, dtype, class_type):
if class_type == "numpy":
if dtype == "int64":
dtype = np.int64
elif dtype == "float32":
dtype = np.float32
return np.random.randn(*shape).astype(dtype)
else:
if dtype == "int64":
dtype = torch.int64
elif dtype == "float32":
dtype = torch.float32
if class_type == "torch":
return torch.randn(*shape).type(dtype)
elif class_type == "treetensor":
return ttorch.randn(*shape).type(dtype)
@pytest.mark.unittest
def test_default_preprocess_learn_action():
for shape in shape_test:
for dtype in dtype_test:
for data_type in data_type_test:
data = [
{
'obs': np.random.randn(4, 84, 84),
'action': get_action(shape, dtype, data_type),
'reward': 1.0,
'next_obs': np.random.randn(4, 84, 84),
'done': False,
'weight': 1.0,
'value': 1.0,
'adv': 1.0,
} for _ in range(10)
]
use_priority_IS_weight = False
use_priority = False
use_nstep = False
ignore_done = False
data = default_preprocess_learn(data, use_priority_IS_weight, use_priority, use_nstep, ignore_done)
assert data['obs'].shape == torch.Size([10, 4, 84, 84])
if dtype in ["int64"] and shape[0] == 1:
assert data['action'].shape == torch.Size([10])
else:
assert data['action'].shape == torch.Size([10, *shape])
assert data['reward'].shape == torch.Size([10])
assert data['next_obs'].shape == torch.Size([10, 4, 84, 84])
assert data['done'].shape == torch.Size([10])
assert data['weight'].shape == torch.Size([10])
assert data['value'].shape == torch.Size([10])
assert data['adv'].shape == torch.Size([10])
@pytest.mark.unittest
def test_default_preprocess_learn_reward_done_adv_1d():
data = [
{
'obs': np.random.randn(4, 84, 84),
'action': np.random.randn(2),
'reward': np.array([1.0]),
'next_obs': np.random.randn(4, 84, 84),
'done': False,
'value': np.array([1.0]),
'adv': np.array([1.0]),
} for _ in range(10)
]
use_priority_IS_weight = False
use_priority = False
use_nstep = False
ignore_done = False
data = default_preprocess_learn(data, use_priority_IS_weight, use_priority, use_nstep, ignore_done)
assert data['reward'].shape == torch.Size([10])
assert data['done'].shape == torch.Size([10])
assert data['weight'] is None
assert data['value'].shape == torch.Size([10])
assert data['adv'].shape == torch.Size([10])
@pytest.mark.unittest
def test_default_preprocess_learn_ignore_done():
data = [
{
'obs': np.random.randn(4, 84, 84),
'action': np.random.randn(2),
'reward': np.array([1.0]),
'next_obs': np.random.randn(4, 84, 84),
'done': True,
'value': np.array([1.0]),
'adv': np.array([1.0]),
} for _ in range(10)
]
use_priority_IS_weight = False
use_priority = False
use_nstep = False
ignore_done = True
data = default_preprocess_learn(data, use_priority_IS_weight, use_priority, use_nstep, ignore_done)
assert data['done'].dtype == torch.float32
assert torch.sum(data['done']) == 0
@pytest.mark.unittest
def test_default_preprocess_learn_use_priority_IS_weight():
data = [
{
'obs': np.random.randn(4, 84, 84),
'action': np.random.randn(2),
'reward': 1.0,
'next_obs': np.random.randn(4, 84, 84),
'done': False,
'priority_IS': 1.0,
'value': 1.0,
'adv': 1.0,
} for _ in range(10)
]
use_priority_IS_weight = True
use_priority = True
use_nstep = False
ignore_done = False
data = default_preprocess_learn(data, use_priority_IS_weight, use_priority, use_nstep, ignore_done)
assert data['weight'].shape == torch.Size([10])
assert torch.sum(data['weight']) == torch.tensor(10.0)
@pytest.mark.unittest
def test_default_preprocess_learn_nstep():
data = [
{
'obs': np.random.randn(4, 84, 84),
'action': np.random.randn(2),
'reward': np.array([1.0, 2.0, 0.0]),
'next_obs': np.random.randn(4, 84, 84),
'done': False,
'value': 1.0,
'adv': 1.0,
} for _ in range(10)
]
use_priority_IS_weight = False
use_priority = False
use_nstep = True
ignore_done = False
data = default_preprocess_learn(data, use_priority_IS_weight, use_priority, use_nstep, ignore_done)
assert data['reward'].shape == torch.Size([3, 10])
assert data['reward'][0][0] == torch.tensor(1.0)
assert data['reward'][1][0] == torch.tensor(2.0)
assert data['reward'][2][0] == torch.tensor(0.0)
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