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import torch
import pytest
from itertools import product
from ding.model.template import ACER
from ding.torch_utils import is_differentiable
B = 4
obs_shape = [4, (8, ), (4, 64, 64)]
act_shape = [3, (6, )]
args = list(product(*[obs_shape, act_shape]))
@pytest.mark.unittest
class TestACER:
@pytest.mark.parametrize('obs_shape, act_shape', args)
def test_ACER(self, obs_shape, act_shape):
if isinstance(obs_shape, int):
inputs = torch.randn(B, obs_shape)
else:
inputs = torch.randn(B, *obs_shape)
model = ACER(obs_shape, act_shape)
outputs_c = model(inputs, mode='compute_critic')
assert isinstance(outputs_c, dict)
if isinstance(act_shape, int):
assert outputs_c['q_value'].shape == (B, act_shape)
elif len(act_shape) == 1:
assert outputs_c['q_value'].shape == (B, *act_shape)
outputs_a = model(inputs, mode='compute_actor')
assert isinstance(outputs_a, dict)
if isinstance(act_shape, int):
assert outputs_a['logit'].shape == (B, act_shape)
elif len(act_shape) == 1:
assert outputs_a['logit'].shape == (B, *act_shape)
outputs = {**outputs_a, **outputs_c}
loss = sum([v.sum() for v in outputs.values()])
is_differentiable(loss, model)
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