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