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import torch
import pytest
from itertools import product
from ding.model.template import EDAC
from ding.torch_utils import is_differentiable
B = 4
obs_shape = [4, (8, )]
act_shape = [3, (6, )]
args = list(product(*[obs_shape, act_shape]))
@pytest.mark.unittest
class TestEDAC:
def output_check(self, model, outputs):
if isinstance(outputs, torch.Tensor):
loss = outputs.sum()
elif isinstance(outputs, list):
loss = sum([t.sum() for t in outputs])
elif isinstance(outputs, dict):
loss = sum([v.sum() for v in outputs.values()])
is_differentiable(loss, model)
@pytest.mark.parametrize('obs_shape, act_shape', args)
def test_EDAC(self, obs_shape, act_shape):
if isinstance(obs_shape, int):
inputs_obs = torch.randn(B, obs_shape)
else:
inputs_obs = torch.randn(B, *obs_shape)
if isinstance(act_shape, int):
inputs_act = torch.randn(B, act_shape)
else:
inputs_act = torch.randn(B, *act_shape)
inputs = {'obs': inputs_obs, 'action': inputs_act}
model = EDAC(obs_shape, act_shape, ensemble_num=2)
outputs_c = model(inputs, mode='compute_critic')
assert isinstance(outputs_c, dict)
assert outputs_c['q_value'].shape == (2, B)
self.output_check(model.critic, outputs_c)
if isinstance(obs_shape, int):
inputs = torch.randn(B, obs_shape)
else:
inputs = torch.randn(B, *obs_shape)
outputs_a = model(inputs, mode='compute_actor')
assert isinstance(outputs_a, dict)
if isinstance(act_shape, int):
assert outputs_a['logit'][0].shape == (B, act_shape)
assert outputs_a['logit'][1].shape == (B, act_shape)
elif len(act_shape) == 1:
assert outputs_a['logit'][0].shape == (B, *act_shape)
assert outputs_a['logit'][1].shape == (B, *act_shape)
outputs = {'mu': outputs_a['logit'][0], 'sigma': outputs_a['logit'][1]}
self.output_check(model.actor, outputs)
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