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import torch
import numpy as np
import pytest
from itertools import product
from ding.model.template import DiscreteBC, ContinuousBC
from ding.torch_utils import is_differentiable
from ding.utils import squeeze
B = 4
T = 6
embedding_size = 32
action_shape_args = [(6, ), [
1,
]]
args = list(product(*[action_shape_args, ['regression', 'reparameterization']]))
@pytest.mark.unittest
@pytest.mark.parametrize('action_shape, action_space', args)
class TestContinuousBC:
def test_continuous_bc(self, action_shape, action_space):
N = 32
inputs = {'obs': torch.randn(B, N), 'action': torch.randn(B, squeeze(action_shape))}
model = ContinuousBC(
obs_shape=(N, ),
action_shape=action_shape,
action_space=action_space,
actor_head_hidden_size=embedding_size,
)
# compute_action
print(model)
if action_space == 'regression':
action = model(inputs['obs'])['action']
if squeeze(action_shape) == 1:
assert action.shape == (B, )
else:
assert action.shape == (B, squeeze(action_shape))
assert action.eq(action.clamp(-1, 1)).all()
is_differentiable(action.sum(), model.actor)
elif action_space == 'reparameterization':
(mu, sigma) = model(inputs['obs'])['logit']
assert mu.shape == (B, *action_shape)
assert sigma.shape == (B, *action_shape)
is_differentiable(mu.sum() + sigma.sum(), model.actor)
T, B = 3, 4
obs_shape = [4, (8, ), (4, 64, 64)]
act_shape = [3, (6, ), [2, 3, 6]]
args = list(product(*[obs_shape, act_shape]))
@pytest.mark.unittest
@pytest.mark.parametrize('obs_shape, act_shape', args)
class TestDiscreteBC:
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)
def test_discrete_bc(self, obs_shape, act_shape):
if isinstance(obs_shape, int):
inputs = torch.randn(B, obs_shape)
else:
inputs = torch.randn(B, *obs_shape)
model = DiscreteBC(obs_shape, act_shape)
outputs = model(inputs)
assert isinstance(outputs, dict)
if isinstance(act_shape, int):
assert outputs['logit'].shape == (B, act_shape)
elif len(act_shape) == 1:
assert outputs['logit'].shape == (B, *act_shape)
else:
for i, s in enumerate(act_shape):
assert outputs['logit'][i].shape == (B, s)
self.output_check(model, outputs['logit'])
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