|
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, |
|
) |
|
|
|
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']) |
|
|
