DI-engine/ding/model/template/tests/test_collaq.py

import pytest
import torch
from ding.torch_utils import is_differentiable
from ding.model.template import CollaQ


@pytest.mark.unittest
def test_collaQ():
    use_mixer = [True, False]
    agent_num, bs, T = 4, 6, 8
    obs_dim, obs_alone_dim, global_obs_dim, action_dim = 32, 24, 32 * 4, 9
    self_feature_range = [8, 10]
    allay_feature_range = [10, 16]
    embedding_dim = 64
    for mix in use_mixer:
        collaQ_model = CollaQ(
            agent_num,
            obs_dim,
            obs_alone_dim,
            global_obs_dim,
            action_dim, [128, embedding_dim],
            True,
            self_feature_range,
            allay_feature_range,
            32,
            mix,
            activation=torch.nn.Tanh()
        )
        print(collaQ_model)
        data = {
            'obs': {
                'agent_state': torch.randn(T, bs, agent_num, obs_dim),
                'agent_alone_state': torch.randn(T, bs, agent_num, obs_alone_dim),
                'agent_alone_padding_state': torch.randn(T, bs, agent_num, obs_dim),
                'global_state': torch.randn(T, bs, global_obs_dim),
                'action_mask': torch.randint(0, 2, size=(T, bs, agent_num, action_dim))
            },
            'prev_state': [[[None for _ in range(agent_num)] for _ in range(3)] for _ in range(bs)],
            'action': torch.randint(0, action_dim, size=(T, bs, agent_num))
        }
        output = collaQ_model(data, single_step=False)
        assert set(output.keys()) == set(['total_q', 'logit', 'next_state', 'action_mask', 'agent_colla_alone_q'])
        assert output['total_q'].shape == (T, bs)
        assert output['logit'].shape == (T, bs, agent_num, action_dim)
        assert len(output['next_state']) == bs and all([len(n) == 3 for n in output['next_state']]) and all(
            [len(q) == agent_num for n in output['next_state'] for q in n]
        )
        print(output['next_state'][0][0][0]['h'].shape)
        # data['prev_state'] = output['next_state']
        loss = output['total_q'].sum()
        is_differentiable(loss, collaQ_model)
        data.pop('action')
        output = collaQ_model(data, single_step=False)