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import pytest |
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import numpy as np |
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import torch |
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from itertools import product |
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from ding.model import mavac |
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from ding.model.template.mavac import MAVAC |
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from ding.torch_utils import is_differentiable |
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B = 32 |
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agent_obs_shape = [216, 265] |
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global_obs_shape = [264, 324] |
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agent_num = 8 |
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action_shape = 14 |
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args = list(product(*[agent_obs_shape, global_obs_shape])) |
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@pytest.mark.unittest |
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@pytest.mark.parametrize('agent_obs_shape, global_obs_shape', args) |
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class TestVAC: |
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def output_check(self, model, outputs, action_shape): |
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if isinstance(action_shape, tuple): |
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loss = sum([t.sum() for t in outputs]) |
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elif np.isscalar(action_shape): |
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loss = outputs.sum() |
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is_differentiable(loss, model) |
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def test_vac(self, agent_obs_shape, global_obs_shape): |
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data = { |
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'agent_state': torch.randn(B, agent_num, agent_obs_shape), |
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'global_state': torch.randn(B, agent_num, global_obs_shape), |
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'action_mask': torch.randint(0, 2, size=(B, agent_num, action_shape)) |
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} |
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model = MAVAC(agent_obs_shape, global_obs_shape, action_shape, agent_num) |
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logit = model(data, mode='compute_actor_critic')['logit'] |
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value = model(data, mode='compute_actor_critic')['value'] |
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outputs = value.sum() + logit.sum() |
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self.output_check(model, outputs, action_shape) |
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for p in model.parameters(): |
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p.grad = None |
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logit = model(data, mode='compute_actor')['logit'] |
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self.output_check(model.actor, logit, model.action_shape) |
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for p in model.parameters(): |
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p.grad = None |
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value = model(data, mode='compute_critic')['value'] |
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assert value.shape == (B, agent_num) |
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self.output_check(model.critic, value, action_shape) |
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