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import pytest
import torch
import numpy as np
from ding.model.template.ebm import EBM, AutoregressiveEBM
from ding.model.template.ebm import DFO, AutoRegressiveDFO, MCMC
# batch, negative_samples, obs_shape, action_shape
B, N, O, A = 32, 1024, 11, 3
@pytest.mark.unittest
class TestEBM:
def test_forward(self):
obs = torch.randn(B, N, O)
action = torch.randn(B, N, A)
ebm = EBM(O, A)
energy = ebm(obs, action)
assert energy.shape == (B, N)
@pytest.mark.unittest
class TestDFO:
opt = DFO(train_samples=N, inference_samples=N)
opt.set_action_bounds(np.stack([np.zeros(A), np.ones(A)], axis=0))
ebm = EBM(O, A)
def test_sample(self):
obs = torch.randn(B, O)
tiled_obs, action_samples = self.opt.sample(obs, self.ebm)
assert tiled_obs.shape == (B, N, O)
assert action_samples.shape == (B, N, A)
def test_infer(self):
obs = torch.randn(B, O)
action = self.opt.infer(obs, self.ebm)
assert action.shape == (B, A)
@pytest.mark.unittest
class TestAutoregressiveEBM:
def test_forward(self):
obs = torch.randn(B, N, O)
action = torch.randn(B, N, A)
arebm = AutoregressiveEBM(O, A)
energy = arebm(obs, action)
assert energy.shape == (B, N, A)
@pytest.mark.unittest
class TestAutoregressiveDFO:
opt = AutoRegressiveDFO(train_samples=N, inference_samples=N)
opt.set_action_bounds(np.stack([np.zeros(A), np.ones(A)], axis=0))
ebm = AutoregressiveEBM(O, A)
def test_sample(self):
obs = torch.randn(B, O)
tiled_obs, action_samples = self.opt.sample(obs, self.ebm)
assert tiled_obs.shape == (B, N, O)
assert action_samples.shape == (B, N, A)
def test_infer(self):
obs = torch.randn(B, O)
action = self.opt.infer(obs, self.ebm)
assert action.shape == (B, A)
@pytest.mark.unittest
class TestMCMC:
opt = MCMC(iters=3, train_samples=N, inference_samples=N)
opt.set_action_bounds(np.stack([np.zeros(A), np.ones(A)], axis=0))
obs = torch.randn(B, N, O)
action = torch.randn(B, N, A)
ebm = EBM(O, A)
def test_gradient_wrt_act(self):
ebm = EBM(O, A)
# inference mode
de_dact = MCMC._gradient_wrt_act(self.obs, self.action, ebm)
assert de_dact.shape == (B, N, A)
# train mode
de_dact = MCMC._gradient_wrt_act(self.obs, self.action, ebm, create_graph=True)
loss = de_dact.pow(2).sum()
loss.backward()
assert de_dact.shape == (B, N, A)
assert ebm.net[0].weight.grad is not None
def test_langevin_step(self):
stepsize = 1
action = self.opt._langevin_step(self.obs, self.action, stepsize, self.ebm)
assert action.shape == (B, N, A)
# TODO: new action should have lower energy
def test_langevin_action_given_obs(self):
action = self.opt._langevin_action_given_obs(self.obs, self.action, self.ebm)
assert action.shape == (B, N, A)
def test_grad_penalty(self):
ebm = EBM(O, A)
self.opt.add_grad_penalty = True
loss = self.opt.grad_penalty(self.obs, self.action, ebm)
loss.backward()
assert ebm.net[0].weight.grad is not None
def test_sample(self):
obs = torch.randn(B, O)
tiled_obs, action_samples = self.opt.sample(obs, self.ebm)
assert tiled_obs.shape == (B, N, O)
assert action_samples.shape == (B, N, A)
def test_infer(self):
obs = torch.randn(B, O)
action = self.opt.infer(obs, self.ebm)
assert action.shape == (B, A)
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