9b61bc620c
This PR focus on refactor of logging method to solve bug of nan reward and log interval. After these two pr, hopefully fundamental change of tianshou/data is finished. We then can concentrate on building benchmarks of tianshou finally. Things changed: 1. trainer now accepts logger (BasicLogger or LazyLogger) instead of writer; 2. remove utils.SummaryWriter;
83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
import torch
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import numpy as np
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from tianshou.utils import MovAvg
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from tianshou.utils.net.common import MLP, Net
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from tianshou.exploration import GaussianNoise, OUNoise
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from tianshou.utils.net.continuous import RecurrentActorProb, RecurrentCritic
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def test_noise():
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noise = GaussianNoise()
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size = (3, 4, 5)
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assert np.allclose(noise(size).shape, size)
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noise = OUNoise()
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noise.reset()
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assert np.allclose(noise(size).shape, size)
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def test_moving_average():
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stat = MovAvg(10)
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assert np.allclose(stat.get(), 0)
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assert np.allclose(stat.mean(), 0)
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assert np.allclose(stat.std() ** 2, 0)
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stat.add(torch.tensor([1]))
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stat.add(np.array([2]))
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stat.add([3, 4])
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stat.add(5.)
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assert np.allclose(stat.get(), 3)
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assert np.allclose(stat.mean(), 3)
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assert np.allclose(stat.std() ** 2, 2)
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def test_net():
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# here test the networks that does not appear in the other script
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bsz = 64
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# MLP
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data = torch.rand([bsz, 3])
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mlp = MLP(3, 6, hidden_sizes=[128])
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assert list(mlp(data).shape) == [bsz, 6]
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# output == 0 and len(hidden_sizes) == 0 means identity model
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mlp = MLP(6, 0)
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assert data.shape == mlp(data).shape
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# common net
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state_shape = (10, 2)
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action_shape = (5, )
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data = torch.rand([bsz, *state_shape])
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expect_output_shape = [bsz, *action_shape]
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net = Net(state_shape, action_shape, hidden_sizes=[128, 128],
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norm_layer=torch.nn.LayerNorm, activation=None)
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assert list(net(data)[0].shape) == expect_output_shape
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assert str(net).count("LayerNorm") == 2
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assert str(net).count("ReLU") == 0
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Q_param = V_param = {"hidden_sizes": [128, 128]}
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net = Net(state_shape, action_shape, hidden_sizes=[128, 128],
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dueling_param=(Q_param, V_param))
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assert list(net(data)[0].shape) == expect_output_shape
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# concat
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net = Net(state_shape, action_shape, hidden_sizes=[128],
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concat=True)
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data = torch.rand([bsz, np.prod(state_shape) + np.prod(action_shape)])
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expect_output_shape = [bsz, 128]
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assert list(net(data)[0].shape) == expect_output_shape
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net = Net(state_shape, action_shape, hidden_sizes=[128],
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concat=True, dueling_param=(Q_param, V_param))
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assert list(net(data)[0].shape) == expect_output_shape
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# recurrent actor/critic
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data = torch.rand([bsz, *state_shape]).flatten(1)
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expect_output_shape = [bsz, *action_shape]
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net = RecurrentActorProb(3, state_shape, action_shape)
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mu, sigma = net(data)[0]
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assert mu.shape == sigma.shape
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assert list(mu.shape) == [bsz, 5]
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net = RecurrentCritic(3, state_shape, action_shape)
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data = torch.rand([bsz, 8, np.prod(state_shape)])
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act = torch.rand(expect_output_shape)
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assert list(net(data, act).shape) == [bsz, 1]
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if __name__ == '__main__':
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test_noise()
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test_moving_average()
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test_net()
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