a633a6a028
This is the first commit of 6 commits mentioned in #274, which features 1. Refactor of `Class Net` to support any form of MLP. 2. Enable type check in utils.network. 3. Relative change in docs/test/examples. 4. Move atari-related network to examples/atari/atari_network.py Co-authored-by: Trinkle23897 <trinkle23897@gmail.com>
102 lines
3.5 KiB
Python
102 lines
3.5 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 import SummaryWriter
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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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def test_summary_writer():
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# get first instance by key of `default` or your own key
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writer1 = SummaryWriter.get_instance(
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key="first", log_dir="log/test_sw/first")
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assert writer1.log_dir == "log/test_sw/first"
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writer2 = SummaryWriter.get_instance()
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assert writer1 is writer2
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# create new instance by specify a new key
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writer3 = SummaryWriter.get_instance(
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key="second", log_dir="log/test_sw/second")
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assert writer3.log_dir == "log/test_sw/second"
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writer4 = SummaryWriter.get_instance(key="second")
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assert writer3 is writer4
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assert writer1 is not writer3
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assert writer1.log_dir != writer4.log_dir
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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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test_summary_writer()
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