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from itertools import product
import pytest
import torch
from ding.torch_utils import is_differentiable
from lzero.model.muzero_model import PredictionNetwork, DynamicsNetwork
batch_size = [10, 100]
num_res_blocks = [3, 4]
num_channels = [3]
reward_head_channels = [2]
fc_reward_layers = [[16, 8]]
output_support_size = [2]
flatten_output_size_for_reward_head = [180]
dynamics_network_args = list(
product(
batch_size, num_res_blocks, num_channels, reward_head_channels, fc_reward_layers, output_support_size,
flatten_output_size_for_reward_head
)
)
action_space_size = [2, 3]
value_head_channels = [8]
policy_head_channels = [8]
fc_value_layers = [[
16,
]]
fc_policy_layers = [[
16,
]]
observation_shape = [1, 3, 3]
prediction_network_args = list(
product(
action_space_size,
batch_size,
num_res_blocks,
num_channels,
value_head_channels,
policy_head_channels,
fc_value_layers,
fc_policy_layers,
output_support_size,
)
)
@pytest.mark.unittest
class TestMuZeroModel:
def output_check(self, model, outputs):
if isinstance(outputs, torch.Tensor):
loss = outputs.sum()
elif isinstance(outputs, list):
loss = sum([t.sum() for t in outputs])
elif isinstance(outputs, dict):
loss = sum([v.sum() for v in outputs.values()])
is_differentiable(loss, model)
@pytest.mark.parametrize(
'action_space_size, batch_size, num_res_blocks, num_channels, value_head_channels, policy_head_channels, fc_value_layers, fc_policy_layers, output_support_size',
prediction_network_args
)
def test_prediction_network(
self, action_space_size, batch_size, num_res_blocks, num_channels, value_head_channels, policy_head_channels,
fc_value_layers, fc_policy_layers, output_support_size
):
obs = torch.rand(batch_size, num_channels, 3, 3)
flatten_output_size_for_value_head = value_head_channels * observation_shape[1] * observation_shape[2]
flatten_output_size_for_policy_head = policy_head_channels * observation_shape[1] * observation_shape[2]
# print('='*20)
# print(batch_size, num_res_blocks, num_channels, action_space_size, fc_value_layers, fc_policy_layers, output_support_size)
# print('='*20)
prediction_network = PredictionNetwork(
observation_shape=observation_shape,
action_space_size=action_space_size,
num_res_blocks=num_res_blocks,
num_channels=num_channels,
value_head_channels=value_head_channels,
policy_head_channels=policy_head_channels,
fc_value_layers=fc_value_layers,
fc_policy_layers=fc_policy_layers,
output_support_size=output_support_size,
flatten_output_size_for_value_head=flatten_output_size_for_value_head,
flatten_output_size_for_policy_head=flatten_output_size_for_policy_head,
last_linear_layer_init_zero=True,
)
policy, value = prediction_network(obs)
assert policy.shape == torch.Size([batch_size, action_space_size])
assert value.shape == torch.Size([batch_size, output_support_size])
@pytest.mark.parametrize(
'batch_size, num_res_blocks, num_channels, reward_head_channels, fc_reward_layers, output_support_size,'
'flatten_output_size_for_reward_head', dynamics_network_args
)
def test_dynamics_network(
self, batch_size, num_res_blocks, num_channels, reward_head_channels, fc_reward_layers, output_support_size,
flatten_output_size_for_reward_head
):
observation_shape = [1, 3, 3]
action_space_size = 1
flatten_output_size_for_reward_head = reward_head_channels * observation_shape[1] * observation_shape[2]
state_action_embedding = torch.rand(batch_size, num_channels, observation_shape[1], observation_shape[2])
dynamics_network = DynamicsNetwork(
observation_shape=observation_shape,
action_encoding_dim=action_space_size,
num_res_blocks=num_res_blocks,
num_channels=num_channels,
reward_head_channels=reward_head_channels,
fc_reward_layers=fc_reward_layers,
output_support_size=output_support_size,
flatten_output_size_for_reward_head=flatten_output_size_for_reward_head
)
next_state, reward = dynamics_network(state_action_embedding)
assert next_state.shape == torch.Size([batch_size, num_channels - action_space_size, 3, 3])
assert reward.shape == torch.Size([batch_size, output_support_size])
if __name__ == "__main__":
batch_size = 10
num_res_blocks = 3
num_channels = 3 + 1 # 3 channels for observation and 1 channel for action
action_space_size = 1
reward_head_channels = 2
fc_reward_layers = [16, 8]
output_support_size = 2
flatten_output_size_for_reward_head = reward_head_channels * 3 * 3
state_action_embedding = torch.rand(batch_size, num_channels, 3, 3)
dynamics_network = DynamicsNetwork(
observation_shape=observation_shape,
action_encoding_dim=action_space_size,
num_res_blocks=num_res_blocks,
num_channels=num_channels,
reward_head_channels=reward_head_channels,
fc_reward_layers=fc_reward_layers,
output_support_size=output_support_size,
flatten_output_size_for_reward_head=flatten_output_size_for_reward_head
)
state, reward = dynamics_network(state_action_embedding)
assert state.shape == torch.Size([10, num_channels - action_space_size, 3, 3])
assert reward.shape == torch.Size([10, output_support_size])
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