取回legacy_mode参数代码
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zhiyang7
parent
08cf434500
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@ -113,6 +113,104 @@ class FarmerLstmModel(nn.Module):
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x = self.dense6(x)
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return dict(values=x)
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class LandlordLstmModelLegacy(nn.Module):
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def __init__(self):
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super().__init__()
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self.lstm = nn.LSTM(432, 128, batch_first=True)
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self.dense1 = nn.Linear(860 + 128, 1024)
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self.dense2 = nn.Linear(1024, 1024)
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self.dense3 = nn.Linear(1024, 768)
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self.dense4 = nn.Linear(768, 512)
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self.dense5 = nn.Linear(512, 256)
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self.dense6 = nn.Linear(256, 1)
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def get_onnx_params(self, device):
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return {
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'args': (
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torch.randn(1, 5, 432, requires_grad=True, device=device),
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torch.randn(1, 887, requires_grad=True, device=device),
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),
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'input_names': ['z_batch','x_batch'],
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'output_names': ['values'],
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'dynamic_axes': {
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'z_batch': {
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0: "batch_size"
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},
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'x_batch': {
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0: "batch_size"
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},
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'values': {
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0: "batch_size"
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}
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}
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}
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def forward(self, z, x):
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lstm_out, (h_n, _) = self.lstm(z)
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lstm_out = lstm_out[:,-1,:]
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x = torch.cat([lstm_out,x], dim=-1)
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x = self.dense1(x)
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x = torch.relu(x)
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x = self.dense2(x)
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x = torch.relu(x)
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x = self.dense3(x)
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x = torch.relu(x)
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x = self.dense4(x)
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x = torch.relu(x)
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x = self.dense5(x)
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x = torch.relu(x)
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x = self.dense6(x)
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return dict(values=x)
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class FarmerLstmModelLegacy(nn.Module):
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def __init__(self):
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super().__init__()
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self.lstm = nn.LSTM(432, 128, batch_first=True)
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self.dense1 = nn.Linear(1192 + 128, 1024)
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self.dense2 = nn.Linear(1024, 1024)
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self.dense3 = nn.Linear(1024, 768)
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self.dense4 = nn.Linear(768, 512)
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self.dense5 = nn.Linear(512, 256)
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self.dense6 = nn.Linear(256, 1)
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def get_onnx_params(self, device):
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return {
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'args': (
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torch.randn(1, 5, 432, requires_grad=True, device=device),
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torch.randn(1, 1219, requires_grad=True, device=device),
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),
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'input_names': ['z_batch','x_batch'],
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'output_names': ['values'],
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'dynamic_axes': {
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'z_batch': {
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0: "legal_actions"
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},
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'x_batch': {
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0: "legal_actions"
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},
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'values': {
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0: "batch_size"
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}
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}
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}
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def forward(self, z, x):
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lstm_out, (h_n, _) = self.lstm(z)
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lstm_out = lstm_out[:,-1,:]
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x = torch.cat([lstm_out,x], dim=-1)
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x = self.dense1(x)
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x = torch.relu(x)
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x = self.dense2(x)
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x = torch.relu(x)
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x = self.dense3(x)
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x = torch.relu(x)
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x = self.dense4(x)
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x = torch.relu(x)
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x = self.dense5(x)
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x = torch.relu(x)
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x = self.dense6(x)
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return dict(values=x)
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# 用于ResNet18和34的残差块，用的是2个3x3的卷积
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class BasicBlock(nn.Module):
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expansion = 1
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@ -214,6 +312,11 @@ model_dict['landlord'] = LandlordLstmModel
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model_dict['landlord_up'] = FarmerLstmModel
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model_dict['landlord_front'] = FarmerLstmModel
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model_dict['landlord_down'] = FarmerLstmModel
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model_dict_legacy = {}
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model_dict_legacy['landlord'] = LandlordLstmModelLegacy
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model_dict_legacy['landlord_up'] = FarmerLstmModelLegacy
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model_dict_legacy['landlord_front'] = FarmerLstmModelLegacy
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model_dict_legacy['landlord_down'] = FarmerLstmModelLegacy
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model_dict_new = {}
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model_dict_new['landlord'] = GeneralModel
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model_dict_new['landlord_up'] = GeneralModel
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@ -41,7 +41,7 @@ log.setLevel(logging.INFO)
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Buffers = typing.Dict[str, typing.List[torch.Tensor]]
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def create_env(flags):
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return Env(flags.objective, flags.old_model)
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return Env(flags.objective, flags.old_model, flags.lagacy_model)
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def get_batch(b_queues, position, flags, lock):
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"""
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@ -33,7 +33,7 @@ class Env:
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Doudizhu multi-agent wrapper
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"""
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def __init__(self, objective, old_model):
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def __init__(self, objective, old_model, legacy_model=False):
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"""
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Objective is wp/adp/logadp. It indicates whether considers
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bomb in reward calculation. Here, we use dummy agents.
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@ -108,7 +108,7 @@ class Env:
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self._env.info_sets[pos].player_id = pid
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self.infoset = self._game_infoset
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return get_obs(self.infoset, self.use_general)
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return get_obs(self.infoset, self.use_general, self.use_legacy)
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def step(self, action):
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"""
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@ -234,7 +234,7 @@ class DummyAgent(object):
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self.action = action
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def get_obs(infoset, use_general=True):
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def get_obs(infoset, use_general=True, use_legacy = False):
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"""
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This function obtains observations with imperfect information
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from the infoset. It has three branches since we encode
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@ -264,13 +264,13 @@ def get_obs(infoset, use_general=True):
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return _get_obs_general(infoset, infoset.player_position)
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else:
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if infoset.player_position == 'landlord':
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return _get_obs_landlord(infoset)
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return _get_obs_landlord(infoset, use_legacy)
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elif infoset.player_position == 'landlord_up':
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return _get_obs_landlord_up(infoset)
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return _get_obs_landlord_up(infoset, use_legacy)
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elif infoset.player_position == 'landlord_front':
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return _get_obs_landlord_front(infoset)
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return _get_obs_landlord_front(infoset, use_legacy)
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elif infoset.player_position == 'landlord_down':
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return _get_obs_landlord_down(infoset)
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return _get_obs_landlord_down(infoset, use_legacy)
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else:
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raise ValueError('')
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@ -377,19 +377,23 @@ def _process_action_seq(sequence, length=20, new_model=True):
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return sequence
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def _get_one_hot_bomb(bomb_num):
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def _get_one_hot_bomb(bomb_num, use_legacy = False):
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"""
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A utility function to encode the number of bombs
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into one-hot representation.
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"""
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one_hot = np.zeros(56) # 14 + 15 + 27
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one_hot[bomb_num[0]] = 1
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one_hot[14 + bomb_num[1]] = 1
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one_hot[29 + bomb_num[2]] = 1
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if use_legacy:
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one_hot = np.zeros(29)
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one_hot[bomb_num[0] + bomb_num[1]] = 1
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else:
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one_hot = np.zeros(56) # 14 + 15 + 27
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one_hot[bomb_num[0]] = 1
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one_hot[14 + bomb_num[1]] = 1
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one_hot[29 + bomb_num[2]] = 1
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return one_hot
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def _get_obs_landlord(infoset):
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def _get_obs_landlord(infoset, use_legacy = False):
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"""
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Obttain the landlord features. See Table 4 in
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https://arxiv.org/pdf/2106.06135.pdf
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@ -448,7 +452,7 @@ def _get_obs_landlord(infoset):
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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infoset.bomb_num, use_legacy)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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@ -489,7 +493,7 @@ def _get_obs_landlord(infoset):
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}
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return obs
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def _get_obs_landlord_up(infoset):
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def _get_obs_landlord_up(infoset, use_legacy = False):
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"""
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Obttain the landlord_up features. See Table 5 in
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https://arxiv.org/pdf/2106.06135.pdf
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@ -563,7 +567,7 @@ def _get_obs_landlord_up(infoset):
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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infoset.bomb_num, use_legacy)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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@ -610,7 +614,7 @@ def _get_obs_landlord_up(infoset):
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}
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return obs
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def _get_obs_landlord_front(infoset):
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def _get_obs_landlord_front(infoset, use_legacy = False):
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"""
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Obttain the landlord_front features. See Table 5 in
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https://arxiv.org/pdf/2106.06135.pdf
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@ -684,7 +688,7 @@ def _get_obs_landlord_front(infoset):
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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infoset.bomb_num, use_legacy)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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@ -731,7 +735,7 @@ def _get_obs_landlord_front(infoset):
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}
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return obs
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def _get_obs_landlord_down(infoset):
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def _get_obs_landlord_down(infoset, use_legacy = False):
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"""
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Obttain the landlord_down features. See Table 5 in
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https://arxiv.org/pdf/2106.06135.pdf
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@ -805,7 +809,7 @@ def _get_obs_landlord_down(infoset):
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num_legal_actions, axis=0)
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bomb_num = _get_one_hot_bomb(
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infoset.bomb_num)
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infoset.bomb_num, use_legacy)
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bomb_num_batch = np.repeat(
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bomb_num[np.newaxis, :],
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num_legal_actions, axis=0)
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@ -3,13 +3,16 @@ import numpy as np
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from douzero.env.env import get_obs
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def _load_model(position, model_path, model_type):
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from douzero.dmc.models import model_dict_new, model_dict, model_dict_new_legacy, model_dict_legacy
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def _load_model(position, model_path, model_type, use_legacy):
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from douzero.dmc.models import model_dict_new, model_dict, model_dict_legacy
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model = None
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if model_type == "general":
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model = model_dict_new[position]()
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else:
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model = model_dict[position]()
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if use_legacy:
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model = model_dict_legacy[position]()
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else:
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model = model_dict[position]()
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model_state_dict = model.state_dict()
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if torch.cuda.is_available():
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pretrained = torch.load(model_path, map_location='cuda:0')
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@ -27,8 +30,9 @@ def _load_model(position, model_path, model_type):
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class DeepAgent:
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def __init__(self, position, model_path):
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self.use_legacy = True if "legacy" in model_path else False
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self.model_type = "general" if "resnet" in model_path else "old"
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self.model = _load_model(position, model_path, self.model_type)
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self.model = _load_model(position, model_path, self.model_type, self.use_legacy)
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self.EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7',
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8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q',
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13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'}
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@ -36,7 +40,7 @@ class DeepAgent:
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if len(infoset.legal_actions) == 1:
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return infoset.legal_actions[0]
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obs = get_obs(infoset, self.model_type == "general")
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obs = get_obs(infoset, self.model_type == "general", self.use_legacy)
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z_batch = torch.from_numpy(obs['z_batch']).float()
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x_batch = torch.from_numpy(obs['x_batch']).float()
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