参数调整
This commit is contained in:
parent
64bf792d15
commit
3fec4a6bc1
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@ -3,3 +3,6 @@ baselines*/
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douzero_checkpoints/
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.vscode/
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*.pkl
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venv/
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.idea/
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*.egg-info/
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@ -13,7 +13,7 @@ class LandlordLstmModel(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.dense1 = nn.Linear(887 + 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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@ -48,7 +48,7 @@ class FarmerLstmModel(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.dense1 = nn.Linear(1219 + 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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@ -79,16 +79,16 @@ class FarmerLstmModel(nn.Module):
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action = torch.argmax(x,dim=0)[0]
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return dict(action=action)
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class LandlordLstmNewModel(nn.Module):
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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(846 + 128, 512)
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self.dense2 = nn.Linear(512, 512)
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self.dense3 = nn.Linear(512, 512)
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self.dense4 = nn.Linear(512, 512)
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self.dense5 = nn.Linear(512, 512)
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self.dense6 = nn.Linear(512, 1)
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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 forward(self, z, x, return_value=False, flags=None):
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lstm_out, (h_n, _) = self.lstm(z)
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@ -114,16 +114,16 @@ class LandlordLstmNewModel(nn.Module):
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action = torch.argmax(x,dim=0)[0]
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return dict(action=action)
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class FarmerLstmNewModel(nn.Module):
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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(1178 + 128, 512)
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self.dense2 = nn.Linear(512, 512)
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self.dense3 = nn.Linear(512, 512)
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self.dense4 = nn.Linear(512, 512)
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self.dense5 = nn.Linear(512, 512)
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self.dense6 = nn.Linear(512, 1)
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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 forward(self, z, x, return_value=False, flags=None):
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lstm_out, (h_n, _) = self.lstm(z)
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@ -249,11 +249,10 @@ class BasicBlock(nn.Module):
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return out
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class GeneralModel(nn.Module):
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def __init__(self, use_legacy = False):
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class GeneralModelLegacy(nn.Module):
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def __init__(self):
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super().__init__()
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self.in_planes = 80
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self.use_legacy = use_legacy
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#input 1*108*41
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self.conv1 = nn.Conv1d(40, 80, kernel_size=(3,),
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stride=(2,), padding=1, bias=False) #1*108*80
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@ -265,9 +264,57 @@ class GeneralModel(nn.Module):
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self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*7*320
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self.layer4 = self._make_layer(BasicBlock, 640, 2, stride=2)#1*4*640
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# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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if self.use_legacy:
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self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 24 * 4, 2048)
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self.linear2 = nn.Linear(2048, 1024)
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self.linear3 = nn.Linear(1024, 512)
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self.linear4 = nn.Linear(512, 256)
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self.linear5 = nn.Linear(256, 1)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def forward(self, z, x, return_value=False, flags=None, debug=False):
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out = F.relu(self.bn1(self.conv1(z)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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out = out.flatten(1,2)
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out = torch.cat([x,x,x,x,out], dim=-1)
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out = F.leaky_relu_(self.linear1(out))
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out = F.leaky_relu_(self.linear2(out))
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out = F.leaky_relu_(self.linear3(out))
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out = F.leaky_relu_(self.linear4(out))
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out = F.leaky_relu_(self.linear5(out))
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if return_value:
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return dict(values=out)
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else:
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if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
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action = torch.randint(out.shape[0], (1,))[0]
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else:
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action = torch.argmax(out,dim=0)[0]
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return dict(action=action, max_value=torch.max(out))
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class GeneralModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.in_planes = 80
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#input 1*108*41
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self.conv1 = nn.Conv1d(40, 80, kernel_size=(3,),
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stride=(2,), padding=1, bias=False) #1*108*80
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self.bn1 = nn.BatchNorm1d(80)
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self.layer1 = self._make_layer(BasicBlock, 80, 2, stride=2)#1*27*80
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self.layer2 = self._make_layer(BasicBlock, 160, 2, stride=2)#1*14*160
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self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*7*320
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self.layer4 = self._make_layer(BasicBlock, 640, 2, stride=2)#1*4*640
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# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 80, 2048)
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self.linear2 = nn.Linear(2048, 1024)
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self.linear3 = nn.Linear(1024, 512)
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@ -289,9 +336,6 @@ class GeneralModel(nn.Module):
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out = self.layer3(out)
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out = self.layer4(out)
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out = out.flatten(1,2)
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if self.use_legacy:
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out = torch.cat([x,x,x,x,out], dim=-1)
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else:
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out = torch.cat([x,out], dim=-1)
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out = F.leaky_relu_(self.linear1(out))
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out = F.leaky_relu_(self.linear2(out))
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@ -355,6 +399,17 @@ 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_legacy = {}
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model_dict_new_legacy['landlord'] = GeneralModelLegacy
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model_dict_new_legacy['landlord_up'] = GeneralModelLegacy
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model_dict_new_legacy['landlord_front'] = GeneralModelLegacy
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model_dict_new_legacy['landlord_down'] = GeneralModelLegacy
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model_dict_new_legacy['bidding'] = BidModel
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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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@ -458,15 +513,15 @@ class Model:
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The wrapper for the three models. We also wrap several
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interfaces such as share_memory, eval, etc.
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"""
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def __init__(self, device=0, use_legacy = False):
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def __init__(self, device=0):
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self.models = {}
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if not device == "cpu":
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device = 'cuda:' + str(device)
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# model = GeneralModel().to(torch.device(device))
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self.models['landlord'] = GeneralModel(use_legacy).to(torch.device(device))
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self.models['landlord_up'] = GeneralModel(use_legacy).to(torch.device(device))
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self.models['landlord_front'] = GeneralModel(use_legacy).to(torch.device(device))
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self.models['landlord_down'] = GeneralModel(use_legacy).to(torch.device(device))
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self.models['landlord'] = GeneralModel().to(torch.device(device))
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self.models['landlord_up'] = GeneralModel().to(torch.device(device))
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self.models['landlord_front'] = GeneralModel().to(torch.device(device))
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self.models['landlord_down'] = GeneralModel().to(torch.device(device))
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self.models['bidding'] = BidModel().to(torch.device(device))
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def forward(self, position, z, x, training=False, flags=None, debug=False):
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@ -3,11 +3,17 @@ 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
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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_new_legacy, model_dict_legacy
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model = None
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if model_type == "general":
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if use_legacy:
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model = model_dict_new_legacy[position]()
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else:
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model = model_dict_new[position]()
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else:
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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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@ -27,8 +33,9 @@ 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 +43,7 @@ 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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32
evaluate.py
32
evaluate.py
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@ -4,7 +4,7 @@ import argparse
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from douzero.evaluation.simulation import evaluate
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def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b = 'baselines'):
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def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b = 'baselines', prefix_a = '', prefix_b = ''):
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if t == 0:
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args.landlord = 'random'
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args.landlord_up = 'random'
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@ -24,16 +24,16 @@ def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b =
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args.landlord_down = '%s/resnet_landlord_down_%i.ckpt' % (folder_a, frame)
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print('random vs %i' % frame)
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elif t == 3:
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args.landlord = '%s/resnet_landlord_%i.ckpt' % (folder_a, frame)
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args.landlord_up = '%s/resnet_landlord_up_%i.ckpt' % (folder_b, adp_frame)
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args.landlord_front = '%s/resnet_landlord_front_%i.ckpt' % (folder_b, adp_frame)
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args.landlord_down = '%s/resnet_landlord_down_%i.ckpt' % (folder_b, adp_frame)
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args.landlord = '%s/%sresnet_landlord_%i.ckpt' % (folder_a, prefix_a, frame)
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args.landlord_up = '%s/%sresnet_landlord_up_%i.ckpt' % (folder_b, prefix_b, adp_frame)
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args.landlord_front = '%s/%sresnet_landlord_front_%i.ckpt' % (folder_b, prefix_b, adp_frame)
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args.landlord_down = '%s/%sresnet_landlord_down_%i.ckpt' % (folder_b, prefix_b, adp_frame)
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print('%i vs %i' % (frame, adp_frame))
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elif t == 4:
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args.landlord = '%s/resnet_landlord_%i.ckpt' % (folder_b, adp_frame)
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args.landlord_up = '%s/resnet_landlord_up_%i.ckpt' % (folder_a, frame)
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args.landlord_front = '%s/resnet_landlord_front_%i.ckpt' % (folder_a, frame)
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args.landlord_down = '%s/resnet_landlord_down_%i.ckpt' % (folder_a, frame)
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args.landlord = '%s/%sresnet_landlord_%i.ckpt' % (folder_b, prefix_b, adp_frame)
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args.landlord_up = '%s/%sresnet_landlord_up_%i.ckpt' % (folder_a, prefix_a, frame)
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args.landlord_front = '%s/%sresnet_landlord_front_%i.ckpt' % (folder_a, prefix_a, frame)
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args.landlord_down = '%s/%sresnet_landlord_down_%i.ckpt' % (folder_a, prefix_a, frame)
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print('%i vs %i' % (adp_frame, frame))
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evaluate(args.landlord,
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@ -108,8 +108,10 @@ if __name__ == '__main__':
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# [14102400, 4968800, 'baselines', 'baselines'],
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# [14102400, 13252000, 'baselines', 'baselines2'],
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# [14102400, 15096800, 'baselines', 'baselines2'],
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[34828000, 40132800, 'baselines2', 'baselines2'],
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# [34828000, 40132800, 'baselines2', 'baselines2'],
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# [14102400, None, 'baselines', 'baselines'],
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[19918400, 19918400, 'baselines', 'baselines', 'legacy_', 'legacy_'],
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[9161600, 19918400, 'baselines', 'baselines', '', 'legacy_'],
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]
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for vs in reversed(eval_list):
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@ -119,11 +121,11 @@ if __name__ == '__main__':
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folder_b = vs[3]
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if adp_frame is None:
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if frame is None:
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make_evaluate(args, 0, None, None)
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make_evaluate(args, 0, None, None, folder_a , folder_b, vs[4], vs[5])
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else:
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make_evaluate(args, 1, frame, None)
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make_evaluate(args, 2, frame, None)
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make_evaluate(args, 1, frame, None, folder_a , folder_b, vs[4], vs[5])
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make_evaluate(args, 2, frame, None, folder_a , folder_b, vs[4], vs[5])
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else:
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make_evaluate(args, 3, frame, adp_frame, folder_a , folder_b)
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make_evaluate(args, 3, frame, adp_frame, folder_a , folder_b, vs[4], vs[5])
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if frame != adp_frame:
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make_evaluate(args, 4, frame, adp_frame, folder_a, folder_b)
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make_evaluate(args, 4, frame, adp_frame, folder_a, folder_b, vs[4], vs[5])
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