rlcard-showdown/pve_server/models.py

import torch
from torch import nn
import torch.nn.functional as F

class LandlordLstmModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(162, 128, batch_first = True)
        self.dense1 = nn.Linear(373 + 128, 512)
        self.dense2 = nn.Linear(512, 512)
        self.dense3 = nn.Linear(512, 512)
        self.dense4 = nn.Linear(512, 512)
        self.dense5 = nn.Linear(512, 512)
        self.dense5 = nn.Linear(512, 512)
        self.dense5 = nn.Linear(512, 512)
        self.dense6 = nn.Linear(512, 1)

    def forward(self, z, x):
        lstm_out, (h_n, _) = self.lstm(z)
        lstm_out = lstm_out[:,-1,:]
        x = torch.cat([lstm_out,x], dim=-1)
        x = self.dense1(x)
        x = torch.relu(x)
        x = self.dense2(x)
        x = torch.relu(x)
        x = self.dense3(x)
        x = torch.relu(x)
        x = self.dense4(x)
        x = torch.relu(x)
        x = self.dense5(x)
        x = torch.relu(x)
        x = self.dense6(x)
        return x

class FarmerLstmModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(162, 128, batch_first = True)
        self.dense1 = nn.Linear(484 + 128 , 512)
        self.dense2 = nn.Linear(512, 512)
        self.dense3 = nn.Linear(512, 512)
        self.dense4 = nn.Linear(512, 512)
        self.dense4 = nn.Linear(512, 512)
        self.dense4 = nn.Linear(512, 512)
        self.dense5 = nn.Linear(512, 512)
        self.dense6 = nn.Linear(512, 1)

    def forward(self, z, x):
        lstm_out, (h_n, _) = self.lstm(z)
        lstm_out = lstm_out[:,-1,:]
        x = torch.cat([lstm_out,x], dim=-1)
        x = self.dense1(x)
        x = torch.relu(x)
        x = self.dense2(x)
        x = torch.relu(x)
        x = self.dense3(x)
        x = torch.relu(x)
        x = self.dense4(x)
        x = torch.relu(x)
        x = self.dense5(x)
        x = torch.relu(x)
        x = self.dense6(x)
        return x


# 用于ResNet18和34的残差块，用的是2个3x3的卷积
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=(3,),
                               stride=(stride,), padding=1, bias=False)
        self.bn1 = nn.BatchNorm1d(planes)
        self.conv2 = nn.Conv1d(planes, planes, kernel_size=(3,),
                               stride=(1,), padding=1, bias=False)
        self.bn2 = nn.BatchNorm1d(planes)
        self.shortcut = nn.Sequential()
        # 经过处理后的x要与x的维度相同(尺寸和深度)
        # 如果不相同，需要添加卷积+BN来变换为同一维度
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv1d(in_planes, self.expansion * planes,
                          kernel_size=(1,), stride=(stride,), bias=False),
                nn.BatchNorm1d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out

class GeneralModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.in_planes = 80
        #input 1*108*41
        self.conv1 = nn.Conv1d(40, 80, kernel_size=(3,),
                               stride=(2,), padding=1, bias=False) #1*108*80

        self.bn1 = nn.BatchNorm1d(80)

        self.layer1 = self._make_layer(BasicBlock, 80, 2, stride=2)#1*27*80
        self.layer2 = self._make_layer(BasicBlock, 160, 2, stride=2)#1*14*160
        self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*7*320
        self.layer4 = self._make_layer(BasicBlock, 640, 2, stride=2)#1*4*640
        # self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 80, 2048)
        self.linear2 = nn.Linear(2048, 1024)
        self.linear3 = nn.Linear(1024, 512)
        self.linear4 = nn.Linear(512, 256)
        self.linear5 = nn.Linear(256, 1)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def get_onnx_params(self):
        return {
            'args': (
                torch.tensor(np.zeros([1, 40, 108]), dtype=torch.float32, device='cuda:0'),
                torch.tensor(np.zeros((1, 80)), dtype=torch.float32, device='cuda:0')
            ),
            'input_names': ['z_batch','x_batch'],
            'output_names': ['values'],
            'dynamic_axes': {
                'z_batch': {
                    0: "legal_actions"
                },
                'x_batch': {
                    0: "legal_actions"
                }
            }
        }

    def forward(self, z, x):
        out = F.relu(self.bn1(self.conv1(z)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = out.flatten(1,2)
        out = torch.cat([x,out], dim=-1)
        out = F.leaky_relu_(self.linear1(out))
        out = F.leaky_relu_(self.linear2(out))
        out = F.leaky_relu_(self.linear3(out))
        out = F.leaky_relu_(self.linear4(out))
        out = F.leaky_relu_(self.linear5(out))
        return dict(values=out)


class BidModel(nn.Module):
    def __init__(self):
        super().__init__()

        self.dense1 = nn.Linear(208, 512)
        self.dense2 = nn.Linear(512, 512)
        self.dense3 = nn.Linear(512, 512)
        self.dense4 = nn.Linear(512, 512)
        self.dense5 = nn.Linear(512, 512)
        self.dense6 = nn.Linear(512, 1)

    def forward(self, z, x):
        x = self.dense1(x)
        x = F.leaky_relu(x)
        # x = F.relu(x)
        x = self.dense2(x)
        x = F.leaky_relu(x)
        # x = F.relu(x)
        x = self.dense3(x)
        x = F.leaky_relu(x)
        # x = F.relu(x)
        x = self.dense4(x)
        x = F.leaky_relu(x)
        # x = F.relu(x)
        x = self.dense5(x)
        # x = F.relu(x)
        x = F.leaky_relu(x)
        x = self.dense6(x)
        return dict(values=x)


model_dict = {}
model_dict['landlord'] = LandlordLstmModel
model_dict['landlord_up'] = FarmerLstmModel
model_dict['landlord_down'] = FarmerLstmModel
model_dict_new = {}
model_dict_new['landlord'] = GeneralModel
model_dict_new['landlord_up'] = GeneralModel
model_dict_new['landlord_front'] = GeneralModel
model_dict_new['landlord_down'] = GeneralModel
model_dict_new['bidding'] = BidModel