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