473 lines
16 KiB
Python
473 lines
16 KiB
Python
"""
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This file includes the torch models. We wrap the three
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models into one class for convenience.
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"""
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import numpy as np
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import torch
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from torch import nn
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import torch.nn.functional as F
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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(162, 128, batch_first=True)
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self.dense1 = nn.Linear(373 + 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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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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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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if return_value:
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return dict(values=x)
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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(x.shape[0], (1,))[0]
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else:
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action = torch.argmax(x,dim=0)[0]
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return dict(action=action)
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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(162, 128, batch_first=True)
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self.dense1 = nn.Linear(484 + 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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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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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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if return_value:
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return dict(values=x)
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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(x.shape[0], (1,))[0]
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else:
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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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def __init__(self):
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super().__init__()
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self.lstm = nn.LSTM(162, 128, batch_first=True)
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self.dense1 = nn.Linear(373 + 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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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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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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if return_value:
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return dict(values=x)
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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(x.shape[0], (1,))[0]
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else:
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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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def __init__(self):
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super().__init__()
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self.lstm = nn.LSTM(162, 128, batch_first=True)
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self.dense1 = nn.Linear(484 + 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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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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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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if return_value:
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return dict(values=x)
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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(x.shape[0], (1,))[0]
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else:
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action = torch.argmax(x,dim=0)[0]
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return dict(action=action)
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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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# input: B * 32 * 57
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# self.lstm = nn.LSTM(162, 512, batch_first=True)
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self.conv_z_1 = torch.nn.Sequential(
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nn.Conv2d(1, 64, kernel_size=(1,57)), # B * 1 * 64 * 32
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nn.ReLU(inplace=True),
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nn.BatchNorm2d(64),
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)
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# Squeeze(-1) B * 64 * 16
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self.conv_z_2 = torch.nn.Sequential(
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nn.Conv1d(64, 128, kernel_size=(5,), padding=2), # 128 * 16
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(128),
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)
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self.conv_z_3 = torch.nn.Sequential(
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nn.Conv1d(128, 256, kernel_size=(3,), padding=1), # 256 * 8
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(256),
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)
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self.conv_z_4 = torch.nn.Sequential(
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nn.Conv1d(256, 512, kernel_size=(3,), padding=1), # 512 * 4
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nn.ReLU(inplace=True),
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nn.BatchNorm1d(512),
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)
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self.dense1 = nn.Linear(519 + 1024, 1024)
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self.dense2 = nn.Linear(1024, 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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def forward(self, z, x, return_value=False, flags=None, debug=False):
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z = z.unsqueeze(1)
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z = self.conv_z_1(z)
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z = z.squeeze(-1)
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z = torch.max_pool1d(z, 2)
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z = self.conv_z_2(z)
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z = torch.max_pool1d(z, 2)
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z = self.conv_z_3(z)
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z = torch.max_pool1d(z, 2)
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z = self.conv_z_4(z)
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z = torch.max_pool1d(z, 2)
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z = z.flatten(1,2)
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x = torch.cat([z,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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if return_value:
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return dict(values=x)
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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(x.shape[0], (1,))[0]
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else:
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action = torch.argmax(x,dim=0)[0]
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return dict(action=action, max_value=torch.max(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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def __init__(self, in_planes, planes, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=(3,),
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stride=(stride,), padding=1, bias=False)
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self.bn1 = nn.BatchNorm1d(planes)
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self.conv2 = nn.Conv1d(planes, planes, kernel_size=(3,),
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stride=(1,), padding=1, bias=False)
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self.bn2 = nn.BatchNorm1d(planes)
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self.shortcut = nn.Sequential()
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# 经过处理后的x要与x的维度相同(尺寸和深度)
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# 如果不相同，需要添加卷积+BN来变换为同一维度
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if stride != 1 or in_planes != self.expansion * planes:
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self.shortcut = nn.Sequential(
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nn.Conv1d(in_planes, self.expansion * planes,
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kernel_size=(1,), stride=(stride,), bias=False),
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nn.BatchNorm1d(self.expansion * planes)
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)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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class ResnetModel(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*54*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*27*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*14*80
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self.layer2 = self._make_layer(BasicBlock, 160, 2, stride=2)#1*7*160
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self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*4*320
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# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.linear1 = nn.Linear(320 * BasicBlock.expansion * 4 + 15 * 4, 1024)
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self.linear2 = nn.Linear(1024, 512)
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self.linear3 = nn.Linear(512, 256)
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self.linear4 = 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 = 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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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 BidModel(nn.Module):
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def __init__(self):
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super().__init__()
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self.dense1 = nn.Linear(114, 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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def forward(self, z, x, return_value=False, flags=None, debug=False):
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x = self.dense1(x)
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x = F.leaky_relu(x)
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# x = F.relu(x)
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x = self.dense2(x)
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x = F.leaky_relu(x)
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# x = F.relu(x)
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x = self.dense3(x)
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x = F.leaky_relu(x)
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# x = F.relu(x)
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x = self.dense4(x)
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x = F.leaky_relu(x)
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# x = F.relu(x)
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x = self.dense5(x)
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# x = F.relu(x)
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x = F.leaky_relu(x)
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x = self.dense6(x)
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if return_value:
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return dict(values=x)
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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(x.shape[0], (1,))[0]
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else:
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action = torch.argmax(x,dim=0)[0]
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return dict(action=action, max_value=torch.max(x))
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# Model dict is only used in evaluation but not training
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model_dict = {}
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model_dict['landlord'] = LandlordLstmModel
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model_dict['landlord_up'] = FarmerLstmModel
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model_dict['landlord_down'] = FarmerLstmModel
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model_dict_resnet = {}
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model_dict_resnet['landlord'] = ResnetModel
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model_dict_resnet['landlord_up'] = ResnetModel
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model_dict_resnet['landlord_down'] = ResnetModel
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model_dict_resnet['bidding'] = BidModel
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model_dict_general = {}
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model_dict_general['landlord'] = GeneralModel
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model_dict_general['landlord_up'] = GeneralModel
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model_dict_general['landlord_down'] = GeneralModel
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model_dict_general['bidding'] = BidModel
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class General_Model:
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"""
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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):
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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'] = GeneralModel1().to(torch.device(device))
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self.models['landlord_up'] = GeneralModel1().to(torch.device(device))
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self.models['landlord_down'] = GeneralModel1().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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model = self.models[position]
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return model.forward(z, x, training, flags, debug)
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def share_memory(self):
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self.models['landlord'].share_memory()
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self.models['landlord_up'].share_memory()
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self.models['landlord_down'].share_memory()
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self.models['bidding'].share_memory()
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def eval(self):
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self.models['landlord'].eval()
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self.models['landlord_up'].eval()
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self.models['landlord_down'].eval()
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self.models['bidding'].eval()
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def parameters(self, position):
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return self.models[position].parameters()
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def get_model(self, position):
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return self.models[position]
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def get_models(self):
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return self.models
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class OldModel:
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"""
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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):
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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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self.models['landlord'] = LandlordLstmModel().to(torch.device(device))
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self.models['landlord_up'] = FarmerLstmModel().to(torch.device(device))
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self.models['landlord_down'] = FarmerLstmModel().to(torch.device(device))
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def forward(self, position, z, x, training=False, flags=None):
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model = self.models[position]
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return model.forward(z, x, training, flags)
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def share_memory(self):
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self.models['landlord'].share_memory()
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self.models['landlord_up'].share_memory()
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self.models['landlord_down'].share_memory()
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def eval(self):
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self.models['landlord'].eval()
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self.models['landlord_up'].eval()
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self.models['landlord_down'].eval()
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def parameters(self, position):
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return self.models[position].parameters()
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def get_model(self, position):
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return self.models[position]
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def get_models(self):
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return self.models
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class Model:
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"""
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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):
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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'] = ResnetModel().to(torch.device(device))
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self.models['landlord_up'] = ResnetModel().to(torch.device(device))
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self.models['landlord_down'] = ResnetModel().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):
|
||
model = self.models[position]
|
||
return model.forward(z, x, training, flags, debug)
|
||
|
||
def share_memory(self):
|
||
self.models['landlord'].share_memory()
|
||
self.models['landlord_up'].share_memory()
|
||
self.models['landlord_down'].share_memory()
|
||
self.models['bidding'].share_memory()
|
||
|
||
def eval(self):
|
||
self.models['landlord'].eval()
|
||
self.models['landlord_up'].eval()
|
||
self.models['landlord_down'].eval()
|
||
self.models['bidding'].eval()
|
||
|
||
def parameters(self, position):
|
||
return self.models[position].parameters()
|
||
|
||
def get_model(self, position):
|
||
return self.models[position]
|
||
|
||
def get_models(self):
|
||
return self.models |