370 lines
13 KiB
Python
370 lines
13 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 os
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import numpy as np
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import torch
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import onnxruntime
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from onnxruntime.datasets import get_example
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from torch import nn
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import torch.nn.functional as F
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def to_numpy(tensor):
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return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
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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(432, 128, batch_first=True)
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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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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 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(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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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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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 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 + 56, 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 get_onnx_params(self, device=None):
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return {
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'args': (
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torch.randn(1, 40, 108, requires_grad=True, device=device),
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torch.randn(1, 56, 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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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,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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return dict(values=out)
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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_front'] = FarmerLstmModel
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model_dict['landlord_down'] = FarmerLstmModel
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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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model_dict_new['landlord_front'] = GeneralModel
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model_dict_new['landlord_down'] = GeneralModel
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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, flags=None):
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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.device = torch.device(device)
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self.models['landlord'] = LandlordLstmModel().to(self.device)
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self.models['landlord_up'] = FarmerLstmModel().to(self.device)
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self.models['landlord_front'] = FarmerLstmModel().to(self.device)
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self.models['landlord_down'] = FarmerLstmModel().to(self.device)
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self.onnx_models = {
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'landlord': None,
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'landlord_up': None,
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'landlord_front': None,
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'landlord_down': None
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}
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def set_onnx_model(self, position, model_path):
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self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path))
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def get_onnx_params(self, position):
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self.models[position].get_onnx_params(self.device)
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def forward(self, position, z, x, return_value=False, flags=None):
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model = self.onnx_models[position]
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if model is None:
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model = self.models[position]
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values = model.forward(z, x)['values']
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else:
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onnx_out = model.run(None, {'z_batch': to_numpy(z), 'x_batch': to_numpy(x)})
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values = torch.tensor(onnx_out[0])
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if return_value:
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return dict(values=values)
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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(values.shape[0], (1,))[0]
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else:
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action = torch.argmax(values,dim=0)[0]
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return dict(action=action)
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def share_memory(self):
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if self.models['landlord'] is not None:
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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_front'].share_memory()
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self.models['landlord_down'].share_memory()
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def eval(self):
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if self.models['landlord'] is not None:
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self.models['landlord'].eval()
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self.models['landlord_up'].eval()
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self.models['landlord_front'].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, flags=None):
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self.models = {}
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self.onnx_models = {}
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self.flags = flags
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if not device == "cpu":
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device = 'cuda:' + str(device)
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self.device = torch.device(device)
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# model = GeneralModel().to(torch.device(device))
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positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
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if flags is not None and flags.enable_onnx:
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for position in positions:
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self.models[position] = None
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else:
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for position in positions:
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self.models[position] = GeneralModel().to(self.device)
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self.onnx_models = {
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'landlord': None,
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'landlord_up': None,
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'landlord_front': None,
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'landlord_down': None
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}
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def set_onnx_model(self, device='cpu'):
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positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
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for position in positions:
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model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.savedir, self.flags.xpid, position))
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if device == 'cpu':
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self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
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else:
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self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
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def get_onnx_params(self, position):
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self.models[position].get_onnx_params(self.device)
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def forward(self, position, z, x, return_value=False, flags=None, debug=False):
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model = self.onnx_models[position]
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if model is None:
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model = self.models[position]
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values = model.forward(z, x)['values']
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else:
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onnx_out = model.run(None, {'z_batch': to_numpy(z), 'x_batch': to_numpy(x)})
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values = torch.tensor(onnx_out[0])
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if return_value:
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return dict(values=values)
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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(values.shape[0], (1,))[0]
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else:
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action = torch.argmax(values,dim=0)[0]
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return dict(action=action)
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def share_memory(self):
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if self.models['landlord'] is not None:
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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_front'].share_memory()
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self.models['landlord_down'].share_memory()
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def eval(self):
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if self.models['landlord'] is not None:
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self.models['landlord'].eval()
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self.models['landlord_up'].eval()
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self.models['landlord_front'].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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