814 lines
28 KiB
Python
814 lines
28 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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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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class LandlordLstmModelLite(nn.Module):
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def __init__(self):
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super().__init__()
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self.lstm = nn.LSTM(276, 128, batch_first=True)
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self.dense1 = nn.Linear(590 + 128, 768)
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self.dense2 = nn.Linear(768, 768)
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self.dense3 = nn.Linear(768, 768)
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self.dense4 = nn.Linear(768, 768)
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self.dense5 = nn.Linear(768, 768)
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self.dense6 = nn.Linear(768, 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, 276, requires_grad=True, device=device),
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torch.randn(1, 590, 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 FarmerLstmModelLite(nn.Module):
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def __init__(self):
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super().__init__()
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self.lstm = nn.LSTM(276, 128, batch_first=True)
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self.dense1 = nn.Linear(798 + 128, 768)
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self.dense2 = nn.Linear(768, 768)
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self.dense3 = nn.Linear(768, 768)
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self.dense4 = nn.Linear(768, 768)
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self.dense5 = nn.Linear(768, 768)
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self.dense6 = nn.Linear(768, 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, 276, requires_grad=True, device=device),
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torch.randn(1, 798, 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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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(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 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, 860, 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 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(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 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, 1192, 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 GeneralModelLite(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*69*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*35*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*18*80
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self.layer2 = self._make_layer(BasicBlock, 160, 2, stride=2)#1*9*160
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self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*5*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 * 5 + 56, 1536)
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self.linear2 = nn.Linear(1536, 768)
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self.linear3 = nn.Linear(768, 384)
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self.linear4 = nn.Linear(384, 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, 69, 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 = 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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return dict(values=out)
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class UnifiedModelLite(nn.Module):
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def __init__(self):
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super().__init__()
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self.in_planes = 30
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#input 1*69*15
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self.conv1 = nn.Conv1d(15, 30, kernel_size=(3,),
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stride=(2,), padding=1, bias=False) #1*35*30
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self.bn1 = nn.BatchNorm1d(30)
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self.layer1 = self._make_layer(BasicBlock, 30, 2, stride=2)#1*18*30
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self.layer2 = self._make_layer(BasicBlock, 60, 2, stride=2)#1*9*60
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self.layer3 = self._make_layer(BasicBlock, 120, 2, stride=2)#1*5*120
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# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.lstm = nn.LSTM(276, 224, batch_first=True)
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self.linear1 = nn.Linear((120 * BasicBlock.expansion * 5 + 224) * 2, 2048)
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self.linear2 = nn.Linear(2048, 1024)
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self.linear3 = nn.Linear(1024, 1024)
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self.linear4 = nn.Linear(1024, 512)
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self.linear5 = nn.Linear(512, 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, 15, 69, requires_grad=True, device=device),
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torch.randn(1, 24, 276, 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 = out.flatten(1,2)
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is_landlord = z[0][0][0]
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lstm_out, (h_n, _) = self.lstm(x)
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lstm_out = lstm_out[:,-1,:]
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out = torch.cat([lstm_out,out], dim=1)
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out = torch.cat([out * is_landlord, out * (1 - is_landlord)], 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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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,),
|
||
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 + 56, 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, device=None):
|
||
return {
|
||
'args': (
|
||
torch.randn(1, 40, 108, requires_grad=True, device=device),
|
||
torch.randn(1, 56, requires_grad=True, device=device),
|
||
),
|
||
'input_names': ['z_batch','x_batch'],
|
||
'output_names': ['values'],
|
||
'dynamic_axes': {
|
||
'z_batch': {
|
||
0: "batch_size"
|
||
},
|
||
'x_batch': {
|
||
0: "batch_size"
|
||
},
|
||
'values': {
|
||
0: "batch_size"
|
||
}
|
||
}
|
||
}
|
||
|
||
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)
|
||
|
||
|
||
# Model dict is only used in evaluation but not training
|
||
model_dict = {}
|
||
model_dict['landlord'] = LandlordLstmModel
|
||
model_dict['landlord_up'] = FarmerLstmModel
|
||
model_dict['landlord_front'] = FarmerLstmModel
|
||
model_dict['landlord_down'] = FarmerLstmModel
|
||
model_dict_lite = {}
|
||
model_dict_lite['landlord'] = LandlordLstmModelLite
|
||
model_dict_lite['landlord_up'] = FarmerLstmModelLite
|
||
model_dict_lite['landlord_front'] = FarmerLstmModelLite
|
||
model_dict_lite['landlord_down'] = FarmerLstmModelLite
|
||
model_dict_legacy = {}
|
||
model_dict_legacy['landlord'] = LandlordLstmModelLegacy
|
||
model_dict_legacy['landlord_up'] = FarmerLstmModelLegacy
|
||
model_dict_legacy['landlord_front'] = FarmerLstmModelLegacy
|
||
model_dict_legacy['landlord_down'] = FarmerLstmModelLegacy
|
||
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_lite = {}
|
||
model_dict_new_lite['landlord'] = GeneralModelLite
|
||
model_dict_new_lite['landlord_up'] = GeneralModelLite
|
||
model_dict_new_lite['landlord_front'] = GeneralModelLite
|
||
model_dict_new_lite['landlord_down'] = GeneralModelLite
|
||
model_dict_uni_lite = {}
|
||
model_dict_uni_lite['landlord'] = UnifiedModelLite
|
||
model_dict_uni_lite['landlord_up'] = UnifiedModelLite
|
||
model_dict_uni_lite['landlord_front'] = UnifiedModelLite
|
||
model_dict_uni_lite['landlord_down'] = UnifiedModelLite
|
||
|
||
def forward_logic(self_model, position, z, x, device='cpu', return_value=False, flags=None):
|
||
legal_count = len(z)
|
||
if not flags.enable_onnx:
|
||
z = torch.tensor(z, device=device)
|
||
x = torch.tensor(x, device=device)
|
||
if legal_count >= 80:
|
||
partition_count = int(legal_count / 40)
|
||
sub_z = np.array_split(z, partition_count)
|
||
sub_x = np.array_split(x, partition_count)
|
||
if flags.enable_onnx:
|
||
model = self_model.onnx_models[position]
|
||
if legal_count >= 80:
|
||
values = np.ndarray((legal_count, 1))
|
||
j = 0
|
||
for i in range(partition_count):
|
||
onnx_out = model.run(None, {'z_batch': sub_z[i], 'x_batch': sub_x[i]})
|
||
values[j:j+len(sub_z[i])] = onnx_out[0]
|
||
j += len(sub_z[i])
|
||
else:
|
||
onnx_out = model.run(None, {'z_batch': z, 'x_batch': x})
|
||
values = onnx_out[0]
|
||
else:
|
||
if legal_count >= 80:
|
||
values = np.ndarray((legal_count, 1))
|
||
j = 0
|
||
for i in range(partition_count):
|
||
model = self_model.models[position]
|
||
model_out = model.forward(sub_z[i], sub_x[i])['values']
|
||
values[j:j+len(sub_z[i])] = model_out.cpu().detach().numpy()
|
||
j += len(sub_z[i])
|
||
else:
|
||
model = self_model.models[position]
|
||
values = model.forward(z, x)['values']
|
||
if return_value:
|
||
return dict(values = values.cpu().detach().numpy() if torch.is_tensor(values) else values)
|
||
else:
|
||
if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon:
|
||
if torch.is_tensor(values):
|
||
action = torch.randint(values.shape[0], (1,))[0].cpu().detach().numpy()
|
||
else:
|
||
action = np.random.randint(0, values.shape[0], (1,))[0]
|
||
else:
|
||
if torch.is_tensor(values):
|
||
action = torch.argmax(values,dim=0)[0].cpu().detach().numpy()
|
||
else:
|
||
action = np.argmax(values, axis=0)[0]
|
||
return dict(action = action)
|
||
|
||
class OldModel:
|
||
"""
|
||
The wrapper for the three models. We also wrap several
|
||
interfaces such as share_memory, eval, etc.
|
||
"""
|
||
def __init__(self, device=0, flags=None, lite_model = False):
|
||
self.models = {}
|
||
self.onnx_models = {}
|
||
self.flags = flags
|
||
if not device == "cpu":
|
||
device = 'cuda:' + str(device)
|
||
self.device = torch.device(device)
|
||
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
|
||
if flags is not None and flags.enable_onnx:
|
||
for position in positions:
|
||
self.models[position] = None
|
||
else:
|
||
for position in positions[1:]:
|
||
self.models[position] = FarmerLstmModelLite().to(self.device) if lite_model else FarmerLstmModel().to(self.device)
|
||
self.models['landlord'] = LandlordLstmModelLite().to(self.device) if lite_model else LandlordLstmModel().to(self.device)
|
||
self.onnx_models = {
|
||
'landlord': None,
|
||
'landlord_up': None,
|
||
'landlord_front': None,
|
||
'landlord_down': None
|
||
}
|
||
|
||
def set_onnx_model(self, device='cpu'):
|
||
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
|
||
for position in positions:
|
||
model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.onnx_model_path, self.flags.xpid, position))
|
||
if device == 'cpu':
|
||
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
|
||
else:
|
||
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
|
||
|
||
def get_onnx_params(self, position):
|
||
self.models[position].get_onnx_params(self.device)
|
||
|
||
def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
|
||
return forward_logic(self, position, z, x, device, return_value, flags)
|
||
|
||
def share_memory(self):
|
||
if self.models['landlord'] is not None:
|
||
self.models['landlord'].share_memory()
|
||
self.models['landlord_up'].share_memory()
|
||
self.models['landlord_front'].share_memory()
|
||
self.models['landlord_down'].share_memory()
|
||
|
||
def eval(self):
|
||
if self.models['landlord'] is not None:
|
||
self.models['landlord'].eval()
|
||
self.models['landlord_up'].eval()
|
||
self.models['landlord_front'].eval()
|
||
self.models['landlord_down'].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
|
||
|
||
|
||
class Model:
|
||
"""
|
||
The wrapper for the three models. We also wrap several
|
||
interfaces such as share_memory, eval, etc.
|
||
"""
|
||
def __init__(self, device=0, flags=None, lite_model = False):
|
||
self.models = {}
|
||
self.onnx_models = {}
|
||
self.flags = flags
|
||
if not device == "cpu":
|
||
device = 'cuda:' + str(device)
|
||
self.device = torch.device(device)
|
||
# model = GeneralModel().to(torch.device(device))
|
||
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
|
||
if flags is not None and flags.enable_onnx:
|
||
for position in positions:
|
||
self.models[position] = None
|
||
else:
|
||
for position in positions:
|
||
if lite_model:
|
||
self.models[position] = GeneralModelLite().to(self.device)
|
||
else:
|
||
self.models[position] = GeneralModel().to(self.device)
|
||
self.onnx_models = {
|
||
'landlord': None,
|
||
'landlord_up': None,
|
||
'landlord_front': None,
|
||
'landlord_down': None
|
||
}
|
||
|
||
def set_onnx_model(self, device='cpu'):
|
||
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
|
||
for position in positions:
|
||
model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.onnx_model_path, self.flags.xpid, position))
|
||
if device == 'cpu':
|
||
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
|
||
else:
|
||
self.onnx_models[position] = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
|
||
|
||
def get_onnx_params(self, position):
|
||
self.models[position].get_onnx_params(self.device)
|
||
|
||
def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
|
||
return forward_logic(self, position, z, x, device, return_value, flags)
|
||
|
||
def share_memory(self):
|
||
if self.models['landlord'] is not None:
|
||
self.models['landlord'].share_memory()
|
||
self.models['landlord_up'].share_memory()
|
||
self.models['landlord_front'].share_memory()
|
||
self.models['landlord_down'].share_memory()
|
||
|
||
def eval(self):
|
||
if self.models['landlord'] is not None:
|
||
self.models['landlord'].eval()
|
||
self.models['landlord_up'].eval()
|
||
self.models['landlord_front'].eval()
|
||
self.models['landlord_down'].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
|
||
|
||
|
||
class UnifiedModel:
|
||
"""
|
||
The wrapper for the three models. We also wrap several
|
||
interfaces such as share_memory, eval, etc.
|
||
"""
|
||
def __init__(self, device=0, flags=None, lite_model = False):
|
||
self.onnx_models = {}
|
||
self.model = None
|
||
self.models = {}
|
||
self.flags = flags
|
||
if not device == "cpu":
|
||
device = 'cuda:' + str(device)
|
||
self.device = torch.device(device)
|
||
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
|
||
if flags is not None and flags.enable_onnx:
|
||
self.model = None
|
||
else:
|
||
if lite_model:
|
||
self.model = UnifiedModelLite().to(self.device)
|
||
for position in positions:
|
||
self.models[position] = self.model
|
||
else:
|
||
self.model = GeneralModel().to(self.device)
|
||
self.onnx_model = None
|
||
|
||
def set_onnx_model(self, device='cpu'):
|
||
model_path = os.path.abspath('%s/%s/model_uni.onnx' % (self.flags.onnx_model_path, self.flags.xpid))
|
||
positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
|
||
if device == 'cpu':
|
||
self.onnx_model = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
|
||
else:
|
||
self.onnx_model = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
|
||
for position in positions:
|
||
self.onnx_models[position] = self.onnx_model
|
||
|
||
def get_onnx_params(self, position):
|
||
self.model.get_onnx_params(self.device)
|
||
|
||
def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
|
||
return forward_logic(self, position, z, x, device, return_value, flags)
|
||
|
||
def share_memory(self):
|
||
if self.model is not None:
|
||
self.model.share_memory()
|
||
|
||
def eval(self):
|
||
if self.model is not None:
|
||
self.model.eval()
|
||
|
||
def parameters(self, position):
|
||
return self.model.parameters()
|
||
|
||
def get_model(self, position):
|
||
return self.model
|
||
|
||
def get_models(self):
|
||
return {
|
||
'uni' : self.model
|
||
}
|
||
|