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model.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Mar 5 11:04:42 2018
@author: liuliang
"""
import torch.nn as nn
import torch
import torch.nn.init as init
from torch.utils.checkpoint import checkpoint
import numpy as np
class VGG16_BackBone(nn.Module):
def __init__(self):
super(VGG16_BackBone, self).__init__()
self.layer0 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1)
self.layer1 = nn.ReLU(inplace=True)
self.layer2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1)
self.layer3 = nn.ReLU(inplace=True)
self.layer4 = nn.MaxPool2d(kernel_size=2, stride=2)
# =============================================================================
self.layer5 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, padding=1)
self.layer6 = nn.ReLU(inplace=True)
self.layer7 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, padding=1)
self.layer8 = nn.ReLU(inplace=True)
self.layer9 = nn.MaxPool2d(kernel_size=2, stride=2)
# =============================================================================
self.layer10 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=3, padding=1)
self.layer11 = nn.ReLU(inplace=True)
self.layer12 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1)
self.layer13 = nn.ReLU(inplace=True)
self.layer14 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=3, padding=1)
self.layer15 = nn.ReLU(inplace=True)
self.layer16 = nn.MaxPool2d(kernel_size=2, stride=2)
# =============================================================================
self.layer17 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=3, padding=1)
self.layer18 = nn.ReLU(inplace=True)
self.layer19 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.layer20 = nn.ReLU(inplace=True)
self.layer21 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.layer22 = nn.ReLU(inplace=True)
self.layer23 = nn.MaxPool2d(kernel_size=2, stride=2)
# =============================================================================
self.layer24 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.layer25 = nn.ReLU(inplace=True)
self.layer26 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.layer27 = nn.ReLU(inplace=True)
self.layer28 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=3, padding=1)
self.layer29 = nn.ReLU(inplace=True)
self.layer30 = nn.MaxPool2d(kernel_size=2, stride=2)
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.xavier_uniform_(m.weight.data)
init.constant_(m.bias.data,0.01)
def forward(self, x):
x = checkpoint(self.layer0,x)
x = self.layer1(x)
x = checkpoint(self.layer2,x)
x = self.layer3(x)
x = checkpoint(self.layer4,x)
x = checkpoint(self.layer5,x)
x = self.layer6(x)
x = checkpoint(self.layer7,x)
x = self.layer8(x)
x = checkpoint(self.layer9,x)
x = checkpoint(self.layer10,x)
x = self.layer11(x)
x = checkpoint(self.layer12,x)
x = self.layer13(x)
x = checkpoint(self.layer14,x)
x = self.layer15(x)
x = checkpoint(self.layer16,x)
x = checkpoint(self.layer17,x)
x = self.layer18(x)
x = checkpoint(self.layer19,x)
x = self.layer20(x)
x = checkpoint(self.layer21,x)
x = self.layer22(x)
x = checkpoint(self.layer23,x)
x = checkpoint(self.layer24,x)
x = self.layer25(x)
x = checkpoint(self.layer26,x)
x = self.layer27(x)
x = checkpoint(self.layer28,x)
x = self.layer29(x)
x = checkpoint(self.layer30,x)
return x
class DQN(nn.Module):
def __init__(self, ACTION_NUMBER, HV_NUMBER):
super(DQN, self).__init__()
self.ACTION_NUMBER = ACTION_NUMBER
self.layer1 = nn.Conv2d(in_channels=HV_NUMBER+512, out_channels=1024, kernel_size=1, padding=0)
self.layer2 = nn.ReLU(inplace=True)
self.layer3 = nn.Conv2d(in_channels=1024, out_channels=1024, kernel_size=1, padding=0)
self.layer4 = nn.ReLU(inplace=True)
self.layer5 = nn.Conv2d(in_channels=1024, out_channels=ACTION_NUMBER, kernel_size=1, padding=0)
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.xavier_uniform_(m.weight.data)
init.constant_(m.bias.data,0.01)
def forward(self, x, hv):
x = [x,hv]
x = torch.cat(x,1)
del hv
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.layer5(x)
return x
class LibraNet(nn.Module):
def __init__(self, parameters):
super(LibraNet, self).__init__()
#Weights definition
Action1 = -10
Action2 = -5
Action3 = -2
Action4 = -1
Action5 = 1
Action6 = 2
Action7 = 5
Action8 = 10
Action9 = 999
self.A = [Action1,Action2,Action3,Action4,Action5,Action6,Action7,Action8,Action9]
self.A_mat = np.array(self.A)
self.A_mat_h_w = np.expand_dims(np.expand_dims(self.A_mat, 1), 2)
#Inverse discretization vector
self.class2num = np.zeros(parameters['Interval_N'])
for i in range(1, parameters['Interval_N']):
if i == 1:
lower = 0
else:
lower = np.exp((i - 2) * parameters['step_log'] + parameters['start_log'])
upper = np.exp((i - 1) * parameters['step_log'] + parameters['start_log'])
self.class2num[i] = (lower + upper) / 2
#Network definition
self.backbone = VGG16_BackBone()
self.DQN = DQN(parameters['ACTION_NUMBER'], parameters['HV_NUMBER'])
self.DQN_faze = DQN(parameters['ACTION_NUMBER'], parameters['HV_NUMBER'])
def get_feature( self, im_data=None):
return self.backbone(im_data)
def get_Q(self, feature=None, history_vectory=None):
return self.DQN(feature,history_vectory) * 100
def get_Q_faze(self, feature=None, history_vectory=None):
return self.DQN_faze(feature, history_vectory) * 100
def weights_normal_init(model, dev=0.01):
if isinstance(model, list):
for m in model:
weights_normal_init(m, dev)
else:
for m in model.modules():
if isinstance(m, nn.Conv2d):
#print torch.sum(m.weight)
m.weight.data.normal_(0, dev)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, nn.ConvTranspose2d):
#print torch.sum(m.weight)
m.weight.data.normal_(0, dev)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0, dev)