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GazeDPTR_V2.py
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GazeDPTR_V2.py
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import torch
import torch.nn as nn
import numpy as np
import math
import copy
from IVModule import Backbone, Transformer, PoseTransformer, TripleDifferentialProj, PositionalEncoder
def ep0(x):
return x.unsqueeze(0)
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
# used for origin.
transIn = 128
convDims = [64, 128, 256, 512]
# origin produces two features, one for gaze zone, one for gaze direction.
self.borigin = Backbone(2, transIn, convDims)
# norm only used for gaze estimation.
self.bnorm = Backbone(1, transIn, convDims)
self.ptrans = PoseTransformer(transIn, 3)
# Proj
self.proj = TripleDifferentialProj()
# 3 * (30 * 2 + 1)
self.PoseEncoder = PositionalEncoder(30, True)
self.MLP_Pos_x = nn.Sequential(
nn.Linear(183, 128),
nn.ReLU(inplace=True),
)
self.MLP_Pos_y = nn.Sequential(
nn.Linear(183, 128),
nn.ReLU(inplace=True),
)
self.MLP_Pos_z = nn.Sequential(
nn.Linear(183, 128),
nn.ReLU(inplace=True),
)
self.MLP_Pos = [self.MLP_Pos_x, self.MLP_Pos_y, self.MLP_Pos_z]
# Transformer to combine gaze point and feature
self.tripleTrans = Transformer(
input_dim = 128,
length = 3,
layer_num=2,
nhead=4
)
self.zoneTrans = Transformer(
input_dim = 128,
length = 2,
layer_num=2,
nhead=4
)
# MLP for gaze estimation
class_num = 122
self.MLP_o_dir = nn.Linear(transIn, 2)
self.MLP_n_dir = nn.Linear(transIn, 2)
module_list = []
for i in range(len(convDims)):
module_list.append(nn.Linear(transIn, 2))
self.MLPList_o = nn.ModuleList(module_list)
module_list = []
for i in range(len(convDims)):
module_list.append(nn.Linear(transIn, 2))
self.MLPList_n = nn.ModuleList(module_list)
self.MLP_o_dir2 = nn.Linear(transIn, 2)
self.MLP_n_dir2 = nn.Linear(transIn, 2)
self.MLP_o_zone = nn.Linear(transIn, class_num)
self.MLP_o_zone2 = nn.Linear(transIn, class_num)
self.MLP_o_zone3 = nn.Linear(transIn, class_num)
# Loss function
self.loss_op_re = nn.L1Loss()
self.loss_op_cls = nn.CrossEntropyLoss()
def forward(self, x_in, train=True):
# feature [outFeatureNum, Batch, transIn], MLfeatgure: list[x1, x2...]
# Extract feature from both two images
feature_o, feature_list_o= self.borigin(x_in['origin_face'])
feature_n, feature_list_n = self.bnorm(x_in['norm_face'])
# Get feature for different task
# [5, 128] [1. 5, 128]
feature_o_zone = feature_o[0,:]
feature_o_dir = feature_o[1,:]
feature_n_dir = feature_n.squeeze()
zone1 = self.MLP_o_zone(feature_o_zone)
# Fuse two direction feature and input it into transformer
features_dir = torch.cat([ep0(feature_o_dir), ep0(feature_n_dir)], 0)
features = self.ptrans(features_dir, x_in['pos'])
# Get fused feature
# feature_o_dir2 = features[0, :]
feature_n_dir2 = features[1, :]
# estimate gaze from fused feature
gaze = self.MLP_n_dir2(feature_n_dir2)
# zone = self.MLP_o_zone(feature_o_zone)
# Proj
gaze_proj = self.proj(gaze.detach(), x_in['gaze_origin'], x_in['norm_cam'])
gaze_proj_list = []
# gaze_proj_list.append(ep0(feature_o_zone))
for i, gaze2d in enumerate(gaze_proj):
gaze_proj_list.append(ep0(self.MLP_Pos[i](self.PoseEncoder.encode(gaze2d))))
triple_feature = torch.cat(gaze_proj_list, 0)
triple_feature = self.tripleTrans(triple_feature)
zone2 = self.MLP_o_zone2(triple_feature)
feature_o_zone2 = torch.cat([ep0(feature_o_zone), ep0(triple_feature)], 0)
feature_o_zone2 = self.zoneTrans(feature_o_zone2)
zone3 = self.MLP_o_zone3(feature_o_zone2)
zone = [zone1, zone2, zone3]
# for loss caculation
loss_gaze_o = []
loss_gaze_n = []
if train:
loss_gaze_n.append(self.MLP_n_dir(feature_n_dir))
loss_gaze_o.append(self.MLP_o_dir(feature_o_dir))
for i, feature in enumerate(feature_list_o):
loss_gaze_o.append(self.MLPList_o[i](feature))
for i, feature in enumerate(feature_list_n):
loss_gaze_n.append(self.MLPList_n[i](feature))
else:
zone = zone2.max(1)[1]
return gaze, zone, loss_gaze_o, loss_gaze_n
def loss(self, x_in, label):
gaze, zones, loss_gaze_o, loss_gaze_n = self.forward(x_in)
loss1 = 2 * self.loss_op_re(gaze, label.normGaze)
loss2 = 0
for zone in zones:
loss2 += (0.2/3) * self.loss_op_cls(zone, label.zone.view(-1))
loss3 = 0
for pred in loss_gaze_o:
loss3 += self.loss_op_re(pred, label.originGaze)
loss4 = 0
for pred in loss_gaze_n:
loss4 += self.loss_op_re(pred, label.normGaze)
loss = loss1 + loss2 + loss3 + loss4
return loss, [loss1, loss2, loss3, loss4]
if __name__ == '__main__':
x_in = {'origin': torch.zeros([5, 3, 224, 224]).cuda(),
'norm': torch.zeros([5, 3, 224, 224]).cuda(),
'pos': torch.zeros(5, 2, 6).cuda()
}
model = Model()
model = model.to('cuda')
print(model)
a = model(x_in)
print(a)