-
Notifications
You must be signed in to change notification settings - Fork 17
/
inference_OneSample_VIPL_PhysFormer.py
154 lines (98 loc) · 5.25 KB
/
inference_OneSample_VIPL_PhysFormer.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
from __future__ import print_function, division
import matplotlib.pyplot as plt
import argparse,os
import torch
import pandas as pd
import cv2
import numpy as np
import random
import math
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from model import ViT_ST_ST_Compact3_TDC_gra_sharp
from Loadtemporal_data_test import VIPL, Normaliztion, ToTensor
import torch.nn.functional as F
from torch.autograd import Variable
import torch.nn as nn
import torch.optim as optim
import copy
import pdb
import scipy.io as sio
from TorchLossComputer import TorchLossComputer
def FeatureMap2Heatmap( x, Score1, Score2, Score3):
## initial images
## initial images
org_img = x[0,:,32,:,:].cpu()
org_img = org_img.data.numpy()*128+127.5
org_img = org_img.transpose((1, 2, 0))
#org_img = cv2.cvtColor(org_img, cv2.COLOR_BGR2RGB)
cv2.imwrite(args.log+'/'+args.log + 'visual.jpg', org_img)
# [B, head, 640, 640]
org_img = Score1[0, 1].cpu().data.numpy()*4000
org_img = cv2.cvtColor(org_img, cv2.COLOR_GRAY2RGB)
cv2.imwrite(args.log+'/'+'Score1_head1.jpg', org_img)
org_img = Score2[0, 1].cpu().data.numpy()*4000
org_img = cv2.cvtColor(org_img, cv2.COLOR_GRAY2RGB)
cv2.imwrite(args.log+'/'+'Score2_head1.jpg', org_img)
org_img = Score3[0, 1].cpu().data.numpy()*4000
org_img = cv2.cvtColor(org_img, cv2.COLOR_GRAY2RGB)
cv2.imwrite(args.log+'/'+'Score3_head1.jpg', org_img)
# main function
def train_test():
isExists = os.path.exists(args.log)
if not isExists:
os.makedirs(args.log)
log_file = open(args.log+'/'+ args.log+'_log.txt', 'w')
VIPL_root_list = args.input_data + '/VIPL_frames/'
VIPL_test_list = 'VIPL_fold1_test1.txt'
dict_list = []
dict_list.append('Physformer_VIPL_fold1.pkl')
print('evaluation~ forward!\n')
model = ViT_ST_ST_Compact3_TDC_gra_sharp(image_size=(160,128,128), patches=(4,4,4), dim=96, ff_dim=144, num_heads=4, num_layers=12, dropout_rate=0.1, theta=0.7)
gra_sharp = 2.0
model = model.cuda()
model.load_state_dict(torch.load(dict_list[0]))
model.eval()
VIPL_testDL = VIPL(VIPL_test_list, VIPL_root_list, transform=transforms.Compose([Normaliztion(), ToTensor()]))
dataloader_test = DataLoader(VIPL_testDL, batch_size=1, shuffle=False, num_workers=2)
results_rPPG = []
results_HR_pred = []
with torch.no_grad():
for i, sample_batched in enumerate(dataloader_test):
inputs = sample_batched['video_x'].cuda()
clip_average_HR, frame_rate = sample_batched['clip_average_HR_peaks'].cuda(), sample_batched['framerate'].cuda()
rPPG_long = torch.randn(1).cuda()
#HR = 0.0
for clip in range(inputs.shape[1]):
rPPG, Score1, Score2, Score3 = model(inputs[:,clip,:,:,:,:], gra_sharp)
rPPG = rPPG[0, 30:30+160]
#HR_predicted = TorchLossComputer.cross_entropy_power_spectrum_forward_pred(rPPG, frame_rate)+40
#HR += HR_predicted
rPPG_long = torch.cat((rPPG_long, rPPG),dim=0)
#HR = HR/inputs.shape[1]
log_file.write('\n sample number :%d \n' % (i+1))
log_file.write('\n')
log_file.flush()
visual = FeatureMap2Heatmap(inputs[:,inputs.shape[1]-1,:,:,:,:], Score1, Score2, Score3)
## save the results as .mat
results_rPPG.append(rPPG_long[1:].cpu().data.numpy())
#results_HR_pred.append([HR.cpu().data.numpy(), clip_average_HR[0].cpu().data.numpy()])
# visual and save
#visual = FeatureMaP2Heatmap(x_visual, x_visual3232, x_visual1616)
sio.savemat( args.log+'/'+args.log+ '.mat' , {'outputs_rPPG_concat': results_rPPG})
#sio.savemat( args.log+'/'+args.log+ '_HR.mat' , {'outputs_HR': results_HR_pred})
print('Finished val')
log_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="save quality using landmarkpose model")
parser.add_argument('--gpu', type=int, default=2, help='the gpu id used for predict')
parser.add_argument('--input_data', type=str, default="/scratch/project_2003204/") # /scratch/project_2003204/VIPL_frames_Matlab/
parser.add_argument('--lr', type=float, default=0.0001, help='initial learning rate') #default=0.0001
parser.add_argument('--step_size', type=int, default=12, help='stepsize of optim.lr_scheduler.StepLR, how many epochs lr decays once')
parser.add_argument('--gamma', type=float, default=0.1, help='gamma of optim.lr_scheduler.StepLR, decay of lr')
parser.add_argument('--echo_batches', type=int, default=50, help='how many batches display once')
parser.add_argument('--epochs', type=int, default=10, help='total training epochs')
parser.add_argument('--log', type=str, default="Inference_Physformer_TDC07_sharp2_hid96_head4_layer12_VIPL", help='log and save model name')
parser.add_argument('--finetune', action='store_true', default=False, help='whether finetune other models')
args = parser.parse_args()
train_test()