-
Notifications
You must be signed in to change notification settings - Fork 18
/
Copy pathdemo_trt_alphapose.py
181 lines (161 loc) · 6.65 KB
/
demo_trt_alphapose.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
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
"""
计算alphapose(yolov3+fastpose)加速比的方法
"""
import torch
import time
from alphapose.models import builder
from easydict import EasyDict as edict
import yaml
import os
import argparse
import numpy as np
from tools.trt_lite import TrtLite
from detector.yolo.darknet_trt import Darknet
def get_parser():
parser = argparse.ArgumentParser(description='AlphaPose Demo')
parser.add_argument('--fastpose_cfg', type=str, default='./configs/coco/resnet/256x192_res50_lr1e-3_1x.yaml',
help='FastPose configure file name')
parser.add_argument('--yolo_cfg', type=str, default='./detector/yolo/cfg/yolov3-spp.cfg',
help='YOLOv3_SPP configure file name')
parser.add_argument('--weight', type=str, default='./detector/yolo/data/yolov3-spp.weights',
help='checkpoint file name')
parser.add_argument('--checkpoint', type=str, default='./pretrained_models/fast_res50_256x192.pth',
help='checkpoint file name')
parser.add_argument('--batch', type=int, default=1, help='batch size')
parser.add_argument('--device', type=str, default='cuda:0', help='gpu id')
parser.add_argument('--fastpose_engine', type=str, default='./alphaPose_-1_3_256_192_dynamic.engine',
help='the path of txt engine')
parser.add_argument('--yolo_engine', type=str, default='./yolov3_spp_-1_608_608_dynamic_folded.engine',
help='the path of txt engine')
args = parser.parse_args()
return args
def update_config(config_file):
with open(config_file) as f:
config = edict(yaml.load(f, Loader=yaml.FullLoader))
return config
def run_fastpose(args):
"""
运行alphapose模型，计算它的推理时间
"""
cfg = update_config(args.fastpose_cfg)
# 创建fastpose的模型
pose_model = builder.build_sppe(cfg.MODEL, preset_cfg=cfg.DATA_PRESET)
# 加载权重
print('Loading pose model from %s...' % (args.checkpoint,))
pose_model.load_state_dict(torch.load(args.checkpoint, map_location=args.device))
pose_model = pose_model.to('cuda:0')
input_data = torch.randn(arg.batch, 3, 256, 192, dtype=torch.float32).to('cuda:0')
# 转成numpy，用于对比加速结果
output_data_pytorch = pose_model(input_data).cpu().detach().numpy()
# 让模型跑100次，然后计算时间
nRound = 100
torch.cuda.synchronize()
t0 = time.time()
for i in range(nRound):
pose_model(input_data)
torch.cuda.synchronize()
time_pytorch = (time.time() - t0) / nRound
# print('PyTorch time:', time_pytorch)
return time_pytorch, output_data_pytorch
def run_fastpose_trt(args):
# 生成了两个trt模型
engine_file_path = args.fastpose_engine
if not os.path.exists(engine_file_path):
print('Engine file', engine_file_path, 'doesn\'t exist. Please run trtexec and re-run this script.')
exit(1)
print('====', engine_file_path, '===')
trt = TrtLite(engine_file_path=engine_file_path)
trt.print_info()
# 这个形状可以不使用
i2shape = {0: (args.batch, 3, 256, 192)}
io_info = trt.get_io_info(i2shape)
# 分配显存
d_buffers = trt.allocate_io_buffers(i2shape, True)
# 保存输出的结果
output_data_trt = np.zeros(io_info[1][2], dtype=np.float32)
input_data = torch.randn(args.batch, 3, 256, 192, dtype=torch.float32, device='cuda')
d_buffers[0] = input_data
trt.execute([t.data_ptr() for t in d_buffers], i2shape)
output_data_trt = d_buffers[1].cpu().numpy()
nRound = 100
torch.cuda.synchronize()
t0 = time.time()
for i in range(nRound):
trt.execute([t.data_ptr() for t in d_buffers], i2shape)
torch.cuda.synchronize()
time_trt = (time.time() - t0) / nRound
# print('TensorRT time:', time_trt)
return time_trt, output_data_trt
def run_yolov3(args):
"""
运行alphapose模型，计算它的推理时间
"""
# cfg = update_config(args.cfg)
# 创建模型
model = Darknet(args.yolo_cfg, args)
model.net_info['height'] = 608
# 加载权重
print('Loading pose model from %s...' % (args.weight,))
model.load_weights(args.weight)
model = model.to(args.device)
model = model.to('cuda:0')
input_data = torch.randn(args.batch, 3, 608, 608, dtype=torch.float32).to('cuda:0')
# 转成numpy，用于对比加速结果
output_data_pytorch = model(input_data).cpu().detach().numpy()
# 让模型跑10次，然后计算时间
nRound = 100
torch.cuda.synchronize()
t0 = time.time()
for i in range(nRound):
model(input_data)
torch.cuda.synchronize()
time_pytorch = (time.time() - t0) / nRound
# print('PyTorch time:', time_pytorch)
return time_pytorch, output_data_pytorch
def run_yolov3_trt(args):
# 生成了两个trt模型
engine_file_path = args.yolo_engine
if not os.path.exists(engine_file_path):
print('Engine file', engine_file_path, 'doesn\'t exist. Please run trtexec and re-run this script.')
exit(1)
print('====', engine_file_path, '===')
trt = TrtLite(dll_file='./build/ScatterND.so', engine_file_path=engine_file_path)
trt.print_info()
# 这个形状可以不使用
i2shape = {0: (args.batch, 3, 608, 608)}
io_info = trt.get_io_info(i2shape)
# 分配显存
d_buffers = trt.allocate_io_buffers(i2shape, True)
# 保存输出的结果
output_data_trt = np.zeros(io_info[1][2], dtype=np.float32)
input_data = torch.randn(args.batch, 3, 608, 608, dtype=torch.float32, device='cuda')
d_buffers[0] = input_data
trt.execute([t.data_ptr() for t in d_buffers], i2shape)
output_data_trt = d_buffers[1].cpu().numpy()
nRound = 100
torch.cuda.synchronize()
t0 = time.time()
for i in range(nRound):
trt.execute([t.data_ptr() for t in d_buffers], i2shape)
torch.cuda.synchronize()
time_trt = (time.time() - t0) / nRound
# print('TensorRT time:', time_trt)
return time_trt, output_data_trt
def run_pytorch(args):
# 执行fastpose + yolo
time_fastpose, _ = run_fastpose(args)
time_yolov3, _ = run_yolov3(args)
return time_fastpose + time_yolov3
def run_trt(args):
time_fastpose_trt, _ = run_fastpose_trt(args)
time_yolov3_trt, _ = run_yolov3_trt(args)
return time_fastpose_trt + time_yolov3_trt
if __name__ == '__main__':
arg = get_parser()
time_pytorch = run_pytorch(arg)
print("Pytorch time:", time_pytorch)
time_trt = run_trt(arg)
print('TensorRT time:', time_trt)
print('Speedup:', time_pytorch / time_trt)
# print('Average diff percentage:',
# np.mean(np.abs(output_data_pytorch - output_data_trt) / np.abs(output_data_pytorch)))