-
Notifications
You must be signed in to change notification settings - Fork 3
/
infer_pycuda.py
120 lines (111 loc) · 5.02 KB
/
infer_pycuda.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
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
import numpy as np
import cv2
import time
class BaseEngine(object):
def __init__(self, engine_path, imgsz=(640,640)):
self.imgsz = imgsz
logger = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(logger)
trt.init_libnvinfer_plugins(logger, namespace="")
with open(engine_path, "rb") as f:
serialized_engine = f.read()
engine = runtime.deserialize_cuda_engine(serialized_engine)
self.context = engine.create_execution_context()
self.inputs, self.outputs, self.bindings = [], [], []
self.stream = cuda.Stream()
for binding in engine:
size = trt.volume(engine.get_binding_shape(binding))
dtype = trt.nptype(engine.get_binding_dtype(binding))
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
self.bindings.append(int(device_mem))
if engine.binding_is_input(binding):
self.inputs.append({'host': host_mem, 'device': device_mem})
else:
self.outputs.append({'host': host_mem, 'device': device_mem})
def predict(self, img,threshold):
self.img = self.preprocess(img)
self.inputs[0]['host'] = np.ravel(self.img)
# transfer data to the gpu
for inp in self.inputs:
cuda.memcpy_htod_async(inp['device'], inp['host'], self.stream)
# run inference
self.context.execute_async_v2(
bindings=self.bindings,
stream_handle=self.stream.handle)
# fetch outputs from gpu
for out in self.outputs:
cuda.memcpy_dtoh_async(out['host'], out['device'], self.stream)
# synchronize stream
self.stream.synchronize()
data = [out['host'] for out in self.outputs]
results = self.postprocess(data,threshold)
return results
def letterbox(self,im,color=(114, 114, 114), auto=False, scaleup=True, stride=32):
# Resize and pad image while meeting stride-multiple constraints
shape = im.shape[:2] # current shape [height, width]
new_shape = self.imgsz
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)
# Scale ratio (new / old)
self.r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup: # only scale down, do not scale up (for better val mAP)
self.r = min(self.r, 1.0)
# Compute padding
new_unpad = int(round(shape[1] * self.r)), int(round(shape[0] * self.r))
self.dw, self.dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
if auto: # minimum rectangle
self.dw, self.dh = np.mod(self.dw, stride), np.mod(self.dh, stride) # wh padding
self.dw /= 2 # divide padding into 2 sides
self.dh /= 2
if shape[::-1] != new_unpad: # resize
im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(self.dh - 0.1)), int(round(self.dh + 0.1))
left, right = int(round(self.dw - 0.1)), int(round(self.dw + 0.1))
self.img = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
return self.img,self.r,self.dw,self.dh
def preprocess(self,image):
self.img,self.r,self.dw,self.dh = self.letterbox(image)
self.img = cv2.cvtColor(self.img,cv2.COLOR_BGR2RGB)
self.img = self.img.astype(np.float32)
self.img = self.img / 255.
self.img -= np.array([0.485, 0.456, 0.406])[None, None, :]
self.img /= np.array([0.229, 0.224, 0.225])[None, None, :]
self.img = self.img.transpose((2, 0, 1))
self.img = np.expand_dims(self.img,0)
return self.img
def postprocess(self,pred,threshold):
new_bboxes = []
num =int(pred[0][0])
bboxes = pred[1].reshape(-1,4)
scores = pred[2]
clas = pred[3]
for i in range(num):
if(scores[i] < threshold):
continue
xmin = (bboxes[i][0] - self.dw)/self.r
ymin = (bboxes[i][1] - self.dh)/self.r
xmax = (bboxes[i][2] - self.dw)/self.r
ymax = (bboxes[i][3] - self.dh)/self.r
new_bboxes.append([clas[i],scores[i],xmin,ymin,xmax,ymax])
return new_bboxes
def visualize(img,bbox_array):
for temp in bbox_array:
xmin = int(temp[2])
ymin = int(temp[3])
xmax = int(temp[4])
ymax = int(temp[5])
clas = int(temp[0])
score = temp[1]
cv2.rectangle(img,(xmin,ymin),(xmax,ymax), (105, 237, 249), 2)
img = cv2.putText(img, "class:"+str(clas)+" "+str(round(score,2)), (xmin,int(ymin)-5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (105, 237, 249), 1)
return img
Predictor = BaseEngine("./trt_model/ppyoloex_nms_fp16.engine")
img1 = cv2.imread("./pictures/bus.jpg")
results = Predictor.predict(img1,threshold=0.5)
img = visualize(img1,results)
cv2.imshow("img",img)
cv2.waitKey(0)