We are trying to run TrafficCamNet pruned model with tensorrt without deepstream.
First, I used tlt-converter command adapted from here to generate a TensorRT engine
tlt-converter resnet18_trafficcamnet_pruned.etlt
-k tlt_encode
-c trafficnet_int8.txt
-o output_cov/Sigmoid,output_bbox/BiasAdd
-d 3,544,960
-i nchw
-e trafficnet_int8.engine
-m 1 -t int8 -b 1
Then, I run the following code
import cv2
import pycuda.autoinit # This is needed for initializing CUDA driver
import numpy as np
import ctypes
import tensorrt as trt
import pycuda.driver as cuda
import os
import sys
import time
import argparse
from utils.display import open_window, set_display, show_fps
from utils.visualization import BBoxVisualizationdef _preprocess_trt(img, shape=(300, 300)):
“”“Preprocess an image before TRT SSD inferencing.”“”
img = cv2.resize(img, shape)
img = np.asarray(img).astype(np.float32)
img = img.transpose(2, 0, 1) / 255.0
img = np.reshape(img,(-1,))
print(img.shape)
return imgclass TrtTrafficCamNet(object):
def _load_plugins(self): if trt.__version__[0] < '7': ctypes.CDLL("ssd/libflattenconcat.so") trt.init_libnvinfer_plugins(self.trt_logger, '') def _load_engine(self): with open(self.model, 'rb') as f, trt.Runtime(self.trt_logger) as runtime: return runtime.deserialize_cuda_engine(f.read()) def _create_context(self): print(self.engine) for binding in self.engine: size = trt.volume(self.engine.get_binding_shape(binding)) * \ self.engine.max_batch_size host_mem = cuda.pagelocked_empty(size, np.float32) cuda_mem = cuda.mem_alloc(host_mem.nbytes) self.bindings.append(int(cuda_mem)) if self.engine.binding_is_input(binding): self.host_inputs.append(host_mem) self.cuda_inputs.append(cuda_mem) else: self.host_outputs.append(host_mem) self.cuda_outputs.append(cuda_mem) return self.engine.create_execution_context() def __init__(self, model, input_shape, output_layout=7): """Initialize TensorRT plugins, engine and conetxt.""" self.model = model self.input_shape = input_shape self.output_layout = output_layout self.trt_logger = trt.Logger(trt.Logger.INFO) self._load_plugins() self.engine = self._load_engine() self.host_inputs = [] self.cuda_inputs = [] self.host_outputs = [] self.cuda_outputs = [] self.bindings = [] self.stream = cuda.Stream() self.context = self._create_context() def __del__(self): """Free CUDA memories.""" del self.stream del self.cuda_outputs del self.cuda_inputs def detect(self, img): """Detect objects in the input image.""" img_resized = _preprocess_trt(img, self.input_shape) np.copyto(self.host_inputs[0], img_resized) cuda.memcpy_htod_async( self.cuda_inputs[0], self.host_inputs[0], self.stream) self.context.execute_async( batch_size=1, bindings=self.bindings, stream_handle=self.stream.handle) cuda.memcpy_dtoh_async( self.host_outputs[1], self.cuda_outputs[1], self.stream) cuda.memcpy_dtoh_async( self.host_outputs[0], self.cuda_outputs[0], self.stream) self.stream.synchronize() output = self.host_outputs[0] print(len(output)) for i in range (0, 50): print(output[i])INPUT_HW = (960, 544)
cls_dict = [
‘car’,
‘bicycle’,
‘person’,
‘road_sign’
]filename = “1.jpg”
result_file_name = str(filename)
img = cv2.imread(filename)
model_name =“TrafficCamNet/trafficnet_int8.engine”
traCamNet = TrtTrafficCamNet(model_name, INPUT_HW)
vis = BBoxVisualization(cls_dict)
print(“start detection!”)traCamNet.detect(img)
print(“finish!”)
Then I got the following output
What is the format of this output result?