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DarkflowTensorRT.py
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DarkflowTensorRT.py
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# ---------------------------------------------------------
# DarkflowTensorRT
# Copyright (c) 2019
# Licensed under The MIT License [see LICENSE for details]
# Written by Rudy Nurhadi
# ---------------------------------------------------------
import os
import time
import json
import numpy as np
import tensorrt as trt
import pycuda.autoinit
import pycuda.driver as cuda
from threading import Thread
from darkflow.net.framework import create_framework
from darkflow.defaults import argHandler
class DarkflowTensorRT():
def __init__(self, model_dir, model_name, max_batch_size, rebuild_engine=False):
self.MODEL_NAME = model_name
self.CWD_PATH = model_dir
self.PATH_TO_MODEL = os.path.join(self.CWD_PATH, '%s.uff' % self.MODEL_NAME)
self.PATH_TO_ENGINE = os.path.join(self.CWD_PATH, '%s.engine' % self.MODEL_NAME)
self.PATH_TO_META = os.path.join(self.CWD_PATH, '%s.meta' % self.MODEL_NAME)
with open(self.PATH_TO_META, 'r') as fp:
self.meta = json.load(fp)
self.FLAGS = argHandler()
self.FLAGS.setDefaults()
self.FLAGS.threshold = 0.1
self.yoloFramework = create_framework(self.meta, self.FLAGS)
self.MAX_BATCH_SIZE = max_batch_size
self.MAX_WORKSPACE_SIZE = 1 << 29
self.TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
self.DTYPE = trt.float16
# Model
self.INPUT_NAME = 'input'
self.INPUT_SHAPE = self.meta['inp_size']
self.OUTPUT_NAME = 'output'
self.prepare_engine(rebuild_engine)
def allocate_buffers(self, engine):
print('allocate buffers')
h_input = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(0)), trt.nptype(engine.get_binding_dtype(0)))
h_output = cuda.pagelocked_empty(trt.volume(engine.get_binding_shape(1)), trt.nptype(engine.get_binding_dtype(1)))
d_input = cuda.mem_alloc(h_input.nbytes)
d_output = cuda.mem_alloc(h_output.nbytes)
stream = cuda.Stream()
return stream, h_input, d_input, h_output, d_output
def build_engine(self, model_file):
print('build engine...')
with trt.Builder(self.TRT_LOGGER) as builder, builder.create_network() as network, trt.UffParser() as parser:
builder.max_workspace_size = self.MAX_WORKSPACE_SIZE
builder.max_batch_size = self.MAX_BATCH_SIZE
if self.DTYPE == trt.float16:
builder.fp16_mode = True
builder.strict_type_constraints = True
print("using float16 precision")
parser.register_input(self.INPUT_NAME, self.INPUT_SHAPE, trt.UffInputOrder.NHWC)
parser.register_output(self.OUTPUT_NAME)
parser.parse(model_file, network, self.DTYPE)
return builder.build_cuda_engine(network)
def load_input(self, img, host_buffer):
img_array = self.yoloFramework.resize_input(img).astype(trt.nptype(self.DTYPE)).ravel()
np.copyto(host_buffer, img_array)
def do_inference(self, context, stream, h_input, d_input, h_output, d_output):
# Transfer input data to the GPU.
cuda.memcpy_htod_async(d_input, h_input, stream)
# Run inference.
context.execute_async(batch_size=1, bindings=[int(d_input), int(d_output)], stream_handle=stream.handle)
# Transfer predictions back from the GPU.
cuda.memcpy_dtoh_async(h_output, d_output, stream)
return h_output
def prepare_engine(self, rebuild_engine):
self.engine = {}
self.stream = {}
self.h_input = {}
self.d_input = {}
self.h_output = {}
self.d_output = {}
self.context = {}
self.output = {}
print("prepare engine")
try:
if rebuild_engine:
raise("rebuild engine")
for i in range(self.MAX_BATCH_SIZE):
with open(self.PATH_TO_ENGINE, "rb") as f, trt.Runtime(self.TRT_LOGGER) as runtime:
self.engine[i] = runtime.deserialize_cuda_engine(f.read())
except:
engine_tmp = self.build_engine(self.PATH_TO_MODEL)
with open(self.PATH_TO_ENGINE, "wb") as f:
f.write(engine_tmp.serialize())
del engine_tmp
for i in range(self.MAX_BATCH_SIZE):
with open(self.PATH_TO_ENGINE, "rb") as f, trt.Runtime(self.TRT_LOGGER) as runtime:
self.engine[i] = runtime.deserialize_cuda_engine(f.read())
for i in range(self.MAX_BATCH_SIZE):
self.stream[i], self.h_input[i], self.d_input[i], self.h_output[i], self.d_output[i] = self.allocate_buffers(self.engine[i])
self.context[i] = self.engine[i].create_execution_context()
print("engine ready")
def return_predict(self, imgs):
results = []
imgs_new = []
imgs_sub = []
num_loop = int((len(imgs) - 1) / self.MAX_BATCH_SIZE) + 1
for i in range(len(imgs)):
if i > 0 and i % self.MAX_BATCH_SIZE == 0:
imgs_new.append(imgs_sub)
imgs_sub = []
imgs_sub.append(imgs[i])
imgs_new.append(imgs_sub)
imgs = imgs_new
for n in range(num_loop):
batch_size = len(imgs[n])
loadInputThreads = {}
for i in range(batch_size):
#self.load_input(imgs[n][i], self.h_input[i])
loadInputThreads[i] = Thread(target=self.load_input,
args=(imgs[n][i], self.h_input[i],))
loadInputThreads[i].start()
for i in range(batch_size):
loadInputThreads[i].join()
self.output[i] = self.do_inference(self.context[i], self.stream[i], self.h_input[i], self.d_input[i], self.h_output[i], self.d_output[i])
for i in range(batch_size):
self.stream[i].synchronize()
output = self.output[i].reshape(self.meta['out_size'])
boxes = self.yoloFramework.findboxes(output)
boxesInfo = list()
for box in boxes:
tmpBox = self.yoloFramework.process_box(box, imgs[n][i].shape[0], imgs[n][i].shape[1], 0)
if tmpBox is None:
continue
boxesInfo.append({
"label": tmpBox[4],
"confidence": tmpBox[6],
"topleft": {
"x": tmpBox[0],
"y": tmpBox[2]},
"bottomright": {
"x": tmpBox[1],
"y": tmpBox[3]}
})
results.append(boxesInfo)
return results