File size: 6,593 Bytes
376ab70 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 | # encoding: utf-8
"""
@author: xingyu liao
@contact: sherlockliao01@gmail.com
"""
import argparse
import glob
import os
import cv2
import numpy as np
import pycuda.driver as cuda
import tensorrt as trt
import tqdm
TRT_LOGGER = trt.Logger()
def get_parser():
parser = argparse.ArgumentParser(description="trt model inference")
parser.add_argument(
"--model-path",
default="outputs/trt_model/baseline.engine",
help="trt model path"
)
parser.add_argument(
"--input",
nargs="+",
help="A list of space separated input images; "
"or a single glob pattern such as 'directory/*.jpg'",
)
parser.add_argument(
"--output",
default="trt_output",
help="path to save trt model inference results"
)
parser.add_argument(
'--batch-size',
default=1,
type=int,
help='the maximum batch size of trt module'
)
parser.add_argument(
"--height",
type=int,
default=256,
help="height of image"
)
parser.add_argument(
"--width",
type=int,
default=128,
help="width of image"
)
return parser
class HostDeviceMem(object):
""" Host and Device Memory Package """
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
class TrtEngine:
def __init__(self, trt_file=None, gpu_idx=0, batch_size=1):
cuda.init()
self._batch_size = batch_size
self._device_ctx = cuda.Device(gpu_idx).make_context()
self._engine = self._load_engine(trt_file)
self._context = self._engine.create_execution_context()
self._input, self._output, self._bindings, self._stream = self._allocate_buffers(self._context)
def _load_engine(self, trt_file):
"""
Load tensorrt engine.
:param trt_file: tensorrt file.
:return:
ICudaEngine
"""
with open(trt_file, "rb") as f, \
trt.Runtime(TRT_LOGGER) as runtime:
engine = runtime.deserialize_cuda_engine(f.read())
return engine
def _allocate_buffers(self, context):
"""
Allocate device memory space for data.
:param context:
:return:
"""
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
for binding in self._engine:
size = trt.volume(self._engine.get_binding_shape(binding)) * self._engine.max_batch_size
dtype = trt.nptype(self._engine.get_binding_dtype(binding))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer to device bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
if self._engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
return inputs, outputs, bindings, stream
def infer(self, data):
"""
Real inference process.
:param model: Model objects
:param data: Preprocessed data
:return:
output
"""
# Copy data to input memory buffer
[np.copyto(_inp.host, data.ravel()) for _inp in self._input]
# Push to device
self._device_ctx.push()
# Transfer input data to the GPU.
# cuda.memcpy_htod_async(self._input.device, self._input.host, self._stream)
[cuda.memcpy_htod_async(inp.device, inp.host, self._stream) for inp in self._input]
# Run inference.
self._context.execute_async_v2(bindings=self._bindings, stream_handle=self._stream.handle)
# Transfer predictions back from the GPU.
# cuda.memcpy_dtoh_async(self._output.host, self._output.device, self._stream)
[cuda.memcpy_dtoh_async(out.host, out.device, self._stream) for out in self._output]
# Synchronize the stream
self._stream.synchronize()
# Pop the device
self._device_ctx.pop()
return [out.host.reshape(self._batch_size, -1) for out in self._output[::-1]]
def inference_on_images(self, imgs, new_size=(256, 128)):
trt_inputs = []
for img in imgs:
input_ndarray = self.preprocess(img, *new_size)
trt_inputs.append(input_ndarray)
trt_inputs = np.vstack(trt_inputs)
valid_bsz = trt_inputs.shape[0]
if valid_bsz < self._batch_size:
trt_inputs = np.vstack([trt_inputs, np.zeros((self._batch_size - valid_bsz, 3, *new_size))])
result, = self.infer(trt_inputs)
result = result[:valid_bsz]
feat = self.postprocess(result, axis=1)
return feat
@classmethod
def preprocess(cls, img, img_height, img_width):
# Apply pre-processing to image.
resize_img = cv2.resize(img, (img_width, img_height), interpolation=cv2.INTER_CUBIC)
type_img = resize_img.astype("float32").transpose(2, 0, 1)[np.newaxis] # (1, 3, h, w)
return type_img
@classmethod
def postprocess(cls, nparray, order=2, axis=-1):
"""Normalize a N-D numpy array along the specified axis."""
norm = np.linalg.norm(nparray, ord=order, axis=axis, keepdims=True)
return nparray / (norm + np.finfo(np.float32).eps)
def __del__(self):
del self._input
del self._output
del self._stream
self._device_ctx.detach() # release device context
if __name__ == "__main__":
args = get_parser().parse_args()
trt = TrtEngine(args.model_path, batch_size=args.batch_size)
if not os.path.exists(args.output): os.makedirs(args.output)
if args.input:
if os.path.isdir(args.input[0]):
args.input = glob.glob(os.path.expanduser(args.input[0]))
assert args.input, "The input path(s) was not found"
inputs = []
for img_path in tqdm.tqdm(args.input):
img = cv2.imread(img_path)
# the model expects RGB inputs
cvt_img = img[:, :, ::-1]
feat = trt.inference_on_images([cvt_img])
np.save(os.path.join(args.output, os.path.basename(img_path).split('.')[0] + '.npy'), feat)
|