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trt_inference.py
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trt_inference.py
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# encoding: utf-8
"""
@author: xingyu liao
@contact: [email protected]
"""
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)