Description
I have a trained yolox model in .onnx format. In TensorRt version 8.2.5.1, in the code examples I found a directory with an example of working with yolov3. I ran this example and got an image of a dog with boxes drawn, everything is cool. Next, I tried to integrate yolox into this pipeline, but everything breaks down at the prediction reshaping stage.
Here is the code in the directory yolov3_onnx:
#!/usr/bin/env python3
#
# Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from __future__ import print_function
import numpy as np
np.bool = np.bool_
import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit
from PIL import ImageDraw
from data_processing import PreprocessYOLO, PostprocessYOLO, ALL_CATEGORIES
import sys, os
sys.path.insert(1, os.path.join(sys.path[0], ".."))
import common
from downloader import getFilePath
TRT_LOGGER = trt.Logger()
def draw_bboxes(image_raw, bboxes, confidences, categories, all_categories, bbox_color='blue'):
"""Draw the bounding boxes on the original input image and return it.
Keyword arguments:
image_raw -- a raw PIL Image
bboxes -- NumPy array containing the bounding box coordinates of N objects, with shape (N,4).
categories -- NumPy array containing the corresponding category for each object,
with shape (N,)
confidences -- NumPy array containing the corresponding confidence for each object,
with shape (N,)
all_categories -- a list of all categories in the correct ordered (required for looking up
the category name)
bbox_color -- an optional string specifying the color of the bounding boxes (default: 'blue')
"""
draw = ImageDraw.Draw(image_raw)
print(bboxes, confidences, categories)
for box, score, category in zip(bboxes, confidences, categories):
x_coord, y_coord, width, height = box
left = max(0, np.floor(x_coord + 0.5).astype(int))
top = max(0, np.floor(y_coord + 0.5).astype(int))
right = min(image_raw.width, np.floor(x_coord + width + 0.5).astype(int))
bottom = min(image_raw.height, np.floor(y_coord + height + 0.5).astype(int))
draw.rectangle(((left, top), (right, bottom)), outline=bbox_color)
draw.text((left, top - 12), '{0} {1:.2f}'.format(all_categories[category], score), fill=bbox_color)
return image_raw
def get_engine(onnx_file_path, engine_file_path=""):
"""Attempts to load a serialized engine if available, otherwise builds a new TensorRT engine and saves it."""
def build_engine():
"""Takes an ONNX file and creates a TensorRT engine to run inference with"""
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(common.EXPLICIT_BATCH) as network, builder.create_builder_config() as config, trt.OnnxParser(network, TRT_LOGGER) as parser, trt.Runtime(TRT_LOGGER) as runtime:
config.max_workspace_size = 1 << 28 # 256MiB
builder.max_batch_size = 1
# Parse model file
if not os.path.exists(onnx_file_path):
print('ONNX file {} not found, please run yolov3_to_onnx.py first to generate it.'.format(onnx_file_path))
exit(0)
print('Loading ONNX file from path {}...'.format(onnx_file_path))
with open(onnx_file_path, 'rb') as model:
print('Beginning ONNX file parsing')
if not parser.parse(model.read()):
print ('ERROR: Failed to parse the ONNX file.')
for error in range(parser.num_errors):
print (parser.get_error(error))
return None
# The actual yolov3.onnx is generated with batch size 64. Reshape input to batch size 1
network.get_input(0).shape = [1, 3, 608, 608]
print('Completed parsing of ONNX file')
print('Building an engine from file {}; this may take a while...'.format(onnx_file_path))
plan = builder.build_serialized_network(network, config)
engine = runtime.deserialize_cuda_engine(plan)
print("Completed creating Engine")
with open(engine_file_path, "wb") as f:
f.write(plan)
return engine
if os.path.exists(engine_file_path):
# If a serialized engine exists, use it instead of building an engine.
print("Reading engine from file {}".format(engine_file_path))
with open(engine_file_path, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime:
return runtime.deserialize_cuda_engine(f.read())
else:
return build_engine()
def main():
"""Create a TensorRT engine for ONNX-based YOLOv3-608 and run inference."""
# Try to load a previously generated YOLOv3-608 network graph in ONNX format:
onnx_file_path = 'yolov3.onnx'
engine_file_path = "yolov3.trt"
# Download a dog image and save it to the following file path:
#input_image_path = getFilePath('dog.jpg')
input_image_path = "./dog.jpg"
# Two-dimensional tuple with the target network's (spatial) input resolution in HW ordered
input_resolution_yolov3_HW = (608, 608)
# Create a pre-processor object by specifying the required input resolution for YOLOv3
preprocessor = PreprocessYOLO(input_resolution_yolov3_HW)
# Load an image from the specified input path, and return it together with a pre-processed version
image_raw, image = preprocessor.process(input_image_path)
# Store the shape of the original input image in WH format, we will need it for later
shape_orig_WH = image_raw.size
# Output shapes expected by the post-processor
output_shapes = [(1, 255, 19, 19), (1, 255, 38, 38), (1, 255, 76, 76)]
# Do inference with TensorRT
trt_outputs = []
with get_engine(onnx_file_path, engine_file_path) as engine, engine.create_execution_context() as context:
inputs, outputs, bindings, stream = common.allocate_buffers(engine)
# Do inference
print('Running inference on image {}...'.format("input_image_path"))
# Set host input to the image. The common.do_inference function will copy the input to the GPU before executing.
inputs[0].host = image
trt_outputs = common.do_inference_v2(context, bindings=bindings, inputs=inputs, outputs=outputs, stream=stream)
# Before doing post-processing, we need to reshape the outputs as the common.do_inference will give us flat arrays.
trt_outputs = [output.reshape(shape) for output, shape in zip(trt_outputs, output_shapes)]
postprocessor_args = {"yolo_masks": [(6, 7, 8), (3, 4, 5), (0, 1, 2)], # A list of 3 three-dimensional tuples for the YOLO masks
"yolo_anchors": [(10, 13), (16, 30), (33, 23), (30, 61), (62, 45), # A list of 9 two-dimensional tuples for the YOLO anchors
(59, 119), (116, 90), (156, 198), (373, 326)],
"obj_threshold": 0.6, # Threshold for object coverage, float value between 0 and 1
"nms_threshold": 0.5, # Threshold for non-max suppression algorithm, float value between 0 and 1
"yolo_input_resolution": input_resolution_yolov3_HW}
postprocessor = PostprocessYOLO(**postprocessor_args)
# Run the post-processing algorithms on the TensorRT outputs and get the bounding box details of detected objects
boxes, classes, scores = postprocessor.process(trt_outputs, (shape_orig_WH))
# Draw the bounding boxes onto the original input image and save it as a PNG file
obj_detected_img = draw_bboxes(image_raw, boxes, scores, classes, ALL_CATEGORIES)
output_image_path = 'dog_bboxes.png'
obj_detected_img.save(output_image_path, 'PNG')
print('Saved image with bounding boxes of detected objects to {}.'.format(output_image_path))
if __name__ == '__main__':
main()
I found a significant difference between yolov3 and yolox. I found a significant difference between yolov3 and yolox. yolov3 stores 3 output tensors, and yolox - 4. I am having great difficulty understanding and interpreting the output of yolox.
Yolox output layer
Yolov3 output layer
Environment
TensorRT Version: 8.2.5.1
GPU Type: NVIDIA GeForce RTX 2070 Super
Nvidia Driver Version: 530.41.03
CUDA Version:
CUDNN Version:
Operating System + Version: Ubuntu 22.04
Python Version (if applicable): 3.8.10
TensorFlow Version (if applicable):
PyTorch Version (if applicable):
Baremetal or Container (if container which image + tag): nvcr.io/nvidia/tensorrt:22.05-py3
Steps To Reproduce
- build the image and start the container
- change directory on workspace/tensorrt/samples/python/yolov3_onnx
- run yolov3_to _tensorrt.py
- get a pic with bounding boxes
- download a yolox_s weights
- check variable on 130 line