Manually import TensorRT converted model and display model outputs

[The-Magicians-Code]

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Question

I've managed to convert yolov5s6 640x640 model to .engine file from .onnx export, now that I've parsed it with tensorrt in python, I received the outputs shaped (1, 25500, 85), assuming the first parameter is the batch size, 85 is the number of classes, what does the middle dimension represent? How to obtain bounding box coordinates, confidence values and confidence classes from the output array by hand?

import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
import numpy as np
import torch
import os
from torch import nn
from collections import namedtuple, OrderedDict
# check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
device = torch.device("cpu")
if device.type == 'cpu':
    device = torch.device('cuda:0')

Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
logger = trt.Logger(trt.Logger.INFO)

w = os.path.join("models/yolov5m6_640x640_batch_1.engine")

with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
    model = runtime.deserialize_cuda_engine(f.read())
context = model.create_execution_context()
bindings = OrderedDict()
output_names = []
fp16 = False  # default updated below
dynamic = False
for i in range(model.num_bindings):
    name = model.get_binding_name(i)
    dtype = trt.nptype(model.get_binding_dtype(i))
    if model.binding_is_input(i):
        if -1 in tuple(model.get_binding_shape(i)):  # dynamic
            dynamic = True
            context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
        if dtype == np.float16:
            fp16 = True
    else:  # output
        output_names.append(name)
    shape = tuple(context.get_binding_shape(i))
    im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
    bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
batch_size = bindings['images'].shape[0]  # if dynamic, this is instead max batch size
# print(binding_addrs)
# print(batch_size)
# print(bindings)
print(bindings['output0'].shape) # This is the output array dimensions parameter
outputs = bindings['output0'] # This array contains the unprocessed values, which I am stuck with

Additional

No response

[github-actions]

👋 Hello @The-Magicians-Code, thank you for your interest in YOLOv5 🚀! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.

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Requirements

Python>=3.7.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

git clone https://github.com/ultralytics/yolov5  # clone
cd yolov5
pip install -r requirements.txt  # install

Environments

YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training, validation, inference, export and benchmarks on MacOS, Windows, and Ubuntu every 24 hours and on every commit.

[glenn-jocher]

@The-Magicians-Code 👋 Hello! Thanks for asking about Export Formats. YOLOv5 🚀 offers export to almost all of the common export formats. See our TFLite, ONNX, CoreML, TensorRT Export Tutorial for full details.

85 is [xywh, objectness, classes 0-79]

Formats

YOLOv5 inference is officially supported in 11 formats:

💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks.
💡 ProTip: Export to TensorRT for up to 5x GPU speedup. See GPU Benchmarks.

Format	export.py --include	Model
PyTorch	-	yolov5s.pt
TorchScript	torchscript	yolov5s.torchscript
ONNX	onnx	yolov5s.onnx
OpenVINO	openvino	yolov5s_openvino_model/
TensorRT	engine	yolov5s.engine
CoreML	coreml	yolov5s.mlmodel
TensorFlow SavedModel	saved_model	yolov5s_saved_model/
TensorFlow GraphDef	pb	yolov5s.pb
TensorFlow Lite	tflite	yolov5s.tflite
TensorFlow Edge TPU	edgetpu	yolov5s_edgetpu.tflite
TensorFlow.js	tfjs	yolov5s_web_model/
PaddlePaddle	paddle	yolov5s_paddle_model/

Benchmarks

Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook . To reproduce:

python benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0

Colab Pro V100 GPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 46.7/166.8 GB disk)

Benchmarks complete (458.07s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623                10.19
1             TorchScript        0.4623                 6.85
2                    ONNX        0.4623                14.63
3                OpenVINO           NaN                  NaN
4                TensorRT        0.4617                 1.89
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623                21.28
7     TensorFlow GraphDef        0.4623                21.22
8         TensorFlow Lite           NaN                  NaN
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Colab Pro CPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 41.5/166.8 GB disk)

Benchmarks complete (241.20s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623               127.61
1             TorchScript        0.4623               131.23
2                    ONNX        0.4623                69.34
3                OpenVINO        0.4623                66.52
4                TensorRT           NaN                  NaN
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623               123.79
7     TensorFlow GraphDef        0.4623               121.57
8         TensorFlow Lite        0.4623               316.61
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Export a Trained YOLOv5 Model

This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. yolov5s.pt is the 'small' model, the second smallest model available. Other options are yolov5n.pt, yolov5m.pt, yolov5l.pt and yolov5x.pt, along with their P6 counterparts i.e. yolov5s6.pt or you own custom training checkpoint i.e. runs/exp/weights/best.pt. For details on all available models please see our README table.

python export.py --weights yolov5s.pt --include torchscript onnx

💡 ProTip: Add --half to export models at FP16 half precision for smaller file sizes

Output:

export: data=data/coco128.yaml, weights=['yolov5s.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 🚀 v6.2-104-ge3e5122 Python-3.7.13 torch-1.12.1+cu113 CPU

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 274MB/s]

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients

PyTorch: starting from yolov5s.pt with output shape (1, 25200, 85) (14.1 MB)

TorchScript: starting export with torch 1.12.1+cu113...
TorchScript: export success ✅ 1.7s, saved as yolov5s.torchscript (28.1 MB)

ONNX: starting export with onnx 1.12.0...
ONNX: export success ✅ 2.3s, saved as yolov5s.onnx (28.0 MB)

Export complete (5.5s)
Results saved to /content/yolov5
Detect:          python detect.py --weights yolov5s.onnx 
Validate:        python val.py --weights yolov5s.onnx 
PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Visualize:       https://netron.app/

The 3 exported models will be saved alongside the original PyTorch model:

Netron Viewer is recommended for visualizing exported models:

Exported Model Usage Examples

detect.py runs inference on exported models:

python detect.py --weights yolov5s.pt                 # PyTorch
                           yolov5s.torchscript        # TorchScript
                           yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                           yolov5s_openvino_model     # OpenVINO
                           yolov5s.engine             # TensorRT
                           yolov5s.mlmodel            # CoreML (macOS only)
                           yolov5s_saved_model        # TensorFlow SavedModel
                           yolov5s.pb                 # TensorFlow GraphDef
                           yolov5s.tflite             # TensorFlow Lite
                           yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                           yolov5s_paddle_model       # PaddlePaddle

val.py runs validation on exported models:

python val.py --weights yolov5s.pt                 # PyTorch
                        yolov5s.torchscript        # TorchScript
                        yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                        yolov5s_openvino_model     # OpenVINO
                        yolov5s.engine             # TensorRT
                        yolov5s.mlmodel            # CoreML (macOS Only)
                        yolov5s_saved_model        # TensorFlow SavedModel
                        yolov5s.pb                 # TensorFlow GraphDef
                        yolov5s.tflite             # TensorFlow Lite
                        yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                        yolov5s_paddle_model       # PaddlePaddle

Use PyTorch Hub with exported YOLOv5 models:

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
                                                       'yolov5s.torchscript ')       # TorchScript
                                                       'yolov5s.onnx')               # ONNX Runtime
                                                       'yolov5s_openvino_model')     # OpenVINO
                                                       'yolov5s.engine')             # TensorRT
                                                       'yolov5s.mlmodel')            # CoreML (macOS Only)
                                                       'yolov5s_saved_model')        # TensorFlow SavedModel
                                                       'yolov5s.pb')                 # TensorFlow GraphDef
                                                       'yolov5s.tflite')             # TensorFlow Lite
                                                       'yolov5s_edgetpu.tflite')     # TensorFlow Edge TPU
                                                       'yolov5s_paddle_model')       # PaddlePaddle

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

OpenCV DNN inference

OpenCV inference with ONNX models:

python export.py --weights yolov5s.pt --include onnx

python detect.py --weights yolov5s.onnx --dnn  # detect
python val.py --weights yolov5s.onnx --dnn  # validate

C++ Inference

YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:

• https://github.com/Hexmagic/ONNX-yolov5/blob/master/src/test.cpp
• https://github.com/doleron/yolov5-opencv-cpp-python

YOLOv5 OpenVINO C++ inference examples:

• https://github.com/dacquaviva/yolov5-openvino-cpp-python
• https://github.com/UNeedCryDear/yolov5-seg-opencv-dnn-cpp

Good luck 🍀 and let us know if you have any other questions!

[The-Magicians-Code]

Thank you for the response.
Apparently I had to use non_maximum_suppression function and reformat the coordinates which ended up with the corresponding tensor, as a result of presenting a photograph of a ship to the model:

Resized to 640x640

The first row from the tensor seems to fit the requested parameters, as in [xmin, ymin, xmax, ymax, confidence, object_id]?

Am I correct?
But for some reason, drawing them with cv2 did not yield correct results.

How to proceed?

Current code:

img = cv2.imread("ship.jpeg")
print(img.shape)

batch_detections = torch.from_numpy(np.array(result))
batch_detections = non_max_suppression(batch_detections)

dh, dw = img.shape[:2] # Extract height and width dimensions
model_input_size = 640
for i in range(batch_detections[0].shape[0]):  # Loop through all filtered detections
    boxes = [i / model_input_size for i in batch_detections[0][i][:4]]  # Bounding box scaling
    xmin, ymin, xmax, ymax = int(boxes[0] * dw), int(boxes[1] * dh), int(boxes[2] * dw), int(boxes[3] * dh)  # Scaling for original image
    print(xmin, ymin, xmax, ymax)
    confidence = batch_detections[0][i][4]
    class_id = batch_detections[0][i][5]

    cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)  # Drawing on image
    
cv2.imwrite("result.jpg", img)

This is what the reference image looks like:

[github-actions]

👋 Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

Access additional YOLOv5 🚀 resources:

• Wiki – https://github.com/ultralytics/yolov5/wiki
• Tutorials – https://docs.ultralytics.com/yolov5
• Docs – https://docs.ultralytics.com

Access additional Ultralytics ⚡ resources:

• Ultralytics HUB – https://ultralytics.com/hub
• Vision API – https://ultralytics.com/yolov5
• About Us – https://ultralytics.com/about
• Join Our Team – https://ultralytics.com/work
• Contact Us – https://ultralytics.com/contact

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLOv5 🚀 and Vision AI ⭐!

[glenn-jocher]

@The-Magicians-Code thanks for reaching out!

It looks like you're on the right track! 😄 The outputs from the model indeed represent [xmin, ymin, xmax, ymax, confidence, object_id] as you mentioned. However, the coordinates must be properly scaled for the original image to draw correct bounding boxes.

It seems the issue might be related to the scaling of the bounding box coordinates. Make sure to perform the proper scaling based on the original image dimensions and the input size used for the model inference.

Also, before writing the outputs to the image, make sure to verify the coordinates by printing them and ensure they correspond to the correct regions in the image.

You might also want to verify the result tensor before and after the non-maximum suppression to ensure that the bounding box coordinates and confidence values are correct.

Feel free to share more details if you need further assistance! Good luck! 🚀