github-actions[bot]
commented
1 year ago 👋 Hello @MEssam711, 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.
If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.
For business inquiries or professional support requests please visit https://ultralytics.com or email support@ultralytics.com.
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 # installEnvironments
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.
MEssam711
glenn-jocher
ego-thales
Search before asking
YOLOv5 Component
Validation
Bug
The appeared ERROR: Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) ninja: no work to do. Loading extension module quant_cpu... Using /home/hanysalah/.cache/torch_extensions/py310_cu117 as PyTorch extensions root... Detected CUDA files, patching ldflags Emitting ninja build file /home/hanysalah/.cache/torch_extensions/py310_cu117/quant_cuda/build.ninja... Building extension module quant_cuda... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) ninja: no work to do. Loading extension module quant_cuda... Using cache found in /home/hanysalah/.cache/torch/hub/ultralytics_yolov5_master YOLOv5 🚀 2023-1-23 Python-3.10.6 torch-1.13.0+cu117 CUDA:0 (NVIDIA GeForce GTX 1650, 4096MiB)
Fusing layers... YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients Adding AutoShape... total 60 layers ; using posit on 60 conv/linear layers loading annotations into memory... Done (t=0.74s) creating index... index created! Evaluation: 0%| | 0/1250 [00:00<?, ?it/s] Traceback (most recent call last): File "/home/hanysalah/technical/posits/conga2022/torchbench_coco-posit.py", line 83, in COCO.benchmark( File "/home/hanysalah/technical/posits/torchbench/torchbench/object_detection/coco.py", line 220, in benchmark test_results, speed_mem_metrics, run_hash = evaluate_detection_coco( File "/home/hanysalah/technical/posits/torchbench/torchbench/object_detection/utils.py", line 209, in evaluate_detection_coco original_output = model(input) File "/home/hanysalah/.local/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, *kwargs) File "/home/hanysalah/.local/lib/python3.10/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context return func(args, **kwargs) File "/home/hanysalah/.cache/torch/hub/ultralytics_yolov5_master/models/common.py", line 690, in forward im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) TypeError: transpose() received an invalid combination of arguments - got (tuple), but expected one of:
(int dim0, int dim1) (name dim0, name dim1)
Environment
-YOLO v5: from pytorchhub OS: Ubuntu 22 Python: 3.8
Minimal Reproducible Example
from torchbench.object_detection import COCO from torchbench.utils import send_model_to_device from torchbench.object_detection.transforms import Compose, ConvertCocoPolysToMask, ToTensor import torchvision import PIL
import torch.nn as nn import qtorch_plus from qtorch_plus.quant import configurable_table_quantize, posit_quantize
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True)
def coco_data_to_device(input, target, device: str = "cuda", non_blocking: bool = True): input = list(inp.to(device=device, non_blocking=non_blocking) for inp in input) target = [{k: v.to(device=device, non_blocking=non_blocking) for k, v in t.items()} for t in target] return input, target
def coco_collate_fn(batch): return tuple(zip(*batch))
def coco_output_transform(output, target): output = [{k: v.to("cpu") for k, v in t.items()} for t in output] return output, target
transforms = Compose([ConvertCocoPolysToMask(), ToTensor()])
def other_weight(input): input = posit_quantize(input, nsize=16, es=1) return input
def other_activation(input):
input = posit_quantize(input, nsize=16, es=1) return input def linear_weight(input): input = posit_quantize(input, nsize=8, es=1, scale= 4.0) return input def linear_activation(input): global act_data input = posit_quantize(input, nsize=8, es=1, scale= 0.5) return input
def forward_pre_hook_other(m, input): return (other_activation(input[0]),)
def forward_pre_hook_linear(m, input):
return (linear_activation(input[0]),) layer_count = 0 total_layer = 0
for name, module in model.named_modules(): if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear) : module.weight.data = linear_weight(module.weight.data) module.register_forward_pre_hook(forward_pre_hook_linear)
else: #should use fixedpoint or posit 16 for other layers 'weight if hasattr(module, 'weight'): total_layer +=1 module.weight.data = other_weight(module.weight.data) module.register_forward_pre_hook(forward_pre_hook_other)
print ("total %d layers ; using posit on %d conv/linear layers"%(total_layer, layer_count))
"""COCO.benchmark( model=model, paper_model_name='Mask R-CNN (ResNet-50-FPN)', paper_arxiv_id='1703.06870', transforms=transforms, model_output_transform=coco_output_transform, send_data_to_device=coco_data_to_device, collate_fn=coco_collate_fn, batch_size=4, num_gpu=1 )"""
COCO.benchmark( model=model, paper_model_name='Yolo', transforms=transforms, model_output_transform=coco_output_transform, send_data_to_device=coco_data_to_device, collate_fn=coco_collate_fn, batch_size=4, num_gpu=1 )
Additional
**I just need to get sotabench benchmark results using coco dataset val2017.
**You must uninstall the existing torchbench and install the below version instead, following the newly updated README in this repo: https://github.com/minhhn2910/conga2022/blob/main/README.md
Are you willing to submit a PR?