Question How could I set my custom classes on yolov6?
I have tried this:
https://github.com/meituan/YOLOv6/blob/main/docs/Train_custom_data.md
my data.yaml:
train: ./data/volleyball_07_03/img_label/images/train
val: ./data/volleyball_07_03/img_label/images/val
test: ./data/volleyball_07_03/img_label/images/val
is_coco: False
nc: 5 # 類別數量
names: ['player','libero','umpire','volleyball','net']
this is my inference result:
https://github.com/pei-ci/photos/blob/main/test_1.jpg?raw=true
I don't have the class named person, but it appears in the results.
my data.yaml can work on yolov5!
I solved the problem.
Aadd --yaml data/data.yaml into:
python tools/infer.py --weights output_dir/name/weights/best_ckpt.pt --source img.jpg --device 0
Related
I latino so mi english is not so good, but i try to explain my problem.
I have a dataset with 3 clasess, but when i try validation the dataset with else image, only recognize a 1 class, but i have 3, i follow the tutorials but i think that the problem is the file coco128.yml when i try to custom.
train: /content/dataset-train/images/train
val: /content/dataset-train/images/validation
test: # test images (optional)
# Classes
nc: 3
names:
0: pitbull
1: husky
2: sanbernando
this is my code, but only recognize the class "pitbull" someone can help me?
train: /content/dataset-train/images/train
val: /content/dataset-train/images/validation
test: # test images (optional)
# Classes
nc: 3
names: ["pitbull","husky","sanbernando"]
before try this way, but cant work same, i dont know how fix.
i git yolov7(https://github.com/WongKinYiu) with yolov7.pt and try to run
detect.py(i just want to run the example). it seems to be normal. but the output image has no mask.Why?
here is my code and log:
(PyTorch) E:\yolov7>python detect.py --weights yolov7.pt --source inference\images\bus.jpg
Namespace(weights=['yolov7.pt'], source='inference\\images\\bus.jpg', img_size=640, conf_thres=0.25, iou_thres=0.45, device='', view_img=False, save_txt=False, save_conf=False, nosave=False, classes=None, agnostic_nms=False, augment=False, update=False, project='runs/detect', name='exp', exist_ok=False, no_trace=False)
YOLOR v0.1-103-g6ded32c torch 1.11.0 CUDA:0 (NVIDIA GeForce GTX 1650, 4095.6875MB)
Fusing layers...
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
RepConv.fuse_repvgg_block
Model Summary: 306 layers, 36905341 parameters, 6652669 gradients
Convert model to Traced-model...
traced_script_module saved!
model is traced!
E:\anaconda\envs\PyTorch\lib\site-packages\torch\functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at C:\cb\pytorch_1000000000000\work\aten\src\ATen\native\TensorShape.cpp:2228.)
return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined]
Done. (151.6ms) Inference, (9.3ms) NMS
The image with the result is saved in: runs\detect\exp4\bus.jpg
Done. (3.713s)
and here is my result:output image
You set the argument classes=None.
The classes variable refers to a list of classes, where you define the index of the entities saved inside the weights you are referencing for the inference.
From detect.py:
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
Since you told the model to check for zero classes, the model itself will not report anything.
I was also facing this issue. After downgrading cuda version to 10.2 my problem was solved. I used Cuda 10.2 with PyTorch 1.10.0 via pip installation. I hope it helps you too.
pip3 install torch==1.10.0+cu102 torchvision==0.11.1+cu102 torchaudio===0.10.0+cu102 -f https://download.pytorch.org/whl/cu102/torch_stable.html
Source of the answer: link
Since when you are working with GPU, It allows half precision by default, which you can change by editing your detect.py file.
Go to detect.py file and not exactly sure but on line 31, you will see this line of code:
half = device.type != 'cpu' # half precision only supported on CUDA
Replace that line with
half = False
and then save default.py file.
Now when you are using your detection command, make sure to use --device 0 that indicates your GPU must be utilize for detection.
python detect.py --weights yolov7.pt --device 0 --source inference\images\bus.jpg
I am trying to come up with a method to test a number of Jupyter notebooks. A test should run when a new notebook is implemented in a Github branch and submitted for a pull request. The tests are not that complicated, they are mostly just testing if the notebook runs end-to-end and without any errors, and maybe a few asserts. However:
There are certain calls in some cells that need to be mocked, e.g. a call to download the data from a database.
There may be some magic cells in the notebooks which run a pip command or something else.
I am open to use any testing library, such as 'pytest' or unittest, although pytest is preferred.
I looked at a few libraries for testing notebooks such as nbmake, treon, and testbook, but I was unable to make them work. I also tried to convert the notebook to a python file, but the magic cells were converted to a get_ipython().run_cell_magic(...) call which became an issue, since pytest uses python and not ipython, and get_ipython() is only available in ipython.
So, I am wondering what is a good way to test jupyter notebooks with all of that in mind. Any help is appreciated.
One straightforward approach I've already used is to execute the entire notebook with nbconvert.
A notebook failed.ipynb raising an exception will result in a failed run thanks to the --execute option that tells nbconvert to execute the notebook prior to its conversion.
jupyter nbconvert --to notebook --execute failed.ipynb
# ...
# Exception: FAILED
echo $?
# 1
Another correct notebook passed.ipynb will result in a successful export.
jupyter nbconvert --to notebook --execute passed.ipynb
# [NbConvertApp] Converting notebook passed.ipynb to notebook
# [NbConvertApp] Writing 1172 bytes to passed.nbconvert.ipynb
echo $?
# 0
Cherry on the cake, you can do the same through the API and so wrap it in Pytest!
import nbformat
import pytest
from nbconvert.preprocessors import ExecutePreprocessor
#pytest.mark.parametrize("notebook", ["passed.ipynb", "failed.ipynb"])
def test_notebook_exec(notebook):
with open(notebook) as f:
nb = nbformat.read(f, as_version=4)
ep = ExecutePreprocessor(timeout=600, kernel_name='python3')
try:
assert ep.preprocess(nb) is not None, f"Got empty notebook for {notebook}"
except Exception:
assert False, f"Failed executing {notebook}"
Running the test gives.
pytest test_nbconv.py
# FAILED test_nbconv.py::test_notebook_exec[failed.ipynb] - AssertionError: Failed executing failed.ipynb
# PASSED test_nbconv.py::test_notebook_exec[passed.ipynb]
Notes
There is several output formats, I've used here notebook.
This doesn’t convert a notebook to a different format per se, instead it allows the running of nbconvert preprocessors on a notebook, and/or conversion to other notebook formats.
The python code example is just a quick draft it can be largely improved.
Here is my own solution using testbook. Let's say I have a notebook called my_notebook.ipynb with the following content:
The trick is to inject a cell before my call to bigquery.Client and mock it:
from testbook import testbook
#testbook('./my_notebook.ipynb')
def test_get_details(tb):
tb.inject(
"""
import mock
mock_client = mock.MagicMock()
mock_df = pd.DataFrame()
mock_df['week'] = range(10)
mock_df['count'] = 5
p1 = mock.patch.object(bigquery, 'Client', return_value=mock_client)
mock_client.query().result().to_dataframe.return_value = mock_df
p1.start()
""",
before=2,
run=False
)
tb.execute()
dataframe = tb.get('dataframe')
assert dataframe.shape == (10, 2)
x = tb.get('x')
assert x == 7
When attempting to serve a new model coded using TensorFlow 2.0 with TensorFlow serving, I get the following error from my Docker container logs:
2019-09-03 08:56:24.984824: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:31] Reading SavedModel from: /models/model_modeFact/1567500955
2019-09-03 08:56:24.989902: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:54] Reading meta graph with tags { serve }
2019-09-03 08:56:25.002593: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:311] SavedModel load for tags { serve }; Status: fail. Took 17772 microseconds.
2019-09-03 08:56:25.002658: E tensorflow_serving/util/retrier.cc:37] Loading servable: {name: model_modeFact version: 1567500955} failed: Internal: Node {{node zero_fraction/total_zero/zero_count/else/_1/zero_fraction/cond}} of type StatelessIf has '_lower_using_switch_merge' attr set but it does not support lowering.
Using the saved_model_cli, the model works fine and can make predictions.
Initially I was getting this error: "TensorFlow Serving crossed columns strange error"
I found that this error might be fixed by swapping to tf-nightly-2.0-preview==2.0.0.dev20190819
But instead I am now I can't even get my model to be served.
The only changes I made to the code to compile my model in TF2 are:
# Added this line to force eager execution, necessary for tf.placeholders
tf.compat.v1.disable_eager_execution()
# For every usage of tf.estimator...
tf.compat.v1.estimator
# For every usage of tf.placeholder...
tf.compat.v1.placeholder
Like the previous problem, the goal is to have a prediction output from my served model, an output similar to when I use saved_model_cli. Something like this:
Result for output key all_class_ids:
[[0 1 2 3 4 5]]
Result for output key all_classes:
[[b'0' b'1' b'2' b'3' b'4' b'5']]
Result for output key class_ids:
[[2]]
Result for output key classes:
[[b'2']]
Result for output key logits:
[[ 0.11128154 -0.44881764 0.31520572 -0.08318427 -0.3479367 -0.08883157]]
Result for output key probabilities:
[[0.19719791 0.11263006 0.2418051 0.16234797 0.12458517 0.16143374]]
Most probably it happens because you are using TF2 docker image, try
tensorflow/serving:1.15.0-rc2
docker image, I hope it fixes this problem .
Try also calling tf.compat.v1.disable_v2_behavior() when app that is saving your model is started.
I wish to run some Tensorflow code on JupyterNotebook.
If run it on terminal, then the link above gives instructions like this:
python src/validate_on_lfw.py ~/datasets/lfw/lfw_mtcnnpy_160 ~/models/facenet/20170512-110547
Question: how do I run it on Jupyter notebook ? Thanks
e.g.,
# Load the model
facenet.load_model(args.model)
Simply replace args.model with ~/models/facenet/20170512-110547
# Load the model
facenet.load_model('~/models/facenet/20170512-110547')
will give error
usage: ipykernel_launcher.py [-h] [--lfw_batch_size LFW_BATCH_SIZE]
[--image_size IMAGE_SIZE] [--lfw_pairs LFW_PAIRS]
[--lfw_file_ext {jpg,png}]
[--lfw_nrof_folds LFW_NROF_FOLDS]
lfw_dir model
ipykernel_launcher.py: error: too few arguments
sys.argv
Out[5]:
['/anaconda/envs/tensorflow/lib/python2.7/site-packages/ipykernel_launcher.py',
'-f',
'/Users/my_name/Library/Jupyter/runtime/kernel-770c12c9-8fbe-44f7-91dd-4b0a5c5d7537.json']
Ok, simple solution...
Simply run it on Terminal as the given GitHub suggested and in the mean time print out the sys.argv on terminal like this
sys.argv = ['src/validate_on_lfw.py', '/Users/../datasets/lfw/lfw_mtcnnpy_160', '/Users/../models/facenet/20170512-110547']
Then use these values of sys.argv in JupyterNotebook in def parse_arguments(argv) as default values, and it worked