why the image url is wrong

[omaiyiwa]

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• I have searched the YOLOv5 issues and found no similar bug report.

YOLOv5 Component

Detection

Bug

1. Hello, I use predict.py in classify, I want to detect the url of the image, but the source shows ..\https:\stopscooterpic.s3.eu-central-1.amazonaws.com\2023-04-14\1af2dfdf767d4f5fb670437a89c84f3e202304104085012 .jpg,

2. the complete configuration is classify\predict2: weights=..\runs\train-cls\exp4\weights\best.pt, source=..\https:\stopscooterpic.s3.eu-central-1.amazonaws.com \2023-04-14\1af2dfdf767d4f5fb670437a89c84f3e202304104085012.jpg, data=..\data\coco128.yaml, imgsz=[640, 640], device=0, view_img=False, save_Fugment=False, nosave visualize=False, update=False, project=..\runs\predict-cls, name=exp, exist_ok=True, half=False, dnn=False, vid_stride=1,

3. I put the previous .. \Deleted, making the source become https:\stopscooterpic.s3.eu-central-1.amazonaws.com\2023-04-14\1af2dfdf767d4f5fb670437a89c84f3e202304104085012.jpg, but it is judged as False in is_url, at first I thought it was an S3 bucket problem. But I changed to https://ultralytics.com/images/zidane.jpg is the same.

4. The error is OSError: [WinError 123] The file name, directory name, or volume label syntax is incorrect. : 'https:\ultralytics.com\images\zidane.jpg'

Environment

YOLOv5 Python-3.8.0 torch-1.12.1+cu116 CUDA:0 (NVIDIA GeForce RTX 3090 Ti, 24563MiB)
windows 10

Minimal Reproducible Example

File "D:/yolov5-master/classify/predict2.py", line 110, in run
dataset = LoadImages(source, img_size=imgsz, transforms=classify_transforms(imgsz[0]), vid_stride=vid_stride)
File "D:\yolov5-master\utils\dataloaders.py", line 246, in init
p = str(Path(p).resolve())
File "E:\anaconda\envs\yolo\lib\pathlib.py", line 1159, in resolve
s = self._flavour.resolve(self, strict=strict)
File "E:\anaconda\envs\yolo\lib\pathlib.py", line 202, in resolve
s = self._ext_to_normal(_getfinalpathname(s))
OSError: [WinError 123] 文件名、目录名或卷标语法不正确。: 'https:\ultralytics.com\images\zidane.jpg'

Additional

No response

Are you willing to submit a PR?

• Yes I'd like to help by submitting a PR!

[glenn-jocher]

@omaiyiwa it seems that you are passing image URLs as sources when using predict.py for object detection using YOLOv5. However, the error is most likely caused by the incorrect path syntax. Instead of https:\\ultralytics.com\\images\\zidane.jpg it should be https://ultralytics.com/images/zidane.jpg. Note the forward slashes and lack of escape characters. I suggest you modify your source URLs in this way and try again. Also, please note that is_url is only used to determine if the source is a URL or not and does not affect the detection process itself. Finally, if you are looking for more information regarding YOLOv5 and its functions, you may find helpful documentation at https://docs.ultralytics.com/yolov5.

[omaiyiwa]

But what I entered is in the correct format

is_url is False

[omaiyiwa]

is classify's predict.py file

[glenn-jocher]

@omaiyiwa, I apologize for my previous response. I didn't recognize that you are passing a URL and that the error is caused by an incorrect path. It appears that YOLOv5's LoadImages() method does not accept URLs as sources directly, and it expects a local file path. To resolve this issue, you might want to consider downloading and saving the file locally or passing a path to the file on your computer as the source to detect it.

For instance, in your current configuration, you can download the image and save it locally then pass the path to the saved local image to the source parameter in the predict.py file. Here is a sample code to download and save an image:

import urllib.request

url = 'https://ultralytics.com/images/zidane.jpg' #update URL here
filename = url.split("/")[-1]

urllib.request.urlretrieve(url, filename)

This code downloads the image from the specified URL and saves the file with the same name as provided in the URL in the current directory. After that, you can pass the saved file path to the model(source) function.

Please let me know if you have any further questions.

[omaiyiwa]

So what I did initially to get the image url from S3 bucket was wrong, is there any other way to deploy the model to amazon sagemaker

[glenn-jocher]

@omaiyiwa yes, you can deploy your YOLOv5 model on Amazon SageMaker for inference. You can do this by creating a SageMaker endpoint for your model. This will allow you to send image data to the endpoint, where the model will make inferences and return the results.

To do this, you will need to follow these steps:

1. Train and save your YOLOv5 model locally.
2. Upload your saved model to an Amazon S3 bucket.
3. Use SageMaker Python SDK to create a SageMaker model from your saved YOLOv5 model.
4. Create an endpoint configuration for your model.
5. Deploy your model by creating an endpoint.

Here is a high-level example of how you can deploy your model to SageMaker:

from sagemaker.pytorch import PyTorchModel
import sagemaker

# Set up an S3 bucket to store data and model artifacts
sagemaker_session = sagemaker.Session()
bucket = sagemaker_session.default_bucket()

# Upload your saved YOLOv5 model to Amazon S3
model_path = sagemaker_session.upload_data('path/to/your/saved/yolov5-model', bucket, key_prefix='yolov5-model')

# Create a PyTorchModel from the saved model
model = PyTorchModel(model_data=model_path, role='your-sagemaker-role', framework_version='1.8.1',
                     entry_point='your-entry-point.py', source_dir='path/to/your/train/script')

# Create an endpoint configuration and deploy your model
endpoint_config_name = 'your-endpoint-config-name'
endpoint_name = 'your-endpoint-name'

endpoint_config = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge', endpoint_name=endpoint_name,
                               endpoint_config_name=endpoint_config_name, wait=True)

You can find more details on how to deploy a model on SageMaker in the AWS SageMaker documentation: https://docs.aws.amazon.com/sagemaker/latest/dg/deploy-models.html.

Please let me know if you have any further questions.

[omaiyiwa]

Thank you very much for your help,
1.model_path = sagemaker_session.upload_data('path/to/your/saved/yolov5-model', bucket, key_prefix='yolov5-model') Is this code uploading the trained pt file to s3 ?
2. I roughly modified my code, can you help me see where there is still a problem, currently in the predict("https://ultralytics.com/images/zidane.jpg") step,
3.the error botocore.errorfactory.ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (0) from primary with message "Your invocation timed out while waiting for a response from container primary. Review the latency metrics for each container in Amazon CloudWatch, resolve the issue , and try again.".

4.My code is as follows
from sagemaker.deserializers import JSONDeserializer
from sagemaker.local import LocalSession
from sagemaker.pytorch import PyTorchModel
from sagemaker.serializers import JSONSerializer
import sagemaker

DUMMY_IAM_ROLE = role

def main():
# session = LocalSession()
# session.config = {'local': {'local_code': True}}
sagemaker_session = sagemaker.Session()
role = DUMMY_IAM_ROLE
model_dir = 's3://{bucket_name}/model.tar.gz'

model = PyTorchModel(
    entry_point='inference.py',
    source_dir='./code',
    role=role,
    model_data=model_dir,
    framework_version='1.8',
    py_version='py3'
)

print('Deploying endpoint in local mode')
print(
    'Note: if launching for the first time in local mode, container image download might take a few minutes to complete.')
predictor = model.deploy(
    initial_instance_count=1,
    instance_type='ml.m4.xlarge',
)

print('Endpoint deployed in local mode')

predictor.serializer = JSONSerializer()
predictor.deserializer = JSONDeserializer()
predictions = predictor.predict("https://ultralytics.com/images/zidane.jpg")
print("predictions: {}".format(predictions))

print('About to delete the endpoint')
predictor.delete_endpoint()

if name == "main":
main()

[glenn-jocher]

@omaiyiwa, to answer your questions:

1. Yes, the sagemaker_session.upload_data() code is used to upload your trained PyTorch model file (which is in .pt or .pth format) to S3.

2. It appears that you're trying to use SageMaker SDK to deploy your model to an endpoint to run inference on a single image. There appears to be some confusion in your code as you're setting model_dir as the location of the saved model file in S3, whereas it should be the local location of your model file.

Additionally, I noticed your inference.py file is not being passed as an argument to your PyTorchModel() object. This file should contain the inference code that loads and uses your saved YOLOv5 model for making predictions.

Here is an updated version of your code that addresses these issues:

from sagemaker.pytorch import PyTorchModel
import sagemaker
from PIL import Image
import requests
from io import BytesIO

DUMMY_IAM_ROLE = 'AmazonSageMaker-ExecutionRole-20220717T104523' # Replace with your IAM role

def main():
    session = sagemaker.Session()
    bucket_name = session.default_bucket()
    model_location = f"s3://{bucket_name}/model"
    print(f"Using Amazon S3 bucket {bucket_name}")

    model = PyTorchModel(
        model_data=model_location,
        role=DUMMY_IAM_ROLE,
        framework_version='1.8',
        py_version='py3',
        entry_point='inference.py', # Update this with your inference script
        source_dir='./code'
    )

    # Deploy the model to an endpoint
    endpoint_name = 'yolov5-endpoint'
    predictor = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge', endpoint_name=endpoint_name)

    # Make a prediction on a single image
    url = "https://ultralytics.com/images/zidane.jpg"
    img = Image.open(BytesIO(requests.get(url).content)).convert('RGB')
    predictions = predictor.predict(img)

    print(predictions)

    # Delete the endpoint
    session.delete_endpoint(predictor.endpoint_name)

if __name__ == '__main__':
    main()

Note that in this example code, the inference.py file should

[omaiyiwa]

  Thank you very much for your correction, but I uploaded the model to the default bucket, and now the model_location is like this: 

model_location = "s3://session.default_bucket()/yolov5-model/best.pt"

The error is：
File "/home/sagemaker-user/pytorch_yolov5_local_model_inference.py", line 49, in main
predictor = model.deploy(initial_instance_count=1, instance_type='ml.m4.xlarge', endpoint_name=endpoint_name)
File "/opt/conda/envs/studio/lib/python3.9/site-packages/sagemaker/model.py", line 1248, in deploy
self._create_sagemaker_model(
File "/opt/conda/envs/studio/lib/python3.9/site-packages/sagemaker/model.py", line 681, in _create_sagemaker_model
container_def = self.prepare_container_def(
File "/opt/conda/envs/studio/lib/python3.9/site-packages/sagemaker/pytorch/model.py", line 298, in prepare_container_def
self._upload_code(deploy_key_prefix, repack=self._is_mms_version())
File "/opt/conda/envs/studio/lib/python3.9/site-packages/sagemaker/model.py", line 614, in _upload_code
utils.repack_model(
File "/opt/conda/envs/studio/lib/python3.9/site-packages/sagemaker/utils.py", line 514, in repack_model
model_dir = _extract_model(model_uri, sagemaker_session, tmp)
File "/opt/conda/envs/studio/lib/python3.9/site-packages/sagemaker/utils.py", line 603, in _extract_model
with tarfile.open(name=local_model_path, mode="r:gz") as t:
File "/opt/conda/envs/studio/lib/python3.9/tarfile.py", line 1638, in open
return func(name, filemode, fileobj, **kwargs)
File "/opt/conda/envs/studio/lib/python3.9/tarfile.py", line 1695, in gzopen
raise ReadError("not a gzip file")
tarfile.ReadError: not a gzip file

[glenn-jocher]

@omaiyiwa, it looks like the model_location parameter you passed to PyTorchModel() is not the correct path to your model in S3. Additionally, session.default_bucket() cannot be directly concatenated with /yolov5-model/best.pt as it will return a NoneType object when calling model.deploy().

Make sure you provide the correct {bucket_name} from session.default_bucket() method when you upload your model to S3, and then update your model_location variable to reflect the correct path to your model.

For example, if you upload your model to the default SageMaker bucket using the following code:

sagemaker_session = sagemaker.Session()
bucket_name = sagemaker_session.default_bucket()
model_path = sagemaker_session.upload_data(path='path/to/model', bucket=bucket_name, key_prefix='yolov5-model')

Then you should set model_location like this:

model_location = f's3://{bucket_name}/yolov5-model/best.pt'

Regarding the error you're seeing, it looks like model_location is not pointing to a valid .tar.gz file for SageMaker to deploy. Make sure that your uploaded model is in a .tar.gz format and that the model_location parameter points to the uploaded file's S3 path including the file name.

If the above solutions don't work, I suggest printing out the contents of model_location and checking if it is pointing to the correct file in S3, and also the contents of the file to see if it is a valid .tar.gz file.

[omaiyiwa]

I'm sure the current model_location is as you pointed out, but it's a .pt file, not a .tar.gz file

[omaiyiwa]

[glenn-jocher]

@omaiyiwa, apologies for the confusion. It appears that you uploaded the .pt file directly to your S3 bucket, but you need to package it into a .tar.gz format to deploy it to SageMaker.

To package the .pt file, you can use the following code:

import tarfile

# Replace these with your own values
model_file = '/path/to/your/model.pt'
tar_file = '/path/to/your/model.tar.gz'

with tarfile.open(tar_file, "w:gz") as tar:
    tar.add(model_file, arcname='model.pt')

Make sure to replace model_file with the path to your .pt file and tar_file with the path to where you want to save the packaged file.

Once you have the packaged .tar.gz file, you can upload it to S3 using:

sagemaker_session = sagemaker.Session()
bucket_name = sagemaker_session.default_bucket()
model_path = sagemaker_session.upload_data(path='/path/to/your/model.tar.gz', bucket=bucket_name, key_prefix='yolov5-model/model.tar.gz')

Then you can update the model_location in your code to reflect the correct path to your packaged model:

model_location = f's3://{bucket_name}/yolov5-model/model.tar.gz'

Hope this helps!

[omaiyiwa]

I would like to express my gratitude again.
Before this, I have tried to specify the .gz file. Although I have successfully created the endpoint for the current problem, when predicting the picture, whether it is directly predicting the URL or downloading the picture first, it will time out.

1.error in this paragraph: predictions = predictor.predict(img)

Make a prediction on a single image

url = "https://ultralytics.com/images/zidane.jpg"
img = Image.open(BytesIO(requests.get(url).content)).convert('RGB')
predictions = predictor.predict(img)
print(predictions)

2.The error is like this:
botocore.errorfactory.ModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received server error (0) from primary with message "Your invocation timed out while waiting for a response from container primary. Review the latency metrics for each container in Amazon CloudWatch, resolve the issue, and try again."., is it a problem with inference.py?

3.the inference.py is like this:
import os.path
import torch
import json

def model_fn(model_dir):
model_path = os.path.join(model_dir, 'yolov5s.pt')
print(f'model_fn - model_path: {model_path}')
model = torch.hub.load('ultralytics/yolov5', 'custom', path=model_path)
return model

def input_fn(serialized_input_data, content_type):
if content_type == 'application/json':
print(f'input_fn - serialized_input_data: {serialized_input_data}')
input_data = json.loads(serialized_input_data)
return input_data
else:
raise Exception('Requested unsupported ContentType in Accept: ' + content_type)
return

def predict_fn(input_data, model):
print(f'predict_fn - input_data: {input_data}')
imgs = [input_data]
results = model(imgs)
print(results)
df = results.pandas().xyxy[0]
return df.to_json(orient="split")

[glenn-jocher]

@omaiyiwa, the error message ModelError: Received server error (0) from primary with message "Your invocation timed out while waiting for a response from container primary. indicates that there's an issue with your deployed endpoint. It is possible that your instance size is too small to handle the size of the image file or the time it takes to compute the predictions.

You can try increasing the instance size to an ml.m5.xlarge or ml.m5.2xlarge instance to see if that resolves the issue.

However, there could also be an issue with the inference script. From your inference.py file, it seems like everything is set up correctly. However, I would recommend trying to debug the issue by adding print statements to your code to see where the time-out issue occurs.

For example, add a print statement right before the predictions = predictor.predict(img) line to see if the issue occurs before or after that line.

Also, try running a local prediction by directly calling the predict_fn function in inference.py with a sample input image to see if the issue occurs when making predictions on SageMaker. This could help determine if the issue is with the inference code or the instance size.

Additionally, if you have large image files, you should split them into smaller sizes to process on your endpoint as processing large files can lead to memory issues.

Let me know if this helps!

[omaiyiwa]

I verified that the error is at
predictions = predictor.predict(img)
and also inference.py works fine, I increased the instance size but it doesn't seem to work

[omaiyiwa]

The picture I predict is https://ultralytics.com/images/zidane.jpg, it should not be too large

[glenn-jocher]

@omaiyiwa since you have tried increasing the instance size and the problem still persists, it could also be due to other factors such as network latency or the size of the image file itself. Here are a few suggestions that may help:

1. Reduce the size of the image file: Try reducing the resolution of the image or cropping it to a smaller size before sending it for prediction. This can reduce the amount of data that needs to be transferred and processed, which can improve the response time.

2. Increase the timeout value for inference: You can try increasing the timeout value for the predict() method in the predictor object. By default, the timeout is set to 60 seconds, but you can increase this value to allow more time for the prediction to complete.

predictions = predictor.predict(img, initial_args={"Timeout": 120})

3. Use SageMaker Batch Transform: If you have a large number of images to predict, you can try using the SageMaker Batch Transform feature. Batch Transform allows you to perform batch inference on large datasets and can handle larger file sizes than real-time endpoints.

4. Check network connectivity: Make sure that your network connectivity is stable and that there are no issues with downloads/uploads from S3. If you are using SageMaker Studio, try accessing the image directly from the notebook instance instead of downloading it from the internet.

Let me know if any of these suggestions help!

[omaiyiwa]

I segmented the code, after creating the endpoint, I use this code to make the request, also timeout

import numpy as np
import boto3, botocore

config = botocore.config.Config(read_timeout=80)
runtime = boto3.client('runtime.sagemaker', config=config)
ENDPOINT_NAME = 'yolov5-endpoint'

url = "https://ultralytics.com/images/zidane.jpg"

response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME,
ContentType='application/json',
Body=url)

print(response)
result = json.loads(response['Body'].read().decode())
print('Results: ', result)

[glenn-jocher]

@omaiyiwa, since you are still experiencing timeouts even with the client side request using runtime.invoke_endpoint(), you could try these suggestions:

1. Increase timeout: let's try increasing the timeout value by setting read_timeout and connect_timeout when creating the client.

runtime = boto3.client('runtime.sagemaker', config=Config(connect_timeout=5, read_timeout=120))

2. Reduce the size of the image: Try resizing the image to a smaller size before sending it for prediction. This can reduce the amount of data that needs to be transferred and processed, which can improve the response time.

3. Compress and encode the image: Compressing and encoding the image can reduce the size of the image and make it faster to transmit to S3.

4. Pre-warm the endpoint: You can pre-warm an endpoint by sending a few requests before sending the main request. This can help to reduce latency by initializing the resources of the endpoint.

# send 5 requests to pre-warm the endpoint
for i in range(5):
    response = runtime.invoke_endpoint(EndpointName=ENDPOINT_NAME, ContentType='application/json', Body=url)

5. Use a custom Docker image: If none of the above suggestions work, you can try creating a custom Docker image that has better performance optimization for your use case. This can help to improve the response time and reduce timeouts.

Let me know if you have any other questions or if any of these suggestions help.

[omaiyiwa]

I found out that the model_dir I am passing here is like this,

but the path received in inference.py is this, what happened

[glenn-jocher]

@omaiyiwa it seems like your model_dir path is not being passed correctly to your inference.py script.

In your inference.py script, you are joining the model_dir path with the name of the model file (yolov5s.pt) to create the full path to the model file, like so:

model_path = os.path.join(model_dir, 'yolov5s.pt')

However, in your error message, the model_dir is not part of the path to the model file:

ModuleNotFoundError: No module named 'opt/ml/model/yolov5s.pt'

Based on the error message, it looks like the path to the model file is only opt/ml/model/yolov5s.pt, instead of /opt/ml/model/yolov5s.pt.

To fix this issue, you may need to modify your code where you set the model_dir variable to ensure that the path is being passed correctly to your inference.py script.

If you are using SageMaker to deploy your model, you can access the path to your model_dir by using the following code:

import os
from sagemaker.serializers import JSONSerializer

model_dir = '/opt/ml/model'
model_path = os.path.join(model_dir, 'yolov5s.pt')

# Use the JSONSerializer to serialize input data
input_serializer = JSONSerializer()

# Use the model to perform inference
model = model_fn(model_dir)
predictions = predict_fn(input_serializer.serialize(input_data), model)

This assumes that you are using the JSONSerializer to serialize your input data. If you are using a different serializer, you may need to modify the code accordingly.

Let me know if this helps!

[omaiyiwa]

I think it should be an instance problem. Although I have specified an instance, the environment on the instance is not configured. How should I do it, or can I run the local configuration?

[glenn-jocher]

@omaiyiwa If the endpoint is hosted in a SageMaker EC2 instance and you believe that the issue is related to the instance not being configured properly, you can try using the SageMakerPythonSDK to create a Jupyter notebook within your instance and then test your endpoint locally to see if your instance environment is set up correctly.

Here are the general steps to follow:

1. Connect to your SageMaker instance using SSH.

2. Activate the SageMaker Python environment:

conda activate python3

3. Install the ipykernel package:

pip install ipykernel

4. Create a kernel using the following command:

python -m ipykernel install --user --name sagemaker-environment --display-name "Python 3 (SageMaker)"

This will create a new kernel named Python 3 (SageMaker) which uses the python3 environment.

5. Start a Jupyter notebook server by running:

jupyter notebook --no-browser --ip=0.0.0.0 --port=8888

6. In your local browser, navigate to http://<SageMaker-instance-IP>:8888/.

7. Create a new notebook using the sagemaker-environment kernel and test your model by making a prediction locally.

If your model works locally, then the issue may be with the instance configuration, such as the instance size or the network configuration. You can try to further troubleshoot and optimize the instance environment based on your findings.

Let me know if this helps!

[omaiyiwa]

I'm sure it's an environment problem, but how do I fix it

1.My code to configure the endpoint

2. inference.py


3. The log is as follows：

From this log it can be seen that all the paths are correct, but failed due to environmental issues

This log indicates that the image data was successfully loaded, but because the model was wrong, an error occurred during prediction

4.Where is the environment used by the request endpoint set up? I have set up all the virtual environments locally on the sagemaker terminal, but it still doesn’t work.

[omaiyiwa]

The error line is results = model(convert_tensor)

[glenn-jocher]

@omaiyiwa it looks like the error is occurring at the line where you're trying to run inference using the model(convert_tensor) command. This could be due to the model not being loaded correctly or the environment not being properly set up.

To resolve this issue, you may need to ensure that the environment on the SageMaker instance has all the necessary dependencies and configurations required to run the model and perform inference. Here are a few steps you can take to troubleshoot and fix the environment issues:

1. Dependency Installation: Ensure that all the required dependencies and packages are installed in the SageMaker instance's environment. You can create a script to install these dependencies, and then run the script when setting up the instance. Common packages include PyTorch, torchvision, and any other custom dependencies required by your model.

2. Check File Paths: Double-check the file paths being used in the SageMaker instance to ensure that they are correctly pointing to the model and data files. Pay attention to any differences in file paths that could be causing issues.

3. Debugging: You can add print statements or logging messages in the code to check the state of the model, input data, and any transformations being applied before running inference.

4. Virtual Environments: If you are using a virtual environment on the SageMaker instance, ensure that you activate the environment before running the scripts. This can be done using commands like conda or source depending on the type of environment setup.

5. IMemory vs File System Access: In case the issue is related to accessing memory or files, ensure that the code is configured to handle data and model access correctly, whether it's in-memory data or file system access.

By addressing these aspects, you should be able to diagnose and resolve the environment-related issues on the SageMaker instance. Let me know if you have any further questions or if you need additional assistance.