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Traffic_sign_detection_YOLO | Implementation of darkflow on traffic sign detection and classification | Machine Learning library

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kandi X-RAY | Traffic_sign_detection_YOLO Summary

Traffic_sign_detection_YOLO is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. Traffic_sign_detection_YOLO has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Traffic_sign_detection_YOLO build file is not available. You can download it from GitHub.
Detecting traffic signs using YOLO algorithm.

kandi-support Support

  • Traffic_sign_detection_YOLO has a low active ecosystem.
  • It has 43 star(s) with 15 fork(s). There are 5 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 5 open issues and 1 have been closed. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Traffic_sign_detection_YOLO is current.

quality kandi Quality

  • Traffic_sign_detection_YOLO has 0 bugs and 0 code smells.

securitySecurity

  • Traffic_sign_detection_YOLO has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • Traffic_sign_detection_YOLO code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.

license License

  • Traffic_sign_detection_YOLO is licensed under the MIT License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.

buildReuse

  • Traffic_sign_detection_YOLO releases are not available. You will need to build from source code and install.
  • Traffic_sign_detection_YOLO has no build file. You will be need to create the build yourself to build the component from source.
  • Installation instructions are not available. Examples and code snippets are available.
  • Traffic_sign_detection_YOLO saves you 1330 person hours of effort in developing the same functionality from scratch.
  • It has 2982 lines of code, 185 functions and 45 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA

kandi has reviewed Traffic_sign_detection_YOLO and discovered the below as its top functions. This is intended to give you an instant insight into Traffic_sign_detection_YOLO implemented functionality, and help decide if they suit your requirements.

  • Generator for the cfg model .
  • Print the loss of the network .
  • capture video
  • Preprocess a single image .
  • Parses Pascal vocab file .
  • Parse the model
  • Generate an XML file .
  • Performs post processing .
  • Train the model .
  • Parse arguments .

Traffic_sign_detection_YOLO Key Features

Implementation of darkflow on traffic sign detection and classification

Traffic_sign_detection_YOLO Examples and Code Snippets

  • Traffic_sign_detection_YOLO

Traffic_sign_detection_YOLO

git clone https://github.com/AmeyaWagh/Traffic_sign_detection_YOLO.git

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Trending Discussions on Machine Learning

QUESTION

Using RNN Trained Model without pytorch installed

Asked 2022-Feb-28 at 20:17

I have trained an RNN model with pytorch. I need to use the model for prediction in an environment where I'm unable to install pytorch because of some strange dependency issue with glibc. However, I can install numpy and scipy and other libraries. So, I want to use the trained model, with the network definition, without pytorch.

I have the weights of the model as I save the model with its state dict and weights in the standard way, but I can also save it using just json/pickle files or similar.

I also have the network definition, which depends on pytorch in a number of ways. This is my RNN network definition.

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random

torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')

class RNN(nn.Module):
  def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
    super(RNN, self).__init__()
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.output_size = output_size
    self.num_layers = num_layers
    self.batch_size = batch_size
    self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
    self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
    self.hidden2out = nn.Linear(hidden_size, output_size)
    self.hidden = self.init_hidden()
  def forward(self, feature_list):
    feature_list=torch.tensor(feature_list)
    
    if self.matching_in_out:
      lstm_out, _ = self.lstm( feature_list.view(len( feature_list), 1, -1))
      output_space = self.hidden2out(lstm_out.view(len( feature_list), -1))
      output_scores = torch.sigmoid(output_space) #we'll need to check if we need this sigmoid
      return output_scores #output_scores
    else:
      for i in range(len(feature_list)):
        cur_ft_tensor=feature_list[i]#.view([1,1,self.input_size])
        cur_ft_tensor=cur_ft_tensor.view([1,1,self.input_size])
        lstm_out, self.hidden = self.lstm(cur_ft_tensor, self.hidden)
        outs=self.hidden2out(lstm_out)
      return outs
  def init_hidden(self):
    #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
    return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device),
            torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device))

I am aware of this question, but I'm willing to go as low level as possible. I can work with numpy array instead of tensors, and reshape instead of view, and I don't need a device setting.

Based on the class definition above, what I can see here is that I only need the following components from torch to get an output from the forward function:

  • nn.LSTM
  • nn.Linear
  • torch.sigmoid

I think I can easily implement the sigmoid function using numpy. However, can I have some implementation for the nn.LSTM and nn.Linear using something not involving pytorch? Also, how will I use the weights from the state dict into the new class?

So, the question is, how can I "translate" this RNN definition into a class that doesn't need pytorch, and how to use the state dict weights for it? Alternatively, is there a "light" version of pytorch, that I can use just to run the model and yield a result?

EDIT

I think it might be useful to include the numpy/scipy equivalent for both nn.LSTM and nn.linear. It would help us compare the numpy output to torch output for the same code, and give us some modular code/functions to use. Specifically, a numpy equivalent for the following would be great:

rnn = nn.LSTM(10, 20, 2)
input = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
c0 = torch.randn(2, 3, 20)
output, (hn, cn) = rnn(input, (h0, c0))

and also for linear:

m = nn.Linear(20, 30)
input = torch.randn(128, 20)
output = m(input)

ANSWER

Answered 2022-Feb-17 at 10:47

You should try to export the model using torch.onnx. The page gives you an example that you can start with.

An alternative is to use TorchScript, but that requires torch libraries.

Both of these can be run without python. You can load torchscript in a C++ application https://pytorch.org/tutorials/advanced/cpp_export.html

ONNX is much more portable and you can use in languages such as C#, Java, or Javascript https://onnxruntime.ai/ (even on the browser)

A running example

Just modifying a little your example to go over the errors I found

Notice that via tracing any if/elif/else, for, while will be unrolled

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import random

torch.manual_seed(1)
random.seed(1)
device = torch.device('cpu')

class RNN(nn.Module):
  def __init__(self, input_size, hidden_size, output_size,num_layers, matching_in_out=False, batch_size=1):
    super(RNN, self).__init__()
    self.input_size = input_size
    self.hidden_size = hidden_size
    self.output_size = output_size
    self.num_layers = num_layers
    self.batch_size = batch_size
    self.matching_in_out = matching_in_out #length of input vector matches the length of output vector 
    self.lstm = nn.LSTM(input_size, hidden_size,num_layers)
    self.hidden2out = nn.Linear(hidden_size, output_size)
  def forward(self, x, h0, c0):
    lstm_out, (hidden_a, hidden_b) = self.lstm(x, (h0, c0))
    outs=self.hidden2out(lstm_out)
    return outs, (hidden_a, hidden_b)
  def init_hidden(self):
    #return torch.rand(self.num_layers, self.batch_size, self.hidden_size)
    return (torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach(),
            torch.rand(self.num_layers, self.batch_size, self.hidden_size).to(device).detach())

# convert the arguments passed during onnx.export call
class MWrapper(nn.Module):
    def __init__(self, model):
        super(MWrapper, self).__init__()
        self.model = model;
    def forward(self, kwargs):
        return self.model(**kwargs)

Run an example

rnn = RNN(10, 10, 10, 3)
X = torch.randn(3,1,10)
h0,c0  = rnn.init_hidden()
print(rnn(X, h0, c0)[0])

Use the same input to trace the model and export an onnx file


torch.onnx.export(MWrapper(rnn), {'x':X,'h0':h0,'c0':c0}, 'rnn.onnx', 
                  dynamic_axes={'x':{1:'N'},
                               'c0':{1: 'N'},
                               'h0':{1: 'N'}
                               },
                  input_names=['x', 'h0', 'c0'],
                  output_names=['y', 'hn', 'cn']
                 )

Notice that you can use symbolic values for the dimensions of some axes of some inputs. Unspecified dimensions will be fixed with the values from the traced inputs. By default LSTM uses dimension 1 as batch.

Next we load the ONNX model and pass the same inputs

import onnxruntime
ort_model = onnxruntime.InferenceSession('rnn.onnx')
print(ort_model.run(['y'], {'x':X.numpy(), 'c0':c0.numpy(), 'h0':h0.numpy()}))

Source https://stackoverflow.com/questions/71146140

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install Traffic_sign_detection_YOLO

You can download it from GitHub.
You can use Traffic_sign_detection_YOLO like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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