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AndroidVisionPipeline | bare bone pipeline infrastructure | Machine Learning library

 by   Credntia Java Version: Current License: No License

 by   Credntia Java Version: Current License: No License

kandi X-RAY | AndroidVisionPipeline Summary

AndroidVisionPipeline is a Java library typically used in Artificial Intelligence, Machine Learning, Tensorflow applications. AndroidVisionPipeline has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub, Maven.
The bare bone pipeline infrastructure required for using google's android vision detectors. Most of the source codes were extracted from Google's android vision sample.
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Support
Quality
Quality
Security
Security
License
License
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kandi-support Support

  • AndroidVisionPipeline has a low active ecosystem.
  • It has 11 star(s) with 5 fork(s). There are 4 watchers for this library.
  • It had no major release in the last 6 months.
  • There are 1 open issues and 1 have been closed. On average issues are closed in 74 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of AndroidVisionPipeline is current.
This Library - Support
Best in #Machine Learning
Average in #Machine Learning
This Library - Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • AndroidVisionPipeline has 0 bugs and 0 code smells.
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • AndroidVisionPipeline has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • AndroidVisionPipeline code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Machine Learning
Average in #Machine Learning
This Library - Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • AndroidVisionPipeline does not have a standard license declared.
  • Check the repository for any license declaration and review the terms closely.
  • Without a license, all rights are reserved, and you cannot use the library in your applications.
This Library - License
Best in #Machine Learning
Average in #Machine Learning
This Library - License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • AndroidVisionPipeline releases are not available. You will need to build from source code and install.
  • Deployable package is available in Maven.
  • Build file is available. You can build the component from source.
  • Installation instructions, examples and code snippets are available.
  • It has 1541 lines of code, 120 functions and 18 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

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

  • Creates a bitmap from the given YUV data
    • Generates a VR21 script with an NV21
    • Calculates an in - sample image size
    • Decodes a bitmap from the specified path
  • Update the layout of the camera
    • Updates the child size based on layout width and height
    • Update the child size and update the view size accordingly
  • Initializes the camera source
    • Request the camera permission
    • Creates the camera source
  • Performs the zoom using the specified scale factor
    • Sets the focus mode
      • Start auto - focus on the camera
        • Draws the overlay
          • Takes a picture
            • Draws a graphic on the supplied canvas
              • Set the flash mode
                • Set the auto - focus callback
                  • Invoked when a user has requested a permission result
                    • Draw the face annotations on the canvas
                      • Updates the camera source

                        Get all kandi verified functions for this library.

                        Get all kandi verified functions for this library.

                        AndroidVisionPipeline Key Features

                        The bare bone pipeline infrastructure required for using google's android vision detectors

                        AndroidVisionPipeline Examples and Code Snippets

                        Community Discussions

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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 AndroidVisionPipeline

                        You can download it from GitHub, Maven.
                        You can use AndroidVisionPipeline like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the AndroidVisionPipeline component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

                        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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                        Clone
                        • https://github.com/Credntia/AndroidVisionPipeline.git

                        • gh repo clone Credntia/AndroidVisionPipeline

                        • git@github.com:Credntia/AndroidVisionPipeline.git

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