kandi background
Explore Kits

DeepVideoAnalytics | distributed visual search and visual data analytics platform | Machine Learning library

 by   AKSHAYUBHAT Python Version: docker.container.6 License: No License

 by   AKSHAYUBHAT Python Version: docker.container.6 License: No License

Download this library from

kandi X-RAY | DeepVideoAnalytics Summary

DeepVideoAnalytics is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Machine Learning applications. DeepVideoAnalytics has build file available and it has medium support. However DeepVideoAnalytics has 1835 bugs and it has 22 vulnerabilities. You can download it from GitHub.
For installation instructions and overview please visit https://www.deepvideoanalytics.com and go through the presentation. The standalone OCR example has been moved to /docs/experiments/ocr directory.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • DeepVideoAnalytics has a medium active ecosystem.
  • It has 2976 star(s) with 749 fork(s). There are 227 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 207 have been closed. On average issues are closed in 174 days. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of DeepVideoAnalytics is docker.container.6
DeepVideoAnalytics Support
Best in #Machine Learning
Average in #Machine Learning
DeepVideoAnalytics Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • DeepVideoAnalytics has 1835 bugs (4 blocker, 0 critical, 1511 major, 320 minor) and 715 code smells.
DeepVideoAnalytics Quality
Best in #Machine Learning
Average in #Machine Learning
DeepVideoAnalytics Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • DeepVideoAnalytics has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • DeepVideoAnalytics code analysis shows 22 unresolved vulnerabilities (22 blocker, 0 critical, 0 major, 0 minor).
  • There are 23 security hotspots that need review.
DeepVideoAnalytics Security
Best in #Machine Learning
Average in #Machine Learning
DeepVideoAnalytics Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • DeepVideoAnalytics 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.
DeepVideoAnalytics License
Best in #Machine Learning
Average in #Machine Learning
DeepVideoAnalytics License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • DeepVideoAnalytics releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • Installation instructions are available. Examples and code snippets are not available.
  • DeepVideoAnalytics saves you 135515 person hours of effort in developing the same functionality from scratch.
  • It has 141648 lines of code, 1899 functions and 859 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
DeepVideoAnalytics Reuse
Best in #Machine Learning
Average in #Machine Learning
DeepVideoAnalytics Reuse
Best in #Machine Learning
Average in #Machine Learning
Top functions reviewed by kandi - BETA

Coming Soon for all Libraries!

Currently covering the most popular Java, JavaScript and Python libraries. See a SAMPLE HERE.
kandi's functional review helps you automatically verify the functionalities of the libraries and avoid rework.

DeepVideoAnalytics Key Features

A distributed visual search and visual data analytics platform.

Community Discussions

Trending Discussions on Machine Learning
  • Using RNN Trained Model without pytorch installed
  • Flux.jl : Customizing optimizer
  • How can I check a confusion_matrix after fine-tuning with custom datasets?
  • CUDA OOM - But the numbers don't add upp?
  • How to compare baseline and GridSearchCV results fair?
  • Getting Error 524 while running jupyter lab in google cloud platform
  • TypeError: brain.NeuralNetwork is not a constructor
  • Ordinal Encoding or One-Hot-Encoding
  • How to increase dimension-vector size of BERT sentence-transformers embedding
  • How to identify what features affect predictions result?
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 DeepVideoAnalytics

For installation instructions and overview please visit https://www.deepvideoanalytics.com and go through the presentation. The standalone OCR example has been moved to /docs/experiments/ocr directory.

Support

Please contact me for more information.

DOWNLOAD this Library from

Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items
Find more libraries
Reuse Solution Kits and Libraries Curated by Popular Use Cases

Save this library and start creating your kit

Share this Page

share link
Reuse Pre-built Kits with DeepVideoAnalytics
Compare Machine Learning Libraries with Highest Support
Compare Machine Learning Libraries with Highest Quality
Compare Machine Learning Libraries with Highest Security
Compare Machine Learning Libraries with Permissive License
Compare Machine Learning Libraries with Highest Reuse
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from
over 430 million Knowledge Items
Find more libraries
Reuse Solution Kits and Libraries Curated by Popular Use Cases

Save this library and start creating your kit

  • © 2022 Open Weaver Inc.