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PyTorch

@olyaTh

Random PyTorch snippets Data: Dataloader summary Iterative forward

https://codeigniter.com/userguide3/database/query_builder.html

$sql = "SELECT * FROM some_table WHERE id = ? AND status = ? AND author = ?";
$this->db->query($sql, array(3, 'live', 'Rick')); // insert in ci

#php #codeigniter #database #model

Validate in Codeigniter

$this->load->library("form_validation");
$this->form_validation->set_rules("username", "User name", "trim|required");
if($this->form_validation->run() === FALSE)
{
     //$this->view_data["errors"] = validation_errors();
}
else
{
     //codes to run on success validation here
}

#pytorch #summary #model

summary

from torchsummary import summary
summ = summary(model, torch.Size((1,28,28)))

#ios #swift #dict #model #dictionary

Convert Model to dictionary old code ios swift

struct Person : Encodable {
    var name: String
    var age: Int
    var address: String?
}
    
    
let person = Person(name: "John Smith", age: 30, address: nil)
let dictionary = person.toDictionary()
print(dictionary) // Output: ["name": "John Smith", "age": 30]
    
    
    extension Encodable {
    func toDictionary() -> [String: Any]? {
        let mirror = Mirror(reflecting: self)
        var dictionary = [String: Any]()
        for child in mirror.children {
            guard let label = child.label else {
                continue
            }
            dictionary[label] = child.value
        }
        return dictionary
    }
}

#ios #swift #dic #dict #model #modelarray #dictonary

Convert ModelArray to Dictonary

Make sure to add encodable like this 

struct modelName : Encodsble

let categories = categoriesArray.map{ $0.toDictionary()  }

extension Encodable {
    func toDictionary() -> [String: Any] {
        let mirror = Mirror(reflecting: self)
        var dictionary = [String: Any]()
        for child in mirror.children {
            guard let label = child.label else {
                continue
            }
            dictionary[label] = child.value
        }
        return dictionary
    }
}

#ios #swift #notification #model

Notification Model

import Foundation

class NotificationModel {
    
    var body = ""
    var senderId = ""
    var receiverId = ""
    var notificationId = ""
    var serviceProviderId = ""
    var timestamp = 0
    var title = ""
    var date = ""
    
    init(notification : [String:Any]) {
        
        self.body = notification["body"] as? String ?? ""
        self.senderId = notification["senderId"] as? String ?? ""
        self.receiverId = notification["receiverId"] as? String ?? ""
        self.notificationId = notification["notificationId"] as? String ?? ""
        self.serviceProviderId = notification["serviceProviderId"] as? String ?? ""
        self.title = notification["title"] as? String ?? ""
        self.date = notification["date"] as? String ?? ""
        self.timestamp = notification["timestamp"] as? Int ?? 0
    }
}

#ios #swift #model #comment #comments

comments Model (model in model)

import Foundation

struct CommentsModel {
    
  var comment = ""
  var gameId = ""
  var timestamp = 0
  var userId = ""
  var userModel = UserModel(user: [:])
    
    init(comment: [String: Any]) {
        
        self.comment = comment["comment"] as? String ?? ""
        self.gameId = comment["gameId"] as? String ?? ""
        self.timestamp = comment["timestamp"] as? Int ?? 0
        self.userId = comment["userId"] as? String ?? ""
        
        if let userDict = comment["userModel"] as? [String: Any] {
            self.userModel = UserModel(user: userDict)
         }
    }
}

#ios #swift #model #dictionary #dict

Convert Model to dictionary (even model have a model in it)

extension Encodable {
    func toDictionary() -> [String: Any]? {
        do {
            let encoder = JSONEncoder()
            let data = try encoder.encode(self)
            let dictionary = try JSONSerialization.jsonObject(with: data, options: .allowFragments) as? [String: Any]
            return dictionary
        } catch {
            print("Error converting to dictionary:", error)
            return nil
        }
    }
}

#laravel #database #eloquent #model

db-models-and-eloquent

https://github.com/LaravelDaily/laravel-tips/blob/master/db-models-and-eloquent.md

#gnn #pytorch #trainset #loaddata

A detailed example of data loaders with PyTorch

# Load entire dataset
X, y = torch.load('some_training_set_with_labels.pt')

# Train model
for epoch in range(max_epochs):
    for i in range(n_batches):
        # Local batches and labels
        local_X, local_y = X[i*n_batches:(i+1)*n_batches,], y[i*n_batches:(i+1)*n_batches,]

        # Your model
        [...]

#gnn #pytorch #loaddata #trainset

A detailed example of data loaders with PyTorch

# Unoptimized generator
training_generator = SomeSingleCoreGenerator('some_training_set_with_labels.pt')

# Train model
for epoch in range(max_epochs):
    for local_X, local_y in training_generator:
        # Your model
        [...]

#textpreprocessing #nlp #gnn #dataload #pytorch

A detailed example of data loaders with PyTorch

# Load entire dataset
X, y = torch.load('some_training_set_with_labels.pt')
 
# Train model
for epoch in range(max_epochs):
    for i in range(n_batches):
        # Local batches and labels
        local_X, local_y = X[i*n_batches:(i+1)*n_batches,], y[i*n_batches:(i+1)*n_batches,]
 
        # Your model
        [...]
         
         
# other
# Unoptimized generator
training_generator = SomeSingleCoreGenerator('some_training_set_with_labels.pt')
 
# Train model
for epoch in range(max_epochs):
    for local_X, local_y in training_generator:
        # Your model
        [...]

#python #pytorch

tf-reed: Demo test image for model

images, boxes = torch.rand(2, 3, 400, 400).to(device), torch.rand(2, 11, 4).to(device)
boxes[:, :, 2:4] = boxes[:, :, 0:2] + boxes[:, :, 2:4]
labels = torch.randint(0, 2, (4, 11)).to(device)
images = list(image for image in images)
targets = []
for i in range(len(images)):
    d = {}
    d['boxes'] = boxes[i]
    d['labels'] = labels[i]
    targets.append(d)

model = model.to(device)
output = model(images, targets)

#python #pytorch

pytorch-sqrtm/sqrtm.py at master · steveli/pytorch-sqrtm

https://github.com/steveli/pytorch-sqrtm/blob/master/sqrtm.py

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