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BIN
NeuralNetwork/best_model.pth
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31
NeuralNetwork/learning_results.txt
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Epoch: 1 Train Loss: 65 Train Accuracy: 0.5754245754245755
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Epoch: 2 Train Loss: 25 Train Accuracy: 0.7457542457542458
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Epoch: 3 Train Loss: 8 Train Accuracy: 0.8431568431568431
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Epoch: 4 Train Loss: 2 Train Accuracy: 0.9010989010989011
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Epoch: 5 Train Loss: 1 Train Accuracy: 0.9335664335664335
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Epoch: 6 Train Loss: 0 Train Accuracy: 0.9545454545454546
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Epoch: 7 Train Loss: 0 Train Accuracy: 0.972027972027972
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Epoch: 8 Train Loss: 0 Train Accuracy: 0.9820179820179821
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Epoch: 9 Train Loss: 0 Train Accuracy: 0.994005994005994
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Epoch: 10 Train Loss: 0 Train Accuracy: 0.9945054945054945
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Epoch: 1 Train Loss: 42 Train Accuracy: 0.6428571428571429
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Epoch: 2 Train Loss: 11 Train Accuracy: 0.8306693306693307
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Epoch: 3 Train Loss: 3 Train Accuracy: 0.8921078921078921
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Epoch: 4 Train Loss: 2 Train Accuracy: 0.8891108891108891
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Epoch: 5 Train Loss: 1 Train Accuracy: 0.9335664335664335
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Epoch: 6 Train Loss: 0 Train Accuracy: 0.952047952047952
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Epoch: 7 Train Loss: 0 Train Accuracy: 0.9545454545454546
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Epoch: 8 Train Loss: 0 Train Accuracy: 0.9655344655344655
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Epoch: 9 Train Loss: 0 Train Accuracy: 0.9815184815184815
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Epoch: 10 Train Loss: 0 Train Accuracy: 0.9805194805194806
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Epoch: 11 Train Loss: 0 Train Accuracy: 0.9855144855144855
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Epoch: 12 Train Loss: 0 Train Accuracy: 0.989010989010989
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Epoch: 13 Train Loss: 0 Train Accuracy: 0.9925074925074925
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Epoch: 14 Train Loss: 0 Train Accuracy: 0.9915084915084915
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Epoch: 15 Train Loss: 0 Train Accuracy: 0.9885114885114885
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Epoch: 16 Train Loss: 0 Train Accuracy: 0.994005994005994
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Epoch: 17 Train Loss: 0 Train Accuracy: 0.997002997002997
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Epoch: 18 Train Loss: 0 Train Accuracy: 0.9965034965034965
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Epoch: 19 Train Loss: 0 Train Accuracy: 0.999000999000999
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Epoch: 20 Train Loss: 0 Train Accuracy: 1.0
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60
NeuralNetwork/neural_network_learning.py
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import glob
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from src.torchvision_resize_dataset import combined_dataset, images_path, classes
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import src.data_model
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from torch.optim import Adam
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import torch
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import torch.nn as nn
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from torch.utils.data import DataLoader
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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train_loader = DataLoader(
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combined_dataset, #dataset of images
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batch_size=256, # accuracy
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shuffle=True # rand order
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)
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model = src.data_model.DataModel(num_objects=2).to(device)
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#optimizer
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optimizer = Adam(model.parameters(), lr=0.001, weight_decay=0.0001)
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#loss function
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criterion = nn.CrossEntropyLoss()
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num_epochs = 20
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# train_size = len(glob.glob(images_path+'*.jpg'))
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train_size = 2002
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go_to_accuracy = 0.0
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for epoch in range(num_epochs):
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#training on dataset
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model.train()
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train_accuracy = 0.0
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train_loss = 0.0
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for i, (images, labels) in enumerate(train_loader):
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if torch.cuda.is_available():
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images = torch.Variable(images.cuda())
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labels = torch.Variable(labels.cuda())
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# clearing the optimizer gradients
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optimizer.zero_grad()
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outputs = model(images) # predoction
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loss = criterion(outputs, labels) #loss calculation
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loss.backward()
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optimizer.step()
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train_loss += loss.cpu().data*images.size(0)
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_, prediction = torch.max(outputs.data, 1)
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train_accuracy += int(torch.sum(prediction == labels.data))
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train_accuracy = train_accuracy/train_size
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train_loss = train_loss/train_size
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model.eval()
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print('Epoch: '+ str(epoch+1) +' Train Loss: '+ str(int(train_loss)) +' Train Accuracy: '+ str(train_accuracy))
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if train_accuracy > go_to_accuracy:
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go_to_accuracy= train_accuracy
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torch.save(model.state_dict(), "best_model.pth")
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BIN
NeuralNetwork/older_best_model.pth
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NeuralNetwork/prediction.py
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import torch
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import torch.nn as nn
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from torchvision.transforms import transforms
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import numpy as np
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from torch.autograd import Variable
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from torchvision.models import squeezenet1_1
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import torch.functional as F
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from io import open
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import os
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from PIL import Image
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import pathlib
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import glob
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from tkinter import Tk, Label
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from PIL import Image, ImageTk
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absolute_path = os.path.abspath('NeuralNetwork/src/train_images')
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train_path = absolute_path
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absolute_path = os.path.abspath('Images/Items_test')
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pred_path = absolute_path
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root=pathlib.Path(train_path)
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classes=sorted([j.name.split('/')[-1] for j in root.iterdir()])
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class DataModel(nn.Module):
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def __init__(self, num_classes):
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super(DataModel, self).__init__()
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#input (batch=256, nr of channels rgb=3 , size=244x244)
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# convolution
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
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#shape (256, 12, 224x224)
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# batch normalization
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self.bn1 = nn.BatchNorm2d(num_features=12)
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#shape (256, 12, 224x224)
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self.reul1 = nn.ReLU()
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self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
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# reduce image size by factor 2
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# pooling window moves by 2 pixels at a time instead of 1
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# shape (256, 12, 112x112)
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self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
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self.bn2 = nn.BatchNorm2d(num_features=24)
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self.reul2 = nn.ReLU()
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# shape (256, 24, 112x112)
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self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
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#shape (256, 48, 112x112)
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self.bn3 = nn.BatchNorm2d(num_features=48)
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#shape (256, 48, 112x112)
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self.reul3 = nn.ReLU()
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# connected layer
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self.fc = nn.Linear(in_features=48*112*112, out_features=num_classes)
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def forward(self, input):
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output = self.conv1(input)
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output = self.bn1(output)
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output = self.reul1(output)
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output = self.pool(output)
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output = self.conv2(output)
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output = self.bn2(output)
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output = self.reul2(output)
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output = self.conv3(output)
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output = self.bn3(output)
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output = self.reul3(output)
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# output shape matrix (256, 48, 112x112)
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#print(output.shape)
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#print(self.fc.weight.shape)
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output = output.view(-1, 48*112*112)
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output = self.fc(output)
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return output
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script_dir = os.path.dirname(os.path.abspath(__file__))
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file_path = os.path.join(script_dir, 'best_model.pth')
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checkpoint=torch.load(file_path)
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model = DataModel(num_classes=2)
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model.load_state_dict(checkpoint)
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model.eval()
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transformer = transforms.Compose([
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transforms.Resize((224, 224)), # Resize images to (224, 224)
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transforms.ToTensor(), # Convert images to tensors, 0-255 to 0-1
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# transforms.RandomHorizontalFlip(), # 0.5 chance to flip the image
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transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
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])
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def prediction(img_path,transformer):
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image=Image.open(img_path)
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image_tensor=transformer(image).float()
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image_tensor=image_tensor.unsqueeze_(0)
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if torch.cuda.is_available():
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image_tensor.cuda()
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input=Variable(image_tensor)
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output=model(input)
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index=output.data.numpy().argmax()
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pred=classes[index]
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return pred
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def prediction_keys():
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#funkcja zwracajaca sciezki do kazdego pliku w folderze w postaci listy
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images_path=glob.glob(pred_path+'/*.jpg')
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pred_list=[]
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for i in images_path:
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pred_list.append(i)
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return pred_list
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def predict_one(path):
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#wyswietlanie obrazka po kazdym podniesieniu itemu
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root = Tk()
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root.title("Okno z obrazkiem")
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image = Image.open(path)
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photo = ImageTk.PhotoImage(image)
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label = Label(root, image=photo)
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label.pack()
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root.mainloop()
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#uruchamia sie funkcja spr czy obrazek to paczka czy list
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pred_print = prediction(path,transformer)
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print('Zdjecie jest: '+pred_print)
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return pred_print
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61
NeuralNetwork/src/data_model.py
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import torch.nn as nn
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import torch
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class DataModel(nn.Module):
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def __init__(self, num_objects):
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super(DataModel, self).__init__()
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#input (batch=256, nr of channels rgb=3 , size=244x244)
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# convolution
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=12, kernel_size=3, stride=1, padding=1)
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#shape (256, 12, 224x224)
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# batch normalization
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self.bn1 = nn.BatchNorm2d(num_features=12)
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#shape (256, 12, 224x224)
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self.reul1 = nn.ReLU()
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self.pool=nn.MaxPool2d(kernel_size=2, stride=2)
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# reduce image size by factor 2
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# pooling window moves by 2 pixels at a time instead of 1
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# shape (256, 12, 112x112)
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|
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|
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|
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self.conv2 = nn.Conv2d(in_channels=12, out_channels=24, kernel_size=3, stride=1, padding=1)
|
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self.bn2 = nn.BatchNorm2d(num_features=24)
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self.reul2 = nn.ReLU()
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|
# shape (256, 24, 112x112)
|
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|
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self.conv3 = nn.Conv2d(in_channels=24, out_channels=48, kernel_size=3, stride=1, padding=1)
|
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#shape (256, 48, 112x112)
|
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self.bn3 = nn.BatchNorm2d(num_features=48)
|
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|
#shape (256, 48, 112x112)
|
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|
self.reul3 = nn.ReLU()
|
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|
||||||
|
# connected layer
|
||||||
|
self.fc = nn.Linear(in_features=48*112*112, out_features=num_objects)
|
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|
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|
def forward(self, input):
|
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|
output = self.conv1(input)
|
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|
output = self.bn1(output)
|
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|
output = self.reul1(output)
|
||||||
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|
||||||
|
output = self.pool(output)
|
||||||
|
output = self.conv2(output)
|
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|
output = self.bn2(output)
|
||||||
|
output = self.reul2(output)
|
||||||
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|
||||||
|
output = self.conv3(output)
|
||||||
|
output = self.bn3(output)
|
||||||
|
output = self.reul3(output)
|
||||||
|
|
||||||
|
# output shape matrix (256, 48, 112x112)
|
||||||
|
#print(output.shape)
|
||||||
|
#print(self.fc.weight.shape)
|
||||||
|
|
||||||
|
output = output.view(-1, 48*112*112)
|
||||||
|
output = self.fc(output)
|
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|
|
||||||
|
return output
|
31
NeuralNetwork/src/torchvision_resize_dataset.py
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@ -0,0 +1,31 @@
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|
import glob
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|
import pathlib
|
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|
import torchvision.transforms as transforms
|
||||||
|
from torchvision.datasets import ImageFolder
|
||||||
|
from torch.utils.data import ConcatDataset
|
||||||
|
|
||||||
|
# images have to be the same size for the algorithm to work
|
||||||
|
transform = transforms.Compose([
|
||||||
|
transforms.Resize((224, 224)), # Resize images to (224, 224)
|
||||||
|
transforms.ToTensor(), # Convert images to tensors, 0-255 to 0-1
|
||||||
|
# transforms.RandomHorizontalFlip(), # 0.5 chance to flip the image
|
||||||
|
transforms.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])
|
||||||
|
])
|
||||||
|
|
||||||
|
letters_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/letters'
|
||||||
|
package_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images/package'
|
||||||
|
images_path = 'C:/Users/wojmed/Documents/VS repositories/Inteligentny_Wozek/NeuralNetwork/src/train_images'
|
||||||
|
|
||||||
|
# # Load images from folders
|
||||||
|
# letter_folder = ImageFolder(letters_path, transform=transform)
|
||||||
|
# package_folder = ImageFolder(package_path, transform=transform)
|
||||||
|
|
||||||
|
# Combine the both datasets into a single dataset
|
||||||
|
#combined_dataset = ConcatDataset([letter_folder, package_folder])
|
||||||
|
combined_dataset = ImageFolder(images_path, transform=transform)
|
||||||
|
|
||||||
|
#image classes
|
||||||
|
path=pathlib.Path(images_path)
|
||||||
|
classes = sorted([i.name.split("/")[-1] for i in path.iterdir()])
|
||||||
|
|
||||||
|
# print(classes)
|
Before Width: | Height: | Size: 2.7 MiB After Width: | Height: | Size: 2.7 MiB |
Before Width: | Height: | Size: 1.8 MiB After Width: | Height: | Size: 1.8 MiB |
Before Width: | Height: | Size: 193 KiB After Width: | Height: | Size: 193 KiB |
Before Width: | Height: | Size: 59 KiB After Width: | Height: | Size: 59 KiB |
Before Width: | Height: | Size: 8.0 KiB After Width: | Height: | Size: 8.0 KiB |
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
Before Width: | Height: | Size: 12 KiB After Width: | Height: | Size: 12 KiB |
Before Width: | Height: | Size: 20 KiB After Width: | Height: | Size: 20 KiB |
Before Width: | Height: | Size: 41 KiB After Width: | Height: | Size: 41 KiB |
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 24 KiB |
Before Width: | Height: | Size: 34 KiB After Width: | Height: | Size: 34 KiB |
Before Width: | Height: | Size: 37 KiB After Width: | Height: | Size: 37 KiB |
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
Before Width: | Height: | Size: 7.7 MiB After Width: | Height: | Size: 7.7 MiB |
Before Width: | Height: | Size: 40 KiB After Width: | Height: | Size: 40 KiB |
Before Width: | Height: | Size: 28 KiB After Width: | Height: | Size: 28 KiB |
Before Width: | Height: | Size: 64 KiB After Width: | Height: | Size: 64 KiB |
Before Width: | Height: | Size: 32 KiB After Width: | Height: | Size: 32 KiB |
Before Width: | Height: | Size: 135 KiB After Width: | Height: | Size: 135 KiB |
Before Width: | Height: | Size: 34 KiB After Width: | Height: | Size: 34 KiB |
Before Width: | Height: | Size: 60 KiB After Width: | Height: | Size: 60 KiB |
Before Width: | Height: | Size: 76 KiB After Width: | Height: | Size: 76 KiB |
Before Width: | Height: | Size: 62 KiB After Width: | Height: | Size: 62 KiB |
Before Width: | Height: | Size: 78 KiB After Width: | Height: | Size: 78 KiB |
Before Width: | Height: | Size: 6.6 MiB After Width: | Height: | Size: 6.6 MiB |
Before Width: | Height: | Size: 91 KiB After Width: | Height: | Size: 91 KiB |
Before Width: | Height: | Size: 36 KiB After Width: | Height: | Size: 36 KiB |
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 24 KiB |
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 24 KiB |
Before Width: | Height: | Size: 44 KiB After Width: | Height: | Size: 44 KiB |
Before Width: | Height: | Size: 35 KiB After Width: | Height: | Size: 35 KiB |
Before Width: | Height: | Size: 85 KiB After Width: | Height: | Size: 85 KiB |
Before Width: | Height: | Size: 53 KiB After Width: | Height: | Size: 53 KiB |
Before Width: | Height: | Size: 6.3 KiB After Width: | Height: | Size: 6.3 KiB |
Before Width: | Height: | Size: 74 KiB After Width: | Height: | Size: 74 KiB |
Before Width: | Height: | Size: 1.8 MiB After Width: | Height: | Size: 1.8 MiB |
Before Width: | Height: | Size: 99 KiB After Width: | Height: | Size: 99 KiB |
Before Width: | Height: | Size: 38 KiB After Width: | Height: | Size: 38 KiB |
Before Width: | Height: | Size: 1004 KiB After Width: | Height: | Size: 1004 KiB |
Before Width: | Height: | Size: 976 B After Width: | Height: | Size: 976 B |
Before Width: | Height: | Size: 434 KiB After Width: | Height: | Size: 434 KiB |
Before Width: | Height: | Size: 131 KiB After Width: | Height: | Size: 131 KiB |
Before Width: | Height: | Size: 111 KiB After Width: | Height: | Size: 111 KiB |
Before Width: | Height: | Size: 17 KiB After Width: | Height: | Size: 17 KiB |
Before Width: | Height: | Size: 3.6 KiB After Width: | Height: | Size: 3.6 KiB |
Before Width: | Height: | Size: 73 KiB After Width: | Height: | Size: 73 KiB |
Before Width: | Height: | Size: 1.6 MiB After Width: | Height: | Size: 1.6 MiB |
Before Width: | Height: | Size: 5.9 KiB After Width: | Height: | Size: 5.9 KiB |
Before Width: | Height: | Size: 5.4 KiB After Width: | Height: | Size: 5.4 KiB |
Before Width: | Height: | Size: 4.2 KiB After Width: | Height: | Size: 4.2 KiB |
Before Width: | Height: | Size: 27 KiB After Width: | Height: | Size: 27 KiB |
Before Width: | Height: | Size: 6.7 KiB After Width: | Height: | Size: 6.7 KiB |
Before Width: | Height: | Size: 38 KiB After Width: | Height: | Size: 38 KiB |
Before Width: | Height: | Size: 555 KiB After Width: | Height: | Size: 555 KiB |
Before Width: | Height: | Size: 52 KiB After Width: | Height: | Size: 52 KiB |
Before Width: | Height: | Size: 51 KiB After Width: | Height: | Size: 51 KiB |
Before Width: | Height: | Size: 362 KiB After Width: | Height: | Size: 362 KiB |
Before Width: | Height: | Size: 1.6 MiB After Width: | Height: | Size: 1.6 MiB |
Before Width: | Height: | Size: 153 KiB After Width: | Height: | Size: 153 KiB |
Before Width: | Height: | Size: 108 KiB After Width: | Height: | Size: 108 KiB |
Before Width: | Height: | Size: 24 KiB After Width: | Height: | Size: 24 KiB |
Before Width: | Height: | Size: 12 KiB After Width: | Height: | Size: 12 KiB |
Before Width: | Height: | Size: 14 KiB After Width: | Height: | Size: 14 KiB |
Before Width: | Height: | Size: 3.7 KiB After Width: | Height: | Size: 3.7 KiB |
Before Width: | Height: | Size: 15 KiB After Width: | Height: | Size: 15 KiB |
Before Width: | Height: | Size: 60 KiB After Width: | Height: | Size: 60 KiB |
Before Width: | Height: | Size: 66 KiB After Width: | Height: | Size: 66 KiB |
Before Width: | Height: | Size: 150 KiB After Width: | Height: | Size: 150 KiB |
Before Width: | Height: | Size: 3.0 MiB After Width: | Height: | Size: 3.0 MiB |
Before Width: | Height: | Size: 20 KiB After Width: | Height: | Size: 20 KiB |
Before Width: | Height: | Size: 120 KiB After Width: | Height: | Size: 120 KiB |
Before Width: | Height: | Size: 3.7 KiB After Width: | Height: | Size: 3.7 KiB |
Before Width: | Height: | Size: 9.8 KiB After Width: | Height: | Size: 9.8 KiB |
Before Width: | Height: | Size: 59 KiB After Width: | Height: | Size: 59 KiB |
Before Width: | Height: | Size: 59 KiB After Width: | Height: | Size: 59 KiB |
Before Width: | Height: | Size: 9.4 KiB After Width: | Height: | Size: 9.4 KiB |
Before Width: | Height: | Size: 7.5 KiB After Width: | Height: | Size: 7.5 KiB |
Before Width: | Height: | Size: 8.8 KiB After Width: | Height: | Size: 8.8 KiB |
Before Width: | Height: | Size: 88 KiB After Width: | Height: | Size: 88 KiB |
Before Width: | Height: | Size: 1.1 MiB After Width: | Height: | Size: 1.1 MiB |
Before Width: | Height: | Size: 96 KiB After Width: | Height: | Size: 96 KiB |
Before Width: | Height: | Size: 9.2 KiB After Width: | Height: | Size: 9.2 KiB |
Before Width: | Height: | Size: 55 KiB After Width: | Height: | Size: 55 KiB |
Before Width: | Height: | Size: 17 KiB After Width: | Height: | Size: 17 KiB |
Before Width: | Height: | Size: 27 KiB After Width: | Height: | Size: 27 KiB |
Before Width: | Height: | Size: 78 KiB After Width: | Height: | Size: 78 KiB |
Before Width: | Height: | Size: 30 KiB After Width: | Height: | Size: 30 KiB |
Before Width: | Height: | Size: 280 KiB After Width: | Height: | Size: 280 KiB |
Before Width: | Height: | Size: 264 KiB After Width: | Height: | Size: 264 KiB |
Before Width: | Height: | Size: 56 KiB After Width: | Height: | Size: 56 KiB |
Before Width: | Height: | Size: 2.8 MiB After Width: | Height: | Size: 2.8 MiB |
Before Width: | Height: | Size: 8.9 KiB After Width: | Height: | Size: 8.9 KiB |
Before Width: | Height: | Size: 52 KiB After Width: | Height: | Size: 52 KiB |