120 lines
3.7 KiB
Python
120 lines
3.7 KiB
Python
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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from torchvision import datasets, transforms, utils
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from torchvision.transforms import Compose, Lambda, ToTensor
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import matplotlib.pyplot as plt
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from NN.model import *
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from PIL import Image
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import pygame
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device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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#data transform to tensors:
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data_transformer = transforms.Compose([
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transforms.Resize((100, 100)),
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transforms.ToTensor(),
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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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#loading data:
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train_set = datasets.ImageFolder(root='resources/train', transform=data_transformer)
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test_set = datasets.ImageFolder(root='resources/test', transform=data_transformer)
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#to mozna nawet przerzucic do funkcji train:
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# train_loader = DataLoader(train_set, batch_size=64, shuffle=True)
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#test_loader = DataLoader(test_set, batch_size=32, shuffle=True)
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#function for training model
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def train(model, dataset, iter=100, batch_size=64):
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optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
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criterion = nn.NLLLoss()
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train_loader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
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model.train()
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for epoch in range(iter):
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for inputs, labels in train_loader:
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optimizer.zero_grad()
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output = model(inputs.to(device))
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loss = criterion(output, labels.to(device))
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loss.backward()
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optimizer.step()
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if epoch % 10 == 0:
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print('epoch: %3d loss: %.4f' % (epoch, loss))
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#function for getting accuracy
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def accuracy(model, dataset):
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model.eval()
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with torch.no_grad():
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correct = sum([
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(model(inputs.to(device)).argmax(dim=1) == labels.to(device)).sum()
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for inputs, labels in DataLoader(dataset, batch_size=64, shuffle=True)
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])
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return correct.float() / len(dataset)
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# model = Conv_Neural_Network_Model()
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# model.to(device)
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#loading the already saved model:
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# model.load_state_dict(torch.load('CNN_model.pth'))
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# model.eval()
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# #training the model:
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# train(model, train_set)
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# print(f"Accuracy of the network is: {100*accuracy(model, test_set)}%")
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# torch.save(model.state_dict(), 'CNN_model.pth')
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def load_model():
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model = Conv_Neural_Network_Model()
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model.load_state_dict(torch.load('CNN_model.pth', map_location=torch.device('cpu')))
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model.eval()
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return model
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def load_image(image_path):
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testImage = Image.open(image_path).convert('RGB')
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testImage = data_transformer(testImage)
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testImage = testImage.unsqueeze(0)
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return testImage
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def display_image(screen, image_path, position):
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image = pygame.image.load(image_path)
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image = pygame.transform.scale(image, (250, 250))
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screen.blit(image, position)
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def display_result(screen, position, predicted_class):
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font = pygame.font.Font(None, 30)
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displayed_text = font.render("The predicted image is: "+str(predicted_class), 1, (255,255,255))
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screen.blit(displayed_text, position)
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def guess_image(model, image_tensor):
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with torch.no_grad():
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testOutput = model(image_tensor)
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_, predicted = torch.max(testOutput, 1)
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predicted_class = train_set.classes[predicted.item()]
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return predicted_class
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#TEST - loading the image and getting results:
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# testImage_path = 'resources/images/plant_photos/1c76aa4d-11f4-47d1-8bdd-2cb78deeeccf.jpg'
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# testImage = Image.open(testImage_path)
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# testImage = data_transformer(testImage)
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# testImage = testImage.unsqueeze(0)
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# testImage = testImage.to(device)
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# model.load_state_dict(torch.load('CNN_model.pth'))
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# model.to(device)
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# model.eval()
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# testOutput = model(testImage)
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# _, predicted = torch.max(testOutput, 1)
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# predicted_class = train_set.classes[predicted.item()]
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# print(f'The predicted class is: {predicted_class}') |