126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
import torch
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import common.helpers
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from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
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from watersandtreegrass import WaterSandTreeGrass
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from torch.utils.data import DataLoader
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from neural_network import NeuralNetwork
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from torchvision.io import read_image, ImageReadMode
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import torch.nn as nn
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from torch.optim import Adam
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import matplotlib.pyplot as plt
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import pytorch_lightning as pl
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from pytorch_lightning.callbacks import EarlyStopping
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import torchvision.transforms.functional as F
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from PIL import Image
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def check_accuracy_tiles():
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/grass_with_tree.jpg') == 'tree':
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answer = answer + 1
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print("Accuracy(%) grass_with_tree.jpg", answer)
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/grass2.png') == 'grass':
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answer = answer + 1
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print("Accuracy(%) grass2.png", answer)
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/grass3.png') == 'grass':
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answer = answer + 1
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print("Accuracy(%) grass3.png", answer)
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/grass4.png') == 'grass':
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answer = answer + 1
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print("Accuracy(%) grass4.png", answer)
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/grass1.png') == 'grass':
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answer = answer + 1
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print("Accuracy(%) grass1.png", answer)
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/water.png') == 'water':
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answer = answer + 1
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print("Accuracy(%) water.png", answer)
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answer = 0
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for i in range(100):
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if what_is_it('../../resources/textures/sand.png') == 'sand':
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answer = answer + 1
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print("Accuracy(%) sand.png", answer)
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def what_is_it(img_path, show_img=False):
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image = Image.open(img_path).convert('RGB')
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if show_img:
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plt.imshow(image)
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plt.show()
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image = SETUP_PHOTOS(image).unsqueeze(0)
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model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_20/checkpoints/epoch=3-step=324.ckpt')
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with torch.no_grad():
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model.eval()
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idx = int(model(image).argmax(dim=1))
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return ID_TO_CLASS[idx]
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def check_accuracy(tset):
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model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt')
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num_correct = 0
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num_samples = 0
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model = model.to(DEVICE)
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model.eval()
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with torch.no_grad():
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for photo, label in tset:
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photo = photo.to(DEVICE)
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label = label.to(DEVICE)
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scores = model(photo)
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predictions = scores.argmax(dim=1)
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num_correct += (predictions == label).sum()
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num_samples += predictions.size(0)
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print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%')
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def check_accuracy_data():
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trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
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testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
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train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
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test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
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print("Accuracy of train_set:")
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check_accuracy(train_loader)
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print("Accuracy of test_set:")
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check_accuracy(test_loader)
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#CNN = NeuralNetwork()
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#common.helpers.createCSV()
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#trainer = pl.Trainer(accelerator='gpu', callbacks=EarlyStopping('val_loss'), devices=1, max_epochs=NUM_EPOCHS)
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#trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
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#trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
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#testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
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#train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
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#test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
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#trainer.fit(CNN, train_loader, test_loader)
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#trainer.tune(CNN, train_loader, test_loader)
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#print(what_is_it('../../resources/textures/grass2.png', True))
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#check_accuracy_data()
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#check_accuracy_tiles()
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