WMICraft/algorithms/neural_network/neural_network_interface.py

import torch
import common.helpers
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
from watersandtreegrass import WaterSandTreeGrass
from torch.utils.data import DataLoader
from neural_network import NeuralNetwork
from torchvision.io import read_image, ImageReadMode
import torch.nn as nn
from torch.optim import Adam
import matplotlib.pyplot as plt
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping
import torchvision.transforms.functional as F
from PIL import Image


def check_accuracy_tiles():
    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/grass_with_tree.jpg') == 'tree':
            answer = answer + 1
    print("Accuracy(%) grass_with_tree.jpg",  answer)

    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/grass2.png') == 'grass':
            answer = answer + 1
    print("Accuracy(%) grass2.png",  answer)

    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/grass3.png') == 'grass':
            answer = answer + 1
    print("Accuracy(%) grass3.png",  answer)

    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/grass4.png') == 'grass':
            answer = answer + 1
    print("Accuracy(%) grass4.png", answer)

    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/grass1.png') == 'grass':
            answer = answer + 1
    print("Accuracy(%) grass1.png", answer)

    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/water.png') == 'water':
            answer = answer + 1
    print("Accuracy(%) water.png", answer)

    answer = 0
    for i in range(100):
        if what_is_it('../../resources/textures/sand.png') == 'sand':
            answer = answer + 1
    print("Accuracy(%) sand.png", answer)


def what_is_it(img_path, show_img=False):
    image = Image.open(img_path).convert('RGB')
    if show_img:
        plt.imshow(image)
        plt.show()

    image = SETUP_PHOTOS(image).unsqueeze(0)
    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_20/checkpoints/epoch=3-step=324.ckpt')

    with torch.no_grad():
        model.eval()
        idx = int(model(image).argmax(dim=1))
        return ID_TO_CLASS[idx]


def check_accuracy(tset):
    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_23/checkpoints/epoch=3-step=324.ckpt')
    num_correct = 0
    num_samples = 0
    model = model.to(DEVICE)
    model.eval()

    with torch.no_grad():
        for photo, label in tset:
            photo = photo.to(DEVICE)
            label = label.to(DEVICE)

            scores = model(photo)
            predictions = scores.argmax(dim=1)
            num_correct += (predictions == label).sum()
            num_samples += predictions.size(0)

        print(f'Got {num_correct} / {num_samples} with accuracy {float(num_correct)/float(num_samples)*100:.2f}%')


def check_accuracy_data():
    trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
    testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
    train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
    test_loader = DataLoader(testset, batch_size=BATCH_SIZE)

    print("Accuracy of train_set:")
    check_accuracy(train_loader)
    print("Accuracy of test_set:")
    check_accuracy(test_loader)

#CNN = NeuralNetwork()
#common.helpers.createCSV()

#trainer = pl.Trainer(accelerator='gpu', callbacks=EarlyStopping('val_loss'), devices=1, max_epochs=NUM_EPOCHS)
#trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)

#trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
#testset = WaterSandTreeGrass('./data/test_csv_file.csv', transform=SETUP_PHOTOS)
#train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
#test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
#trainer.fit(CNN, train_loader, test_loader)
#trainer.tune(CNN, train_loader, test_loader)


#print(what_is_it('../../resources/textures/grass2.png', True))

#check_accuracy_data()

#check_accuracy_tiles()