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work on what_is_it func;

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Angelika Iskra 2022-05-27 00:15:32 +02:00
parent 36f20d8895
commit 6aee7bb207
4 changed files with 15 additions and 16 deletions
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5
algorithms/neural_network/neural_network.py
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@ -1,10 +1,9 @@
import torch import torch
import pytorch_lightning as pl import pytorch_lightning as pl
import torch.nn as nn import torch.nn as nn
from torch.optim import SGD, Adam, lr_scheduler from torch.optim import Adam
import torch.nn.functional as F import torch.nn.functional as F
from torch.utils.data import DataLoader from common.constants import BATCH_SIZE, LEARNING_RATE
from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETUP_PHOTOS, ID_TO_CLASS
class NeuralNetwork(pl.LightningModule): class NeuralNetwork(pl.LightningModule):

22
algorithms/neural_network/neural_network_interface.py
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@ -100,7 +100,7 @@ def what_is_it(img_path, show_img=False):
plt.imshow(plt.imread(img_path)) plt.imshow(plt.imread(img_path))
plt.show() plt.show()
image = SETUP_PHOTOS(image).unsqueeze(0) image = SETUP_PHOTOS(image).unsqueeze(0)
model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_0/checkpoints/epoch=4-step=405.ckpt') model = NeuralNetwork.load_from_checkpoint('D:/DEV/UAM/WMICraft/algorithms/neural_network/lightning_logs/version_3/checkpoints/epoch=8-step=810.ckpt')
with torch.no_grad(): with torch.no_grad():
model.eval() model.eval()
@ -108,18 +108,18 @@ def what_is_it(img_path, show_img=False):
return ID_TO_CLASS[idx] return ID_TO_CLASS[idx]
CNN = NeuralNetwork() # CNN = NeuralNetwork()
common.helpers.createCSV() # common.helpers.createCSV()
#trainer = pl.Trainer(accelerator='gpu', devices=1, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS) #trainer = pl.Trainer(accelerator='gpu', devices=1, callbacks=[EarlyStopping('val_loss')], max_epochs=NUM_EPOCHS)
trainer = pl.Trainer(accelerator='gpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS) # trainer = pl.Trainer(accelerator='cpu', devices=1, auto_lr_find=True, max_epochs=NUM_EPOCHS)
#
trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS) # trainset = WaterSandTreeGrass('./data/train_csv_file.csv', transform=SETUP_PHOTOS)
testset = WaterSandTreeGrass('./data/test_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) # train_loader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True)
test_loader = DataLoader(testset, batch_size=BATCH_SIZE) # test_loader = DataLoader(testset, batch_size=BATCH_SIZE)
#
trainer.fit(CNN, train_loader, test_loader) # trainer.fit(CNN, train_loader, test_loader)
#trainer.tune(CNN, train_loader, test_loader) #trainer.tune(CNN, train_loader, test_loader)
#check_accuracy_tiles() #check_accuracy_tiles()
#print(what_is_it('../../resources/textures/sand.png', True)) #print(what_is_it('../../resources/textures/sand.png', True))

2
logic/level.py
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@ -33,7 +33,7 @@ class Level:
def create_map(self): def create_map(self):
print("Create map") print("Create map")
print(what_is_it('../../resources/textures/grass1.png')) print(what_is_it('D:/DEV/UAM/WMICraft/resources/textures/t2.jpg'))
# self.generate_map() # self.generate_map()
# self.setup_base_tiles() # self.setup_base_tiles()
# self.setup_objects() # self.setup_objects()