update
1
.gitignore
vendored
@ -150,3 +150,4 @@ cython_debug/
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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.idea/
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/algorithms/neural_network/data/
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@ -10,23 +10,33 @@ from common.constants import DEVICE, BATCH_SIZE, NUM_EPOCHS, LEARNING_RATE, SETU
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class NeuralNetwork(pl.LightningModule):
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def __init__(self, numChannels=3, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, num_classes=4):
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super().__init__()
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self.layer = nn.Sequential(
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nn.Linear(36*36*3, 300),
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nn.ReLU(),
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nn.Linear(300, 4),
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nn.LogSoftmax(dim=-1)
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)
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super(NeuralNetwork, self).__init__()
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self.conv1 = nn.Conv2d(numChannels, 24, (3, 3), padding=1)
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self.relu1 = nn.ReLU()
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self.maxpool1 = nn.MaxPool2d((2, 2), stride=2)
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self.conv2 = nn.Conv2d(24, 48, (3, 3), padding=1)
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self.relu2 = nn.ReLU()
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self.fc1 = nn.Linear(48*18*18, 4)
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self.relu3 = nn.ReLU()
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self.fc2 = nn.Linear(500, num_classes)
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self.logSoftmax = nn.LogSoftmax(dim=1)
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self.batch_size = batch_size
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self.learning_rate = learning_rate
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def forward(self, x):
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x = self.conv1(x)
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x = self.relu1(x)
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x = self.maxpool1(x)
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x = self.conv2(x)
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x = self.relu2(x)
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x = x.reshape(x.shape[0], -1)
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x = self.layer(x)
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x = self.fc1(x)
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x = self.logSoftmax(x)
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return x
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def configure_optimizers(self):
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optimizer = SGD(self.parameters(), lr=self.learning_rate)
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optimizer = Adam(self.parameters(), lr=self.learning_rate)
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return optimizer
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def training_step(self, batch, batch_idx):
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@ -100,7 +100,7 @@ def what_is_it(img_path, show_img=False):
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plt.imshow(plt.imread(img_path))
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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_3/checkpoints/epoch=8-step=810.ckpt')
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model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_13/checkpoints/epoch=4-step=405.ckpt')
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with torch.no_grad():
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model.eval()
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@ -109,17 +109,17 @@ def what_is_it(img_path, show_img=False):
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CNN = NeuralNetwork()
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common.helpers.createCSV()
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trainer = pl.Trainer(accelerator='gpu', devices=1, auto_scale_batch_size=True, callbacks=[EarlyStopping('val_loss')], 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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#trainer = pl.Trainer(accelerator='gpu', devices=1, callbacks=[EarlyStopping('val_loss')], 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.fit(CNN, train_loader, test_loader)
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#trainer.tune(CNN, train_loader, test_loader)
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check_accuracy_tiles()
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print(what_is_it('../../resources/textures/sand.png', True))
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#check_accuracy_tiles()
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#print(what_is_it('../../resources/textures/sand.png', True))
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@ -77,19 +77,18 @@ BAR_HEIGHT_MULTIPLIER = 0.1
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#NEURAL_NETWORK
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LEARNING_RATE = 0.13182567385564073
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LEARNING_RATE = 0.00478630092322638
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BATCH_SIZE = 64
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NUM_EPOCHS = 50
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NUM_EPOCHS = 20
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DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
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print("Using ", DEVICE)
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CLASSES = ['grass', 'sand', 'tree', 'water']
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SETUP_PHOTOS = transforms.Compose([
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transforms.Resize(36),
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transforms.CenterCrop(36),
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transforms.ToPILImage(),
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transforms.ToTensor(),
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transforms.Resize((36, 36)),
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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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