update

algorithms/neural_network/lightning_logs/version_20/hparams.yaml

@@ -0,0 +1 @@
+{}

algorithms/neural_network/neural_network.py

@@ -16,9 +16,11 @@ class NeuralNetwork(pl.LightningModule):
         self.maxpool1 = nn.MaxPool2d((2, 2), stride=2)
         self.conv2 = nn.Conv2d(24, 48, (3, 3), padding=1)
         self.relu2 = nn.ReLU()
-        self.fc1 = nn.Linear(48*18*18, 4)
+        self.fc1 = nn.Linear(48*18*18, 800)
         self.relu3 = nn.ReLU()
-        self.fc2 = nn.Linear(500, num_classes)
+        self.fc2 = nn.Linear(800, 400)
+        self.relu4 = nn.ReLU()
+        self.fc3 = nn.Linear(400, 4)
         self.logSoftmax = nn.LogSoftmax(dim=1)
 
         self.batch_size = batch_size
@@ -32,6 +34,10 @@ class NeuralNetwork(pl.LightningModule):
         x = self.relu2(x)
         x = x.reshape(x.shape[0], -1)
         x = self.fc1(x)
+        x = self.relu3(x)
+        x = self.fc2(x)
+        x = self.relu4(x)
+        x = self.fc3(x)
         x = self.logSoftmax(x)
         return x
 

algorithms/neural_network/neural_network_interface.py

@@ -10,7 +10,8 @@ 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 train(model):
     model = model.to(DEVICE)
@@ -95,12 +96,13 @@ def check_accuracy_tiles():
 
 
 def what_is_it(img_path, show_img=False):
-    image = read_image(img_path, mode=ImageReadMode.RGB)
+    image = Image.open(img_path).convert('RGB')
     if show_img:
-        plt.imshow(plt.imread(img_path))
+        plt.imshow(image)
         plt.show()
+
     image = SETUP_PHOTOS(image).unsqueeze(0)
-    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_13/checkpoints/epoch=4-step=405.ckpt')
+    model = NeuralNetwork.load_from_checkpoint('./lightning_logs/version_20/checkpoints/epoch=3-step=324.ckpt')
 
     with torch.no_grad():
         model.eval()
@@ -108,18 +110,19 @@ def what_is_it(img_path, show_img=False):
         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', 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)
+#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)
+#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)
 #check_accuracy_tiles()
-#print(what_is_it('../../resources/textures/sand.png', True))
+
+#print(what_is_it('../../resources/textures/grass2.png', True))

algorithms/neural_network/watersandtreegrass.py

@@ -3,6 +3,7 @@ from torch.utils.data import Dataset
 import pandas as pd
 from torchvision.io import read_image, ImageReadMode
 from common.helpers import createCSV
+from PIL import Image
 
 
 class WaterSandTreeGrass(Dataset):
@@ -15,7 +16,8 @@ class WaterSandTreeGrass(Dataset):
         return len(self.img_labels)
 
     def __getitem__(self, idx):
-        image = read_image(self.img_labels.iloc[idx, 0], mode=ImageReadMode.RGB)
+        image = Image.open(self.img_labels.iloc[idx, 0]).convert('RGB')
+
         label = torch.tensor(int(self.img_labels.iloc[idx, 1]))
 
         if self.transform:

common/constants.py

@@ -77,16 +77,15 @@ BAR_HEIGHT_MULTIPLIER = 0.1
 
 
 #NEURAL_NETWORK
-LEARNING_RATE = 0.00478630092322638
+LEARNING_RATE = 0.000630957344480193
 BATCH_SIZE = 64
-NUM_EPOCHS = 20
+NUM_EPOCHS = 9
 
 DEVICE = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
 print("Using ", DEVICE)
 CLASSES = ['grass', 'sand', 'tree', 'water']
 
 SETUP_PHOTOS = transforms.Compose([
-    transforms.ToPILImage(),
     transforms.ToTensor(),
     transforms.Resize((36, 36)),
     transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])