first commit

bird_cnn/main.py

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+import torch
+import torch.nn as nn
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader, random_split
+
+from bird_cnn import Bird_CNN, sample, trainCNN
+
+from enum import Enum
+
+from PIL import Image
+
+SAVE_PATH = "./saved_models"
+IMAGE_SIZE = (300, 300)
+
+class bird_species(Enum):
+    Common_Kingfisher = 0
+    CommonMyna = 1
+    House_Crow = 2
+    Indian_Peacock = 3
+    Indian_Pitta = 4
+    Ruddy_Shelduck = 5
+    Sarus_Crane = 6
+
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+dataset = datasets.ImageFolder("data/train", transform=transform)
+
+train_size = int(0.3 * len(dataset))
+val_size = int(len(dataset) * 0.3)
+throw_away = len(dataset) - val_size - train_size
+
+train_dataset, val_dataset, _ = random_split(dataset, [train_size, val_size, throw_away])
+
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+
+device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
+print("Using device", device)
+
+model = Bird_CNN(c_in=3, c_hidden=15, c_out=7, kernel_size=3, img_width=IMAGE_SIZE[0], img_height=IMAGE_SIZE[1])
+model.to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
+loss_module = nn.CrossEntropyLoss()
+
+trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 50, SAVE_PATH=SAVE_PATH, save=True)
+exit()
+
+image_path = "test.jpg"
+
+image = Image.open(image_path).convert("RGB")
+image = transform(image)
+image = image.unsqueeze(0)
+
+confidence, pred = sample(model=model, img=image, device=device, SAVE_PATH=SAVE_PATH)
+print(f"Species: {bird_species(pred.item()).name} | Confidence: {int(confidence.item()*100)/100}")