changed project structure, merged docker compose files

api/r-cnn.py

@@ -0,0 +1,51 @@
+import torch
+import torch.nn as nn
+import torchvision
+from torchvision import datasets, transforms
+from torch.utils.data import DataLoader, random_split
+import matplotlib.pyplot as plt
+
+from bird_cnn import Bird_CNN, sample, trainCNN
+
+from enum import Enum
+
+from PIL import Image
+
+SAVE_PATH = "./saved_models"
+IMAGE_SIZE = 64
+
+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.CenterCrop(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+dataset = datasets.ImageFolder("data/train", transform=transform)
+
+train_size = int(0.8 * len(dataset))
+val_size = len(dataset) - train_size
+
+train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
+
+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=32, c_out=7)
+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)