added review sql database

api/train_bird.py

@@ -5,6 +5,7 @@ from torchvision import datasets, transforms
 from torch.utils.data import DataLoader, random_split
 import matplotlib.pyplot as plt
 
+from util import TransformedSubset, visualizeData
 from bird_cnn import Bird_CNN, sample, trainCNN
 
 from enum import Enum
@@ -12,7 +13,7 @@ from enum import Enum
 from PIL import Image
 
 SAVE_PATH = "./saved_models"
-IMAGE_SIZE = 64
+IMAGE_SIZE = 128
 
 class bird_species(Enum):
     Common_Kingfisher = 0
@@ -23,24 +24,34 @@ class bird_species(Enum):
     Ruddy_Shelduck = 5
     Sarus_Crane = 6
 
-transform = transforms.Compose([
+transform_augemnt = transforms.Compose([
+    transforms.RandomAffine(
+        degrees=35,                # no rotation
+        translate=(0.2, 0.2)      # shift up to 20% horizontally/vertically
+    ),
     transforms.RandomHorizontalFlip(p=0.5),
-    transforms.RandomRotation(35),
     transforms.Resize(IMAGE_SIZE),
     transforms.CenterCrop(IMAGE_SIZE),
     transforms.ToTensor()
 ])
 
-dataset = datasets.ImageFolder("data/CUB_200_2011/images", transform=transform)
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.CenterCrop(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+dataset = datasets.ImageFolder("data/CUB_200_2011/images")
 
 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)
+train_subset, val_subset = random_split(dataset, [train_size, val_size])
+train_dataset = TransformedSubset(train_subset, transform_augemnt)
+val_dataset = TransformedSubset(val_subset, transform)
 
+train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
+val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)
 
 device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
 print("Using device", device)
@@ -50,7 +61,7 @@ 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)
+trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 500, SAVE_PATH=SAVE_PATH, save=True)
 exit()
 
 image_path = "test1.jpg"