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from datetime import datetime
import torch
import torchvision
import torch.nn as nn
from tqdm.auto import tqdm
from torchvision import transforms
from .config import CatDogClassifierConfigs
class CatDogClassifier(nn.Module):
def __init__(self, configs: CatDogClassifierConfigs):
super(CatDogClassifier, self).__init__()
self.configs = configs
self.kernel_size = configs.kernel_size
self.stride = configs.stride
self.padding = configs.padding
self.num_layers = configs.num_layers
self.learning_rate = configs.learning_rate
self.num_classes = configs.num_classes
self.input_channels = configs.input_channels
self.device = configs.device
self.use_amp = configs.use_amp
# Initialize the model architecture
self._build_model()
def _build_model(self):
# Placeholder for model building logic
self.conv_layer_1 = nn.Sequential(
nn.Conv2d(
in_channels=self.input_channels,
out_channels=64,
kernel_size=self.kernel_size,
padding=self.padding
),
nn.ReLU(),
nn.BatchNorm2d(num_features=64),
nn.MaxPool2d(kernel_size=2)
)
self.conv_layer_2 = nn.Sequential(
nn.Conv2d(
in_channels=64,
out_channels=128,
kernel_size=self.kernel_size,
padding=self.padding
),
nn.BatchNorm2d(num_features=128),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.conv_layer_3 = nn.Sequential(
nn.Conv2d(
in_channels=128,
out_channels=256,
kernel_size=self.kernel_size,
padding=self.padding
),
nn.BatchNorm2d(num_features=256),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2)
)
self.conv_layer_4 = nn.Sequential(
nn.Conv2d(
in_channels=256,
out_channels=512,
kernel_size=self.kernel_size,
padding=self.padding
),
nn.BatchNorm2d(num_features=512),
nn.ReLU(),
nn.AdaptiveAvgPool2d((1, 1))
)
self.classifier = nn.Sequential(
nn.Flatten(),
nn.Dropout(p=0.5),
nn.Linear(in_features=512, out_features=256),
nn.ReLU(),
nn.Dropout(p=0.3),
nn.Linear(in_features=256, out_features=self.num_classes)
)
def forward(self, x: torch.Tensor):
x = self.conv_layer_1(x)
x = self.conv_layer_2(x)
x = self.conv_layer_3(x)
x = self.conv_layer_4(x)
x = self.classifier(x)
return x
def train_process(
self,
model: nn.Module,
train_dataloader: torch.utils.data.DataLoader,
test_dataloader: torch.utils.data.DataLoader,
num_epochs: int,
loss_fn: nn.Module,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler._LRScheduler = None,
):
# Initialize the loss function and optimizer
scaler = torch.amp.GradScaler(device=self.configs.device, enabled=self.configs.use_amp)
print("Training the model with provided data")
best_acc = 0.0
# Implement training loop here
results = {
"train_loss": [],
"train_acc": [],
"test_loss": [],
"test_acc": []
}
# Loop through each epoch
for epoch in tqdm(range(num_epochs)):
train_loss, train_acc = self._train_step(
model=model,
dataloader=train_dataloader,
loss_fn=loss_fn,
optimizer=optimizer,
epoch=epoch,
num_epochs=num_epochs,
scaler=scaler
)
test_loss, test_acc = self._test_step(
model=model,
dataloader=test_dataloader,
loss_fn=loss_fn
)
# ----- Scheduler update -----
if scheduler:
scheduler.step()
# ----- Save best model -----
if test_acc > best_acc:
best_acc = test_acc
torch.save(model.state_dict(), f"best_cat_dog_classifier_model_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pth")
results["train_loss"].append(train_loss)
results["train_acc"].append(train_acc)
results["test_loss"].append(test_loss)
results["test_acc"].append(test_acc)
print(
f"Epoch [{epoch+1}/{num_epochs}] "
f"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f} | "
f"Test Loss: {test_loss:.4f}, Test Acc: {test_acc:.4f}"
)
print(f"\n✅ Training complete! Best Test Accuracy: {best_acc:.4f}")
return results
def _train_step(
self,
model: nn.Module,
dataloader: torch.utils.data.DataLoader,
loss_fn: nn.Module,
optimizer: torch.optim.Optimizer,
epoch: int,
num_epochs: int,
scaler: torch.amp.GradScaler,
):
# Define model in training mode
model.train()
train_loss, train_acc, correct, total_train_examples = 0, 0, 0, 0
# Loop through each batch
pbar = tqdm(enumerate(dataloader), desc=f"Epoch [{epoch+1}/{num_epochs}]")
for batch_idx, (data, target) in pbar:
data, target = data.to(self.configs.device), target.to(self.configs.device)
# print(f"Batch {batch_idx+1}: data shape {data.shape}, target shape {target.shape}")
# Forward pass
# y_pred = model(data)
with torch.amp.autocast(device_type=self.configs.device, enabled=self.configs.use_amp):
y_pred = model(data)
# Calculate and accumulate loss
loss = loss_fn(y_pred, target)
train_loss += loss.item()
# Backward pass
optimizer.zero_grad()
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
# Calculate and accumulate accuracy metric
y_pred_labels = torch.argmax(y_pred, dim=1)
correct += (y_pred_labels == target).sum().item()
total_train_examples += target.size(0)
# Adjust loss and accuracy to get average loss and accuracy based on number of batches
train_loss /= len(dataloader)
train_acc = correct / total_train_examples
return train_loss, train_acc
def _test_step(
self,
model: nn.Module,
dataloader: torch.utils.data.DataLoader,
loss_fn: nn.Module,
):
# Define model in evaluation
model.eval()
test_loss, test_acc, correct, total_test_examples = 0, 0, 0, 0
with torch.inference_mode():
for batch_idx, (data, target) in enumerate(dataloader):
data, target = data.to(self.configs.device), target.to(self.configs.device)
# Forward pass
y_pred = model(data)
# Calculate and accumulate loss
loss = loss_fn(y_pred, target)
test_loss += loss.item()
# Calculate and accumulate accuracy metric
y_pred_labels = torch.argmax(y_pred, dim=1)
correct += (y_pred_labels == target).sum().item()
total_test_examples += target.size(0)
# Adjust loss and accuracy to get average loss and accuracy based on number of batches
test_loss /= len(dataloader)
test_acc = correct / total_test_examples
return test_loss, test_acc
def predict(
self,
model: nn.Module,
image_path: str
) -> str:
# Load and preprocess the image converting it to a tensor
# and normalizing the pixel values between 0 and 1
image_tensor = torchvision.io.read_image(str(image_path)).float() / 255.0
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
),
])
image_tensor_transformed = transform(image_tensor).unsqueeze(0).to(self.configs.device)
# Set model to evaluation mode and make prediction
model = model.to(self.configs.device)
model.eval()
with torch.inference_mode():
image_tensor_pred = model(image_tensor_transformed)
predicted_label = torch.argmax(image_tensor_pred, dim=1).item()
return "cat" if predicted_label == 0 else "dog"
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