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2a2559c 106788a 2a2559c 106788a 2a2559c 106788a 2a2559c 106788a 2a2559c 106788a 2a2559c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import os
import torch
import torch.nn as nn
from datetime import datetime
from src.config import (
CatDogDatasetConfigsInput,
CatDogClassifierConfigs,
DataPreprocessorConfigsInput
)
from src.data_ingestion import CatDogDataset
from src.data_preprocessing import DataPreprocessor
from src.model import CatDogClassifier
def train_pipeline():
# Setup Intialize configurations
# Data ingestion
data_path = "datasets/datasets.zip"
train_data_path = "datasets/train"
test_data_path = "datasets/test"
data_path = os.path.join(os.getcwd(), data_path)
train_data_path = os.path.join(os.getcwd(), train_data_path)
test_data_path = os.path.join(os.getcwd(), test_data_path)
# data_ingestion_configs = CatDogDatasetConfigsInput(
# data_path=data_path,
# train_data_path=train_data_path,
# test_data_path=test_data_path,
# test_size=0.2,
# random_state=42
# )
# print(data_ingestion_configs)
# dataset = CatDogDataset(data_ingestion_configs)
# dataset.load_data()
# Data preprocessing
data_preprocessing_configs = DataPreprocessorConfigsInput(
train_dataset_path=train_data_path,
test_dataset_path=test_data_path,
shuffle=True,
batch_size=32,
horizontal_flip_prob=0.5,
image_size=224,
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]
)
preprocessor = DataPreprocessor(data_preprocessing_configs)
train_dataloader, test_dataloader = preprocessor.create_dataloader()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {device}")
# Model training
model_configs = CatDogClassifierConfigs(
device=device,
input_channels=3,
num_classes=2,
learning_rate=0.001,
kernel_size=3,
stride=2,
padding=1,
num_layers=3,
use_amp=True
)
model = CatDogClassifier(model_configs)
model.to(device)
# Set random seeds
torch.manual_seed(42)
torch.cuda.manual_seed(42)
# Setup loss function and optimizer
loss_fn = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=model.parameters(), lr=0.001)
# # Calculate training time using timeit
# Start the timer
from timeit import default_timer as timer
start_time = timer()
model.train_process(
model=model,
train_dataloader=train_dataloader,
test_dataloader=test_dataloader,
num_epochs=20,
loss_fn=loss_fn,
optimizer=optimizer
)
end_time = timer()
print(f"Training completed in {end_time - start_time} seconds.")
# Save the trained model
torch.save(model.state_dict(), f"cat_dog_classifier_{datetime.now().strftime('%Y%m%d_%H%M%S')}.pth")
print("Model saved to cat_dog_classifier.pth")
if __name__ == "__main__":
train_pipeline() |