54 lines
1.8 KiB
Python
54 lines
1.8 KiB
Python
import random
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from lightning import Trainer
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from sklearn.model_selection import train_test_split
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from torch.utils.data import DataLoader
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from data import (
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FootballSegDataset,
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calculate_mean_std,
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get_data,
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)
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from model import UNetLightning
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def main():
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all_images, all_paths = get_data()
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image_train_paths, image_test_paths, label_train_paths, label_test_paths = (
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train_test_split(all_images, all_paths, test_size=0.2, random_state=42)
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)
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mean, std = calculate_mean_std(image_train_paths)
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label_mean, label_std = calculate_mean_std(label_train_paths)
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test_mean, test_std = calculate_mean_std(image_test_paths)
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test_label_mean, test_label_std = calculate_mean_std(label_test_paths)
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train_dataset = FootballSegDataset(
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image_train_paths, label_train_paths, mean, std, label_mean, label_std)
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test_dataset = FootballSegDataset(
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image_test_paths, label_test_paths, test_mean, test_std, test_label_mean, test_label_std)
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train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
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test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)
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train_indices = random.sample(range(len(train_loader.dataset)), 5)
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test_indices = random.sample(range(len(test_loader.dataset)), 5)
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# plot_random_images(train_indices, train_loader, "train")
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# plot_random_images(test_indices, test_loader, "test")
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# statistics = SegmentationStatistics(all_paths, train_loader, test_loader, mean, std)
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# statistics.count_colors()
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# statistics.print_statistics()
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model = UNetLightning(3, 3, learning_rate=1e-3)
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trainer = Trainer(max_epochs=5, logger=True, log_every_n_steps=1)
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trainer.fit(model, train_loader, test_loader)
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model.eval()
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model.freeze()
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trainer.test(model, test_loader)
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if __name__ == "__main__":
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main()
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