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87 lines
3.0 KiB
87 lines
3.0 KiB
import matplotlib.colors as mcolors
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import torch
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from torch.utils.data import DataLoader, TensorDataset
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def extract_colors():
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# Extracting the list of xkcd colors as RGB triples
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xkcd_colors = mcolors.XKCD_COLORS
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rgb_values = [mcolors.to_rgb(color) for color in xkcd_colors.values()]
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# Extracting the list of xkcd color names
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xkcd_color_names = list(xkcd_colors.keys())
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# Convert the list of RGB triples to a PyTorch tensor
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rgb_tensor = torch.tensor(rgb_values, dtype=torch.float32)
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return rgb_tensor, xkcd_color_names
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def create_dataloader(N: int = 50, **kwargs):
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rgb_tensor, _ = extract_colors()
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rgb_tensor = preprocess_data(rgb_tensor)
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# Creating a dataset and data loader
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dataset = TensorDataset(rgb_tensor, torch.zeros(len(rgb_tensor)))
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train_dataloader = DataLoader(dataset, **kwargs)
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return train_dataloader
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def create_gray_supplement(N: int = 50):
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linear_space = torch.linspace(0, 1, N)
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gray_tensor = linear_space.unsqueeze(1).repeat(1, 3)
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gray_tensor = preprocess_data(gray_tensor)
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return [(gray_tensor[i], f"gray{i/N:2.4f}") for i in range(len(gray_tensor))]
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def create_named_dataloader(N: int = 50, **kwargs):
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rgb_tensor, xkcd_color_names = extract_colors()
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rgb_tensor = preprocess_data(rgb_tensor)
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# Creating a dataset with RGB values and their corresponding color names
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dataset_with_names = [
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(rgb_tensor[i], xkcd_color_names[i].replace("xkcd:", ""))
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for i in range(len(rgb_tensor))
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]
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dataset_with_names += create_gray_supplement(N)
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train_dataloader_with_names = DataLoader(dataset_with_names, **kwargs)
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return train_dataloader_with_names
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def preprocess_data(data):
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# Assuming 'data' is a tensor of shape [n_samples, 3]
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# Compute argmin and argmax for each row
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argmin_values = torch.argmin(data, dim=1, keepdim=True).float()
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argmax_values = torch.argmax(data, dim=1, keepdim=True).float()
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# Normalize or scale argmin and argmax if necessary
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# For example, here I am just dividing by the number of features
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argmin_values /= data.shape[1]
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argmax_values /= data.shape[1]
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# Concatenate the argmin and argmax values to the original data
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new_data = torch.cat((data, argmin_values, argmax_values), dim=1)
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return new_data
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if __name__ == "__main__":
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batch_size = 4
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train_dataloader = create_dataloader(batch_size=batch_size, shuffle=True)
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train_dataloader_with_names = create_named_dataloader(
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batch_size=batch_size, shuffle=True
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)
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# Extract a sample from the DataLoader
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sample_data = next(iter(train_dataloader))
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# Sample RGB values and their corresponding dummy labels
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sample_rgb_values, _ = sample_data
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print(sample_rgb_values)
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# Extract a sample from the new DataLoader
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sample_data_with_names = next(iter(train_dataloader_with_names))
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# Sample RGB values and their corresponding color names
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sample_rgb_values_with_names, sample_color_names = sample_data_with_names
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print(sample_rgb_values_with_names, sample_color_names)
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