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| # Copyright (C) 2021-2025, Mindee. | |
| # This program is licensed under the Apache License 2.0. | |
| # See LICENSE or go to <https://opensource.org/licenses/Apache-2.0> for full license details. | |
| import math | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| def plot_samples(images, targets): | |
| # Unnormalize image | |
| num_samples = min(len(images), 12) | |
| num_cols = min(len(images), 8) | |
| num_rows = int(math.ceil(num_samples / num_cols)) | |
| _, axes = plt.subplots(num_rows, num_cols, figsize=(20, 5)) | |
| for idx in range(num_samples): | |
| img = (255 * images[idx].numpy()).round().clip(0, 255).astype(np.uint8) | |
| if img.shape[0] == 3 and img.shape[2] != 3: | |
| img = img.transpose(1, 2, 0) | |
| row_idx = idx // num_cols | |
| col_idx = idx % num_cols | |
| ax = axes[row_idx] if num_rows > 1 else axes | |
| ax = ax[col_idx] if num_cols > 1 else ax | |
| ax.imshow(img) | |
| ax.set_title(targets[idx]) | |
| # Disable axis | |
| for ax in axes.ravel(): | |
| ax.axis("off") | |
| plt.show() | |
| def plot_recorder(lr_recorder, loss_recorder, beta: float = 0.95, **kwargs) -> None: | |
| """Display the results of the LR grid search. | |
| Adapted from https://github.com/frgfm/Holocron/blob/master/holocron/trainer/core.py | |
| Args: | |
| lr_recorder: list of LR values | |
| loss_recorder: list of loss values | |
| beta (float, optional): smoothing factor | |
| **kwargs: keyword arguments from `matplotlib.pyplot.show` | |
| """ | |
| if len(lr_recorder) != len(loss_recorder) or len(lr_recorder) == 0: | |
| raise AssertionError("Both `lr_recorder` and `loss_recorder` should have the same length") | |
| # Exp moving average of loss | |
| smoothed_losses = [] | |
| avg_loss = 0.0 | |
| for idx, loss in enumerate(loss_recorder): | |
| avg_loss = beta * avg_loss + (1 - beta) * loss | |
| smoothed_losses.append(avg_loss / (1 - beta ** (idx + 1))) | |
| # Properly rescale Y-axis | |
| data_slice = slice( | |
| min(len(loss_recorder) // 10, 10), | |
| -min(len(loss_recorder) // 20, 5) if len(loss_recorder) >= 20 else len(loss_recorder), | |
| ) | |
| vals = np.array(smoothed_losses[data_slice]) | |
| min_idx = vals.argmin() | |
| max_val = vals.max() if min_idx is None else vals[: min_idx + 1].max() # type: ignore[misc] | |
| delta = max_val - vals[min_idx] | |
| plt.plot(lr_recorder[data_slice], smoothed_losses[data_slice]) | |
| plt.xscale("log") | |
| plt.xlabel("Learning Rate") | |
| plt.ylabel("Training loss") | |
| plt.ylim(vals[min_idx] - 0.1 * delta, max_val + 0.2 * delta) | |
| plt.grid(True, linestyle="--", axis="x") | |
| plt.show(**kwargs) | |
| class EarlyStopper: | |
| def __init__(self, patience: int = 5, min_delta: float = 0.01): | |
| self.patience = patience | |
| self.min_delta = min_delta | |
| self.counter = 0 | |
| self.min_validation_loss = float("inf") | |
| def early_stop(self, validation_loss: float) -> bool: | |
| if validation_loss < self.min_validation_loss: | |
| self.min_validation_loss = validation_loss | |
| self.counter = 0 | |
| elif validation_loss > (self.min_validation_loss + self.min_delta): | |
| self.counter += 1 | |
| if self.counter >= self.patience: | |
| return True | |
| return False | |