Source code for catalyst.contrib.utils.visualization

from typing import List, Optional, Union
import itertools
from pathlib import Path

import numpy as np

from .image import tensor_from_rgb_image
from .plotly import plot_tensorboard_log

[docs]def plot_confusion_matrix( cm, class_names=None, normalize=False, title="confusion matrix", fname=None, show=True, figsize=12, fontsize=32, colormap="Blues", ): """ Render the confusion matrix and return matplotlib"s figure with it. Normalization can be applied by setting `normalize=True`. """ import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt plt.ioff() cmap =[colormap] if class_names is None: class_names = [str(i) for i in range(len(np.diag(cm)))] if normalize: cm = cm.astype(np.float32) / cm.sum(axis=1)[:, np.newaxis] plt.rcParams.update( {"font.size": int(fontsize / np.log2(len(class_names)))} ) f = plt.figure(figsize=(figsize, figsize)) plt.title(title) plt.imshow(cm, interpolation="nearest", cmap=cmap) plt.colorbar() tick_marks = np.arange(len(class_names)) plt.xticks(tick_marks, class_names, rotation=45, ha="right") plt.yticks(tick_marks, class_names) fmt = ".2f" if normalize else "d" thresh = cm.max() / 2.0 for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text( j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black", ) plt.tight_layout() plt.ylabel("True label") plt.xlabel("Predicted label") if fname is not None: plt.savefig(fname=fname) if show: return f
[docs]def render_figure_to_tensor(figure): """@TODO: Docs. Contribution is welcome.""" import matplotlib matplotlib.use("Agg") import matplotlib.pyplot as plt plt.ioff() figure.canvas.draw() image = np.array(figure.canvas.renderer._renderer) plt.close(figure) del figure image = tensor_from_rgb_image(image) return image
[docs]def plot_metrics( logdir: Union[str, Path], step: Optional[str] = "epoch", metrics: Optional[List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, ) -> None: """Plots your learning results. Args: logdir: the logdir that was specified during training. step: 'batch' or 'epoch' - what logs to show: for batches or for epochs metrics: list of metrics to plot. The loss should be specified as 'loss', learning rate = '_base/lr' and other metrics should be specified as names in metrics dict that was specified during training height: the height of the whole resulting plot width: the width of the whole resulting plot """ assert step in [ "batch", "epoch", ], f"Step should be either 'batch' or 'epoch', got '{step}'" metrics = metrics or ["loss"] plot_tensorboard_log(logdir, step, metrics, height, width)
__all__ = ["plot_confusion_matrix", "render_figure_to_tensor", "plot_metrics"]