Machine-Learning-Collection/ML/TensorFlow/Basics/tutorial17-tensorboard/utils.py

import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
import numpy as np
import io
import sklearn.metrics
from tensorboard.plugins import projector
import cv2
import os
import shutil

# Stolen from tensorflow official guide:
# https://www.tensorflow.org/tensorboard/image_summaries
def plot_to_image(figure):
    """Converts the matplotlib plot specified by 'figure' to a PNG image and
    returns it. The supplied figure is closed and inaccessible after this call."""

    # Save the plot to a PNG in memory.
    buf = io.BytesIO()
    plt.savefig(buf, format="png")

    # Closing the figure prevents it from being displayed directly inside
    # the notebook.
    plt.close(figure)
    buf.seek(0)

    # Convert PNG buffer to TF image
    image = tf.image.decode_png(buf.getvalue(), channels=4)

    # Add the batch dimension
    image = tf.expand_dims(image, 0)
    return image


def image_grid(data, labels, class_names):
    # Data should be in (BATCH_SIZE, H, W, C)
    assert data.ndim == 4

    figure = plt.figure(figsize=(10, 10))
    num_images = data.shape[0]
    size = int(np.ceil(np.sqrt(num_images)))

    for i in range(data.shape[0]):
        plt.subplot(size, size, i + 1, title=class_names[labels[i]])
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)

        # if grayscale
        if data.shape[3] == 1:
            plt.imshow(data[i], cmap=plt.cm.binary)

        else:
            plt.imshow(data[i])

    return figure


def get_confusion_matrix(y_labels, logits, class_names):
    preds = np.argmax(logits, axis=1)
    cm = sklearn.metrics.confusion_matrix(
        y_labels, preds, labels=np.arange(len(class_names)),
    )

    return cm


def plot_confusion_matrix(cm, class_names):
    size = len(class_names)
    figure = plt.figure(figsize=(size, size))
    plt.imshow(cm, interpolation="nearest", cmap=plt.cm.Blues)
    plt.title("Confusion Matrix")

    indices = np.arange(len(class_names))
    plt.xticks(indices, class_names, rotation=45)
    plt.yticks(indices, class_names)

    # Normalize Confusion Matrix
    cm = np.around(cm.astype("float") / cm.sum(axis=1)[:, np.newaxis], decimals=3,)

    threshold = cm.max() / 2.0
    for i in range(size):
        for j in range(size):
            color = "white" if cm[i, j] > threshold else "black"
            plt.text(
                i, j, cm[i, j], horizontalalignment="center", color=color,
            )

    plt.tight_layout()
    plt.xlabel("True Label")
    plt.ylabel("Predicted label")

    cm_image = plot_to_image(figure)
    return cm_image


# Stolen from:
# https://gist.github.com/AndrewBMartin/ab06f4708124ccb4cacc4b158c3cef12
def create_sprite(data):
    """
    Tile images into sprite image.
    Add any necessary padding
    """

    # For B&W or greyscale images
    if len(data.shape) == 3:
        data = np.tile(data[..., np.newaxis], (1, 1, 1, 3))

    n = int(np.ceil(np.sqrt(data.shape[0])))
    padding = ((0, n ** 2 - data.shape[0]), (0, 0), (0, 0), (0, 0))
    data = np.pad(data, padding, mode="constant", constant_values=0)

    # Tile images into sprite
    data = data.reshape((n, n) + data.shape[1:]).transpose((0, 2, 1, 3, 4))
    # print(data.shape) => (n, image_height, n, image_width, 3)

    data = data.reshape((n * data.shape[1], n * data.shape[3]) + data.shape[4:])
    # print(data.shape) => (n * image_height, n * image_width, 3)
    return data


def plot_to_projector(
    x,
    feature_vector,
    y,
    class_names,
    log_dir="default_log_dir",
    meta_file="metadata.tsv",
):
    assert x.ndim == 4  # (BATCH, H, W, C)

    if os.path.isdir(log_dir):
        shutil.rmtree(log_dir)

    # Create a new clean fresh folder :)
    os.mkdir(log_dir)

    SPRITES_FILE = os.path.join(log_dir, "sprites.png")
    sprite = create_sprite(x)
    cv2.imwrite(SPRITES_FILE, sprite)

    # Generate label names
    labels = [class_names[y[i]] for i in range(int(y.shape[0]))]

    with open(os.path.join(log_dir, meta_file), "w") as f:
        for label in labels:
            f.write("{}\n".format(label))

    if feature_vector.ndim != 2:
        print(
            "NOTE: Feature vector is not of form (BATCH, FEATURES)"
            " reshaping to try and get it to this form!"
        )
        feature_vector = tf.reshape(feature_vector, [feature_vector.shape[0], -1])

        feature_vector = tf.Variable(feature_vector)
        checkpoint = tf.train.Checkpoint(embedding=feature_vector)
        checkpoint.save(os.path.join(log_dir, "embeddings.ckpt"))

        # Set up config
        config = projector.ProjectorConfig()
        embedding = config.embeddings.add()
        embedding.tensor_name = "embedding/.ATTRIBUTES/VARIABLE_VALUE"
        embedding.metadata_path = meta_file
        embedding.sprite.image_path = "sprites.png"
        embedding.sprite.single_image_dim.extend((x.shape[1], x.shape[2]))
        projector.visualize_embeddings(log_dir, config)