Add main code

Author: Behrooz

projector.py

@@ -0,0 +1,140 @@
+import os
+from typing import Iterable
+
+import numpy as np
+import tensorflow as tf
+from matplotlib import pyplot as plt
+from tensorboard.plugins import projector
+
+
+class Projector:
+    def __init__(
+            self,
+            images: np.array,
+            labels: Iterable,
+            log_dir: str,
+            data_name: str):
+        """
+        Create all necessary artifacts and configs for Tensorboard Projector.
+
+        Parameters
+        ----------
+        images : np.array
+            An n-d array of images (NumberOFImages X Width X Height).
+        labels : Iterable
+            A one-to-one asociated labes to images. It can be a list, numpy array, or any iterable.
+        log_dir : str
+            The location all the artifacts are being saved. The directory to which Tensorboard is directd.
+            `Tensorboard --logdir "log_dir"`
+        data_name : str
+            The name of the dataset, which is appended to the name of all artifacts.
+        """
+        self.log_dir = log_dir
+        self.images = images
+        self.labels = labels
+        self.data_name = data_name
+
+        self.n_images = None
+        self.image_width = None
+        self.image_height = None
+        self.points = None
+
+        if self.images:
+            self.convert_images_to_points()
+
+    def convert_images_to_points(self):
+        """
+        Convert images array to high-dimentional data points (NumberOFImages X NumberOfDimensions).
+        """
+        self.n_images, self.image_width, self.image_height = self.images.shape
+        self.points = np.reshape(
+            self.images, (-1, self.image_width * self.image_height))
+
+    def save_points(self):
+        """
+        Save high-dimensional data points into a model checkpoint.
+        """
+        points_filename = os.path.join(self.log_dir, f'images_{self.data_name}.ckpt')
+        points_tensor = tf.Variable(self.points, name=self.data_name)
+        ckpt = tf.train.Checkpoint(**{self.data_name: points_tensor})
+        ckpt.save(points_filename)
+        print('> Images are saved in {}'.format(points_filename))
+
+    def save_labels(self):
+        """
+        Save labels into a metadata tab-separated-value file.
+        """
+        meta_filename = os.path.join(
+            self.log_dir, f'metadata_{self.data_name}.tsv')
+        with open(meta_filename, 'w') as metadata_file:
+            for row in self.labels:
+                metadata_file.write(f'{row}\n')
+        print('> Metadata file is saved in {}'.format(meta_filename))
+
+    def write_sprite_image(self):
+        """
+        Create and write a sprite image, a single PNG file containing all images (possibly downsampled).
+        """
+        # Calculate number of plot
+        n_plots = int(np.ceil(np.sqrt(self.n_images)))
+
+        # Preallocate the sprite image
+        sprite_image = np.ones(
+            (self.image_height * n_plots, self.image_width * n_plots))
+
+        for i in range(n_plots):
+            for j in range(n_plots):
+                img_idx = i * n_plots + j
+                if img_idx < self.n_images:
+                    img = self.images[img_idx]
+                    sprite_image[i * self.image_height: (i + 1) * self.image_height,
+                                 j * self.image_width: (j + 1) * self.image_width] = img
+
+        sprite_filename = os.path.join(
+            self.log_dir, f'sprite_{self.data_name}.png')
+        plt.imsave(sprite_filename, sprite_image, cmap='gray')
+        print('> Sprite image saved in {}'.format(sprite_filename))
+
+    def create_config(self, with_sprite=True):
+        """
+        Create a congfig files that defines image tensor name, path to metadata file, path to the sprite image,
+        and the size of individual image whithin the sprite image.
+
+        Parameters
+        ----------
+        with_sprite : bool, optional
+            If to save sprite or not, by default True
+        """
+        config = projector.ProjectorConfig()
+        embedding = config.embeddings.add()
+        embedding.tensor_name = f'{self.data_name}/.ATTRIBUTES/VARIABLE_VALUE'
+        embedding.metadata_path = f'metadata_{self.data_name}.tsv'
+        if with_sprite:
+            embedding.sprite.image_path = f'sprite_{self.data_name}.png'
+            embedding.sprite.single_image_dim.extend(
+                [self.image_width, self.image_height])
+        projector.visualize_embeddings(self.log_dir, config)
+
+    def make(self):
+        self.save_points()
+        self.save_labels()
+        self.write_sprite_image()
+        self.create_config()
+
+
+if __name__ == "__main__":
+    fashion_mnist = tf.keras.datasets.fashion_mnist
+    (train_images, train_labels), (test_images,
+                                   test_labels) = fashion_mnist.load_data()
+
+    log_dir = '/Users/behrooz/workspace/unsupervised/logs/projector3'
+    data_name = 'fmnist_with_image'
+    labels = train_labels[:1000]
+    images = train_images[:1000]
+
+    proj = Projector(
+        images=images,
+        labels=labels,
+        log_dir=log_dir,
+        data_name=data_name)
+    proj.make()