301-tensorflow-training-openvino: From Training to Deployment with TensorFlow and OpenVINO

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Sat Jun 18 2022 21:03:36 GMT+0000 (UTC)

Saved by @OpenVINOtoolkit #python #openvino #openvino-notebooks #deeplearning #accelerated-inference #optimization #tensorflow

# Import TensorFlow and Other Libraries
import os
import sys
from pathlib import Path

import PIL
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from PIL import Image
from openvino.runtime import Core
from openvino.tools.mo import mo_tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential

sys.path.append("../utils")
from notebook_utils import download_file

# Download and Explore the Dataset
import pathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file('flower_photos', origin=dataset_url, untar=True)
data_dir = pathlib.Path(data_dir)

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
PIL.Image.open(str(roses[1]))

tulips = list(data_dir.glob('tulips/*'))
PIL.Image.open(str(tulips[0]))
PIL.Image.open(str(tulips[1]))

# Create a Dataset
batch_size = 32
img_height = 180
img_width = 180

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="training",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
  data_dir,
  validation_split=0.2,
  subset="validation",
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)

# Visualize the Data
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
    for i in range(9):
        ax = plt.subplot(3, 3, i + 1)
        plt.imshow(images[i].numpy().astype("uint8"))
        plt.title(class_names[labels[i]])
        plt.axis("off")

for image_batch, labels_batch in train_ds:
    print(image_batch.shape)
    print(labels_batch.shape)
    break

# Configure the Dataset for Performance
# AUTOTUNE = tf.data.AUTOTUNE
AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

# Standardize the Data
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixels values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image)) 

# Create the Model
num_classes = 5

model = Sequential([
  layers.experimental.preprocessing.Rescaling(1./255, input_shape=(img_height, img_width, 3)),
  layers.Conv2D(16, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(32, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Conv2D(64, 3, padding='same', activation='relu'),
  layers.MaxPooling2D(),
  layers.Flatten(),
  layers.Dense(128, activation='relu'),
  layers.Dense(num_classes)
])

# Compile the Model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
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In this directory, you will find two Jupyter notebooks. The first is an end-to-end deep learning training tutorial which borrows the open source code from the TensorFlow image classification tutorial, demonstrating how to train the model and then convert to OpenVINO™ Intermediate Representation (IR). It leverages the tf_flowers dataset which includes about 3,700 photos of flowers. This tutorial demonstrates how to train, convert, and deploy an image classification model with TensorFlow and OpenVINO. This particular notebook shows the process where we perform the inference step on the freshly trained model that is converted to OpenVINO IR with Model Optimizer The training code is based on the official TensorFlow Image Classification Tutorial. The flower_ir.bin and flower_ir.xml (pre-trained models) can be obtained by executing the code with 'Runtime->Run All' or the Ctrl+F9 command.

https://github.com/openvinotoolkit/openvino_notebooks/blob/main/notebooks/301-tensorflow-training-openvino/301-tensorflow-training-openvino.ipynb