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# Copyright 2018 The TensorFlow Authors
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# Modified for OpenVINO Notebooks
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. For faster inference speed on the model created in this notebook, check out the Post-Training Quantization with TensorFlow Classification Model notebook.
The training code is based on the official TensorFlow Image Classification Tutorial: (https://www.tensorflow.org/tutorials/images/classification).
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.
The first part of the tutorial shows how to classify images of flowers (based on the TensorFlow's official tutorial). It creates an image classifier using a keras.Sequential model, and loads data using preprocessing.image_dataset_from_directory. You will gain practical experience with the following concepts:
This tutorial follows a basic machine learning workflow:
import matplotlib.pyplot as plt
import numpy as np
import os
import PIL
from PIL import Image
from pathlib import Path
import urllib
from openvino.inference_engine import IECore
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.models import Sequential
This tutorial uses a dataset of about 3,700 photos of flowers. The dataset contains 5 sub-directories, one per class:
flower_photo/
daisy/
dandelion/
roses/
sunflowers/
tulips/
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)
After downloading, you should now have a copy of the dataset available. There are 3,670 total images:
image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)
Here are some roses:
roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
PIL.Image.open(str(roses[1]))
And some tulips:
tulips = list(data_dir.glob('tulips/*'))
PIL.Image.open(str(tulips[0]))
PIL.Image.open(str(tulips[1]))
Let's load these images off disk using the helpful image_dataset_from_directory utility. This will take you from a directory of images on disk to a tf.data.Dataset in just a couple lines of code. If you like, you can also write your own data loading code from scratch by visiting the load images tutorial.
Define some parameters for the loader:
batch_size = 32
img_height = 180
img_width = 180
It's good practice to use a validation split when developing your model. Let's use 80% of the images for training, and 20% for validation.
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)
You can find the class names in the class_names attribute on these datasets. These correspond to the directory names in alphabetical order.
class_names = train_ds.class_names
print(class_names)
Here are the first 9 images from the training dataset.
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")
You will train a model using these datasets by passing them to model.fit in a moment. If you like, you can also manually iterate over the dataset and retrieve batches of images:
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
The image_batch is a tensor of the shape (32, 180, 180, 3). This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images.
You can call .numpy() on the image_batch and labels_batch tensors to convert them to a numpy.ndarray.
Let's make sure to use buffered prefetching so you can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data.
Dataset.cache() keeps the images in memory after they're loaded off disk during the first epoch. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.
Dataset.prefetch() overlaps data preprocessing and model execution while training.
Interested readers can learn more about both methods, as well as how to cache data to disk in the data performance guide.
# 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)
The RGB channel values are in the [0, 255] range. This is not ideal for a neural network; in general you should seek to make your input values small. Here, you will standardize values to be in the [0, 1] range by using a Rescaling layer.
normalization_layer = layers.experimental.preprocessing.Rescaling(1./255)
Note: The Keras Preprocessing utilities and layers introduced in this section are currently experimental and may change.
There are two ways to use this layer. You can apply it to the dataset by calling map:
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))
Or, you can include the layer inside your model definition, which can simplify deployment. Let's use the second approach here.
Note: you previously resized images using the image_size argument of image_dataset_from_directory. If you want to include the resizing logic in your model as well, you can use the Resizing layer.
The model consists of three convolution blocks with a max pool layer in each of them. There's a fully connected layer with 128 units on top of it that is activated by a relu activation function. This model has not been tuned for high accuracy, the goal of this tutorial is to show a standard approach.
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)
])
For this tutorial, choose the optimizers.Adam optimizer and losses.SparseCategoricalCrossentropy loss function. To view training and validation accuracy for each training epoch, pass the metrics argument.
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
View all the layers of the network using the model's summary method.
NOTE: This section is commented out for performance reasons. Please feel free to uncomment these to compare the results.
# model.summary()
# epochs=10
# history = model.fit(
# train_ds,
# validation_data=val_ds,
# epochs=epochs
# )
Create plots of loss and accuracy on the training and validation sets.
# acc = history.history['accuracy']
# val_acc = history.history['val_accuracy']
# loss = history.history['loss']
# val_loss = history.history['val_loss']
# epochs_range = range(epochs)
# plt.figure(figsize=(8, 8))
# plt.subplot(1, 2, 1)
# plt.plot(epochs_range, acc, label='Training Accuracy')
# plt.plot(epochs_range, val_acc, label='Validation Accuracy')
# plt.legend(loc='lower right')
# plt.title('Training and Validation Accuracy')
# plt.subplot(1, 2, 2)
# plt.plot(epochs_range, loss, label='Training Loss')
# plt.plot(epochs_range, val_loss, label='Validation Loss')
# plt.legend(loc='upper right')
# plt.title('Training and Validation Loss')
# plt.show()
As you can see from the plots, training accuracy and validation accuracy are off by large margin and the model has achieved only around 60% accuracy on the validation set.
Let's look at what went wrong and try to increase the overall performance of the model.
In the plots above, the training accuracy is increasing linearly over time, whereas validation accuracy stalls around 60% in the training process. Also, the difference in accuracy between training and validation accuracy is noticeable—a sign of overfitting.
When there are a small number of training examples, the model sometimes learns from noises or unwanted details from training examples—to an extent that it negatively impacts the performance of the model on new examples. This phenomenon is known as overfitting. It means that the model will have a difficult time generalizing on a new dataset.
There are multiple ways to fight overfitting in the training process. In this tutorial, you'll use data augmentation and add Dropout to your model.
Overfitting generally occurs when there are a small number of training examples. Data augmentation takes the approach of generating additional training data from your existing examples by augmenting them using random transformations that yield believable-looking images. This helps expose the model to more aspects of the data and generalize better.
You will implement data augmentation using the layers from tf.keras.layers.experimental.preprocessing. These can be included inside your model like other layers, and run on the GPU.
data_augmentation = keras.Sequential(
[
layers.experimental.preprocessing.RandomFlip("horizontal",
input_shape=(img_height,
img_width,
3)),
layers.experimental.preprocessing.RandomRotation(0.1),
layers.experimental.preprocessing.RandomZoom(0.1),
]
)
Let's visualize what a few augmented examples look like by applying data augmentation to the same image several times:
plt.figure(figsize=(10, 10))
for images, _ in train_ds.take(1):
for i in range(9):
augmented_images = data_augmentation(images)
ax = plt.subplot(3, 3, i + 1)
plt.imshow(augmented_images[0].numpy().astype("uint8"))
plt.axis("off")
You will use data augmentation to train a model in a moment.
Another technique to reduce overfitting is to introduce Dropout to the network, a form of regularization.
When you apply Dropout to a layer it randomly drops out (by setting the activation to zero) a number of output units from the layer during the training process. Dropout takes a fractional number as its input value, in the form such as 0.1, 0.2, 0.4, etc. This means dropping out 10%, 20% or 40% of the output units randomly from the applied layer.
Let's create a new neural network using layers.Dropout, then train it using augmented images.
model = Sequential([
data_augmentation,
layers.experimental.preprocessing.Rescaling(1./255),
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.Dropout(0.2),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.summary()
epochs = 15
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=epochs
)
After applying data augmentation and Dropout, there is less overfitting than before, and training and validation accuracy are closer aligned.
acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs_range = range(epochs)
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()
Finally, let's use our model to classify an image that wasn't included in the training or validation sets.
Note: Data augmentation and Dropout layers are inactive at inference time.
sunflower_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/592px-Red_sunflower.jpg"
sunflower_path = tf.keras.utils.get_file('Red_sunflower', origin=sunflower_url)
img = keras.preprocessing.image.load_img(
sunflower_path, target_size=(img_height, img_width)
)
img_array = keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0) # Create a batch
predictions = model.predict(img_array)
score = tf.nn.softmax(predictions[0])
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
#save the trained model - a new folder flower will be created
#and the file "saved_model.pb" is the pre-trained model
model_dir = "model"
model_fname = f"{model_dir}/flower"
model.save(model_fname)
# The paths of the source and converted models
model_name = "flower"
model_path = Path(model_fname)
ir_data_type = "FP16"
ir_model_name = "flower_ir"
# Get the path to the Model Optimizer script
# Construct the command for Model Optimizer
mo_command = f"""mo
--saved_model_dir "{model_fname}"
--input_shape "[1,180,180,3]"
--data_type "{ir_data_type}"
--output_dir "{model_fname}"
--model_name "{ir_model_name}"
"""
mo_command = " ".join(mo_command.split())
print("Model Optimizer command to convert TensorFlow to OpenVINO:")
print(mo_command)
# Run the Model Optimizer (overwrites the older model)
print("Exporting TensorFlow model to IR... This may take a few minutes.")
mo_result = %sx $mo_command
print("\n".join(mo_result))
def pre_process_image(imagePath, img_height=180):
# Model input format
n, c, h, w = [1, 3, img_height, img_height]
image = Image.open(imagePath)
image = image.resize((h, w), resample=Image.BILINEAR)
# Convert to array and change data layout from HWC to CHW
image = np.array(image)
image = image.transpose((2, 0, 1))
input_image = image.reshape((n, c, h, w))
return input_image
class_names=["daisy", "dandelion", "roses", "sunflowers", "tulips"]
model_xml = f"{model_fname}/flower_ir.xml"
# Load network to the plugin
ie = IECore()
net = ie.read_network(model=model_xml)
# Neural Compute Stick
# exec_net = ie.load_network(network=net, device_name="MYRIAD")
exec_net = ie.load_network(network=net, device_name="CPU")
del net
input_layer = next(iter(exec_net.input_info))
output_layer = next(iter(exec_net.outputs))
# Run inference on the input image...
inp_img_url = "https://upload.wikimedia.org/wikipedia/commons/4/48/A_Close_Up_Photo_of_a_Dandelion.jpg"
OUTPUT_DIR = "output"
inp_file_name = f"{OUTPUT_DIR}/A_Close_Up_Photo_of_a_Dandelion.jpg"
os.makedirs(OUTPUT_DIR, exist_ok=True)
# Download the image
opener = urllib.request.build_opener()
opener.addheaders = [("User-agent", "Mozilla/5.0")]
urllib.request.install_opener(opener)
urllib.request.urlretrieve(inp_img_url, inp_file_name)
# Pre-process the image and get it ready for inference.
input_image = pre_process_image(inp_file_name)
res = exec_net.infer(inputs={input_layer: input_image})
res = res[output_layer]
score = tf.nn.softmax(res[0])
# Show the results
image = Image.open(inp_file_name)
plt.imshow(image)
print(
"This image most likely belongs to {} with a {:.2f} percent confidence."
.format(class_names[np.argmax(score)], 100 * np.max(score))
)
This tutorial showed how to train a TensorFlow model, how to convert that model to OpenVINO's IR format, and how to do inference on the converted model. For faster inference speed, you can quantize the IR model. To see how to quantize this model with OpenVINO's Post-Training Optimization Tool, check out the Post-Training Quantization with TensorFlow Classification Model notebook.