03_image_models/flowers.py

import math, re, os, sys
import tensorflow as tf
import numpy as np
from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix
print("Tensorflow version " + tf.__version__)
AUTO = tf.data.experimental.AUTOTUNE

try: # detect TPUs
    tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect()
    strategy = tf.distribute.TPUStrategy(tpu)
except ValueError: # detect GPUs or multi-GPU machines
    strategy = tf.distribute.MirroredStrategy()

print("REPLICAS: ", strategy.num_replicas_in_sync)

GCS_DS_PATH = "gs://practical-ml-vision-book-data/flowers_104_tfr"

# Settings for TPUv3. When running on hardware with less memory such as a TPUv2 (Colab)
# or a GPU, you might have to use lower BATCH_SIZE and/or IMAGE_SIZE values.

IMAGE_SIZE = [512, 512] # available image sizes in flowers104 dataset: 512x512, 331x331, 224x224, 192,192
BATCH_SIZE = 32 * strategy.num_replicas_in_sync
EPOCHS = 13

# Learning rate schedule
LR_START = 0.00001
LR_MAX = 0.0001 * strategy.num_replicas_in_sync
LR_MIN = 0.00001
LR_RAMPUP_EPOCHS = 3
LR_SUSTAIN_EPOCHS = 3
LR_EXP_DECAY = .5

GCS_PATH_SELECT = { # available image sizes
    192: GCS_DS_PATH + '/tfrecords-jpeg-192x192',
    224: GCS_DS_PATH + '/tfrecords-jpeg-224x224',
    331: GCS_DS_PATH + '/tfrecords-jpeg-331x331',
    512: GCS_DS_PATH + '/tfrecords-jpeg-512x512'
}
GCS_PATH = GCS_PATH_SELECT[IMAGE_SIZE[0]]

# This dataset is split three ways, training, validation, test
# but we will use it split two ways only: training and validation.
TRAINING_FILENAMES = tf.io.gfile.glob(GCS_PATH + '/train/*.tfrec') + tf.io.gfile.glob(GCS_PATH + '/val/*.tfrec')
VALIDATION_FILENAMES =  tf.io.gfile.glob(GCS_PATH + '/test/*.tfrec')

CLASSES = ['pink primrose',    'hard-leaved pocket orchid', 'canterbury bells', 'sweet pea',     'wild geranium',     'tiger lily',           'moon orchid',              'bird of paradise', 'monkshood',        'globe thistle',         # 00 - 09
           'snapdragon',       "colt's foot",               'king protea',      'spear thistle', 'yellow iris',       'globe-flower',         'purple coneflower',        'peruvian lily',    'balloon flower',   'giant white arum lily', # 10 - 19
           'fire lily',        'pincushion flower',         'fritillary',       'red ginger',    'grape hyacinth',    'corn poppy',           'prince of wales feathers', 'stemless gentian', 'artichoke',        'sweet william',         # 20 - 29
           'carnation',        'garden phlox',              'love in the mist', 'cosmos',        'alpine sea holly',  'ruby-lipped cattleya', 'cape flower',              'great masterwort', 'siam tulip',       'lenten rose',           # 30 - 39
           'barberton daisy',  'daffodil',                  'sword lily',       'poinsettia',    'bolero deep blue',  'wallflower',           'marigold',                 'buttercup',        'daisy',            'common dandelion',      # 40 - 49
           'petunia',          'wild pansy',                'primula',          'sunflower',     'lilac hibiscus',    'bishop of llandaff',   'gaura',                    'geranium',         'orange dahlia',    'pink-yellow dahlia',    # 50 - 59
           'cautleya spicata', 'japanese anemone',          'black-eyed susan', 'silverbush',    'californian poppy', 'osteospermum',         'spring crocus',            'iris',             'windflower',       'tree poppy',            # 60 - 69
           'gazania',          'azalea',                    'water lily',       'rose',          'thorn apple',       'morning glory',        'passion flower',           'lotus',            'toad lily',        'anthurium',             # 70 - 79
           'frangipani',       'clematis',                  'hibiscus',         'columbine',     'desert-rose',       'tree mallow',          'magnolia',                 'cyclamen ',        'watercress',       'canna lily',            # 80 - 89
           'hippeastrum ',     'bee balm',                  'pink quill',       'foxglove',      'bougainvillea',     'camellia',             'mallow',                   'mexican petunia',  'bromelia',         'blanket flower',        # 90 - 99
           'trumpet creeper',  'blackberry lily',           'common tulip',     'wild rose']                                                                                                                                               # 100 - 102


def lrfn(epoch):
    if epoch < LR_RAMPUP_EPOCHS:
        lr = (LR_MAX - LR_START) / LR_RAMPUP_EPOCHS * epoch + LR_START
    elif epoch < LR_RAMPUP_EPOCHS + LR_SUSTAIN_EPOCHS:
        lr = LR_MAX
    else:
        lr = (LR_MAX - LR_MIN) * LR_EXP_DECAY**(epoch - LR_RAMPUP_EPOCHS - LR_SUSTAIN_EPOCHS) + LR_MIN
    return lr

lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=True)


# DATASET
def decode_image(image_data):
    image = tf.image.decode_jpeg(image_data, channels=3) # decoded inamge in uint8 format range [0,255]
    image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU
    return image

def read_tfrecord(example):
    TFREC_FORMAT = {
        "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring
        "class": tf.io.FixedLenFeature([], tf.int64),  # shape [] means single element
        "id": tf.io.FixedLenFeature([], tf.string),  # shape [] means single element
    }
    example = tf.io.parse_single_example(example, TFREC_FORMAT)
    image = decode_image(example['image'])
    label = tf.cast(example['class'], tf.int32)
    idnum = example['id'] # image id, not used
    return image, label # returns a dataset of (image, label) pairs

def load_dataset(filenames, ordered=False):
    # Read from TFRecords. For optimal performance, reading from multiple files at once and
    # disregarding data order. Order does not matter since we will be shuffling the data anyway.

    ignore_order = tf.data.Options()
    if not ordered:
        ignore_order.experimental_deterministic = False # disable order, increase speed

    dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=AUTO) # automatically interleaves reads from multiple files
    dataset = dataset.with_options(ignore_order) # uses data as soon as it streams in, rather than in its original order
    dataset = dataset.map(read_tfrecord, num_parallel_calls=AUTO)
    # returns a dataset of (image, label) pairs
    return dataset

def data_augment(image, label):
    # data augmentation. Thanks to the dataset.prefetch(AUTO) statement in the next function (below),
    # this happens essentially for free on TPU. Data pipeline code is executed on the "CPU" part
    # of the TPU while the TPU itself is computing gradients.
    image = tf.image.random_flip_left_right(image)
    #image = tf.image.random_saturation(image, 0, 2)
    return image, label

def get_training_dataset():
    dataset = load_dataset(TRAINING_FILENAMES)
    dataset = dataset.map(data_augment, num_parallel_calls=AUTO)
    dataset = dataset.repeat() # the training dataset must repeat for several epochs
    dataset = dataset.shuffle(2048)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

def get_validation_dataset(ordered=False):
    dataset = load_dataset(VALIDATION_FILENAMES, ordered=ordered)
    dataset = dataset.batch(BATCH_SIZE)
    dataset = dataset.cache()
    dataset = dataset.prefetch(AUTO) # prefetch next batch while training (autotune prefetch buffer size)
    return dataset

def count_data_items(filenames):
    # the number of data items is written in the name of the .tfrec files, i.e. flowers00-230.tfrec = 230 data items
    n = [int(re.compile(r"-([0-9]*)\.").search(filename).group(1)) for filename in filenames]
    return np.sum(n)


NUM_TRAINING_IMAGES = count_data_items(TRAINING_FILENAMES)
NUM_VALIDATION_IMAGES = count_data_items(VALIDATION_FILENAMES)
STEPS_PER_EPOCH = NUM_TRAINING_IMAGES // BATCH_SIZE
VALIDATION_STEPS = -(-NUM_VALIDATION_IMAGES // BATCH_SIZE) # The "-(-//)" trick rounds up instead of down :-)
print('Dataset: {} training images, {} validation images'.format(NUM_TRAINING_IMAGES, NUM_VALIDATION_IMAGES))


# MODEL

with strategy.scope():
    pretrained_model = tf.keras.applications.Xception(weights='imagenet', include_top=False)
    #pretrained_model = efficientnet.tfkeras.EfficientNetB7(weights='imagenet', include_top=False, input_shape=[*IMAGE_SIZE, 3])
    pretrained_model.trainable = True # fine-tuning

    model = tf.keras.Sequential([
        # convert image format from int [0,255] to the format expected by this model
        tf.keras.layers.Lambda(lambda data: tf.keras.applications.xception.preprocess_input(tf.cast(data, tf.float32)), input_shape=[*IMAGE_SIZE, 3]),
        pretrained_model,
        tf.keras.layers.GlobalAveragePooling2D(),
        tf.keras.layers.Dense(len(CLASSES), activation='softmax', name='flower_prob')
    ])

model.compile(
    optimizer='adam',
    loss = 'sparse_categorical_crossentropy',
    metrics=['sparse_categorical_accuracy'],
    steps_per_execution=8
)
model.summary()

# TRAINING

history = model.fit(get_training_dataset(), steps_per_epoch=STEPS_PER_EPOCH, epochs=EPOCHS,
                    validation_data=get_validation_dataset(), validation_steps=VALIDATION_STEPS,
                    callbacks=[lr_callback])

# CONFUSION MATRIX
cmdataset = get_validation_dataset(ordered=True) # since we are splitting the dataset and iterating separately on images and labels, order matters.
images_ds = cmdataset.map(lambda image, label: image)
labels_ds = cmdataset.map(lambda image, label: label).unbatch()
cm_correct_labels = next(iter(labels_ds.batch(NUM_VALIDATION_IMAGES))).numpy() # get everything as one batch
cm_probabilities = model.predict(images_ds, steps=VALIDATION_STEPS)
cm_predictions = np.argmax(cm_probabilities, axis=-1)
print("Correct   labels: ", cm_correct_labels.shape, cm_correct_labels)
print("Predicted labels: ", cm_predictions.shape, cm_predictions)

cmat = confusion_matrix(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)))
score = f1_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')
precision = precision_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')
recall = recall_score(cm_correct_labels, cm_predictions, labels=range(len(CLASSES)), average='macro')
cmat = (cmat.T / cmat.sum(axis=1)).T # normalized
print('f1 score: {:.3f}, precision: {:.3f}, recall: {:.3f}'.format(score, precision, recall))