'''module with utility functions''' import math, re, os import numpy as np import tensorflow as tf from tqdm import tqdm from matplotlib import pyplot as plt from numpy.random import default_rng print("Tensorflow version " + tf.__version__) from functools import partial 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 AUTO = tf.data.experimental.AUTOTUNE def decode_image(image_data, IMAGE_SIZE, RESIZE): image = tf.image.decode_jpeg(image_data, channels=3) image = tf.cast(image, tf.float32) / 255.0 # convert image to floats in [0, 1] range image = tf.reshape(image, [*IMAGE_SIZE, 3]) # explicit size needed for TPU if RESIZE: target_height, target_width = RESIZE image = tf.image.resize_with_pad( image, target_height, target_width #method=ResizeMethod.BILINEAR, #antialias=False ) return image def read_labeled_tfrecord(example, IMAGE_SIZE, RESIZE, with_id=False): LABELED_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, LABELED_TFREC_FORMAT) image = decode_image(example['image'], IMAGE_SIZE, RESIZE) label = tf.cast(example['class'], tf.int32) depth = tf.constant(104) one_hot_encoded = tf.one_hot(indices=label, depth=depth) if not with_id: return image, one_hot_encoded # returns a dataset of (image, label) pairs else: image_id = tf.cast(example['id'], tf.string) return image, one_hot_encoded, image_id def read_unlabeled_tfrecord(example, IMAGE_SIZE, RESIZE): UNLABELED_TFREC_FORMAT = { "image": tf.io.FixedLenFeature([], tf.string), # tf.string means bytestring "id": tf.io.FixedLenFeature([], tf.string), # shape [] means single element # class is missing, this competitions's challenge is to predict flower classes for the test dataset } example = tf.io.parse_single_example(example, UNLABELED_TFREC_FORMAT) image = decode_image(example['image'], IMAGE_SIZE, RESIZE) idnum = example['id'] return image, idnum # returns a dataset of image(s) def load_dataset(filenames, IMAGE_SIZE, RESIZE, labeled=True, ordered=False, with_id=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 read_labeled_tfrecord2 = partial(read_labeled_tfrecord, IMAGE_SIZE=IMAGE_SIZE, RESIZE=RESIZE, with_id=with_id) read_unlabeled_tfrecord2 = partial(read_unlabeled_tfrecord, IMAGE_SIZE=IMAGE_SIZE, RESIZE=RESIZE) dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=1)# 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_labeled_tfrecord2 if labeled else read_unlabeled_tfrecord2, num_parallel_calls=1)#AUTO) # returns a dataset of (image, label) pairs if labeled=True or (image, id) pairs if labeled=False return dataset def data_augment(image, label): # 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(TRAINING_FILENAMES, BATCH_SIZE, IMAGE_SIZE, RESIZE, with_id): dataset = load_dataset(TRAINING_FILENAMES, IMAGE_SIZE, RESIZE, labeled=True, with_id=with_id) #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(VALIDATION_FILENAMES, BATCH_SIZE, IMAGE_SIZE, RESIZE, with_id, ordered=False): dataset = load_dataset(VALIDATION_FILENAMES, IMAGE_SIZE, RESIZE, labeled=True, ordered=ordered, with_id=with_id) dataset = dataset.batch(BATCH_SIZE) #dataset = dataset.cache() #dataset = dataset.prefetch(AUTO) return dataset def get_test_dataset(TEST_FILENAMES, BATCH_SIZE, IMAGE_SIZE, RESIZE, ordered=False): dataset = load_dataset(TEST_FILENAMES, IMAGE_SIZE, RESIZE, labeled=False, ordered=ordered) dataset = dataset.batch(BATCH_SIZE) dataset = dataset.prefetch(AUTO) 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) def get_datasets(BATCH_SIZE, IMAGE_SIZE, RESIZE, tpu=False, with_id=False): if not tpu: data_root = "../input/tpu-getting-started" else: #data_root = GCS_DS_PATH print('tpu not implemented yet') return if 512 in IMAGE_SIZE: data_path = data_root + '/tfrecords-jpeg-512x512' #IMAGE_SIZE = [512, 512] elif 224 in IMAGE_SIZE: data_path = data_root + "/tfrecords-jpeg-224x224" #IMAGE_SIZE = [224, 224] elif 331 in IMAGE_SIZE: data_path = data_root + "/tfrecords-jpeg-331x331" #IMAGE_SIZE = [331, 331] elif 192 in IMAGE_SIZE: data_path = data_root + "/tfrecords-jpeg-192x192" #IMAGE_SIZE = [192, 192] else: print("wrong image size") return TRAINING_FILENAMES = tf.io.gfile.glob(data_path + '/train/*.tfrec') VALIDATION_FILENAMES = tf.io.gfile.glob(data_path + '/val/*.tfrec') TEST_FILENAMES = tf.io.gfile.glob(data_path + '/test/*.tfrec') ds_train = get_training_dataset(TRAINING_FILENAMES, BATCH_SIZE, IMAGE_SIZE, RESIZE, with_id=with_id) ds_valid = get_validation_dataset(VALIDATION_FILENAMES, BATCH_SIZE, IMAGE_SIZE, RESIZE, with_id=with_id) ds_test = get_test_dataset(TEST_FILENAMES, BATCH_SIZE, IMAGE_SIZE, RESIZE) print("Training:", ds_train) print ("Validation:", ds_valid) print("Test:", ds_test) return ds_train, ds_valid, ds_test def batch_to_numpy_images_and_labels(data): images, labels = data numpy_images = images.numpy() numpy_labels = labels.numpy() if numpy_labels.dtype == object: # binary string in this case, # these are image ID strings numpy_labels = [None for _ in enumerate(numpy_images)] # If no labels, only image IDs, return None for labels (this is # the case for test data) return numpy_images, numpy_labels def title_from_label_and_target(label, correct_label): if correct_label is None: return CLASSES[label], True correct = (label == correct_label) return "{} [{}{}{}]".format(CLASSES[label], 'OK' if correct else 'NO', u"\u2192" if not correct else '', CLASSES[correct_label] if not correct else ''), correct def display_one_flower(image, title, subplot, red=False, titlesize=16): plt.subplot(*subplot) plt.axis('off') plt.imshow(image) if len(title) > 0: plt.title(title, fontsize=int(titlesize) if not red else int(titlesize/1.2), color='red' if red else 'black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5)) return (subplot[0], subplot[1], subplot[2]+1) def display_training_curves(training, validation, title, subplot): if subplot%10==1: # set up the subplots on the first call plt.subplots(figsize=(10,10), facecolor='#F0F0F0') plt.tight_layout() ax = plt.subplot(subplot) ax.set_facecolor('#F8F8F8') ax.plot(training) ax.plot(validation) ax.set_title('model '+ title) ax.set_ylabel(title) #ax.set_ylim(0.28,1.05) ax.set_xlabel('epoch') ax.legend(['train', 'valid.']) #### image analysis def display_batch_of_images(databatch, predictions=None, FIGSIZE=13, image_ids=None): """This will work with: display_batch_of_images(images) display_batch_of_images(images, predictions) display_batch_of_images((images, labels)) display_batch_of_images((images, labels), predictions) """ # data images, labels = batch_to_numpy_images_and_labels(databatch) if labels is None: labels = [None for _ in enumerate(images)] # auto-squaring: this will drop data that does not fit into square # or square-ish rectangle rows = int(math.sqrt(len(images))) cols = len(images)//rows + 1 # size and spacing #FIGSIZE = 13.0 SPACING = 0.1 subplot=(rows,cols,1) if rows < cols: plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows)) else: plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE)) # display for i, (image, label) in enumerate(zip(images[:rows*cols], labels[:rows*cols])): title = '' if label is None else CLASSES[label] correct = True if predictions is not None: title, correct = title_from_label_and_target(predictions[i], label) if image_ids is not None: title = title + "\n" + image_ids[i] dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols)*40+3 # magic formula tested to work from 1x1 to 10x10 images subplot = display_one_flower(image, title, subplot, not correct, titlesize=dynamic_titlesize) #layout plt.tight_layout() if label is None and predictions is None: plt.subplots_adjust(wspace=0, hspace=0) else: plt.subplots_adjust(wspace=SPACING, hspace=SPACING) plt.show() def display_batch_by_class(files, name = 'iris', top_n= 10, FIGSIZE=13): class_name_mapping = {i:name for i, name in enumerate(CLASSES)} inverse_class_name_mapping = {class_name_mapping[i]: i for i in class_name_mapping} class_idx = inverse_class_name_mapping[name] print(class_idx) # get position of class images in dataset sample_idx = [] ds = load_dataset(files, labeled=True, IMAGE_SIZE=(224, 224), RESIZE=None) ds = ds.batch(1) for i, (img, label) in tqdm(enumerate(ds)): #print(label) if label.numpy()[0].argmax() == class_idx: sample_idx.append(i) print(f"found {len(sample_idx)} images and take sample of {top_n} images") # choose randomly top_n images rng = default_rng(42) sample_idx_shuffled = sample_idx.copy() rng.shuffle(sample_idx_shuffled) top_n_sample = sample_idx_shuffled[:top_n] ds = load_dataset(files, labeled=True, IMAGE_SIZE=(224, 224), RESIZE=None) ds = ds.batch(1) # get thte images for each data point images_class = [] tmp = [] for i, (img, label) in tqdm(enumerate(ds)): if i in top_n_sample: images_class.append(img) tmp.append(label) batch = tf.stack([tf.squeeze(img) for img in images_class]), tf.stack([class_idx for i in range(len(images_class))]) display_batch_of_images(batch, FIGSIZE=FIGSIZE)