Kaggle competition: APTOS 2019 Blindness Detection
<SOS> metric: QuadraticWeightedKappa Kaggle data source: aptos-2019-blindness-detection<prepare_x_and_y>
%reload_ext autoreload
%autoreload 2
%matplotlib inline
warnings.filterwarnings("ignore")
 %matplotlib inline
def read_labeled_tfrecord(example):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
'patient_id' : tf.io.FixedLenFeature([], tf.int64),
'sex' : tf.io.FixedLenFeature([], tf.int64),
'age_approx' : tf.io.FixedLenFeature([], tf.int64),
'anatom_site_general_challenge': tf.io.FixedLenFeature([], tf.int64),
'diagnosis' : tf.io.FixedLenFeature([], tf.int64),
'target' : tf.io.FixedLenFeature([], tf.int64)
 }
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['target']
def read_unlabeled_tfrecord(example, return_image_name):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'image_name' : tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['image_name'] if return_image_name else 0
def prepare_image(img, cfg=None, augment=True):
img = tf.image.decode_jpeg(img, channels=3)
 img = tf.image.resize(img, [cfg['read_size'], cfg['read_size']])
 img = tf.cast(img, tf.float32)/ 255.0
if augment:
img = transform(img, cfg)
 img = tf.image.random_crop(img, [cfg['crop_size'], cfg['crop_size'], 3])
 img = tf.image.random_flip_left_right(img)
 img = tf.image.random_hue(img, 0.01)
 img = tf.image.random_saturation(img, 0.7, 1.3)
 img = tf.image.random_contrast(img, 0.8, 1.2)
 img = tf.image.random_brightness(img, 0.1)
 else:
img = tf.image.central_crop(img, cfg['crop_size'] / cfg['read_size'])
 img = tf.image.resize(img, [cfg['net_size'], cfg['net_size']])
 img = tf.reshape(img, [cfg['net_size'], cfg['net_size'], 3])
 return img
def count_data_items(filenames):
n = [int(re.compile(r"-([0-9]*)\." ).search(filename ).group(1))
for filename in filenames]
return np.sum(n )<categorify>
md_ef = EfficientNet.from_pretrained('efficientnet-b5', num_classes=1)
 !mkdir models
!cp '.. /input/kaggle-public/abcdef.pth' 'models'
def get_dataset(files, cfg, augment = False, shuffle = False, repeat = False,
labeled=True, return_image_names=True):
ds = tf.data.TFRecordDataset(files, num_parallel_reads=AUTO)
 ds = ds.cache()
if repeat:
ds = ds.repeat()
if shuffle:
ds = ds.shuffle(1024*8)
 opt = tf.data.Options()
opt.experimental_deterministic = False
ds = ds.with_options(opt)
 if labeled:
ds = ds.map(read_labeled_tfrecord, num_parallel_calls=AUTO)
 else:
ds = ds.map(lambda example: read_unlabeled_tfrecord(example, return_image_names),
num_parallel_calls=AUTO)
 ds = ds.map(lambda img, imgname_or_label:(prepare_image(img, augment=augment, cfg=cfg),
imgname_or_label),
num_parallel_calls=AUTO)
 ds = ds.batch(cfg['batch_size'] * REPLICAS)
 ds = ds.prefetch(AUTO)
 return ds<prepare_x_and_y>
bs = 64
tfms = get_transforms(do_flip=True,flip_vert=True )
def show_dataset(thumb_size, cols, rows, ds):
mosaic = PIL.Image.new(mode='RGB', size=(thumb_size*cols +(cols-1),
thumb_size*rows +(rows-1)))
 for idx, data in enumerate(iter(ds)) :
img, target_or_imgid = data
ix = idx % cols
iy = idx // cols
img = np.clip(img.numpy() * 255, 0, 255 ).astype(np.uint8)
 img = PIL.Image.fromarray(img)
 img = img.resize(( thumb_size, thumb_size), resample=PIL.Image.BILINEAR)
 mosaic.paste(img,(ix*thumb_size + ix,
iy*thumb_size + iy))
display(mosaic)
 ds = get_dataset(files_train, CFG ).unbatch().take(12*5)
 show_dataset(64, 12, 5, ds )<create_dataframe>
learn.load('abcdef');
opt = OptimizedRounder()
ds = tf.data.TFRecordDataset(files_train, num_parallel_reads=AUTO)
 ds = ds.take(1 ).cache().repeat()
ds = ds.map(read_labeled_tfrecord, num_parallel_calls=AUTO)
 ds = ds.map(lambda img, target:(prepare_image(img, cfg=CFG, augment=True), target),
num_parallel_calls=AUTO)
 ds = ds.take(12*5)
 ds = ds.prefetch(AUTO)
 show_dataset(64, 12, 5, ds )<create_dataframe>
preds0,y = learn.get_preds(DatasetType.Test )
ds = get_dataset(files_test, CFG, labeled=False ).unbatch().take(12*5)
 show_dataset(64, 12, 5, ds )<choose_model_class>
preds =(preds0 + preds1 + preds2 + preds3 + preds4)/5
<choose_model_class><EOS> 
tst_pred = opt.predict(preds, coef=[0.5, 1.5, 2.5, 3.5])
 test_df.diagnosis = tst_pred.astype(int)
 test_df.to_csv('submission.csv',index=False)
 print('done' )