Kaggle competition: SIIM-ISIC Melanoma Classification
<SOS> metric: MCAUC Kaggle data source: siim-isic-melanoma-classification<load_from_csv>
DEVICE = "TPU"
CFG = dict(
 net_count = 8,
batch_size = 8,
read_size = 256,
crop_size = 250,
net_size = 224,
LR_START = 0.000004,
LR_MAX = 0.000020,
LR_MIN = 0.000001,
LR_RAMPUP_EPOCHS = 5,
LR_SUSTAIN_EPOCHS = 0,
LR_EXP_DECAY = 0.0075,
epochs = 12,
rot = 180.0,
shr = 2.0,
hzoom = 8.0,
wzoom = 8.0,
hshift = 8.0,
wshift = 8.0,
optimizer = 'adam',
label_smooth_fac = 0.05,
tta_steps = 50
)
train = pd.read_csv('.. /input/train.csv')
 test = pd.read_csv('.. /input/test.csv' )<load_from_csv>
!pip install -q efficientnet
train = pd.read_csv('.. /input/train.csv')
 test = pd.read_csv('.. /input/test.csv' )<drop_column>
random.seed(a=42)
train.drop(['PassengerId'],axis=1,inplace=True )<count_missing_values>
BASEPATH = ".. /input/siim-isic-melanoma-classification"
GCS_PATH = KaggleDatasets().get_gcs_path('oc-d-512512')
 files_train = np.sort(np.array(tf.io.gfile.glob(GCS_PATH + '/*')))
pd.isnull(train ).sum()<count_missing_values>
if DEVICE == "TPU":
print("connecting to TPU...")
 try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
 except ValueError:
print("Could not connect to TPU")
 tpu = None
if tpu:
try:
print("initializing TPU...")
 tf.config.experimental_connect_to_cluster(tpu)
 tf.tpu.experimental.initialize_tpu_system(tpu)
 strategy = tf.distribute.experimental.TPUStrategy(tpu)
 print("TPU initialized")
 except _:
print("failed to initialize TPU")
 else:
DEVICE = "GPU"
if DEVICE != "TPU":
print("Using default strategy for CPU and single GPU")
 strategy = tf.distribute.get_strategy()
if DEVICE == "GPU":
print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices('GPU')))
 AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
print(f'REPLICAS: {REPLICAS}' )
pd.isnull(test ).sum()<count_unique_values>
def get_mat(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = math.pi * rotation / 180.
 shear = math.pi * shear / 180.
 def get_3x3_mat(lst):
return tf.reshape(tf.concat([lst],axis=0), [3,3])
 c1 = tf.math.cos(rotation)
 s1 = tf.math.sin(rotation)
 one = tf.constant([1],dtype='float32')
 zero = tf.constant([0],dtype='float32')
 rotation_matrix = get_3x3_mat([c1, s1, zero,
-s1, c1, zero,
zero, zero, one])
 c2 = tf.math.cos(shear)
 s2 = tf.math.sin(shear)
 shear_matrix = get_3x3_mat([one, s2, zero,
zero, c2, zero,
zero, zero, one])
 zoom_matrix = get_3x3_mat([one/height_zoom, zero, zero,
zero, one/width_zoom, zero,
zero, zero, one])
 shift_matrix = get_3x3_mat([one, zero, height_shift,
zero, one, width_shift,
zero, zero, one])
 return K.dot(K.dot(rotation_matrix, shear_matrix),
K.dot(zoom_matrix, shift_matrix))
def transform(image, cfg):
DIM = cfg["read_size"]
XDIM = DIM%2
rot = cfg['rot'] * tf.random.normal([1], dtype='float32')
 shr = cfg['shr'] * tf.random.normal([1], dtype='float32')
 h_zoom = 1.0 + tf.random.normal([1], dtype='float32')/ cfg['hzoom']
w_zoom = 1.0 + tf.random.normal([1], dtype='float32')/ cfg['wzoom']
h_shift = cfg['hshift'] * tf.random.normal([1], dtype='float32')
 w_shift = cfg['wshift'] * tf.random.normal([1], dtype='float32')
 m = get_mat(rot,shr,h_zoom,w_zoom,h_shift,w_shift)
 x = tf.repeat(tf.range(DIM//2, -DIM//2,-1), DIM)
 y = tf.tile(tf.range(-DIM//2, DIM//2), [DIM])
 z = tf.ones([DIM*DIM], dtype='int32')
 idx = tf.stack([x,y,z])
 idx2 = K.dot(m, tf.cast(idx, dtype='float32'))
idx2 = K.cast(idx2, dtype='int32')
 idx2 = K.clip(idx2, -DIM//2+XDIM+1, DIM//2)
 idx3 = tf.stack([DIM//2-idx2[0,], DIM//2-1+idx2[1,]])
 d = tf.gather_nd(image, tf.transpose(idx3))
return tf.reshape(d,[DIM, DIM,3] )
for i in train.select_dtypes(include=['object']):
print('Column ',i,' has ',train[i].nunique() ,' unique values' )<count_unique_values>
def read_labeled_tfrecord(example):
tfrec_format = {
'image' : tf.io.FixedLenFeature([], tf.string),
'filename' : tf.io.FixedLenFeature([], tf.string),
'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),
'filename' : tf.io.FixedLenFeature([], tf.string),
}
example = tf.io.parse_single_example(example, tfrec_format)
 return example['image'], example['filename'] 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 )
for i in test.select_dtypes(include=['object']):
print('Column ',i,' has ',test[i].nunique() ,' unique values' )<concatenate>
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
target = train.Survived
train_df = train.drop('Survived',axis=1)
 test_df = test.drop('PassengerId',axis=1)
 train_df['is_train'] = 1
test_df['is_train'] = 0
train_test = pd.concat([train_df,test_df],axis=0 )<feature_engineering>
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 )
<categorify><EOS> 
print("image before the augmentaion ")
 ds = get_dataset(files_train, CFG ).unbatch().take(1)
 show_dataset(200, 1, 1, ds)
 print("image after the augmentaion ")
 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 )