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import tensorflow as tf
from tensorflow import keras

class ColorJitter(keras.layers.Layer):
    def __init__(
        self,
        brightness_factor=0.5,
        contrast_factor=(0.5, 0.9),
        saturation_factor=(0.5, 0.9),
        hue_factor=0.5,

innat

import tensorflow as tf
from tensorflow import keras

class ChannelShuffle(keras.layers.Layer):
    def __init__(self, groups=3, seed=None, **kwargs):
        super().__init__(**kwargs)
        self.groups = groups
        self.seed = seed

    def _channel_shuffling(self, images):

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innat

innat

```
# original repo: https://github.com/isears/tf_clahe
# check https://github.com/isears/tf_clahe/issues/1#issuecomment-1356676495
!pip install -q --no-deps /kaggle/input/tfpackages/tf_clahe-0.1.0-py3-none-any.whl
```

import tf_clahe

class CLAHE(keras.layers.Layer):
    def __init__(

innat

import tensorflow as tf

def weighted_binary_loss(weight, from_logits=True, reduction="mean"):
    def inverse_sigmoid(sigmoidal):
        return - tf.math.log(1. / sigmoidal - 1.)

    def weighted_loss(labels, predictions):
        predictions = tf.convert_to_tensor(predictions)
        labels = tf.cast(labels, predictions.dtype)
        num_samples = tf.cast(tf.shape(labels)[-1], dtype=labels.dtype)

innat

# Base Image: Ubuntu + Cuda
FROM nvidia/cuda:11.0.3-devel-ubuntu20.04 AS python_base_cuda

# ENV SET    
ENV PYTHONUNBUFFERED=1 \
  PYTHONFAULTHANDLER=1 \
  PYTHONHASHSEED=random \
  PYTHONDONTWRITEBYTECODE=1 \
  PIP_NO_CACHE_DIR=off \
  PIP_DISABLE_PIP_VERSION_CHECK=on \

innat

"""
Reference
# https://github.com/fuenwang/PanoramaUtility
# https://github.com/fuenwang/Equirec2Perspec
# https://github.com/fuenwang/PanoramaUtility
# https://github.com/timy90022/Perspective-and-Equirectangular
"""

import os
import sys

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# RESTRICT TENSORFLOW TO 2GB OF GPU RAM
# SO THAT WE HAVE 14GB RAM free
LIMIT = 2.0
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
    try:
        tf.config.experimental.set_virtual_device_configuration(
            gpus[0],
            [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024*LIMIT)])
        logical_gpus = tf.config.experimental.list_logical_devices('GPU')

innat

class WarmupLearningRateSchedule(optimizers.schedules.LearningRateSchedule):
    """WarmupLearningRateSchedule a variety of learning rate
    decay schedules with warm up."""

    def __init__(
        self,
        initial_lr,
        steps_per_epoch=None,
        lr_decay_type="exponential",
        decay_factor=0.97,