innat

# 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,

innat

class RandomGrayscale(layers.Layer):
    """Grayscale is a preprocessing layer that transforms 
    RGB images to Grayscale images.

    Input shape:
        3D (unbatched) or 4D (batched) tensor with shape:
        `(..., height, width, channels)` format
    Output shape:
        3D (unbatched) or 4D (batched) tensor with shape:
        `(..., height, width, channels)` format

innat

def make_plot(tfdata, take_batch=1, title=True, figsize=(20, 20)):
    '''ref: https://gist.github.com/innat/4dc4080cfdf5cf20ef0fc93d3623ca9b
    '''
    font = {
        "family": "serif",
        "color": "darkred",
        "weight": "normal",
        "size": 10,
    }

innat

from functools import partial

import numpy as np
from jax import jit
from jax import random
from jax.experimental import jax2tf
from tensorflow.keras import layers


class RandomChannelShuffle(layers.Layer):

innat

import tensorflow as tf
from tensorflow.keras import layers


class CutMix(layers.Layer):
    """Original implementation: https://github.com/keras-team/keras-cv.
    The original implementaiton provide more interface to apply mixup on
    various CV related task, i.e. object detection etc. It also provides
    many effective validation check.

innat

import tensorflow as tf
from tensorflow.keras import layers


class MixUp(layers.Layer):
    """Original implementation: https://github.com/keras-team/keras-cv.
    The original implementaiton provide more interface to apply mixup on
    various CV related task, i.e. object detection etc. It also provides
    many effective validation check.

innat

import tensorflow as tf
# https://github.com/tensorflow/tensorflow/issues/55646

def unique_uniform(num_samples, 
                   minval, 
                   maxval, 
                   seed, 
                   shape, 
                   dtype):  # maxval is inclusive

innat

# Case 2: Multiple Class Maks

# String to integer labels. 
# Assuming we havae 5 classes like below (excluding background). 
categories =  {
    'human' : 1,
    'dog'   : 2,
    'cat'   : 3,
    'bus'   : 4,
    'road'  : 5

innat

import numpy as np
import tensorflow as tf
from tensorflow import keras 
from tensorflow.keras import Model, Input
from tensorflow.keras.layers import Conv2DTranspose
from tensorflow.keras.layers import UpSampling2D
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Activation
from tensorflow.keras.layers import Concatenate