innat

import tensorflow as tf
from tensorflow import keras 
from tensorflow.keras import Model 
from tensorflow.keras import layers 

class ConvoBlocks(tf.keras.layers.Layer):
    def __init__(self, num_filters=256, 
                 kernel_size=3, dilation_rate=1, 
                 padding="same", use_bias=False, **kwargs):
        super(ConvoBlocks, self).__init__(**kwargs)

innat

# Reference: https://keras.io/examples/structured_data/deep_neural_decision_forests/

import tensorflow as tf 
from tensorflow.keras import layers 
from tensorflow import keras 

class NeuralDecisionTree(keras.Model):
    def __init__(self, depth, num_features, used_features_rate, num_classes):
        super(NeuralDecisionTree, self).__init__()
        self.depth = depth

innat

import tensorflow as tf
from tensorflow.keras import layers 
from tensorflow import keras 
print('TensorFlow', tf.__version__)

class ResidualBlock(layers.Layer):
    def __init__(self, block_type=None, n_filters=None):
        super(ResidualBlock, self).__init__()
        self.n_filters = n_filters
        if block_type == 'identity':

innat

innat

Reflection Removal with Deep Learning

Few interesting model on reflection removal algorithm.

• ERRNet

• RAGNet

innat

import tensorflow as tf 
from tensorflow.keras.layers import * 
from tensorflow.keras.models import Model
from tensorflow.keras.utils import plot_model

class Conv3DBatchNorm(tf.keras.layers.Layer):
    def __init__(self, nb_filters, kernel_size, padding, strides):
        super(Conv3DBatchNorm, self).__init__()
        # parameters 
        self.nb_filters = nb_filters

innat

def vis(path1, path2, n_images, is_random=True, figsize=(16, 16)):
    '''
    https://github.com/innat
    '''
    plt.figure(figsize=figsize)
    image_names = os.listdir(path1)
    masks_names = os.listdir(path2)
    
    for i in range(n_images):
        if is_random:

innat

"""
pip install tensorflow
pip install tf2onnx keras2onnx onnxmltools
"""

import os
import pdb
import json
import traceback
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3" 

innat

import tensorflow as tf

# credit: https://stackoverflow.com/a/66524901/9215780
class CustomTrainStep(tf.keras.Model):
    def __init__(self, n_gradients, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.n_gradients = tf.constant(n_gradients, dtype=tf.int32)
        self.n_acum_step = tf.Variable(0, dtype=tf.int32, trainable=False)
        self.gradient_accumulation = [tf.Variable(tf.zeros_like(v, dtype=tf.float32), 
                                                  trainable=False) for v in self.trainable_variables]

innat

import numpy as np
import random, math
from scipy.stats import beta

def binarise_mask(mask, lam, in_shape, max_soft=0.0):
    """ Binarises a given low frequency image such that it has mean lambda.
    :param mask: Low frequency image, usually the result of `make_low_freq_image`
    :param lam: Mean value of final mask
    :param in_shape: Shape of inputs
    :param max_soft: Softening value between 0 and 0.5 which smooths hard edges in the mask.