Добавлена первая версия ЛР по сжатию изображений при помощи нейронных сетей.

Версия сделана для учебных целей.

CNNImageCodec.py

+#This code is the simplest example of image compression based on neural networks
+#Comparison with JPEG is provided as well
+#It is a demonstation for Information Theory course
+#Written by Evgeny Belyaev, February 2024.
+import os
+import math
+import numpy
+from matplotlib import pyplot as plt
+from PIL import Image
+import imghdr
+import tensorflow
+from tensorflow import keras
+from tensorflow.keras import layers
+from tensorflow.keras.models import Model
+from keras import backend as K
+
+#import C-implementation of Witten&Neal&Cleary-1987 arithmetic coding as a external module
+from EntropyCodec import *
+
+#source folder with test images
+testfolder = './test/'
+#source folder with train images
+trainfolder = './train/'
+#size of test and train images
+w=128
+h=128
+#If 0, then the training will be started, otherwise the model will be readed from a file
+LoadModel = 1
+#Training parameters
+batch_size = 10
+#Number of bits for representation of the layers sample in the training process
+bt = 3
+epochs = 3000
+#epochs = 100
+#Model parameters
+n1=128
+n2=32
+n3=16
+
+#Number of images to be compressed and shown from the test folder
+NumImagesToShow = 5
+
+#Number of bits for representation of the layers sample
+b = 3
+
+#Compute PSNR in RGB domain
+def PSNR_RGB(image1,image2):
+    width, height = image1.size
+    I1 = numpy.array(image1.getdata()).reshape(image1.size[0], image1.size[1], 3)
+    I2 = numpy.array(image2.getdata()).reshape(image2.size[0], image2.size[1], 3)
+    I1 = numpy.reshape(I1, width * height * 3)
+    I2 = numpy.reshape(I2, width * height * 3)
+    I1 = I1.astype(float)
+    I2 = I2.astype(float)
+    mse = numpy.mean((I1 - I2) ** 2)
+    if (mse == 0):  # MSE is zero means no noise is present in the signal .
+        psnr=100.0
+    else:
+        max_pixel = 255.0
+        psnr = 20 * math.log10(max_pixel / math.sqrt(mse))
+    #print("PSNR = %5.2f dB" % psnr)
+    return psnr
+
+#Compute PSNR between two vectors
+def PSNR(y_true, y_pred):
+    max_pixel = 1.0
+    return 10.0 * (1.0 / math.log(10)) * K.log((max_pixel ** 2) / (K.mean(K.square(y_pred - y_true))))
+
+#reads all images from folder and puts them into x array
+def LoadImagesFromFolder (foldername):
+    dir_list = os.listdir(foldername)
+    N = 0
+    Nmax = 0
+    for name in dir_list:
+        fullname = foldername + name
+        filetype = imghdr.what(fullname)
+        if filetype is None:
+            print('')
+        else:
+            Nmax = Nmax + 1
+
+    x = numpy.zeros([Nmax, w, h, 3])
+    N = 0
+    for name in dir_list:
+        fullname = foldername + name
+        filetype = imghdr.what(fullname)
+        if filetype is None:
+            print('Unknown image format for file: ', name)
+        else:
+            print('Progress: N = %i' % N)
+            image = Image.open(fullname)
+            I1 = numpy.array(image.getdata()).reshape(image.size[0], image.size[1], 3)
+            x[N, :, :, :] = I1
+            N = N + 1
+    return x
+
+#Model training function
+def ImageCodecModel(trainfolder):
+    input = layers.Input(shape=(w, h, 3))
+    # Encoder
+    e1 = layers.Conv2D(n1, (7, 7), activation="relu", padding="same")(input)
+    e1 = layers.MaxPooling2D((2, 2), padding="same")(e1)
+    e2 = layers.Conv2D(n2, (5, 5), activation="relu", padding="same")(e1)
+    e2 = layers.MaxPooling2D((2, 2), padding="same")(e2)
+    e3 = layers.Conv2D(n3, (3, 3), activation="relu", padding="same")(e2)
+    e3 = layers.MaxPooling2D((2, 2), padding="same")(e3)
+    #add noise during training (needed for layer quantinzation)
+    e3 = e3 + tensorflow.random.uniform(tensorflow.shape(e3), 0, tensorflow.math.reduce_max(e3)/pow(2, bt+1))
+
+    # Decoder
+    x = layers.Conv2DTranspose(n3, (3, 3), strides=2, activation="relu", padding="same")(e3)
+    x = layers.Conv2DTranspose(n2, (5, 5), strides=2, activation="relu", padding="same")(x)
+    x = layers.Conv2DTranspose(n1, (7, 7), strides=2, activation="relu", padding="same")(x)
+    x = layers.Conv2D(3, (3, 3), activation="sigmoid", padding="same")(x)
+
+    # Autoencoder
+    encoder = Model(input, e3)
+    decoder = Model(e3, x)
+    autoencoder = Model(input, x)
+    autoencoder.compile(optimizer="adam", loss='mean_squared_error')
+    autoencoder.summary()
+
+    if LoadModel == 0:
+        xtrain = LoadImagesFromFolder(trainfolder)
+        xtrain = xtrain / 255
+        autoencoder.fit(xtrain, xtrain, epochs=epochs, batch_size=batch_size,shuffle=True)
+        autoencoder.save('autoencodertemp.mdl')
+        encoder.save('encoder.mdl')
+        decoder.save('decoder.mdl')
+    else:
+        autoencoder = keras.models.load_model('autoencodertemp.mdl')
+        encoder = keras.models.load_model('encoder.mdl')
+        decoder = keras.models.load_model('decoder.mdl')
+    return encoder,decoder
+
+#Compresses input layer by multi-alphabet arithmetic coding using memoryless source model
+def EntropyEncoder (filename,enclayers,size_z,size_h,size_w):
+    temp = numpy.zeros((size_z, size_h, size_w), numpy.uint8, 'C')
+    for z in range(size_z):
+        for h in range(size_h):
+            for w in range(size_w):
+                temp[z][h][w] = enclayers[z][h][w]
+    maxbinsize = (size_h * size_w * size_z)
+    bitstream = numpy.zeros(maxbinsize, numpy.uint8, 'C')
+    StreamSize = numpy.zeros(1, numpy.int32, 'C')
+    HiddenLayersEncoder(temp, size_w, size_h, size_z, bitstream, StreamSize)
+    name = filename
+    path = './'
+    fp = open(os.path.join(path, name), 'wb')
+    out = bitstream[0:StreamSize[0]]
+    out.astype('uint8').tofile(fp)
+    fp.close()
+
+#Decompresses input layer by multi-alphabet arithmetic coding using memoryless source model
+def EntropyDecoder (filename,size_z,size_h,size_w):
+    fp = open(filename, 'rb')
+    bitstream = fp.read()
+    fp.close()
+    bitstream = numpy.frombuffer(bitstream, dtype=numpy.uint8)
+    declayers = numpy.zeros((size_z, size_h, size_w), numpy.uint8, 'C')
+    FrameOffset = numpy.zeros(1, numpy.int32, 'C')
+    FrameOffset[0] = 0
+    HiddenLayersDecoder(declayers, size_w, size_h, size_z, bitstream, FrameOffset)
+    return declayers
+
+#This function is searching for the JPEG quality factor (QF)
+#which provides neares compression to TargetBPP
+def JPEGRDSingleImage(X,TargetBPP,i):
+    X = X*255
+    image = Image.fromarray(X.astype('uint8'), 'RGB')
+    width, height = image. size
+    realbpp = 0
+    realpsnr = 0
+    realQ = 0
+    for Q in range(101):
+        image.save('test.jpeg', "JPEG", quality=Q)
+        image_dec = Image.open('test.jpeg')
+        bytesize = os.path.getsize('test.jpeg')
+        bpp = bytesize*8/(width*height)
+        psnr = PSNR_RGB(image, image_dec)
+        if abs(realbpp-TargetBPP)>abs(bpp-TargetBPP):
+            realbpp=bpp
+            realpsnr=psnr
+            realQ = Q
+    JPEGfilename = 'image%i.jpeg' % i
+    image.save(JPEGfilename, "JPEG", quality=realQ)
+    return realQ, realbpp, realpsnr
+
+# Main function
+if __name__ == '__main__':
+    #Load test images
+    xtest = LoadImagesFromFolder(testfolder)
+    xtest = xtest / 255
+
+    #Train the model
+    encoder, decoder = ImageCodecModel(trainfolder)
+
+    #Run the model for first NumImagesToShow images from the test set
+    encoded_layers = encoder.predict(xtest, batch_size=NumImagesToShow)
+    max_encoded_layers = numpy.zeros(NumImagesToShow, numpy.float16, 'C')
+
+    #normalization the layer to interval [0,1)
+    for i in range(NumImagesToShow):
+        max_encoded_layers[i] = numpy.max(encoded_layers[i])
+        encoded_layers[i] = encoded_layers[i] / max_encoded_layers[i]
+
+    #Quantization of layer to b bits
+    encoded_layers1 = numpy.clip(encoded_layers, 0, 0.9999999)
+    encoded_layers1 = K.cast(encoded_layers1*pow(2, b), "int32")
+
+    #Encoding and decoding of each quantized layer by arithmetic coding
+    bpp = numpy.zeros(NumImagesToShow, numpy.float16, 'C')
+    declayers = numpy.zeros((NumImagesToShow,16, 16, 16), numpy.uint8, 'C')
+    for i in range(NumImagesToShow):
+        binfilename = 'image%i.bin' % i
+        EntropyEncoder(binfilename, encoded_layers1[i], 16, 16, 16)
+        bytesize = os.path.getsize(binfilename)
+        bpp[i] = bytesize * 8 / (w * h)
+        declayers[i] = EntropyDecoder(binfilename,  16, 16, 16)
+
+    #Dequantization and denormalization of each layer
+    print(bpp)
+    shift = 1.0/pow(2, b+1)
+    declayers = K.cast(declayers, "float32") / pow(2, b)
+    declayers = declayers + shift
+    encoded_layers_quantized = numpy.zeros((NumImagesToShow, 16, 16, 16), numpy.double, 'C')
+    for i in range(NumImagesToShow):
+        encoded_layers_quantized[i] = K.cast(declayers[i]*max_encoded_layers[i], "float32")
+        encoded_layers[i] = K.cast(encoded_layers[i] * max_encoded_layers[i], "float32")
+    decoded_imgs = decoder.predict(encoded_layers, batch_size=NumImagesToShow)
+    decoded_imgsQ = decoder.predict(encoded_layers_quantized, batch_size=NumImagesToShow)
+
+    #Shows NumImagesToShow images from the test set
+    #For each image the following results are presented
+    #Original image
+    #Image, represented by the model (without quantization)
+    #Image, represented by the model with quantization and compression of the layers samples
+    #Corresponding JPEG image at the same compression level
+    for i in range(NumImagesToShow):
+        title = ''
+        plt.subplot(4, NumImagesToShow, i + 1).set_title(title, fontsize=10)
+        plt.imshow(xtest[i, :, :, :], interpolation='nearest')
+        plt.axis(False)
+    for i in range(NumImagesToShow):
+        psnr = PSNR(xtest[i, :, :, :], decoded_imgs[i, :, :, :])
+        title = '%2.2f' % psnr
+        plt.subplot(4, NumImagesToShow, NumImagesToShow + i + 1).set_title(title, fontsize=10)
+        plt.imshow(decoded_imgs[i, :, :, :], interpolation='nearest')
+        plt.axis(False)
+    for i in range(NumImagesToShow):
+        psnr = PSNR(xtest[i, :, :, :], decoded_imgsQ[i, :, :, :])
+        title = '%2.2f %2.2f' % (psnr, bpp[i])
+        plt.subplot(4, NumImagesToShow, 2*NumImagesToShow + i + 1).set_title(title, fontsize=10)
+        plt.imshow(decoded_imgsQ[i, :, :, :], interpolation='nearest')
+        plt.axis(False)
+    for i in range(NumImagesToShow):
+        JPEGQP,JPEGrealbpp, JPEGrealpsnr = JPEGRDSingleImage(xtest[i, :, :, :], bpp[i],i)
+        JPEGfilename = 'image%i.jpeg' % i
+        JPEGimage = Image.open(JPEGfilename)
+        title = '%2.2f %2.2f' % (JPEGrealpsnr,JPEGrealbpp)
+        plt.subplot(4, NumImagesToShow, 3*NumImagesToShow + i + 1).set_title(title, fontsize=10)
+        plt.imshow(JPEGimage, interpolation='nearest')
+        plt.axis(False)
+    plt.show()
\ No newline at end of file

EntropyCompile.bat

+del EntropyCodec.cp39-win_amd64.pyd
+del ..\EntropyCodec.cp39-win_amd64.pyd
+python.exe EntropySetup.py build_ext --inplace
+pause
\ No newline at end of file

EntropySetup.py

+from setuptools import setup, Extension
+import os
+import pybind11
+
+functions_module = Extension(
+    name='EntropyCodec',
+    sources=['wrapper.cpp'],
+    include_dirs=[os.path.join(os.getenv('PYTHON_DIR'), 'include'),
+                os.path.join(pybind11.__path__[0], 'include')]
+)
+
+setup(ext_modules=[functions_module], options={"build_ext": {"build_lib": ".."}})

ImageCoder.cpp

+// ImageCodec.cpp : This file contains the 'main' function. Program execution begins and ends there.
+//
+#define _CRT_SECURE_NO_WARNINGS
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <math.h>
+#include <time.h>
+
+#include "mcoder.h"
+
+int BitPlaneEncoder(unsigned char *obuffer, unsigned char *layer1, int w1, int h1, int z1, int *bitsize)
+{
+	BiContextType bin_ctx;
+	int code_len = 0;
+	EncodingEnvironmentPtr eep;
+	unsigned char *bufptr = obuffer;
+
+	eep = arienco_create_encoding_environment();
+	arienco_start_encoding(eep, bufptr, &code_len);
+	biari_init_context(&bin_ctx, "ctx");
+
+	for (int z = 0;z < z1;z++)
+	{
+		for (int i = 0;i < h1;i++)
+		{
+			for (int j = 0;j < w1;j++)
+			{
+				biari_encode_symbol(eep, layer1[z*h1*w1+i*w1+j], &bin_ctx);
+			}
+		}
+	}
+
+	biari_encode_symbol(eep, 0, &bin_ctx);
+	biari_encode_symbol(eep, 1, &bin_ctx);
+	biari_encode_symbol(eep, 0, &bin_ctx);
+	biari_encode_symbol(eep, 1, &bin_ctx);
+
+	arienco_done_encoding(eep);
+	arienco_delete_encoding_environment(eep);
+	*bitsize = code_len / 8;
+
+	return code_len / 8;
+}
+
+int BitPlaneDecoder(unsigned char *obuffer, unsigned char *layer1, int w1, int h1, int z1, int *offset)
+{
+	BiContextType bin_ctx;
+	int code_len = 0;
+	DecodingEnvironmentPtr dep;
+	unsigned char *bufptr = obuffer;
+
+	bufptr += (*offset);
+	dep = arideco_create_decoding_environment();
+	arideco_start_decoding(dep, bufptr, 0, &code_len);
+	biari_init_context(&bin_ctx, "ctx");
+
+	for (int z = 0;z < z1;z++)
+	{
+		for (int i = 0;i < h1;i++)
+		{
+			for (int j = 0;j < w1;j++)
+			{
+				layer1[z*h1*w1 + i * w1 + j] = biari_decode_symbol(dep, &bin_ctx);
+			}
+		}
+	}
+	*offset = code_len;
+	arideco_delete_decoding_environment(dep);
+
+	return 0;
+}
+
+int main(int argc, char* argv[])
+{
+	
+	return 0;
+}
\ No newline at end of file

ac_dec.cpp

+#include <stdlib.h>
+#include <stdio.h>
+#include "mcoder.h"
+#include <math.h>
+
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Allocates memory for the DecodingEnvironment struct
+ * \return DecodingContextPtr
+ *    allocates memory
+ ************************************************************************
+ */
+DecodingEnvironmentPtr arideco_create_decoding_environment()
+{
+  DecodingEnvironmentPtr dep;
+
+  if ((dep = (DecodingEnvironmentPtr)calloc(1,sizeof(DecodingEnvironment))) == NULL)
+    printf("arideco_create_decoding_environment: dep");
+  return dep;
+}
+
+
+/*!
+ ***********************************************************************
+ * \brief
+ *    Frees memory of the DecodingEnvironment struct
+ ***********************************************************************
+ */
+void arideco_delete_decoding_environment(DecodingEnvironmentPtr dep)
+{
+  free(dep);
+}
+
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Initializes the DecodingEnvironment for the arithmetic coder
+ ************************************************************************
+ */
+void arideco_start_decoding(DecodingEnvironmentPtr dep, unsigned char *cpixcode,
+                            int firstbyte, int *cpixcode_len )
+{
+  register unsigned int bbit = 0;
+ 
+  int value = 0;
+  dep->Dcodestrm = cpixcode;
+  dep->Dcodestrm_len = (unsigned int*)cpixcode_len;
+  dep->Dvalue = 0;
+  for (int i = 1; i <= BITS_IN_REGISTER; i++)
+  {
+	Get1Bit(dep->Dcodestrm, *dep->Dcodestrm_len, bbit);
+    dep->Dvalue = 2 * dep->Dvalue + bbit;
+  }
+  dep->Dlow = 0;
+  dep->Drange = HALF-2;
+}
+
+unsigned int  biari_decode_symbol(DecodingEnvironmentPtr dep, BiContextTypePtr bi_ct)
+{
+  int bbit;
+  register unsigned int low = dep->Dlow;
+  register unsigned int value = dep->Dvalue;
+  register unsigned int range = dep->Drange;
+  unsigned int symbol;
+
+  unsigned int cum = (int)((((long) (value - low) + 1) * bi_ct->freq_all - 1) / range);
+  for (int i=0;i<ALPHABET_SIZE;i++)
+  {
+	if (bi_ct->cum_freq [i] > cum)
+	{
+		break;		
+	}
+	symbol = i;
+  }
+
+  low = low + (range * bi_ct->cum_freq [symbol]) / bi_ct->freq_all;
+  range = (range*bi_ct->freq [symbol])/bi_ct->freq_all;
+  range = max(1,range);
+
+  bi_ct->freq[symbol]++;
+  bi_ct->freq_all++;
+  if (bi_ct->freq_all>16384)
+  {
+	bi_ct->freq_all=0;
+	for (int i=0;i<ALPHABET_SIZE;i++)
+	{
+		bi_ct->freq[i]=max(1,bi_ct->freq[i]>>1);
+		bi_ct->freq_all+=bi_ct->freq[i];
+	}
+  }
+  bi_ct->cum_freq[0]=0;
+  for (int i=1;i<=ALPHABET_SIZE;i++)
+  {
+	bi_ct->cum_freq[i] = bi_ct->cum_freq[i-1]+bi_ct->freq[i-1];
+  }
+
+  while (range < QUARTER)
+  {
+ 	if (low >= HALF)
+    {
+        low -= HALF;
+		value -= HALF;
+    }
+    else 
+      if (low < QUARTER)
+      {
+      }
+      else
+      {
+        low -= QUARTER;
+		value -= QUARTER;
+      }
+    // Double range 
+    range <<= 1;
+	low <<= 1;
+	Get1Bit(dep->Dcodestrm, *dep->Dcodestrm_len, bbit);
+    value = (value << 1) | bbit;
+  }
+
+  dep->Drange = range;
+  dep->Dvalue = value;
+  dep->Dlow = low;
+ 
+  return(symbol);
+}
+
+

ac_enc.cpp

+#include <stdlib.h>
+#include <stdio.h>
+#include <string.h>
+#include <math.h>
+#include "mcoder.h"
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Initializes a given context with some pre-defined probability state
+ ************************************************************************
+ */
+void biari_init_context 
+(
+	BiContextTypePtr ctx, 
+	char* name
+)
+{
+  ctx->freq_all = 0;
+  for (int i=0;i<ALPHABET_SIZE;i++)
+  {
+	ctx->freq[i] = 1;
+	ctx->freq_all+=ctx->freq[i];
+  }
+
+  ctx->cum_freq[0]=0;
+  for (int i=1;i<=ALPHABET_SIZE;i++)
+  {
+	  ctx->cum_freq[i] = ctx->cum_freq[i-1]+ctx->freq[i-1];
+  }
+}
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Allocates memory for the EncodingEnvironment struct
+ ************************************************************************
+ */
+EncodingEnvironmentPtr arienco_create_encoding_environment()
+{
+  EncodingEnvironmentPtr eep;
+
+  if ( (eep = (EncodingEnvironmentPtr) calloc(1,sizeof(EncodingEnvironment))) == NULL)
+    printf("arienco_create_encoding_environment: eep");
+
+  return eep;
+}
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Frees memory of the EncodingEnvironment struct
+ ************************************************************************
+ */
+void arienco_delete_encoding_environment(EncodingEnvironmentPtr eep)
+{
+  if (eep != NULL)
+  {
+    free(eep);
+  }
+}
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Initializes the EncodingEnvironment for the arithmetic coder
+ ************************************************************************
+ */
+void arienco_start_encoding(EncodingEnvironmentPtr eep,
+                            unsigned char *code_buffer,
+                            int *code_len )
+{
+  eep->Elow = 0;
+  eep->Erange = HALF-2;
+  eep->Ebits_to_follow = 0;
+  eep->Ecodestrm = code_buffer;
+  eep->Ecodestrm_len = (unsigned int*)code_len;
+}
+
+/*!
+ ************************************************************************
+ * \brief
+ *    Terminates the arithmetic codeword, writes stop bit and stuffing bytes (if any)
+ ************************************************************************
+ */
+
+void arienco_done_encoding(EncodingEnvironmentPtr eep)
+{
+  if((eep->Elow >> (BITS_IN_REGISTER-1)) & 1)
+  {
+    put_one_bit_1_plus_outstanding;
+  }
+  else
+  {
+    put_one_bit_0_plus_outstanding;
+  }
+  
+  Put1Bit(eep->Ecodestrm, *eep->Ecodestrm_len, (eep->Elow >> (BITS_IN_REGISTER-2))&1);
+  Put1Bit(eep->Ecodestrm, *eep->Ecodestrm_len, 1);
+    *eep->Ecodestrm_len = (*eep->Ecodestrm_len+7) & ~7;
+}
+
+
+
+void biari_encode_symbol(EncodingEnvironmentPtr eep, signed int symbol, BiContextTypePtr bi_ct)
+{
+  register unsigned int range = eep->Erange;
+  register unsigned int low = eep->Elow;
+
+  low = low + (range * bi_ct->cum_freq [symbol]) / bi_ct->freq_all;
+  range = (range*bi_ct->freq [symbol])/bi_ct->freq_all;
+  range=max(1,range);
+
+  bi_ct->freq[symbol]++;
+  bi_ct->freq_all++;
+  if (bi_ct->freq_all>16384)
+  {
+	bi_ct->freq_all=0;
+	for (int i=0;i<ALPHABET_SIZE;i++)
+	{
+		bi_ct->freq[i]=max(1,bi_ct->freq[i]>>1);
+		bi_ct->freq_all+=bi_ct->freq[i];
+	}
+  }
+  bi_ct->cum_freq[0]=0;
+  for (int i=1;i<=ALPHABET_SIZE;i++)
+  {
+	bi_ct->cum_freq[i] = bi_ct->cum_freq[i-1]+bi_ct->freq[i-1];
+  }
+
+  // renormalization 
+  while (range < QUARTER)
+  {
+    if (low >= HALF)
+    {
+      put_one_bit_1_plus_outstanding;
+      low -= HALF;
+    }
+    else 
+      if (low < QUARTER)
+      {
+        put_one_bit_0_plus_outstanding;
+      }
+      else
+      {
+        eep->Ebits_to_follow++;
+        low -= QUARTER;
+      }
+    low <<= 1;
+    range <<= 1;
+  }
+  
+  eep->Erange = range;
+  eep->Elow = low;
+  
+  bi_ct->freq_stat[symbol]++;
+}
+

decoder.mdl/fingerprint.pb

+ގœ浂
* ȾG(e2

\ No newline at end of file

decoder.mdl/keras_metadata.pb

+
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{"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 3, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_3", "inbound_nodes": [[["conv2d_transpose_2", 0, 0, {}]]]}], "input_layers": [["input_2", 0, 0]], "output_layers": [["conv2d_3", 0, 0]]}, "shared_object_id": 13, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 16, 16, 16]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 16, 16, 16]}, "float32", null]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 16, 16, 16]}, "float32", null]}, "keras_version": "2.15.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model_1", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 16, 16, 16]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}, "name": "input_2", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose", "trainable": 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"items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "name": "conv2d_transpose_1", "inbound_nodes": [[["conv2d_transpose", 0, 0, {}]]], "shared_object_id": 6}, {"class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_2", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [7, 7]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, 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"shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_3", "inbound_nodes": [[["conv2d_transpose_2", 0, 0, {}]]], "shared_object_id": 12}], "input_layers": [["input_2", 0, 0]], "output_layers": [["conv2d_3", 0, 0]]}}}2

+
root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_2", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 16, 16, 16]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 16, 16, 16]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_2"}}2

+
+root.layer_with_weights-0"_tf_keras_layer*
+{"name": "conv2d_transpose", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "inbound_nodes": [[["input_2", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"-1": 16}}, "shared_object_id": 15}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}}2

+
+root.layer_with_weights-1"_tf_keras_layer*
+{"name": "conv2d_transpose_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [5, 5]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 4}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 5}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "inbound_nodes": [[["conv2d_transpose", 0, 0, {}]]], "shared_object_id": 6, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"-1": 16}}, "shared_object_id": 16}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 32, 16]}}2

+
+root.layer_with_weights-2"_tf_keras_layer*
+{"name": "conv2d_transpose_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2DTranspose", "config": {"name": "conv2d_transpose_2", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [7, 7]}, "strides": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 7}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 8}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null, "output_padding": null}, "inbound_nodes": [[["conv2d_transpose_1", 0, 0, {}]]], "shared_object_id": 9, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {"-1": 32}}, "shared_object_id": 17}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64, 64, 32]}}2

+
+root.layer_with_weights-3"_tf_keras_layer*
+{"name": "conv2d_3", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "conv2d_3", "trainable": true, "dtype": "float32", "filters": 3, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 10}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 11}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["conv2d_transpose_2", 0, 0, {}]]], "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 18}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128, 128, 128]}}2

\ No newline at end of file

encoder.mdl/fingerprint.pb

+
Ֆ֯
i ǹL(Ͳ
2

\ No newline at end of file

encoder.mdl/keras_metadata.pb

+
+Xroot"_tf_keras_network*X{"name": "model", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Functional", "config": {"name": "model", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 128, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": []}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [7, 7]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d", "inbound_nodes": [[["input_1", 0, 0, {}]]]}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d", "inbound_nodes": [[["conv2d", 0, 0, {}]]]}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [5, 5]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, 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"tf.__operators__.add", "inbound_nodes": [["max_pooling2d_2", 0, 0, {"y": ["tf.random.uniform", 0, 0], "name": null}]]}], "input_layers": [["input_1", 0, 0]], "output_layers": [["tf.__operators__.add", 0, 0]]}, "shared_object_id": 18, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 128, 128, 3]}, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 128, 128, 3]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 128, 128, 3]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 128, 128, 3]}, "float32", "input_1"]}, "keras_version": "2.15.0", "backend": "tensorflow", "model_config": {"class_name": "Functional", "config": {"name": "model", "trainable": true, "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 128, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "name": "input_1", "inbound_nodes": [], "shared_object_id": 0}, {"class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [7, 7]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d", "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 3}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d", "inbound_nodes": [[["conv2d", 0, 0, {}]]], "shared_object_id": 4}, {"class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [5, 5]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_1", "inbound_nodes": [[["max_pooling2d", 0, 0, {}]]], "shared_object_id": 7}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_1", "inbound_nodes": [[["conv2d_1", 0, 0, {}]]], "shared_object_id": 8}, {"class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 9}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 10}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "name": "conv2d_2", "inbound_nodes": [[["max_pooling2d_1", 0, 0, {}]]], "shared_object_id": 11}, {"class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "name": "max_pooling2d_2", "inbound_nodes": [[["conv2d_2", 0, 0, {}]]], "shared_object_id": 12}, {"class_name": "TFOpLambda", "config": {"name": "tf.math.reduce_max", "trainable": true, "dtype": "float32", "function": "math.reduce_max"}, "name": "tf.math.reduce_max", "inbound_nodes": [["max_pooling2d_2", 0, 0, {}]], "shared_object_id": 13}, {"class_name": "TFOpLambda", "config": {"name": "tf.compat.v1.shape", "trainable": true, "dtype": "float32", "function": "compat.v1.shape"}, "name": "tf.compat.v1.shape", "inbound_nodes": [["max_pooling2d_2", 0, 0, {"name": null, "out_type": "int32"}]], "shared_object_id": 14}, {"class_name": "TFOpLambda", "config": {"name": "tf.math.truediv", "trainable": true, "dtype": "float32", "function": "math.truediv"}, "name": "tf.math.truediv", "inbound_nodes": [["tf.math.reduce_max", 0, 0, {"y": 16, "name": null}]], "shared_object_id": 15}, {"class_name": "TFOpLambda", "config": {"name": "tf.random.uniform", "trainable": true, "dtype": "float32", "function": "random.uniform"}, "name": "tf.random.uniform", "inbound_nodes": [["tf.compat.v1.shape", 0, 0, {"minval": 0, "maxval": ["tf.math.truediv", 0, 0]}]], "shared_object_id": 16}, {"class_name": "TFOpLambda", "config": {"name": "tf.__operators__.add", "trainable": true, "dtype": "float32", "function": "__operators__.add"}, "name": "tf.__operators__.add", "inbound_nodes": [["max_pooling2d_2", 0, 0, {"y": ["tf.random.uniform", 0, 0], "name": null}]], "shared_object_id": 17}], "input_layers": [["input_1", 0, 0]], "output_layers": [["tf.__operators__.add", 0, 0]]}}}2

+
root.layer-0"_tf_keras_input_layer*{"class_name": "InputLayer", "name": "input_1", "dtype": "float32", "sparse": false, "ragged": false, "batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 128, 3]}, "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 128, 128, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}2

+
+root.layer_with_weights-0"_tf_keras_layer*	{"name": "conv2d", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "conv2d", "trainable": true, "dtype": "float32", "filters": 128, "kernel_size": {"class_name": "__tuple__", "items": [7, 7]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 2}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["input_1", 0, 0, {}]]], "shared_object_id": 3, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 3}}, "shared_object_id": 20}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128, 128, 3]}}2

+root.layer-2"_tf_keras_layer*{"name": "max_pooling2d", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d", 0, 0, {}]]], "shared_object_id": 4, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 21}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 128, 128, 128]}}2

+
+root.layer_with_weights-1"_tf_keras_layer*
+{"name": "conv2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "conv2d_1", "trainable": true, "dtype": "float32", "filters": 32, "kernel_size": {"class_name": "__tuple__", "items": [5, 5]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 5}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 6}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d", 0, 0, {}]]], "shared_object_id": 7, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 128}}, "shared_object_id": 22}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64, 64, 128]}}2

+root.layer-4"_tf_keras_layer*{"name": "max_pooling2d_1", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_1", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_1", 0, 0, {}]]], "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 23}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 64, 64, 32]}}2

+
+root.layer_with_weights-2"_tf_keras_layer*
+{"name": "conv2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Conv2D", "config": {"name": "conv2d_2", "trainable": true, "dtype": "float32", "filters": 16, "kernel_size": {"class_name": "__tuple__", "items": [3, 3]}, "strides": {"class_name": "__tuple__", "items": [1, 1]}, "padding": "same", "data_format": "channels_last", "dilation_rate": {"class_name": "__tuple__", "items": [1, 1]}, "groups": 1, "activation": "relu", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 9}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 10}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "inbound_nodes": [[["max_pooling2d_1", 0, 0, {}]]], "shared_object_id": 11, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 4, "axes": {"-1": 32}}, "shared_object_id": 24}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 32, 32]}}2

+root.layer-6"_tf_keras_layer*{"name": "max_pooling2d_2", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "MaxPooling2D", "config": {"name": "max_pooling2d_2", "trainable": true, "dtype": "float32", "pool_size": {"class_name": "__tuple__", "items": [2, 2]}, "padding": "same", "strides": {"class_name": "__tuple__", "items": [2, 2]}, "data_format": "channels_last"}, "inbound_nodes": [[["conv2d_2", 0, 0, {}]]], "shared_object_id": 12, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": 4, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 25}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32, 32, 16]}}2

+root.layer-7"_tf_keras_layer*{"name": "tf.math.reduce_max", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.math.reduce_max", "trainable": true, "dtype": "float32", "function": "math.reduce_max"}, "inbound_nodes": [["max_pooling2d_2", 0, 0, {}]], "shared_object_id": 13, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}}2

+	root.layer-8"_tf_keras_layer*{"name": "tf.compat.v1.shape", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.compat.v1.shape", "trainable": true, "dtype": "float32", "function": "compat.v1.shape"}, "inbound_nodes": [["max_pooling2d_2", 0, 0, {"name": null, "out_type": "int32"}]], "shared_object_id": 14, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}}2

+
+root.layer-9"_tf_keras_layer*{"name": "tf.math.truediv", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.math.truediv", "trainable": true, "dtype": "float32", "function": "math.truediv"}, "inbound_nodes": [["tf.math.reduce_max", 0, 0, {"y": 16, "name": null}]], "shared_object_id": 15, "build_input_shape": {"class_name": "TensorShape", "items": []}}2

+
root.layer-10"_tf_keras_layer*{"name": "tf.random.uniform", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.random.uniform", "trainable": true, "dtype": "float32", "function": "random.uniform"}, "inbound_nodes": [["tf.compat.v1.shape", 0, 0, {"minval": 0, "maxval": ["tf.math.truediv", 0, 0]}]], "shared_object_id": 16, "build_input_shape": {"class_name": "TensorShape", "items": [4]}}2

+
root.layer-11"_tf_keras_layer*{"name": "tf.__operators__.add", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": true, "preserve_input_structure_in_config": true, "autocast": false, "class_name": "TFOpLambda", "config": {"name": "tf.__operators__.add", "trainable": true, "dtype": "float32", "function": "__operators__.add"}, "inbound_nodes": [["max_pooling2d_2", 0, 0, {"y": ["tf.random.uniform", 0, 0], "name": null}]], "shared_object_id": 17, "build_input_shape": {"class_name": "TensorShape", "items": [null, 16, 16, 16]}}2

\ No newline at end of file

mcoder.h

+#ifndef MCODER_H
+#define MCODER_H
+
+#include "stream.h"
+#include <stdio.h>
+
+//new parameters
+
+#define BITS_IN_REGISTER 16
+#define TOP_VALUE (((long) 1 << BITS_IN_REGISTER) - 1)
+#define QUARTER (TOP_VALUE / 4 + 1)
+#define FIRST_QTR (TOP_VALUE / 4 + 1)
+#define HALF      (2 * FIRST_QTR)
+#define THIRD_QTR (3 * FIRST_QTR)
+
+#define ALPHABET_SIZE 256
+#define max(a,b) (((a) > (b)) ? (a) : (b))
+
+typedef struct
+{
+  unsigned long freq[ALPHABET_SIZE+1];
+  unsigned long freq_all;
+  unsigned long cum_freq[ALPHABET_SIZE+1];
+  unsigned long freq_stat[ALPHABET_SIZE+1];
+} BiContextType;
+
+typedef BiContextType *BiContextTypePtr;
+
+#define putLogToFile(fname,...) { \
+  FILE *fp = fopen(fname,"at"); \
+  fprintf(fp,__VA_ARGS__); \
+  fclose(fp); \
+}
+
+
+
+#define SIGN_TO_ENCODE(x) ((x) >= 0 ? 0 : 1)
+#define CABAC_ABS(x) ((x) > 0 ? (x) : -(x))
+#define MAX_BITS_IN_SERIE 25
+///   25 MAXIMUM
+  #define PutZeros(nbits) {\
+    unsigned int iii;\
+    for(iii = 0; iii<(nbits); iii++ )\
+    {\
+      Put1Bit(eep->Ecodestrm, *eep->Ecodestrm_len, 0);\
+    }\
+      }
+
+  #define put_one_bit_1_plus_outstanding { \
+                                          Put1Bit(eep->Ecodestrm, *eep->Ecodestrm_len, 1); \
+                                          PutZeros(eep->Ebits_to_follow);\
+                                          eep->Ebits_to_follow = 0;\
+                                         }
+
+  #define PutLongOnes(nbits) {\
+      unsigned int i1=0xFFFFFFFF;\
+      int bits1;\
+      int main = (nbits)/MAX_BITS_IN_SERIE;\
+      int tail = (nbits)%MAX_BITS_IN_SERIE;\
+      for(bits1 = 0; bits1<main; bits1++ ) {\
+        PutBits(eep->Ecodestrm, *eep->Ecodestrm_len,i1,MAX_BITS_IN_SERIE);\
+      }\
+      PutBits(eep->Ecodestrm, *eep->Ecodestrm_len,i1,tail);\
+    }
+
+  #define put_one_bit_0_plus_outstanding { \
+                                          Put1Bit(eep->Ecodestrm, *eep->Ecodestrm_len, 0); \
+                                          if( eep->Ebits_to_follow > MAX_BITS_IN_SERIE ) \
+                                          { \
+                                            PutLongOnes(eep->Ebits_to_follow);\
+                                          }\
+                                          else \
+                                          {\
+                                            PutBitsOnes(eep->Ecodestrm, *eep->Ecodestrm_len, eep->Ebits_to_follow);\
+                                          }\
+                                          eep->Ebits_to_follow = 0;\
+                                         }
+
+
+//! struct to characterize the state of the arithmetic coding engine
+typedef struct
+{
+  unsigned int  Elow, Erange, Ehigh;
+  unsigned int  Ebits_to_follow;
+  unsigned char *Ecodestrm;
+  unsigned int  *Ecodestrm_len;
+} EncodingEnvironment;
+
+typedef EncodingEnvironment *EncodingEnvironmentPtr;
+
+// cabac_enc.c
+void biari_init_context 
+(
+	BiContextTypePtr ctx, 
+	char* name
+);
+
+
+EncodingEnvironmentPtr arienco_create_encoding_environment();
+void arienco_delete_encoding_environment(EncodingEnvironmentPtr eep);
+void arienco_start_encoding(EncodingEnvironmentPtr eep,
+                            unsigned char *code_buffer,
+                            int *code_len );
+void arienco_done_encoding(EncodingEnvironmentPtr eep);
+
+void biari_encode_symbol(EncodingEnvironmentPtr eep, int symbol, BiContextTypePtr bi_ct);
+
+
+//! struct to characterize the state of the arithmetic coding engine
+typedef struct
+{
+  unsigned int    Dlow, Drange, Dhigh;
+  unsigned int    Dvalue;
+  unsigned int    Dbuffer;
+  int             Dbits_to_go;
+  unsigned char   *Dcodestrm;
+  unsigned int    *Dcodestrm_len;
+} DecodingEnvironment;
+
+typedef DecodingEnvironment *DecodingEnvironmentPtr;
+
+// cabac_dec.c
+DecodingEnvironmentPtr arideco_create_decoding_environment();
+void arideco_delete_decoding_environment(DecodingEnvironmentPtr dep);
+void arideco_start_decoding(DecodingEnvironmentPtr dep, unsigned char *cpixcode,
+                            int firstbyte, int *cpixcode_len );
+int arideco_bits_read(DecodingEnvironmentPtr dep);
+void arideco_done_decoding(DecodingEnvironmentPtr dep);
+unsigned int biari_decode_symbol(DecodingEnvironmentPtr dep, BiContextTypePtr bi_ct);
+#endif // !MCODER_H
\ No newline at end of file

stream.h

+#ifndef _STREAM_H
+#define _STREAM_H
+
+// ****************************************************
+// *     Bits Order (w/o swap):                       *
+// * the 1st bit in a bitstream is 0th bit (not 7th)  *
+// ****************************************************
+
+#define mask(y,nb) ((unsigned int)((int)(y) & ((1L<<(nb)) - 1)))
+
+#ifdef TMS
+#define UINT32P(x)  _mem4(x)
+#else
+#define UINT32P(x)  (*((unsigned int*)(x)))
+#endif
+
+#define UINT32(x) ((unsigned int)(x))
+#define UCHAR(x)  ((unsigned char)(x))
+#define UCHARP(x) ((unsigned char*)(x))
+
+//-----------------------------------------------
+#define PutBitsAC(stream, bitptr, x, nbits)   	\
+{ 												\
+   UINT32P(&(UCHARP(stream)[(bitptr)>>3]))  |=	\
+           UINT32(x) << ((bitptr)&0x7);         \
+   bitptr += nbits;                             \
+}												\
+//-----------------------------------------------
+//-------------------------------------------------------------------
+#define Put1BitAC(stream, bitptr, x)  								\
+{       				 	  										\
+  if(x) UCHARP(stream)[(bitptr)>>3] |= UCHAR(1L<<((bitptr)&0x7));	\
+  (bitptr)++;                                                	  	\
+}																	\
+//-------------------------------------------------------------------
+
+//---------------------------------------------------
+#define PutBits(stream, bitptr, x, nbits)    		\
+{                                         			\
+   UINT32P(&(UCHARP(stream)[((bitptr)+7)>>3])) = 0;	\
+   UINT32P(&(UCHARP(stream)[(bitptr)>>3]))    |= 	\
+           UINT32(mask(x,nbits) << ((bitptr)&0x7));	\
+   bitptr += nbits;									\
+}													\
+//---------------------------------------------------
+
+//---------------------------------------------------
+#define PutBitsOnes(stream, bitptr, nbits)       	\
+{                                                   \
+   UINT32P(&(UCHARP(stream)[((bitptr)+7)>>3])) = 0;	\
+   UINT32P(&(UCHARP(stream)[(bitptr)>>3]))    |=    \
+    UINT32(mask(0xFFFFFFFF,nbits) << ((bitptr)&0x7)); \
+   bitptr += nbits;                                 \
+}													\
+//---------------------------------------------------
+//---------------------------------------------------
+#define PutBitsZeros(stream, bitptr, nbits)     	\
+{                                                   \
+   UINT32P(&(UCHARP(stream)[((bitptr)+7)>>3])) = 0;	\
+   bitptr += nbits;                                 \
+}													\
+//---------------------------------------------------
+
+//------------------------------------------------------------------
+#define ShiftBitPtr(bitptr, nbits) bitptr += nbits;
+//------------------------------------------------------------------
+
+// *********************************************
+// *    Put a bit into the bitstream.          *
+// * x can be any value;                       *
+// * only if x~=0, the current bit is set to 1 *
+// *********************************************
+//---------------------------------------------------------------
+#define Put1Bit(stream, bitptr, x)                  			\
+{                                                       		\
+   UINT32P(&(UCHARP(stream)[((bitptr)+7)>>3]) ) = 0;     		\
+   if((x)!=0) UINT32P(&(UCHARP(stream)[(bitptr)>>3])) |=       	\
+                                UINT32(1L << ((bitptr)&0x7));	\
+   (bitptr)++;                                            		\
+}																\
+//---------------------------------------------------------------
+
+//-------------------------------------------------------
+#define Put1BitOne(stream, bitptr)                   	\
+{                                                       \
+   UINT32P(&(UCHARP(stream)[((bitptr)+7)>>3]) ) = 0;	\
+   UINT32P(&(UCHARP(stream)[(bitptr)>>3]) )    |=  		\
+               	       UINT32(1L << ((bitptr)&0x7));	\
+   (bitptr)++;                                          \
+}														\
+//-------------------------------------------------------
+//-------------------------------------------------------
+#define Put1BitZero(stream, bitptr)                   	\
+{                                                       \
+   UINT32P(&(UCHARP(stream)[((bitptr)+7)>>3]) ) = 0; 	\
+   (bitptr)++;                                          \
+}														\
+//-------------------------------------------------------
+
+//-------------------------------------------------------
+#define Get1BitCABAC(stream, bitptr, x)                                 \
+{                                                                  \
+   x   = (stream)[(bitptr)>>3];   \
+   x >>= (bitptr) & 0x7 ;                                          \
+   x  &= 1;                                                        \
+   (bitptr)++;                                                     \
+}
+
+//-------------------------------------------------------
+#define GetBits(stream, bitptr, x, nbits)                      \
+{                                                       \
+   x   = UINT32P(&(UCHARP(stream)[(bitptr)>>3]));	\
+   x >>= (bitptr) & 0x7 ;                               \
+   bitptr += nbits;                                          \
+}														\
+//-------------------------------------------------------
+
+
+//-------------------------------------------------------
+#define Get1Bit(stream, bitptr, x)                      \
+{                                                       \
+   x   = UINT32P(&(UCHARP(stream)[(bitptr)>>3]));	\
+   x >>= (bitptr) & 0x7 ;                               \
+   x  &= 1;                                             \
+   (bitptr)++;                                          \
+}														\
+//-------------------------------------------------------
+#define Get1BitCABAC(stream, bitptr, x)                                 \
+{                                                                  \
+   x   = (stream)[(bitptr)>>3];   \
+   x >>= (bitptr) & 0x7 ;                                          \
+   x  &= 1;                                                        \
+   (bitptr)++;                                                     \
+}
+
+#endif // _STREAM_H

wrapper.cpp

+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <pybind11/numpy.h>
+
+#include "ImageCoder.cpp"
+#include "ac_enc.cpp"
+#include "ac_dec.cpp"
+
+
+namespace py = pybind11;
+
+template<typename T>
+T*** from_py_array2(py::array_t<T>& py_array)
+{
+	return (T***)py_array.data();
+}
+
+template<typename T>
+T* from_py_array(py::array_t<T>& py_array)
+{
+	return (T*)py_array.data();
+}
+
+
+PYBIND11_MODULE(EntropyCodec, m){
+    m.def("HiddenLayersEncoder", [](py::array_t<unsigned char>& layer1, int w1, int h1, int z1, 
+		py::array_t<unsigned char>& stream, py::array_t<int>& bitsize)
+    {
+		auto l1 = from_py_array(layer1);
+		auto st = from_py_array(stream);
+		auto bs = from_py_array(bitsize);
+		
+		BitPlaneEncoder(st, l1, w1, h1, z1, bs);
+    });
+
+	m.def("HiddenLayersDecoder", [](py::array_t<unsigned char>& layer1, int w1, int h1, int z1,
+		py::array_t<unsigned char>& stream, py::array_t<int>& offset)
+    {
+		auto l1 = from_py_array(layer1);
+		auto st = from_py_array(stream);
+		auto of = from_py_array(offset);
+
+		BitPlaneDecoder(st, l1, w1, h1, z1, of);
+    });
+}
+