From be187ee0a61a4dbfc3f2726d5c741b6502fdaee7 Mon Sep 17 00:00:00 2001 From: Francesco Date: Tue, 23 Mar 2021 14:17:27 +0100 Subject: [PATCH 1/6] First commit for the creation of a qkeras zoo. Added five networks: quicknet, quicknet_small, quicknet_large, alexnet and birealnet. Each network is built in its own class and tested with the methods in utils.py file. --- zoo/alexnet.py | 182 +++++++++++ zoo/birealnet.py | 209 ++++++++++++ zoo/larq_models/alexNet.json | 1 + zoo/larq_models/biRealNet.json | 1 + zoo/larq_models/quickNet_.json | 1 + zoo/larq_models/quickNet_large_large.json | 1 + zoo/larq_models/quickNet_small_small.json | 1 + zoo/qkeras_models/alexNet.json | 1 + zoo/qkeras_models/biRealNet.json | 1 + zoo/qkeras_models/quickNet_.json | 1 + zoo/qkeras_models/quickNet_large_large.json | 1 + zoo/qkeras_models/quickNet_small_small.json | 1 + zoo/quicknet.py | 343 ++++++++++++++++++++ zoo/readme.md | 21 ++ zoo/results.txt | 21 ++ zoo/utils.py | 132 ++++++++ 16 files changed, 918 insertions(+) create mode 100644 zoo/alexnet.py create mode 100644 zoo/birealnet.py create mode 100644 zoo/larq_models/alexNet.json create mode 100644 zoo/larq_models/biRealNet.json create mode 100644 zoo/larq_models/quickNet_.json create mode 100644 zoo/larq_models/quickNet_large_large.json create mode 100644 zoo/larq_models/quickNet_small_small.json create mode 100644 zoo/qkeras_models/alexNet.json create mode 100644 zoo/qkeras_models/biRealNet.json create mode 100644 zoo/qkeras_models/quickNet_.json create mode 100644 zoo/qkeras_models/quickNet_large_large.json create mode 100644 zoo/qkeras_models/quickNet_small_small.json create mode 100644 zoo/quicknet.py create mode 100644 zoo/readme.md create mode 100644 zoo/results.txt create mode 100644 zoo/utils.py diff --git a/zoo/alexnet.py b/zoo/alexnet.py new file mode 100644 index 00000000..d643ce4b --- /dev/null +++ b/zoo/alexnet.py @@ -0,0 +1,182 @@ +############################################################################### +# .. attention:: +# +# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. +# All rights reserved. +# +# This software component is licensed by Apache License Version 2.0 +# http://www.apache.org/licenses/ +# Same as QKeras +# You may not use this file except in compliance with# the License. +# +############################################################################### + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +# Download pretrained weight from: +# Alexnet -> https://github.com/larq/zoo/releases/download/binary_alexnet-v0.2.0/binary_alexnet_weights.h5 + +import qkeras as q +import tensorflow as tf +import larq as lq +from utils import compare_network, create_random_dataset, dump_network_to_json + +# Define path to the pre-trained weights +path_alexnet = "./weigths/binary_alexnet_weights.h5" +alexnet_name = "alexNet" + + +class alexnet(): + """ + Class to create and load weights of: alexnet + """ + + def __init__(self): + self.__weights_path = path_alexnet + self.network_name = alexnet_name + + @staticmethod + def add_qkeras_conv_block(model, filters_num, kernel_size, pool, + qnt, strides=1): + """ + Add a sequence of: Activation quantization, Quantized Conv2D, MaxPooling + and BatchNormalization to the given model + :param model: model where to add the sequence + :param filters_num: number of filters for Cov2D + :param kernel_size: kernel size for Cov2D + :param pool: boolean to decide if MaxPool is performed or not + :param qnt: boolean to decide if Activation quantization is performed + or not + :param strides: strides for Conv2D + :return: model plus the sequence + """ + if qnt: + model.add(q.QActivation("binary(alpha=1)")) + model.add( + q.QConv2D(filters_num, kernel_size, strides=strides, padding="same", + use_bias=False, kernel_quantizer="binary(alpha=1)")) + if pool: + model.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2)) + model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) + return model + + @staticmethod + def add_qkeras_dense_block(model, units): + """ + Add a sequence of: Activation quantization, Quantized Dense to the given + model + :param model: model where to add the sequence + :param units: neurons of the Dense + :return: model plus the sequence + """ + model.add(q.QActivation("binary(alpha=1)")) + model.add( + q.QDense(units, kernel_quantizer="binary(alpha=1)", use_bias=False)) + model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) + return model + + @staticmethod + def add_larq_conv_block(model, filters_num, kernel_size, pool, qnt, strides=1): + """ + Same method of add_qkeras_conv_block but for a larq network + """ + model.add( + lq.layers.QuantConv2D(filters_num, kernel_size, strides=strides, + padding="same", use_bias=False, + input_quantizer=None if not qnt else "ste_sign", + kernel_quantizer="ste_sign", + kernel_constraint="weight_clip")) + if pool: + model.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2)) + model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) + return model + + @staticmethod + def add_larq_dense_block(model, units): + """ + Same method of add_qkeras_dense_block but for a larq network + """ + model.add(lq.layers.QuantDense(units, use_bias=False, + input_quantizer="ste_sign", + kernel_quantizer="ste_sign", + kernel_constraint="weight_clip")) + model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) + return model + + def build(self): + """ + Build the model + :return: qkeras and larq models + """ + qkeras_network = self.build_qkeras_alexnet() + print("\nQKeras network successfully created") + larq_network = self.build_larq_alexnet() + print("Larq network successfully created") + return qkeras_network, larq_network + + def build_qkeras_alexnet(self): + """ + Build the qkeras version of the alexnet + :return: qkeras model of the alexnet + """ + qkeras_alexNet = tf.keras.models.Sequential() + qkeras_alexNet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + self.add_qkeras_conv_block(qkeras_alexNet, filters_num=64, kernel_size=11, + strides=4, pool=True, qnt=False) + self.add_qkeras_conv_block(qkeras_alexNet, filters_num=192, kernel_size=5, + pool=True, qnt=True) + self.add_qkeras_conv_block(qkeras_alexNet, filters_num=384, kernel_size=3, + pool=False, qnt=True) + self.add_qkeras_conv_block(qkeras_alexNet, filters_num=384, kernel_size=3, + pool=False, qnt=True) + self.add_qkeras_conv_block(qkeras_alexNet, filters_num=256, kernel_size=3, + pool=True, qnt=True) + qkeras_alexNet.add(tf.keras.layers.Flatten()) + self.add_qkeras_dense_block(qkeras_alexNet, units=4096) + self.add_qkeras_dense_block(qkeras_alexNet, units=4096) + self.add_qkeras_dense_block(qkeras_alexNet, units=1000) + qkeras_alexNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + qkeras_alexNet.load_weights(self.__weights_path) + return qkeras_alexNet + + def build_larq_alexnet(self): + """ + Build the larq version of the alexnet + :return: larq model of the alexnet + """ + larq_alexnet = tf.keras.models.Sequential() + larq_alexnet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + self.add_larq_conv_block(larq_alexnet, filters_num=64, kernel_size=11, + strides=4, pool=True, qnt=False) + self.add_larq_conv_block(larq_alexnet, filters_num=192, kernel_size=5, + pool=True, qnt=True) + self.add_larq_conv_block(larq_alexnet, filters_num=384, kernel_size=3, + pool=False, qnt=True) + self.add_larq_conv_block(larq_alexnet, filters_num=384, kernel_size=3, + pool=False, qnt=True) + self.add_larq_conv_block(larq_alexnet, filters_num=256, kernel_size=3, + pool=True, qnt=True) + larq_alexnet.add(tf.keras.layers.Flatten()) + self.add_larq_dense_block(larq_alexnet, units=4096) + self.add_larq_dense_block(larq_alexnet, units=4096) + self.add_larq_dense_block(larq_alexnet, units=1000) + larq_alexnet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + larq_alexnet.load_weights(self.__weights_path) + return larq_alexnet + + +if __name__ == "__main__": + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + + network = alexnet() + qkeras_network, larq_network = network.build() + # Compare mean MSE and Absolute error of the the networks + compare_network(qkeras_network=qkeras_network, larq_network=larq_network, + dataset=random_data, network_name=alexnet_name) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, + network_name=alexnet_name) diff --git a/zoo/birealnet.py b/zoo/birealnet.py new file mode 100644 index 00000000..b7091407 --- /dev/null +++ b/zoo/birealnet.py @@ -0,0 +1,209 @@ +############################################################################### +# .. attention:: +# +# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. +# All rights reserved. +# +# This software component is licensed by Apache License Version 2.0 +# http://www.apache.org/licenses/ +# Same as QKeras +# You may not use this file except in compliance with# the License. +# +############################################################################### + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +# Download pretrained weight from: +# Birealnet -> https://github.com/larq/zoo/releases/download/birealnet-v0.3.0/birealnet_weights.h5 + +import qkeras as q +import tensorflow as tf +import larq as lq +from utils import compare_network, create_random_dataset, dump_network_to_json + +# Define path to the pre-trained weights +path_birealnet = "./weigths/birealnet_weights.h5" +birealnet_name = "biRealNet" + + +class birealnet(): + """ + Class to create and load weights of: biRealNet + """ + + def __init__(self): + self.__weights_path = path_birealnet + self.network_name = birealnet_name + + @staticmethod + def add_qkeras_residual_block(model, filters_num, strides=1): + """ + Add a sequence of: Activation quantization, Quantized Conv2D + :param model: model where to add the sequence + :param filters_num: number of filters for Cov2D + :param strides: strides for Conv2D + """ + model.add(q.QActivation("binary(alpha=1)")) + model.add(q.QConv2D(filters_num, (3, 3), strides=strides, padding="same", + kernel_quantizer="binary(alpha=1)", use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + + @staticmethod + def add_qkeras_connection_block(model, filters_num): + """ + Add a sequence of: Activation quantization, Quantized Conv2D, reshape, + Average Pooling, Conv2D, 2x BatchNormalization + :param model: model where to add the sequence + :param filters_num: number of filters for Cov2D + """ + model.add(q.QActivation("binary")) + model.add(q.QConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, + padding="same", kernel_quantizer="binary(alpha=1)")) + # Prepare shapes for reshape layers + shape_in = model.output_shape[1] * model.output_shape[2] * \ + model.output_shape[3] + shape_out = (model.output_shape[1], model.output_shape[2], + model.output_shape[3] // 2) + model.add(tf.keras.layers.Flatten()) + model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) + model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) + model.add(tf.keras.layers.Reshape(target_shape=shape_out)) + model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + model.add(tf.keras.layers.BatchNormalization()) + + @staticmethod + def add_larq_residual_block(model, features, strides=1): + """ + Same method of add_qkeras_residual_block but for a larq network + """ + model.add(lq.layers.QuantConv2D(features, (3, 3), strides=strides, + padding="same", use_bias=False, + input_quantizer="approx_sign", + kernel_quantizer= + "magnitude_aware_sign", + kernel_constraint="weight_clip", )) + model.add(tf.keras.layers.BatchNormalization()) + + @staticmethod + def add_larq_connection_block(model, filters_num): + """ + Same method of add_qkeras_connection_block but for a larq network + """ + model.add( + lq.layers.QuantConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, + padding="same", + input_quantizer="approx_sign", + kernel_quantizer="magnitude_aware_sign", + kernel_constraint="weight_clip")) + + shape_in = model.output_shape[1] * model.output_shape[2] * \ + model.output_shape[3] + shape_out = (model.output_shape[1], model.output_shape[2], + model.output_shape[3] // 2) + # Prepare shapes for reshape layers + model.add(tf.keras.layers.Flatten()) + model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) + model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) + model.add(tf.keras.layers.Reshape(target_shape=shape_out)) + model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + model.add(tf.keras.layers.BatchNormalization()) + + def build(self): + """ + Build the model + :return: qkeras and larq models + """ + qkeras_network = self.build_qkeras_birealnet() + print("\nQKeras network successfully created") + larq_network = self.build_larq_birealnet() + print("Larq network successfully created") + return qkeras_network, larq_network + + def build_qkeras_birealnet(self): + """ + Build the qkeras version of the birealnet + :return: qkeras model of the birealnet + """ + qkeras_biRealNet = tf.keras.models.Sequential() + qkeras_biRealNet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + qkeras_biRealNet.add( + tf.keras.layers.Conv2D(64, (7, 7), strides=2, padding="same", + use_bias=False)) + qkeras_biRealNet.add(tf.keras.layers.BatchNormalization(momentum=0.8)) + qkeras_biRealNet.add( + tf.keras.layers.MaxPool2D((3, 3), strides=2, padding="same")) + for _ in range(0, 4): + self.add_qkeras_residual_block(qkeras_biRealNet, 64) + self.add_qkeras_connection_block(qkeras_biRealNet, 128) + + for _ in range(0, 3): + self.add_qkeras_residual_block(qkeras_biRealNet, 128) + self.add_qkeras_connection_block(qkeras_biRealNet, 256) + + for _ in range(0, 3): + self.add_qkeras_residual_block(qkeras_biRealNet, 256) + self.add_qkeras_connection_block(qkeras_biRealNet, 512) + + for _ in range(0, 3): + self.add_qkeras_residual_block(qkeras_biRealNet, 512) + qkeras_biRealNet.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) + qkeras_biRealNet.add(tf.keras.layers.Flatten()) + qkeras_biRealNet.add(tf.keras.layers.Dense(1000)) + qkeras_biRealNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + qkeras_biRealNet.load_weights(path_birealnet) + return qkeras_biRealNet + + def build_larq_birealnet(self): + """ + Build the larq version of the birealnet + :return: larq model of the birealnet + """ + larq_biRealNet = tf.keras.models.Sequential() + larq_biRealNet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + larq_biRealNet.add( + tf.keras.layers.Conv2D(64, (7, 7), strides=2, padding="same", + use_bias=False)) + larq_biRealNet.add(tf.keras.layers.BatchNormalization(momentum=0.8)) + larq_biRealNet.add( + tf.keras.layers.MaxPool2D((3, 3), strides=2, padding="same")) + for _ in range(0, 4): + self.add_larq_residual_block(larq_biRealNet, 64) + self.add_larq_connection_block(larq_biRealNet, 128) + + for _ in range(0, 3): + self.add_larq_residual_block(larq_biRealNet, 128) + self.add_larq_connection_block(larq_biRealNet, 256) + + for _ in range(0, 3): + self.add_larq_residual_block(larq_biRealNet, 256) + self.add_larq_connection_block(larq_biRealNet, 512) + + for _ in range(0, 3): + self.add_larq_residual_block(larq_biRealNet, 512) + larq_biRealNet.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) + larq_biRealNet.add(tf.keras.layers.Flatten()) + larq_biRealNet.add(tf.keras.layers.Dense(1000)) + larq_biRealNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + larq_biRealNet.load_weights(path_birealnet) + return larq_biRealNet + + +if __name__ == "__main__": + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + + network = birealnet() + qkeras_network, larq_network = network.build() + # Compare mean MSE and Absolute error of the the networks + compare_network(qkeras_network=qkeras_network, larq_network=larq_network, + dataset=random_data, network_name=birealnet_name) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, + network_name=birealnet_name) diff --git a/zoo/larq_models/alexNet.json b/zoo/larq_models/alexNet.json new file mode 100644 index 00000000..0e56f5d2 --- /dev/null +++ b/zoo/larq_models/alexNet.json @@ -0,0 +1 @@ +"{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_1\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_2\"}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [11, 11], \"strides\": [4, 4], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": null, \"kernel_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [3, 3], \"padding\": \"valid\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_8\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 0.001, \"center\": true, \"scale\": false, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 192, \"kernel_size\": [5, 5], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign_2\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}, \"kernel_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign_1\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_4\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [3, 3], \"padding\": \"valid\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_9\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 0.001, \"center\": true, \"scale\": false, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_2\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 384, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign_4\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}, \"kernel_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign_3\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_10\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 0.001, \"center\": true, \"scale\": false, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, 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7], \"padding\": \"valid\", \"strides\": [7, 7], \"data_format\": \"channels_last\"}}, {\"class_name\": \"Flatten\", \"config\": {\"name\": \"flatten_4\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"QDense\", \"config\": {\"name\": \"q_dense_1\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 1000, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null, \"kernel_quantizer\": null, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_29\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"softmax\"}}]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file diff --git a/zoo/quicknet.py b/zoo/quicknet.py new file mode 100644 index 00000000..ef70cee6 --- /dev/null +++ b/zoo/quicknet.py @@ -0,0 +1,343 @@ +############################################################################### +# .. attention:: +# +# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. +# All rights reserved. +# +# This software component is licensed by Apache License Version 2.0 +# http://www.apache.org/licenses/ +# Same as QKeras +# You may not use this file except in compliance with# the License. +# +############################################################################### + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +# Download pretrained weight from: +# QuickNet -> https://github.com/larq/zoo/releases/download/quicknet-v1.0/quicknet_weights.h5 +# QuickNetSmall -> https://github.com/larq/zoo/releases/download/quicknet-v1.0/quicknet_small_weights.h5 +# QuickNetLarge -> https://github.com/larq/zoo/releases/download/quicknet-v1.0/quicknet_large_weights.h5 + +import qkeras as q +import tensorflow as tf +import larq as lq +from utils import compare_network, create_random_dataset, dump_network_to_json + + +# Define path to the pre-trained weights +path_quicknet = "./weigths/quicknet_weights.h5" +path_quicknet_small = "./weigths/quicknet_small_weights.h5" +path_quicknet_large = "./weigths/quicknet_large_weights.h5" +quicknet_large_name = ["quickNet_large"] +quicknet_small_name = ["quickNet_small"] +quicknet_name = ["quickNet"] + + +class quicknet(): + """ + Class to create and load weights of: quicknet, quicknet small and quicknet + large networks. Select the size of the network from size param. If None size + is provided creates the quicknet version. + :param size: size of the network + """ + + def __init__(self, size=None): + if str(size).lower() == "large": + self.__id = 0 + self.__filters = ((64, 128, 256, 512)) + self.__weights_path = path_quicknet_large + self.network_name = quicknet_large_name + elif str(size).lower() == "small": + self.__id = 1 + self.__filters = ((32, 64, 256, 512)) + self.__weights_path = path_quicknet_small + self.network_name = quicknet_small_name + elif str(size) == "": + self.__id = 2 + self.__filters = ((64, 128, 256, 512)) + self.__weights_path = path_quicknet + self.network_name = quicknet_name + else: + raise NameError("name:", str, "not recognized") + + @staticmethod + def add_qkeras_residual(model, filters_num): + """ + Add a sequence of: Activation quantization, Quantized Conv2D, + BatchNormalization to the given model + :param model: model where to add the sequence + :param filters_num: number of filters for QConv2D + :return: model plus the sequence + """ + model.add(q.QActivation("binary")) + model.add(q.QConv2D(filters_num, (3, 3), activation="relu", + kernel_quantizer="binary(alpha=1)", + kernel_initializer="glorot_normal", + padding="same", use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + return model + + @staticmethod + def add_qkeras_transistion(model, strides, filters_num): + """ + Add a sequence of: Activation quantization, Quantized Conv2D, + BatchNormalization to the given model + :param model: model where to add the sequence + :param strides: strides param for MaxPool2d and QConv2D + :param filters_num: number of filters for QConv2D + :return: model plus the sequence + """ + model.add(tf.keras.layers.Activation("relu")) + model.add(tf.keras.layers.MaxPool2D(pool_size=strides, strides=1)) + model.add(tf.keras.layers.DepthwiseConv2D((3, 3), padding="same", + strides=strides, trainable=False, + use_bias=False)) + model.add(q.QConv2D(filters_num, (1, 1), kernel_initializer="glorot_normal", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + return model + + @staticmethod + def add_larq_residual(model, filters_num): + """ + Same method of add_qkeras_residual but for a larq network + """ + model.add(lq.layers.QuantConv2D(filters_num, (3, 3), activation="relu", + input_quantizer="ste_sign", + kernel_quantizer= + lq.quantizers.SteSign(clip_value=1.25), + kernel_constraint= + lq.constraints.WeightClip(clip_value=1.25), + kernel_initializer="glorot_normal", + padding="same", use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + return model + + @staticmethod + def add_larq_transistion(model, strides, filters_num): + """ + Same method of add_qkeras_transistion but for a larq network + """ + model.add(tf.keras.layers.Activation("relu")) + model.add(tf.keras.layers.MaxPool2D(pool_size=strides, strides=1)) + model.add(tf.keras.layers.DepthwiseConv2D((3, 3), padding="same", + strides=strides, + trainable=False, + use_bias=False)) + model.add(lq.layers.QuantConv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + return model + + def add_qkeras_first_block(self, model): + """ + Add a sequence of: Input, QConv2D, BatchNormalization, Activation, + QdepthWiseConv2D, BatchNormalization, QConv2d, BatchNormalization + :param model: model where to add the sequence + :return: model plus the sequence + """ + model.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + model.add(q.QConv2D(self.__filters[0] // 4, (3, 3), + kernel_initializer="he_normal", + padding="same", + strides=2, use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + model.add(tf.keras.layers.Activation("relu")) + model.add(q.QDepthwiseConv2D((3, 3), padding="same", strides=2, + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization(scale=False, + center=False)) + model.add(q.QConv2D(self.__filters[0], 1, + kernel_initializer="he_normal", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + return model + + def add_qkeras_last_block(self, model): + """ + Add a sequence of: Activation, AveragePooling2D, Flatten, Dense + :param model: model where to add the sequence + :return: model plus the sequence + """ + model.add(tf.keras.layers.Activation("relu")) + model.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) + model.add(tf.keras.layers.Flatten()) + model.add(q.QDense(1000, kernel_initializer="glorot_normal")) + model.add(tf.keras.layers.Activation("softmax", dtype="float32")) + model.load_weights(self.__weights_path) + return model + + def add_larq_first_block(self, model): + """ + Same method of add_qkeras_first_block but for a larq network + """ + model.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + model.add(lq.layers.QuantConv2D(self.__filters[0] // 4, (3, 3), + kernel_initializer="he_normal", + padding="same", strides=2, + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + model.add(tf.keras.layers.Activation("relu")) + model.add(lq.layers.QuantDepthwiseConv2D((3, 3), padding="same", + strides=2, use_bias=False)) + model.add(tf.keras.layers.BatchNormalization(scale=False, + center=False)) + model.add(lq.layers.QuantConv2D(self.__filters[0], 1, + kernel_initializer="he_normal", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + + def add_larq_last_block(self, model): + """ + Same method of add_larq_first_block but for a larq network + """ + model.add(tf.keras.layers.Activation("relu")) + model.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) + model.add(tf.keras.layers.Flatten()) + model.add(lq.layers.QuantDense(1000, kernel_initializer="glorot_normal")) + model.add(tf.keras.layers.Activation("softmax", dtype="float32")) + model.load_weights(self.__weights_path) + + def build(self): + """ + Build the model based on its ID + :return: qkeras and larq models + """ + if self.__id == 0: + qkeras_network = self.build_larq_quicknet_large() + print("\nQKeras network successfully created") + larq_network = self.build_larq_quicknet_large() + print("Larq network successfully created") + return qkeras_network, larq_network + + else: + qkeras_network = self.build_qkeras_quicknet() + print("\nQKeras network successfully created") + larq_network = self.build_larq_quicknet() + print("Larq network successfully created") + return qkeras_network, larq_network + + def build_qkeras_quicknet_large(self): + """ + Build the qkeras version of the quicknet large + :return: qkeras model of the quicknet large + """ + # Input layer + qkeras_quicknet = tf.keras.models.Sequential() + self.add_qkeras_first_block(qkeras_quicknet) + for _ in range(0, 6): + self.add_qkeras_residual(qkeras_quicknet, filters_num=self.__filters[0]) + self.add_qkeras_transistion(qkeras_quicknet, strides=2, + filters_num=self.__filters[1]) + + for _ in range(0, 8): + self.add_qkeras_residual(qkeras_quicknet, filters_num=self.__filters[1]) + self.add_qkeras_transistion(qkeras_quicknet, strides=2, + filters_num=self.__filters[2]) + + for _ in range(0, 12): + self.add_qkeras_residual(qkeras_quicknet, filters_num=self.__filters[2]) + self.add_qkeras_transistion(qkeras_quicknet, strides=2, + filters_num=self.__filters[3]) + + for _ in range(0, 6): + self.add_qkeras_residual(qkeras_quicknet, filters_num=self.__filters[3]) + self.add_qkeras_last_block(qkeras_quicknet) + return qkeras_quicknet + + def build_larq_quicknet_large(self): + """ + Build the larq version of the quicknet large + :return: larq model of the quicknet large + """ + # Input layer + larq_quicknet = tf.keras.models.Sequential() + self.add_larq_first_block(larq_quicknet) + for _ in range(0, 6): + self.add_qkeras_residual(larq_quicknet, filters_num=self.__filters[0]) + self.add_qkeras_transistion(larq_quicknet, strides=2, + filters_num=self.__filters[1]) + + for _ in range(0, 8): + self.add_qkeras_residual(larq_quicknet, filters_num=self.__filters[1]) + self.add_qkeras_transistion(larq_quicknet, strides=2, + filters_num=self.__filters[2]) + + for _ in range(0, 12): + self.add_qkeras_residual(larq_quicknet, filters_num=self.__filters[2]) + self.add_qkeras_transistion(larq_quicknet, strides=2, + filters_num=self.__filters[3]) + + for _ in range(0, 6): + self.add_qkeras_residual(larq_quicknet, filters_num=self.__filters[3]) + self.add_larq_last_block(larq_quicknet) + return larq_quicknet + + def build_qkeras_quicknet(self): + """ + Build the qkeras version of the quicknet + :return: qkeras model of the quicknet + """ + # Input layer + qkeras_quicknet = tf.keras.models.Sequential() + self.add_qkeras_first_block(qkeras_quicknet) + for filters_index in range(0, 3): + # Residual block + for _ in range(0, 4): + filters_num = self.__filters[filters_index] + self.add_qkeras_residual(qkeras_quicknet, filters_num=filters_num) + # Transition block + filters_num = self.__filters[filters_index + 1] + self.add_qkeras_transistion(qkeras_quicknet, strides=2, + filters_num=filters_num) + # Residual block + for _ in range(0, 4): + filters_num = self.__filters[3] + self.add_qkeras_residual(qkeras_quicknet, filters_num=filters_num) + self.add_qkeras_last_block(qkeras_quicknet) + return qkeras_quicknet + + def build_larq_quicknet(self): + """ + Build the larq version of the quicknet + :return: larq model of the quicknet + """ + # Input layer + larq_quicknet = tf.keras.models.Sequential() + self.add_larq_first_block(larq_quicknet) + for filters_index in range(0, 3): + # Residual block + for _ in range(0, 4): + filters_num = self.__filters[filters_index] + self.add_larq_residual(larq_quicknet, filters_num=filters_num) + # Transition block + filters_num = self.__filters[filters_index + 1] + self.add_larq_transistion(larq_quicknet, strides=2, + filters_num=filters_num) + # Residual block + for _ in range(0, 4): + filters_num = self.__filters[3] + self.add_larq_residual(larq_quicknet, filters_num=filters_num) + self.add_larq_last_block(larq_quicknet) + return larq_quicknet + + +if __name__ == "__main__": + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + + network_names = ["quickNet", "quickNet_large", "quickNet_small"] + sizes = ["", "large", "small"] + + for size, name in zip(sizes, network_names): + network = quicknet(size) + qkeras_network, larq_network = network.build() + # Compare mean MSE and Absolute error of the the networks + compare_network(qkeras_network=qkeras_network, larq_network=larq_network, + dataset=random_data, network_name=name) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, network_name=name+"_"+size) \ No newline at end of file diff --git a/zoo/readme.md b/zoo/readme.md new file mode 100644 index 00000000..8cdebbb3 --- /dev/null +++ b/zoo/readme.md @@ -0,0 +1,21 @@ +# QKeras neural network zoo +#### Collection of pre-trained neural networks +This folder contains a collection of networks written using two different frameworks: qkeras and larq. +Each network can be built and tested using a randomly generated dataset, the output will consists of two measurements: +- Mean MSE, calculate the mean MSE between all the output for both networks +- Absolute errors, calculates how many times the class predicted by one network does + not coincide with the class predicted by the other network + +The folder is divided in: +- network_name.py is the class to build and test the networks. +- utils.pyt contains share methods betweenh all the classes. +- results.txt are the results of the comparison between the networks. +- ./qkeras_models contains the .json qkeras saved models. +- ./larq_models contains the .json larq saved models. +- ./weights please put the downloaded weights here, link to download weights is provided in the network class. + +Link to the folder with all weights: https://drive.google.com/drive/folders/1pGZ6dGWvJyc9aH-TOQohm0PhORihQZ5I?usp=sharing + +An example can by run with: +```python + python3 quicknet.py \ No newline at end of file diff --git a/zoo/results.txt b/zoo/results.txt new file mode 100644 index 00000000..3e631cd6 --- /dev/null +++ b/zoo/results.txt @@ -0,0 +1,21 @@ +Results on 100 random samples: + +Alexnet: + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 + +BiRealNet: + Mean MSE for biRealNet -> 8.4-05 + Absolute errors for biRealNet -> 6 + +Quicknet: + Mean MSE for quickNet -> 5.6e-18 + Absolute errors for quickNet -> 0 + +Quicknet Large: + Mean MSE for quickNet_large -> 6.9e-18 + Absolute errors for quickNet_large -> 0 + +Quicknet Small: + Mean MSE for quickNet_small -> 0.0 + Absolute errors for quickNet_small -> 0 \ No newline at end of file diff --git a/zoo/utils.py b/zoo/utils.py new file mode 100644 index 00000000..395911d1 --- /dev/null +++ b/zoo/utils.py @@ -0,0 +1,132 @@ +############################################################################### +# .. attention:: +# +# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. +# All rights reserved. +# +# This software component is licensed by Apache License Version 2.0 +# http://www.apache.org/licenses/ +# Same as QKeras +# You may not use this file except in compliance with# the License. +# +############################################################################### + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +import numpy as np +from tqdm import tqdm +import os +import json +#import cv2 + +path_to_larq = "./larq_models" +path_to_qkeras = "./qkeras_models" + + +def calculate_MSE(res_qkeras, res_larq): + """ + Calculate the MSE between the two lists + :param res_qkeras: list with the prediction given by the qkeras network + :param res_larq: list with the prediction given by the larq network + :return: the MSE + """ + qres = np.squeeze(np.asarray(res_qkeras)) + lres = np.squeeze(np.asarray(res_larq)) + mse = (np.square(qres - lres)).mean() + return mse + + +def calculate_absolute_error(res_qkeras, res_larq): + """ + calculates how many times the class predicted by the qkeras network does + not coincide with the class predicted by + the larq network + :param res_qkeras: list with the prediction given by the qkeras network + :param res_larq: list with the prediction given by the larq network + :return: number of misclassifications + """ + pred = np.argmax(np.asarray(res_qkeras), axis=2) + real = np.argmax(np.asarray(res_larq), axis=2) + return np.count_nonzero(pred - real) + + +def create_random_dataset(sample_num=100): + """ + Generate a random dataset to simulate imageNet + :param sample_num: number of wanted samples, default is 100 samples + :return: numpy array that represents the dataset + """ + return np.random.randint(low=0, high=254, size=(sample_num, 1, 224, 224, 3)) + + +def compare_network(qkeras_network, larq_network, dataset, network_name): + """ + Given a shape dataset that conforms to the input of the networks. + Compare the two input networks, based on the + average MSE of their predictions and the number of times the two networks + predict a different class. + Prints the mean MSE and the Absolute error + :param qkeras_network: qkeras network + :param larq_network: larq network + :param dataset: dataset + :param network_name: network name + """ + # list where predictions are stored + res_qkeras = [] + res_larq = [] + + for data in tqdm(dataset): + res_qkeras.append(qkeras_network.predict(data)) + res_larq.append(larq_network.predict(data)) + mse = calculate_MSE(res_qkeras, res_larq) + print("Mean MSE for", network_name, "->", np.asarray(mse).mean()) + print("Absolute errors for", network_name, "->", + calculate_absolute_error(res_qkeras, res_larq)) + + +def dump_network_to_json(qkeras_network, larq_network, network_name): + """ + Dumps the given network to .json in the correct directory + :param qkeras_network: qkeras network + :param larq_network: larq network + :param network_name: name of the network + """ + network = qkeras_network.to_json() + with open(os.path.join(path_to_qkeras, network_name + ".json"), "w") \ + as outfile: + json.dump(network, outfile) + print("Network:", network_name, "successfully saved into:", + os.path.join(path_to_qkeras, network_name)) + + network = larq_network.to_json() + with open(os.path.join(path_to_larq, network_name + ".json"), "w") \ + as outfile: + json.dump(network, outfile) + print("Network:", network_name, "successfully saved into:", + os.path.join(path_to_larq, str(network_name))) + + +def loadImageNetData(path, image_num=1000): + """ + Load and preprocess images from the imageNet dataset, please specify the + directory where the dataset is + and the desired number of samples + :param path: path to the dataset + :param image_num: number of wanted images + :return: numpy array that contains the images with the correct shape for + the networks + """ + file_list = os.listdir(path) + file_list.sort() + file_list = file_list[0:image_num] + im = [] + for i in tqdm(file_list): + img = cv2.imread(os.path.join(path, i)) + img = cv2.resize(img, (224, 224)) + im.append(img) + del img + im = np.expand_dims(np.asarray(im), axis=1) + return im \ No newline at end of file From 12e59af7af95c636f0f4b345231add23f0925f7d Mon Sep 17 00:00:00 2001 From: Francesco Date: Tue, 23 Mar 2021 14:36:06 +0100 Subject: [PATCH 2/6] modified readme.md --- zoo/readme.md | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/zoo/readme.md b/zoo/readme.md index 8cdebbb3..247f5e4f 100644 --- a/zoo/readme.md +++ b/zoo/readme.md @@ -1,14 +1,32 @@ +############################################################################### +# .. attention:: +# +# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. +# All rights reserved. +# +# This software component is licensed by Apache License Version 2.0 +# http://www.apache.org/licenses/ +# Same as QKeras +# You may not use this file except in compliance with# the License. +# +############################################################################### + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + # QKeras neural network zoo #### Collection of pre-trained neural networks This folder contains a collection of networks written using two different frameworks: qkeras and larq. -Each network can be built and tested using a randomly generated dataset, the output will consists of two measurements: +Each network can be built and tested using a randomly generated dataset, the output will consist of two measurements: - Mean MSE, calculate the mean MSE between all the output for both networks - Absolute errors, calculates how many times the class predicted by one network does not coincide with the class predicted by the other network The folder is divided in: - network_name.py is the class to build and test the networks. -- utils.pyt contains share methods betweenh all the classes. +- utils.py contains share methods between all the classes. - results.txt are the results of the comparison between the networks. - ./qkeras_models contains the .json qkeras saved models. - ./larq_models contains the .json larq saved models. From 2da840aa48f27cf3bde131001863e2e599983886 Mon Sep 17 00:00:00 2001 From: Francesco Date: Thu, 25 Mar 2021 14:51:38 +0100 Subject: [PATCH 3/6] added binary resnet E18 --- zoo/binary_resnet_e18.py | 219 +++++++++++++++++++++++ zoo/larq_models/binary_resnet_e18.json | 1 + zoo/qkeras_models/binary_resnet_e18.json | 1 + zoo/results.txt | 4 + 4 files changed, 225 insertions(+) create mode 100644 zoo/binary_resnet_e18.py create mode 100644 zoo/larq_models/binary_resnet_e18.json create mode 100644 zoo/qkeras_models/binary_resnet_e18.json diff --git a/zoo/binary_resnet_e18.py b/zoo/binary_resnet_e18.py new file mode 100644 index 00000000..9014efbc --- /dev/null +++ b/zoo/binary_resnet_e18.py @@ -0,0 +1,219 @@ +############################################################################### +# .. attention:: +# +# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. +# All rights reserved. +# +# This software component is licensed by Apache License Version 2.0 +# http://www.apache.org/licenses/ +# Same as QKeras +# You may not use this file except in compliance with# the License. +# +############################################################################### + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +# Download pretrained weight from: +# Alexnet -> https://drive.google.com/file/d/1-eRhwVTzIKm3D0WoOls3eyeXmJE7Q6Cn/view?usp=sharing + +import qkeras as q +import tensorflow as tf +import larq as lq +from utils import compare_network, create_random_dataset, dump_network_to_json + +# Define path to the pre-trained weights +path_resnet_e18 = "./weigths/resnet_e_18_weights.h5" +resnet_e18_name = "binary_resnet_e18" + + +class resnet_e18(): + """ + Class to create and load weights of: resnet_e18 + """ + + def __init__(self): + self.__weights_path = path_resnet_e18 + self.network_name = resnet_e18_name + self.filters = (64,128,256,512) + + @staticmethod + def add_qkeras_quant_block(model, filters_num, strides=1): + model.add(q.QActivation("binary(alpha=1)")) + model.add( + q.QConv2D(filters_num, kernel_size=3, strides=strides, + padding="same", + kernel_quantizer="binary(alpha=1)", + kernel_initializer="glorot_normal", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + + @staticmethod + def add_qkeras_connection_block(model, filters_num): + """ + Same method of add_qkeras_connection_block but for a larq network + """ + model.add(q.QActivation("binary(alpha=1)")) + model.add( + q.QConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, + padding="same", + kernel_quantizer="binary(alpha=1)", + kernel_constraint="weight_clip")) + shape_in = model.output_shape[1] * model.output_shape[2] * \ + model.output_shape[3] + shape_out = (model.output_shape[1], model.output_shape[2], + model.output_shape[3] // 2) + # Prepare shapes for reshape layers + model.add(tf.keras.layers.Flatten()) + model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) + model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) + model.add(tf.keras.layers.Reshape(target_shape=shape_out)) + model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + model.add(tf.keras.layers.BatchNormalization()) + + @staticmethod + def add_larq_quant_block(model, filters_num, strides=1): + model.add( + lq.layers.QuantConv2D(filters_num, kernel_size=3, strides=strides, + padding="same", + input_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_constraint=lq.constraints.WeightClip( + clip_value=1.25), + kernel_initializer="glorot_normal", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + + @staticmethod + def add_larq_connection_block(model, filters_num): + """ + Same method of add_qkeras_connection_block but for a larq network + """ + model.add( + lq.layers.QuantConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, + padding="same", + input_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_constraint=lq.constraints.WeightClip( + clip_value=1.25),)) + + shape_in = model.output_shape[1] * model.output_shape[2] * \ + model.output_shape[3] + shape_out = (model.output_shape[1], model.output_shape[2], + model.output_shape[3] // 2) + # Prepare shapes for reshape layers + model.add(tf.keras.layers.Flatten()) + model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) + model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) + model.add(tf.keras.layers.Reshape(target_shape=shape_out)) + model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", + use_bias=False)) + model.add(tf.keras.layers.BatchNormalization()) + model.add(tf.keras.layers.BatchNormalization()) + + def build(self): + """ + Build the model + :return: qkeras and larq models + """ + qkeras_network = self.build_qkeras_resnet_e18() + print("\nQKeras network successfully created") + larq_network = self.build_larq_resnet_e18() + print("Larq network successfully created") + return qkeras_network, larq_network + + def build_qkeras_resnet_e18(self): + """ + Build the qkeras version of the resnet_e18 + :return: qkeras model of the resnet_e18 + """ + qkeras_resnet = tf.keras.models.Sequential() + qkeras_resnet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + qkeras_resnet.add( + tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding="same", + kernel_initializer="he_normal", use_bias=False)) + qkeras_resnet.add( + tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + qkeras_resnet.add(tf.keras.layers.Activation("relu")) + qkeras_resnet.add(tf.keras.layers.MaxPool2D(3, strides=2, padding="same")) + qkeras_resnet.add( + tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + + for _ in range(0, 4): + self.add_qkeras_quant_block(qkeras_resnet, 64) + self.add_qkeras_connection_block(qkeras_resnet, 128) + + for i in range(1, 3): + for _ in range(0, 3): + self.add_qkeras_quant_block(qkeras_resnet, filters_num=self.filters[i]) + self.add_qkeras_connection_block(qkeras_resnet, + filters_num=self.filters[i + 1]) + for _ in range(0, 3): + self.add_qkeras_quant_block(qkeras_resnet, filters_num=self.filters[-1]) + qkeras_resnet.add(tf.keras.layers.Activation("relu")) + qkeras_resnet.add(tf.keras.layers.MaxPool2D(pool_size=7)) + qkeras_resnet.add(tf.keras.layers.Flatten()) + qkeras_resnet.add( + tf.keras.layers.Dense(1000, kernel_initializer="glorot_normal")) + qkeras_resnet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + qkeras_resnet.load_weights(self.__weights_path) + return qkeras_resnet + + + def build_larq_resnet_e18(self): + """ + Build the larq version of the resnet_e18 + :return: larq model of the resnet_e18 + """ + larq_resnet = tf.keras.models.Sequential() + larq_resnet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + larq_resnet.add( + tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding="same", + kernel_initializer="he_normal", use_bias=False)) + larq_resnet.add( + tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + larq_resnet.add(tf.keras.layers.Activation("relu")) + larq_resnet.add(tf.keras.layers.MaxPool2D(3, strides=2, padding="same")) + larq_resnet.add( + tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + + for _ in range(0, 4): + self.add_larq_quant_block(larq_resnet, 64) + self.add_larq_connection_block(larq_resnet, 128) + + for i in range(1, 3): + for _ in range(0, 3): + self.add_larq_quant_block(larq_resnet, filters_num=self.filters[i]) + self.add_larq_connection_block(larq_resnet, filters_num=self.filters[i+1]) + for _ in range(0, 3): + self.add_larq_quant_block(larq_resnet, filters_num=self.filters[-1]) + larq_resnet.add(tf.keras.layers.Activation("relu")) + larq_resnet.add(tf.keras.layers.MaxPool2D(pool_size=7)) + larq_resnet.add(tf.keras.layers.Flatten()) + larq_resnet.add( + tf.keras.layers.Dense(1000, kernel_initializer="glorot_normal")) + larq_resnet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + larq_resnet.load_weights(self.__weights_path) + return larq_resnet + + +if __name__ == "__main__": + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + + network = resnet_e18() + qkeras_network, larq_network = network.build() + # Compare mean MSE and Absolute error of the the networks + compare_network(qkeras_network=qkeras_network, larq_network=larq_network, + dataset=random_data, network_name=resnet_e18_name) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, + network_name=resnet_e18_name) diff --git a/zoo/larq_models/binary_resnet_e18.json b/zoo/larq_models/binary_resnet_e18.json new file mode 100644 index 00000000..b87aa28b --- /dev/null +++ b/zoo/larq_models/binary_resnet_e18.json @@ -0,0 +1 @@ +"{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_1\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_2\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d_4\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [7, 7], \"strides\": [2, 2], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"HeNormal\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, 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\"AveragePooling1D\", \"config\": {\"name\": \"average_pooling1d_2\", \"trainable\": true, \"dtype\": \"float32\", \"strides\": [2], \"pool_size\": [1], \"padding\": \"same\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Reshape\", \"config\": {\"name\": \"reshape_5\", \"trainable\": true, \"dtype\": \"float32\", \"target_shape\": [7, 7, 256]}}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d_3\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_16\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_17\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation_13\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d_13\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": {\"min_value\": -1, \"max_value\": 1}}, \"bias_constraint\": null, \"kernel_quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}}, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_18\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation_14\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d_14\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": {\"min_value\": -1, \"max_value\": 1}}, \"bias_constraint\": null, \"kernel_quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}}, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_19\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation_15\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d_15\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": {\"min_value\": -1, \"max_value\": 1}}, \"bias_constraint\": null, \"kernel_quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}}, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_20\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_1\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [7, 7], \"padding\": \"valid\", \"strides\": [7, 7], \"data_format\": \"channels_last\"}}, {\"class_name\": \"Flatten\", \"config\": {\"name\": \"flatten_3\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 1000, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_2\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"softmax\"}}]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file diff --git a/zoo/results.txt b/zoo/results.txt index 3e631cd6..b97945f5 100644 --- a/zoo/results.txt +++ b/zoo/results.txt @@ -8,6 +8,10 @@ BiRealNet: Mean MSE for biRealNet -> 8.4-05 Absolute errors for biRealNet -> 6 +Binary ResNEt e18: + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 + Quicknet: Mean MSE for quickNet -> 5.6e-18 Absolute errors for quickNet -> 0 From 6fcd4886eff1574b078c5b0fe8eaed6c9cfea66e Mon Sep 17 00:00:00 2001 From: Francesco Date: Fri, 26 Mar 2021 15:34:50 +0100 Subject: [PATCH 4/6] reformat code following: https://google.github.io/styleguide/pyguide.html\#3162-naming-conventions and https://google.github.io/styleguide/pyguide.html\#384-classes --- zoo/alexnet.py | 20 +++++++++++--------- zoo/binary_resnet_e18.py | 36 ++++++++++++++++++++++++++---------- zoo/birealnet.py | 24 +++++++++++++----------- zoo/quicknet.py | 37 +++++++++++++++++++------------------ zoo/utils.py | 14 +++++++------- 5 files changed, 76 insertions(+), 55 deletions(-) diff --git a/zoo/alexnet.py b/zoo/alexnet.py index d643ce4b..1512cd65 100644 --- a/zoo/alexnet.py +++ b/zoo/alexnet.py @@ -17,7 +17,7 @@ __email__ = "danilo.pau@st.com" # Download pretrained weight from: -# Alexnet -> https://github.com/larq/zoo/releases/download/binary_alexnet-v0.2.0/binary_alexnet_weights.h5 +# Alexnet -> https://drive.google.com/file/d/1-65sB1xnJuOoPhL00TYY0s3Fov0zxBHJ/view?usp=sharing import qkeras as q import tensorflow as tf @@ -25,18 +25,20 @@ from utils import compare_network, create_random_dataset, dump_network_to_json # Define path to the pre-trained weights -path_alexnet = "./weigths/binary_alexnet_weights.h5" -alexnet_name = "alexNet" +PATH_ALEXNET = "weights/binary_alexnet_weights.h5" +ALEXNET_NAME = "alexNet" -class alexnet(): +class AlexNet: """ Class to create and load weights of: alexnet + Attributes: + network_name: Name of the network """ def __init__(self): - self.__weights_path = path_alexnet - self.network_name = alexnet_name + self.__weights_path = PATH_ALEXNET + self.network_name = ALEXNET_NAME @staticmethod def add_qkeras_conv_block(model, filters_num, kernel_size, pool, @@ -172,11 +174,11 @@ def build_larq_alexnet(self): # Create a random dataset with 100 samples random_data = create_random_dataset(100) - network = alexnet() + network = AlexNet() qkeras_network, larq_network = network.build() # Compare mean MSE and Absolute error of the the networks compare_network(qkeras_network=qkeras_network, larq_network=larq_network, - dataset=random_data, network_name=alexnet_name) + dataset=random_data, network_name=ALEXNET_NAME) dump_network_to_json(qkeras_network=qkeras_network, larq_network=larq_network, - network_name=alexnet_name) + network_name=ALEXNET_NAME) diff --git a/zoo/binary_resnet_e18.py b/zoo/binary_resnet_e18.py index 9014efbc..cfbd97b6 100644 --- a/zoo/binary_resnet_e18.py +++ b/zoo/binary_resnet_e18.py @@ -17,7 +17,7 @@ __email__ = "danilo.pau@st.com" # Download pretrained weight from: -# Alexnet -> https://drive.google.com/file/d/1-eRhwVTzIKm3D0WoOls3eyeXmJE7Q6Cn/view?usp=sharing +# resnet_e18 -> https://drive.google.com/file/d/1-eRhwVTzIKm3D0WoOls3eyeXmJE7Q6Cn/view?usp=sharing import qkeras as q import tensorflow as tf @@ -25,22 +25,32 @@ from utils import compare_network, create_random_dataset, dump_network_to_json # Define path to the pre-trained weights -path_resnet_e18 = "./weigths/resnet_e_18_weights.h5" -resnet_e18_name = "binary_resnet_e18" +PATH_RESNET_E18 = "weights/resnet_e_18_weights.h5" +RESNET_E18_NAME = "binary_resnet_e18" -class resnet_e18(): +class ResNetE18: """ Class to create and load weights of: resnet_e18 + Attributes: + network_name: Name of the network + filters: Number of filters for Conv2D """ def __init__(self): - self.__weights_path = path_resnet_e18 - self.network_name = resnet_e18_name + self.__weights_path = PATH_RESNET_E18 + self.network_name = RESNET_E18_NAME self.filters = (64,128,256,512) @staticmethod def add_qkeras_quant_block(model, filters_num, strides=1): + """ + Add a sequence of: Activation quantization, Quantized Conv2D, + Batch Normalization + :param model: model where to add the sequence + :param filters_num: number of filters for Cov2D + :param strides: strides for Conv2D + """ model.add(q.QActivation("binary(alpha=1)")) model.add( q.QConv2D(filters_num, kernel_size=3, strides=strides, @@ -53,7 +63,10 @@ def add_qkeras_quant_block(model, filters_num, strides=1): @staticmethod def add_qkeras_connection_block(model, filters_num): """ - Same method of add_qkeras_connection_block but for a larq network + Add a sequence of: Activation quantization, Quantized Conv2D, reshape, + Average Pooling, Conv2D, 2x BatchNormalization + :param model: model where to add the sequence + :param filters_num: number of filters for Cov2D """ model.add(q.QActivation("binary(alpha=1)")) model.add( @@ -77,6 +90,9 @@ def add_qkeras_connection_block(model, filters_num): @staticmethod def add_larq_quant_block(model, filters_num, strides=1): + """ + Same method of add_qkeras_quant_block but for a larq network + """ model.add( lq.layers.QuantConv2D(filters_num, kernel_size=3, strides=strides, padding="same", @@ -209,11 +225,11 @@ def build_larq_resnet_e18(self): # Create a random dataset with 100 samples random_data = create_random_dataset(100) - network = resnet_e18() + network = ResNetE18() qkeras_network, larq_network = network.build() # Compare mean MSE and Absolute error of the the networks compare_network(qkeras_network=qkeras_network, larq_network=larq_network, - dataset=random_data, network_name=resnet_e18_name) + dataset=random_data, network_name=RESNET_E18_NAME) dump_network_to_json(qkeras_network=qkeras_network, larq_network=larq_network, - network_name=resnet_e18_name) + network_name=RESNET_E18_NAME) diff --git a/zoo/birealnet.py b/zoo/birealnet.py index b7091407..c34aa79a 100644 --- a/zoo/birealnet.py +++ b/zoo/birealnet.py @@ -17,7 +17,7 @@ __email__ = "danilo.pau@st.com" # Download pretrained weight from: -# Birealnet -> https://github.com/larq/zoo/releases/download/birealnet-v0.3.0/birealnet_weights.h5 +# Birealnet -> https://drive.google.com/file/d/1BuDYhydNAy-sFdvoh24gJbpA0zRDvGVq/view?usp=sharing import qkeras as q import tensorflow as tf @@ -25,18 +25,20 @@ from utils import compare_network, create_random_dataset, dump_network_to_json # Define path to the pre-trained weights -path_birealnet = "./weigths/birealnet_weights.h5" -birealnet_name = "biRealNet" +PATH_BIREALNET = "weights/birealnet_weights.h5" +BIREALNET_NAME = "biRealNet" -class birealnet(): +class BirealNet: """ Class to create and load weights of: biRealNet + Attributes: + network_name: Name of the network """ def __init__(self): - self.__weights_path = path_birealnet - self.network_name = birealnet_name + self.__weights_path = PATH_BIREALNET + self.network_name = BIREALNET_NAME @staticmethod def add_qkeras_residual_block(model, filters_num, strides=1): @@ -157,7 +159,7 @@ def build_qkeras_birealnet(self): qkeras_biRealNet.add(tf.keras.layers.Flatten()) qkeras_biRealNet.add(tf.keras.layers.Dense(1000)) qkeras_biRealNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) - qkeras_biRealNet.load_weights(path_birealnet) + qkeras_biRealNet.load_weights(PATH_BIREALNET) return qkeras_biRealNet def build_larq_birealnet(self): @@ -191,7 +193,7 @@ def build_larq_birealnet(self): larq_biRealNet.add(tf.keras.layers.Flatten()) larq_biRealNet.add(tf.keras.layers.Dense(1000)) larq_biRealNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) - larq_biRealNet.load_weights(path_birealnet) + larq_biRealNet.load_weights(PATH_BIREALNET) return larq_biRealNet @@ -199,11 +201,11 @@ def build_larq_birealnet(self): # Create a random dataset with 100 samples random_data = create_random_dataset(100) - network = birealnet() + network = BirealNet() qkeras_network, larq_network = network.build() # Compare mean MSE and Absolute error of the the networks compare_network(qkeras_network=qkeras_network, larq_network=larq_network, - dataset=random_data, network_name=birealnet_name) + dataset=random_data, network_name=BIREALNET_NAME) dump_network_to_json(qkeras_network=qkeras_network, larq_network=larq_network, - network_name=birealnet_name) + network_name=BIREALNET_NAME) diff --git a/zoo/quicknet.py b/zoo/quicknet.py index ef70cee6..7e45edd0 100644 --- a/zoo/quicknet.py +++ b/zoo/quicknet.py @@ -17,9 +17,9 @@ __email__ = "danilo.pau@st.com" # Download pretrained weight from: -# QuickNet -> https://github.com/larq/zoo/releases/download/quicknet-v1.0/quicknet_weights.h5 -# QuickNetSmall -> https://github.com/larq/zoo/releases/download/quicknet-v1.0/quicknet_small_weights.h5 -# QuickNetLarge -> https://github.com/larq/zoo/releases/download/quicknet-v1.0/quicknet_large_weights.h5 +# QuickNet -> https://drive.google.com/file/d/1-JieqQOWmQ4sA8_A4akfS84O8xFT9_x8/view?usp=sharing +# QuickNetSmall -> https://drive.google.com/file/d/1-N7GTBYI1dkibbxG-lKtvnRtEpveFGj_/view?usp=sharing +# QuickNetLarge -> https://drive.google.com/file/d/1-Nm-kAYagGche_31eKDuvH2l9i9ANygN/view?usp=sharing import qkeras as q import tensorflow as tf @@ -28,38 +28,39 @@ # Define path to the pre-trained weights -path_quicknet = "./weigths/quicknet_weights.h5" -path_quicknet_small = "./weigths/quicknet_small_weights.h5" -path_quicknet_large = "./weigths/quicknet_large_weights.h5" -quicknet_large_name = ["quickNet_large"] -quicknet_small_name = ["quickNet_small"] -quicknet_name = ["quickNet"] +PATH_QUICKNET = "./weights/quicknet_weights.h5" +PATH_QUICKNET_SMALL = "weights/quicknet_small_weights.h5" +PATH_QUICKNET_LARGE = "weights/quicknet_large_weights.h5" +QUICKNET_LARGE_NAME = "quickNet_large" +QUICKNET_SMALL_NAME = "quickNet_small" +QUICKNET_NAME = "quickNet" -class quicknet(): +class QuickNet: """ Class to create and load weights of: quicknet, quicknet small and quicknet large networks. Select the size of the network from size param. If None size is provided creates the quicknet version. - :param size: size of the network + Attributes: + network_name: Name of the network """ def __init__(self, size=None): if str(size).lower() == "large": self.__id = 0 self.__filters = ((64, 128, 256, 512)) - self.__weights_path = path_quicknet_large - self.network_name = quicknet_large_name + self.__weights_path = PATH_QUICKNET_LARGE + self.network_name = QUICKNET_LARGE_NAME elif str(size).lower() == "small": self.__id = 1 self.__filters = ((32, 64, 256, 512)) - self.__weights_path = path_quicknet_small - self.network_name = quicknet_small_name + self.__weights_path = PATH_QUICKNET_SMALL + self.network_name = QUICKNET_SMALL_NAME elif str(size) == "": self.__id = 2 self.__filters = ((64, 128, 256, 512)) - self.__weights_path = path_quicknet - self.network_name = quicknet_name + self.__weights_path = PATH_QUICKNET + self.network_name = QUICKNET_NAME else: raise NameError("name:", str, "not recognized") @@ -334,7 +335,7 @@ def build_larq_quicknet(self): sizes = ["", "large", "small"] for size, name in zip(sizes, network_names): - network = quicknet(size) + network = QuickNet(size) qkeras_network, larq_network = network.build() # Compare mean MSE and Absolute error of the the networks compare_network(qkeras_network=qkeras_network, larq_network=larq_network, diff --git a/zoo/utils.py b/zoo/utils.py index 395911d1..bfd3b128 100644 --- a/zoo/utils.py +++ b/zoo/utils.py @@ -20,10 +20,10 @@ from tqdm import tqdm import os import json -#import cv2 +import cv2 -path_to_larq = "./larq_models" -path_to_qkeras = "./qkeras_models" +PATH_TO_LARQ = "./larq_models" +PATH_TO_QKERAS = "./qkeras_models" def calculate_MSE(res_qkeras, res_larq): @@ -95,18 +95,18 @@ def dump_network_to_json(qkeras_network, larq_network, network_name): :param network_name: name of the network """ network = qkeras_network.to_json() - with open(os.path.join(path_to_qkeras, network_name + ".json"), "w") \ + with open(os.path.join(PATH_TO_QKERAS, network_name + ".json"), "w") \ as outfile: json.dump(network, outfile) print("Network:", network_name, "successfully saved into:", - os.path.join(path_to_qkeras, network_name)) + os.path.join(PATH_TO_QKERAS, network_name)) network = larq_network.to_json() - with open(os.path.join(path_to_larq, network_name + ".json"), "w") \ + with open(os.path.join(PATH_TO_LARQ, network_name + ".json"), "w") \ as outfile: json.dump(network, outfile) print("Network:", network_name, "successfully saved into:", - os.path.join(path_to_larq, str(network_name))) + os.path.join(PATH_TO_LARQ, str(network_name))) def loadImageNetData(path, image_num=1000): From 0de042412a3e01bfb40bdb64c2b640827a010b7d Mon Sep 17 00:00:00 2001 From: Francesco Date: Fri, 26 Mar 2021 16:04:39 +0100 Subject: [PATCH 5/6] Updated Apache License header --- zoo/alexnet.py | 19 ++++++++++--------- zoo/binary_resnet_e18.py | 19 ++++++++++--------- zoo/birealnet.py | 19 ++++++++++--------- zoo/quicknet.py | 19 ++++++++++--------- zoo/readme.md | 18 ------------------ zoo/utils.py | 21 +++++++++++---------- 6 files changed, 51 insertions(+), 64 deletions(-) diff --git a/zoo/alexnet.py b/zoo/alexnet.py index 1512cd65..f187ae17 100644 --- a/zoo/alexnet.py +++ b/zoo/alexnet.py @@ -1,15 +1,16 @@ -############################################################################### -# .. attention:: +# Copyright 2021 Loro Francesco # -# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. -# All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at # -# This software component is licensed by Apache License Version 2.0 -# http://www.apache.org/licenses/ -# Same as QKeras -# You may not use this file except in compliance with# the License. +# http://www.apache.org/licenses/LICENSE-2.0 # -############################################################################### +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. __author__ = "Francesco Loro" __email__ = "francesco.official@gmail.com" diff --git a/zoo/binary_resnet_e18.py b/zoo/binary_resnet_e18.py index cfbd97b6..c2ddd575 100644 --- a/zoo/binary_resnet_e18.py +++ b/zoo/binary_resnet_e18.py @@ -1,15 +1,16 @@ -############################################################################### -# .. attention:: +# Copyright 2021 Loro Francesco # -# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. -# All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at # -# This software component is licensed by Apache License Version 2.0 -# http://www.apache.org/licenses/ -# Same as QKeras -# You may not use this file except in compliance with# the License. +# http://www.apache.org/licenses/LICENSE-2.0 # -############################################################################### +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. __author__ = "Francesco Loro" __email__ = "francesco.official@gmail.com" diff --git a/zoo/birealnet.py b/zoo/birealnet.py index c34aa79a..1fb6147b 100644 --- a/zoo/birealnet.py +++ b/zoo/birealnet.py @@ -1,15 +1,16 @@ -############################################################################### -# .. attention:: +# Copyright 2021 Loro Francesco # -# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. -# All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at # -# This software component is licensed by Apache License Version 2.0 -# http://www.apache.org/licenses/ -# Same as QKeras -# You may not use this file except in compliance with# the License. +# http://www.apache.org/licenses/LICENSE-2.0 # -############################################################################### +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. __author__ = "Francesco Loro" __email__ = "francesco.official@gmail.com" diff --git a/zoo/quicknet.py b/zoo/quicknet.py index 7e45edd0..f6aa1f06 100644 --- a/zoo/quicknet.py +++ b/zoo/quicknet.py @@ -1,15 +1,16 @@ -############################################################################### -# .. attention:: +# Copyright 2021 Loro Francesco # -# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. -# All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at # -# This software component is licensed by Apache License Version 2.0 -# http://www.apache.org/licenses/ -# Same as QKeras -# You may not use this file except in compliance with# the License. +# http://www.apache.org/licenses/LICENSE-2.0 # -############################################################################### +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. __author__ = "Francesco Loro" __email__ = "francesco.official@gmail.com" diff --git a/zoo/readme.md b/zoo/readme.md index 247f5e4f..2c938568 100644 --- a/zoo/readme.md +++ b/zoo/readme.md @@ -1,21 +1,3 @@ -############################################################################### -# .. attention:: -# -# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. -# All rights reserved. -# -# This software component is licensed by Apache License Version 2.0 -# http://www.apache.org/licenses/ -# Same as QKeras -# You may not use this file except in compliance with# the License. -# -############################################################################### - -__author__ = "Francesco Loro" -__email__ = "francesco.official@gmail.com" -__supervisor__ = "Danilo Pau" -__email__ = "danilo.pau@st.com" - # QKeras neural network zoo #### Collection of pre-trained neural networks This folder contains a collection of networks written using two different frameworks: qkeras and larq. diff --git a/zoo/utils.py b/zoo/utils.py index bfd3b128..16577355 100644 --- a/zoo/utils.py +++ b/zoo/utils.py @@ -1,15 +1,16 @@ -############################################################################### -# .. attention:: +# Copyright 2021 Loro Francesco # -# Copyright(c) 2021 Francesco Loro, Master Degree Student Università degli studi di Padova. -# All rights reserved. +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at # -# This software component is licensed by Apache License Version 2.0 -# http://www.apache.org/licenses/ -# Same as QKeras -# You may not use this file except in compliance with# the License. +# http://www.apache.org/licenses/LICENSE-2.0 # -############################################################################### +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. __author__ = "Francesco Loro" __email__ = "francesco.official@gmail.com" @@ -20,7 +21,7 @@ from tqdm import tqdm import os import json -import cv2 +#import cv2 PATH_TO_LARQ = "./larq_models" PATH_TO_QKERAS = "./qkeras_models" From 54d6a173a97001c852dbc601297ff0c523ae29c8 Mon Sep 17 00:00:00 2001 From: Francesco Date: Thu, 1 Apr 2021 10:17:41 +0200 Subject: [PATCH 6/6] Added binary_densenet E28,E37,E45, binary_densnet_dilated, complete refactor of biRealNet fixing consecutive batch norm layers --- zoo/alexnet.py | 122 ++--- zoo/binary_densenet.py | 429 ++++++++++++++++++ zoo/binary_densenet37_dilated.py | 220 +++++++++ zoo/binary_resnet_e18.py | 296 ++++++------ zoo/birealnet.py | 249 +++++----- zoo/larq_models/biRealNet.json | 2 +- zoo/larq_models/binary_densenet_e28.json | 1 + zoo/larq_models/binary_densenet_e37.json | 1 + .../binary_densenet_e37_dilated.json | 1 + zoo/larq_models/binary_densenet_e45.json | 1 + zoo/larq_models/binary_resnet_e18.json | 2 +- zoo/qkeras_models/biRealNet.json | 2 +- zoo/qkeras_models/binary_densenet_e28.json | 1 + zoo/qkeras_models/binary_densenet_e37.json | 1 + .../binary_densenet_e37_dilated.json | 1 + zoo/qkeras_models/binary_densenet_e45.json | 1 + zoo/qkeras_models/binary_resnet_e18.json | 2 +- zoo/quicknet.py | 262 ++++++----- zoo/results.txt | 24 +- 19 files changed, 1185 insertions(+), 433 deletions(-) create mode 100644 zoo/binary_densenet.py create mode 100644 zoo/binary_densenet37_dilated.py create mode 100644 zoo/larq_models/binary_densenet_e28.json create mode 100644 zoo/larq_models/binary_densenet_e37.json create mode 100644 zoo/larq_models/binary_densenet_e37_dilated.json create mode 100644 zoo/larq_models/binary_densenet_e45.json create mode 100644 zoo/qkeras_models/binary_densenet_e28.json create mode 100644 zoo/qkeras_models/binary_densenet_e37.json create mode 100644 zoo/qkeras_models/binary_densenet_e37_dilated.json create mode 100644 zoo/qkeras_models/binary_densenet_e45.json diff --git a/zoo/alexnet.py b/zoo/alexnet.py index f187ae17..910b4b0b 100644 --- a/zoo/alexnet.py +++ b/zoo/alexnet.py @@ -31,8 +31,8 @@ class AlexNet: - """ - Class to create and load weights of: alexnet + """Class to create and load weights of: alexnet + Attributes: network_name: Name of the network """ @@ -42,77 +42,91 @@ def __init__(self): self.network_name = ALEXNET_NAME @staticmethod - def add_qkeras_conv_block(model, filters_num, kernel_size, pool, + def add_qkeras_conv_block(given_model, filters_num, kernel_size, pool, qnt, strides=1): - """ + """Adds a sequence of layers to the given model + Add a sequence of: Activation quantization, Quantized Conv2D, MaxPooling and BatchNormalization to the given model - :param model: model where to add the sequence - :param filters_num: number of filters for Cov2D - :param kernel_size: kernel size for Cov2D - :param pool: boolean to decide if MaxPool is performed or not - :param qnt: boolean to decide if Activation quantization is performed - or not - :param strides: strides for Conv2D - :return: model plus the sequence + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + kernel_size: kernel size for Conv2D + pool: boolean to decide if MaxPool is performed or not + qnt: boolean to decide if Activation quantization is performed + or not + strides: strides for Conv2D + + Returns: + Given Model plus the sequence """ if qnt: - model.add(q.QActivation("binary(alpha=1)")) - model.add( + given_model.add(q.QActivation("binary(alpha=1)")) + given_model.add( q.QConv2D(filters_num, kernel_size, strides=strides, padding="same", use_bias=False, kernel_quantizer="binary(alpha=1)")) if pool: - model.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2)) - model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) - return model + given_model.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2)) + given_model.add(tf.keras.layers.BatchNormalization(scale=False, + momentum=0.9)) + return given_model @staticmethod - def add_qkeras_dense_block(model, units): - """ - Add a sequence of: Activation quantization, Quantized Dense to the given - model - :param model: model where to add the sequence - :param units: neurons of the Dense - :return: model plus the sequence + def add_qkeras_dense_block(given_model, units): + """Adds a sequence of layers to the given model + + Add a sequence of: Activation quantization, Quantized Dense and + Batch Normalization to the given model + + Args: + given_model: model where to add the sequence + units: neurons of the Dense + + Returns: + Given Model plus the sequence """ - model.add(q.QActivation("binary(alpha=1)")) - model.add( + given_model.add(q.QActivation("binary(alpha=1)")) + given_model.add( q.QDense(units, kernel_quantizer="binary(alpha=1)", use_bias=False)) - model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) - return model + given_model.add(tf.keras.layers.BatchNormalization(scale=False, + momentum=0.9)) + return given_model @staticmethod - def add_larq_conv_block(model, filters_num, kernel_size, pool, qnt, strides=1): + def add_larq_conv_block(given_model, filters_num, kernel_size, pool, qnt, + strides=1): + """Same method of add_qkeras_conv_block but for a larq network """ - Same method of add_qkeras_conv_block but for a larq network - """ - model.add( + given_model.add( lq.layers.QuantConv2D(filters_num, kernel_size, strides=strides, padding="same", use_bias=False, input_quantizer=None if not qnt else "ste_sign", kernel_quantizer="ste_sign", kernel_constraint="weight_clip")) if pool: - model.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2)) - model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) - return model + given_model.add(tf.keras.layers.MaxPool2D(pool_size=3, strides=2)) + given_model.add(tf.keras.layers.BatchNormalization(scale=False, + momentum=0.9)) + return given_model @staticmethod - def add_larq_dense_block(model, units): - """ - Same method of add_qkeras_dense_block but for a larq network + def add_larq_dense_block(given_model, units): + """Same method of add_qkeras_dense_block but for a larq network """ - model.add(lq.layers.QuantDense(units, use_bias=False, - input_quantizer="ste_sign", - kernel_quantizer="ste_sign", - kernel_constraint="weight_clip")) - model.add(tf.keras.layers.BatchNormalization(scale=False, momentum=0.9)) - return model + given_model.add(lq.layers.QuantDense(units, use_bias=False, + input_quantizer="ste_sign", + kernel_quantizer="ste_sign", + kernel_constraint="weight_clip")) + given_model.add(tf.keras.layers.BatchNormalization(scale=False, + momentum=0.9)) + return given_model def build(self): - """ - Build the model - :return: qkeras and larq models + """Build the model + + Returns: + Qkeras and larq models """ qkeras_network = self.build_qkeras_alexnet() print("\nQKeras network successfully created") @@ -121,9 +135,10 @@ def build(self): return qkeras_network, larq_network def build_qkeras_alexnet(self): - """ - Build the qkeras version of the alexnet - :return: qkeras model of the alexnet + """Build the qkeras version of the alexnet + + Return: + Qkeras model of the alexnet """ qkeras_alexNet = tf.keras.models.Sequential() qkeras_alexNet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) @@ -146,9 +161,10 @@ def build_qkeras_alexnet(self): return qkeras_alexNet def build_larq_alexnet(self): - """ - Build the larq version of the alexnet - :return: larq model of the alexnet + """Build the larq version of the alexnet + + Return: + Larq model of the alexnet """ larq_alexnet = tf.keras.models.Sequential() larq_alexnet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) diff --git a/zoo/binary_densenet.py b/zoo/binary_densenet.py new file mode 100644 index 00000000..3eb5e89e --- /dev/null +++ b/zoo/binary_densenet.py @@ -0,0 +1,429 @@ +# Copyright 2021 Loro Francesco +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +# Download pretrained weight from: +# densenet_e28 -> https://drive.google.com/file/d/1-Plw5XKKCeTP17nUpnzRM3JnUdlwv_MJ/view?usp=sharing +# densenet_e37 -> https://drive.google.com/file/d/1PldbeERqq9-Xz8HQtaRAHtbtqItRznLp/view?usp=sharing +# densenet e45 -> https://drive.google.com/file/d/1Lpc-rRAleNJSF-Y4SlDab9bW2L8cUkn0/view?usp=sharing + +import qkeras as q +import tensorflow as tf +import larq as lq +from utils import compare_network, create_random_dataset, dump_network_to_json + +# Define path to the pre-trained weights +PATH_DENSENET_E28 = "./weights/binary_densenet_28_weights.h5" +DENSENET_E28_NAME = "binary_densenet_e28" +PATH_DENSENET_E37 = "./weights/binary_densenet_37_weights.h5" +DENSENET_E37_NAME = "binary_densenet_e37" +PATH_DENSENET_E45 = "./weights/binary_densenet_45_weights.h5" +DENSENET_E45_NAME = "binary_densenet_e45" + + +class DenseNet(): + """Class to create and load weights of: densenet + + Attributes: + size : int to specify the size of the network + network_name: Name of the network + + Raises: + NameError: if the size is not one of these: 28, 37, 45 + """ + + def __init__(self, size=28): + if size == 28: + self.__weights_path = PATH_DENSENET_E28 + self.network_name = DENSENET_E28_NAME + self.__filters = (160, 192, 256) + self.__ID = 0 + + elif size == 37: + self.__weights_path = PATH_DENSENET_E37 + self.network_name = DENSENET_E37_NAME + self.__filters = (128, 192, 256) + self.__filter_repetition = (6, 8, 12, 6) + self.__ID = 1 + + elif size == 45: + self.__weights_path = PATH_DENSENET_E45 + self.network_name = DENSENET_E45_NAME + self.__filters = (160, 288, 288) + self.__filter_repetition = (6, 12, 14, 6, 8) + self.__ID = 2 + + else: + raise NameError("size:", str(size), "not recognized") + + @staticmethod + def add_qkeras_quant_block(given_model, filters_num): + """Adds a sequence of layers to the given model + + Add a sequence of: Batch Normalization, Quantization Activations, Conv2D + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence + """ + y = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5) \ + (given_model) + y = q.QActivation("binary(alpha=1)")(y) + y = q.QConv2D(filters=filters_num, kernel_size=3, + kernel_quantizer="binary(alpha=1)", + kernel_initializer="glorot_normal", + padding="same", + use_bias=False, + )(y) + return tf.keras.layers.concatenate([given_model, y]) + + @staticmethod + def add_larq_quant_block(given_model, filters_num): + """Same method of add_qkeras_quant_block but for a larq network + """ + y = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)( + given_model) + y = lq.layers.QuantConv2D( + filters=filters_num, + kernel_size=3, + input_quantizer=lq.quantizers.SteSign(clip_value=1.3), + kernel_quantizer=lq.quantizers.SteSign(clip_value=1.3), + kernel_initializer="glorot_normal", + kernel_constraint=lq.constraints.WeightClip(clip_value=1.3), + padding="same", + use_bias=False, + )(y) + return tf.keras.layers.concatenate([given_model, y]) + + @staticmethod + def add_connection_block(given_model, filters_num): + """Adds a sequence of layers to the given model + + Add a sequence of: Batch Normalization, MaxPooling2D, Activation, Conv2D + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence + """ + x = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)( + given_model) + x = tf.keras.layers.MaxPooling2D(2, strides=2)(x) + x = tf.keras.layers.Activation("relu")(x) + x = tf.keras.layers.Conv2D(filters=filters_num, kernel_size=1, + kernel_initializer="he_normal", + use_bias=False)(x) + return x + + def build(self): + """Builds the model by its ID + + Returns: + Qkeras and larq models + """ + if self.__ID == 0: + qkeras_network = self.build_qkeras_densenet_e28() + print("\nQKeras network successfully created") + larq_network = self.build_larq_densenet_e28() + print("Larq network successfully created") + return qkeras_network, larq_network + + if self.__ID == 1: + qkeras_network = self.build_qkeras_densenet_e37() + print("\nQKeras network successfully created") + larq_network = self.build_larq_densenet_e37() + print("Larq network successfully created") + return qkeras_network, larq_network + + if self.__ID == 2: + qkeras_network = self.build_qkeras_densenet_e45() + print("\nQKeras network successfully created") + larq_network = self.build_larq_densenet_e45() + print("Larq network successfully created") + return qkeras_network, larq_network + + def build_qkeras_densenet_e28(self): + """Build the qkeras version of the densenet_e28 + + Returns: + Qkeras model of the densenet_e28 + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + qkeras_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + qkeras_densenet) + + for filter_num in self.__filters: + for _ in range(0, 6): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + qkeras_densenet = self.add_connection_block(qkeras_densenet, filter_num) + + for _ in range(0, 5): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(pool_size=7)(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Flatten()(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + qkeras_densenet) + qkeras_densenet = tf.keras.Model(inputs=input_layer, + outputs=qkeras_densenet) + qkeras_densenet.load_weights(self.__weights_path) + return qkeras_densenet + + def build_larq_densenet_e28(self): + """Build the larq version of the densenet_e28 + + Returns: + Larq model of the densenet_e28 + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + larq_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + larq_densenet) + + for filter_num in self.__filters: + for _ in range(0, 6): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + larq_densenet = self.add_connection_block(larq_densenet, filter_num) + + for _ in range(0, 5): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(pool_size=7)(larq_densenet) + larq_densenet = tf.keras.layers.Flatten()(larq_densenet) + larq_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + larq_densenet) + larq_densenet = tf.keras.Model(inputs=input_layer, outputs=larq_densenet) + larq_densenet.load_weights(self.__weights_path) + return larq_densenet + + def build_qkeras_densenet_e37(self): + """Build the qkeras version of the densenet_e37 + + Returns: + Qkeras model of the densenet_e37 + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + qkeras_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + qkeras_densenet) + + for filter_index, filter_num in enumerate(self.__filters): + for _ in range(0, self.__filter_repetition[filter_index]): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + qkeras_densenet = self.add_connection_block(qkeras_densenet, filter_num) + + for _ in range(0, self.__filter_repetition[-1]): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(pool_size=7)(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Flatten()(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + qkeras_densenet) + qkeras_densenet = tf.keras.Model(inputs=input_layer, + outputs=qkeras_densenet) + qkeras_densenet.load_weights(self.__weights_path) + return qkeras_densenet + + def build_larq_densenet_e37(self): + """Build the larq version of the densenet_e28 + + Returns: + Larq model of the densenet_e28 + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + larq_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + larq_densenet) + + for filter_index, filter_num in enumerate(self.__filters): + for _ in range(0, self.__filter_repetition[filter_index]): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + larq_densenet = self.add_connection_block(larq_densenet, filter_num) + + for _ in range(0, self.__filter_repetition[-1]): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(pool_size=7)(larq_densenet) + larq_densenet = tf.keras.layers.Flatten()(larq_densenet) + larq_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + larq_densenet) + larq_densenet = tf.keras.Model(inputs=input_layer, outputs=larq_densenet) + larq_densenet.load_weights(self.__weights_path) + return larq_densenet + + def build_qkeras_densenet_e45(self): + """Build the qkeras version of the densenet_e45 + + Returns: + Qkeras model of the densenet_e45 + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + qkeras_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + qkeras_densenet) + + for filter_index, filter_num in enumerate(self.__filters): + for _ in range(0, self.__filter_repetition[filter_index]): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + qkeras_densenet = self.add_connection_block(qkeras_densenet, filter_num) + + for _ in range(0, self.__filter_repetition[-1]): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(pool_size=7)(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Flatten()(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + qkeras_densenet) + qkeras_densenet = tf.keras.Model(inputs=input_layer, + outputs=qkeras_densenet) + qkeras_densenet.load_weights(self.__weights_path) + return qkeras_densenet + + def build_larq_densenet_e45(self): + """Build the larq version of the densenet_e45 + + Returns: + Larq model of the densenet_e45 + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + larq_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + larq_densenet) + + for filter_index, filter_num in enumerate(self.__filters): + for _ in range(0, self.__filter_repetition[filter_index]): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + larq_densenet = self.add_connection_block(larq_densenet, filter_num) + + for _ in range(0, self.__filter_repetition[-1]): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(pool_size=7)(larq_densenet) + larq_densenet = tf.keras.layers.Flatten()(larq_densenet) + larq_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + larq_densenet) + larq_densenet = tf.keras.Model(inputs=input_layer, outputs=larq_densenet) + larq_densenet.load_weights(self.__weights_path) + return larq_densenet + + +if __name__ == "__main__": + network_names = [DENSENET_E28_NAME, DENSENET_E37_NAME, DENSENET_E45_NAME] + sizes = [28, 37, 45] + + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + for size, name in zip(sizes, network_names): + network = DenseNet(size) + qkeras_network, larq_network = network.build() + # Compare mean MSE and Absolute error of the the networks + compare_network(qkeras_network=qkeras_network, larq_network=larq_network, + dataset=random_data, network_name=name) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, + network_name=name) diff --git a/zoo/binary_densenet37_dilated.py b/zoo/binary_densenet37_dilated.py new file mode 100644 index 00000000..7d9011bb --- /dev/null +++ b/zoo/binary_densenet37_dilated.py @@ -0,0 +1,220 @@ +# Copyright 2021 Loro Francesco +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +__author__ = "Francesco Loro" +__email__ = "francesco.official@gmail.com" +__supervisor__ = "Danilo Pau" +__email__ = "danilo.pau@st.com" + +# Download pretrained weight from: +# densenet_e37_dilated -> https://drive.google.com/file/d/1JZoiPcQlMJ8KAe3vdli4ixM_hGWHkPXm/view?usp=sharing + +import qkeras as q +import tensorflow as tf +import larq as lq +from utils import compare_network, create_random_dataset, dump_network_to_json + +# Define path to the pre-trained weights +PATH_DENSENET_E37_dilated = "./weights/binary_densenet_37_dilated_weights.h5" +DENSENET_E37_DILATED_NAME = "binary_densenet_e37_dilated" + + +class DenseNetE37Dilated: + """Class to create and load weights of: densenet_e37_dilated + + Attributes: + network_name: Name of the network + filters: Number of filters for Conv2D + """ + + def __init__(self): + self.__weights_path = PATH_DENSENET_E37_dilated + self.network_name = DENSENET_E37_DILATED_NAME + self.filters = (128, 192, 256) + self.__filter_repetition = (6, 8, 12, 6) + + @staticmethod + def add_qkeras_quant_block(given_model, filters_num): + """Adds a sequence of layers to the given model + + Add a sequence of: Batch Normalization, Quantization Activations, Conv2D + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence + """ + y = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)(given_model) + y = q.QActivation("binary(alpha=1)")(y) + y = q.QConv2D(filters=filters_num, kernel_size=3, + kernel_quantizer="binary(alpha=1)", + kernel_initializer="glorot_normal", + padding="same", + use_bias=False, + )(y) + return tf.keras.layers.concatenate([given_model, y]) + + @staticmethod + def add_larq_quant_block(given_model, filters_num): + """Same method of add_qkeras_quant_block but for a larq network + """ + y = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)(given_model) + y = lq.layers.QuantConv2D( + filters=filters_num, + kernel_size=3, + input_quantizer=lq.quantizers.SteSign(clip_value=1.3), + kernel_quantizer=lq.quantizers.SteSign(clip_value=1.3), + kernel_initializer="glorot_normal", + kernel_constraint=lq.constraints.WeightClip(clip_value=1.3), + padding="same", + use_bias=False, + )(y) + return tf.keras.layers.concatenate([given_model, y]) + + @staticmethod + def add_connection_block(given_model, filters_num, max_pool=0): + """Adds a sequence of layers to the given model + + Add a sequence of: Batch Normalization, MaxPooling2D, Activation, Conv2D + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence + """ + x = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)(given_model) + if max_pool: + x = tf.keras.layers.MaxPooling2D(2, strides=2)(x) + x = tf.keras.layers.Activation("relu")(x) + x = tf.keras.layers.Conv2D(filters=filters_num, kernel_size=1, + kernel_initializer="he_normal", + use_bias=False)(x) + return x + + def build(self): + """Build the model + + Returns: + Qkeras and larq models + """ + qkeras_network = self.build_qkeras_densenet_e37_dilated() + print("\nQKeras network successfully created") + larq_network = self.build_larq_densenet_e37_dilated() + print("Larq network successfully created") + return qkeras_network, larq_network + + def build_qkeras_densenet_e37_dilated(self): + """Build the qkeras version of the densenet_e37_dilated + + Returns: + Qkeras model of the densenet_e37_dilated + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + qkeras_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + qkeras_densenet) + + for filter_index, filter_num in enumerate(self.filters): + for _ in range(0, self.__filter_repetition[filter_index]): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + qkeras_densenet = \ + self.add_connection_block(qkeras_densenet, filter_num, + max_pool=1 if filter_index == 1 else 0) + + for _ in range(0, self.__filter_repetition[-1]): + qkeras_densenet = self.add_larq_quant_block(qkeras_densenet, 64) + + qkeras_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("relu")(qkeras_densenet) + qkeras_densenet = tf.keras.layers.MaxPool2D(pool_size=28)(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Flatten()(qkeras_densenet) + qkeras_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + qkeras_densenet) + qkeras_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + qkeras_densenet) + qkeras_densenet = tf.keras.Model(inputs=input_layer, + outputs=qkeras_densenet) + qkeras_densenet.load_weights(self.__weights_path) + return qkeras_densenet + + def build_larq_densenet_e37_dilated(self): + """Build the larq version of the densenet_e37_dilated + + Returns: + Larq model of the densenet_e37_dilated + """ + input_layer = tf.keras.Input(shape=(224, 224, 3)) + larq_densenet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)(input_layer) + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + larq_densenet) + + for filter_index, filter_num in enumerate(self.filters): + for _ in range(0, self.__filter_repetition[filter_index]): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + larq_densenet = \ + self.add_connection_block(larq_densenet, filter_num, + max_pool=1 if filter_index == 1 else 0) + + for _ in range(0, self.__filter_repetition[-1]): + larq_densenet = self.add_larq_quant_block(larq_densenet, 64) + + larq_densenet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5)( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("relu")(larq_densenet) + larq_densenet = tf.keras.layers.MaxPool2D(pool_size=28)(larq_densenet) + larq_densenet = tf.keras.layers.Flatten()(larq_densenet) + larq_densenet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal")( + larq_densenet) + larq_densenet = tf.keras.layers.Activation("softmax", dtype="float32")( + larq_densenet) + larq_densenet = tf.keras.Model(inputs=input_layer, outputs=larq_densenet) + larq_densenet.load_weights(self.__weights_path) + return larq_densenet + + +if __name__ == "__main__": + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + + network = DenseNetE37Dilated() + qkeras_network, larq_network = network.build() + # Compare mean MSE and Absolute error of the the networks + compare_network(qkeras_network=qkeras_network, larq_network=larq_network, + dataset=random_data, network_name=DENSENET_E37_DILATED_NAME) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, + network_name=DENSENET_E37_DILATED_NAME) diff --git a/zoo/binary_resnet_e18.py b/zoo/binary_resnet_e18.py index c2ddd575..b050e8ee 100644 --- a/zoo/binary_resnet_e18.py +++ b/zoo/binary_resnet_e18.py @@ -31,8 +31,8 @@ class ResNetE18: - """ - Class to create and load weights of: resnet_e18 + """Class to create and load weights of: resnet_e18 + Attributes: network_name: Name of the network filters: Number of filters for Conv2D @@ -41,105 +41,102 @@ class ResNetE18: def __init__(self): self.__weights_path = PATH_RESNET_E18 self.network_name = RESNET_E18_NAME - self.filters = (64,128,256,512) + self.filters = (64, 128, 256, 512) @staticmethod - def add_qkeras_quant_block(model, filters_num, strides=1): - """ + def add_qkeras_quant_block(given_model, filters_num, strides=1): + """Adds a sequence of layers to the given model + Add a sequence of: Activation quantization, Quantized Conv2D, Batch Normalization - :param model: model where to add the sequence - :param filters_num: number of filters for Cov2D - :param strides: strides for Conv2D + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + strides: strides for Conv2D + + Returns: + Given Model plus the sequence """ - model.add(q.QActivation("binary(alpha=1)")) - model.add( - q.QConv2D(filters_num, kernel_size=3, strides=strides, - padding="same", - kernel_quantizer="binary(alpha=1)", - kernel_initializer="glorot_normal", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + x = q.QActivation("binary(alpha=1)")(given_model) + x = q.QConv2D(filters_num, kernel_size=3, strides=strides, + padding="same", + kernel_quantizer="binary(alpha=1)", + kernel_initializer="glorot_normal", + use_bias=False)(x) + return tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)(x) @staticmethod - def add_qkeras_connection_block(model, filters_num): - """ - Add a sequence of: Activation quantization, Quantized Conv2D, reshape, - Average Pooling, Conv2D, 2x BatchNormalization - :param model: model where to add the sequence - :param filters_num: number of filters for Cov2D + def add_larq_quant_block(given_model, filters_num, strides=1): + """Same method of add_qkeras_quant_block but for a larq network """ - model.add(q.QActivation("binary(alpha=1)")) - model.add( - q.QConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, - padding="same", - kernel_quantizer="binary(alpha=1)", - kernel_constraint="weight_clip")) - shape_in = model.output_shape[1] * model.output_shape[2] * \ - model.output_shape[3] - shape_out = (model.output_shape[1], model.output_shape[2], - model.output_shape[3] // 2) - # Prepare shapes for reshape layers - model.add(tf.keras.layers.Flatten()) - model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) - model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) - model.add(tf.keras.layers.Reshape(target_shape=shape_out)) - model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - model.add(tf.keras.layers.BatchNormalization()) + x = lq.layers.QuantConv2D(filters_num, kernel_size=3, strides=strides, + padding="same", + input_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_constraint=lq.constraints.WeightClip( + clip_value=1.25), + kernel_initializer="glorot_normal", + use_bias=False)(given_model) + return tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)(x) @staticmethod - def add_larq_quant_block(model, filters_num, strides=1): + def add_qkeras_connection_block(given_model, filters_num): + """Adds a sequence of layers to the given model + + Adds two sequences one of Activation quantization, Quantized Conv2D, + Batch Normalization the other of Average Pooling2D, Conv2D, BatchNorm + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence """ - Same method of add_qkeras_quant_block but for a larq network - """ - model.add( - lq.layers.QuantConv2D(filters_num, kernel_size=3, strides=strides, - padding="same", - input_quantizer=lq.quantizers.SteSign( - clip_value=1.25), - kernel_quantizer=lq.quantizers.SteSign( - clip_value=1.25), - kernel_constraint=lq.constraints.WeightClip( - clip_value=1.25), - kernel_initializer="glorot_normal", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + shortcut = given_model + shortcut = tf.keras.layers.AvgPool2D(2, strides=2, padding="same")(shortcut) + shortcut = tf.keras.layers.Conv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)(shortcut) + shortcut = tf.keras.layers.BatchNormalization(momentum=0.8)(shortcut) + x = q.QActivation("binary(alpha=1)")(given_model) + x = q.QConv2D(filters_num, (3, 3), strides=(2, 2), + padding="same", use_bias=False, + kernel_quantizer="binary(alpha=1)", + kernel_constraint="weight_clip")(x) + x = tf.keras.layers.BatchNormalization(momentum=0.8)(x) + return tf.keras.layers.add([x, shortcut]) @staticmethod - def add_larq_connection_block(model, filters_num): + def add_larq_connection_block(give_model, filters_num): + """Same method of add_qkeras_connection_block but for a larq network """ - Same method of add_qkeras_connection_block but for a larq network - """ - model.add( - lq.layers.QuantConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, - padding="same", - input_quantizer=lq.quantizers.SteSign( - clip_value=1.25), - kernel_quantizer=lq.quantizers.SteSign( - clip_value=1.25), - kernel_constraint=lq.constraints.WeightClip( - clip_value=1.25),)) - - shape_in = model.output_shape[1] * model.output_shape[2] * \ - model.output_shape[3] - shape_out = (model.output_shape[1], model.output_shape[2], - model.output_shape[3] // 2) - # Prepare shapes for reshape layers - model.add(tf.keras.layers.Flatten()) - model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) - model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) - model.add(tf.keras.layers.Reshape(target_shape=shape_out)) - model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - model.add(tf.keras.layers.BatchNormalization()) + shortcut = give_model + shortcut = tf.keras.layers.AvgPool2D(2, strides=2, padding="same")(shortcut) + shortcut = tf.keras.layers.Conv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)(shortcut) + shortcut = tf.keras.layers.BatchNormalization(momentum=0.8)(shortcut) + x = lq.layers.QuantConv2D(filters_num, (3, 3), strides=(2, 2), + use_bias=False, + padding="same", + input_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_quantizer=lq.quantizers.SteSign( + clip_value=1.25), + kernel_constraint=lq.constraints.WeightClip( + clip_value=1.25))(give_model) + x = tf.keras.layers.BatchNormalization(momentum=0.8)(x) + return tf.keras.layers.add([x, shortcut]) def build(self): - """ - Build the model - :return: qkeras and larq models + """Build the model + + Returns: + qkeras and larq models """ qkeras_network = self.build_qkeras_resnet_e18() print("\nQKeras network successfully created") @@ -148,76 +145,97 @@ def build(self): return qkeras_network, larq_network def build_qkeras_resnet_e18(self): + """Build the qkeras version of the resnet_e18 + + Returns: + qkeras model of the resnet_e18 """ - Build the qkeras version of the resnet_e18 - :return: qkeras model of the resnet_e18 - """ - qkeras_resnet = tf.keras.models.Sequential() - qkeras_resnet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) - qkeras_resnet.add( - tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding="same", - kernel_initializer="he_normal", use_bias=False)) - qkeras_resnet.add( - tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) - qkeras_resnet.add(tf.keras.layers.Activation("relu")) - qkeras_resnet.add(tf.keras.layers.MaxPool2D(3, strides=2, padding="same")) - qkeras_resnet.add( - tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + input_layer = tf.keras.Input(shape=(224, 224, 3)) + qkeras_resnet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)( + input_layer) + qkeras_resnet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5) \ + (qkeras_resnet) + qkeras_resnet = tf.keras.layers.Activation("relu")(qkeras_resnet) + qkeras_resnet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + qkeras_resnet) + qkeras_resnet = tf.keras.layers.BatchNormalization(momentum=0.9, + epsilon=1e-5) \ + (qkeras_resnet) for _ in range(0, 4): - self.add_qkeras_quant_block(qkeras_resnet, 64) - self.add_qkeras_connection_block(qkeras_resnet, 128) - + qkeras_resnet = self.add_qkeras_quant_block(qkeras_resnet, 64) + qkeras_resnet = self.add_qkeras_connection_block(qkeras_resnet, 128) for i in range(1, 3): for _ in range(0, 3): - self.add_qkeras_quant_block(qkeras_resnet, filters_num=self.filters[i]) - self.add_qkeras_connection_block(qkeras_resnet, - filters_num=self.filters[i + 1]) + qkeras_resnet = self.add_qkeras_quant_block(qkeras_resnet, + filters_num=self.filters[i]) + qkeras_resnet = self.add_qkeras_connection_block(qkeras_resnet, + filters_num=self.filters[ + i + 1]) for _ in range(0, 3): - self.add_qkeras_quant_block(qkeras_resnet, filters_num=self.filters[-1]) - qkeras_resnet.add(tf.keras.layers.Activation("relu")) - qkeras_resnet.add(tf.keras.layers.MaxPool2D(pool_size=7)) - qkeras_resnet.add(tf.keras.layers.Flatten()) - qkeras_resnet.add( - tf.keras.layers.Dense(1000, kernel_initializer="glorot_normal")) - qkeras_resnet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + qkeras_resnet = self.add_qkeras_quant_block(qkeras_resnet, + filters_num=self.filters[-1]) + + qkeras_resnet = tf.keras.layers.Activation("relu")(qkeras_resnet) + + qkeras_resnet = tf.keras.layers.MaxPool2D(pool_size=7)(qkeras_resnet) + qkeras_resnet = tf.keras.layers.Flatten()(qkeras_resnet) + qkeras_resnet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal") \ + (qkeras_resnet) + qkeras_resnet = tf.keras.layers.Activation("softmax", dtype="float32")( + qkeras_resnet) + qkeras_resnet = tf.keras.Model(inputs=input_layer, outputs=qkeras_resnet) qkeras_resnet.load_weights(self.__weights_path) return qkeras_resnet - def build_larq_resnet_e18(self): + """Build the larq version of the resnet_e18 + + Returns: + Larq model of the resnet_e18 """ - Build the larq version of the resnet_e18 - :return: larq model of the resnet_e18 - """ - larq_resnet = tf.keras.models.Sequential() - larq_resnet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) - larq_resnet.add( - tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, padding="same", - kernel_initializer="he_normal", use_bias=False)) - larq_resnet.add( - tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) - larq_resnet.add(tf.keras.layers.Activation("relu")) - larq_resnet.add(tf.keras.layers.MaxPool2D(3, strides=2, padding="same")) - larq_resnet.add( - tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5)) + input_layer = tf.keras.Input(shape=(224, 224, 3)) + larq_resnet = tf.keras.layers.Conv2D(64, kernel_size=7, strides=2, + padding="same", + kernel_initializer="he_normal", + use_bias=False)( + input_layer) + larq_resnet = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5) \ + (larq_resnet) + larq_resnet = tf.keras.layers.Activation("relu")(larq_resnet) + larq_resnet = tf.keras.layers.MaxPool2D(3, strides=2, padding="same")( + larq_resnet) + larq_resnet = tf.keras.layers.BatchNormalization(momentum=0.9, epsilon=1e-5) \ + (larq_resnet) for _ in range(0, 4): - self.add_larq_quant_block(larq_resnet, 64) - self.add_larq_connection_block(larq_resnet, 128) - + larq_resnet = self.add_larq_quant_block(larq_resnet, 64) + larq_resnet = self.add_larq_connection_block(larq_resnet, 128) for i in range(1, 3): for _ in range(0, 3): - self.add_larq_quant_block(larq_resnet, filters_num=self.filters[i]) - self.add_larq_connection_block(larq_resnet, filters_num=self.filters[i+1]) + larq_resnet = self.add_larq_quant_block(larq_resnet, + filters_num=self.filters[i]) + larq_resnet = self.add_larq_connection_block(larq_resnet, + filters_num=self.filters[ + i + 1]) for _ in range(0, 3): - self.add_larq_quant_block(larq_resnet, filters_num=self.filters[-1]) - larq_resnet.add(tf.keras.layers.Activation("relu")) - larq_resnet.add(tf.keras.layers.MaxPool2D(pool_size=7)) - larq_resnet.add(tf.keras.layers.Flatten()) - larq_resnet.add( - tf.keras.layers.Dense(1000, kernel_initializer="glorot_normal")) - larq_resnet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + larq_resnet = self.add_larq_quant_block(larq_resnet, + filters_num=self.filters[-1]) + + larq_resnet = tf.keras.layers.Activation("relu")(larq_resnet) + larq_resnet = tf.keras.layers.MaxPool2D(pool_size=7)(larq_resnet) + larq_resnet = tf.keras.layers.Flatten()(larq_resnet) + larq_resnet = tf.keras.layers.Dense(1000, + kernel_initializer="glorot_normal") \ + (larq_resnet) + larq_resnet = tf.keras.layers.Activation("softmax", dtype="float32")( + larq_resnet) + larq_resnet = tf.keras.Model(inputs=input_layer, outputs=larq_resnet) larq_resnet.load_weights(self.__weights_path) return larq_resnet diff --git a/zoo/birealnet.py b/zoo/birealnet.py index 1fb6147b..719172ae 100644 --- a/zoo/birealnet.py +++ b/zoo/birealnet.py @@ -31,8 +31,8 @@ class BirealNet: - """ - Class to create and load weights of: biRealNet + """Class to create and load weights of: biRealNet + Attributes: network_name: Name of the network """ @@ -42,86 +42,88 @@ def __init__(self): self.network_name = BIREALNET_NAME @staticmethod - def add_qkeras_residual_block(model, filters_num, strides=1): - """ + def add_qkeras_residual_block(given_model, filters_num): + """Adds a sequence of layers to the given model + Add a sequence of: Activation quantization, Quantized Conv2D - :param model: model where to add the sequence - :param filters_num: number of filters for Cov2D - :param strides: strides for Conv2D + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Cov2D + strides: strides for Conv2D + + Returns: + Given Model plus the sequence """ - model.add(q.QActivation("binary(alpha=1)")) - model.add(q.QConv2D(filters_num, (3, 3), strides=strides, padding="same", - kernel_quantizer="binary(alpha=1)", use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) + x = q.QActivation("binary(alpha=1)")(given_model) + x = q.QConv2D(filters_num, (3, 3), padding="same", + kernel_quantizer="binary(alpha=1)", use_bias=False)(x) + x = tf.keras.layers.BatchNormalization(momentum=0.8)(x) + return tf.keras.layers.add([given_model, x]) @staticmethod - def add_qkeras_connection_block(model, filters_num): - """ - Add a sequence of: Activation quantization, Quantized Conv2D, reshape, - Average Pooling, Conv2D, 2x BatchNormalization - :param model: model where to add the sequence - :param filters_num: number of filters for Cov2D + def add_qkeras_connection_block(given_model, filters_num): + """Adds a sequence of layers to the given model + + Adds two sequences one of Activation quantization, Quantized Conv2D, + Batch Normalization the other of Average Pooling2D, Conv2D, BatchNorm + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence """ - model.add(q.QActivation("binary")) - model.add(q.QConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, - padding="same", kernel_quantizer="binary(alpha=1)")) - # Prepare shapes for reshape layers - shape_in = model.output_shape[1] * model.output_shape[2] * \ - model.output_shape[3] - shape_out = (model.output_shape[1], model.output_shape[2], - model.output_shape[3] // 2) - model.add(tf.keras.layers.Flatten()) - model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) - model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) - model.add(tf.keras.layers.Reshape(target_shape=shape_out)) - model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - model.add(tf.keras.layers.BatchNormalization()) + shortcut = given_model + shortcut = tf.keras.layers.AvgPool2D(2, strides=2, padding="same")(shortcut) + shortcut = tf.keras.layers.Conv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)(shortcut) + shortcut = tf.keras.layers.BatchNormalization(momentum=0.8)(shortcut) + x = q.QActivation("binary")(given_model) + x = q.QConv2D(filters_num, (3, 3), strides=(2, 2), + padding="same", use_bias=False, + kernel_quantizer="binary(alpha=1)")(x) + x = tf.keras.layers.BatchNormalization(momentum=0.8)(x) + return tf.keras.layers.add([x, shortcut]) @staticmethod - def add_larq_residual_block(model, features, strides=1): + def add_larq_residual_block(given_model, features): + """Same method of add_qkeras_residual_block but for a larq network """ - Same method of add_qkeras_residual_block but for a larq network - """ - model.add(lq.layers.QuantConv2D(features, (3, 3), strides=strides, - padding="same", use_bias=False, - input_quantizer="approx_sign", - kernel_quantizer= - "magnitude_aware_sign", - kernel_constraint="weight_clip", )) - model.add(tf.keras.layers.BatchNormalization()) + x = lq.layers.QuantConv2D(features, (3, 3), padding="same", use_bias=False, + input_quantizer="approx_sign", + kernel_quantizer= + "magnitude_aware_sign", + kernel_constraint="weight_clip")(given_model) + x = tf.keras.layers.BatchNormalization(momentum=0.8)(x) + return tf.keras.layers.add([given_model, x]) @staticmethod - def add_larq_connection_block(model, filters_num): - """ - Same method of add_qkeras_connection_block but for a larq network + def add_larq_connection_block(given_model, filters_num): + """Same method of add_qkeras_connection_block but for a larq network """ - model.add( - lq.layers.QuantConv2D(filters_num, (3, 3), strides=(2, 2), use_bias=False, - padding="same", - input_quantizer="approx_sign", - kernel_quantizer="magnitude_aware_sign", - kernel_constraint="weight_clip")) - - shape_in = model.output_shape[1] * model.output_shape[2] * \ - model.output_shape[3] - shape_out = (model.output_shape[1], model.output_shape[2], - model.output_shape[3] // 2) - # Prepare shapes for reshape layers - model.add(tf.keras.layers.Flatten()) - model.add(tf.keras.layers.Reshape(target_shape=(shape_in, 1))) - model.add(tf.keras.layers.AvgPool1D(1, strides=2, padding="same")) - model.add(tf.keras.layers.Reshape(target_shape=shape_out)) - model.add(tf.keras.layers.Conv2D(filters_num, (1, 1), padding="same", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - model.add(tf.keras.layers.BatchNormalization()) + shortcut = given_model + shortcut = tf.keras.layers.AvgPool2D(2, strides=2, padding="same")(shortcut) + shortcut = tf.keras.layers.Conv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)(shortcut) + shortcut = tf.keras.layers.BatchNormalization(momentum=0.8)(shortcut) + x = lq.layers.QuantConv2D(filters_num, (3, 3), strides=(2, 2), + padding="same", use_bias=False, + input_quantizer="approx_sign", + kernel_quantizer= + "magnitude_aware_sign", + kernel_constraint="weight_clip")(given_model) + x = tf.keras.layers.BatchNormalization(momentum=0.8)(x) + return tf.keras.layers.add([x, shortcut]) def build(self): - """ - Build the model - :return: qkeras and larq models + """Builds the model + + Returns: + qkeras and larq models """ qkeras_network = self.build_qkeras_birealnet() print("\nQKeras network successfully created") @@ -130,70 +132,79 @@ def build(self): return qkeras_network, larq_network def build_qkeras_birealnet(self): + """Builds the qkeras version of the birealnet + + Returns: + Qkeras model of the birealnet """ - Build the qkeras version of the birealnet - :return: qkeras model of the birealnet - """ - qkeras_biRealNet = tf.keras.models.Sequential() - qkeras_biRealNet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) - qkeras_biRealNet.add( - tf.keras.layers.Conv2D(64, (7, 7), strides=2, padding="same", - use_bias=False)) - qkeras_biRealNet.add(tf.keras.layers.BatchNormalization(momentum=0.8)) - qkeras_biRealNet.add( - tf.keras.layers.MaxPool2D((3, 3), strides=2, padding="same")) - for _ in range(0, 4): - self.add_qkeras_residual_block(qkeras_biRealNet, 64) - self.add_qkeras_connection_block(qkeras_biRealNet, 128) + input_layer = tf.keras.Input(shape=(224, 224, 3)) + qkeras_biRealNet = tf.keras.layers.Conv2D(64, (7, 7), strides=2, + padding="same", + use_bias=False)(input_layer) + qkeras_biRealNet = tf.keras.layers.BatchNormalization(momentum=0.8)( + qkeras_biRealNet) + qkeras_biRealNet = tf.keras.layers.MaxPool2D((3, 3), strides=2, + padding="same") \ + (qkeras_biRealNet) + for _ in range(0, 4): + qkeras_biRealNet = self.add_larq_residual_block(qkeras_biRealNet, 64) + qkeras_biRealNet = self.add_larq_connection_block(qkeras_biRealNet, 128) for _ in range(0, 3): - self.add_qkeras_residual_block(qkeras_biRealNet, 128) - self.add_qkeras_connection_block(qkeras_biRealNet, 256) - + qkeras_biRealNet = self.add_larq_residual_block(qkeras_biRealNet, 128) + qkeras_biRealNet = self.add_larq_connection_block(qkeras_biRealNet, 256) for _ in range(0, 3): - self.add_qkeras_residual_block(qkeras_biRealNet, 256) - self.add_qkeras_connection_block(qkeras_biRealNet, 512) - + qkeras_biRealNet = self.add_larq_residual_block(qkeras_biRealNet, 256) + qkeras_biRealNet = self.add_larq_connection_block(qkeras_biRealNet, 512) for _ in range(0, 3): - self.add_qkeras_residual_block(qkeras_biRealNet, 512) - qkeras_biRealNet.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) - qkeras_biRealNet.add(tf.keras.layers.Flatten()) - qkeras_biRealNet.add(tf.keras.layers.Dense(1000)) - qkeras_biRealNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + qkeras_biRealNet = self.add_larq_residual_block(qkeras_biRealNet, 512) + + qkeras_biRealNet = tf.keras.layers.AveragePooling2D(pool_size=(7, 7))( + qkeras_biRealNet) + qkeras_biRealNet = tf.keras.layers.Flatten()(qkeras_biRealNet) + qkeras_biRealNet = tf.keras.layers.Dense(1000)(qkeras_biRealNet) + qkeras_biRealNet = tf.keras.layers.Activation("softmax", dtype="float32")( + qkeras_biRealNet) + qkeras_biRealNet = tf.keras.Model(inputs=input_layer, + outputs=qkeras_biRealNet) qkeras_biRealNet.load_weights(PATH_BIREALNET) return qkeras_biRealNet def build_larq_birealnet(self): + """Builds the larq version of the birealnet + + Returns: + Larq model of the birealnet """ - Build the larq version of the birealnet - :return: larq model of the birealnet - """ - larq_biRealNet = tf.keras.models.Sequential() - larq_biRealNet.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) - larq_biRealNet.add( - tf.keras.layers.Conv2D(64, (7, 7), strides=2, padding="same", - use_bias=False)) - larq_biRealNet.add(tf.keras.layers.BatchNormalization(momentum=0.8)) - larq_biRealNet.add( - tf.keras.layers.MaxPool2D((3, 3), strides=2, padding="same")) - for _ in range(0, 4): - self.add_larq_residual_block(larq_biRealNet, 64) - self.add_larq_connection_block(larq_biRealNet, 128) + input_layer = tf.keras.Input(shape=(224, 224, 3)) + larq_biRealNet = tf.keras.layers.Conv2D(64, (7, 7), strides=2, + padding="same", + use_bias=False)(input_layer) + larq_biRealNet = tf.keras.layers.BatchNormalization(momentum=0.8)( + larq_biRealNet) + larq_biRealNet = tf.keras.layers.MaxPool2D((3, 3), strides=2, + padding="same") \ + (larq_biRealNet) + for _ in range(0, 4): + larq_biRealNet = self.add_larq_residual_block(larq_biRealNet, 64) + larq_biRealNet = self.add_larq_connection_block(larq_biRealNet, 128) for _ in range(0, 3): - self.add_larq_residual_block(larq_biRealNet, 128) - self.add_larq_connection_block(larq_biRealNet, 256) - + larq_biRealNet = self.add_larq_residual_block(larq_biRealNet, 128) + larq_biRealNet = self.add_larq_connection_block(larq_biRealNet, 256) for _ in range(0, 3): - self.add_larq_residual_block(larq_biRealNet, 256) - self.add_larq_connection_block(larq_biRealNet, 512) - + larq_biRealNet = self.add_larq_residual_block(larq_biRealNet, 256) + larq_biRealNet = self.add_larq_connection_block(larq_biRealNet, 512) for _ in range(0, 3): - self.add_larq_residual_block(larq_biRealNet, 512) - larq_biRealNet.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) - larq_biRealNet.add(tf.keras.layers.Flatten()) - larq_biRealNet.add(tf.keras.layers.Dense(1000)) - larq_biRealNet.add(tf.keras.layers.Activation("softmax", dtype="float32")) + larq_biRealNet = self.add_larq_residual_block(larq_biRealNet, 512) + + larq_biRealNet = tf.keras.layers.AveragePooling2D(pool_size=(7, 7))( + larq_biRealNet) + larq_biRealNet = tf.keras.layers.Flatten()(larq_biRealNet) + larq_biRealNet = tf.keras.layers.Dense(1000)(larq_biRealNet) + larq_biRealNet = tf.keras.layers.Activation("softmax", dtype="float32")( + larq_biRealNet) + larq_biRealNet = tf.keras.Model(inputs=input_layer, outputs=larq_biRealNet) larq_biRealNet.load_weights(PATH_BIREALNET) return larq_biRealNet diff --git a/zoo/larq_models/biRealNet.json b/zoo/larq_models/biRealNet.json index aa67b6b4..93e595d2 100644 --- a/zoo/larq_models/biRealNet.json +++ b/zoo/larq_models/biRealNet.json @@ -1 +1 @@ -"{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_1\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_2\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d_4\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [7, 7], \"strides\": [2, 2], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_20\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.8, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [3, 3], \"padding\": \"same\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": {\"name\": \"magnitude_aware_sign\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_21\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign_1\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": {\"name\": \"magnitude_aware_sign_1\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_22\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_2\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": 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\"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_3\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign_3\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": 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\"reshape_6\", \"trainable\": true, \"dtype\": \"float32\", \"target_shape\": [100352, 1]}}, {\"class_name\": \"AveragePooling1D\", \"config\": {\"name\": \"average_pooling1d_3\", \"trainable\": true, \"dtype\": \"float32\", \"strides\": [2], \"pool_size\": [1], \"padding\": \"same\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Reshape\", \"config\": {\"name\": \"reshape_7\", \"trainable\": true, \"dtype\": \"float32\", \"target_shape\": [28, 28, 64]}}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d_5\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 128, \"kernel_size\": [1, 1], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, 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\"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign_5\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": {\"name\": \"magnitude_aware_sign_5\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_27\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_6\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 128, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign_6\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": {\"name\": \"magnitude_aware_sign_6\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_28\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_7\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 128, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign_7\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": {\"name\": \"magnitude_aware_sign_7\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_29\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.99, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"QuantConv2D\", \"config\": {\"name\": \"quant_conv2d_8\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 256, \"kernel_size\": [3, 3], \"strides\": [2, 2], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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\": {\"class_name\": \"weight_clip\", \"config\": {\"clip_value\": 1}}, \"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"ApproxSign\", \"config\": {\"name\": \"approx_sign_8\", \"trainable\": true, \"dtype\": \"float32\"}}, \"kernel_quantizer\": {\"class_name\": \"MagnitudeAwareSign\", \"config\": {\"name\": \"magnitude_aware_sign_8\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.0}}}}, {\"class_name\": \"Flatten\", \"config\": {\"name\": \"flatten_5\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Reshape\", \"config\": {\"name\": \"reshape_8\", \"trainable\": true, \"dtype\": \"float32\", \"target_shape\": [50176, 1]}}, {\"class_name\": \"AveragePooling1D\", \"config\": {\"name\": \"average_pooling1d_4\", \"trainable\": true, \"dtype\": \"float32\", \"strides\": [2], \"pool_size\": [1], \"padding\": \"same\", \"data_format\": \"channels_last\"}}, 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\"bias_constraint\": null, \"pad_values\": 0.0, \"input_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign_30\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.25}}, \"kernel_quantizer\": {\"class_name\": \"SteSign\", \"config\": {\"name\": \"ste_sign_31\", \"trainable\": true, \"dtype\": \"float32\", \"clip_value\": 1.25}}}, \"name\": \"quant_conv2d_15\", \"inbound_nodes\": [[[\"batch_normalization_40\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_41\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"batch_normalization_41\", \"inbound_nodes\": [[[\"quant_conv2d_15\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_4\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"activation_4\", \"inbound_nodes\": [[[\"batch_normalization_41\", 0, 0, {}]]]}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_3\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [7, 7], \"padding\": \"valid\", \"strides\": [7, 7], \"data_format\": \"channels_last\"}, \"name\": \"max_pooling2d_3\", \"inbound_nodes\": [[[\"activation_4\", 0, 0, {}]]]}, {\"class_name\": \"Flatten\", \"config\": {\"name\": \"flatten_1\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}, \"name\": \"flatten_1\", \"inbound_nodes\": [[[\"max_pooling2d_3\", 0, 0, {}]]]}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_1\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 1000, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotNormal\", \"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\": \"dense_1\", \"inbound_nodes\": [[[\"flatten_1\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_5\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"softmax\"}, \"name\": \"activation_5\", \"inbound_nodes\": [[[\"dense_1\", 0, 0, {}]]]}], \"input_layers\": [[\"input_2\", 0, 0]], \"output_layers\": [[\"activation_5\", 0, 0]]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file diff --git a/zoo/qkeras_models/biRealNet.json b/zoo/qkeras_models/biRealNet.json index f7f08a93..bc7c48a9 100644 --- a/zoo/qkeras_models/biRealNet.json +++ b/zoo/qkeras_models/biRealNet.json @@ -1 +1 @@ -"{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_1\"}}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv2d\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [7, 7], \"strides\": [2, 2], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"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}}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.8, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [3, 3], \"padding\": \"same\", \"strides\": [2, 2], \"data_format\": \"channels_last\"}}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 64, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"HeNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": 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\"dense_4\", \"inbound_nodes\": [[[\"flatten_4\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_29\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"softmax\"}, \"name\": \"activation_29\", \"inbound_nodes\": [[[\"dense_4\", 0, 0, {}]]]}], \"input_layers\": [[\"input_5\", 0, 0]], \"output_layers\": [[\"activation_29\", 0, 0]]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file diff --git a/zoo/qkeras_models/binary_resnet_e18.json b/zoo/qkeras_models/binary_resnet_e18.json index 516cdc9f..4f4c6bb7 100644 --- a/zoo/qkeras_models/binary_resnet_e18.json +++ b/zoo/qkeras_models/binary_resnet_e18.json @@ -1 +1 @@ -"{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_1\"}}, {\"class_name\": \"Conv2D\", 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\"inbound_nodes\": [[[\"average_pooling2d_2\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_17\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.8, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"batch_normalization_17\", \"inbound_nodes\": [[[\"q_conv2d_12\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_16\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.8, \"epsilon\": 0.001, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"batch_normalization_16\", \"inbound_nodes\": [[[\"conv2d_3\", 0, 0, {}]]]}, {\"class_name\": \"Add\", \"config\": {\"name\": \"add_2\", \"trainable\": true, \"dtype\": \"float32\"}, \"name\": \"add_2\", \"inbound_nodes\": [[[\"batch_normalization_17\", 0, 0, {}], [\"batch_normalization_16\", 0, 0, {}]]]}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation_13\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}, \"name\": \"q_activation_13\", \"inbound_nodes\": [[[\"add_2\", 0, 0, {}]]]}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d_13\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": {\"min_value\": -1, \"max_value\": 1}}, \"bias_constraint\": null, \"kernel_quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}}, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}, \"name\": \"q_conv2d_13\", \"inbound_nodes\": [[[\"q_activation_13\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_18\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"batch_normalization_18\", \"inbound_nodes\": [[[\"q_conv2d_13\", 0, 0, {}]]]}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation_14\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}, \"name\": \"q_activation_14\", \"inbound_nodes\": [[[\"batch_normalization_18\", 0, 0, {}]]]}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d_14\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": {\"min_value\": -1, \"max_value\": 1}}, \"bias_constraint\": null, \"kernel_quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}}, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}, \"name\": \"q_conv2d_14\", \"inbound_nodes\": [[[\"q_activation_14\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_19\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"batch_normalization_19\", \"inbound_nodes\": [[[\"q_conv2d_14\", 0, 0, {}]]]}, {\"class_name\": \"QActivation\", \"config\": {\"name\": \"q_activation_15\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"binary(alpha=1)\"}, \"name\": \"q_activation_15\", \"inbound_nodes\": [[[\"batch_normalization_19\", 0, 0, {}]]]}, {\"class_name\": \"QConv2D\", \"config\": {\"name\": \"q_conv2d_15\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 512, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"same\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"groups\": 1, \"activation\": \"linear\", \"use_bias\": false, \"kernel_initializer\": {\"class_name\": \"QInitializer\", \"config\": {\"initializer\": {\"class_name\": \"GlorotNormal\", \"config\": {\"seed\": null}, \"__passive_serialization__\": true}, \"use_scale\": true, \"quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}, \"__passive_serialization__\": true}}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": {\"class_name\": \"Clip\", \"config\": {\"min_value\": -1, \"max_value\": 1}}, \"bias_constraint\": null, \"kernel_quantizer\": {\"class_name\": \"binary\", \"config\": {\"use_01\": false, \"alpha\": 1, \"use_stochastic_rounding\": false}}, \"bias_quantizer\": null, \"kernel_range\": null, \"bias_range\": null}, \"name\": \"q_conv2d_15\", \"inbound_nodes\": [[[\"q_activation_15\", 0, 0, {}]]]}, {\"class_name\": \"BatchNormalization\", \"config\": {\"name\": \"batch_normalization_20\", \"trainable\": true, \"dtype\": \"float32\", \"axis\": [3], \"momentum\": 0.9, \"epsilon\": 1e-05, \"center\": true, \"scale\": true, \"beta_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"gamma_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"moving_mean_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"moving_variance_initializer\": {\"class_name\": \"Ones\", \"config\": {}}, \"beta_regularizer\": null, \"gamma_regularizer\": null, \"beta_constraint\": null, \"gamma_constraint\": null}, \"name\": \"batch_normalization_20\", \"inbound_nodes\": [[[\"q_conv2d_15\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_1\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}, \"name\": \"activation_1\", \"inbound_nodes\": [[[\"batch_normalization_20\", 0, 0, {}]]]}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"max_pooling2d_1\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [7, 7], \"padding\": \"valid\", \"strides\": [7, 7], \"data_format\": \"channels_last\"}, \"name\": \"max_pooling2d_1\", \"inbound_nodes\": [[[\"activation_1\", 0, 0, {}]]]}, {\"class_name\": \"Flatten\", \"config\": {\"name\": \"flatten\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}, \"name\": \"flatten\", \"inbound_nodes\": [[[\"max_pooling2d_1\", 0, 0, {}]]]}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 1000, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotNormal\", \"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\": \"dense\", \"inbound_nodes\": [[[\"flatten\", 0, 0, {}]]]}, {\"class_name\": \"Activation\", \"config\": {\"name\": \"activation_2\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"softmax\"}, \"name\": \"activation_2\", \"inbound_nodes\": [[[\"dense\", 0, 0, {}]]]}], \"input_layers\": [[\"input_1\", 0, 0]], \"output_layers\": [[\"activation_2\", 0, 0]]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file diff --git a/zoo/quicknet.py b/zoo/quicknet.py index f6aa1f06..8cedc25b 100644 --- a/zoo/quicknet.py +++ b/zoo/quicknet.py @@ -38,15 +38,23 @@ class QuickNet: - """ - Class to create and load weights of: quicknet, quicknet small and quicknet - large networks. Select the size of the network from size param. If None size + """Class to create quicknet, quicknet small and quicknet large networks. + + Select the size of the network from size param. If None size is provided creates the quicknet version. Attributes: network_name: Name of the network """ def __init__(self, size=None): + """Constructor for the network + + Attributes: + size: string to specify size of the network + + Raises: + NameError: if the size is not one of these: "", "small", "large" + """ if str(size).lower() == "large": self.__id = 0 self.__filters = ((64, 128, 256, 512)) @@ -63,119 +71,140 @@ def __init__(self, size=None): self.__weights_path = PATH_QUICKNET self.network_name = QUICKNET_NAME else: - raise NameError("name:", str, "not recognized") + raise NameError("name:", str(size), "not recognized") @staticmethod - def add_qkeras_residual(model, filters_num): - """ + def add_qkeras_residual(given_model, filters_num): + """Adds a sequence of layers to the given model + Add a sequence of: Activation quantization, Quantized Conv2D, BatchNormalization to the given model - :param model: model where to add the sequence - :param filters_num: number of filters for QConv2D - :return: model plus the sequence + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence """ - model.add(q.QActivation("binary")) - model.add(q.QConv2D(filters_num, (3, 3), activation="relu", - kernel_quantizer="binary(alpha=1)", - kernel_initializer="glorot_normal", - padding="same", use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - return model + given_model.add(q.QActivation("binary")) + given_model.add(q.QConv2D(filters_num, (3, 3), activation="relu", + kernel_quantizer="binary(alpha=1)", + kernel_initializer="glorot_normal", + padding="same", use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + return given_model @staticmethod - def add_qkeras_transistion(model, strides, filters_num): - """ + def add_qkeras_transistion(given_model, strides, filters_num): + """Adds a sequence of layers to the given model + Add a sequence of: Activation quantization, Quantized Conv2D, BatchNormalization to the given model - :param model: model where to add the sequence - :param strides: strides param for MaxPool2d and QConv2D - :param filters_num: number of filters for QConv2D - :return: model plus the sequence + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + kernel_size: kernel size for Conv2D + strides: strides for Conv2D + + Returns: + Given Model plus the sequence """ - model.add(tf.keras.layers.Activation("relu")) - model.add(tf.keras.layers.MaxPool2D(pool_size=strides, strides=1)) - model.add(tf.keras.layers.DepthwiseConv2D((3, 3), padding="same", - strides=strides, trainable=False, - use_bias=False)) - model.add(q.QConv2D(filters_num, (1, 1), kernel_initializer="glorot_normal", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - return model + given_model.add(tf.keras.layers.Activation("relu")) + given_model.add(tf.keras.layers.MaxPool2D(pool_size=strides, strides=1)) + given_model.add(tf.keras.layers.DepthwiseConv2D((3, 3), padding="same", + strides=strides, + trainable=False, + use_bias=False)) + given_model.add(q.QConv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + return given_model @staticmethod - def add_larq_residual(model, filters_num): - """ - Same method of add_qkeras_residual but for a larq network + def add_larq_residual(given_model, filters_num): + """Same method of add_qkeras_residual but for a larq network """ - model.add(lq.layers.QuantConv2D(filters_num, (3, 3), activation="relu", - input_quantizer="ste_sign", - kernel_quantizer= + given_model.add(lq.layers.QuantConv2D(filters_num, (3, 3), + activation="relu", + input_quantizer="ste_sign", + kernel_quantizer= lq.quantizers.SteSign(clip_value=1.25), - kernel_constraint= + kernel_constraint= lq.constraints.WeightClip(clip_value=1.25), - kernel_initializer="glorot_normal", - padding="same", use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - return model + kernel_initializer="glorot_normal", + padding="same", use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + return given_model @staticmethod - def add_larq_transistion(model, strides, filters_num): + def add_larq_transistion(given_model, strides, filters_num): + """Same method of add_qkeras_transistion but for a larq network """ - Same method of add_qkeras_transistion but for a larq network - """ - model.add(tf.keras.layers.Activation("relu")) - model.add(tf.keras.layers.MaxPool2D(pool_size=strides, strides=1)) - model.add(tf.keras.layers.DepthwiseConv2D((3, 3), padding="same", - strides=strides, - trainable=False, - use_bias=False)) - model.add(lq.layers.QuantConv2D(filters_num, (1, 1), - kernel_initializer="glorot_normal", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - return model + given_model.add(tf.keras.layers.Activation("relu")) + given_model.add(tf.keras.layers.MaxPool2D(pool_size=strides, strides=1)) + given_model.add(tf.keras.layers.DepthwiseConv2D((3, 3), padding="same", + strides=strides, + trainable=False, + use_bias=False)) + given_model.add(lq.layers.QuantConv2D(filters_num, (1, 1), + kernel_initializer="glorot_normal", + use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + return given_model + + def add_qkeras_first_block(self, given_model): + """Adds a sequence of layers to the given model - def add_qkeras_first_block(self, model): - """ Add a sequence of: Input, QConv2D, BatchNormalization, Activation, QdepthWiseConv2D, BatchNormalization, QConv2d, BatchNormalization - :param model: model where to add the sequence - :return: model plus the sequence - """ - model.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) - model.add(q.QConv2D(self.__filters[0] // 4, (3, 3), - kernel_initializer="he_normal", - padding="same", - strides=2, use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - model.add(tf.keras.layers.Activation("relu")) - model.add(q.QDepthwiseConv2D((3, 3), padding="same", strides=2, - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization(scale=False, - center=False)) - model.add(q.QConv2D(self.__filters[0], 1, - kernel_initializer="he_normal", - use_bias=False)) - model.add(tf.keras.layers.BatchNormalization()) - return model - def add_qkeras_last_block(self, model): + Args: + given_model: model where to add the sequence + + Returns: + Given Model plus the sequence """ + given_model.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) + given_model.add(q.QConv2D(self.__filters[0] // 4, (3, 3), + kernel_initializer="he_normal", + padding="same", + strides=2, use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + given_model.add(tf.keras.layers.Activation("relu")) + given_model.add(q.QDepthwiseConv2D((3, 3), padding="same", strides=2, + use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization(scale=False, + center=False)) + given_model.add(q.QConv2D(self.__filters[0], 1, + kernel_initializer="he_normal", + use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + return given_model + + def add_qkeras_last_block(self, given_model): + """Adds a sequence of layers to the given model + Add a sequence of: Activation, AveragePooling2D, Flatten, Dense - :param model: model where to add the sequence - :return: model plus the sequence + + Args: + given_model: model where to add the sequence + + Returns: + Given Model plus the sequence """ - model.add(tf.keras.layers.Activation("relu")) - model.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) - model.add(tf.keras.layers.Flatten()) - model.add(q.QDense(1000, kernel_initializer="glorot_normal")) - model.add(tf.keras.layers.Activation("softmax", dtype="float32")) - model.load_weights(self.__weights_path) - return model + given_model.add(tf.keras.layers.Activation("relu")) + given_model.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) + given_model.add(tf.keras.layers.Flatten()) + given_model.add(q.QDense(1000, kernel_initializer="glorot_normal")) + given_model.add(tf.keras.layers.Activation("softmax", dtype="float32")) + given_model.load_weights(self.__weights_path) + return given_model def add_larq_first_block(self, model): - """ - Same method of add_qkeras_first_block but for a larq network + """Same method of add_qkeras_first_block but for a larq network """ model.add(tf.keras.layers.InputLayer(input_shape=(224, 224, 3))) model.add(lq.layers.QuantConv2D(self.__filters[0] // 4, (3, 3), @@ -193,21 +222,22 @@ def add_larq_first_block(self, model): use_bias=False)) model.add(tf.keras.layers.BatchNormalization()) - def add_larq_last_block(self, model): + def add_larq_last_block(self, given_model): + """Same method of add_larq_first_block but for a larq network """ - Same method of add_larq_first_block but for a larq network - """ - model.add(tf.keras.layers.Activation("relu")) - model.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) - model.add(tf.keras.layers.Flatten()) - model.add(lq.layers.QuantDense(1000, kernel_initializer="glorot_normal")) - model.add(tf.keras.layers.Activation("softmax", dtype="float32")) - model.load_weights(self.__weights_path) + given_model.add(tf.keras.layers.Activation("relu")) + given_model.add(tf.keras.layers.AveragePooling2D(pool_size=(7, 7))) + given_model.add(tf.keras.layers.Flatten()) + given_model.add(lq.layers.QuantDense(1000, + kernel_initializer="glorot_normal")) + given_model.add(tf.keras.layers.Activation("softmax", dtype="float32")) + given_model.load_weights(self.__weights_path) def build(self): - """ - Build the model based on its ID - :return: qkeras and larq models + """Build the model based on its ID + + Returns: + Qkeras and larq models """ if self.__id == 0: qkeras_network = self.build_larq_quicknet_large() @@ -224,9 +254,10 @@ def build(self): return qkeras_network, larq_network def build_qkeras_quicknet_large(self): - """ - Build the qkeras version of the quicknet large - :return: qkeras model of the quicknet large + """Build the qkeras version of the quicknet large + + Returns: + Qkeras model of the quicknet large """ # Input layer qkeras_quicknet = tf.keras.models.Sequential() @@ -252,9 +283,10 @@ def build_qkeras_quicknet_large(self): return qkeras_quicknet def build_larq_quicknet_large(self): - """ - Build the larq version of the quicknet large - :return: larq model of the quicknet large + """Build the larq version of the quicknet large + + Returns: + larq model of the quicknet large """ # Input layer larq_quicknet = tf.keras.models.Sequential() @@ -280,9 +312,10 @@ def build_larq_quicknet_large(self): return larq_quicknet def build_qkeras_quicknet(self): - """ - Build the qkeras version of the quicknet - :return: qkeras model of the quicknet + """Build the qkeras version of the quicknet + + Returns: + qkeras model of the quicknet """ # Input layer qkeras_quicknet = tf.keras.models.Sequential() @@ -304,9 +337,10 @@ def build_qkeras_quicknet(self): return qkeras_quicknet def build_larq_quicknet(self): - """ - Build the larq version of the quicknet - :return: larq model of the quicknet + """Build the larq version of the quicknet + + Returns: + larq model of the quicknet """ # Input layer larq_quicknet = tf.keras.models.Sequential() @@ -332,7 +366,7 @@ def build_larq_quicknet(self): # Create a random dataset with 100 samples random_data = create_random_dataset(100) - network_names = ["quickNet", "quickNet_large", "quickNet_small"] + network_names = [QUICKNET_NAME, QUICKNET_LARGE_NAME, QUICKNET_SMALL_NAME] sizes = ["", "large", "small"] for size, name in zip(sizes, network_names): diff --git a/zoo/results.txt b/zoo/results.txt index b97945f5..badf6f83 100644 --- a/zoo/results.txt +++ b/zoo/results.txt @@ -4,14 +4,30 @@ Alexnet: Mean MSE for quickNet -> 0.0 Absolute errors for quickNet -> 0 -BiRealNet: - Mean MSE for biRealNet -> 8.4-05 - Absolute errors for biRealNet -> 6 +Binary DenseNet e28 + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 + +Binary DenseNet e37 + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 + +Binary DenseNet e37 dilated + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 + +Binary DenseNet e45 + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 -Binary ResNEt e18: +Binary ResNet e18: Mean MSE for quickNet -> 0.0 Absolute errors for quickNet -> 0 +BiRealNet: + Mean MSE for biRealNet -> 8.4-05 + Absolute errors for biRealNet -> 6 + Quicknet: Mean MSE for quickNet -> 5.6e-18 Absolute errors for quickNet -> 0