diff --git a/zoo/alexnet.py b/zoo/alexnet.py new file mode 100644 index 00000000..910b4b0b --- /dev/null +++ b/zoo/alexnet.py @@ -0,0 +1,201 @@ +# 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: +# Alexnet -> https://drive.google.com/file/d/1-65sB1xnJuOoPhL00TYY0s3Fov0zxBHJ/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_ALEXNET = "weights/binary_alexnet_weights.h5" +ALEXNET_NAME = "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 + + @staticmethod + 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 + + 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: + 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: + 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(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 + """ + given_model.add(q.QActivation("binary(alpha=1)")) + given_model.add( + q.QDense(units, kernel_quantizer="binary(alpha=1)", use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization(scale=False, + momentum=0.9)) + return given_model + + @staticmethod + 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 + """ + 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: + 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(given_model, units): + """Same method of add_qkeras_dense_block but for a larq network + """ + 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 + + Returns: + 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/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 new file mode 100644 index 00000000..b050e8ee --- /dev/null +++ b/zoo/binary_resnet_e18.py @@ -0,0 +1,254 @@ +# 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: +# resnet_e18 -> 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 = "weights/resnet_e_18_weights.h5" +RESNET_E18_NAME = "binary_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.filters = (64, 128, 256, 512) + + @staticmethod + 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 + + 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 + """ + 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_larq_quant_block(given_model, filters_num, strides=1): + """Same method of add_qkeras_quant_block but for a larq network + """ + 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_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 + """ + 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(give_model, filters_num): + """Same method of add_qkeras_connection_block but for a larq network + """ + 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 + + Returns: + 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 + + Returns: + qkeras model of the resnet_e18 + """ + 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): + 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): + 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): + 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 + """ + 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): + 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): + 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): + 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 + + +if __name__ == "__main__": + # Create a random dataset with 100 samples + random_data = create_random_dataset(100) + + 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) + dump_network_to_json(qkeras_network=qkeras_network, + larq_network=larq_network, + network_name=RESNET_E18_NAME) diff --git a/zoo/birealnet.py b/zoo/birealnet.py new file mode 100644 index 00000000..719172ae --- /dev/null +++ b/zoo/birealnet.py @@ -0,0 +1,223 @@ +# 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: +# Birealnet -> https://drive.google.com/file/d/1BuDYhydNAy-sFdvoh24gJbpA0zRDvGVq/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_BIREALNET = "weights/birealnet_weights.h5" +BIREALNET_NAME = "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 + + @staticmethod + 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 + + 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 + """ + 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(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 + """ + 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(given_model, features): + """Same method of add_qkeras_residual_block but for a larq network + """ + 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(given_model, filters_num): + """Same method of add_qkeras_connection_block but for a larq network + """ + 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): + """Builds the model + + Returns: + 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): + """Builds the qkeras version of the birealnet + + Returns: + Qkeras model of the birealnet + """ + 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): + 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): + 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): + 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 + """ + 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): + 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): + 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): + 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 + + +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}}, 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\"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\": \"softmax\"}}]}, \"keras_version\": \"2.4.0\", \"backend\": \"tensorflow\"}" \ No newline at end of file diff --git a/zoo/larq_models/biRealNet.json b/zoo/larq_models/biRealNet.json new file mode 100644 index 00000000..93e595d2 --- /dev/null +++ b/zoo/larq_models/biRealNet.json @@ -0,0 +1 @@ +"{\"class_name\": \"Functional\", \"config\": {\"name\": \"model_1\", \"layers\": [{\"class_name\": \"InputLayer\", \"config\": {\"batch_input_shape\": [null, 224, 224, 3], \"dtype\": \"float32\", \"sparse\": false, \"ragged\": false, \"name\": \"input_2\"}, \"name\": \"input_2\", \"inbound_nodes\": []}, 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\"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_28\", \"trainable\": true, \"dtype\": \"float32\", \"activation\": \"relu\"}}, {\"class_name\": \"AveragePooling2D\", \"config\": {\"name\": \"average_pooling2d_4\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [7, 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..8cedc25b --- /dev/null +++ b/zoo/quicknet.py @@ -0,0 +1,379 @@ +# 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: +# 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 +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 = "./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 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)) + 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(size), "not recognized") + + @staticmethod + 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 + + Args: + given_model: model where to add the sequence + filters_num: number of filters for Conv2D + + Returns: + Given Model plus the sequence + """ + 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(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 + + 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 + """ + 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(given_model, filters_num): + """Same method of add_qkeras_residual but for a larq network + """ + 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= + lq.constraints.WeightClip(clip_value=1.25), + kernel_initializer="glorot_normal", + padding="same", use_bias=False)) + given_model.add(tf.keras.layers.BatchNormalization()) + return given_model + + @staticmethod + def add_larq_transistion(given_model, strides, filters_num): + """Same method of add_qkeras_transistion but for a larq network + """ + 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 + + Add a sequence of: Input, QConv2D, BatchNormalization, Activation, + QdepthWiseConv2D, BatchNormalization, QConv2d, BatchNormalization + + 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 + + Args: + given_model: model where to add the sequence + + Returns: + Given Model plus the sequence + """ + 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 + """ + 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, given_model): + """Same method of add_larq_first_block but for a larq network + """ + 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 + + Returns: + 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 + + Returns: + 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 + + Returns: + 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 + + Returns: + 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 + + Returns: + 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_NAME, QUICKNET_LARGE_NAME, QUICKNET_SMALL_NAME] + 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..2c938568 --- /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 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.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. +- ./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..badf6f83 --- /dev/null +++ b/zoo/results.txt @@ -0,0 +1,41 @@ +Results on 100 random samples: + +Alexnet: + Mean MSE for quickNet -> 0.0 + Absolute errors for quickNet -> 0 + +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: + 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..16577355 --- /dev/null +++ b/zoo/utils.py @@ -0,0 +1,133 @@ +# 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" + +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