# -*- coding: utf-8 -*-
"""
keras_resnet.models._2d
~~~~~~~~~~~~~~~~~~~~~~~
This module implements popular two-dimensional residual models.
"""
import keras.backend
import keras.layers
import keras.models
import keras.regularizers
def ResNet(inputs, blocks, block, include_top=True, classes=1000, numerical_names=None, *args, **kwargs):
"""
Constructs a `keras.models.Model` object using the given block count.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param block: a residual block (e.g. an instance of `keras_resnet.blocks.basic_2d`)
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:param numerical_names: list of bool, same size as blocks, used to indicate whether names of layers should include numbers or letters
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.blocks
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> blocks = [2, 2, 2, 2]
>>> block = keras_resnet.blocks.basic_2d
>>> model = keras_resnet.models.ResNet(x, classes, blocks, block, classes=classes)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if keras.backend.image_data_format() == "channels_last":
axis = 3
else:
axis = 1
if numerical_names is None:
numerical_names = [True] * len(blocks)
x = keras.layers.ZeroPadding2D(padding=3, name="padding_conv1")(inputs)
x = keras.layers.Conv2D(64, (7, 7), strides=(2, 2), use_bias=False, name="conv1")(x)
x = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn_conv1")(x)
x = keras.layers.Activation("relu", name="conv1_relu")(x)
x = keras.layers.MaxPooling2D((3, 3), strides=(2, 2), padding="same", name="pool1")(x)
features = 64
outputs = []
for stage_id, iterations in enumerate(blocks):
for block_id in range(iterations):
x = block(features, stage_id, block_id, numerical_name=(block_id > 0 and numerical_names[stage_id]))(x)
features *= 2
outputs.append(x)
if include_top:
assert classes > 0
x = keras.layers.GlobalAveragePooling2D(name="pool5")(x)
x = keras.layers.Dense(classes, activation="softmax", name="fc1000")(x)
return keras.models.Model(inputs=inputs, outputs=x, *args, **kwargs)
else:
# Else output each stages features
return keras.models.Model(inputs=inputs, outputs=outputs, *args, **kwargs)
def ResNet18(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
"""
Constructs a `keras.models.Model` according to the ResNet18 specifications.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> model = keras_resnet.models.ResNet18(x, classes=classes)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if blocks is None:
blocks = [2, 2, 2, 2]
return ResNet(inputs, blocks, block=keras_resnet.blocks.basic_2d, include_top=include_top, classes=classes, *args, **kwargs)
def ResNet34(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
"""
Constructs a `keras.models.Model` according to the ResNet34 specifications.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> model = keras_resnet.models.ResNet34(x, classes=classes)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if blocks is None:
blocks = [3, 4, 6, 3]
return ResNet(inputs, blocks, block=keras_resnet.blocks.basic_2d, include_top=include_top, classes=classes, *args, **kwargs)
def ResNet50(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
"""
Constructs a `keras.models.Model` according to the ResNet50 specifications.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> model = keras_resnet.models.ResNet50(x)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if blocks is None:
blocks = [3, 4, 6, 3]
numerical_names = [False, False, False, False]
return ResNet(inputs, blocks, numerical_names=numerical_names, block=bottleneck_2d, include_top=include_top, classes=classes, *args, **kwargs)
def ResNet101(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
"""
Constructs a `keras.models.Model` according to the ResNet101 specifications.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> model = keras_resnet.models.ResNet101(x, classes=classes)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if blocks is None:
blocks = [3, 4, 23, 3]
numerical_names = [False, True, True, False]
return ResNet(inputs, blocks, numerical_names=numerical_names, block=bottleneck_2d, include_top=include_top, classes=classes, *args, **kwargs)
def ResNet152(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
"""
Constructs a `keras.models.Model` according to the ResNet152 specifications.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> model = keras_resnet.models.ResNet152(x, classes=classes)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if blocks is None:
blocks = [3, 8, 36, 3]
numerical_names = [False, True, True, False]
return ResNet(inputs, blocks, numerical_names=numerical_names, block=bottleneck_2d, include_top=include_top, classes=classes, *args, **kwargs)
def ResNet200(inputs, blocks=None, include_top=True, classes=1000, *args, **kwargs):
"""
Constructs a `keras.models.Model` according to the ResNet200 specifications.
:param inputs: input tensor (e.g. an instance of `keras.layers.Input`)
:param blocks: the network’s residual architecture
:param include_top: if true, includes classification layers
:param classes: number of classes to classify (include_top must be true)
:return model: ResNet model with encoding output (if `include_top=False`) or classification output (if `include_top=True`)
Usage:
>>> import keras_resnet.models
>>> shape, classes = (224, 224, 3), 1000
>>> x = keras.layers.Input(shape)
>>> model = keras_resnet.models.ResNet200(x, classes=classes)
>>> model.compile("adam", "categorical_crossentropy", ["accuracy"])
"""
if blocks is None:
blocks = [3, 24, 36, 3]
numerical_names = [False, True, True, False]
return ResNet(inputs, blocks, numerical_names=numerical_names, block=bottleneck_2d, include_top=include_top, classes=classes, *args, **kwargs)
import keras.layers
import keras.regularizers
import keras_resnet.layers
parameters = {
"kernel_initializer": "he_normal"
}
def basic_2d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
"""
A two-dimensional basic block.
:param filters: the output’s feature space
:param stage: int representing the stage of this block (starting from 0)
:param block: int representing this block (starting from 0)
:param kernel_size: size of the kernel
:param numerical_name: if true, uses numbers to represent blocks instead of chars (ResNet{101, 152, 200})
:param stride: int representing the stride used in the shortcut and the first conv layer, default derives stride from block id
Usage:
>>> import keras_resnet.blocks
>>> keras_resnet.blocks.basic_2d(64)
"""
if stride is None:
if block != 0 or stage == 0:
stride = 1
else:
stride = 2
if keras.backend.image_data_format() == "channels_last":
axis = 3
else:
axis = 1
if block > 0 and numerical_name:
block_char = "b{}".format(block)
else:
block_char = chr(ord('a') + block)
stage_char = str(stage + 2)
def f(x):
y = keras.layers.ZeroPadding2D(padding=1, name="padding{}{}_branch2a".format(stage_char, block_char))(x)
y = keras.layers.Conv2D(filters, kernel_size, strides=stride, use_bias=False, name="res{}{}_branch2a".format(stage_char, block_char), **parameters)(y)
y = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch2a".format(stage_char, block_char))(y)
y = keras.layers.Activation("relu", name="res{}{}_branch2a_relu".format(stage_char, block_char))(y)
y = keras.layers.ZeroPadding2D(padding=1, name="padding{}{}_branch2b".format(stage_char, block_char))(y)
y = keras.layers.Conv2D(filters, kernel_size, use_bias=False, name="res{}{}_branch2b".format(stage_char, block_char), **parameters)(y)
y = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch2b".format(stage_char, block_char))(y)
if block == 0:
shortcut = keras.layers.Conv2D(filters, (1, 1), strides=stride, use_bias=False, name="res{}{}_branch1".format(stage_char, block_char), **parameters)(x)
shortcut = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch1".format(stage_char, block_char))(shortcut)
else:
shortcut = x
y = keras.layers.Add(name="res{}{}".format(stage_char, block_char))([y, shortcut])
y = keras.layers.Activation("relu", name="res{}{}_relu".format(stage_char, block_char))(y)
return y
return f
def bottleneck_2d(filters, stage=0, block=0, kernel_size=3, numerical_name=False, stride=None):
"""
A two-dimensional bottleneck block.
:param filters: the output’s feature space
:param stage: int representing the stage of this block (starting from 0)
:param block: int representing this block (starting from 0)
:param kernel_size: size of the kernel
:param numerical_name: if true, uses numbers to represent blocks instead of chars (ResNet{101, 152, 200})
:param stride: int representing the stride used in the shortcut and the first conv layer, default derives stride from block id
Usage:
>>> import keras_resnet.blocks
>>> bottleneck_2d(64)
"""
if stride is None:
if block != 0 or stage == 0:
stride = 1
else:
stride = 2
if keras.backend.image_data_format() == "channels_last":
axis = 3
else:
axis = 1
if block > 0 and numerical_name:
block_char = "b{}".format(block)
else:
block_char = chr(ord('a') + block)
stage_char = str(stage + 2)
def f(x):
y = keras.layers.Conv2D(filters, (1, 1), strides=stride, use_bias=False, name="res{}{}_branch2a".format(stage_char, block_char), **parameters)(x)
y = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch2a".format(stage_char, block_char))(y)
y = keras.layers.Activation("relu", name="res{}{}_branch2a_relu".format(stage_char, block_char))(y)
y = keras.layers.ZeroPadding2D(padding=1, name="padding{}{}_branch2b".format(stage_char, block_char))(y)
y = keras.layers.Conv2D(filters, kernel_size, use_bias=False, name="res{}{}_branch2b".format(stage_char, block_char), **parameters)(y)
y = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch2b".format(stage_char, block_char))(y)
y = keras.layers.Activation("relu", name="res{}{}_branch2b_relu".format(stage_char, block_char))(y)
y = keras.layers.Conv2D(filters * 4, (1, 1), use_bias=False, name="res{}{}_branch2c".format(stage_char, block_char), **parameters)(y)
y = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch2c".format(stage_char, block_char))(y)
if block == 0:
shortcut = keras.layers.Conv2D(filters * 4, (1, 1), strides=stride, use_bias=False, name="res{}{}_branch1".format(stage_char, block_char), **parameters)(x)
shortcut = keras.layers.BatchNormalization(axis=axis, epsilon=1e-5, name="bn{}{}_branch1".format(stage_char, block_char))(shortcut)
else:
shortcut = x
y = keras.layers.Add(name="res{}{}".format(stage_char, block_char))([y, shortcut])
y = keras.layers.Activation("relu", name="res{}{}_relu".format(stage_char, block_char))(y)
return y
return f
Datasets
Models
