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Update DenseNet with ImageNet weights and major code cleanup

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Somshubra Majumdar
2017-09-06 13:01:50 -05:00
parent 156c7e8e24
commit 7bae1db1f2
1 changed files with 552 additions and 225 deletions
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keras_contrib/applications/densenet.py
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@@ -1,5 +1,38 @@
# -*- coding: utf-8 -*-
'''DenseNet models for Keras.
'''DenseNet and DenseNet-FCN models for Keras.
DenseNet is a network architecture where each layer is directly connected
to every other layer in a feed-forward fashion (within each dense block).
For each layer, the feature maps of all preceding layers are treated as
separate inputs whereas its own feature maps are passed on as inputs to
all subsequent layers. This connectivity pattern yields state-of-the-art
accuracies on CIFAR10/100 (with or without data augmentation) and SVHN.
On the large scale ILSVRC 2012 (ImageNet) dataset, DenseNet achieves a
similar accuracy as ResNet, but using less than half the amount of
parameters and roughly half the number of FLOPs.
DenseNets support any input image size of 32x32 or greater, and are thus
suited for CIFAR-10 or CIFAR-100 datasets. There are two types of DenseNets,
one suited for smaller images (DenseNet) and one suited for ImageNet,
called DenseNetImageNet. They are differentiated by the strided convolution
and pooling operations prior to the initial dense block.
The following table describes the size and accuracy of DenseNetImageNet models
on the ImageNet dataset (single crop), for which weights are provided:
------------------------------------------------------------------------------------
Model type | ImageNet Acc (Top 1) | ImageNet Acc (Top 5) | Params (M) |
------------------------------------------------------------------------------------
| DenseNet-121 | 25.02 % | 7.71 % | 8.0 |
| DenseNet-169 | 23.80 % | 6.85 % | 14.3 |
| DenseNet-201 | 22.58 % | 6.34 % | 20.2 |
| DenseNet-161 | 22.20 % | - % | 28.9 |
------------------------------------------------------------------------------------
DenseNets can be extended to image segmentation tasks as described in the
paper "The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for
Semantic Segmentation". Here, the dense blocks are arranged and concatenated
with long skip connections for state of the art performance on CamVid dataset.
# Reference
- [Densely Connected Convolutional Networks](https://arxiv.org/pdf/1608.06993.pdf)
- [The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/pdf/1611.09326.pdf)
@@ -11,89 +44,169 @@ from __future__ import division
import warnings
from keras.models import Model
from keras.layers.core import Dense, Dropout, Activation, Reshape
from keras.layers.convolutional import Conv2D, Conv2DTranspose, UpSampling2D
from keras.layers.pooling import AveragePooling2D
from keras.layers.pooling import GlobalAveragePooling2D
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Activation
from keras.layers import Reshape
from keras.layers import Conv2D
from keras.layers import Conv2DTranspose
from keras.layers import UpSampling2D
from keras.layers import MaxPooling2D
from keras.layers import AveragePooling2D
from keras.layers import GlobalMaxPooling2D
from keras.layers import GlobalAveragePooling2D
from keras.layers import Input
from keras.layers.merge import concatenate
from keras.layers.normalization import BatchNormalization
from keras.layers import concatenate
from keras.layers import BatchNormalization
from keras.regularizers import l2
from keras.utils.layer_utils import convert_all_kernels_in_model
from keras.utils.data_utils import get_file
from keras.engine.topology import get_source_inputs
from keras.applications.imagenet_utils import _obtain_input_shape
from keras.applications.imagenet_utils import decode_predictions
import keras.backend as K
from keras_contrib.layers.convolutional import SubPixelUpscaling
TH_WEIGHTS_PATH = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Theano-Backend-TH-dim-ordering.h5'
TF_WEIGHTS_PATH = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Tensorflow-Backend-TF-dim-ordering.h5'
TH_WEIGHTS_PATH_NO_TOP = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Theano-Backend-TH-dim-ordering-no-top.h5'
TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/titu1994/DenseNet/releases/download/v2.0/DenseNet-40-12-Tensorflow-Backend-TF-dim-ordering-no-top.h5'
DENSENET_121_WEIGHTS_PATH = r'https://github.com/titu1994/DenseNet/releases/download/v3.0/DenseNet-BC-121-32.h5'
DENSENET_161_WEIGHTS_PATH = r'https://github.com/titu1994/DenseNet/releases/download/v3.0/DenseNet-BC-161-48.h5'
DENSENET_169_WEIGHTS_PATH = r'https://github.com/titu1994/DenseNet/releases/download/v3.0/DenseNet-BC-169-32.h5'
DENSENET_121_WEIGHTS_PATH_NO_TOP = r'https://github.com/titu1994/DenseNet/releases/download/v3.0/DenseNet-BC-121-32-no-top.h5'
DENSENET_161_WEIGHTS_PATH_NO_TOP = r'https://github.com/titu1994/DenseNet/releases/download/v3.0/DenseNet-BC-161-48-no-top.h5'
DENSENET_169_WEIGHTS_PATH_NO_TOP = r'https://github.com/titu1994/DenseNet/releases/download/v3.0/DenseNet-BC-169-32-no-top.h5'
def DenseNet(input_shape=None, depth=40, nb_dense_block=3, growth_rate=12, nb_filter=16, nb_layers_per_block=-1,
bottleneck=False, reduction=0.0, dropout_rate=0.0, weight_decay=1E-4,
include_top=True, weights='cifar10', input_tensor=None,
classes=10, activation='softmax'):
'''Instantiate the DenseNet architecture,
optionally loading weights pre-trained
on CIFAR-10. Note that when using TensorFlow,
for best performance you should set
`image_data_format='channels_last'` in your Keras config
at ~/.keras/keras.json.
The model and the weights are compatible with both
TensorFlow and Theano. The dimension ordering
convention used by the model is the one
specified in your Keras config file.
# Arguments
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(32, 32, 3)` (with `channels_last` dim ordering)
or `(3, 32, 32)` (with `channels_first` dim ordering).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 8.
E.g. `(200, 200, 3)` would be one valid value.
depth: number or layers in the DenseNet
nb_dense_block: number of dense blocks to add to end (generally = 3)
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters. -1 indicates initial
number of filters is 2 * growth_rate
nb_layers_per_block: number of layers in each dense block.
Can be a -1, positive integer or a list.
If -1, calculates nb_layer_per_block from the network depth.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be (nb_dense_block + 1)
bottleneck: flag to add bottleneck blocks in between dense blocks
reduction: reduction factor of transition blocks.
Note : reduction value is inverted to compute compression.
dropout_rate: dropout rate
weight_decay: weight decay factor
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization) or
'cifar10' (pre-training on CIFAR-10)..
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
# Returns
A Keras model instance.
'''
def preprocess_input(x, data_format=None):
"""Preprocesses a tensor encoding a batch of images.
if weights not in {'cifar10', None}:
# Arguments
x: input Numpy tensor, 4D.
data_format: data format of the image tensor.
# Returns
Preprocessed tensor.
"""
if data_format is None:
data_format = K.image_data_format()
assert data_format in {'channels_last', 'channels_first'}
if data_format == 'channels_first':
if x.ndim == 3:
# 'RGB'->'BGR'
x = x[::-1, ...]
# Zero-center by mean pixel
x[0, :, :] -= 103.939
x[1, :, :] -= 116.779
x[2, :, :] -= 123.68
else:
x = x[:, ::-1, ...]
x[:, 0, :, :] -= 103.939
x[:, 1, :, :] -= 116.779
x[:, 2, :, :] -= 123.68
else:
# 'RGB'->'BGR'
x = x[..., ::-1]
# Zero-center by mean pixel
x[..., 0] -= 103.939
x[..., 1] -= 116.779
x[..., 2] -= 123.68
x *= 0.017 # scale values
return x
def DenseNet(input_shape=None,
depth=40,
nb_dense_block=3,
growth_rate=12,
nb_filter=-1,
nb_layers_per_block=-1,
bottleneck=False,
reduction=0.0,
dropout_rate=0.0,
weight_decay=1e-4,
subsample_initial_block=False,
include_top=True,
weights=None,
input_tensor=None,
pooling=None,
classes=10,
activation='softmax'):
'''Instantiate the DenseNet architecture.
The model and the weights are compatible with both
TensorFlow and Theano. The dimension ordering
convention used by the model is the one
specified in your Keras config file.
# Arguments
input_shape: optional shape tuple, only to be specified
if `include_top` is False (otherwise the input shape
has to be `(224, 224, 3)` (with `channels_last` dim ordering)
or `(3, 224, 224)` (with `channels_first` dim ordering).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 8.
E.g. `(224, 224, 3)` would be one valid value.
depth: number or layers in the DenseNet
nb_dense_block: number of dense blocks to add to end
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters. -1 indicates initial
number of filters will default to 2 * growth_rate
nb_layers_per_block: number of layers in each dense block.
Can be a -1, positive integer or a list.
If -1, calculates nb_layer_per_block from the network depth.
If positive integer, a set number of layers per dense block.
If list, nb_layer is used as provided. Note that list size must
be nb_dense_block
bottleneck: flag to add bottleneck blocks in between dense blocks
reduction: reduction factor of transition blocks.
Note : reduction value is inverted to compute compression.
dropout_rate: dropout rate
weight_decay: weight decay rate
subsample_initial_block: Set to True to subsample the initial
convolution and add a MaxPool2D before the dense blocks are added.
include_top: whether to include the fully-connected
layer at the top of the network.
weights: one of `None` (random initialization) or
'imagenet' (pre-training on ImageNet)..
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
to use as image input for the model.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
- `max` means that global max pooling will
be applied.
classes: optional number of classes to classify images
into, only to be specified if `include_top` is True, and
if no `weights` argument is specified.
activation: Type of activation at the top layer. Can be one of
'softmax' or 'sigmoid'. Note that if sigmoid is used,
classes must be 1.
# Returns
A Keras model instance.
# Raises
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
'''
if weights not in {'imagenet', None}:
raise ValueError('The `weights` argument should be either '
'`None` (random initialization) or `cifar10` '
'(pre-training on CIFAR-10).')
'`None` (random initialization) or `imagenet` '
'(pre-training on ImageNet).')
if weights == 'cifar10' and include_top and classes != 10:
raise ValueError('If using `weights` as CIFAR 10 with `include_top`'
' as true, `classes` should be 10')
if weights == 'imagenet' and include_top and classes != 1000:
raise ValueError('If using `weights` as ImageNet with `include_top` '
'as true, `classes` should be 1000')
if activation not in ['softmax', 'sigmoid']:
raise ValueError('activation must be one of "softmax" or "sigmoid"')
@@ -106,7 +219,7 @@ def DenseNet(input_shape=None, depth=40, nb_dense_block=3, growth_rate=12, nb_fi
default_size=32,
min_size=8,
data_format=K.image_data_format(),
include_top=include_top)
require_flatten=include_top)
if input_tensor is None:
img_input = Input(shape=input_shape)
@@ -117,8 +230,9 @@ def DenseNet(input_shape=None, depth=40, nb_dense_block=3, growth_rate=12, nb_fi
img_input = input_tensor
x = __create_dense_net(classes, img_input, include_top, depth, nb_dense_block,
growth_rate, nb_filter, nb_layers_per_block, bottleneck, reduction,
dropout_rate, weight_decay, activation)
growth_rate, nb_filter, nb_layers_per_block, bottleneck,
reduction, dropout_rate, weight_decay, subsample_initial_block,
pooling, activation)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
@@ -130,47 +244,69 @@ def DenseNet(input_shape=None, depth=40, nb_dense_block=3, growth_rate=12, nb_fi
model = Model(inputs, x, name='densenet')
# load weights
if weights == 'cifar10':
if (depth == 40) and (nb_dense_block == 3) and (growth_rate == 12) and (nb_filter == 16) and \
(bottleneck is False) and (reduction == 0.0) and (dropout_rate == 0.0) and (weight_decay == 1E-4):
# Default parameters match. Weights for this model exist:
if weights == 'imagenet':
weights_loaded = False
if K.image_data_format() == 'channels_first':
if include_top:
weights_path = get_file('densenet_40_12_th_dim_ordering_th_kernels.h5',
TH_WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('densenet_40_12_th_dim_ordering_th_kernels_no_top.h5',
TH_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
model.load_weights(weights_path)
if K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image dimension ordering convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
convert_all_kernels_in_model(model)
if (depth == 121) and (nb_dense_block == 4) and (growth_rate == 32) and (nb_filter == 64) and \
(bottleneck is True) and (reduction == 0.5) and (dropout_rate == 0.0) and (subsample_initial_block):
if include_top:
weights_path = get_file('DenseNet-BC-121-32.h5',
DENSENET_121_WEIGHTS_PATH,
cache_subdir='models',
md5_hash='a439dd41aa672aef6daba4ee1fd54abd')
else:
if include_top:
weights_path = get_file('densenet_40_12_tf_dim_ordering_tf_kernels.h5',
TF_WEIGHTS_PATH,
cache_subdir='models')
else:
weights_path = get_file('densenet_40_12_tf_dim_ordering_tf_kernels_no_top.h5',
TF_WEIGHTS_PATH_NO_TOP,
cache_subdir='models')
weights_path = get_file('DenseNet-BC-121-32-no-top.h5',
DENSENET_121_WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='8804bcb37da5be4a52dc4e45d4425ba7')
model.load_weights(weights_path)
weights_loaded = True
model.load_weights(weights_path)
if (depth == 161) and (nb_dense_block == 4) and (growth_rate == 48) and (nb_filter == 96) and \
(bottleneck is True) and (reduction == 0.5) and (dropout_rate == 0.0) and (subsample_initial_block):
if include_top:
weights_path = get_file('DenseNet-BC-161-48.h5',
DENSENET_161_WEIGHTS_PATH,
cache_subdir='models',
md5_hash='6c326cf4fbdb57d31eff04333a23fcca')
else:
weights_path = get_file('DenseNet-BC-161-48-no-top.h5',
DENSENET_161_WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='d38903b8732fe238c91dac7859271f26')
model.load_weights(weights_path)
weights_loaded = True
if K.backend() == 'theano':
convert_all_kernels_in_model(model)
if (depth == 169) and (nb_dense_block == 4) and (growth_rate == 32) and (nb_filter == 64) and \
(bottleneck is True) and (reduction == 0.5) and (dropout_rate == 0.0) and (subsample_initial_block):
if include_top:
weights_path = get_file('DenseNet-BC-169-32.h5',
DENSENET_169_WEIGHTS_PATH,
cache_subdir='models',
md5_hash='914869c361303d2e39dec640b4e606a6')
else:
weights_path = get_file('DenseNet-BC-169-32-no-top.h5',
DENSENET_169_WEIGHTS_PATH_NO_TOP,
cache_subdir='models',
md5_hash='a664d78a30ddd217dd38c0bb8d258461')
model.load_weights(weights_path)
weights_loaded = True
if weights_loaded:
if K.backend() == 'theano':
convert_all_kernels_in_model(model)
if K.image_data_format() == 'channels_first' and K.backend() == 'tensorflow':
warnings.warn('You are using the TensorFlow backend, yet you '
'are using the Theano '
'image data format convention '
'(`image_data_format="channels_first"`). '
'For best performance, set '
'`image_data_format="channels_last"` in '
'your Keras config '
'at ~/.keras/keras.json.')
print("Weights for the model were loaded successfully")
return model
@@ -297,95 +433,182 @@ def DenseNetFCN(input_shape, nb_dense_block=5, growth_rate=16, nb_layers_per_blo
return model
def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_decay=1E-4):
''' Apply BatchNorm, Relu, 3x3 Conv2D, optional bottleneck block and dropout
Args:
ip: Input keras tensor
nb_filter: number of filters
bottleneck: add bottleneck block
def DenseNetImageNet121(input_shape=None,
bottleneck=True,
reduction=0.5,
dropout_rate=0.0,
weight_decay=1e-4,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000,
activation='softmax'):
return DenseNet(input_shape, depth=121, nb_dense_block=4, growth_rate=32, nb_filter=64,
nb_layers_per_block=[6, 12, 24, 16], bottleneck=bottleneck, reduction=reduction,
dropout_rate=dropout_rate, weight_decay=weight_decay, subsample_initial_block=True,
include_top=include_top, weights=weights, input_tensor=input_tensor,
pooling=pooling, classes=classes, activation=activation)
def DenseNetImageNet169(input_shape=None,
bottleneck=True,
reduction=0.5,
dropout_rate=0.0,
weight_decay=1e-4,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000,
activation='softmax'):
return DenseNet(input_shape, depth=169, nb_dense_block=4, growth_rate=32, nb_filter=64,
nb_layers_per_block=[6, 12, 32, 32], bottleneck=bottleneck, reduction=reduction,
dropout_rate=dropout_rate, weight_decay=weight_decay, subsample_initial_block=True,
include_top=include_top, weights=weights, input_tensor=input_tensor,
pooling=pooling, classes=classes, activation=activation)
def DenseNetImageNet201(input_shape=None,
bottleneck=True,
reduction=0.5,
dropout_rate=0.0,
weight_decay=1e-4,
include_top=True,
weights=None,
input_tensor=None,
pooling=None,
classes=1000,
activation='softmax'):
return DenseNet(input_shape, depth=201, nb_dense_block=4, growth_rate=32, nb_filter=64,
nb_layers_per_block=[6, 12, 48, 32], bottleneck=bottleneck, reduction=reduction,
dropout_rate=dropout_rate, weight_decay=weight_decay, subsample_initial_block=True,
include_top=include_top, weights=weights, input_tensor=input_tensor,
pooling=pooling, classes=classes, activation=activation)
def DenseNetImageNet264(input_shape=None,
bottleneck=True,
reduction=0.5,
dropout_rate=0.0,
weight_decay=1e-4,
include_top=True,
weights=None,
input_tensor=None,
pooling=None,
classes=1000,
activation='softmax'):
return DenseNet(input_shape, depth=201, nb_dense_block=4, growth_rate=32, nb_filter=64,
nb_layers_per_block=[6, 12, 64, 48], bottleneck=bottleneck, reduction=reduction,
dropout_rate=dropout_rate, weight_decay=weight_decay, subsample_initial_block=True,
include_top=include_top, weights=weights, input_tensor=input_tensor,
pooling=pooling, classes=classes, activation=activation)
def DenseNetImageNet161(input_shape=None,
bottleneck=True,
reduction=0.5,
dropout_rate=0.0,
weight_decay=1e-4,
include_top=True,
weights='imagenet',
input_tensor=None,
pooling=None,
classes=1000,
activation='softmax'):
return DenseNet(input_shape, depth=161, nb_dense_block=4, growth_rate=48, nb_filter=96,
nb_layers_per_block=[6, 12, 36, 24], bottleneck=bottleneck, reduction=reduction,
dropout_rate=dropout_rate, weight_decay=weight_decay, subsample_initial_block=True,
include_top=include_top, weights=weights, input_tensor=input_tensor,
pooling=pooling, classes=classes, activation=activation)
def __conv_block(ip, nb_filter, bottleneck=False, dropout_rate=None, weight_decay=1e-4):
'''
Adds a convolution layer (with batch normalization and relu),
and optionally a bottleneck layer.
# Arguments
ip: Input tensor
nb_filter: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
bottleneck: if True, adds a bottleneck convolution block
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor with batch_norm, relu and convolution2d added (optional bottleneck)
'''
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, filters, new_rows, new_cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, new_rows, new_cols, filters)` if data_format='channels_last'.
`rows` and `cols` values might have changed due to stride.
# Returns
output tensor of block
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(ip)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
x = Activation('relu')(x)
if bottleneck:
inter_channel = nb_filter * 4 # Obtained from https://github.com/liuzhuang13/DenseNet/blob/master/densenet.lua
inter_channel = nb_filter * 4
x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False,
x = Conv2D(inter_channel, (1, 1), kernel_initializer='he_normal', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
x = Activation('relu')(x)
x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_uniform', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_normal', padding='same', use_bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def __transition_block(ip, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):
''' Apply BatchNorm, Relu 1x1, Conv2D, optional compression, dropout and Maxpooling2D
Args:
ip: keras tensor
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps
in the transition block.
def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropout_rate=None,
weight_decay=1e-4, grow_nb_filters=True, return_concat_list=False):
'''
Build a dense_block where the output of each conv_block is fed
to subsequent ones
# Arguments
x: input keras tensor
nb_layers: the number of conv_blocks to append to the model
nb_filter: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
growth_rate: growth rate of the dense block
bottleneck: if True, adds a bottleneck convolution block to
each conv_block
dropout_rate: dropout rate
weight_decay: weight decay factor
Returns: keras tensor, after applying batch_norm, relu-conv, dropout, maxpool
grow_nb_filters: if True, allows number of filters to grow
return_concat_list: set to True to return the list of
feature maps along with the actual output
# Return
If return_concat_list is True, returns a list of the output
keras tensor, the number of filters and a list of all the
dense blocks added to the keras tensor
If return_concat_list is False, returns a list of the output
keras tensor and the number of filters
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(ip)
x = Activation('relu')(x)
x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_uniform', padding='same', use_bias=False,
kernel_regularizer=l2(weight_decay))(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
x = AveragePooling2D((2, 2), strides=(2, 2))(x)
return x
def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropout_rate=None, weight_decay=1E-4,
grow_nb_filters=True, return_concat_list=False):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
Args:
x: keras tensor
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
bottleneck: bottleneck block
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
return_concat_list: return the list of feature maps along with the actual output
Returns: keras tensor with nb_layers of conv_block appended
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x_list = [x]
for i in range(nb_layers):
conv_block = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
x_list.append(conv_block)
cb = __conv_block(x, growth_rate, bottleneck, dropout_rate, weight_decay)
x_list.append(cb)
x = concatenate(x_list, axis=concat_axis)
x = concatenate([x, cb], axis=concat_axis)
if grow_nb_filters:
nb_filter += growth_rate
@@ -396,36 +619,96 @@ def __dense_block(x, nb_layers, nb_filter, growth_rate, bottleneck=False, dropou
return x, nb_filter
def __transition_up_block(ip, nb_filters, type='upsampling', weight_decay=1E-4):
''' SubpixelConvolutional Upscaling (factor = 2)
Args:
ip: keras tensor
nb_filters: number of layers
type: can be 'upsampling', 'subpixel', 'deconv'. Determines type of upsampling performed
def __transition_block(ip, nb_filter, compression=1.0, weight_decay=1e-4):
'''
Adds a pointwise convolution layer (with batch normalization and relu),
and an average pooling layer. The number of output convolution filters
can be reduced by appropriately reducing the compression parameter.
# Arguments
ip: input keras tensor
nb_filter: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
compression: calculated as 1 - reduction. Reduces the number
of feature maps in the transition block.
weight_decay: weight decay factor
Returns: keras tensor, after applying upsampling operation.
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, nb_filter * compression, rows / 2, cols / 2)`
if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows / 2, cols / 2, nb_filter * compression)`
if data_format='channels_last'.
# Returns
a keras tensor
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(ip)
x = Activation('relu')(x)
x = Conv2D(int(nb_filter * compression), (1, 1), kernel_initializer='he_normal', padding='same',
use_bias=False, kernel_regularizer=l2(weight_decay))(x)
x = AveragePooling2D((2, 2), strides=(2, 2))(x)
return x
def __transition_up_block(ip, nb_filters, type='deconv', weight_decay=1E-4):
'''Adds an upsampling block. Upsampling operation relies on the the type parameter.
# Arguments
ip: input keras tensor
nb_filters: integer, the dimensionality of the output space
(i.e. the number output of filters in the convolution)
type: can be 'upsampling', 'subpixel', 'deconv'. Determines
type of upsampling performed
weight_decay: weight decay factor
# Input shape
4D tensor with shape:
`(samples, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(samples, nb_filter, rows * 2, cols * 2)` if data_format='channels_first'
or 4D tensor with shape:
`(samples, rows * 2, cols * 2, nb_filter)` if data_format='channels_last'.
# Returns
a keras tensor
'''
if type == 'upsampling':
x = UpSampling2D()(ip)
elif type == 'subpixel':
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', W_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_uniform')(ip)
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_normal')(ip)
x = SubPixelUpscaling(scale_factor=2)(x)
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', W_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_uniform')(x)
x = Conv2D(nb_filters, (3, 3), activation='relu', padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False, kernel_initializer='he_normal')(x)
else:
x = Conv2DTranspose(nb_filters, (3, 3), activation='relu', padding='same', strides=(2, 2),
kernel_initializer='he_uniform')(ip)
kernel_initializer='he_normal', kernel_regularizer=l2(weight_decay))(ip)
return x
def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_block=3, growth_rate=12, nb_filter=-1,
nb_layers_per_block=-1, bottleneck=False, reduction=0.0, dropout_rate=None, weight_decay=1E-4,
activation='softmax'):
nb_layers_per_block=-1, bottleneck=False, reduction=0.0, dropout_rate=None, weight_decay=1e-4,
subsample_initial_block=False, pooling=None, activation='softmax'):
''' Build the DenseNet model
Args:
# Arguments
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
@@ -442,28 +725,51 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl
bottleneck: add bottleneck blocks
reduction: reduction factor of transition blocks. Note : reduction value is inverted to compute compression
dropout_rate: dropout rate
weight_decay: weight decay
weight_decay: weight decay rate
subsample_initial_block: Set to True to subsample the initial convolution and
add a MaxPool2D before the dense blocks are added.
pooling: Optional pooling mode for feature extraction
when `include_top` is `False`.
- `None` means that the output of the model
will be the 4D tensor output of the
last convolutional layer.
- `avg` means that global average pooling
will be applied to the output of the
last convolutional layer, and thus
the output of the model will be a
2D tensor.
- `max` means that global max pooling will
be applied.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
Returns: keras tensor with nb_layers of conv_block appended
# Returns
a keras tensor
# Raises
ValueError: in case of invalid argument for `reduction`
or `nb_dense_block`
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4'
if reduction != 0.0:
assert reduction <= 1.0 and reduction > 0.0, 'reduction value must lie between 0.0 and 1.0'
if not (reduction <= 1.0 and reduction > 0.0):
raise ValueError('`reduction` value must lie between 0.0 and 1.0')
# layers in each dense block
if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
nb_layers = list(nb_layers_per_block) # Convert tuple to list
assert len(nb_layers) == (nb_dense_block + 1), 'If list, nb_layer is used as provided. ' \
'Note that list size must be (nb_dense_block + 1)'
if len(nb_layers) != (nb_dense_block):
raise ValueError('If `nb_dense_block` is a list, its length must match '
'the number of layers provided by `nb_layers`.')
final_nb_layer = nb_layers[-1]
nb_layers = nb_layers[:-1]
else:
if nb_layers_per_block == -1:
assert (depth - 4) % 3 == 0, 'Depth must be 3 N + 4 if nb_layers_per_block == -1'
count = int((depth - 4) / 3)
nb_layers = [count for _ in range(nb_dense_block)]
final_nb_layer = count
@@ -471,9 +777,6 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl
final_nb_layer = nb_layers_per_block
nb_layers = [nb_layers_per_block] * nb_dense_block
if bottleneck:
nb_layers = [int(layer // 2) for layer in nb_layers]
# compute initial nb_filter if -1, else accept users initial nb_filter
if nb_filter <= 0:
nb_filter = 2 * growth_rate
@@ -482,39 +785,55 @@ def __create_dense_net(nb_classes, img_input, include_top, depth=40, nb_dense_bl
compression = 1.0 - reduction
# Initial convolution
x = Conv2D(nb_filter, (3, 3), kernel_initializer='he_uniform', padding='same', name='initial_conv2D',
use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
if subsample_initial_block:
initial_kernel = (7, 7)
initial_strides = (2, 2)
else:
initial_kernel = (3, 3)
initial_strides = (1, 1)
x = Conv2D(nb_filter, initial_kernel, kernel_initializer='he_normal', padding='same',
strides=initial_strides, use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
if subsample_initial_block:
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
x = Activation('relu')(x)
x = MaxPooling2D((3, 3), strides=(2, 2), padding='same')(x)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, bottleneck=bottleneck,
dropout_rate=dropout_rate, weight_decay=weight_decay)
# add transition_block
x = __transition_block(x, nb_filter, compression=compression, dropout_rate=dropout_rate,
weight_decay=weight_decay)
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay)
nb_filter = int(nb_filter * compression)
# The last dense_block does not have a transition_block
x, nb_filter = __dense_block(x, final_nb_layer, nb_filter, growth_rate, bottleneck=bottleneck,
dropout_rate=dropout_rate, weight_decay=weight_decay)
x = BatchNormalization(axis=concat_axis, gamma_regularizer=l2(weight_decay),
beta_regularizer=l2(weight_decay))(x)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
x = Activation('relu')(x)
x = GlobalAveragePooling2D()(x)
if include_top:
x = Dense(nb_classes, activation=activation, W_regularizer=l2(weight_decay), b_regularizer=l2(weight_decay))(x)
x = GlobalAveragePooling2D()(x)
x = Dense(nb_classes, activation=activation)(x)
else:
if pooling == 'avg':
x = GlobalAveragePooling2D()(x)
if pooling == 'max':
x = GlobalMaxPooling2D()(x)
return x
def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5, growth_rate=12,
reduction=0.0, dropout_rate=None, weight_decay=1E-4,
nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='deconv',
init_conv_filters=48, input_shape=None, activation='softmax'):
''' Build the DenseNet model
Args:
reduction=0.0, dropout_rate=None, weight_decay=1e-4,
nb_layers_per_block=4, nb_upsampling_conv=128, upsampling_type='upsampling',
init_conv_filters=48, input_shape=None, activation='deconv'):
''' Build the DenseNet-FCN model
# Arguments
nb_classes: number of classes
img_input: tuple of shape (channels, rows, columns) or (rows, columns, channels)
include_top: flag to include the final Dense layer
@@ -534,7 +853,13 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
input_shape: Only used for shape inference in fully convolutional networks.
activation: Type of activation at the top layer. Can be one of 'softmax' or 'sigmoid'.
Note that if sigmoid is used, classes must be 1.
Returns: keras tensor with nb_layers of conv_block appended
# Returns
a keras tensor
# Raises
ValueError: in case of invalid argument for `reduction`,
`nb_dense_block` or `nb_upsampling_conv`.
'''
concat_axis = 1 if K.image_data_format() == 'channels_first' else -1
@@ -545,20 +870,22 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
rows, cols, _ = input_shape
if reduction != 0.0:
assert reduction <= 1.0 and reduction > 0.0, 'reduction value must lie between 0.0 and 1.0'
if not (reduction <= 1.0 and reduction > 0.0):
raise ValueError('`reduction` value must lie between 0.0 and 1.0')
# check if upsampling_conv has minimum number of filters
# minimum is set to 12, as at least 3 color channels are needed for correct upsampling
assert nb_upsampling_conv > 12 and nb_upsampling_conv % 4 == 0, 'Parameter `upsampling_conv` number of channels must ' \
'be a positive number divisible by 4 and greater ' \
'than 12'
if not (nb_upsampling_conv > 12 and nb_upsampling_conv % 4 == 0):
raise ValueError('Parameter `nb_upsampling_conv` number of channels must '
'be a positive number divisible by 4 and greater than 12')
# layers in each dense block
if type(nb_layers_per_block) is list or type(nb_layers_per_block) is tuple:
nb_layers = list(nb_layers_per_block) # Convert tuple to list
assert len(nb_layers) == (nb_dense_block + 1), 'If list, nb_layer is used as provided. ' \
'Note that list size must be (nb_dense_block + 1)'
if len(nb_layers) != (nb_dense_block + 1):
raise ValueError('If `nb_dense_block` is a list, its length must be '
'(`nb_dense_block` + 1)')
bottleneck_nb_layers = nb_layers[-1]
rev_layers = nb_layers[::-1]
@@ -571,8 +898,10 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
compression = 1.0 - reduction
# Initial convolution
x = Conv2D(init_conv_filters, (3, 3), kernel_initializer='he_uniform', padding='same', name='initial_conv2D',
x = Conv2D(init_conv_filters, (7, 7), kernel_initializer='he_normal', padding='same', name='initial_conv2D',
use_bias=False, kernel_regularizer=l2(weight_decay))(img_input)
x = BatchNormalization(axis=concat_axis, epsilon=1.1e-5)(x)
x = Activation('relu')(x)
nb_filter = init_conv_filters
@@ -580,15 +909,14 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
# Add dense blocks and transition down block
for block_idx in range(nb_dense_block):
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate,
dropout_rate=dropout_rate, weight_decay=weight_decay)
x, nb_filter = __dense_block(x, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate,
weight_decay=weight_decay)
# Skip connection
skip_list.append(x)
# add transition_block
x = __transition_block(x, nb_filter, compression=compression, dropout_rate=dropout_rate,
weight_decay=weight_decay)
x = __transition_block(x, nb_filter, compression=compression, weight_decay=weight_decay)
nb_filter = int(nb_filter * compression) # this is calculated inside transition_down_block
@@ -608,7 +936,7 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
# not the concatenation of the input with the feature maps (concat_list[0].
l = concatenate(concat_list[1:], axis=concat_axis)
t = __transition_up_block(l, nb_filters=n_filters_keep, type=upsampling_type)
t = __transition_up_block(l, nb_filters=n_filters_keep, type=upsampling_type, weight_decay=weight_decay)
# concatenate the skip connection with the transition block
x = concatenate([t, skip_list[block_idx]], axis=concat_axis)
@@ -616,12 +944,11 @@ def __create_fcn_dense_net(nb_classes, img_input, include_top, nb_dense_block=5,
# Dont allow the feature map size to grow in upsampling dense blocks
x_up, nb_filter, concat_list = __dense_block(x, nb_layers[nb_dense_block + block_idx + 1], nb_filter=growth_rate,
growth_rate=growth_rate, dropout_rate=dropout_rate,
weight_decay=weight_decay,
return_concat_list=True, grow_nb_filters=False)
weight_decay=weight_decay, return_concat_list=True,
grow_nb_filters=False)
if include_top:
x = Conv2D(nb_classes, (1, 1), activation='linear', padding='same', kernel_regularizer=l2(weight_decay),
use_bias=False)(x_up)
x = Conv2D(nb_classes, (1, 1), activation='linear', padding='same', use_bias=False)(x_up)
if K.image_data_format() == 'channels_first':
channel, row, col = input_shape