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delf_model.py
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delf_model.py
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# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# 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.
# ==============================================================================
"""DELF model implementation based on the following paper.
Large-Scale Image Retrieval with Attentive Deep Local Features
https://arxiv.org/abs/1612.06321
"""
import tensorflow as tf
from delf.python.training.model import resnet50 as resnet
layers = tf.keras.layers
reg = tf.keras.regularizers
_DECAY = 0.0001
class AttentionModel(tf.keras.Model):
"""Instantiates attention model.
Uses two [kernel_size x kernel_size] convolutions and softplus as activation
to compute an attention map with the same resolution as the featuremap.
Features l2-normalized and aggregated using attention probabilities as weights.
The features (targets) to be aggregated can be the input featuremap, or a
different one with the same resolution.
"""
def __init__(self, kernel_size=1, decay=_DECAY, name='attention'):
"""Initialization of attention model.
Args:
kernel_size: int, kernel size of convolutions.
decay: float, decay for l2 regularization of kernel weights.
name: str, name to identify model.
"""
super(AttentionModel, self).__init__(name=name)
# First convolutional layer (called with relu activation).
self.conv1 = layers.Conv2D(
512,
kernel_size,
kernel_regularizer=reg.l2(decay),
padding='same',
name='attn_conv1')
self.bn_conv1 = layers.BatchNormalization(axis=3, name='bn_conv1')
# Second convolutional layer, with softplus activation.
self.conv2 = layers.Conv2D(
1,
kernel_size,
kernel_regularizer=reg.l2(decay),
padding='same',
name='attn_conv2')
self.activation_layer = layers.Activation('softplus')
def call(self, inputs, targets=None, training=True):
x = self.conv1(inputs)
x = self.bn_conv1(x, training=training)
x = tf.nn.relu(x)
score = self.conv2(x)
prob = self.activation_layer(score)
# Aggregate inputs if targets is None.
if targets is None:
targets = inputs
# L2-normalize the featuremap before pooling.
targets = tf.nn.l2_normalize(targets, axis=-1)
feat = tf.reduce_mean(tf.multiply(targets, prob), [1, 2], keepdims=False)
return feat, prob, score
class AutoencoderModel(tf.keras.Model):
"""Instantiates the Keras Autoencoder model."""
def __init__(self, reduced_dimension, expand_dimension, kernel_size=1,
name='autoencoder'):
"""Initialization of Autoencoder model.
Args:
reduced_dimension: int, the output dimension of the autoencoder layer.
expand_dimension: int, the input dimension of the autoencoder layer.
kernel_size: int or tuple, height and width of the 2D convolution window.
name: str, name to identify model.
"""
super(AutoencoderModel, self).__init__(name=name)
self.conv1 = layers.Conv2D(
reduced_dimension,
kernel_size,
padding='same',
name='autoenc_conv1')
self.conv2 = layers.Conv2D(
expand_dimension,
kernel_size,
activation=tf.keras.activations.relu,
padding='same',
name='autoenc_conv2')
def call(self, inputs):
dim_reduced_features = self.conv1(inputs)
dim_expanded_features = self.conv2(dim_reduced_features)
return dim_expanded_features, dim_reduced_features
class Delf(tf.keras.Model):
"""Instantiates Keras DELF model using ResNet50 as backbone.
This class implements the [DELF](https://arxiv.org/abs/1612.06321) model for
extracting local features from images. The backbone is a ResNet50 network
that extracts featuremaps from both conv_4 and conv_5 layers. Activations
from conv_4 are used to compute an attention map of the same resolution.
"""
def __init__(self,
block3_strides=True,
name='DELF',
pooling='avg',
gem_power=3.0,
embedding_layer=False,
embedding_layer_dim=2048,
use_dim_reduction=False,
reduced_dimension=128,
dim_expand_channels=1024):
"""Initialization of DELF model.
Args:
block3_strides: bool, whether to add strides to the output of block3.
name: str, name to identify model.
pooling: str, pooling mode for global feature extraction; possible values
are 'None', 'avg', 'max', 'gem.'
gem_power: float, GeM power for GeM pooling. Only used if pooling ==
'gem'.
embedding_layer: bool, whether to create an embedding layer (FC whitening
layer).
embedding_layer_dim: int, size of the embedding layer.
use_dim_reduction: Whether to integrate dimensionality reduction layers.
If True, extra layers are added to reduce the dimensionality of the
extracted features.
reduced_dimension: int, only used if use_dim_reduction is True. The output
dimension of the autoencoder layer.
dim_expand_channels: int, only used if use_dim_reduction is True. The
number of channels of the backbone block used. Default value 1024 is the
number of channels of backbone block 'block3'.
"""
super(Delf, self).__init__(name=name)
# Backbone using Keras ResNet50.
self.backbone = resnet.ResNet50(
'channels_last',
name='backbone',
include_top=False,
pooling=pooling,
block3_strides=block3_strides,
average_pooling=False,
gem_power=gem_power,
embedding_layer=embedding_layer,
embedding_layer_dim=embedding_layer_dim)
# Attention model.
self.attention = AttentionModel(name='attention')
# Autoencoder model.
self._use_dim_reduction = use_dim_reduction
if self._use_dim_reduction:
self.autoencoder = AutoencoderModel(reduced_dimension,
dim_expand_channels,
name='autoencoder')
def init_classifiers(self, num_classes, desc_classification=None):
"""Define classifiers for training backbone and attention models."""
self.num_classes = num_classes
if desc_classification is None:
self.desc_classification = layers.Dense(
num_classes, activation=None, kernel_regularizer=None, name='desc_fc')
else:
self.desc_classification = desc_classification
self.attn_classification = layers.Dense(
num_classes, activation=None, kernel_regularizer=None, name='att_fc')
def global_and_local_forward_pass(self, images, training=True):
"""Run a forward to calculate global descriptor and attention prelogits.
Args:
images: Tensor containing the dataset on which to run the forward pass.
training: Indicator of whether the forward pass is running in training mode
or not.
Returns:
Global descriptor prelogits, attention prelogits, attention scores,
backbone weights.
"""
backbone_blocks = {}
desc_prelogits = self.backbone.build_call(
images, intermediates_dict=backbone_blocks, training=training)
# Prevent gradients from propagating into the backbone. See DELG paper:
# https://arxiv.org/abs/2001.05027.
block3 = backbone_blocks['block3'] # pytype: disable=key-error
block3 = tf.stop_gradient(block3)
if self._use_dim_reduction:
(dim_expanded_features, dim_reduced_features) = self.autoencoder(block3)
attn_prelogits, attn_scores, _ = self.attention(
block3,
targets=dim_expanded_features,
training=training)
else:
attn_prelogits, attn_scores, _ = self.attention(block3, training=training)
dim_expanded_features = None
dim_reduced_features = None
return (desc_prelogits, attn_prelogits, attn_scores, backbone_blocks,
dim_expanded_features, dim_reduced_features)
def build_call(self, input_image, training=True):
(global_feature, _, attn_scores, backbone_blocks, _,
dim_reduced_features) = self.global_and_local_forward_pass(input_image,
training)
if self._use_dim_reduction:
features = dim_reduced_features
else:
features = backbone_blocks['block3'] # pytype: disable=key-error
return global_feature, attn_scores, features
def call(self, input_image, training=True):
_, probs, features = self.build_call(input_image, training=training)
return probs, features