""" Copyright 2018 vidosits (https://github.com/vidosits/) 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. """ import keras from keras.applications import densenet from keras.utils import get_file from . import retinanet from . import Backbone from crfnet.utils.image import preprocess_image allowed_backbones = { 'densenet121': ([6, 12, 24, 16], densenet.DenseNet121), 'densenet169': ([6, 12, 32, 32], densenet.DenseNet169), 'densenet201': ([6, 12, 48, 32], densenet.DenseNet201), } class DenseNetBackbone(Backbone): """ Describes backbone information and provides utility functions. """ def retinanet(self, *args, **kwargs): """ Returns a retinanet model using the correct backbone. """ return densenet_retinanet(*args, backbone=self.backbone, **kwargs) def download_imagenet(self): """ Download pre-trained weights for the specified backbone name. This name is in the format {backbone}_weights_tf_dim_ordering_tf_kernels_notop where backbone is the densenet + number of layers (e.g. densenet121). For more info check the explanation from the keras densenet script itself: https://github.com/keras-team/keras/blob/master/keras/applications/densenet.py """ origin = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.8/' file_name = '{}_weights_tf_dim_ordering_tf_kernels_notop.h5' # load weights if keras.backend.image_data_format() == 'channels_first': raise ValueError('Weights for "channels_first" format are not available.') weights_url = origin + file_name.format(self.backbone) return get_file(file_name.format(self.backbone), weights_url, cache_subdir='models') def validate(self): """ Checks whether the backbone string is correct. """ backbone = self.backbone.split('_')[0] if backbone not in allowed_backbones: raise ValueError('Backbone (\'{}\') not in allowed backbones ({}).'.format(backbone, allowed_backbones.keys())) def preprocess_image(self, inputs): """ Takes as input an image and prepares it for being passed through the network. """ return preprocess_image(inputs, mode='tf') def densenet_retinanet(num_classes, backbone='densenet121', inputs=None, modifier=None, cfg=None, **kwargs): """ Constructs a retinanet model using a densenet backbone. Args num_classes: Number of classes to predict. backbone: Which backbone to use (one of ('densenet121', 'densenet169', 'densenet201')). inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)). modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example). Returns RetinaNet model with a DenseNet backbone. """ # choose default input if inputs is None: inputs = keras.layers.Input((None, None, 3)) elif isinstance(inputs, tuple): inputs = keras.layers.Input(inputs) blocks, creator = allowed_backbones[backbone] model = creator(input_tensor=inputs, include_top=False, pooling=None, weights=None) # get last conv layer from the end of each dense block layer_outputs = [model.get_layer(name='conv{}_block{}_concat'.format(idx + 2, block_num)).output for idx, block_num in enumerate(blocks)] # create the densenet backbone model = keras.models.Model(inputs=inputs, outputs=layer_outputs[1:], name=model.name) # invoke modifier if given if modifier: model = modifier(model) # create the full model model = retinanet.retinanet(inputs=inputs, num_classes=num_classes, backbone_layers=model.outputs, **kwargs) return model