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Diffstat (limited to 'tensorflow/contrib/eager/python/examples/densenet/densenet.py')
| -rw-r--r-- | tensorflow/contrib/eager/python/examples/densenet/densenet.py | 274 |
1 files changed, 274 insertions, 0 deletions
diff --git a/tensorflow/contrib/eager/python/examples/densenet/densenet.py b/tensorflow/contrib/eager/python/examples/densenet/densenet.py new file mode 100644 index 0000000000..3a2b2de250 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/densenet/densenet.py @@ -0,0 +1,274 @@ +# Copyright 2017 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. +# ============================================================================== +"""Densely Connected Convolutional Networks. + +Reference [ +Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993) + +""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf +l2 = tf.keras.regularizers.l2 + + +class ConvBlock(tf.keras.Model): + """Convolutional Block consisting of (batchnorm->relu->conv). + + Arguments: + num_filters: number of filters passed to a convolutional layer. + bottleneck: if True, then a 1x1 Conv is performed followed by 3x3 Conv. + weight_decay: weight decay + dropout_rate: dropout rate. + """ + + def __init__(self, num_filters, bottleneck, weight_decay=1e-4, + dropout_rate=0): + super(ConvBlock, self).__init__() + self.bottleneck = bottleneck + inter_filter = num_filters * 4 + # don't forget to set use_bias=False when using batchnorm + self.conv2 = tf.keras.layers.Conv2D(num_filters, + (3, 3), + padding="same", + use_bias=False, + kernel_initializer="he_normal", + kernel_regularizer=l2(weight_decay)) + self.batchnorm1 = tf.keras.layers.BatchNormalization() + self.dropout = tf.keras.layers.Dropout(dropout_rate) + + if self.bottleneck: + self.conv1 = tf.keras.layers.Conv2D(inter_filter, + (1, 1), + padding="same", + use_bias=False, + kernel_initializer="he_normal", + kernel_regularizer=l2(weight_decay)) + self.batchnorm2 = tf.keras.layers.BatchNormalization() + + def call(self, x, training=True): + output = self.batchnorm1(x, training=training) + + if self.bottleneck: + output = self.conv1(tf.nn.relu(output)) + output = self.batchnorm2(output, training=training) + + output = self.conv2(tf.nn.relu(output)) + output = self.dropout(output, training=training) + + return output + + +class TransitionBlock(tf.keras.Model): + """Transition Block to reduce the number of features. + + Arguments: + num_filters: number of filters passed to a convolutional layer. + weight_decay: weight decay + dropout_rate: dropout rate. + """ + + def __init__(self, num_filters, weight_decay=1e-4, dropout_rate=0): + super(TransitionBlock, self).__init__() + self.batchnorm = tf.keras.layers.BatchNormalization() + self.conv = tf.keras.layers.Conv2D(num_filters, + (1, 1), + padding="same", + use_bias=False, + kernel_initializer="he_normal", + kernel_regularizer=l2(weight_decay)) + self.avg_pool = tf.keras.layers.AveragePooling2D() + + def call(self, x, training=True): + output = self.batchnorm(x, training=training) + output = self.conv(tf.nn.relu(output)) + output = self.avg_pool(output) + return output + + +class DenseBlock(tf.keras.Model): + """Dense Block consisting of ConvBlocks where each block's + output is concatenated with its input. + + Arguments: + num_layers: Number of layers in each block. + growth_rate: number of filters to add per conv block. + bottleneck: boolean, that decides which part of ConvBlock to call. + weight_decay: weight decay + dropout_rate: dropout rate. + """ + + def __init__(self, num_layers, growth_rate, bottleneck, + weight_decay=1e-4, dropout_rate=0): + super(DenseBlock, self).__init__() + self.num_layers = num_layers + + self.blocks = [] + for _ in range(int(self.num_layers)): + self.blocks.append(ConvBlock(growth_rate, + bottleneck, + weight_decay, + dropout_rate)) + + def call(self, x, training=True): + for i in range(int(self.num_layers)): + output = self.blocks[i](x, training=training) + x = tf.concat([x, output], axis=-1) + + return x + + +class DenseNet(tf.keras.Model): + """Creating the Densenet Architecture. + + Arguments: + depth_of_model: number of layers in the model. + growth_rate: number of filters to add per conv block. + num_of_blocks: number of dense blocks. + output_classes: number of output classes. + num_layers_in_each_block: number of layers in each block. + If -1, then we calculate this by (depth-3)/4. + If positive integer, then the it is used as the + number of layers per block. + If list or tuple, then this list is used directly. + bottleneck: boolean, to decide which part of conv block to call. + compression: reducing the number of inputs(filters) to the transition block. + weight_decay: weight decay + rate: dropout rate. + pool_initial: If True add a 7x7 conv with stride 2 followed by 3x3 maxpool + else, do a 3x3 conv with stride 1. + include_top: If true, GlobalAveragePooling Layer and Dense layer are + included. + """ + + def __init__(self, depth_of_model, growth_rate, num_of_blocks, + output_classes, num_layers_in_each_block, + bottleneck=True, compression=0.5, weight_decay=1e-4, + dropout_rate=0, pool_initial=False, include_top=True): + super(DenseNet, self).__init__() + self.depth_of_model = depth_of_model + self.growth_rate = growth_rate + self.num_of_blocks = num_of_blocks + self.output_classes = output_classes + self.num_layers_in_each_block = num_layers_in_each_block + self.bottleneck = bottleneck + self.compression = compression + self.weight_decay = weight_decay + self.dropout_rate = dropout_rate + self.pool_initial = pool_initial + self.include_top = include_top + + # deciding on number of layers in each block + if isinstance(self.num_layers_in_each_block, list) or isinstance( + self.num_layers_in_each_block, tuple): + self.num_layers_in_each_block = list(self.num_layers_in_each_block) + else: + if self.num_layers_in_each_block == -1: + if self.num_of_blocks != 3: + raise ValueError( + "Number of blocks must be 3 if num_layers_in_each_block is -1") + if (self.depth_of_model - 4) % 3 == 0: + num_layers = (self.depth_of_model - 4) / 3 + if self.bottleneck: + num_layers //= 2 + self.num_layers_in_each_block = [num_layers] * self.num_of_blocks + else: + raise ValueError("Depth must be 3N+4 if num_layer_in_each_block=-1") + else: + self.num_layers_in_each_block = [ + self.num_layers_in_each_block] * self.num_of_blocks + + # setting the filters and stride of the initial covn layer. + if self.pool_initial: + init_filters = (7, 7) + stride = (2, 2) + else: + init_filters = (3, 3) + stride = (1, 1) + + self.num_filters = 2 * self.growth_rate + + # first conv and pool layer + self.conv1 = tf.keras.layers.Conv2D(self.num_filters, + init_filters, + strides=stride, + padding="same", + use_bias=False, + kernel_initializer="he_normal", + kernel_regularizer=l2( + self.weight_decay)) + if self.pool_initial: + self.pool1 = tf.keras.layers.MaxPooling2D(pool_size=(3, 3), + strides=(2, 2), + padding="same") + self.batchnorm1 = tf.keras.layers.BatchNormalization() + + self.batchnorm2 = tf.keras.layers.BatchNormalization() + + # last pooling and fc layer + if self.include_top: + self.last_pool = tf.keras.layers.GlobalAveragePooling2D() + self.classifier = tf.keras.layers.Dense(self.output_classes) + + # calculating the number of filters after each block + num_filters_after_each_block = [self.num_filters] + for i in range(1, self.num_of_blocks): + temp_num_filters = num_filters_after_each_block[i-1] + ( + self.growth_rate * self.num_layers_in_each_block[i-1]) + # using compression to reduce the number of inputs to the + # transition block + temp_num_filters = int(temp_num_filters * compression) + num_filters_after_each_block.append(temp_num_filters) + + # dense block initialization + self.dense_blocks = [] + self.transition_blocks = [] + for i in range(self.num_of_blocks): + self.dense_blocks.append(DenseBlock(self.num_layers_in_each_block[i], + self.growth_rate, + self.bottleneck, + self.weight_decay, + self.dropout_rate)) + if i+1 < self.num_of_blocks: + self.transition_blocks.append( + TransitionBlock(num_filters_after_each_block[i+1], + self.weight_decay, + self.dropout_rate)) + + def call(self, x, training=True): + output = self.conv1(x) + + if self.pool_initial: + output = self.batchnorm1(output, training=training) + output = tf.nn.relu(output) + output = self.pool1(output) + + for i in range(self.num_of_blocks - 1): + output = self.dense_blocks[i](output, training=training) + output = self.transition_blocks[i](output, training=training) + + output = self.dense_blocks[ + self.num_of_blocks - 1](output, training=training) + output = self.batchnorm2(output, training=training) + output = tf.nn.relu(output) + + if self.include_top: + output = self.last_pool(output) + output = self.classifier(output) + + return output |