495 lines
17 KiB
Python
495 lines
17 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""DenseNet models for Keras.
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Reference:
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- [Densely Connected Convolutional Networks](
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https://arxiv.org/abs/1608.06993) (CVPR 2017)
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"""
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.applications import imagenet_utils
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from keras.engine import training
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from keras.layers import VersionAwareLayers
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from keras.utils import data_utils
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from keras.utils import layer_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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BASE_WEIGHTS_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/densenet/"
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)
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DENSENET121_WEIGHT_PATH = (
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BASE_WEIGHTS_PATH + "densenet121_weights_tf_dim_ordering_tf_kernels.h5"
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)
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DENSENET121_WEIGHT_PATH_NO_TOP = (
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BASE_WEIGHTS_PATH
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+ "densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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DENSENET169_WEIGHT_PATH = (
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BASE_WEIGHTS_PATH + "densenet169_weights_tf_dim_ordering_tf_kernels.h5"
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)
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DENSENET169_WEIGHT_PATH_NO_TOP = (
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BASE_WEIGHTS_PATH
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+ "densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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DENSENET201_WEIGHT_PATH = (
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BASE_WEIGHTS_PATH + "densenet201_weights_tf_dim_ordering_tf_kernels.h5"
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)
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DENSENET201_WEIGHT_PATH_NO_TOP = (
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BASE_WEIGHTS_PATH
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+ "densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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layers = VersionAwareLayers()
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def dense_block(x, blocks, name):
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"""A dense block.
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Args:
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x: input tensor.
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blocks: integer, the number of building blocks.
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name: string, block label.
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Returns:
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Output tensor for the block.
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"""
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for i in range(blocks):
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x = conv_block(x, 32, name=name + "_block" + str(i + 1))
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return x
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def transition_block(x, reduction, name):
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"""A transition block.
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Args:
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x: input tensor.
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reduction: float, compression rate at transition layers.
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name: string, block label.
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Returns:
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output tensor for the block.
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"""
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bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_bn"
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)(x)
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x = layers.Activation("relu", name=name + "_relu")(x)
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x = layers.Conv2D(
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int(backend.int_shape(x)[bn_axis] * reduction),
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1,
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use_bias=False,
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name=name + "_conv",
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)(x)
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x = layers.AveragePooling2D(2, strides=2, name=name + "_pool")(x)
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return x
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def conv_block(x, growth_rate, name):
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"""A building block for a dense block.
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Args:
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x: input tensor.
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growth_rate: float, growth rate at dense layers.
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name: string, block label.
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Returns:
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Output tensor for the block.
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"""
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bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
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x1 = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn"
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)(x)
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x1 = layers.Activation("relu", name=name + "_0_relu")(x1)
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x1 = layers.Conv2D(
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4 * growth_rate, 1, use_bias=False, name=name + "_1_conv"
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)(x1)
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x1 = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
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)(x1)
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x1 = layers.Activation("relu", name=name + "_1_relu")(x1)
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x1 = layers.Conv2D(
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growth_rate, 3, padding="same", use_bias=False, name=name + "_2_conv"
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)(x1)
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x = layers.Concatenate(axis=bn_axis, name=name + "_concat")([x, x1])
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return x
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def DenseNet(
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blocks,
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation="softmax",
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):
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"""Instantiates the DenseNet architecture.
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Reference:
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- [Densely Connected Convolutional Networks](
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https://arxiv.org/abs/1608.06993) (CVPR 2017)
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This function returns a Keras image classification model,
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optionally loaded with weights pre-trained on ImageNet.
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For image classification use cases, see
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[this page for detailed examples](
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https://keras.io/api/applications/#usage-examples-for-image-classification-models).
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For transfer learning use cases, make sure to read the
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[guide to transfer learning & fine-tuning](
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https://keras.io/guides/transfer_learning/).
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Note: each Keras Application expects a specific kind of input preprocessing.
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For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your
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inputs before passing them to the model.
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`densenet.preprocess_input` will scale pixels between 0 and 1 and then
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will normalize each channel with respect to the ImageNet dataset statistics.
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Args:
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blocks: numbers of building blocks for the four dense layers.
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include_top: whether to include the fully-connected
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layer at the top of the network.
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weights: one of `None` (random initialization),
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'imagenet' (pre-training on ImageNet),
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or the path to the weights file to be loaded.
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input_tensor: optional Keras tensor
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(i.e. output of `layers.Input()`)
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to use as image input for the model.
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input_shape: optional shape tuple, only to be specified
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if `include_top` is False (otherwise the input shape
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has to be `(224, 224, 3)` (with `'channels_last'` data format)
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or `(3, 224, 224)` (with `'channels_first'` data format).
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 32.
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E.g. `(200, 200, 3)` would be one valid value.
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pooling: optional pooling mode for feature extraction
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when `include_top` is `False`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional block.
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- `avg` means that global average pooling
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will be applied to the output of the
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last convolutional block, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will
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be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified.
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classifier_activation: A `str` or callable. The activation function to use
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on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top" layer.
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When loading pretrained weights, `classifier_activation` can only
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be `None` or `"softmax"`.
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Returns:
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A `keras.Model` instance.
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"""
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if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
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raise ValueError(
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"The `weights` argument should be either "
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"`None` (random initialization), `imagenet` "
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"(pre-training on ImageNet), "
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"or the path to the weights file to be loaded."
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)
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if weights == "imagenet" and include_top and classes != 1000:
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raise ValueError(
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'If using `weights` as `"imagenet"` with `include_top`'
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" as true, `classes` should be 1000"
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)
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# Determine proper input shape
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input_shape = imagenet_utils.obtain_input_shape(
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input_shape,
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default_size=224,
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min_size=32,
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data_format=backend.image_data_format(),
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require_flatten=include_top,
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weights=weights,
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)
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if input_tensor is None:
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img_input = layers.Input(shape=input_shape)
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else:
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if not backend.is_keras_tensor(input_tensor):
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img_input = layers.Input(tensor=input_tensor, shape=input_shape)
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else:
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img_input = input_tensor
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bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
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x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
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x = layers.Conv2D(64, 7, strides=2, use_bias=False, name="conv1/conv")(x)
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name="conv1/bn"
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)(x)
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x = layers.Activation("relu", name="conv1/relu")(x)
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x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
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x = layers.MaxPooling2D(3, strides=2, name="pool1")(x)
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x = dense_block(x, blocks[0], name="conv2")
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x = transition_block(x, 0.5, name="pool2")
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x = dense_block(x, blocks[1], name="conv3")
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x = transition_block(x, 0.5, name="pool3")
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x = dense_block(x, blocks[2], name="conv4")
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x = transition_block(x, 0.5, name="pool4")
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x = dense_block(x, blocks[3], name="conv5")
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x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name="bn")(x)
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x = layers.Activation("relu", name="relu")(x)
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if include_top:
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x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
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imagenet_utils.validate_activation(classifier_activation, weights)
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x = layers.Dense(
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classes, activation=classifier_activation, name="predictions"
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)(x)
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else:
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if pooling == "avg":
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x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
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elif pooling == "max":
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x = layers.GlobalMaxPooling2D(name="max_pool")(x)
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# Ensure that the model takes into account
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# any potential predecessors of `input_tensor`.
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if input_tensor is not None:
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inputs = layer_utils.get_source_inputs(input_tensor)
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else:
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inputs = img_input
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# Create model.
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if blocks == [6, 12, 24, 16]:
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model = training.Model(inputs, x, name="densenet121")
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elif blocks == [6, 12, 32, 32]:
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model = training.Model(inputs, x, name="densenet169")
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elif blocks == [6, 12, 48, 32]:
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model = training.Model(inputs, x, name="densenet201")
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else:
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model = training.Model(inputs, x, name="densenet")
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# Load weights.
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if weights == "imagenet":
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if include_top:
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if blocks == [6, 12, 24, 16]:
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weights_path = data_utils.get_file(
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"densenet121_weights_tf_dim_ordering_tf_kernels.h5",
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DENSENET121_WEIGHT_PATH,
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cache_subdir="models",
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file_hash="9d60b8095a5708f2dcce2bca79d332c7",
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)
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elif blocks == [6, 12, 32, 32]:
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weights_path = data_utils.get_file(
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"densenet169_weights_tf_dim_ordering_tf_kernels.h5",
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DENSENET169_WEIGHT_PATH,
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cache_subdir="models",
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file_hash="d699b8f76981ab1b30698df4c175e90b",
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)
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elif blocks == [6, 12, 48, 32]:
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weights_path = data_utils.get_file(
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"densenet201_weights_tf_dim_ordering_tf_kernels.h5",
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DENSENET201_WEIGHT_PATH,
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cache_subdir="models",
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file_hash="1ceb130c1ea1b78c3bf6114dbdfd8807",
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)
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else:
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if blocks == [6, 12, 24, 16]:
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weights_path = data_utils.get_file(
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"densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5",
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DENSENET121_WEIGHT_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="30ee3e1110167f948a6b9946edeeb738",
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)
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elif blocks == [6, 12, 32, 32]:
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weights_path = data_utils.get_file(
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"densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5",
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DENSENET169_WEIGHT_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="b8c4d4c20dd625c148057b9ff1c1176b",
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)
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elif blocks == [6, 12, 48, 32]:
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weights_path = data_utils.get_file(
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"densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5",
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DENSENET201_WEIGHT_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="c13680b51ded0fb44dff2d8f86ac8bb1",
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)
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model.load_weights(weights_path)
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elif weights is not None:
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model.load_weights(weights)
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return model
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@keras_export(
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"keras.applications.densenet.DenseNet121", "keras.applications.DenseNet121"
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)
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def DenseNet121(
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation="softmax",
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):
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"""Instantiates the Densenet121 architecture."""
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return DenseNet(
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[6, 12, 24, 16],
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include_top,
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weights,
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input_tensor,
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input_shape,
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pooling,
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classes,
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classifier_activation,
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)
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@keras_export(
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"keras.applications.densenet.DenseNet169", "keras.applications.DenseNet169"
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)
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def DenseNet169(
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation="softmax",
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):
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"""Instantiates the Densenet169 architecture."""
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return DenseNet(
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[6, 12, 32, 32],
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include_top,
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weights,
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input_tensor,
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input_shape,
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pooling,
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classes,
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classifier_activation,
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)
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@keras_export(
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"keras.applications.densenet.DenseNet201", "keras.applications.DenseNet201"
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)
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def DenseNet201(
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include_top=True,
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weights="imagenet",
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input_tensor=None,
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input_shape=None,
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pooling=None,
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classes=1000,
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classifier_activation="softmax",
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):
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"""Instantiates the Densenet201 architecture."""
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return DenseNet(
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[6, 12, 48, 32],
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include_top,
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weights,
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input_tensor,
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input_shape,
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pooling,
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classes,
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classifier_activation,
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)
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@keras_export("keras.applications.densenet.preprocess_input")
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def preprocess_input(x, data_format=None):
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return imagenet_utils.preprocess_input(
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x, data_format=data_format, mode="torch"
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)
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@keras_export("keras.applications.densenet.decode_predictions")
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def decode_predictions(preds, top=5):
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return imagenet_utils.decode_predictions(preds, top=top)
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preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
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mode="",
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ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TORCH,
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error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
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)
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decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
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DOC = """
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Reference:
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- [Densely Connected Convolutional Networks](
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https://arxiv.org/abs/1608.06993) (CVPR 2017)
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Optionally loads weights pre-trained on ImageNet.
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Note that the data format convention used by the model is
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the one specified in your Keras config at `~/.keras/keras.json`.
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Note: each Keras Application expects a specific kind of input preprocessing.
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For DenseNet, call `tf.keras.applications.densenet.preprocess_input` on your
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inputs before passing them to the model.
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|
Args:
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include_top: whether to include the fully-connected
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layer at the top of the network.
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weights: one of `None` (random initialization),
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'imagenet' (pre-training on ImageNet),
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or the path to the weights file to be loaded.
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input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
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to use as image input for the model.
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input_shape: optional shape tuple, only to be specified
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if `include_top` is False (otherwise the input shape
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has to be `(224, 224, 3)` (with `'channels_last'` data format)
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|
or `(3, 224, 224)` (with `'channels_first'` data format).
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|
It should have exactly 3 inputs channels,
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and width and height should be no smaller than 32.
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E.g. `(200, 200, 3)` would be one valid value.
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|
pooling: Optional pooling mode for feature extraction
|
|
when `include_top` is `False`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional block.
|
|
- `avg` means that global average pooling
|
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will be applied to the output of the
|
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last convolutional block, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will
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be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified.
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classifier_activation: A `str` or callable. The activation function to use
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on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top" layer.
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When loading pretrained weights, `classifier_activation` can only
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be `None` or `"softmax"`.
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Returns:
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A Keras model instance.
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"""
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setattr(DenseNet121, "__doc__", DenseNet121.__doc__ + DOC)
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setattr(DenseNet169, "__doc__", DenseNet169.__doc__ + DOC)
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setattr(DenseNet201, "__doc__", DenseNet201.__doc__ + DOC)
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