439 lines
16 KiB
Python
439 lines
16 KiB
Python
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# Copyright 2017 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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"""Inception-ResNet V2 model for Keras.
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Reference:
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- [Inception-v4, Inception-ResNet and the Impact of
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Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
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(AAAI 2017)
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"""
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import tensorflow.compat.v2 as tf
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import keras
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from keras import backend
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from keras import layers as keras_layers
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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_WEIGHT_URL = (
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"https://storage.googleapis.com/tensorflow/"
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"keras-applications/inception_resnet_v2/"
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)
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layers = None
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@keras_export(
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"keras.applications.inception_resnet_v2.InceptionResNetV2",
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"keras.applications.InceptionResNetV2",
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)
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def InceptionResNetV2(
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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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**kwargs,
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):
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"""Instantiates the Inception-ResNet v2 architecture.
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Reference:
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- [Inception-v4, Inception-ResNet and the Impact of
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Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
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(AAAI 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 InceptionResNetV2, call
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`tf.keras.applications.inception_resnet_v2.preprocess_input`
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on your inputs before passing them to the model.
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`inception_resnet_v2.preprocess_input`
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will scale input pixels between -1 and 1.
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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 `(299, 299, 3)` (with `'channels_last'` data format)
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or `(3, 299, 299)` (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 75.
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E.g. `(150, 150, 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 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 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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**kwargs: For backwards compatibility only.
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Returns:
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A `keras.Model` instance.
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"""
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global layers
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if "layers" in kwargs:
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layers = kwargs.pop("layers")
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else:
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layers = VersionAwareLayers()
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if kwargs:
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raise ValueError(f"Unknown argument(s): {kwargs}")
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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=299,
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min_size=75,
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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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# Stem block: 35 x 35 x 192
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x = conv2d_bn(img_input, 32, 3, strides=2, padding="valid")
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x = conv2d_bn(x, 32, 3, padding="valid")
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x = conv2d_bn(x, 64, 3)
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x = layers.MaxPooling2D(3, strides=2)(x)
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x = conv2d_bn(x, 80, 1, padding="valid")
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x = conv2d_bn(x, 192, 3, padding="valid")
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x = layers.MaxPooling2D(3, strides=2)(x)
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# Mixed 5b (Inception-A block): 35 x 35 x 320
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branch_0 = conv2d_bn(x, 96, 1)
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branch_1 = conv2d_bn(x, 48, 1)
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branch_1 = conv2d_bn(branch_1, 64, 5)
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branch_2 = conv2d_bn(x, 64, 1)
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branch_2 = conv2d_bn(branch_2, 96, 3)
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branch_2 = conv2d_bn(branch_2, 96, 3)
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branch_pool = layers.AveragePooling2D(3, strides=1, padding="same")(x)
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branch_pool = conv2d_bn(branch_pool, 64, 1)
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branches = [branch_0, branch_1, branch_2, branch_pool]
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channel_axis = 1 if backend.image_data_format() == "channels_first" else 3
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x = layers.Concatenate(axis=channel_axis, name="mixed_5b")(branches)
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# 10x block35 (Inception-ResNet-A block): 35 x 35 x 320
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for block_idx in range(1, 11):
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x = inception_resnet_block(
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x, scale=0.17, block_type="block35", block_idx=block_idx
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)
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# Mixed 6a (Reduction-A block): 17 x 17 x 1088
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branch_0 = conv2d_bn(x, 384, 3, strides=2, padding="valid")
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branch_1 = conv2d_bn(x, 256, 1)
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branch_1 = conv2d_bn(branch_1, 256, 3)
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branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding="valid")
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branch_pool = layers.MaxPooling2D(3, strides=2, padding="valid")(x)
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branches = [branch_0, branch_1, branch_pool]
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x = layers.Concatenate(axis=channel_axis, name="mixed_6a")(branches)
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# 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088
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for block_idx in range(1, 21):
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x = inception_resnet_block(
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x, scale=0.1, block_type="block17", block_idx=block_idx
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)
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# Mixed 7a (Reduction-B block): 8 x 8 x 2080
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branch_0 = conv2d_bn(x, 256, 1)
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branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding="valid")
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branch_1 = conv2d_bn(x, 256, 1)
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branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding="valid")
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branch_2 = conv2d_bn(x, 256, 1)
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branch_2 = conv2d_bn(branch_2, 288, 3)
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branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding="valid")
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branch_pool = layers.MaxPooling2D(3, strides=2, padding="valid")(x)
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branches = [branch_0, branch_1, branch_2, branch_pool]
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x = layers.Concatenate(axis=channel_axis, name="mixed_7a")(branches)
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# 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080
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for block_idx in range(1, 10):
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x = inception_resnet_block(
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x, scale=0.2, block_type="block8", block_idx=block_idx
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)
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x = inception_resnet_block(
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x, scale=1.0, activation=None, block_type="block8", block_idx=10
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)
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# Final convolution block: 8 x 8 x 1536
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x = conv2d_bn(x, 1536, 1, name="conv_7b")
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if include_top:
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# Classification block
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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()(x)
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elif pooling == "max":
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x = layers.GlobalMaxPooling2D()(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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model = training.Model(inputs, x, name="inception_resnet_v2")
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# Load weights.
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if weights == "imagenet":
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if include_top:
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fname = "inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5"
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weights_path = data_utils.get_file(
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fname,
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BASE_WEIGHT_URL + fname,
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cache_subdir="models",
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file_hash="e693bd0210a403b3192acc6073ad2e96",
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)
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else:
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fname = (
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"inception_resnet_v2_weights_"
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"tf_dim_ordering_tf_kernels_notop.h5"
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)
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weights_path = data_utils.get_file(
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fname,
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BASE_WEIGHT_URL + fname,
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cache_subdir="models",
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file_hash="d19885ff4a710c122648d3b5c3b684e4",
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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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def conv2d_bn(
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x,
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filters,
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kernel_size,
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strides=1,
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padding="same",
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activation="relu",
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use_bias=False,
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name=None,
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):
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"""Utility function to apply conv + BN.
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Args:
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x: input tensor.
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filters: filters in `Conv2D`.
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kernel_size: kernel size as in `Conv2D`.
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strides: strides in `Conv2D`.
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padding: padding mode in `Conv2D`.
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activation: activation in `Conv2D`.
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use_bias: whether to use a bias in `Conv2D`.
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name: name of the ops; will become `name + '_ac'` for the activation
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and `name + '_bn'` for the batch norm layer.
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Returns:
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Output tensor after applying `Conv2D` and `BatchNormalization`.
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"""
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x = layers.Conv2D(
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filters,
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kernel_size,
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strides=strides,
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padding=padding,
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use_bias=use_bias,
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name=name,
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)(x)
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if not use_bias:
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bn_axis = 1 if backend.image_data_format() == "channels_first" else 3
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bn_name = None if name is None else name + "_bn"
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x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(
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x
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)
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if activation is not None:
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ac_name = None if name is None else name + "_ac"
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x = layers.Activation(activation, name=ac_name)(x)
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return x
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@keras.utils.register_keras_serializable()
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class CustomScaleLayer(keras_layers.Layer):
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def __init__(self, scale, **kwargs):
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super().__init__(**kwargs)
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self.scale = scale
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def get_config(self):
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config = super().get_config()
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config.update({"scale": self.scale})
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return config
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def call(self, inputs):
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return inputs[0] + inputs[1] * self.scale
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def inception_resnet_block(x, scale, block_type, block_idx, activation="relu"):
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"""Adds an Inception-ResNet block.
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This function builds 3 types of Inception-ResNet blocks mentioned
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in the paper, controlled by the `block_type` argument (which is the
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block name used in the official TF-slim implementation):
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- Inception-ResNet-A: `block_type='block35'`
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- Inception-ResNet-B: `block_type='block17'`
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- Inception-ResNet-C: `block_type='block8'`
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Args:
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x: input tensor.
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scale: scaling factor to scale the residuals (i.e., the output of passing
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`x` through an inception module) before adding them to the shortcut
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branch. Let `r` be the output from the residual branch, the output of
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this block will be `x + scale * r`.
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block_type: `'block35'`, `'block17'` or `'block8'`, determines the network
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structure in the residual branch.
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block_idx: an `int` used for generating layer names. The Inception-ResNet
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blocks are repeated many times in this network. We use `block_idx` to
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identify each of the repetitions. For example, the first
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Inception-ResNet-A block will have `block_type='block35', block_idx=0`,
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and the layer names will have a common prefix `'block35_0'`.
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activation: activation function to use at the end of the block (see
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[activations](../activations.md)). When `activation=None`, no activation
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is applied
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(i.e., "linear" activation: `a(x) = x`).
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Returns:
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Output tensor for the block.
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Raises:
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ValueError: if `block_type` is not one of `'block35'`,
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`'block17'` or `'block8'`.
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"""
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if block_type == "block35":
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branch_0 = conv2d_bn(x, 32, 1)
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branch_1 = conv2d_bn(x, 32, 1)
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branch_1 = conv2d_bn(branch_1, 32, 3)
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branch_2 = conv2d_bn(x, 32, 1)
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branch_2 = conv2d_bn(branch_2, 48, 3)
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branch_2 = conv2d_bn(branch_2, 64, 3)
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branches = [branch_0, branch_1, branch_2]
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elif block_type == "block17":
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branch_0 = conv2d_bn(x, 192, 1)
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branch_1 = conv2d_bn(x, 128, 1)
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branch_1 = conv2d_bn(branch_1, 160, [1, 7])
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branch_1 = conv2d_bn(branch_1, 192, [7, 1])
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branches = [branch_0, branch_1]
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elif block_type == "block8":
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branch_0 = conv2d_bn(x, 192, 1)
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branch_1 = conv2d_bn(x, 192, 1)
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branch_1 = conv2d_bn(branch_1, 224, [1, 3])
|
||
|
branch_1 = conv2d_bn(branch_1, 256, [3, 1])
|
||
|
branches = [branch_0, branch_1]
|
||
|
else:
|
||
|
raise ValueError(
|
||
|
"Unknown Inception-ResNet block type. "
|
||
|
'Expects "block35", "block17" or "block8", '
|
||
|
"but got: " + str(block_type)
|
||
|
)
|
||
|
|
||
|
block_name = block_type + "_" + str(block_idx)
|
||
|
channel_axis = 1 if backend.image_data_format() == "channels_first" else 3
|
||
|
mixed = layers.Concatenate(axis=channel_axis, name=block_name + "_mixed")(
|
||
|
branches
|
||
|
)
|
||
|
up = conv2d_bn(
|
||
|
mixed,
|
||
|
backend.int_shape(x)[channel_axis],
|
||
|
1,
|
||
|
activation=None,
|
||
|
use_bias=True,
|
||
|
name=block_name + "_conv",
|
||
|
)
|
||
|
|
||
|
x = CustomScaleLayer(scale)([x, up])
|
||
|
if activation is not None:
|
||
|
x = layers.Activation(activation, name=block_name + "_ac")(x)
|
||
|
return x
|
||
|
|
||
|
|
||
|
@keras_export("keras.applications.inception_resnet_v2.preprocess_input")
|
||
|
def preprocess_input(x, data_format=None):
|
||
|
return imagenet_utils.preprocess_input(
|
||
|
x, data_format=data_format, mode="tf"
|
||
|
)
|
||
|
|
||
|
|
||
|
@keras_export("keras.applications.inception_resnet_v2.decode_predictions")
|
||
|
def decode_predictions(preds, top=5):
|
||
|
return imagenet_utils.decode_predictions(preds, top=top)
|
||
|
|
||
|
|
||
|
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
|
||
|
mode="",
|
||
|
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
|
||
|
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
|
||
|
)
|
||
|
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
|