464 lines
16 KiB
Python
464 lines
16 KiB
Python
# Copyright 2015 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 V3 model for Keras.
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Reference:
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- [Rethinking the Inception Architecture for Computer Vision](
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http://arxiv.org/abs/1512.00567) (CVPR 2016)
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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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WEIGHTS_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/"
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"inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels.h5"
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)
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WEIGHTS_PATH_NO_TOP = (
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"https://storage.googleapis.com/tensorflow/keras-applications/"
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"inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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layers = VersionAwareLayers()
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@keras_export(
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"keras.applications.inception_v3.InceptionV3",
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"keras.applications.InceptionV3",
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)
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def InceptionV3(
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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 Inception v3 architecture.
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Reference:
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- [Rethinking the Inception Architecture for Computer Vision](
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http://arxiv.org/abs/1512.00567) (CVPR 2016)
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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 `InceptionV3`, call
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`tf.keras.applications.inception_v3.preprocess_input` on your inputs before
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passing them to the model. `inception_v3.preprocess_input` will scale input
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pixels between -1 and 1.
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Args:
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include_top: Boolean, whether to include the fully-connected
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layer at the top, as the last layer of the network. Default to `True`.
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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. Default to `imagenet`.
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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. `input_tensor` is useful for
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sharing inputs between multiple different networks. Default to None.
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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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`input_shape` will be ignored if the `input_tensor` is provided.
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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` (default) 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. Default to 1000.
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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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f"Received: weights={weights}"
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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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f"Received classes={classes}"
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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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if backend.image_data_format() == "channels_first":
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channel_axis = 1
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else:
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channel_axis = 3
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x = conv2d_bn(img_input, 32, 3, 3, strides=(2, 2), padding="valid")
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x = conv2d_bn(x, 32, 3, 3, padding="valid")
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x = conv2d_bn(x, 64, 3, 3)
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x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = conv2d_bn(x, 80, 1, 1, padding="valid")
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x = conv2d_bn(x, 192, 3, 3, padding="valid")
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x = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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# mixed 0: 35 x 35 x 256
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branch1x1 = conv2d_bn(x, 64, 1, 1)
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branch5x5 = conv2d_bn(x, 48, 1, 1)
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branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 32, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch5x5, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name="mixed0",
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)
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# mixed 1: 35 x 35 x 288
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branch1x1 = conv2d_bn(x, 64, 1, 1)
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branch5x5 = conv2d_bn(x, 48, 1, 1)
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branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch5x5, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name="mixed1",
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)
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# mixed 2: 35 x 35 x 288
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branch1x1 = conv2d_bn(x, 64, 1, 1)
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branch5x5 = conv2d_bn(x, 48, 1, 1)
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branch5x5 = conv2d_bn(branch5x5, 64, 5, 5)
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 64, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch5x5, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name="mixed2",
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)
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# mixed 3: 17 x 17 x 768
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branch3x3 = conv2d_bn(x, 384, 3, 3, strides=(2, 2), padding="valid")
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branch3x3dbl = conv2d_bn(x, 64, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 96, 3, 3)
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branch3x3dbl = conv2d_bn(
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branch3x3dbl, 96, 3, 3, strides=(2, 2), padding="valid"
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)
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branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = layers.concatenate(
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[branch3x3, branch3x3dbl, branch_pool], axis=channel_axis, name="mixed3"
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)
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# mixed 4: 17 x 17 x 768
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branch1x1 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(x, 128, 1, 1)
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branch7x7 = conv2d_bn(branch7x7, 128, 1, 7)
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branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
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branch7x7dbl = conv2d_bn(x, 128, 1, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 1, 7)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 128, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch7x7, branch7x7dbl, branch_pool],
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axis=channel_axis,
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name="mixed4",
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)
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# mixed 5, 6: 17 x 17 x 768
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for i in range(2):
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branch1x1 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(x, 160, 1, 1)
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branch7x7 = conv2d_bn(branch7x7, 160, 1, 7)
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branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
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branch7x7dbl = conv2d_bn(x, 160, 1, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 1, 7)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 160, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch7x7, branch7x7dbl, branch_pool],
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axis=channel_axis,
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name="mixed" + str(5 + i),
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)
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# mixed 7: 17 x 17 x 768
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branch1x1 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(x, 192, 1, 1)
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branch7x7 = conv2d_bn(branch7x7, 192, 1, 7)
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branch7x7 = conv2d_bn(branch7x7, 192, 7, 1)
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branch7x7dbl = conv2d_bn(x, 192, 1, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 7, 1)
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branch7x7dbl = conv2d_bn(branch7x7dbl, 192, 1, 7)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch7x7, branch7x7dbl, branch_pool],
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axis=channel_axis,
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name="mixed7",
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)
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# mixed 8: 8 x 8 x 1280
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branch3x3 = conv2d_bn(x, 192, 1, 1)
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branch3x3 = conv2d_bn(branch3x3, 320, 3, 3, strides=(2, 2), padding="valid")
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branch7x7x3 = conv2d_bn(x, 192, 1, 1)
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branch7x7x3 = conv2d_bn(branch7x7x3, 192, 1, 7)
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branch7x7x3 = conv2d_bn(branch7x7x3, 192, 7, 1)
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branch7x7x3 = conv2d_bn(
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branch7x7x3, 192, 3, 3, strides=(2, 2), padding="valid"
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)
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branch_pool = layers.MaxPooling2D((3, 3), strides=(2, 2))(x)
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x = layers.concatenate(
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[branch3x3, branch7x7x3, branch_pool], axis=channel_axis, name="mixed8"
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)
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# mixed 9: 8 x 8 x 2048
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for i in range(2):
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branch1x1 = conv2d_bn(x, 320, 1, 1)
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branch3x3 = conv2d_bn(x, 384, 1, 1)
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branch3x3_1 = conv2d_bn(branch3x3, 384, 1, 3)
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branch3x3_2 = conv2d_bn(branch3x3, 384, 3, 1)
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branch3x3 = layers.concatenate(
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[branch3x3_1, branch3x3_2],
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axis=channel_axis,
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name="mixed9_" + str(i),
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)
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branch3x3dbl = conv2d_bn(x, 448, 1, 1)
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branch3x3dbl = conv2d_bn(branch3x3dbl, 384, 3, 3)
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branch3x3dbl_1 = conv2d_bn(branch3x3dbl, 384, 1, 3)
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branch3x3dbl_2 = conv2d_bn(branch3x3dbl, 384, 3, 1)
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branch3x3dbl = layers.concatenate(
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[branch3x3dbl_1, branch3x3dbl_2], axis=channel_axis
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)
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branch_pool = layers.AveragePooling2D(
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(3, 3), strides=(1, 1), padding="same"
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)(x)
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branch_pool = conv2d_bn(branch_pool, 192, 1, 1)
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x = layers.concatenate(
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[branch1x1, branch3x3, branch3x3dbl, branch_pool],
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axis=channel_axis,
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name="mixed" + str(9 + i),
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)
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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_v3")
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# Load weights.
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if weights == "imagenet":
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if include_top:
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weights_path = data_utils.get_file(
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"inception_v3_weights_tf_dim_ordering_tf_kernels.h5",
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WEIGHTS_PATH,
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cache_subdir="models",
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file_hash="9a0d58056eeedaa3f26cb7ebd46da564",
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)
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else:
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weights_path = data_utils.get_file(
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"inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5",
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WEIGHTS_PATH_NO_TOP,
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cache_subdir="models",
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file_hash="bcbd6486424b2319ff4ef7d526e38f63",
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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, filters, num_row, num_col, padding="same", strides=(1, 1), 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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num_row: height of the convolution kernel.
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num_col: width of the convolution kernel.
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padding: padding mode in `Conv2D`.
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strides: strides in `Conv2D`.
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name: name of the ops; will become `name + '_conv'`
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for the convolution and `name + '_bn'` for the
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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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if name is not None:
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bn_name = name + "_bn"
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conv_name = name + "_conv"
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else:
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bn_name = None
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conv_name = None
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if backend.image_data_format() == "channels_first":
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bn_axis = 1
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else:
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bn_axis = 3
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x = layers.Conv2D(
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filters,
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(num_row, num_col),
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strides=strides,
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padding=padding,
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use_bias=False,
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name=conv_name,
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)(x)
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x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)(x)
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x = layers.Activation("relu", name=name)(x)
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return x
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@keras_export("keras.applications.inception_v3.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="tf"
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)
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@keras_export("keras.applications.inception_v3.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_TF,
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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__
|