# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Inception-ResNet V2 model for Keras. Reference: - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) (AAAI 2017) """ import tensorflow.compat.v2 as tf from keras import backend from keras.applications import imagenet_utils from keras.engine import training from keras.layers import VersionAwareLayers from keras.utils import data_utils from keras.utils import layer_utils # isort: off from tensorflow.python.util.tf_export import keras_export BASE_WEIGHT_URL = ( "https://storage.googleapis.com/tensorflow/" "keras-applications/inception_resnet_v2/" ) layers = None @keras_export( "keras.applications.inception_resnet_v2.InceptionResNetV2", "keras.applications.InceptionResNetV2", ) def InceptionResNetV2( include_top=True, weights="imagenet", input_tensor=None, input_shape=None, pooling=None, classes=1000, classifier_activation="softmax", **kwargs, ): """Instantiates the Inception-ResNet v2 architecture. Reference: - [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261) (AAAI 2017) This function returns a Keras image classification model, optionally loaded with weights pre-trained on ImageNet. For image classification use cases, see [this page for detailed examples]( https://keras.io/api/applications/#usage-examples-for-image-classification-models). For transfer learning use cases, make sure to read the [guide to transfer learning & fine-tuning]( https://keras.io/guides/transfer_learning/). Note: each Keras Application expects a specific kind of input preprocessing. For InceptionResNetV2, call `tf.keras.applications.inception_resnet_v2.preprocess_input` on your inputs before passing them to the model. `inception_resnet_v2.preprocess_input` will scale input pixels between -1 and 1. Args: include_top: whether to include the fully-connected layer at the top of the network. weights: one of `None` (random initialization), 'imagenet' (pre-training on ImageNet), or the path to the weights file to be loaded. input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) to use as image input for the model. input_shape: optional shape tuple, only to be specified if `include_top` is `False` (otherwise the input shape has to be `(299, 299, 3)` (with `'channels_last'` data format) or `(3, 299, 299)` (with `'channels_first'` data format). It should have exactly 3 inputs channels, and width and height should be no smaller than 75. E.g. `(150, 150, 3)` would be one valid value. pooling: Optional pooling mode for feature extraction when `include_top` is `False`. - `None` means that the output of the model will be the 4D tensor output of the last convolutional block. - `'avg'` means that global average pooling will be applied to the output of the last convolutional block, and thus the output of the model will be a 2D tensor. - `'max'` means that global max pooling will be applied. classes: optional number of classes to classify images into, only to be specified if `include_top` is `True`, and if no `weights` argument is specified. classifier_activation: A `str` or callable. The activation function to use on the "top" layer. Ignored unless `include_top=True`. Set `classifier_activation=None` to return the logits of the "top" layer. When loading pretrained weights, `classifier_activation` can only be `None` or `"softmax"`. **kwargs: For backwards compatibility only. Returns: A `keras.Model` instance. """ global layers if "layers" in kwargs: layers = kwargs.pop("layers") else: layers = VersionAwareLayers() if kwargs: raise ValueError(f"Unknown argument(s): {kwargs}") if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)): raise ValueError( "The `weights` argument should be either " "`None` (random initialization), `imagenet` " "(pre-training on ImageNet), " "or the path to the weights file to be loaded." ) if weights == "imagenet" and include_top and classes != 1000: raise ValueError( 'If using `weights` as `"imagenet"` with `include_top`' " as true, `classes` should be 1000" ) # Determine proper input shape input_shape = imagenet_utils.obtain_input_shape( input_shape, default_size=299, min_size=75, data_format=backend.image_data_format(), require_flatten=include_top, weights=weights, ) if input_tensor is None: img_input = layers.Input(shape=input_shape) else: if not backend.is_keras_tensor(input_tensor): img_input = layers.Input(tensor=input_tensor, shape=input_shape) else: img_input = input_tensor # Stem block: 35 x 35 x 192 x = conv2d_bn(img_input, 32, 3, strides=2, padding="valid") x = conv2d_bn(x, 32, 3, padding="valid") x = conv2d_bn(x, 64, 3) x = layers.MaxPooling2D(3, strides=2)(x) x = conv2d_bn(x, 80, 1, padding="valid") x = conv2d_bn(x, 192, 3, padding="valid") x = layers.MaxPooling2D(3, strides=2)(x) # Mixed 5b (Inception-A block): 35 x 35 x 320 branch_0 = conv2d_bn(x, 96, 1) branch_1 = conv2d_bn(x, 48, 1) branch_1 = conv2d_bn(branch_1, 64, 5) branch_2 = conv2d_bn(x, 64, 1) branch_2 = conv2d_bn(branch_2, 96, 3) branch_2 = conv2d_bn(branch_2, 96, 3) branch_pool = layers.AveragePooling2D(3, strides=1, padding="same")(x) branch_pool = conv2d_bn(branch_pool, 64, 1) branches = [branch_0, branch_1, branch_2, branch_pool] channel_axis = 1 if backend.image_data_format() == "channels_first" else 3 x = layers.Concatenate(axis=channel_axis, name="mixed_5b")(branches) # 10x block35 (Inception-ResNet-A block): 35 x 35 x 320 for block_idx in range(1, 11): x = inception_resnet_block( x, scale=0.17, block_type="block35", block_idx=block_idx ) # Mixed 6a (Reduction-A block): 17 x 17 x 1088 branch_0 = conv2d_bn(x, 384, 3, strides=2, padding="valid") branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 256, 3) branch_1 = conv2d_bn(branch_1, 384, 3, strides=2, padding="valid") branch_pool = layers.MaxPooling2D(3, strides=2, padding="valid")(x) branches = [branch_0, branch_1, branch_pool] x = layers.Concatenate(axis=channel_axis, name="mixed_6a")(branches) # 20x block17 (Inception-ResNet-B block): 17 x 17 x 1088 for block_idx in range(1, 21): x = inception_resnet_block( x, scale=0.1, block_type="block17", block_idx=block_idx ) # Mixed 7a (Reduction-B block): 8 x 8 x 2080 branch_0 = conv2d_bn(x, 256, 1) branch_0 = conv2d_bn(branch_0, 384, 3, strides=2, padding="valid") branch_1 = conv2d_bn(x, 256, 1) branch_1 = conv2d_bn(branch_1, 288, 3, strides=2, padding="valid") branch_2 = conv2d_bn(x, 256, 1) branch_2 = conv2d_bn(branch_2, 288, 3) branch_2 = conv2d_bn(branch_2, 320, 3, strides=2, padding="valid") branch_pool = layers.MaxPooling2D(3, strides=2, padding="valid")(x) branches = [branch_0, branch_1, branch_2, branch_pool] x = layers.Concatenate(axis=channel_axis, name="mixed_7a")(branches) # 10x block8 (Inception-ResNet-C block): 8 x 8 x 2080 for block_idx in range(1, 10): x = inception_resnet_block( x, scale=0.2, block_type="block8", block_idx=block_idx ) x = inception_resnet_block( x, scale=1.0, activation=None, block_type="block8", block_idx=10 ) # Final convolution block: 8 x 8 x 1536 x = conv2d_bn(x, 1536, 1, name="conv_7b") if include_top: # Classification block x = layers.GlobalAveragePooling2D(name="avg_pool")(x) imagenet_utils.validate_activation(classifier_activation, weights) x = layers.Dense( classes, activation=classifier_activation, name="predictions" )(x) else: if pooling == "avg": x = layers.GlobalAveragePooling2D()(x) elif pooling == "max": x = layers.GlobalMaxPooling2D()(x) # Ensure that the model takes into account # any potential predecessors of `input_tensor`. if input_tensor is not None: inputs = layer_utils.get_source_inputs(input_tensor) else: inputs = img_input # Create model. model = training.Model(inputs, x, name="inception_resnet_v2") # Load weights. if weights == "imagenet": if include_top: fname = "inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5" weights_path = data_utils.get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir="models", file_hash="e693bd0210a403b3192acc6073ad2e96", ) else: fname = ( "inception_resnet_v2_weights_" "tf_dim_ordering_tf_kernels_notop.h5" ) weights_path = data_utils.get_file( fname, BASE_WEIGHT_URL + fname, cache_subdir="models", file_hash="d19885ff4a710c122648d3b5c3b684e4", ) model.load_weights(weights_path) elif weights is not None: model.load_weights(weights) return model def conv2d_bn( x, filters, kernel_size, strides=1, padding="same", activation="relu", use_bias=False, name=None, ): """Utility function to apply conv + BN. Args: x: input tensor. filters: filters in `Conv2D`. kernel_size: kernel size as in `Conv2D`. strides: strides in `Conv2D`. padding: padding mode in `Conv2D`. activation: activation in `Conv2D`. use_bias: whether to use a bias in `Conv2D`. name: name of the ops; will become `name + '_ac'` for the activation and `name + '_bn'` for the batch norm layer. Returns: Output tensor after applying `Conv2D` and `BatchNormalization`. """ x = layers.Conv2D( filters, kernel_size, strides=strides, padding=padding, use_bias=use_bias, name=name, )(x) if not use_bias: bn_axis = 1 if backend.image_data_format() == "channels_first" else 3 bn_name = None if name is None else name + "_bn" x = layers.BatchNormalization(axis=bn_axis, scale=False, name=bn_name)( x ) if activation is not None: ac_name = None if name is None else name + "_ac" x = layers.Activation(activation, name=ac_name)(x) return x def inception_resnet_block(x, scale, block_type, block_idx, activation="relu"): """Adds an Inception-ResNet block. This function builds 3 types of Inception-ResNet blocks mentioned in the paper, controlled by the `block_type` argument (which is the block name used in the official TF-slim implementation): - Inception-ResNet-A: `block_type='block35'` - Inception-ResNet-B: `block_type='block17'` - Inception-ResNet-C: `block_type='block8'` Args: x: input tensor. scale: scaling factor to scale the residuals (i.e., the output of passing `x` through an inception module) before adding them to the shortcut branch. Let `r` be the output from the residual branch, the output of this block will be `x + scale * r`. block_type: `'block35'`, `'block17'` or `'block8'`, determines the network structure in the residual branch. block_idx: an `int` used for generating layer names. The Inception-ResNet blocks are repeated many times in this network. We use `block_idx` to identify each of the repetitions. For example, the first Inception-ResNet-A block will have `block_type='block35', block_idx=0`, and the layer names will have a common prefix `'block35_0'`. activation: activation function to use at the end of the block (see [activations](../activations.md)). When `activation=None`, no activation is applied (i.e., "linear" activation: `a(x) = x`). Returns: Output tensor for the block. Raises: ValueError: if `block_type` is not one of `'block35'`, `'block17'` or `'block8'`. """ if block_type == "block35": branch_0 = conv2d_bn(x, 32, 1) branch_1 = conv2d_bn(x, 32, 1) branch_1 = conv2d_bn(branch_1, 32, 3) branch_2 = conv2d_bn(x, 32, 1) branch_2 = conv2d_bn(branch_2, 48, 3) branch_2 = conv2d_bn(branch_2, 64, 3) branches = [branch_0, branch_1, branch_2] elif block_type == "block17": branch_0 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(x, 128, 1) branch_1 = conv2d_bn(branch_1, 160, [1, 7]) branch_1 = conv2d_bn(branch_1, 192, [7, 1]) branches = [branch_0, branch_1] elif block_type == "block8": branch_0 = conv2d_bn(x, 192, 1) branch_1 = conv2d_bn(x, 192, 1) 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 = layers.Lambda( lambda inputs, scale: inputs[0] + inputs[1] * scale, output_shape=backend.int_shape(x)[1:], arguments={"scale": scale}, name=block_name, )([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__