585 lines
19 KiB
Python
585 lines
19 KiB
Python
from tensorflow.io import gfile
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from keras_core import backend
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from keras_core import layers
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from keras_core.api_export import keras_core_export
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from keras_core.applications import imagenet_utils
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from keras_core.models import Functional
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from keras_core.operations import operation_utils
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from keras_core.utils import file_utils
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BASE_WEIGHTS_PATH = (
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"https://storage.googleapis.com/tensorflow/keras-applications/resnet/"
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)
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WEIGHTS_HASHES = {
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"resnet50": (
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"2cb95161c43110f7111970584f804107",
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"4d473c1dd8becc155b73f8504c6f6626",
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),
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"resnet101": (
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"f1aeb4b969a6efcfb50fad2f0c20cfc5",
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"88cf7a10940856eca736dc7b7e228a21",
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),
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"resnet152": (
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"100835be76be38e30d865e96f2aaae62",
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"ee4c566cf9a93f14d82f913c2dc6dd0c",
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),
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"resnet50v2": (
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"3ef43a0b657b3be2300d5770ece849e0",
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"fac2f116257151a9d068a22e544a4917",
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),
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"resnet101v2": (
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"6343647c601c52e1368623803854d971",
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"c0ed64b8031c3730f411d2eb4eea35b5",
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),
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"resnet152v2": (
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"a49b44d1979771252814e80f8ec446f9",
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"ed17cf2e0169df9d443503ef94b23b33",
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),
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"resnext50": (
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"67a5b30d522ed92f75a1f16eef299d1a",
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"62527c363bdd9ec598bed41947b379fc",
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),
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"resnext101": (
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"34fb605428fcc7aa4d62f44404c11509",
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"0f678c91647380debd923963594981b3",
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),
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}
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def ResNet(
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stack_fn,
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preact,
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use_bias,
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model_name="resnet",
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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 ResNet, ResNetV2, and ResNeXt architecture.
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Args:
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stack_fn: A function that returns output tensor for the
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stacked residual blocks.
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preact: Whether to use pre-activation or not. `True` for ResNetV2,
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`False` for ResNet and ResNeXt.
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use_bias: Whether to use biases for convolutional layers or not.
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`True` for ResNet and ResNetV2, `False` for ResNeXt.
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model_name: Name of the model.
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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). It
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should have exactly 3 inputs channels.
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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 layer.
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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 layer, 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`,
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and if no `weights` argument is specified.
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classifier_activation: A `str` or callable. The activation
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function to use on the "top" layer. Ignored unless
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`include_top=True`. Set `classifier_activation=None` to
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return the logits of the "top" layer. When loading
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pretrained weights, `classifier_activation` can only be
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`None` or `"softmax"`.
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Returns:
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A Model instance.
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"""
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if not (weights in {"imagenet", None} or 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. Received: "
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f"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='imagenet'` with `include_top=True`, "
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"`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=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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if backend.image_data_format() == "channels_last":
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bn_axis = 3
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else:
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bn_axis = 1
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x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)), name="conv1_pad")(
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img_input
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)
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x = layers.Conv2D(64, 7, strides=2, use_bias=use_bias, name="conv1_conv")(x)
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if not preact:
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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)), name="pool1_pad")(x)
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x = layers.MaxPooling2D(3, strides=2, name="pool1_pool")(x)
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x = stack_fn(x)
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if preact:
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name="post_bn"
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)(x)
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x = layers.Activation("relu", name="post_relu")(x)
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if include_top:
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x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
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# Validate activation for the classifier layer
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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 = operation_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 = Functional(inputs, x, name=model_name)
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# Load weights.
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if (weights == "imagenet") and (model_name in WEIGHTS_HASHES):
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if include_top:
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file_name = model_name + "_weights_tf_dim_ordering_tf_kernels.h5"
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file_hash = WEIGHTS_HASHES[model_name][0]
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else:
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file_name = (
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model_name + "_weights_tf_dim_ordering_tf_kernels_notop.h5"
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)
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file_hash = WEIGHTS_HASHES[model_name][1]
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weights_path = file_utils.get_file(
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file_name,
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BASE_WEIGHTS_PATH + file_name,
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cache_subdir="models",
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file_hash=file_hash,
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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 residual_block_v1(
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x, filters, kernel_size=3, stride=1, conv_shortcut=True, name=None
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):
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"""A residual block for ResNet*_v1.
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Args:
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x: Input tensor.
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filters: No of filters in the bottleneck layer.
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kernel_size: Kernel size of the bottleneck layer. Defaults to 3
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stride: Stride of the first layer. Defaults to 1
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conv_shortcut: Use convolution shortcut if `True`, otherwise
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use identity shortcut. Defaults to `True`
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name(optional): Name of the block
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Returns:
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Output tensor for the residual block.
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"""
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if backend.image_data_format() == "channels_last":
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bn_axis = 3
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else:
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bn_axis = 1
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if conv_shortcut:
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shortcut = layers.Conv2D(
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4 * filters, 1, strides=stride, name=name + "_0_conv"
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)(x)
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shortcut = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_0_bn"
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)(shortcut)
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else:
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shortcut = x
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x = layers.Conv2D(filters, 1, strides=stride, name=name + "_1_conv")(x)
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
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)(x)
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x = layers.Activation("relu", name=name + "_1_relu")(x)
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x = layers.Conv2D(
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filters, kernel_size, padding="SAME", name=name + "_2_conv"
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)(x)
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn"
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)(x)
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x = layers.Activation("relu", name=name + "_2_relu")(x)
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x = layers.Conv2D(4 * filters, 1, name=name + "_3_conv")(x)
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_3_bn"
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)(x)
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x = layers.Add(name=name + "_add")([shortcut, x])
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x = layers.Activation("relu", name=name + "_out")(x)
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return x
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def stack_residual_blocks_v1(x, filters, blocks, stride1=2, name=None):
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"""A set of stacked residual blocks.
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Args:
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x: Input tensor.
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filters: Number of filters in the bottleneck layer in a block.
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blocks: Number of blocks in the stacked blocks.
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stride1: Stride of the first layer in the first block. Defaults to 2.
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name: Stack label.
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Returns:
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Output tensor for the stacked blocks.
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"""
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x = residual_block_v1(x, filters, stride=stride1, name=name + "_block1")
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for i in range(2, blocks + 1):
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x = residual_block_v1(
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x, filters, conv_shortcut=False, name=name + "_block" + str(i)
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)
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return x
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def residual_block_v2(
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x, filters, kernel_size=3, stride=1, conv_shortcut=False, name=None
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):
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"""A residual block for ResNet*_v2.
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Args:
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x: Input tensor.
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filters: No of filters in the bottleneck layer.
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kernel_size: Kernel size of the bottleneck layer. Defaults to 3
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stride: Stride of the first layer. Defaults to 1
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conv_shortcut: Use convolution shortcut if `True`, otherwise
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use identity shortcut. Defaults to `True`
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name(optional): Name of the block
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Returns:
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Output tensor for the residual block.
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"""
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if backend.image_data_format() == "channels_last":
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bn_axis = 3
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else:
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bn_axis = 1
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preact = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_preact_bn"
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)(x)
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preact = layers.Activation("relu", name=name + "_preact_relu")(preact)
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if conv_shortcut:
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shortcut = layers.Conv2D(
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4 * filters, 1, strides=stride, name=name + "_0_conv"
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)(preact)
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else:
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shortcut = (
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layers.MaxPooling2D(1, strides=stride)(x) if stride > 1 else x
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)
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x = layers.Conv2D(
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filters, 1, strides=1, use_bias=False, name=name + "_1_conv"
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)(preact)
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_1_bn"
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)(x)
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x = layers.Activation("relu", name=name + "_1_relu")(x)
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x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)), name=name + "_2_pad")(x)
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x = layers.Conv2D(
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filters,
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kernel_size,
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strides=stride,
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use_bias=False,
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name=name + "_2_conv",
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)(x)
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x = layers.BatchNormalization(
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axis=bn_axis, epsilon=1.001e-5, name=name + "_2_bn"
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)(x)
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x = layers.Activation("relu", name=name + "_2_relu")(x)
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x = layers.Conv2D(4 * filters, 1, name=name + "_3_conv")(x)
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x = layers.Add(name=name + "_out")([shortcut, x])
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return x
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def stack_residual_blocks_v2(x, filters, blocks, stride1=2, name=None):
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"""A set of stacked residual blocks.
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Args:
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x: Input tensor.
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filters: Number of filters in the bottleneck layer in a block.
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blocks: Number of blocks in the stacked blocks.
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stride1: Stride of the first layer in the first block. Defaults to 2.
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name: Stack label.
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Returns:
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Output tensor for the stacked blocks.
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"""
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x = residual_block_v2(x, filters, conv_shortcut=True, name=name + "_block1")
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for i in range(2, blocks):
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x = residual_block_v2(x, filters, name=name + "_block" + str(i))
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x = residual_block_v2(
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x, filters, stride=stride1, name=name + "_block" + str(blocks)
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)
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return x
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@keras_core_export(
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[
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"keras_core.applications.resnet50.ResNet50",
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"keras_core.applications.resnet.ResNet50",
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"keras_core.applications.ResNet50",
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]
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)
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def ResNet50(
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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 ResNet50 architecture."""
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def stack_fn(x):
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x = stack_residual_blocks_v1(x, 64, 3, stride1=1, name="conv2")
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x = stack_residual_blocks_v1(x, 128, 4, name="conv3")
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x = stack_residual_blocks_v1(x, 256, 6, name="conv4")
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return stack_residual_blocks_v1(x, 512, 3, name="conv5")
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return ResNet(
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stack_fn,
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False,
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True,
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"resnet50",
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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=classifier_activation,
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)
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@keras_core_export(
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[
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"keras_core.applications.resnet.ResNet101",
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"keras_core.applications.ResNet101",
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]
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)
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def ResNet101(
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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 ResNet101 architecture."""
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def stack_fn(x):
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x = stack_residual_blocks_v1(x, 64, 3, stride1=1, name="conv2")
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x = stack_residual_blocks_v1(x, 128, 4, name="conv3")
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x = stack_residual_blocks_v1(x, 256, 23, name="conv4")
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return stack_residual_blocks_v1(x, 512, 3, name="conv5")
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return ResNet(
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stack_fn,
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False,
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True,
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"resnet101",
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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=classifier_activation,
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)
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@keras_core_export(
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[
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"keras_core.applications.resnet.ResNet152",
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"keras_core.applications.ResNet152",
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]
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)
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def ResNet152(
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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 ResNet152 architecture."""
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def stack_fn(x):
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x = stack_residual_blocks_v1(x, 64, 3, stride1=1, name="conv2")
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x = stack_residual_blocks_v1(x, 128, 8, name="conv3")
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x = stack_residual_blocks_v1(x, 256, 36, name="conv4")
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return stack_residual_blocks_v1(x, 512, 3, name="conv5")
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return ResNet(
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stack_fn,
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False,
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True,
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"resnet152",
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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=classifier_activation,
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)
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@keras_core_export(
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[
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"keras_core.applications.resnet50.preprocess_input",
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"keras_core.applications.resnet.preprocess_input",
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]
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)
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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="caffe"
|
|
)
|
|
|
|
|
|
@keras_core_export(
|
|
[
|
|
"keras_core.applications.resnet50.decode_predictions",
|
|
"keras_core.applications.resnet.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_CAFFE,
|
|
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC,
|
|
)
|
|
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
|
|
|
|
DOC = """
|
|
|
|
Reference:
|
|
- [Deep Residual Learning for Image Recognition](
|
|
https://arxiv.org/abs/1512.03385) (CVPR 2015)
|
|
|
|
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 ResNet, call `keras_core.applications.resnet.preprocess_input` on your
|
|
inputs before passing them to the model. `resnet.preprocess_input` will convert
|
|
the input images from RGB to BGR, then will zero-center each color channel with
|
|
respect to the ImageNet dataset, without scaling.
|
|
|
|
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 `(224, 224, 3)`
|
|
(with `"channels_last"` data format) or `(3, 224, 224)`
|
|
(with `"channels_first"` data format). It should have exactly 3
|
|
inputs channels, and width and height should be no smaller than 32.
|
|
E.g. `(200, 200, 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"`.
|
|
|
|
Returns:
|
|
A Model instance.
|
|
"""
|
|
|
|
setattr(ResNet50, "__doc__", ResNet50.__doc__ + DOC)
|
|
setattr(ResNet101, "__doc__", ResNet101.__doc__ + DOC)
|
|
setattr(ResNet152, "__doc__", ResNet152.__doc__ + DOC)
|