215 lines
6.7 KiB
Python
215 lines
6.7 KiB
Python
# Copyright 2019 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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"""ResNet v2 models for Keras.
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Reference:
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- [Identity Mappings in Deep Residual Networks](
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https://arxiv.org/abs/1603.05027) (CVPR 2016)
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"""
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from keras.applications import imagenet_utils
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from keras.applications import resnet
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export(
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"keras.applications.resnet_v2.ResNet50V2", "keras.applications.ResNet50V2"
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)
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def ResNet50V2(
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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 ResNet50V2 architecture."""
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def stack_fn(x):
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x = resnet.stack2(x, 64, 3, name="conv2")
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x = resnet.stack2(x, 128, 4, name="conv3")
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x = resnet.stack2(x, 256, 6, name="conv4")
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return resnet.stack2(x, 512, 3, stride1=1, name="conv5")
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return resnet.ResNet(
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stack_fn,
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True,
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True,
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"resnet50v2",
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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_export(
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"keras.applications.resnet_v2.ResNet101V2", "keras.applications.ResNet101V2"
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)
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def ResNet101V2(
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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 ResNet101V2 architecture."""
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def stack_fn(x):
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x = resnet.stack2(x, 64, 3, name="conv2")
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x = resnet.stack2(x, 128, 4, name="conv3")
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x = resnet.stack2(x, 256, 23, name="conv4")
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return resnet.stack2(x, 512, 3, stride1=1, name="conv5")
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return resnet.ResNet(
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stack_fn,
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True,
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True,
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"resnet101v2",
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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_export(
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"keras.applications.resnet_v2.ResNet152V2", "keras.applications.ResNet152V2"
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)
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def ResNet152V2(
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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 ResNet152V2 architecture."""
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def stack_fn(x):
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x = resnet.stack2(x, 64, 3, name="conv2")
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x = resnet.stack2(x, 128, 8, name="conv3")
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x = resnet.stack2(x, 256, 36, name="conv4")
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return resnet.stack2(x, 512, 3, stride1=1, name="conv5")
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return resnet.ResNet(
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stack_fn,
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True,
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True,
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"resnet152v2",
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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_export("keras.applications.resnet_v2.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.resnet_v2.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__
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DOC = """
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Reference:
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- [Identity Mappings in Deep Residual Networks](
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https://arxiv.org/abs/1603.05027) (CVPR 2016)
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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 ResNetV2, call `tf.keras.applications.resnet_v2.preprocess_input` on your
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inputs before passing them to the model.
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`resnet_v2.preprocess_input` 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 `(224, 224, 3)` (with `'channels_last'` data format)
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or `(3, 224, 224)` (with `'channels_first'` data format).
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It should have exactly 3 inputs channels,
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and width and height should be no smaller than 32.
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E.g. `(200, 200, 3)` would be one valid value.
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pooling: Optional pooling mode for feature extraction
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when `include_top` is `False`.
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- `None` means that the output of the model will be
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the 4D tensor output of the
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last convolutional block.
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- `avg` means that global average pooling
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will be applied to the output of the
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last convolutional block, and thus
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the output of the model will be a 2D tensor.
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- `max` means that global max pooling will
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be applied.
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classes: optional number of classes to classify images
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into, only to be specified if `include_top` is True, and
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if no `weights` argument is specified.
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classifier_activation: A `str` or callable. The activation function to use
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on the "top" layer. Ignored unless `include_top=True`. Set
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`classifier_activation=None` to return the logits of the "top" layer.
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When loading pretrained weights, `classifier_activation` can only
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be `None` or `"softmax"`.
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Returns:
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A `keras.Model` instance.
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"""
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setattr(ResNet50V2, "__doc__", ResNet50V2.__doc__ + DOC)
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setattr(ResNet101V2, "__doc__", ResNet101V2.__doc__ + DOC)
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setattr(ResNet152V2, "__doc__", ResNet152V2.__doc__ + DOC)
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