| from keras.src.api_export import keras_export |
| from keras.src.applications import imagenet_utils |
| from keras.src.applications import resnet |
|
|
|
|
| @keras_export( |
| [ |
| "keras.applications.ResNet50V2", |
| "keras.applications.resnet_v2.ResNet50V2", |
| ] |
| ) |
| def ResNet50V2( |
| include_top=True, |
| weights="imagenet", |
| input_tensor=None, |
| input_shape=None, |
| pooling=None, |
| classes=1000, |
| classifier_activation="softmax", |
| name="resnet50v2", |
| ): |
| """Instantiates the ResNet50V2 architecture.""" |
|
|
| def stack_fn(x): |
| x = resnet.stack_residual_blocks_v2(x, 64, 3, name="conv2") |
| x = resnet.stack_residual_blocks_v2(x, 128, 4, name="conv3") |
| x = resnet.stack_residual_blocks_v2(x, 256, 6, name="conv4") |
| return resnet.stack_residual_blocks_v2( |
| x, 512, 3, stride1=1, name="conv5" |
| ) |
|
|
| return resnet.ResNet( |
| stack_fn, |
| True, |
| True, |
| name=name, |
| weights_name="resnet50v2", |
| include_top=include_top, |
| weights=weights, |
| input_tensor=input_tensor, |
| input_shape=input_shape, |
| pooling=pooling, |
| classes=classes, |
| classifier_activation=classifier_activation, |
| ) |
|
|
|
|
| @keras_export( |
| [ |
| "keras.applications.ResNet101V2", |
| "keras.applications.resnet_v2.ResNet101V2", |
| ] |
| ) |
| def ResNet101V2( |
| include_top=True, |
| weights="imagenet", |
| input_tensor=None, |
| input_shape=None, |
| pooling=None, |
| classes=1000, |
| classifier_activation="softmax", |
| name="resnet101v2", |
| ): |
| """Instantiates the ResNet101V2 architecture.""" |
|
|
| def stack_fn(x): |
| x = resnet.stack_residual_blocks_v2(x, 64, 3, name="conv2") |
| x = resnet.stack_residual_blocks_v2(x, 128, 4, name="conv3") |
| x = resnet.stack_residual_blocks_v2(x, 256, 23, name="conv4") |
| return resnet.stack_residual_blocks_v2( |
| x, 512, 3, stride1=1, name="conv5" |
| ) |
|
|
| return resnet.ResNet( |
| stack_fn, |
| True, |
| True, |
| name=name, |
| weights_name="resnet101v2", |
| include_top=include_top, |
| weights=weights, |
| input_tensor=input_tensor, |
| input_shape=input_shape, |
| pooling=pooling, |
| classes=classes, |
| classifier_activation=classifier_activation, |
| ) |
|
|
|
|
| @keras_export( |
| [ |
| "keras.applications.ResNet152V2", |
| "keras.applications.resnet_v2.ResNet152V2", |
| ] |
| ) |
| def ResNet152V2( |
| include_top=True, |
| weights="imagenet", |
| input_tensor=None, |
| input_shape=None, |
| pooling=None, |
| classes=1000, |
| classifier_activation="softmax", |
| name="resnet152v2", |
| ): |
| """Instantiates the ResNet152V2 architecture.""" |
|
|
| def stack_fn(x): |
| x = resnet.stack_residual_blocks_v2(x, 64, 3, name="conv2") |
| x = resnet.stack_residual_blocks_v2(x, 128, 8, name="conv3") |
| x = resnet.stack_residual_blocks_v2(x, 256, 36, name="conv4") |
| return resnet.stack_residual_blocks_v2( |
| x, 512, 3, stride1=1, name="conv5" |
| ) |
|
|
| return resnet.ResNet( |
| stack_fn, |
| True, |
| True, |
| name=name, |
| weights_name="resnet152v2", |
| include_top=include_top, |
| weights=weights, |
| input_tensor=input_tensor, |
| input_shape=input_shape, |
| pooling=pooling, |
| classes=classes, |
| classifier_activation=classifier_activation, |
| ) |
|
|
|
|
| @keras_export("keras.applications.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.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__ |
|
|
|
|
| DOC = """ |
| |
| Reference: |
| - [Identity Mappings in Deep Residual Networks]( |
| https://arxiv.org/abs/1603.05027) (CVPR 2016) |
| |
| 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.applications.resnet_v2.preprocess_input` on your |
| inputs before passing them to the model. `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 `(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"`. |
| name: The name of the model (string). |
| |
| Returns: |
| A Model instance. |
| """ |
|
|
| setattr(ResNet50V2, "__doc__", ResNet50V2.__doc__ + DOC) |
| setattr(ResNet101V2, "__doc__", ResNet101V2.__doc__ + DOC) |
| setattr(ResNet152V2, "__doc__", ResNet152V2.__doc__ + DOC) |
|
|
