753 lines
24 KiB
Python
753 lines
24 KiB
Python
import numpy as np
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from tensorflow.io import gfile
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from keras_core import backend
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from keras_core import initializers
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from keras_core import layers
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from keras_core import operations as ops
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from keras_core import random
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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.layers.layer import Layer
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from keras_core.models import Functional
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from keras_core.models import Sequential
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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/convnext/"
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)
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WEIGHTS_HASHES = {
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"convnext_tiny": (
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"8ae6e78ce2933352b1ef4008e6dd2f17bc40771563877d156bc6426c7cf503ff",
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"d547c096cabd03329d7be5562c5e14798aa39ed24b474157cef5e85ab9e49ef1",
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),
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"convnext_small": (
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"ce1277d8f1ee5a0ef0e171469089c18f5233860ceaf9b168049cb9263fd7483c",
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"6fc8009faa2f00c1c1dfce59feea9b0745eb260a7dd11bee65c8e20843da6eab",
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),
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"convnext_base": (
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"52cbb006d3dadd03f6e095a8ca1aca47aecdd75acb4bc74bce1f5c695d0086e6",
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"40a20c5548a5e9202f69735ecc06c990e6b7c9d2de39f0361e27baeb24cb7c45",
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),
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"convnext_large": (
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"070c5ed9ed289581e477741d3b34beffa920db8cf590899d6d2c67fba2a198a6",
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"96f02b6f0753d4f543261bc9d09bed650f24dd6bc02ddde3066135b63d23a1cd",
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),
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"convnext_xlarge": (
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"c1f5ccab661354fc3a79a10fa99af82f0fbf10ec65cb894a3ae0815f17a889ee",
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"de3f8a54174130e0cecdc71583354753d557fcf1f4487331558e2a16ba0cfe05",
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),
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}
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MODEL_CONFIGS = {
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"tiny": {
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"depths": [3, 3, 9, 3],
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"projection_dims": [96, 192, 384, 768],
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"default_size": 224,
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},
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"small": {
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"depths": [3, 3, 27, 3],
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"projection_dims": [96, 192, 384, 768],
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"default_size": 224,
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},
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"base": {
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"depths": [3, 3, 27, 3],
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"projection_dims": [128, 256, 512, 1024],
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"default_size": 224,
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},
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"large": {
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"depths": [3, 3, 27, 3],
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"projection_dims": [192, 384, 768, 1536],
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"default_size": 224,
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},
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"xlarge": {
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"depths": [3, 3, 27, 3],
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"projection_dims": [256, 512, 1024, 2048],
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"default_size": 224,
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},
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}
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BASE_DOCSTRING = """Instantiates the {name} architecture.
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References:
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- [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
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(CVPR 2022)
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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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The `base`, `large`, and `xlarge` models were first pre-trained on the
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ImageNet-21k dataset and then fine-tuned on the ImageNet-1k dataset. The
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pre-trained parameters of the models were assembled from the
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[official repository](https://github.com/facebookresearch/ConvNeXt). To get a
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sense of how these parameters were converted to Keras compatible parameters,
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please refer to
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[this repository](https://github.com/sayakpaul/keras-convnext-conversion).
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Note: Each Keras Application expects a specific kind of input preprocessing.
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For ConvNeXt, preprocessing is included in the model using a `Normalization`
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layer. ConvNeXt models expect their inputs to be float or uint8 tensors of
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pixels with values in the [0-255] range.
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When calling the `summary()` method after instantiating a ConvNeXt model,
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prefer setting the `expand_nested` argument `summary()` to `True` to better
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investigate the instantiated model.
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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. Defaults to `True`.
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weights: One of `None` (random initialization),
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`"imagenet"` (pre-training on ImageNet-1k), or the path to the weights
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file to be loaded. Defaults to `"imagenet"`.
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input_tensor: Optional Keras tensor
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(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`.
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It 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`. Defaults to None.
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- `None` means that the output of the model will be
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the 4D tensor output of the 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`, and
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if no `weights` argument is specified. Defaults to 1000 (number of
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ImageNet classes).
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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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Defaults to `"softmax"`.
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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 model instance.
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"""
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class StochasticDepth(Layer):
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"""Stochastic Depth module.
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It performs batch-wise dropping rather than sample-wise. In libraries like
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`timm`, it's similar to `DropPath` layers that drops residual paths
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sample-wise.
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References:
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- https://github.com/rwightman/pytorch-image-models
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Args:
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drop_path_rate (float): Probability of dropping paths. Should be within
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[0, 1].
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Returns:
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Tensor either with the residual path dropped or kept.
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"""
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def __init__(self, drop_path_rate, **kwargs):
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super().__init__(**kwargs)
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self.drop_path_rate = drop_path_rate
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def call(self, x, training=None):
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if training:
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keep_prob = 1 - self.drop_path_rate
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shape = (ops.shape(x)[0],) + (1,) * (len(ops.shape(x)) - 1)
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random_tensor = keep_prob + random.uniform(shape, 0, 1)
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random_tensor = ops.floor(random_tensor)
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return (x / keep_prob) * random_tensor
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return x
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def get_config(self):
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config = super().get_config()
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config.update({"drop_path_rate": self.drop_path_rate})
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return config
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class LayerScale(Layer):
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"""Layer scale module.
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References:
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- https://arxiv.org/abs/2103.17239
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Args:
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init_values (float): Initial value for layer scale. Should be within
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[0, 1].
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projection_dim (int): Projection dimensionality.
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Returns:
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Tensor multiplied to the scale.
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"""
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def __init__(self, init_values, projection_dim, **kwargs):
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super().__init__(**kwargs)
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self.init_values = init_values
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self.projection_dim = projection_dim
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def build(self, _):
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self.gamma = self.add_weight(
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shape=(self.projection_dim,),
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initializer=initializers.Constant(self.init_values),
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trainable=True,
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)
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def call(self, x):
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return x * self.gamma
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def get_config(self):
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config = super().get_config()
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config.update(
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{
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"init_values": self.init_values,
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"projection_dim": self.projection_dim,
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}
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)
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return config
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def ConvNeXtBlock(
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projection_dim, drop_path_rate=0.0, layer_scale_init_value=1e-6, name=None
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):
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"""ConvNeXt block.
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References:
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- https://arxiv.org/abs/2201.03545
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- https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
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Notes:
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In the original ConvNeXt implementation (linked above), the authors use
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`Dense` layers for pointwise convolutions for increased efficiency.
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Following that, this implementation also uses the same.
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Args:
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projection_dim (int): Number of filters for convolution layers. In the
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ConvNeXt paper, this is referred to as projection dimension.
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drop_path_rate (float): Probability of dropping paths. Should be within
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[0, 1].
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layer_scale_init_value (float): Layer scale value.
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Should be a small float number.
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name: name to path to the keras layer.
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Returns:
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A function representing a ConvNeXtBlock block.
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"""
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if name is None:
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name = "prestem" + str(backend.get_uid("prestem"))
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def apply(inputs):
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x = inputs
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x = layers.Conv2D(
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filters=projection_dim,
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kernel_size=7,
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padding="same",
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groups=projection_dim,
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name=name + "_depthwise_conv",
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)(x)
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x = layers.LayerNormalization(epsilon=1e-6, name=name + "_layernorm")(x)
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x = layers.Dense(4 * projection_dim, name=name + "_pointwise_conv_1")(x)
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x = layers.Activation("gelu", name=name + "_gelu")(x)
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x = layers.Dense(projection_dim, name=name + "_pointwise_conv_2")(x)
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if layer_scale_init_value is not None:
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x = LayerScale(
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layer_scale_init_value,
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projection_dim,
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name=name + "_layer_scale",
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)(x)
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if drop_path_rate:
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layer = StochasticDepth(
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drop_path_rate, name=name + "_stochastic_depth"
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)
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else:
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layer = layers.Activation("linear", name=name + "_identity")
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return inputs + layer(x)
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return apply
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def PreStem(name=None):
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"""Normalizes inputs with ImageNet-1k mean and std."""
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if name is None:
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name = "prestem" + str(backend.get_uid("prestem"))
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def apply(x):
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x = layers.Normalization(
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mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
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variance=[
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(0.229 * 255) ** 2,
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(0.224 * 255) ** 2,
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(0.225 * 255) ** 2,
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],
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name=name + "_prestem_normalization",
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)(x)
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return x
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return apply
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def Head(num_classes=1000, classifier_activation=None, name=None):
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"""Implementation of classification head of ConvNeXt.
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Args:
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num_classes: number of classes for Dense layer
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classifier_activation: activation function for the Dense layer
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name: name prefix
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Returns:
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Classification head function.
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"""
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if name is None:
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name = str(backend.get_uid("head"))
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def apply(x):
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x = layers.GlobalAveragePooling2D(name=name + "_head_gap")(x)
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x = layers.LayerNormalization(
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epsilon=1e-6, name=name + "_head_layernorm"
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)(x)
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x = layers.Dense(
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num_classes,
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activation=classifier_activation,
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name=name + "_head_dense",
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)(x)
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return x
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return apply
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def ConvNeXt(
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depths,
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projection_dims,
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drop_path_rate=0.0,
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layer_scale_init_value=1e-6,
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default_size=224,
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model_name="convnext",
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include_preprocessing=True,
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include_top=True,
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weights=None,
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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 ConvNeXt architecture given specific configuration.
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Args:
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depths: An iterable containing depths for each individual stages.
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projection_dims: An iterable containing output number of channels of
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each individual stages.
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drop_path_rate: Stochastic depth probability. If 0.0, then stochastic
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depth won't be used.
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layer_scale_init_value: Layer scale coefficient. If 0.0, layer scaling
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won't be used.
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default_size: Default input image size.
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model_name: An optional name for the model.
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include_preprocessing: boolean denoting whther to
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include preprocessing in the model.
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When `weights="imagenet"` this should always be `True`.
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But for other models (e.g., randomly initialized) you should set it
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to `False` and apply preprocessing to data accordingly.
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include_top: Boolean denoting whether to include classification
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head to the model.
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weights: one of `None` (random initialization), `"imagenet"`
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(pre-training on ImageNet-1k),
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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()`) to
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use as image input for the model.
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input_shape: optional shape tuple, only to be specified if `include_top`
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is `False`. It should have exactly 3 inputs channels.
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pooling: optional pooling mode for feature extraction when `include_top`
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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 last convolutional layer.
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- `avg` means that global average pooling will be applied
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to the output of the last convolutional layer,
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and thus 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 into,
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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.
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The activation function to use
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on the "top" layer. Ignored unless `include_top=True`.
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Set `classifier_activation=None` to return the logits
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of the "top" layer.
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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."
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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=default_size,
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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 input_tensor is not None:
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inputs = operation_utils.get_source_inputs(input_tensor)[0]
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else:
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inputs = img_input
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x = inputs
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if include_preprocessing:
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channel_axis = (
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3 if backend.image_data_format() == "channels_last" else 1
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)
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num_channels = input_shape[channel_axis - 1]
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if num_channels == 3:
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x = PreStem(name=model_name)(x)
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# Stem block.
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stem = Sequential(
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[
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layers.Conv2D(
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projection_dims[0],
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kernel_size=4,
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strides=4,
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name=model_name + "_stem_conv",
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),
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layers.LayerNormalization(
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epsilon=1e-6, name=model_name + "_stem_layernorm"
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),
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],
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name=model_name + "_stem",
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)
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# Downsampling blocks.
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downsample_layers = []
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downsample_layers.append(stem)
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num_downsample_layers = 3
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for i in range(num_downsample_layers):
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downsample_layer = Sequential(
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[
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layers.LayerNormalization(
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epsilon=1e-6,
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name=model_name + "_downsampling_layernorm_" + str(i),
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),
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layers.Conv2D(
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projection_dims[i + 1],
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kernel_size=2,
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strides=2,
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name=model_name + "_downsampling_conv_" + str(i),
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),
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],
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name=model_name + "_downsampling_block_" + str(i),
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)
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downsample_layers.append(downsample_layer)
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# Stochastic depth schedule.
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# This is referred from the original ConvNeXt codebase:
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# https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py#L86
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depth_drop_rates = [
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float(x) for x in np.linspace(0.0, drop_path_rate, sum(depths))
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]
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# First apply downsampling blocks and then apply ConvNeXt stages.
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cur = 0
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num_convnext_blocks = 4
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for i in range(num_convnext_blocks):
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x = downsample_layers[i](x)
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for j in range(depths[i]):
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x = ConvNeXtBlock(
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projection_dim=projection_dims[i],
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drop_path_rate=depth_drop_rates[cur + j],
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layer_scale_init_value=layer_scale_init_value,
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name=model_name + f"_stage_{i}_block_{j}",
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)(x)
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cur += depths[i]
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if include_top:
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imagenet_utils.validate_activation(classifier_activation, weights)
|
|
x = Head(
|
|
num_classes=classes,
|
|
classifier_activation=classifier_activation,
|
|
name=model_name,
|
|
)(x)
|
|
|
|
else:
|
|
if pooling == "avg":
|
|
x = layers.GlobalAveragePooling2D()(x)
|
|
elif pooling == "max":
|
|
x = layers.GlobalMaxPooling2D()(x)
|
|
x = layers.LayerNormalization(epsilon=1e-6)(x)
|
|
|
|
model = Functional(inputs=inputs, outputs=x, name=model_name)
|
|
|
|
# Load weights.
|
|
if weights == "imagenet":
|
|
if include_top:
|
|
file_suffix = ".h5"
|
|
file_hash = WEIGHTS_HASHES[model_name][0]
|
|
else:
|
|
file_suffix = "_notop.h5"
|
|
file_hash = WEIGHTS_HASHES[model_name][1]
|
|
file_name = model_name + file_suffix
|
|
weights_path = file_utils.get_file(
|
|
file_name,
|
|
BASE_WEIGHTS_PATH + file_name,
|
|
cache_subdir="models",
|
|
file_hash=file_hash,
|
|
)
|
|
model.load_weights(weights_path)
|
|
elif weights is not None:
|
|
model.load_weights(weights)
|
|
|
|
return model
|
|
|
|
|
|
## Instantiating variants ##
|
|
|
|
|
|
@keras_core_export(
|
|
[
|
|
"keras_core.applications.convnext.ConvNeXtTiny",
|
|
"keras_core.applications.ConvNeXtTiny",
|
|
]
|
|
)
|
|
def ConvNeXtTiny(
|
|
model_name="convnext_tiny",
|
|
include_top=True,
|
|
include_preprocessing=True,
|
|
weights="imagenet",
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
classifier_activation="softmax",
|
|
):
|
|
return ConvNeXt(
|
|
depths=MODEL_CONFIGS["tiny"]["depths"],
|
|
projection_dims=MODEL_CONFIGS["tiny"]["projection_dims"],
|
|
drop_path_rate=0.0,
|
|
layer_scale_init_value=1e-6,
|
|
default_size=MODEL_CONFIGS["tiny"]["default_size"],
|
|
model_name=model_name,
|
|
include_top=include_top,
|
|
include_preprocessing=include_preprocessing,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
input_shape=input_shape,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
classifier_activation=classifier_activation,
|
|
)
|
|
|
|
|
|
@keras_core_export(
|
|
[
|
|
"keras_core.applications.convnext.ConvNeXtSmall",
|
|
"keras_core.applications.ConvNeXtSmall",
|
|
]
|
|
)
|
|
def ConvNeXtSmall(
|
|
model_name="convnext_small",
|
|
include_top=True,
|
|
include_preprocessing=True,
|
|
weights="imagenet",
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
classifier_activation="softmax",
|
|
):
|
|
return ConvNeXt(
|
|
depths=MODEL_CONFIGS["small"]["depths"],
|
|
projection_dims=MODEL_CONFIGS["small"]["projection_dims"],
|
|
drop_path_rate=0.0,
|
|
layer_scale_init_value=1e-6,
|
|
default_size=MODEL_CONFIGS["small"]["default_size"],
|
|
model_name=model_name,
|
|
include_top=include_top,
|
|
include_preprocessing=include_preprocessing,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
input_shape=input_shape,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
classifier_activation=classifier_activation,
|
|
)
|
|
|
|
|
|
@keras_core_export(
|
|
[
|
|
"keras_core.applications.convnext.ConvNeXtBase",
|
|
"keras_core.applications.ConvNeXtBase",
|
|
]
|
|
)
|
|
def ConvNeXtBase(
|
|
model_name="convnext_base",
|
|
include_top=True,
|
|
include_preprocessing=True,
|
|
weights="imagenet",
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
classifier_activation="softmax",
|
|
):
|
|
return ConvNeXt(
|
|
depths=MODEL_CONFIGS["base"]["depths"],
|
|
projection_dims=MODEL_CONFIGS["base"]["projection_dims"],
|
|
drop_path_rate=0.0,
|
|
layer_scale_init_value=1e-6,
|
|
default_size=MODEL_CONFIGS["base"]["default_size"],
|
|
model_name=model_name,
|
|
include_top=include_top,
|
|
include_preprocessing=include_preprocessing,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
input_shape=input_shape,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
classifier_activation=classifier_activation,
|
|
)
|
|
|
|
|
|
@keras_core_export(
|
|
[
|
|
"keras_core.applications.convnext.ConvNeXtLarge",
|
|
"keras_core.applications.ConvNeXtLarge",
|
|
]
|
|
)
|
|
def ConvNeXtLarge(
|
|
model_name="convnext_large",
|
|
include_top=True,
|
|
include_preprocessing=True,
|
|
weights="imagenet",
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
classifier_activation="softmax",
|
|
):
|
|
return ConvNeXt(
|
|
depths=MODEL_CONFIGS["large"]["depths"],
|
|
projection_dims=MODEL_CONFIGS["large"]["projection_dims"],
|
|
drop_path_rate=0.0,
|
|
layer_scale_init_value=1e-6,
|
|
default_size=MODEL_CONFIGS["large"]["default_size"],
|
|
model_name=model_name,
|
|
include_top=include_top,
|
|
include_preprocessing=include_preprocessing,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
input_shape=input_shape,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
classifier_activation=classifier_activation,
|
|
)
|
|
|
|
|
|
@keras_core_export(
|
|
[
|
|
"keras_core.applications.convnext.ConvNeXtXLarge",
|
|
"keras_core.applications.ConvNeXtXLarge",
|
|
]
|
|
)
|
|
def ConvNeXtXLarge(
|
|
model_name="convnext_xlarge",
|
|
include_top=True,
|
|
include_preprocessing=True,
|
|
weights="imagenet",
|
|
input_tensor=None,
|
|
input_shape=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
classifier_activation="softmax",
|
|
):
|
|
return ConvNeXt(
|
|
depths=MODEL_CONFIGS["xlarge"]["depths"],
|
|
projection_dims=MODEL_CONFIGS["xlarge"]["projection_dims"],
|
|
drop_path_rate=0.0,
|
|
layer_scale_init_value=1e-6,
|
|
default_size=MODEL_CONFIGS["xlarge"]["default_size"],
|
|
model_name=model_name,
|
|
include_top=include_top,
|
|
include_preprocessing=include_preprocessing,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
input_shape=input_shape,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
classifier_activation=classifier_activation,
|
|
)
|
|
|
|
|
|
ConvNeXtTiny.__doc__ = BASE_DOCSTRING.format(name="ConvNeXtTiny")
|
|
ConvNeXtSmall.__doc__ = BASE_DOCSTRING.format(name="ConvNeXtSmall")
|
|
ConvNeXtBase.__doc__ = BASE_DOCSTRING.format(name="ConvNeXtBase")
|
|
ConvNeXtLarge.__doc__ = BASE_DOCSTRING.format(name="ConvNeXtLarge")
|
|
ConvNeXtXLarge.__doc__ = BASE_DOCSTRING.format(name="ConvNeXtXLarge")
|
|
|
|
|
|
@keras_core_export("keras_core.applications.convnext.preprocess_input")
|
|
def preprocess_input(x, data_format=None):
|
|
"""A placeholder method for backward compatibility.
|
|
|
|
The preprocessing logic has been included in the convnext model
|
|
implementation. Users are no longer required to call this method to
|
|
normalize the input data. This method does nothing and only kept as a
|
|
placeholder to align the API surface between old and new version of model.
|
|
|
|
Args:
|
|
x: A floating point `numpy.array` or a tensor.
|
|
data_format: Optional data format of the image tensor/array. Defaults to
|
|
None, in which case the global setting
|
|
`keras_core.backend.image_data_format()` is used
|
|
(unless you changed it, it defaults to `"channels_last"`).{mode}
|
|
|
|
Returns:
|
|
Unchanged `numpy.array` or tensor.
|
|
"""
|
|
return x
|
|
|
|
|
|
@keras_core_export("keras_core.applications.convnext.decode_predictions")
|
|
def decode_predictions(preds, top=5):
|
|
return imagenet_utils.decode_predictions(preds, top=top)
|
|
|
|
|
|
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
|