[WIP] ConvneXT Models for Classification and Segmentation (#210)

* Update CHANGELOG.md [skip ci]

* Create DepthwiseConv3d.py

* for classification

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* Update convnext.py

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* Update DepthwiseConv3d.py

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fix typo

---------

Co-authored-by: Satrajit Ghosh <[email protected]>
Co-authored-by: Nobrainer Bot <[email protected]>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: H Gazula <[email protected]>
Co-authored-by: Aakanksha Rana <[email protected]>

nobrainer/layers/DepthwiseConv3d.py

@@ -0,0 +1,338 @@
+"""
+Directly taken from
+https://github.com/alexandrosstergiou/keras-DepthwiseConv3D/blob/master/DepthwiseConv3D.py
+This is a modification of the SeparableConv3D code in Keras,
+to perform just the Depthwise Convolution (1st step) of the
+Depthwise Separable Convolution layer.
+"""
+from __future__ import absolute_import
+
+from keras import backend as K
+from keras import constraints, initializers, regularizers
+from keras.backend.tensorflow_backend import (
+    _preprocess_conv3d_input,
+    _preprocess_padding,
+)
+from keras.engine import InputSpec
+from keras.layers import Conv3D
+from keras.legacy.interfaces import conv3d_args_preprocessor
+from keras.utils import conv_utils
+import tensorflow as tf
+
+
+def depthwise_conv3d_args_preprocessor(args, kwargs):
+    converted = []
+
+    if "init" in kwargs:
+        init = kwargs.pop("init")
+        kwargs["depthwise_initializer"] = init
+        converted.append(("init", "depthwise_initializer"))
+
+    args, kwargs, _converted = conv3d_args_preprocessor(args, kwargs)
+    return args, kwargs, converted + _converted
+
+
+#    legacy_depthwise_conv3d_support = generate_legacy_interface(
+#        allowed_positional_args=["filters", "kernel_size"],
+#        conversions=[
+#            ("nb_filter", "filters"),
+#            ("subsample", "strides"),
+#            ("border_mode", "padding"),
+#            ("dim_ordering", "data_format"),
+#            ("b_regularizer", "bias_regularizer"),
+#            ("b_constraint", "bias_constraint"),
+#            ("bias", "use_bias"),
+#        ],
+#        value_conversions={
+#            "dim_ordering": {
+#                "tf": "channels_last",
+#                "th": "channels_first",
+#                "default": None,
+#            }
+#        },
+#        preprocessor=depthwise_conv3d_args_preprocessor,
+#    )
+
+
+class DepthwiseConv3D(Conv3D):
+    """Depthwise 3D convolution.
+    Depth-wise part of separable convolutions consist in performing
+    just the first step/operation
+    (which acts on each input channel separately).
+    It does not perform the pointwise convolution (second step).
+    The `depth_multiplier` argument controls how many
+    output channels are generated per input channel in the depthwise step.
+    # Arguments
+        kernel_size: An integer or tuple/list of 3 integers, specifying the
+            depth, width and height of the 3D convolution window.
+            Can be a single integer to specify the same value for
+            all spatial dimensions.
+        strides: An integer or tuple/list of 3 integers,
+            specifying the strides of the convolution along the depth, width and height.
+            Can be a single integer to specify the same value for
+            all spatial dimensions.
+        padding: one of `"valid"` or `"same"` (case-insensitive).
+        depth_multiplier: The number of depthwise convolution output channels
+            for each input channel.
+            The total number of depthwise convolution output
+            channels will be equal to `filterss_in * depth_multiplier`.
+        groups: The depth size of the convolution (as a variant of the original Depthwise conv)
+        data_format: A string,
+            one of `channels_last` (default) or `channels_first`.
+            The ordering of the dimensions in the inputs.
+            `channels_last` corresponds to inputs with shape
+            `(batch, height, width, channels)` while `channels_first`
+            corresponds to inputs with shape
+            `(batch, channels, height, width)`.
+            It defaults to the `image_data_format` value found in your
+            Keras config file at `~/.keras/keras.json`.
+            If you never set it, then it will be "channels_last".
+        activation: Activation function to use
+            (see [activations](../activations.md)).
+            If you don't specify anything, no activation is applied
+            (ie. "linear" activation: `a(x) = x`).
+        use_bias: Boolean, whether the layer uses a bias vector.
+        depthwise_initializer: Initializer for the depthwise kernel matrix
+            (see [initializers](../initializers.md)).
+        bias_initializer: Initializer for the bias vector
+            (see [initializers](../initializers.md)).
+        depthwise_regularizer: Regularizer function applied to
+            the depthwise kernel matrix
+            (see [regularizer](../regularizers.md)).
+        bias_regularizer: Regularizer function applied to the bias vector
+            (see [regularizer](../regularizers.md)).
+        dialation_rate: List of ints.
+                        Defines the dilation factor for each dimension in the
+                        input. Defaults to (1,1,1)
+        activity_regularizer: Regularizer function applied to
+            the output of the layer (its "activation").
+            (see [regularizer](../regularizers.md)).
+        depthwise_constraint: Constraint function applied to
+            the depthwise kernel matrix
+            (see [constraints](../constraints.md)).
+        bias_constraint: Constraint function applied to the bias vector
+            (see [constraints](../constraints.md)).
+    # Input shape
+        5D tensor with shape:
+        `(batch, depth, channels, rows, cols)` if data_format='channels_first'
+        or 5D tensor with shape:
+        `(batch, depth, rows, cols, channels)` if data_format='channels_last'.
+    # Output shape
+        5D tensor with shape:
+        `(batch, filters * depth, new_depth, new_rows, new_cols)` if data_format='channels_first'
+        or 4D tensor with shape:
+        `(batch, new_depth, new_rows, new_cols, filters * depth)` if data_format='channels_last'.
+        `rows` and `cols` values might have changed due to padding.
+    """
+
+    # @legacy_depthwise_conv3d_support
+    def __init__(
+        self,
+        kernel_size,
+        strides=(1, 1, 1),
+        padding="valid",
+        depth_multiplier=1,
+        groups=None,
+        data_format=None,
+        activation=None,
+        use_bias=True,
+        depthwise_initializer="glorot_uniform",
+        bias_initializer="zeros",
+        dilation_rate=(1, 1, 1),
+        depthwise_regularizer=None,
+        bias_regularizer=None,
+        activity_regularizer=None,
+        depthwise_constraint=None,
+        bias_constraint=None,
+        **kwargs
+    ):
+        super(DepthwiseConv3D, self).__init__(
+            filters=None,
+            kernel_size=kernel_size,
+            strides=strides,
+            padding=padding,
+            data_format=data_format,
+            activation=activation,
+            use_bias=use_bias,
+            bias_regularizer=bias_regularizer,
+            dilation_rate=dilation_rate,
+            activity_regularizer=activity_regularizer,
+            bias_constraint=bias_constraint,
+            **kwargs
+        )
+        self.depth_multiplier = depth_multiplier
+        self.groups = groups
+        self.depthwise_initializer = initializers.get(depthwise_initializer)
+        self.depthwise_regularizer = regularizers.get(depthwise_regularizer)
+        self.depthwise_constraint = constraints.get(depthwise_constraint)
+        self.bias_initializer = initializers.get(bias_initializer)
+        self.dilation_rate = dilation_rate
+        self._padding = _preprocess_padding(self.padding)
+        self._strides = (1,) + self.strides + (1,)
+        self._data_format = "NDHWC"
+        self.input_dim = None
+
+    def build(self, input_shape):
+        if len(input_shape) < 5:
+            raise ValueError(
+                "Inputs to `DepthwiseConv3D` should have rank 5. "
+                "Received input shape:",
+                str(input_shape),
+            )
+        if self.data_format == "channels_first":
+            channel_axis = 1
+        else:
+            channel_axis = -1
+        if input_shape[channel_axis] is None:
+            raise ValueError(
+                "The channel dimension of the inputs to "
+                "`DepthwiseConv3D` "
+                "should be defined. Found `None`."
+            )
+        self.input_dim = int(input_shape[channel_axis])
+
+        if self.groups is None:
+            self.groups = self.input_dim
+
+        if self.groups > self.input_dim:
+            raise ValueError(
+                "The number of groups cannot exceed the number of channels"
+            )
+
+        if self.input_dim % self.groups != 0:
+            raise ValueError(
+                "Warning! The channels dimension is not divisible by the group size chosen"
+            )
+
+        depthwise_kernel_shape = (
+            self.kernel_size[0],
+            self.kernel_size[1],
+            self.kernel_size[2],
+            self.input_dim,
+            self.depth_multiplier,
+        )
+
+        self.depthwise_kernel = self.add_weight(
+            shape=depthwise_kernel_shape,
+            initializer=self.depthwise_initializer,
+            name="depthwise_kernel",
+            regularizer=self.depthwise_regularizer,
+            constraint=self.depthwise_constraint,
+        )
+
+        if self.use_bias:
+            self.bias = self.add_weight(
+                shape=(self.groups * self.depth_multiplier,),
+                initializer=self.bias_initializer,
+                name="bias",
+                regularizer=self.bias_regularizer,
+                constraint=self.bias_constraint,
+            )
+        else:
+            self.bias = None
+        # Set input spec.
+        self.input_spec = InputSpec(ndim=5, axes={channel_axis: self.input_dim})
+        self.built = True
+
+    def call(self, inputs, training=None):
+        inputs = _preprocess_conv3d_input(inputs, self.data_format)
+
+        if self.data_format == "channels_last":
+            dilation = (1,) + self.dilation_rate + (1,)
+        else:
+            dilation = self.dilation_rate + (1,) + (1,)
+
+        if self._data_format == "NCDHW":
+            outputs = tf.concat(
+                [
+                    tf.nn.conv3d(
+                        inputs[0][:, i : i + self.input_dim // self.groups, :, :, :],
+                        self.depthwise_kernel[
+                            :, :, :, i : i + self.input_dim // self.groups, :
+                        ],
+                        strides=self._strides,
+                        padding=self._padding,
+                        dilations=dilation,
+                        data_format=self._data_format,
+                    )
+                    for i in range(0, self.input_dim, self.input_dim // self.groups)
+                ],
+                axis=1,
+            )
+
+        else:
+            outputs = tf.concat(
+                [
+                    tf.nn.conv3d(
+                        inputs[0][:, :, :, :, i : i + self.input_dim // self.groups],
+                        self.depthwise_kernel[
+                            :, :, :, i : i + self.input_dim // self.groups, :
+                        ],
+                        strides=self._strides,
+                        padding=self._padding,
+                        dilations=dilation,
+                        data_format=self._data_format,
+                    )
+                    for i in range(0, self.input_dim, self.input_dim // self.groups)
+                ],
+                axis=-1,
+            )
+
+        if self.bias is not None:
+            outputs = K.bias_add(outputs, self.bias, data_format=self.data_format)
+
+        if self.activation is not None:
+            return self.activation(outputs)
+
+        return outputs
+
+    def compute_output_shape(self, input_shape):
+        if self.data_format == "channels_first":
+            depth = input_shape[2]
+            rows = input_shape[3]
+            cols = input_shape[4]
+            out_filters = self.groups * self.depth_multiplier
+        elif self.data_format == "channels_last":
+            depth = input_shape[1]
+            rows = input_shape[2]
+            cols = input_shape[3]
+            out_filters = self.groups * self.depth_multiplier
+
+        depth = conv_utils.conv_output_length(
+            depth, self.kernel_size[0], self.padding, self.strides[0]
+        )
+
+        rows = conv_utils.conv_output_length(
+            rows, self.kernel_size[1], self.padding, self.strides[1]
+        )
+
+        cols = conv_utils.conv_output_length(
+            cols, self.kernel_size[2], self.padding, self.strides[2]
+        )
+
+        if self.data_format == "channels_first":
+            return (input_shape[0], out_filters, depth, rows, cols)
+
+        elif self.data_format == "channels_last":
+            return (input_shape[0], depth, rows, cols, out_filters)
+
+    def get_config(self):
+        config = super(DepthwiseConv3D, self).get_config()
+        config.pop("filters")
+        config.pop("kernel_initializer")
+        config.pop("kernel_regularizer")
+        config.pop("kernel_constraint")
+        config["depth_multiplier"] = self.depth_multiplier
+        config["depthwise_initializer"] = initializers.serialize(
+            self.depthwise_initializer
+        )
+        config["depthwise_regularizer"] = regularizers.serialize(
+            self.depthwise_regularizer
+        )
+        config["depthwise_constraint"] = constraints.serialize(
+            self.depthwise_constraint
+        )
+        return config
+
+
+DepthwiseConvolution3D = DepthwiseConv3D