Add TFConvNextModel (#15750)

* feat: initial implementation of convnext in tensorflow.

* fix: sample code for the classification model.

* chore: added checked for  from the classification model.

* chore: set bias initializer in the classification head.

* chore: updated license terms.

* chore: removed ununsed imports

* feat: enabled  argument during using drop_path.

* chore: replaced tf.identity with layers.Activation(linear).

* chore: edited default checkpoint.

* fix: minor bugs in the initializations.

* partial-fix: tf model errors for loading pretrained pt weights.

* partial-fix: call method updated

* partial-fix: cross loading of weights (4x3 variables to be matched)

* chore: removed unneeded comment.

* removed playground.py

* rebasing

* rebasing and removing playground.py.

* fix: renaming TFConvNextStage conv and layer norm layers

* chore: added initializers and other minor additions.

* chore: added initializers and other minor additions.

* add: tests for convnext.

* fix: integration tester class.

* fix: issues mentioned in pr feedback (round 1).

* fix: how output_hidden_states arg is propoagated inside the network.

* feat: handling of  arg for pure cnn models.

* chore: added a note on equal contribution in model docs.

* rebasing

* rebasing and removing playground.py.

* feat: encapsulation for the convnext trunk.

* Fix variable naming; Test-related corrections; Run make fixup

* chore: added Joao as a contributor to convnext.

* rebasing

* rebasing and removing playground.py.

* rebasing

* rebasing and removing playground.py.

* chore: corrected copyright year and added comment on NHWC.

* chore: fixed the black version and ran formatting.

* chore: ran make style.

* chore: removed from_pt argument from test, ran make style.

* rebasing

* rebasing and removing playground.py.

* rebasing

* rebasing and removing playground.py.

* fix: tests in the convnext subclass, ran make style.

* rebasing

* rebasing and removing playground.py.

* rebasing

* rebasing and removing playground.py.

* chore: moved convnext test to the correct location

* fix: locations for the test file of convnext.

* fix: convnext tests.

* chore: applied  sgugger's suggestion for dealing w/ output_attentions.

* chore: added comments.

* chore: applied updated quality enviornment style.

* chore: applied formatting with quality enviornment.

* chore: revert to the previous tests/test_modeling_common.py.

* chore: revert to the original test_modeling_common.py

* chore: revert to previous states for test_modeling_tf_common.py and modeling_tf_utils.py

* fix: tests for convnext.

* chore: removed output_attentions argument from convnext config.

* chore: revert to the earlier tf utils.

* fix: output shapes of the hidden states

* chore: removed unnecessary comment

* chore: reverting to the right test_modeling_tf_common.py.

* Styling nits

Co-authored-by: ariG23498 <aritra.born2fly@gmail.com>
Co-authored-by: Joao Gante <joao@huggingface.co>
Co-authored-by: Sylvain Gugger <Sylvain.gugger@gmail.com>

docs/source/index.mdx

@@ -179,7 +179,7 @@ Flax), PyTorch, and/or TensorFlow.
 |           Canine            |       ✅       |       ❌       |       ✅        |         ❌         |      ❌      |
 |            CLIP             |       ✅       |       ✅       |       ✅        |         ✅         |      ✅      |
 |          ConvBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
-|          ConvNext           |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|          ConvNext           |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            CTRL             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           DeBERTa           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |         DeBERTa-v2          |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |

docs/source/model_doc/convnext.mdx

@@ -37,7 +37,8 @@ alt="drawing" width="600"/>
 
 <small> ConvNeXT architecture. Taken from the <a href="https://arxiv.org/abs/2201.03545">original paper</a>.</small>
 
-This model was contributed by [nielsr](https://huggingface.co/nielsr). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
+This model was contributed by [nielsr](https://huggingface.co/nielsr). TensorFlow version of the model was contributed by [ariG23498](https://github.com/ariG23498),
+[gante](https://github.com/gante), and [sayakpaul](https://github.com/sayakpaul) (equal contribution). The original code can be found [here](https://github.com/facebookresearch/ConvNeXt).
 
 ## ConvNeXT specific outputs
 
@@ -63,4 +64,16 @@ This model was contributed by [nielsr](https://huggingface.co/nielsr). The origi
 ## ConvNextForImageClassification
 
 [[autodoc]] ConvNextForImageClassification
-    - forward
+    - forward
+
+
+## TFConvNextModel
+
+[[autodoc]] TFConvNextModel
+    - call
+
+
+## TFConvNextForImageClassification
+
+[[autodoc]] TFConvNextForImageClassification
+    - call

src/transformers/__init__.py

@@ -1743,6 +1743,13 @@ if is_tf_available():
             "TFConvBertPreTrainedModel",
         ]
     )
+    _import_structure["models.convnext"].extend(
+        [
+            "TFConvNextForImageClassification",
+            "TFConvNextModel",
+            "TFConvNextPreTrainedModel",
+        ]
+    )
     _import_structure["models.ctrl"].extend(
         [
             "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -3751,6 +3758,7 @@ if TYPE_CHECKING:
             TFConvBertModel,
             TFConvBertPreTrainedModel,
         )
+        from .models.convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
         from .models.ctrl import (
             TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFCTRLForSequenceClassification,

src/transformers/modeling_tf_utils.py

@@ -311,9 +311,10 @@ def booleans_processing(config, **kwargs):
     final_booleans = {}
 
     if tf.executing_eagerly():
-        final_booleans["output_attentions"] = (
-            kwargs["output_attentions"] if kwargs["output_attentions"] is not None else config.output_attentions
-        )
+        # Pure conv models (such as ConvNext) do not have `output_attentions`
+        final_booleans["output_attentions"] = kwargs.get("output_attentions", None)
+        if final_booleans["output_attentions"] is None:
+            final_booleans["output_attentions"] = config.output_attentions
         final_booleans["output_hidden_states"] = (
             kwargs["output_hidden_states"]
             if kwargs["output_hidden_states"] is not None

src/transformers/models/auto/modeling_tf_auto.py

@@ -36,6 +36,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("rembert", "TFRemBertModel"),
         ("roformer", "TFRoFormerModel"),
         ("convbert", "TFConvBertModel"),
+        ("convnext", "TFConvNextModel"),
         ("led", "TFLEDModel"),
         ("lxmert", "TFLxmertModel"),
         ("mt5", "TFMT5Model"),
@@ -155,6 +156,7 @@ TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Image-classsification
         ("vit", "TFViTForImageClassification"),
+        ("convnext", "TFConvNextForImageClassification"),
     ]
 )
 

src/transformers/models/convnext/__init__.py

@@ -18,7 +18,7 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...file_utils import _LazyModule, is_torch_available, is_vision_available
+from ...file_utils import _LazyModule, is_tf_available, is_torch_available, is_vision_available
 
 
 _import_structure = {
@@ -36,6 +36,12 @@ if is_torch_available():
         "ConvNextPreTrainedModel",
     ]
 
+if is_tf_available():
+    _import_structure["modeling_tf_convnext"] = [
+        "TFConvNextForImageClassification",
+        "TFConvNextModel",
+        "TFConvNextPreTrainedModel",
+    ]
 
 if TYPE_CHECKING:
     from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig
@@ -51,6 +57,9 @@ if TYPE_CHECKING:
             ConvNextPreTrainedModel,
         )
 
+    if is_tf_available():
+        from .modeling_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
+
 
 else:
     import sys

src/transformers/models/convnext/configuration_convnext.py

@@ -85,6 +85,7 @@ class ConvNextConfig(PretrainedConfig):
         is_encoder_decoder=False,
         layer_scale_init_value=1e-6,
         drop_path_rate=0.0,
+        image_size=224,
         **kwargs
     ):
         super().__init__(**kwargs)
@@ -99,3 +100,4 @@ class ConvNextConfig(PretrainedConfig):
         self.layer_norm_eps = layer_norm_eps
         self.layer_scale_init_value = layer_scale_init_value
         self.drop_path_rate = drop_path_rate
+        self.image_size = image_size

src/transformers/models/convnext/modeling_tf_convnext.py

@@ -0,0 +1,618 @@
+# coding=utf-8
+# Copyright 2022 Meta Platforms Inc. and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" TF 2.0 ConvNext model."""
+
+
+from typing import Dict, Optional, Tuple, Union
+
+import numpy as np
+import tensorflow as tf
+
+from ...activations_tf import get_tf_activation
+from ...file_utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
+from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling, TFSequenceClassifierOutput
+from ...modeling_tf_utils import (
+    TFModelInputType,
+    TFPreTrainedModel,
+    TFSequenceClassificationLoss,
+    get_initializer,
+    input_processing,
+    keras_serializable,
+)
+from ...utils import logging
+from .configuration_convnext import ConvNextConfig
+
+
+logger = logging.get_logger(__name__)
+
+
+_CONFIG_FOR_DOC = "ConvNextConfig"
+_CHECKPOINT_FOR_DOC = "facebook/convnext-tiny-224"
+
+
+class TFConvNextDropPath(tf.keras.layers.Layer):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    References:
+        (1) github.com:rwightman/pytorch-image-models
+    """
+
+    def __init__(self, drop_path, **kwargs):
+        super().__init__(**kwargs)
+        self.drop_path = drop_path
+
+    def call(self, x, training=None):
+        if training:
+            keep_prob = 1 - self.drop_path
+            shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)
+            random_tensor = keep_prob + tf.random.uniform(shape, 0, 1)
+            random_tensor = tf.floor(random_tensor)
+            return (x / keep_prob) * random_tensor
+        return x
+
+
+class TFConvNextEmbeddings(tf.keras.layers.Layer):
+    """This class is comparable to (and inspired by) the SwinEmbeddings class
+    found in src/transformers/models/swin/modeling_swin.py.
+    """
+
+    def __init__(self, config, **kwargs):
+        super().__init__(**kwargs)
+        self.patch_embeddings = tf.keras.layers.Conv2D(
+            filters=config.hidden_sizes[0],
+            kernel_size=config.patch_size,
+            strides=config.patch_size,
+            name="patch_embeddings",
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+        )
+        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm")
+
+    def call(self, pixel_values):
+        if isinstance(pixel_values, dict):
+            pixel_values = pixel_values["pixel_values"]
+
+        # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
+        # So change the input format from `NCHW` to `NHWC`.
+        # shape = (batch_size, in_height, in_width, in_channels=num_channels)
+        pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
+
+        embeddings = self.patch_embeddings(pixel_values)
+        embeddings = self.layernorm(embeddings)
+        return embeddings
+
+
+class TFConvNextLayer(tf.keras.layers.Layer):
+    """This corresponds to the `Block` class in the original implementation.
+
+    There are two equivalent implementations: [DwConv, LayerNorm (channels_first), Conv, GELU,1x1 Conv]; all in (N, C,
+    H, W) (2) [DwConv, Permute to (N, H, W, C), LayerNorm (channels_last), Linear, GELU, Linear]; Permute back
+
+    The authors used (2) as they find it slightly faster in PyTorch. Since we already permuted the inputs to follow
+    NHWC ordering, we can just apply the operations straight-away without the permutation.
+
+    Args:
+        config ([`ConvNextConfig`]): Model configuration class.
+        dim (`int`): Number of input channels.
+        drop_path (`float`): Stochastic depth rate. Default: 0.0.
+    """
+
+    def __init__(self, config, dim, drop_path=0.0, **kwargs):
+        super().__init__(**kwargs)
+        self.dim = dim
+        self.config = config
+        self.dwconv = tf.keras.layers.Conv2D(
+            filters=dim,
+            kernel_size=7,
+            padding="same",
+            groups=dim,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="dwconv",
+        )  # depthwise conv
+        self.layernorm = tf.keras.layers.LayerNormalization(
+            epsilon=1e-6,
+            name="layernorm",
+        )
+        self.pwconv1 = tf.keras.layers.Dense(
+            units=4 * dim,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="pwconv1",
+        )  # pointwise/1x1 convs, implemented with linear layers
+        self.act = get_tf_activation(config.hidden_act)
+        self.pwconv2 = tf.keras.layers.Dense(
+            units=dim,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="pwconv2",
+        )
+        # Using `layers.Activation` instead of `tf.identity` to better control `training`
+        # behaviour.
+        self.drop_path = (
+            TFConvNextDropPath(drop_path, name="drop_path")
+            if drop_path > 0.0
+            else tf.keras.layers.Activation("linear", name="drop_path")
+        )
+
+    def build(self, input_shape: tf.TensorShape):
+        # PT's `nn.Parameters` must be mapped to a TF layer weight to inherit the same name hierarchy (and vice-versa)
+        self.layer_scale_parameter = (
+            self.add_weight(
+                shape=(self.dim,),
+                initializer=tf.keras.initializers.Constant(value=self.config.layer_scale_init_value),
+                trainable=True,
+                name="layer_scale_parameter",
+            )
+            if self.config.layer_scale_init_value > 0
+            else None
+        )
+        super().build(input_shape)
+
+    def call(self, hidden_states, training=False):
+        input = hidden_states
+        x = self.dwconv(hidden_states)
+        x = self.layernorm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+
+        if self.layer_scale_parameter is not None:
+            x = self.layer_scale_parameter * x
+
+        x = input + self.drop_path(x, training=training)
+        return x
+
+
+class TFConvNextStage(tf.keras.layers.Layer):
+    """ConvNext stage, consisting of an optional downsampling layer + multiple residual blocks.
+
+    Args:
+        config ([`ConvNextConfig`]): Model configuration class.
+        in_channels (`int`): Number of input channels.
+        out_channels (`int`): Number of output channels.
+        depth (`int`): Number of residual blocks.
+        drop_path_rates(`List[float]`): Stochastic depth rates for each layer.
+    """
+
+    def __init__(
+        self, config, in_channels, out_channels, kernel_size=2, stride=2, depth=2, drop_path_rates=None, **kwargs
+    ):
+        super().__init__(**kwargs)
+        if in_channels != out_channels or stride > 1:
+            self.downsampling_layer = [
+                tf.keras.layers.LayerNormalization(
+                    epsilon=1e-6,
+                    name="downsampling_layer.0",
+                ),
+                # Inputs to this layer will follow NHWC format since we
+                # transposed the inputs from NCHW to NHWC in the `TFConvNextEmbeddings`
+                # layer. All the outputs throughout the model will be in NHWC
+                # from this point on until the output where we again change to
+                # NCHW.
+                tf.keras.layers.Conv2D(
+                    filters=out_channels,
+                    kernel_size=kernel_size,
+                    strides=stride,
+                    kernel_initializer=get_initializer(config.initializer_range),
+                    bias_initializer="zeros",
+                    name="downsampling_layer.1",
+                ),
+            ]
+        else:
+            self.downsampling_layer = [tf.identity]
+
+        drop_path_rates = drop_path_rates or [0.0] * depth
+        self.layers = [
+            TFConvNextLayer(
+                config,
+                dim=out_channels,
+                drop_path=drop_path_rates[j],
+                name=f"layers.{j}",
+            )
+            for j in range(depth)
+        ]
+
+    def call(self, hidden_states):
+        for layer in self.downsampling_layer:
+            hidden_states = layer(hidden_states)
+        for layer in self.layers:
+            hidden_states = layer(hidden_states)
+        return hidden_states
+
+
+class TFConvNextEncoder(tf.keras.layers.Layer):
+    def __init__(self, config, **kwargs):
+        super().__init__(**kwargs)
+        self.stages = []
+        drop_path_rates = [x for x in tf.linspace(0.0, config.drop_path_rate, sum(config.depths))]
+        cur = 0
+        prev_chs = config.hidden_sizes[0]
+        for i in range(config.num_stages):
+            out_chs = config.hidden_sizes[i]
+            stage = TFConvNextStage(
+                config,
+                in_channels=prev_chs,
+                out_channels=out_chs,
+                stride=2 if i > 0 else 1,
+                depth=config.depths[i],
+                drop_path_rates=drop_path_rates[cur],
+                name=f"stages.{i}",
+            )
+            self.stages.append(stage)
+            cur += config.depths[i]
+            prev_chs = out_chs
+
+    def call(self, hidden_states, output_hidden_states=False, return_dict=True):
+        all_hidden_states = () if output_hidden_states else None
+
+        for i, layer_module in enumerate(self.stages):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            hidden_states = layer_module(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, all_hidden_states] if v is not None)
+
+        return TFBaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states)
+
+
+@keras_serializable
+class TFConvNextMainLayer(tf.keras.layers.Layer):
+    config_class = ConvNextConfig
+
+    def __init__(self, config: ConvNextConfig, add_pooling_layer: bool = True, **kwargs):
+        super().__init__(**kwargs)
+
+        self.config = config
+        self.embeddings = TFConvNextEmbeddings(config, name="embeddings")
+        self.encoder = TFConvNextEncoder(config, name="encoder")
+        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
+        # We are setting the `data_format` like so because from here on we will revert to the
+        # NCHW output format
+        self.pooler = tf.keras.layers.GlobalAvgPool2D(data_format="channels_first") if add_pooling_layer else None
+
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=pixel_values,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if "input_ids" in inputs:
+            inputs["pixel_values"] = inputs.pop("input_ids")
+
+        if inputs["pixel_values"] is None:
+            raise ValueError("You have to specify pixel_values")
+
+        embedding_output = self.embeddings(inputs["pixel_values"], training=inputs["training"])
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=inputs["training"],
+        )
+
+        last_hidden_state = encoder_outputs[0]
+        # Change to NCHW output format have uniformity in the modules
+        last_hidden_state = tf.transpose(last_hidden_state, perm=(0, 3, 1, 2))
+        pooled_output = self.layernorm(self.pooler(last_hidden_state))
+
+        # Change the other hidden state outputs to NCHW as well
+        if output_hidden_states:
+            hidden_states = tuple([tf.transpose(h, perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])
+
+        if not return_dict:
+            return (last_hidden_state, pooled_output) + encoder_outputs[1:]
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=last_hidden_state,
+            pooler_output=pooled_output,
+            hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,
+        )
+
+
+class TFConvNextPreTrainedModel(TFPreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = ConvNextConfig
+    base_model_prefix = "convnext"
+    main_input_name = "pixel_values"
+
+    @property
+    def dummy_inputs(self) -> Dict[str, tf.Tensor]:
+        """
+        Dummy inputs to build the network.
+
+        Returns:
+            `Dict[str, tf.Tensor]`: The dummy inputs.
+        """
+        VISION_DUMMY_INPUTS = tf.random.uniform(
+            shape=(
+                3,
+                self.config.num_channels,
+                self.config.image_size,
+                self.config.image_size,
+            ),
+            dtype=tf.float32,
+        )
+        return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
+
+    @tf.function(
+        input_signature=[
+            {
+                "pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
+            }
+        ]
+    )
+    def serving(self, inputs):
+        """
+        Method used for serving the model.
+
+        Args:
+            inputs (`Dict[str, tf.Tensor]`):
+                The input of the saved model as a dictionary of tensors.
+        """
+        return self.call(inputs)
+
+
+CONVNEXT_START_DOCSTRING = r"""
+    This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
+    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+    etc.)
+
+    This model is also a [tf.keras.Model](https://www.tensorflow.org/api_docs/python/tf/keras/Model) subclass. Use it
+    as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general usage and
+    behavior.
+
+    <Tip>
+
+    TF 2.0 models accepts two formats as inputs:
+
+    - having all inputs as keyword arguments (like PyTorch models), or
+    - having all inputs as a list, tuple or dict in the first positional arguments.
+
+    This second option is useful when using [`tf.keras.Model.fit`] method which currently requires having all the
+    tensors in the first argument of the model call function: `model(inputs)`.
+
+    </Tip>
+
+    Parameters:
+        config ([`ConvNextConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+CONVNEXT_INPUTS_DOCSTRING = r"""
+    Args:
+        pixel_values (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `(batch_size, num_channels, height, width)`):
+            Pixel values. Pixel values can be obtained using [`ConvNextFeatureExtractor`]. See
+            [`ConvNextFeatureExtractor.__call__`] for details.
+
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail. This argument can be used only in eager mode, in graph mode the value in the config will be
+            used instead.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple. This argument can be used
+            in eager mode, in graph mode the value will always be set to True.
+"""
+
+
+@add_start_docstrings(
+    "The bare ConvNext model outputting raw features without any specific head on top.",
+    CONVNEXT_START_DOCSTRING,
+)
+class TFConvNextModel(TFConvNextPreTrainedModel):
+    def __init__(self, config, *inputs, add_pooling_layer=True, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+        self.convnext = TFConvNextMainLayer(config, add_pooling_layer=add_pooling_layer, name="convnext")
+
+    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        training: bool = False,
+        **kwargs,
+    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
+        r"""
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import ConvNextFeatureExtractor, TFConvNextModel
+        >>> from PIL import Image
+        >>> import requests
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224")
+        >>> model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
+
+        >>> inputs = feature_extractor(images=image, return_tensors="tf")
+        >>> outputs = model(**inputs)
+        >>> last_hidden_states = outputs.last_hidden_state
+        ```"""
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=pixel_values,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if "input_ids" in inputs:
+            inputs["pixel_values"] = inputs.pop("input_ids")
+
+        if inputs["pixel_values"] is None:
+            raise ValueError("You have to specify pixel_values")
+
+        outputs = self.convnext(
+            pixel_values=inputs["pixel_values"],
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=inputs["training"],
+        )
+
+        if not return_dict:
+            return (outputs[0],) + outputs[1:]
+
+        return TFBaseModelOutputWithPooling(
+            last_hidden_state=outputs.last_hidden_state,
+            pooler_output=outputs.pooler_output,
+            hidden_states=outputs.hidden_states,
+        )
+
+
+@add_start_docstrings(
+    """
+    ConvNext Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
+    ImageNet.
+    """,
+    CONVNEXT_START_DOCSTRING,
+)
+class TFConvNextForImageClassification(TFConvNextPreTrainedModel, TFSequenceClassificationLoss):
+    def __init__(self, config: ConvNextConfig, *inputs, **kwargs):
+        super().__init__(config, *inputs, **kwargs)
+
+        self.num_labels = config.num_labels
+        self.convnext = TFConvNextMainLayer(config, name="convnext")
+
+        # Classifier head
+        self.classifier = tf.keras.layers.Dense(
+            units=config.num_labels,
+            kernel_initializer=get_initializer(config.initializer_range),
+            bias_initializer="zeros",
+            name="classifier",
+        )
+
+    @add_start_docstrings_to_model_forward(CONVNEXT_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
+    def call(
+        self,
+        pixel_values: Optional[TFModelInputType] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
+        training: Optional[bool] = False,
+        **kwargs,
+    ) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
+        r"""
+        labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+
+        Returns:
+
+        Examples:
+
+        ```python
+        >>> from transformers import ConvNextFeatureExtractor, TFConvNextForImageClassification
+        >>> import tensorflow as tf
+        >>> from PIL import Image
+        >>> import requests
+
+        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        >>> image = Image.open(requests.get(url, stream=True).raw)
+
+        >>> feature_extractor = ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224")
+        >>> model = TFViTForImageClassification.from_pretrained("facebook/convnext-tiny-224")
+
+        >>> inputs = feature_extractor(images=image, return_tensors="tf")
+        >>> outputs = model(**inputs)
+        >>> logits = outputs.logits
+        >>> # model predicts one of the 1000 ImageNet classes
+        >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
+        >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
+        ```"""
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        inputs = input_processing(
+            func=self.call,
+            config=self.config,
+            input_ids=pixel_values,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            labels=labels,
+            training=training,
+            kwargs_call=kwargs,
+        )
+
+        if "input_ids" in inputs:
+            inputs["pixel_values"] = inputs.pop("input_ids")
+
+        if inputs["pixel_values"] is None:
+            raise ValueError("You have to specify pixel_values")
+
+        outputs = self.convnext(
+            inputs["pixel_values"],
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            training=inputs["training"],
+        )
+
+        pooled_output = outputs.pooler_output if return_dict else outputs[1]
+
+        logits = self.classifier(pooled_output)
+        loss = None if inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=logits)
+
+        if not inputs["return_dict"]:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return TFSequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+        )

src/transformers/utils/dummy_tf_objects.py

@@ -641,6 +641,27 @@ class TFConvBertPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+class TFConvNextForImageClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFConvNextModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFConvNextPreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/convnext/test_modeling_tf_convnext.py

@@ -0,0 +1,281 @@
+# coding=utf-8
+# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Testing suite for the TensorFlow ConvNext model. """
+
+import inspect
+import unittest
+from typing import List, Tuple
+
+from transformers import ConvNextConfig
+from transformers.file_utils import cached_property, is_tf_available, is_vision_available
+from transformers.testing_utils import require_tf, require_vision, slow
+
+from ..test_configuration_common import ConfigTester
+from ..test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFConvNextForImageClassification, TFConvNextModel
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ConvNextFeatureExtractor
+
+
+class TFConvNextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=13,
+        image_size=32,
+        num_channels=3,
+        num_stages=4,
+        hidden_sizes=[10, 20, 30, 40],
+        depths=[2, 2, 3, 2],
+        is_training=True,
+        use_labels=True,
+        intermediate_size=37,
+        hidden_act="gelu",
+        type_sequence_label_size=10,
+        initializer_range=0.02,
+        num_labels=3,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.num_channels = num_channels
+        self.num_stages = num_stages
+        self.hidden_sizes = hidden_sizes
+        self.depths = depths
+        self.is_training = is_training
+        self.use_labels = use_labels
+        self.intermediate_size = intermediate_size
+        self.hidden_act = hidden_act
+        self.type_sequence_label_size = type_sequence_label_size
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+
+        labels = None
+        if self.use_labels:
+            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+
+        config = self.get_config()
+
+        return config, pixel_values, labels
+
+    def get_config(self):
+        return ConvNextConfig(
+            num_channels=self.num_channels,
+            hidden_sizes=self.hidden_sizes,
+            depths=self.depths,
+            num_stages=self.num_stages,
+            hidden_act=self.hidden_act,
+            is_decoder=False,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values, labels):
+        model = TFConvNextModel(config=config)
+        result = model(pixel_values, training=False)
+        # expected last hidden states: B, C, H // 32, W // 32
+        self.parent.assertEqual(
+            result.last_hidden_state.shape,
+            (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),
+        )
+
+    def create_and_check_for_image_classification(self, config, pixel_values, labels):
+        config.num_labels = self.type_sequence_label_size
+        model = TFConvNextForImageClassification(config)
+        result = model(pixel_values, labels=labels, training=False)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, labels = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_tf
+class TFConvNextModelTest(TFModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as ConvNext does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
+
+    test_pruning = False
+    test_onnx = False
+    test_resize_embeddings = False
+    test_head_masking = False
+
+    def setUp(self):
+        self.model_tester = TFConvNextModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=ConvNextConfig,
+            has_text_modality=False,
+            hidden_size=37,
+        )
+
+    @unittest.skip(reason="ConvNext does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="ConvNext does not support input and output embeddings")
+    def test_model_common_attributes(self):
+        pass
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.call)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="Model doesn't have attention layers")
+    def test_attention_outputs(self):
+        pass
+
+    def test_hidden_states_output(self):
+        def check_hidden_states_output(inputs_dict, config, model_class):
+            model = model_class(config)
+
+            outputs = model(**self._prepare_for_class(inputs_dict, model_class))
+            hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
+
+            expected_num_stages = self.model_tester.num_stages
+            self.assertEqual(len(hidden_states), expected_num_stages + 1)
+
+            # ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
+            self.assertListEqual(
+                list(hidden_states[0].shape[-2:]),
+                [self.model_tester.image_size // 4, self.model_tester.image_size // 4],
+            )
+
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            inputs_dict["output_hidden_states"] = True
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+            # check that output_hidden_states also work using config
+            del inputs_dict["output_hidden_states"]
+            config.output_hidden_states = True
+
+            check_hidden_states_output(inputs_dict, config, model_class)
+
+    # Since ConvNext does not have any attention we need to rewrite this test.
+    def test_model_outputs_equivalence(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        def check_equivalence(model, tuple_inputs, dict_inputs, additional_kwargs={}):
+            tuple_output = model(tuple_inputs, return_dict=False, **additional_kwargs)
+            dict_output = model(dict_inputs, return_dict=True, **additional_kwargs).to_tuple()
+
+            def recursive_check(tuple_object, dict_object):
+                if isinstance(tuple_object, (List, Tuple)):
+                    for tuple_iterable_value, dict_iterable_value in zip(tuple_object, dict_object):
+                        recursive_check(tuple_iterable_value, dict_iterable_value)
+                elif tuple_object is None:
+                    return
+                else:
+                    self.assertTrue(
+                        all(tf.equal(tuple_object, dict_object)),
+                        msg=f"Tuple and dict output are not equal. Difference: {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}",
+                    )
+
+                recursive_check(tuple_output, dict_output)
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            check_equivalence(model, tuple_inputs, dict_inputs)
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class)
+            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
+
+            tuple_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            dict_inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
+            check_equivalence(model, tuple_inputs, dict_inputs, {"output_hidden_states": True})
+
+    def test_for_image_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)
+
+    @slow
+    def test_model_from_pretrained(self):
+        model = TFConvNextModel.from_pretrained("facebook/convnext-tiny-224")
+        self.assertIsNotNone(model)
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@require_tf
+@require_vision
+class TFConvNextModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_feature_extractor(self):
+        return (
+            ConvNextFeatureExtractor.from_pretrained("facebook/convnext-tiny-224") if is_vision_available() else None
+        )
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224")
+
+        feature_extractor = self.default_feature_extractor
+        image = prepare_img()
+        inputs = feature_extractor(images=image, return_tensors="tf")
+
+        # forward pass
+        outputs = model(**inputs)
+
+        # verify the logits
+        expected_shape = tf.TensorShape((1, 1000))
+        self.assertEqual(outputs.logits.shape, expected_shape)
+
+        expected_slice = tf.constant([-0.0260, -0.4739, 0.1911])
+
+        tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4)

tests/test_modeling_tf_common.py

@@ -474,8 +474,8 @@ class TFModelTesterMixin:
                     ),
                     "input_ids": tf.keras.Input(batch_shape=(2, max_input), name="input_ids", dtype="int32"),
                 }
-            # TODO: A better way to handle vision models
-            elif model_class.__name__ in ["TFViTModel", "TFViTForImageClassification", "TFCLIPVisionModel"]:
+            # `pixel_values` implies that the input is an image
+            elif model_class.main_input_name == "pixel_values":
                 inputs = tf.keras.Input(
                     batch_shape=(
                         3,