Add TensorFlow implementation of ConvNeXTv2 (#25558)

* Add type annotations to TFConvNextDropPath

* Use tf.debugging.assert_equal for TFConvNextEmbeddings shape check

* Add TensorFlow implementation of ConvNeXTV2

* check_docstrings: add TFConvNextV2Model to exclusions

TFConvNextV2Model and TFConvNextV2ForImageClassification have docstrings
which are equivalent to their PyTorch cousins, but a parsing issue prevents them
from passing the test.

Adding exclusions for these two classes as discussed in #25558.

docs/source/en/index.md

@@ -97,7 +97,7 @@ Flax), PyTorch, and/or TensorFlow.
 |              [Conditional DETR](model_doc/conditional_detr)              |       ✅        |         ❌         |      ❌      |
 |                      [ConvBERT](model_doc/convbert)                      |       ✅        |         ✅         |      ❌      |
 |                      [ConvNeXT](model_doc/convnext)                      |       ✅        |         ✅         |      ❌      |
-|                    [ConvNeXTV2](model_doc/convnextv2)                    |       ✅        |         ❌         |      ❌      |
+|                    [ConvNeXTV2](model_doc/convnextv2)                    |       ✅        |         ✅         |      ❌      |
 |                           [CPM](model_doc/cpm)                           |       ✅        |         ✅         |      ✅      |
 |                       [CPM-Ant](model_doc/cpmant)                        |       ✅        |         ❌         |      ❌      |
 |                          [CTRL](model_doc/ctrl)                          |       ✅        |         ✅         |      ❌      |

docs/source/en/model_doc/convnextv2.md

@@ -58,4 +58,15 @@ If you're interested in submitting a resource to be included here, please feel f
 ## ConvNextV2ForImageClassification
 
 [[autodoc]] ConvNextV2ForImageClassification
-    - forward
+    - forward
\ No newline at end of file
+
+## TFConvNextV2Model
+
+[[autodoc]] TFConvNextV2Model
+    - call
+
+
+## TFConvNextV2ForImageClassification
+
+[[autodoc]] TFConvNextV2ForImageClassification
+    - call

src/transformers/__init__.py

@@ -3415,6 +3415,13 @@ else:
             "TFConvNextPreTrainedModel",
         ]
     )
+    _import_structure["models.convnextv2"].extend(
+        [
+            "TFConvNextV2ForImageClassification",
+            "TFConvNextV2Model",
+            "TFConvNextV2PreTrainedModel",
+        ]
+    )
     _import_structure["models.ctrl"].extend(
         [
             "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -7127,6 +7134,11 @@ if TYPE_CHECKING:
             TFConvBertPreTrainedModel,
         )
         from .models.convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel
+        from .models.convnextv2 import (
+            TFConvNextV2ForImageClassification,
+            TFConvNextV2Model,
+            TFConvNextV2PreTrainedModel,
+        )
         from .models.ctrl import (
             TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFCTRLForSequenceClassification,

src/transformers/models/auto/modeling_tf_auto.py

@@ -39,6 +39,7 @@ TF_MODEL_MAPPING_NAMES = OrderedDict(
         ("clip", "TFCLIPModel"),
         ("convbert", "TFConvBertModel"),
         ("convnext", "TFConvNextModel"),
+        ("convnextv2", "TFConvNextV2Model"),
         ("ctrl", "TFCTRLModel"),
         ("cvt", "TFCvtModel"),
         ("data2vec-vision", "TFData2VecVisionModel"),
@@ -200,6 +201,7 @@ TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
     [
         # Model for Image-classsification
         ("convnext", "TFConvNextForImageClassification"),
+        ("convnextv2", "TFConvNextV2ForImageClassification"),
         ("cvt", "TFCvtForImageClassification"),
         ("data2vec-vision", "TFData2VecVisionForImageClassification"),
         ("deit", ("TFDeiTForImageClassification", "TFDeiTForImageClassificationWithTeacher")),

src/transformers/models/convnext/modeling_tf_convnext.py

@@ -17,7 +17,7 @@
 
 from __future__ import annotations
 
-from typing import Optional, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 import numpy as np
 import tensorflow as tf
@@ -50,11 +50,11 @@ class TFConvNextDropPath(tf.keras.layers.Layer):
         (1) github.com:rwightman/pytorch-image-models
     """
 
-    def __init__(self, drop_path, **kwargs):
+    def __init__(self, drop_path: float, **kwargs):
         super().__init__(**kwargs)
         self.drop_path = drop_path
 
-    def call(self, x, training=None):
+    def call(self, x: tf.Tensor, training=None):
         if training:
             keep_prob = 1 - self.drop_path
             shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)
@@ -69,7 +69,7 @@ class TFConvNextEmbeddings(tf.keras.layers.Layer):
     found in src/transformers/models/swin/modeling_swin.py.
     """
 
-    def __init__(self, config, **kwargs):
+    def __init__(self, config: ConvNextConfig, **kwargs):
         super().__init__(**kwargs)
         self.patch_embeddings = tf.keras.layers.Conv2D(
             filters=config.hidden_sizes[0],
@@ -77,7 +77,7 @@ class TFConvNextEmbeddings(tf.keras.layers.Layer):
             strides=config.patch_size,
             name="patch_embeddings",
             kernel_initializer=get_initializer(config.initializer_range),
-            bias_initializer="zeros",
+            bias_initializer=tf.keras.initializers.Zeros(),
         )
         self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6, name="layernorm")
         self.num_channels = config.num_channels
@@ -86,15 +86,15 @@ class TFConvNextEmbeddings(tf.keras.layers.Layer):
         if isinstance(pixel_values, dict):
             pixel_values = pixel_values["pixel_values"]
 
-        num_channels = shape_list(pixel_values)[1]
-        if tf.executing_eagerly() and num_channels != self.num_channels:
-            raise ValueError(
-                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
-            )
+        tf.debugging.assert_equal(
+            shape_list(pixel_values)[1],
+            self.num_channels,
+            message="Make sure that the channel dimension of the pixel values match with the one set in the configuration.",
+        )
 
         # 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)
+        # shape = (batch_size, in_height, in_width, in_channels)
         pixel_values = tf.transpose(pixel_values, perm=(0, 2, 3, 1))
 
         embeddings = self.patch_embeddings(pixel_values)
@@ -188,15 +188,28 @@ 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.
+        config (`ConvNextV2Config`):
+            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
+        self,
+        config: ConvNextConfig,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int = 2,
+        stride: int = 2,
+        depth: int = 2,
+        drop_path_rates: Optional[List[float]] = None,
+        **kwargs,
     ):
         super().__init__(**kwargs)
         if in_channels != out_channels or stride > 1:
@@ -215,7 +228,7 @@ class TFConvNextStage(tf.keras.layers.Layer):
                     kernel_size=kernel_size,
                     strides=stride,
                     kernel_initializer=get_initializer(config.initializer_range),
-                    bias_initializer="zeros",
+                    bias_initializer=tf.keras.initializers.Zeros(),
                     name="downsampling_layer.1",
                 ),
             ]

src/transformers/models/convnextv2/__init__.py

@@ -22,6 +22,7 @@ from ...utils import (
     OptionalDependencyNotAvailable,
     _LazyModule,
     is_torch_available,
+    is_tf_available,
 )
 
 
@@ -46,6 +47,17 @@ else:
         "ConvNextV2Backbone",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_convnextv2"] = [
+        "TFConvNextV2ForImageClassification",
+        "TFConvNextV2Model",
+        "TFConvNextV2PreTrainedModel",
+    ]
 
 if TYPE_CHECKING:
     from .configuration_convnextv2 import (
@@ -67,6 +79,18 @@ if TYPE_CHECKING:
             ConvNextV2PreTrainedModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_convnextv2 import (
+            TFConvNextV2ForImageClassification,
+            TFConvNextV2Model,
+            TFConvNextV2PreTrainedModel,
+        )
+
 else:
     import sys
 

src/transformers/models/efficientformer/modeling_tf_efficientformer.py

@@ -337,11 +337,11 @@ class TFEfficientFormerDropPath(tf.keras.layers.Layer):
         (1) github.com:rwightman/pytorch-image-models
     """
 
-    def __init__(self, drop_path, **kwargs):
+    def __init__(self, drop_path: float, **kwargs):
         super().__init__(**kwargs)
         self.drop_path = drop_path
 
-    def call(self, x, training=None):
+    def call(self, x: tf.Tensor, training=None):
         if training:
             keep_prob = 1 - self.drop_path
             shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)

src/transformers/models/segformer/modeling_tf_segformer.py

@@ -62,11 +62,11 @@ class TFSegformerDropPath(tf.keras.layers.Layer):
         (1) github.com:rwightman/pytorch-image-models
     """
 
-    def __init__(self, drop_path, **kwargs):
+    def __init__(self, drop_path: float, **kwargs):
         super().__init__(**kwargs)
         self.drop_path = drop_path
 
-    def call(self, x, training=None):
+    def call(self, x: tf.Tensor, training=None):
         if training:
             keep_prob = 1 - self.drop_path
             shape = (tf.shape(x)[0],) + (1,) * (len(tf.shape(x)) - 1)

src/transformers/utils/dummy_tf_objects.py

@@ -836,6 +836,27 @@ class TFConvNextPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["tf"])
 
 
+class TFConvNextV2ForImageClassification(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFConvNextV2Model(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
+class TFConvNextV2PreTrainedModel(metaclass=DummyObject):
+    _backends = ["tf"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["tf"])
+
+
 TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

tests/models/convnextv2/test_modeling_tf_convnextv2.py

+# coding=utf-8
+# Copyright 2023 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. """
+
+from __future__ import annotations
+
+import inspect
+import unittest
+from typing import List, Tuple
+
+import numpy as np
+
+from transformers import ConvNextV2Config
+from transformers.testing_utils import require_tf, require_vision, slow
+from transformers.utils import cached_property, is_tf_available, is_vision_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_tf_available():
+    import tensorflow as tf
+
+    from transformers import TFConvNextV2ForImageClassification, TFConvNextV2Model
+
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import ConvNextImageProcessor
+
+
+class TFConvNextV2ModelTester:
+    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 ConvNextV2Config(
+            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 = TFConvNextV2Model(config=config)
+        result = model(pixel_values, training=False)
+        # expected last hidden states: batch_size, channels, height // 32, width // 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 = TFConvNextV2ForImageClassification(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 TFConvNextV2ModelTest(TFModelTesterMixin, PipelineTesterMixin, 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 = (TFConvNextV2Model, TFConvNextV2ForImageClassification) if is_tf_available() else ()
+    pipeline_model_mapping = (
+        {"feature-extraction": TFConvNextV2Model, "image-classification": TFConvNextV2ForImageClassification}
+        if is_tf_available()
+        else {}
+    )
+
+    test_pruning = False
+    test_onnx = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    has_attentions = False
+
+    def setUp(self):
+        self.model_tester = TFConvNextV2ModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=ConvNextV2Config,
+            has_text_modality=False,
+            hidden_size=37,
+        )
+
+    @unittest.skip(reason="ConvNext does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skipIf(
+        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
+        reason="TF does not support backprop for grouped convolutions on CPU.",
+    )
+    @slow
+    def test_keras_fit(self):
+        super().test_keras_fit()
+
+    @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.skipIf(
+        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0,
+        reason="TF does not support backprop for grouped convolutions on CPU.",
+    )
+    def test_dataset_conversion(self):
+        super().test_dataset_conversion()
+
+    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=(
+                            "Tuple and dict output are not equal. Difference:"
+                            f" {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 = TFConvNextV2Model.from_pretrained("facebook/convnextv2-tiny-1k-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 TFConvNextV2ModelIntegrationTest(unittest.TestCase):
+    @cached_property
+    def default_image_processor(self):
+        return (
+            ConvNextImageProcessor.from_pretrained("facebook/convnextv2-tiny-1k-224")
+            if is_vision_available()
+            else None
+        )
+
+    @slow
+    def test_inference_image_classification_head(self):
+        model = TFConvNextV2ForImageClassification.from_pretrained("facebook/convnextv2-tiny-1k-224")
+
+        image_processor = self.default_image_processor
+        image = prepare_img()
+        inputs = image_processor(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 = np.array([0.9996, 0.1966, -0.4386])
+
+        self.assertTrue(np.allclose(outputs.logits[0, :3].numpy(), expected_slice, atol=1e-4))

utils/check_docstrings.py

@@ -533,6 +533,8 @@ OBJECTS_TO_IGNORE = [
     "TFConvBertModel",
     "TFConvNextForImageClassification",
     "TFConvNextModel",
+    "TFConvNextV2Model",  # Parsing issue. Equivalent to PT ConvNextV2Model, see PR #25558
+    "TFConvNextV2ForImageClassification",
     "TFCvtForImageClassification",
     "TFCvtModel",
     "TFDPRReader",