TF implementation of RegNets

docs/source/en/index.mdx

@@ -261,7 +261,7 @@ Flax), PyTorch, and/or TensorFlow.
 |             RAG             |       ✅       |       ❌       |       ✅        |         ✅         |      ❌      |
 |            REALM            |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
 |          Reformer           |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |
-|           RegNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
+|           RegNet            |       ❌       |       ❌       |       ✅        |         ✅         |      ❌      |
 |           RemBERT           |       ✅       |       ✅       |       ✅        |         ✅         |      ❌      |
 |           ResNet            |       ❌       |       ❌       |       ✅        |         ❌         |      ❌      |
 |          RetriBERT          |       ✅       |       ✅       |       ✅        |         ❌         |      ❌      |

docs/source/en/model_doc/regnet.mdx

@@ -27,7 +27,8 @@ Tips:
 - One can use [`AutoFeatureExtractor`] to prepare images for the model.
 - The huge 10B model from [Self-supervised Pretraining of Visual Features in the Wild](https://arxiv.org/abs/2103.01988), trained on one billion Instagram images, is available on the [hub](https://huggingface.co/facebook/regnet-y-10b-seer)
 
-This model was contributed by [Francesco](https://huggingface.co/Francesco).
+This model was contributed by [Francesco](https://huggingface.co/Francesco). The TensorFlow version of the model
+was contributed by [sayakpaul](https://huggingface.com/sayakpaul) and [ariG23498](https://huggingface.com/ariG23498).
 The original code can be found [here](https://github.com/facebookresearch/pycls).
 
 
@@ -45,4 +46,15 @@ The original code can be found [here](https://github.com/facebookresearch/pycls)
 ## RegNetForImageClassification
 
 [[autodoc]] RegNetForImageClassification
-    - forward
+    - forward
+
+## TFRegNetModel
+
+[[autodoc]] TFRegNetModel
+    - call
+
+
+## TFRegNetForImageClassification
+
+[[autodoc]] TFRegNetForImageClassification
+    - call

src/transformers/__init__.py

@@ -2279,6 +2279,14 @@
             "TFRagTokenForGeneration",
         ]
     )
+    _import_structure["models.regnet"].extend(
+        [
+            "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "TFRegNetForImageClassification",
+            "TFRegNetModel",
+            "TFRegNetPreTrainedModel",
+        ]
+    )
     _import_structure["models.rembert"].extend(
         [
             "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -4551,6 +4559,12 @@
         from .models.opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel
         from .models.pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel
         from .models.rag import TFRagModel, TFRagPreTrainedModel, TFRagSequenceForGeneration, TFRagTokenForGeneration
+        from .models.regnet import (
+            TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRegNetForImageClassification,
+            TFRegNetModel,
+            TFRegNetPreTrainedModel,
+        )
         from .models.rembert import (
             TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
             TFRemBertForCausalLM,

src/transformers/modeling_tf_outputs.py

@@ -46,6 +46,25 @@ class TFBaseModelOutput(ModelOutput):
     attentions: Optional[Tuple[tf.Tensor]] = None
 
 
+@dataclass
+class TFBaseModelOutputWithNoAttention(ModelOutput):
+    """
+    Base class for model's outputs, with potential hidden states.
+
+    Args:
+        last_hidden_state (`tf.Tensor` shape `(batch_size, num_channels, height, width)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, +
+            one for the output of each layer) of shape `(batch_size, num_channels, height, width)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    last_hidden_state: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+
+
 @dataclass
 class TFBaseModelOutputWithPooling(ModelOutput):
     """
@@ -80,6 +99,28 @@ class TFBaseModelOutputWithPooling(ModelOutput):
     attentions: Optional[Tuple[tf.Tensor]] = None
 
 
+@dataclass
+class TFBaseModelOutputWithPoolingAndNoAttention(ModelOutput):
+    """
+    Base class for model's outputs that also contains a pooling of the last hidden states.
+
+    Args:
+        last_hidden_state (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
+            Sequence of hidden-states at the output of the last layer of the model.
+        pooler_output (`tf.Tensor` of shape `(batch_size, hidden_size)`):
+            Last layer hidden-state after a pooling operation on the spatial dimensions.
+        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
+            the output of each layer) of shape `(batch_size, num_channels, height, width)`.
+
+            Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.
+    """
+
+    last_hidden_state: tf.Tensor = None
+    pooler_output: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None
+
+
 @dataclass
 class TFBaseModelOutputWithPoolingAndCrossAttentions(ModelOutput):
     """
@@ -825,3 +866,24 @@ class TFSequenceClassifierOutputWithPast(ModelOutput):
     past_key_values: Optional[List[tf.Tensor]] = None
     hidden_states: Optional[Tuple[tf.Tensor]] = None
     attentions: Optional[Tuple[tf.Tensor]] = None
+
+
+@dataclass
+class TFImageClassifierOutputWithNoAttention(ModelOutput):
+    """
+    Base class for outputs of image classification models.
+
+    Args:
+        loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
+            Classification (or regression if config.num_labels==1) loss.
+        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
+            Classification (or regression if config.num_labels==1) scores (before SoftMax).
+        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
+            Tuple of `tf.Tensor` (one for the output of the embeddings, if the model has an embedding layer, + one for
+            the output of each stage) of shape `(batch_size, num_channels, height, width)`. Hidden-states (also called
+            feature maps) of the model at the output of each stage.
+    """
+
+    loss: Optional[tf.Tensor] = None
+    logits: tf.Tensor = None
+    hidden_states: Optional[Tuple[tf.Tensor]] = None

src/transformers/models/auto/modeling_tf_auto.py

@@ -62,6 +62,7 @@
         ("openai-gpt", "TFOpenAIGPTModel"),
         ("opt", "TFOPTModel"),
         ("pegasus", "TFPegasusModel"),
+        ("regnet", "TFRegNetModel"),
         ("rembert", "TFRemBertModel"),
         ("roberta", "TFRobertaModel"),
         ("roformer", "TFRoFormerModel"),
@@ -173,6 +174,7 @@
         # Model for Image-classsification
         ("convnext", "TFConvNextForImageClassification"),
         ("data2vec-vision", "TFData2VecVisionForImageClassification"),
+        ("regnet", "TFRegNetForImageClassification"),
         ("swin", "TFSwinForImageClassification"),
         ("vit", "TFViTForImageClassification"),
     ]

src/transformers/models/regnet/__init__.py

@@ -18,8 +18,7 @@
 from typing import TYPE_CHECKING
 
 # rely on isort to merge the imports
-from ...file_utils import _LazyModule, is_torch_available
-from ...utils import OptionalDependencyNotAvailable
+from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
 
 
 _import_structure = {"configuration_regnet": ["REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "RegNetConfig"]}
@@ -37,6 +36,19 @@
         "RegNetPreTrainedModel",
     ]
 
+try:
+    if not is_tf_available():
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    pass
+else:
+    _import_structure["modeling_tf_regnet"] = [
+        "TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST",
+        "TFRegNetForImageClassification",
+        "TFRegNetModel",
+        "TFRegNetPreTrainedModel",
+    ]
+
 
 if TYPE_CHECKING:
     from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig
@@ -54,6 +66,19 @@
             RegNetPreTrainedModel,
         )
 
+    try:
+        if not is_tf_available():
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        pass
+    else:
+        from .modeling_tf_regnet import (
+            TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
+            TFRegNetForImageClassification,
+            TFRegNetModel,
+            TFRegNetPreTrainedModel,
+        )
+
 
 else:
     import sys