/usr/local/lib/python3.6/site-packages/transformers/models/clip
Edit: /usr/local/lib/python3.6/site-packages/transformers/models/clip/modeling_tf_clip.py (55491B)
# coding=utf-8
# Copyright 2021 The OpenAI Team Authors and The HuggingFace 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 CLIP model."""
import math
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFModelInputType,
TFPreTrainedModel,
get_initializer,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list
from ...utils import (
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai/clip-vit-base-patch32"
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
"openai/clip-vit-base-patch32",
# See all CLIP models at https://huggingface.co/models?filter=clip
]
LARGE_NEGATIVE = -1e8
# Copied from transformers.models.bart.modeling_tf_bart._expand_mask
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None, past_key_values_length: int = 0):
"""
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
"""
src_len = shape_list(mask)[1]
tgt_len = tgt_len if tgt_len is not None else src_len
one_cst = tf.constant(1.0)
mask = tf.cast(mask, dtype=one_cst.dtype)
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1))
return (one_cst - expanded_mask) * LARGE_NEGATIVE
# contrastive loss function, adapted from
# https://sachinruk.github.io/blog/pytorch/pytorch%20lightning/loss%20function/gpu/2021/03/07/CLIP.html
def contrastive_loss(logits: tf.Tensor) -> tf.Tensor:
return tf.math.reduce_mean(
tf.keras.metrics.sparse_categorical_crossentropy(
y_true=tf.range(shape_list(logits)[0]), y_pred=logits, from_logits=True
)
)
def clip_loss(similarity: tf.Tensor) -> tf.Tensor:
caption_loss = contrastive_loss(similarity)
image_loss = contrastive_loss(tf.transpose(similarity))
return (caption_loss + image_loss) / 2.0
@dataclass
class TFCLIPOutput(ModelOutput):
"""
Args:
loss (`tf.Tensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`):
Contrastive loss for image-text similarity.
logits_per_image:(`tf.Tensor` of shape `(image_batch_size, text_batch_size)`):
The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text
similarity scores.
logits_per_text:(`tf.Tensor` of shape `(text_batch_size, image_batch_size)`):
The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image
similarity scores.
text_embeds(`tf.Tensor` of shape `(batch_size, output_dim`):
The text embeddings obtained by applying the projection layer to the pooled output of [`TFCLIPTextModel`].
image_embeds(`tf.Tensor` of shape `(batch_size, output_dim`):
The image embeddings obtained by applying the projection layer to the pooled output of
[`TFCLIPVisionModel`].
text_model_output([`~modeling_tf_utils.TFBaseModelOutputWithPooling`]):
The output of the [`TFCLIPTextModel`].
vision_model_output([`~modeling_tf_utils.TFBaseModelOutputWithPooling`]):
The output of the [`TFCLIPVisionModel`].
"""
loss: Optional[tf.Tensor] = None
logits_per_image: tf.Tensor = None
logits_per_text: tf.Tensor = None
text_embeds: tf.Tensor = None
image_embeds: tf.Tensor = None
text_model_output: TFBaseModelOutputWithPooling = None
vision_model_output: TFBaseModelOutputWithPooling = None
def to_tuple(self) -> Tuple[Any]:
return tuple(
self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple()
for k in self.keys()
)
class TFCLIPVisionEmbeddings(tf.keras.layers.Layer):
def __init__(self, config: CLIPVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.image_size = config.image_size
self.patch_size = config.patch_size
self.num_patches = (self.image_size // self.patch_size) ** 2
self.num_positions = self.num_patches + 1
self.config = config
self.patch_embedding = tf.keras.layers.Conv2D(
filters=self.embed_dim,
kernel_size=self.patch_size,
strides=self.patch_size,
padding="valid",
data_format="channels_last",
use_bias=False,
kernel_initializer=get_initializer(self.config.initializer_range * self.config.initializer_factor),
name="patch_embedding",
)
def build(self, input_shape: tf.TensorShape):
factor = self.config.initializer_factor
self.class_embedding = self.add_weight(
shape=(self.embed_dim,),
initializer=get_initializer(self.embed_dim**-0.5 * factor),
trainable=True,
name="class_embedding",
)
with tf.name_scope("position_embedding"):
self.position_embedding = self.add_weight(
shape=(self.num_positions, self.embed_dim),
initializer=get_initializer(self.config.initializer_range * factor),
trainable=True,
name="embeddings",
)
super().build(input_shape)
def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
"""`pixel_values` is expected to be of NCHW format."""
batch_size, num_channels, height, width = shape_list(pixel_values)
# When running on CPU, `tf.nn.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))
patch_embeds = self.patch_embedding(pixel_values)
# Change the 2D spatial dimensions to a single temporal dimension.
# shape = (batch_size, num_patches, out_channels=embed_dim)
patch_embeds = tf.reshape(tensor=patch_embeds, shape=(batch_size, self.num_patches, -1))
# add the [CLS] token to the embedded patch tokens
class_embeds = tf.broadcast_to(self.class_embedding, shape=(batch_size, 1, self.embed_dim))
embeddings = tf.concat((class_embeds, patch_embeds), axis=1)
embeddings = embeddings + self.position_embedding
return embeddings
class TFCLIPTextEmbeddings(tf.keras.layers.Layer):
def __init__(self, config: CLIPTextConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.vocab_size = config.vocab_size
self.config = config
def build(self, input_shape: tf.TensorShape):
with tf.name_scope("token_embedding"):
self.weight = self.add_weight(
shape=(self.vocab_size, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="weight",
)
with tf.name_scope("position_embedding"):
self.position_embedding = self.add_weight(
shape=(self.config.max_position_embeddings, self.embed_dim),
initializer=get_initializer(self.config.initializer_factor * self.config.initializer_range),
trainable=True,
name="embeddings",
)
super().build(input_shape)
def call(
self,
input_ids: tf.Tensor = None,
position_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
"""
if input_ids is None and inputs_embeds is None:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = tf.gather(params=self.weight, indices=input_ids)
input_shape = shape_list(inputs_embeds)[:-1]
if position_ids is None:
position_ids = tf.expand_dims(tf.range(start=0, limit=input_shape[-1]), axis=0)
position_embeds = tf.gather(params=self.position_embedding, indices=position_ids)
position_embeds = tf.tile(input=position_embeds, multiples=(input_shape[0], 1, 1))
final_embeddings = inputs_embeds + position_embeds
return final_embeddings
class TFCLIPAttention(tf.keras.layers.Layer):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.num_attention_heads = config.num_attention_heads
self.attention_head_size = self.embed_dim // self.num_attention_heads
if self.attention_head_size * self.num_attention_heads != self.embed_dim:
raise ValueError(
f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_attention_heads})."
)
factor = config.initializer_factor
in_proj_std = (self.embed_dim**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) * factor
out_proj_std = (self.embed_dim**-0.5) * factor
self.sqrt_att_head_size = math.sqrt(self.attention_head_size)
self.q_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="q_proj"
)
self.k_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="k_proj"
)
self.v_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(in_proj_std), name="v_proj"
)
self.dropout = tf.keras.layers.Dropout(rate=config.attention_dropout)
self.out_proj = tf.keras.layers.Dense(
units=self.embed_dim, kernel_initializer=get_initializer(out_proj_std), name="out_proj"
)
# copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention.transpose_for_scores
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
return tf.transpose(tensor, perm=[0, 2, 1, 3])
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""Input shape: Batch x Time x Channel"""
batch_size = shape_list(hidden_states)[0]
mixed_query_layer = self.q_proj(inputs=hidden_states)
mixed_key_layer = self.k_proj(inputs=hidden_states)
mixed_value_layer = self.v_proj(inputs=hidden_states)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
# (batch size, num_heads, seq_len_q, seq_len_k)
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
attention_scores = tf.divide(attention_scores, dk)
# apply the causal_attention_mask first
if causal_attention_mask is not None:
# Apply the causal attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, causal_attention_mask)
if attention_mask is not None:
# Apply the attention mask (precomputed for all layers in TFCLIPModel call() function)
attention_scores = tf.add(attention_scores, attention_mask)
# Normalize the attention scores to probabilities.
_attention_probs = tf.nn.softmax(logits=attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(inputs=_attention_probs, training=training)
attention_output = tf.matmul(attention_probs, value_layer)
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])
# (batch_size, seq_len_q, embed_dim)
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.embed_dim))
attention_output = self.out_proj(attention_output, training=training)
# In TFBert, attention weights are returned after dropout.
# However, in CLIP, they are returned before dropout.
outputs = (attention_output, _attention_probs) if output_attentions else (attention_output,)
return outputs
class TFCLIPMLP(tf.keras.layers.Layer):
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
self.activation_fn = get_tf_activation(config.hidden_act)
factor = config.initializer_factor
in_proj_std = (config.hidden_size**-0.5) * ((2 * config.num_hidden_layers) ** -0.5) * factor
fc_std = (2 * config.hidden_size) ** -0.5 * factor
self.fc1 = tf.keras.layers.Dense(
units=config.intermediate_size, kernel_initializer=get_initializer(fc_std), name="fc1"
)
self.fc2 = tf.keras.layers.Dense(
units=config.hidden_size, kernel_initializer=get_initializer(in_proj_std), name="fc2"
)
def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
hidden_states = self.fc1(inputs=hidden_states)
hidden_states = self.activation_fn(hidden_states)
hidden_states = self.fc2(inputs=hidden_states)
return hidden_states
class TFCLIPEncoderLayer(tf.keras.layers.Layer):
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
self.embed_dim = config.hidden_size
self.self_attn = TFCLIPAttention(config, name="self_attn")
self.layer_norm1 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm1")
self.mlp = TFCLIPMLP(config, name="mlp")
self.layer_norm2 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layer_norm2")
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
causal_attention_mask (`tf.Tensor`): causal attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
output_attentions (`bool`):
Whether or not to return the attentions tensors of all attention layers. See `outputs` under returned
tensors for more detail.
"""
residual = hidden_states
hidden_states = self.layer_norm1(inputs=hidden_states)
attention_outputs = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = attention_outputs[0]
hidden_states = residual + hidden_states
residual = hidden_states
hidden_states = self.layer_norm2(inputs=hidden_states)
hidden_states = self.mlp(hidden_states=hidden_states)
hidden_states = residual + hidden_states
outputs = (hidden_states,) + attention_outputs[1:] # add attentions if we output them
return outputs
class TFCLIPEncoder(tf.keras.layers.Layer):
"""
Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a
[`TFCLIPEncoderLayer`].
Args:
config: CLIPConfig
"""
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
self.layers = [TFCLIPEncoderLayer(config, name=f"layers_._{i}") for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states=hidden_states,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
training=training,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
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, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class TFCLIPTextTransformer(tf.keras.layers.Layer):
def __init__(self, config: CLIPTextConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFCLIPTextEmbeddings(config, name="embeddings")
self.encoder = TFCLIPEncoder(config, name="encoder")
self.final_layer_norm = tf.keras.layers.LayerNormalization(
epsilon=config.layer_norm_eps, name="final_layer_norm"
)
def call(
self,
input_ids: TFModelInputType,
attention_mask: tf.Tensor,
position_ids: tf.Tensor,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
input_shape = shape_list(input_ids)
embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids)
batch_size, seq_length = input_shape
# CLIP's text model uses causal mask, prepare it here.
# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
causal_attention_mask = self._build_causal_attention_mask(batch_size, seq_length, dtype=embedding_output.dtype)
# check attention mask and invert
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
attention_mask = _expand_mask(attention_mask)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=attention_mask,
causal_attention_mask=causal_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
sequence_output = self.final_layer_norm(inputs=sequence_output)
# text_embeds.shape = [batch_size, n_ctx, transformer.width]
# take features from the eot embedding (eot_token is the highest number in each sequence)
pooled_output = tf.gather_nd(
params=sequence_output,
indices=tf.stack(
values=(tf.range(input_shape[0], dtype=tf.int64), tf.math.argmax(input_ids, axis=-1)), axis=1
),
)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
def _build_causal_attention_mask(self, batch_size, seq_length, dtype=tf.float32):
diag = tf.constant(0.0, shape=(seq_length,), dtype=dtype)
# set an additive 2D attention mask with all places being masked
to_mask = tf.constant(-10000.0, shape=(seq_length, seq_length), dtype=dtype)
# set diagonal & lower triangular parts to 0 (i.e. the places not to be masked)
# TIP: think the 2D matrix as the space of (query_seq, key_seq)
to_mask = tf.linalg.band_part(to_mask, 0, -1)
# to_mask = tf.linalg.band_part(to_mask, -1, 0)
to_mask = tf.linalg.set_diag(to_mask, diagonal=diag)
return tf.broadcast_to(input=to_mask, shape=(batch_size, 1, seq_length, seq_length))
@keras_serializable
class TFCLIPTextMainLayer(tf.keras.layers.Layer):
config_class = CLIPTextConfig
def __init__(self, config: CLIPTextConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.text_model = TFCLIPTextTransformer(config, name="text_model")
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.text_model.embeddings
def set_input_embeddings(self, value: tf.Variable):
self.text_model.embeddings.weight = value
self.text_model.embeddings.vocab_size = shape_list(value)[0]
@unpack_inputs
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if input_ids is None:
raise ValueError("You have to specify input_ids")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
text_model_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return text_model_outputs
class TFCLIPVisionTransformer(tf.keras.layers.Layer):
def __init__(self, config: CLIPVisionConfig, **kwargs):
super().__init__(**kwargs)
self.embeddings = TFCLIPVisionEmbeddings(config, name="embeddings")
self.pre_layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="pre_layrnorm")
self.encoder = TFCLIPEncoder(config, name="encoder")
self.post_layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="post_layernorm")
def call(
self,
pixel_values: TFModelInputType,
output_attentions: bool,
output_hidden_states: bool,
return_dict: bool,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
embedding_output = self.embeddings(pixel_values=pixel_values)
embedding_output = self.pre_layernorm(inputs=embedding_output)
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=None,
causal_attention_mask=None,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = sequence_output[:, 0, :]
pooled_output = self.post_layernorm(inputs=pooled_output)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@keras_serializable
class TFCLIPVisionMainLayer(tf.keras.layers.Layer):
config_class = CLIPVisionConfig
def __init__(self, config: CLIPVisionConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.vision_model = TFCLIPVisionTransformer(config, name="vision_model")
def get_input_embeddings(self) -> tf.keras.layers.Layer:
return self.vision_model.embeddings
@unpack_inputs
def call(
self,
pixel_values: Optional[TFModelInputType] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
vision_model_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return vision_model_outputs
@keras_serializable
class TFCLIPMainLayer(tf.keras.layers.Layer):
config_class = CLIPConfig
def __init__(self, config: CLIPConfig, **kwargs):
super().__init__(**kwargs)
if not isinstance(config.text_config, CLIPTextConfig):
raise ValueError(
f"config.text_config is expected to be of type CLIPTextConfig but is of type {type(config.text_config)}."
)
if not isinstance(config.vision_config, CLIPVisionConfig):
raise ValueError(
f"config.vision_config is expected to be of type CLIPVisionConfig but is of type {type(config.vision_config)}."
)
self.config = config
text_config = config.text_config
vision_config = config.vision_config
self.projection_dim = config.projection_dim
self.text_model = TFCLIPTextTransformer(text_config, name="text_model")
self.vision_model = TFCLIPVisionTransformer(vision_config, name="vision_model")
self.visual_projection = tf.keras.layers.Dense(
units=self.projection_dim,
kernel_initializer=get_initializer(vision_config.hidden_size**-0.5 * self.config.initializer_factor),
use_bias=False,
name="visual_projection",
)
self.text_projection = tf.keras.layers.Dense(
units=self.projection_dim,
kernel_initializer=get_initializer(text_config.hidden_size**-0.5 * self.config.initializer_factor),
use_bias=False,
name="text_projection",
)
def build(self, input_shape: tf.TensorShape):
self.logit_scale = self.add_weight(
shape=(1,),
initializer=tf.keras.initializers.Constant(self.config.logit_scale_init_value),
trainable=True,
name="logit_scale",
)
super().build(input_shape)
@unpack_inputs
def get_text_features(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
if input_ids is None:
raise ValueError("You have to specify either input_ids")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = text_outputs[1]
text_features = self.text_projection(inputs=pooled_output)
return text_features
@unpack_inputs
def get_image_features(
self,
pixel_values: Optional[TFModelInputType] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = vision_outputs[1] # pooled_output
image_features = self.visual_projection(inputs=pooled_output)
return image_features
@unpack_inputs
def call(
self,
input_ids: Optional[TFModelInputType] = None,
pixel_values: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFCLIPOutput, Tuple[tf.Tensor]]:
if input_ids is None:
raise ValueError("You have to specify either input_ids")
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
input_shape = shape_list(input_ids)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
vision_outputs = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
text_outputs = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
image_embeds = vision_outputs[1]
image_embeds = self.visual_projection(inputs=image_embeds)
text_embeds = text_outputs[1]
text_embeds = self.text_projection(inputs=text_embeds)
# normalized features
image_embeds = image_embeds / tf.norm(tensor=image_embeds, ord="euclidean", axis=-1, keepdims=True)
text_embeds = text_embeds / tf.norm(tensor=text_embeds, ord="euclidean", axis=-1, keepdims=True)
# cosine similarity as logits
logit_scale = tf.math.exp(self.logit_scale)
logits_per_text = tf.matmul(text_embeds, image_embeds, transpose_b=True) * logit_scale
logits_per_image = tf.transpose(logits_per_text)
loss = None
if return_loss:
loss = clip_loss(logits_per_text)
if not return_dict:
output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs)
return (loss,) + output if loss is not None else output
return TFCLIPOutput(
loss=loss,
logits_per_image=logits_per_image,
logits_per_text=logits_per_text,
text_embeds=text_embeds,
image_embeds=image_embeds,
text_model_output=text_outputs,
vision_model_output=vision_outputs,
)
class TFCLIPPreTrainedModel(TFPreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models.
"""
config_class = CLIPConfig
base_model_prefix = "clip"
CLIP_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.
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)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors in the
first positional argument :
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
`model([input_ids, attention_mask])` or `model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Args:
config ([`CLIPConfig`]): 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.
"""
CLIP_TEXT_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` 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.
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 [`~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.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
CLIP_VISION_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 [`CLIPFeatureExtractor`]. See
[`CLIPFeatureExtractor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to
return the attentions tensors of all attention layers. See `attentions` 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.
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 [`~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.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
CLIP_INPUTS_DOCSTRING = r"""
Args:
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]` ``Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`BertTokenizer`]. See [`PreTrainedTokenizer.__call__`] and
[`PreTrainedTokenizer.encode`] for details.
[What are input IDs?](../glossary#input-ids)
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 [`CLIPFeatureExtractor`]. See
[`CLIPFeatureExtractor.__call__`] for details.
attention_mask (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
position_ids (`np.ndarray` or `tf.Tensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
return_loss (`bool`, *optional*):
Whether or not to return the contrastive loss.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` 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.
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 [`~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.
training (`bool`, *optional*, defaults to `False``):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
class TFCLIPTextModel(TFCLIPPreTrainedModel):
config_class = CLIPTextConfig
def __init__(self, config: CLIPTextConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.clip = TFCLIPTextMainLayer(config, name="clip")
@unpack_inputs
@add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=CLIPTextConfig)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, TFCLIPTextModel
>>> model = TFCLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled (EOS token) states
```"""
outputs = self.clip(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hs,
attentions=attns,
)
class TFCLIPVisionModel(TFCLIPPreTrainedModel):
config_class = CLIPVisionConfig
main_input_name = "pixel_values"
def __init__(self, config: CLIPVisionConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.clip = TFCLIPVisionMainLayer(config, name="clip")
@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=(len(DUMMY_INPUTS), 3, self.config.image_size, self.config.image_size), dtype=tf.float32
)
return {"pixel_values": 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.
"""
output = self.call(inputs)
return self.serving_output(output)
@unpack_inputs
@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=CLIPVisionConfig)
def call(
self,
pixel_values: Optional[TFModelInputType] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, TFCLIPVisionModel
>>> model = TFCLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled CLS states
```"""
outputs = self.clip(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
return outputs
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings(CLIP_START_DOCSTRING)
class TFCLIPModel(TFCLIPPreTrainedModel):
config_class = CLIPConfig
def __init__(self, config: CLIPConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.clip = TFCLIPMainLayer(config, name="clip")
@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=(len(DUMMY_INPUTS), 3, self.config.vision_config.image_size, self.config.vision_config.image_size),
dtype=tf.float32,
)
return {
"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32),
"pixel_values": VISION_DUMMY_INPUTS,
}
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
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.
"""
output = self.call(inputs)
return self.serving_output(output)
@unpack_inputs
@add_start_docstrings_to_model_forward(CLIP_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def get_text_features(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Returns:
text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
the projection layer to the pooled output of [`TFCLIPTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="tf")
>>> text_features = model.get_text_features(**inputs)
```"""
text_features = self.clip.get_text_features(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return text_features
@unpack_inputs
@add_start_docstrings_to_model_forward(CLIP_VISION_INPUTS_DOCSTRING)
def get_image_features(
self,
pixel_values: Optional[TFModelInputType] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Returns:
image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
the projection layer to the pooled output of [`TFCLIPVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="tf")
>>> image_features = model.get_image_features(**inputs)
```"""
image_features = self.clip.get_image_features(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return image_features
@unpack_inputs
@add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFCLIPOutput, config_class=CLIPConfig)
def call(
self,
input_ids: Optional[TFModelInputType] = None,
pixel_values: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFCLIPOutput, Tuple[tf.Tensor]]:
r"""
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(
... text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="tf", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = tf.nn.softmax(logits_per_image, axis=1) # we can take the softmax to get the label probabilities
```"""
outputs = self.clip(
input_ids=input_ids,
pixel_values=pixel_values,
attention_mask=attention_mask,
position_ids=position_ids,
return_loss=return_loss,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
return outputs
def serving_output(self, output: TFCLIPOutput) -> TFCLIPOutput:
return output