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Refactor CLIP and update SD3. #2316

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105 changes: 3 additions & 102 deletions keras_hub/src/models/clip/clip_backbone.py
Original file line number Diff line number Diff line change
@@ -1,109 +1,10 @@
import math

from keras import layers
from keras import ops

from keras_hub.src.api_export import keras_hub_export
from keras_hub.src.models.backbone import Backbone


class CLIPVisionPooler(layers.Layer):
"""The vision pooler layer of CLIP.

`CLIPVisionPooler` will extracts the first token (index `0`) from the
sequence of the vision embeddings as the pooled outputs.

Call arguments:
vision_embeddings: A tensor of shape
`(batch_size, sequence_length, hidden_dim)`.
"""

def call(self, vision_embeddings):
return vision_embeddings[:, 0, :]

def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[-1])


class CLIPTextPooler(layers.Layer):
"""The text pooler layer of CLIP.

`CLIPTextPooler` extracts the text embeddings at the positions of EOS tokens
as the pooled outputs.

Call arguments:
text_embeddings: A tensor of shape
`(batch_size, sequence_length, hidden_dim)`.
token_ids: A tensor of shape `(batch_size, max_tokens)`, used to
identify the positions of EOS tokens.
"""

def call(self, text_embeddings, token_ids):
# `keepdims` is not supported in `keras<=3.1`.
eos_index = ops.argmax(token_ids, axis=-1)
eos_index = ops.expand_dims(eos_index, axis=-1)
eos_index = ops.expand_dims(eos_index, axis=-1)
pooled_outputs = ops.take_along_axis(text_embeddings, eos_index, axis=1)
return ops.squeeze(pooled_outputs, axis=1)

def compute_output_shape(self, input_shape):
return (input_shape[0], input_shape[-1])


class CLIPHead(layers.Layer):
"""The head layer of CLIP.

`CLIPHead` takes `vision_embedding` and `text_embedding` as inputs to
compute the corresponding logits. Both embeddings are L2 normalized and used
to compute pairwise cosine similarity. The resulting logits are then scaled
by a learnable `logit_scale` parameter.

Call arguments:
vision_embedding: A tensor of shape `(batch_size, hidden_dim)`.
text_embedding: A tensor of shape `(batch_size, hidden_dim)`.
"""

def build(self, input_shape):
self.logit_scale = self.add_weight(
shape=(),
initializer=lambda *a, **kw: math.log(1 / 0.07),
trainable=True,
dtype=self.variable_dtype,
name="logit_scale",
)

def call(self, vision_embedding, text_embedding):
normalized_vision_embedding = ops.sqrt(
ops.sum(ops.power(vision_embedding, 2), axis=-1, keepdims=True)
)
normalized_text_embedding = ops.sqrt(
ops.sum(ops.power(text_embedding, 2), axis=-1, keepdims=True)
)
vision_embedding = vision_embedding / normalized_vision_embedding
text_embedding = text_embedding / normalized_text_embedding
logit_scale = ops.exp(self.logit_scale)
text_logits = (
ops.matmul(
text_embedding,
ops.transpose(vision_embedding),
)
* logit_scale
)
vision_logits = ops.transpose(text_logits)
return vision_logits, text_logits

def compute_output_shape(
self, vision_embedding_shape, text_embedding_shape
):
vision_logits_shape = (
vision_embedding_shape[0],
text_embedding_shape[0],
)
text_logits_shape = (
text_embedding_shape[0],
vision_embedding_shape[0],
)
return vision_logits_shape, text_logits_shape
from keras_hub.src.models.clip.clip_layers import CLIPHead
from keras_hub.src.models.clip.clip_layers import CLIPTextPooler
from keras_hub.src.models.clip.clip_layers import CLIPVisionPooler


@keras_hub_export("keras_hub.models.CLIPBackbone")
Expand Down
111 changes: 0 additions & 111 deletions keras_hub/src/models/clip/clip_encoder_block.py

This file was deleted.

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