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# Pass Image color channels information to Transformers #2846

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# Pass Image color channels information to Transformers #2846

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7a7cb61
# Pass Image color channels information to Transformers
davychxn Jul 18, 2024
33a4ebc
# Made 2 modifications.
davychxn Jul 25, 2024
e683051
# Modified 2 files
davychxn Jul 27, 2024
531c59a
Merge branch 'master' of https://github.com/UKPLab/sentence-transformers
davychxn Sep 20, 2024
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11 changes: 10 additions & 1 deletion sentence_transformers/SentenceTransformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@
from .models import Normalize, Pooling, Transformer
from .quantization import quantize_embeddings
from .util import (
ImageChannelDimension,
batch_to_device,
get_device_name,
import_from_string,
Expand Down Expand Up @@ -367,6 +368,7 @@ def encode(
convert_to_tensor: Literal[False] = ...,
device: str = ...,
normalize_embeddings: bool = ...,
input_data_format: str = ImageChannelDimension.LAST,
) -> Tensor: ...

@overload
Expand All @@ -383,6 +385,7 @@ def encode(
convert_to_tensor: Literal[False] = ...,
device: str = ...,
normalize_embeddings: bool = ...,
input_data_format: str = ImageChannelDimension.LAST,
) -> np.ndarray: ...

@overload
Expand All @@ -399,6 +402,7 @@ def encode(
convert_to_tensor: Literal[True] = ...,
device: str = ...,
normalize_embeddings: bool = ...,
input_data_format: str = ImageChannelDimension.LAST,
) -> Tensor: ...

@overload
Expand All @@ -415,6 +419,7 @@ def encode(
convert_to_tensor: Literal[False] = ...,
device: str = ...,
normalize_embeddings: bool = ...,
input_data_format: str = ImageChannelDimension.LAST,
) -> list[Tensor]: ...

def encode(
Expand All @@ -430,6 +435,7 @@ def encode(
convert_to_tensor: bool = False,
device: str = None,
normalize_embeddings: bool = False,
input_data_format: str = ImageChannelDimension.LAST,
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) -> list[Tensor] | np.ndarray | Tensor:
"""
Computes sentence embeddings.
Expand Down Expand Up @@ -491,6 +497,9 @@ def encode(
self.is_hpu_graph_enabled = True

self.eval()
# Will be used in Image Tokenizer
self.input_data_format = input_data_format

if show_progress_bar is None:
show_progress_bar = logger.getEffectiveLevel() in (logging.INFO, logging.DEBUG)

Expand Down Expand Up @@ -991,7 +1000,7 @@ def tokenize(self, texts: list[str] | list[dict] | list[tuple[str, str]]) -> dic
Dict[str, Tensor]: A dictionary of tensors with the tokenized texts. Common keys are "input_ids",
"attention_mask", and "token_type_ids".
"""
return self._first_module().tokenize(texts)
return self._first_module().tokenize(texts, input_data_format=self.input_data_format)

def get_sentence_features(self, *features) -> dict[Literal["sentence_embedding"], torch.Tensor]:
return self._first_module().get_sentence_features(*features)
Expand Down
5 changes: 3 additions & 2 deletions sentence_transformers/models/CLIPModel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import transformers
from PIL import Image
from torch import nn
from ..util import ImageChannelDimension


class CLIPModel(nn.Module):
Expand Down Expand Up @@ -51,7 +52,7 @@ def forward(self, features: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:

return features

def tokenize(self, texts, padding: str | bool = True) -> dict[str, torch.Tensor]:
def tokenize(self, texts, padding: str | bool = True, input_data_format: str = ImageChannelDimension.LAST) -> dict[str, torch.Tensor]:
images = []
texts_values = []
image_text_info = []
Expand All @@ -69,7 +70,7 @@ def tokenize(self, texts, padding: str | bool = True) -> dict[str, torch.Tensor]
encoding = self.processor.tokenizer(texts_values, return_tensors="pt", padding=padding)

if len(images):
image_features = self.processor.image_processor(images, return_tensors="pt")
image_features = self.processor.image_processor(images, return_tensors="pt", input_data_format=input_data_format)
encoding["pixel_values"] = image_features.pixel_values

encoding["image_text_info"] = image_text_info
Expand Down
6 changes: 6 additions & 0 deletions sentence_transformers/util.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,6 +28,12 @@
from sentence_transformers.cross_encoder.CrossEncoder import CrossEncoder
from sentence_transformers.SentenceTransformer import SentenceTransformer

class ImageChannelDimension():
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This should probably be an Enum

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This class is copied from Transformers' repo. It is defined like this there. Because the string defined in the class is needed by Transformers' image-processor. If we use Enum, I think, we'll get integer values? And we need to convert to string before passing to Transformers?

"""
Defines the color channels' position in an Image's shape
"""
FIRST = "channels_first"
LAST = "channels_last"

def _convert_to_tensor(a: list | np.ndarray | Tensor) -> Tensor:
"""
Expand Down