in optimum/intel/openvino/modeling_visual_language.py [0:0]
def pack_image_features(self, image_features, image_sizes, image_newline=None):
"""
Reshape, unpad and then pack each image_feature into a single image_features tensor containing all visual vectors.
Args:
image_features (`List[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
List of image feature tensor, each contains all the visual feature of all patches.
image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
Actual image size of each images (H, W).
vision_feature_select_strategy (`str`)
The feature selection strategy used to select the vision feature from the vision backbone.
image_newline (`torch.Tensor` of shape `(embed_dim)`)
New line embedding vector.
Returns:
image_features (`torch.Tensor` of shape `(all_feat_len, embed_dim)`)
feature_lens (`List[int]`)
token length of each image in image_features
"""
from transformers.models.llava_next_video.modeling_llava_next_video import (
get_anyres_image_grid_shape,
unpad_image,
)
new_image_features = []
feature_lens = []
vision_feature_select_strategy = self.config.vision_feature_select_strategy
for image_idx, image_feature in enumerate(image_features):
if image_feature.shape[0] > 1:
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
num_patch_height, num_patch_width = get_anyres_image_grid_shape(
image_sizes[image_idx],
self.config.image_grid_pinpoints,
self.config.vision_config.image_size,
)
if (
np.prod(image_feature.shape) % (num_patch_height * num_patch_width * height * width) != 0
and vision_feature_select_strategy == "default"
):
logger.warning_once(
"Image feature shape does not line up with the provided patch size. "
"You may be using the `default` vision_feature_select_strategy with a"
" visual encoder that does not have CLS."
)
image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)
image_feature = image_feature.permute(4, 0, 2, 1, 3).contiguous()
image_feature = image_feature.flatten(1, 2).flatten(2, 3)
image_feature = unpad_image(image_feature, image_sizes[image_idx])
if image_newline is not None:
image_feature = torch.cat(
(
image_feature,
image_newline[:, None, None]
.expand(*image_feature.shape[:-1], 1)
.to(image_feature.device, image_feature.dtype),
),
dim=-1,
)
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
else:
image_feature = image_feature[0]
if image_newline is not None:
image_feature = torch.cat((image_feature, image_newline[None].to(image_feature)), dim=0)
new_image_features.append(image_feature)
feature_lens.append(image_feature.size(0))
image_features = torch.cat(new_image_features, dim=0)
feature_lens = torch.tensor(feature_lens, dtype=torch.long, device=image_features.device)
return image_features, feature_lens