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team-10/venv/Lib/site-packages/transformers/models/aimv2/modular_aimv2.py

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# coding=utf-8
# Copyright 2025 Apple Inc. 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.
"""Pytorch implementation of AIMv2 Model"""
import math
from typing import Optional
import torch
import torch.nn.functional as F
from torch import nn
from ...masking_utils import create_causal_mask
from ...modeling_layers import GradientCheckpointingLayer
from ...modeling_outputs import BaseModelOutputWithPooling
from ...modeling_utils import PreTrainedModel
from ...utils import (
auto_docstring,
can_return_tuple,
)
from ..clip.modeling_clip import CLIPModel, CLIPTextEmbeddings, _get_vector_norm
from ..llama.modeling_llama import LlamaMLP, LlamaRMSNorm
from ..siglip.configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
from ..siglip.modeling_siglip import SiglipAttention, SiglipEncoder, SiglipOutput
class Aimv2VisionConfig(SiglipVisionConfig):
r"""
This is the configuration class to store the configuration of a [`Aimv2VisionModel`]. It is used to instantiate a
AIMv2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the vision encoder of the AIMv2
[apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 2816):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
num_channels (`int`, *optional*, defaults to 3):
Number of channels in the input images.
image_size (`int`, *optional*, defaults to 224):
The size (resolution) of each image.
patch_size (`int`, *optional*, defaults to 14):
The size (resolution) of each patch.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
qkv_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the queries, keys and values.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the Linear layers or Not.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the for initializing all weight matrices.
use_head (`str`, *optional*, defaults to `True`):
Whether to use Attention Pooling Head or Not.
is_native (`str`, *optional*, defaults to `False`):
Whether to use ckpt trained for image native resolution or not.
Example:
```python
>>> from transformers import SiglipVisionConfig, SiglipVisionModel
>>> # Initializing a Aimv2VisionConfig with apple/aimv2-large-patch14-224 style configuration
>>> configuration = Aimv2VisionConfig()
>>> # Initializing a Aimv2VisionModel (with random weights) from the apple/aimv2-large-patch14-224 style configuration
>>> model = Aimv2VisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
def __init__(
self,
hidden_size: int = 1024,
intermediate_size: int = 2816,
num_hidden_layers: int = 24,
num_attention_heads: int = 8,
num_channels: int = 3,
image_size: int = 224,
patch_size: int = 14,
rms_norm_eps: float = 1e-5,
attention_dropout: float = 0.0,
qkv_bias: bool = False,
mlp_bias: bool = False,
hidden_act: str = "silu",
initializer_range: float = 0.02,
use_head: bool = True,
is_native: bool = False,
**kwargs,
):
super().__init__(
hidden_size=hidden_size,
intermediate_size=intermediate_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
hidden_act=hidden_act,
num_channels=num_channels,
image_size=image_size,
patch_size=patch_size,
qkv_bias=qkv_bias,
**kwargs,
)
self.use_head = use_head
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.qkv_bias = qkv_bias
self.rms_norm_eps = rms_norm_eps
self.is_native = is_native
del self.layer_norm_eps
class Aimv2TextConfig(SiglipTextConfig):
r"""
This is the configuration class to store the configuration of a [`Aimv2TextModel`]. It is used to instantiate a
AIMv2 text encoder according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the text encoder of the AIMv2
[apple/aimv2-large-patch14-224-lit](https://huggingface.co/apple/aimv2-large-patch14-224-lit) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 49408):
Vocabulary size of the AIMv2 text model. Defines the number of different tokens that can be represented by
the `inputs_ids` passed when calling [`Aimv2Model`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
intermediate_size (`int`, *optional*, defaults to 2048):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 6):
Number of attention heads for each attention layer in the Transformer encoder.
rms_norm_eps (`float`, *optional*, defaults to 1e-05):
The epsilon used by the rms normalization layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout ratio for the attention probabilities.
qkv_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the queries, keys and values.
mlp_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the Linear layers or Not.
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
pad_token_id (`int`, *optional*, defaults to 1):
The id of the padding token in the vocabulary.
bos_token_id (`int`, *optional*, defaults to 49406):
The id of the beginning-of-sequence token in the vocabulary.
eos_token_id (`int`, *optional*, defaults to 49407):
The id of the end-of-sequence token in the vocabulary.
max_position_embeddings (`int`, *optional*, defaults to 77):
The maximum sequence length that this model might ever be used with. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the for initializing all weight matrices.
"""
def __init__(
self,
vocab_size: int = 49408,
hidden_size: int = 768,
intermediate_size: int = 2048,
num_hidden_layers: int = 12,
num_attention_heads: int = 6,
rms_norm_eps: float = 1e-5,
attention_dropout: float = 0.0,
qkv_bias: bool = False,
mlp_bias: bool = False,
hidden_act: str = "silu",
pad_token_id: Optional[int] = None,
bos_token_id: Optional[int] = None,
eos_token_id: int = 49407,
max_position_embeddings: int = 77,
initializer_range: bool = 0.02,
**kwargs,
):
super().__init__(
vocab_size=vocab_size,
hidden_size=hidden_size,
intermediate_size=intermediate_size,
num_hidden_layers=num_hidden_layers,
num_attention_heads=num_attention_heads,
hidden_act=hidden_act,
max_position_embeddings=max_position_embeddings,
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.initializer_range = initializer_range
self.attention_dropout = attention_dropout
self.mlp_bias = mlp_bias
self.qkv_bias = qkv_bias
self.rms_norm_eps = rms_norm_eps
del self.bos_token_id
del self.pad_token_id
del self.projection_size
del self.layer_norm_eps
class Aimv2Config(SiglipConfig):
r"""
[`Aimv2Config`] is the configuration class to store the configuration of a [`Aimv2Model`]. It is used to
instantiate a AIMv2 model according to the specified arguments, defining the text model and vision model configs.
Instantiating a configuration with the defaults will yield a similar configuration to that of the AIMv2
[apple/aimv2-large-patch14-224-lit](https://huggingface.co/apple/aimv2-large-patch14-224-lit) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
text_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Aimv2TextConfig`].
vision_config (`dict`, *optional*):
Dictionary of configuration options used to initialize [`Aimv2VisionConfig`].
projection_dim (`int`, *optional*, defaults to 512):
Dimensionality of text and vision projection layers.
logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
The initial value of the *logit_scale* parameter.
kwargs (*optional*):
Dictionary of keyword arguments.
Example:
```python
>>> from transformers import Aimv2Config, Aimv2Model
>>> # Initializing a Aimv2Config with apple/aimv2-large-patch14-224-lit style configuration
>>> configuration = Aimv2Config()
>>> # Initializing a Aimv2Model (with random weights) from the apple/aimv2-large-patch14-224-lit style configuration
>>> model = Aimv2Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
>>> # We can also initialize a Aimv2Config from a Aimv2TextConfig and a Aimv2VisionConfig
>>> from transformers import Aimv2TextConfig, Aimv2VisionConfig
>>> # Initializing a AIMv2Text and AIMv2Vision configuration
>>> config_text = Aimv2TextConfig()
>>> config_vision = Aimv2VisionConfig()
>>> config = Aimv2Config(text_config=config_text, vision_config=config_vision)
```"""
def __init__(
self, text_config=None, vision_config=None, projection_dim=512, logit_scale_init_value=2.6592, **kwargs
):
super().__init__(text_config, vision_config, **kwargs)
self.projection_dim = projection_dim
self.logit_scale_init_value = logit_scale_init_value
self.max_logit_scale = 100.0
del self.initializer_factor
class Aimv2Output(SiglipOutput):
pass
class Aimv2RMSNorm(LlamaRMSNorm):
pass
class Aimv2MLP(LlamaMLP):
pass
class Aimv2VisionEmbeddings(nn.Module):
def __init__(self, config: Aimv2VisionConfig):
super().__init__()
self.config = config
self.patch_size = config.patch_size
self.patch_embed = nn.Conv2d(
config.num_channels, config.hidden_size, kernel_size=config.patch_size, stride=config.patch_size
)
self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
num_patches = (config.image_size // config.patch_size) ** 2
if not self.config.is_native:
self.position_embedding = nn.Embedding(num_patches, config.hidden_size)
self.register_buffer("position_ids", torch.arange(num_patches).expand((1, -1)), persistent=False)
@staticmethod
def build_2d_sincos_position_embedding(
height, width, embed_dim=256, temperature=10000.0, device="cpu", dtype=torch.float32
) -> torch.Tensor:
grid_w = torch.arange(int(width), dtype=dtype, device=device)
grid_h = torch.arange(int(height), dtype=dtype, device=device)
grid_h, grid_w = torch.meshgrid(grid_w, grid_h, indexing="xy")
pos_dim = embed_dim // 4
omega = torch.arange(pos_dim, dtype=dtype, device=device) / pos_dim
omega = 1.0 / (temperature**omega)
out_h = grid_h.flatten()[..., None] @ omega[None, :]
out_w = grid_w.flatten()[..., None] @ omega[None, :]
return torch.concat([out_h.sin(), out_h.cos(), out_w.sin(), out_w.cos()], dim=1)[None, :, :]
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
_, _, height, width = pixel_values.size()
hidden_states = self.patch_embed(pixel_values).flatten(2).transpose(1, 2)
hidden_states = self.rms_norm(hidden_states)
if self.config.is_native:
pos_embed = self.build_2d_sincos_position_embedding(
height // self.patch_size,
width // self.patch_size,
embed_dim=self.config.hidden_size,
device=hidden_states.device,
dtype=hidden_states.dtype,
)
else:
pos_embed = self.position_embedding(self.position_ids)
hidden_states = hidden_states + pos_embed
return hidden_states
class Aimv2TextEmbeddings(CLIPTextEmbeddings):
pass
class Aimv2Attention(SiglipAttention):
def __init__(self, config):
super().__init__(config)
self.k_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
self.v_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
self.q_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=config.qkv_bias)
class Aimv2EncoderLayer(GradientCheckpointingLayer):
def __init__(self, config: Aimv2VisionConfig):
super().__init__()
self.attention = Aimv2Attention(config)
self.ffn = Aimv2MLP(config)
self.rms_norm1 = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
self.rms_norm2 = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
) -> tuple[torch.Tensor, torch.Tensor]:
norm_hidden_states = self.rms_norm1(hidden_states)
attn_output, attn_weights = self.attention(hidden_states=norm_hidden_states, attention_mask=attention_mask)
hidden_states = hidden_states + attn_output
norm_hidden_states = self.rms_norm2(hidden_states)
mlp_output = self.ffn(norm_hidden_states)
hidden_states = hidden_states + mlp_output
return (hidden_states, attn_weights) if output_attentions else (hidden_states, None)
class Aimv2Encoder(SiglipEncoder):
pass
class Aimv2AttentionPoolingHead(nn.Module):
def __init__(self, config: Aimv2VisionConfig):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.num_attention_heads
self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.qkv_bias)
self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=config.qkv_bias)
self.cls_token = nn.Parameter(torch.zeros(1, 1, self.hidden_size))
self.output_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=True)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
batch_size, seq_len, hidden_dim = hidden_states.shape
cls_token = self.cls_token.expand(batch_size, -1, -1)
key = self.k_proj(hidden_states).reshape(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads)
value = self.v_proj(hidden_states).reshape(batch_size, seq_len, self.num_heads, hidden_dim // self.num_heads)
query = cls_token.reshape(batch_size, 1, self.num_heads, hidden_dim // self.num_heads)
key = key.permute(0, 2, 1, 3)
value = value.permute(0, 2, 1, 3)
query = query.permute(0, 2, 1, 3)
attn_output = F.scaled_dot_product_attention(query, key, value)
attn_output = attn_output.transpose(1, 2).reshape(batch_size, 1, hidden_dim)
attn_output = attn_output.mean(dim=1)
output = self.output_proj(attn_output)
return output
@auto_docstring
class Aimv2PreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models. The model is only intended for inference and doesn't support finetuning.
"""
config: Aimv2Config
base_model_prefix = "aimv2"
supports_gradient_checkpointing = True
_no_split_modules = [
"Aimv2EncoderLayer",
"Aimv2AttentionPoolingHead",
"Aimv2VisionEmbeddings",
"Aimv2TextEmbeddings",
]
_supports_sdpa = True
_supports_flash_attn = True
_supports_flex_attn = True
def _init_weights(self, module):
super()._init_weights(module)
if hasattr(module, "logit_scale"):
if isinstance(module.logit_scale, nn.Parameter):
module.logit_scale.data.fill_(math.log(1 / 0.07))
elif isinstance(module, Aimv2AttentionPoolingHead):
module.cls_token.data.normal_(mean=0.0, std=self.config.initializer_range)
@auto_docstring(
custom_intro="""
The Vision model from AIMv2 without any head or projection on top.
"""
)
class Aimv2VisionModel(Aimv2PreTrainedModel):
config: Aimv2VisionConfig
main_input_name = "pixel_values"
def __init__(self, config: Aimv2VisionConfig):
super().__init__(config)
self.config = config
self.embeddings = Aimv2VisionEmbeddings(config)
self.encoder = Aimv2Encoder(config)
# The only change from SiglipVisionTransformer is, layernorm -> rms_norm.
self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
self.use_head = config.use_head
if self.use_head:
self.head = Aimv2AttentionPoolingHead(config)
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.embeddings.patch_embed
@can_return_tuple
@auto_docstring
def forward(
self,
pixel_values,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> BaseModelOutputWithPooling:
r"""
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Siglip2VisionModel
>>> model = Aimv2VisionModel.from_pretrained("apple/aimv2-large-patch14-native")
>>> processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-native")
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> inputs = processor(images=image, return_tensors="pt")
>>> outputs = model(**inputs)
>>> last_hidden_state = outputs.last_hidden_state
>>> pooled_output = outputs.pooler_output # pooled features
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
hidden_states = self.embeddings(pixel_values)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.rms_norm(last_hidden_state)
pooler_output = self.head(last_hidden_state) if self.use_head else None
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooler_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@auto_docstring(
custom_intro="""
The text model from AIMv2 without any head or projection on top.
"""
)
class Aimv2TextModel(Aimv2PreTrainedModel):
main_input_name = "input_ids"
def __init__(self, config: Aimv2TextConfig):
super().__init__(config)
self.config = config
self.embeddings = Aimv2TextEmbeddings(config)
self.encoder = Aimv2Encoder(config)
self.rms_norm = Aimv2RMSNorm(config.hidden_size, config.rms_norm_eps)
self.eos_token_id = config.eos_token_id
self.post_init()
def get_input_embeddings(self) -> nn.Module:
return self.embeddings.token_embedding
def set_input_embeddings(self, value):
self.embeddings.token_embedding = value
@can_return_tuple
@auto_docstring
def forward(
self,
input_ids,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> BaseModelOutputWithPooling:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
hidden_states = self.embeddings(input_ids)
batch_size, seq_len, _ = hidden_states.shape
cache_position = torch.arange(seq_len, dtype=torch.long, device=hidden_states.device)
position_ids = cache_position.unsqueeze(0).expand(batch_size, -1)
if attention_mask is not None:
attention_mask = create_causal_mask(
config=self.config,
input_embeds=hidden_states,
position_ids=position_ids,
attention_mask=attention_mask,
cache_position=cache_position,
past_key_values=None,
)
encoder_outputs = self.encoder(
inputs_embeds=hidden_states,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
last_hidden_state = encoder_outputs[0]
last_hidden_state = self.rms_norm(last_hidden_state)
# Get pooled output
pooled_output = last_hidden_state[
torch.arange(last_hidden_state.shape[0], device=last_hidden_state.device),
(input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id).int().argmax(dim=-1),
]
return BaseModelOutputWithPooling(
last_hidden_state=last_hidden_state,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@auto_docstring
class Aimv2Model(CLIPModel, nn.Module):
def __init__(self, config: Aimv2Config):
nn.Module().__init__(config)
self.projection_dim = config.projection_dim
self.vision_embed_dim = config.vision_config.hidden_size
self.text_embed_dim = config.text_config.hidden_size
self.vision_model = Aimv2VisionModel._from_config(config.vision_config)
self.text_model = Aimv2TextModel._from_config(config.text_config)
self.visual_projection = nn.Linear(self.vision_embed_dim, self.projection_dim, bias=False)
self.text_projection = nn.Linear(self.text_embed_dim, self.projection_dim, bias=False)
self.logit_scale = nn.Parameter(torch.tensor(self.config.logit_scale_init_value))
self.max_log_logit_scale = math.log(config.max_logit_scale)
self.post_init()
@auto_docstring
@can_return_tuple
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
) -> Aimv2Output:
r"""
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import AutoProcessor, Aimv2Model
>>> model = Aimv2Model.from_pretrained("apple/aimv2-large-patch14-224-lit")
>>> processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit")
>>> 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="pt", padding=True
... )
>>> outputs = model(**inputs)
>>> logits_per_image = outputs.logits_per_image # this is the image-text similarity score
>>> probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
vision_outputs: BaseModelOutputWithPooling = self.vision_model(
pixel_values=pixel_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
text_outputs: BaseModelOutputWithPooling = self.text_model(
input_ids=input_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
)
image_embeds = vision_outputs.pooler_output
image_embeds = self.visual_projection(image_embeds)
text_embeds = text_outputs.pooler_output
text_embeds = self.text_projection(text_embeds)
# normalized features
image_embeds = image_embeds / _get_vector_norm(image_embeds)
text_embeds = text_embeds / _get_vector_norm(text_embeds)
logit_scale = self.logit_scale.clamp(0.0, self.max_log_logit_scale).exp().to(text_embeds.device)
logits_per_text = (logit_scale * text_embeds) @ image_embeds.t()
logits_per_image = logits_per_text.t()
return Aimv2Output(
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,
)
__all__ = [
"Aimv2Config",
"Aimv2VisionConfig",
"Aimv2TextConfig",
"Aimv2VisionModel",
"Aimv2Model",
"Aimv2PreTrainedModel",
"Aimv2TextModel",
]
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