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# coding=utf-8
# Copyright 2023 Meta AI and The HuggingFace Inc. 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.
# Reference:
# * transformers/models/dinov2/modeling_dinov2.py
# * https://github.com/facebookresearch/DiT/blob/main/models.py#L101
# * https://github.com/3DTopia/OpenLRM/tree/main/openlrm/models/encoders/dinov2
"""PyTorch DINOv2 model."""
from typing import Dict, List, Optional, Set, Tuple, Union
import torch
import torch.nn as nn
from .modeling_dinov2 import (
Dinov2Config,
Dinov2Layer,
Dinov2Model,
Dinov2Embeddings,
BaseModelOutput,
BaseModelOutputWithPooling,
)
class ModLN(nn.Module):
def __init__(self, inner_dim: int, mod_dim: int = 1024):
super().__init__()
self.mlp = nn.Sequential(
nn.SiLU(),
nn.Linear(mod_dim, inner_dim * 2),
)
for m in self.modules():
if isinstance(m, nn.Linear):
nn.init.zeros_(m.weight)
nn.init.zeros_(m.bias)
def forward(self, x: torch.Tensor, condition: torch.Tensor):
"""
x: [N, M, C_in], M: num of tokens
condition: [N, C_mod]
"""
shift, scale = self.mlp(condition).unsqueeze(1).chunk(2, dim=-1)
return x * (1 + scale) + shift
class ConditionalDinov2Config(Dinov2Config):
def __init__(self, modulation_dim: int = 1024, *args, **kwargs):
super().__init__(*args, **kwargs)
self.modulation_dim = modulation_dim
class ConditionalDinov2Layer(Dinov2Layer):
"""This corresponds to the Block class in the original implementation."""
def __init__(self, config: ConditionalDinov2Config) -> None:
super().__init__(config)
self.mod_norm1 = ModLN(config.hidden_size, config.modulation_dim)
self.mod_norm2 = ModLN(config.hidden_size, config.modulation_dim)
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
condition: Optional[torch.Tensor] = None,
output_attentions: bool = False,
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
self_attention_outputs = self.attention(
self.mod_norm1(
self.norm1(hidden_states), condition
), # in Dinov2, layernorm is applied before self-attention
head_mask,
output_attentions=output_attentions,
)
attention_output = self_attention_outputs[0]
attention_output = self.layer_scale1(attention_output)
outputs = self_attention_outputs[
1:
] # add self attentions if we output attention weights
# first residual connection
hidden_states = self.drop_path(attention_output) + hidden_states
# in Dinov2, layernorm is also applied after self-attention
layer_output = self.mod_norm2(self.norm2(hidden_states), condition)
layer_output = self.mlp(layer_output)
layer_output = self.layer_scale2(layer_output)
# second residual connection
layer_output = self.drop_path(layer_output) + hidden_states
outputs = (layer_output,) + outputs
return outputs
# Copied from transformers.models.vit.modeling_vit.ViTEncoder with ViT->Dinov2
class ConditionalDinov2Encoder(nn.Module):
def __init__(self, config: ConditionalDinov2Config) -> None:
super().__init__()
self.config = config
self.layer = nn.ModuleList(
[ConditionalDinov2Layer(config) for _ in range(config.num_hidden_layers)]
)
self.gradient_checkpointing = False
def forward(
self,
hidden_states: torch.Tensor,
head_mask: Optional[torch.Tensor] = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
condition: Optional[torch.Tensor] = None,
return_dict: bool = True,
) -> Union[tuple, BaseModelOutput]:
all_hidden_states = () if output_hidden_states else None
all_self_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_head_mask = head_mask[i] if head_mask is not None else None
if self.gradient_checkpointing and self.training:
layer_outputs = self._gradient_checkpointing_func(
layer_module.__call__,
hidden_states,
layer_head_mask,
condition,
output_attentions,
)
else:
layer_outputs = layer_module(
hidden_states,
layer_head_mask,
condition,
output_attentions,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attentions = all_self_attentions + (layer_outputs[1],)
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_self_attentions]
if v is not None
)
return BaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class ConditionalDinov2Model(Dinov2Model):
config_class = ConditionalDinov2Config
def __init__(self, config: ConditionalDinov2Config):
super().__init__(config)
self.config = config
self.embeddings = Dinov2Embeddings(config)
self.encoder = ConditionalDinov2Encoder(config)
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
# Initialize weights and apply final processing
self.post_init()
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
bool_masked_pos: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
condition: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, 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
)
return_dict = (
return_dict if return_dict is not None else self.config.use_return_dict
)
if pixel_values is None:
raise ValueError("You have to specify pixel_values")
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
embedding_output = self.embeddings(
pixel_values, bool_masked_pos=bool_masked_pos
)
encoder_outputs = self.encoder(
embedding_output,
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
condition=condition,
return_dict=return_dict,
)
sequence_output = encoder_outputs[0]
sequence_output = self.layernorm(sequence_output)
pooled_output = sequence_output[:, 0, :]
if not return_dict:
head_outputs = (sequence_output, pooled_output)
return head_outputs + encoder_outputs[1:]
return BaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
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