857 lines
35 KiB
Python
857 lines
35 KiB
Python
# 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.
|
|
""" PyTorch DINOv2 model."""
|
|
|
|
|
|
import collections.abc
|
|
import math
|
|
from typing import Dict, List, Optional, Set, Tuple, Union
|
|
|
|
import torch
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from ...activations import ACT2FN
|
|
from ...modeling_outputs import (
|
|
BackboneOutput,
|
|
BaseModelOutput,
|
|
BaseModelOutputWithPooling,
|
|
ImageClassifierOutput,
|
|
)
|
|
from ...modeling_utils import PreTrainedModel
|
|
from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
|
|
from ...utils import (
|
|
add_code_sample_docstrings,
|
|
add_start_docstrings,
|
|
add_start_docstrings_to_model_forward,
|
|
logging,
|
|
replace_return_docstrings,
|
|
)
|
|
from ...utils.backbone_utils import BackboneMixin
|
|
from .configuration_dinov2 import Dinov2Config
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
# General docstring
|
|
_CONFIG_FOR_DOC = "Dinov2Config"
|
|
|
|
# Base docstring
|
|
_CHECKPOINT_FOR_DOC = "facebook/dinov2-base"
|
|
_EXPECTED_OUTPUT_SHAPE = [1, 257, 768]
|
|
|
|
# Image classification docstring
|
|
_IMAGE_CLASS_CHECKPOINT = "facebook/dinov2-small-imagenet1k-1-layer"
|
|
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"
|
|
|
|
|
|
from ..deprecated._archive_maps import DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST # noqa: F401, E402
|
|
|
|
|
|
class Dinov2Embeddings(nn.Module):
|
|
"""
|
|
Construct the CLS token, mask token, position and patch embeddings.
|
|
"""
|
|
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__()
|
|
|
|
self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
|
|
self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size))
|
|
self.patch_embeddings = Dinov2PatchEmbeddings(config)
|
|
num_patches = self.patch_embeddings.num_patches
|
|
self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.config = config
|
|
|
|
def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
|
|
"""
|
|
This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher
|
|
resolution images.
|
|
|
|
Source:
|
|
https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174
|
|
"""
|
|
|
|
num_patches = embeddings.shape[1] - 1
|
|
num_positions = self.position_embeddings.shape[1] - 1
|
|
if num_patches == num_positions and height == width:
|
|
return self.position_embeddings
|
|
class_pos_embed = self.position_embeddings[:, 0]
|
|
patch_pos_embed = self.position_embeddings[:, 1:]
|
|
dim = embeddings.shape[-1]
|
|
height = height // self.config.patch_size
|
|
width = width // self.config.patch_size
|
|
# we add a small number to avoid floating point error in the interpolation
|
|
# see discussion at https://github.com/facebookresearch/dino/issues/8
|
|
height, width = height + 0.1, width + 0.1
|
|
patch_pos_embed = patch_pos_embed.reshape(1, int(math.sqrt(num_positions)), int(math.sqrt(num_positions)), dim)
|
|
patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
|
|
target_dtype = patch_pos_embed.dtype
|
|
patch_pos_embed = nn.functional.interpolate(
|
|
patch_pos_embed.to(dtype=torch.float32),
|
|
scale_factor=(float(height / math.sqrt(num_positions)), float(width / math.sqrt(num_positions))),
|
|
mode="bicubic",
|
|
align_corners=False,
|
|
).to(dtype=target_dtype)
|
|
if int(height) != patch_pos_embed.shape[-2] or int(width) != patch_pos_embed.shape[-1]:
|
|
raise ValueError("Width or height does not match with the interpolated position embeddings")
|
|
patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
|
|
return torch.cat((class_pos_embed.unsqueeze(0), patch_pos_embed), dim=1)
|
|
|
|
def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
|
|
batch_size, _, height, width = pixel_values.shape
|
|
target_dtype = self.patch_embeddings.projection.weight.dtype
|
|
embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
|
|
|
|
if bool_masked_pos is not None:
|
|
embeddings = torch.where(
|
|
bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings
|
|
)
|
|
|
|
# add the [CLS] token to the embedded patch tokens
|
|
cls_tokens = self.cls_token.expand(batch_size, -1, -1)
|
|
embeddings = torch.cat((cls_tokens, embeddings), dim=1)
|
|
|
|
# add positional encoding to each token
|
|
embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
|
|
|
|
embeddings = self.dropout(embeddings)
|
|
|
|
return embeddings
|
|
|
|
|
|
class Dinov2PatchEmbeddings(nn.Module):
|
|
"""
|
|
This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
|
|
`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
|
|
Transformer.
|
|
"""
|
|
|
|
def __init__(self, config):
|
|
super().__init__()
|
|
image_size, patch_size = config.image_size, config.patch_size
|
|
num_channels, hidden_size = config.num_channels, config.hidden_size
|
|
|
|
image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
|
|
patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
|
|
num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_channels = num_channels
|
|
self.num_patches = num_patches
|
|
|
|
self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
|
|
|
|
def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
|
|
num_channels = pixel_values.shape[1]
|
|
if num_channels != self.num_channels:
|
|
raise ValueError(
|
|
"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
|
|
f" Expected {self.num_channels} but got {num_channels}."
|
|
)
|
|
embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
|
|
return embeddings
|
|
|
|
|
|
# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2
|
|
class Dinov2SelfAttention(nn.Module):
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__()
|
|
if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
|
|
raise ValueError(
|
|
f"The hidden size {config.hidden_size,} is not a multiple of the number of attention "
|
|
f"heads {config.num_attention_heads}."
|
|
)
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
|
|
self.all_head_size = self.num_attention_heads * self.attention_head_size
|
|
|
|
self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
|
|
self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
|
|
self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
|
|
|
|
self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
|
|
|
|
def transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
|
|
new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
|
|
x = x.view(new_x_shape)
|
|
return x.permute(0, 2, 1, 3)
|
|
|
|
def forward(
|
|
self, hidden_states, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False
|
|
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
|
|
mixed_query_layer = self.query(hidden_states)
|
|
|
|
key_layer = self.transpose_for_scores(self.key(hidden_states))
|
|
value_layer = self.transpose_for_scores(self.value(hidden_states))
|
|
query_layer = self.transpose_for_scores(mixed_query_layer)
|
|
|
|
# Take the dot product between "query" and "key" to get the raw attention scores.
|
|
attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
|
|
|
|
attention_scores = attention_scores / math.sqrt(self.attention_head_size)
|
|
|
|
# Normalize the attention scores to probabilities.
|
|
attention_probs = nn.functional.softmax(attention_scores, dim=-1)
|
|
|
|
# This is actually dropping out entire tokens to attend to, which might
|
|
# seem a bit unusual, but is taken from the original Transformer paper.
|
|
attention_probs = self.dropout(attention_probs)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attention_probs = attention_probs * head_mask
|
|
|
|
context_layer = torch.matmul(attention_probs, value_layer)
|
|
|
|
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
|
|
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
|
|
context_layer = context_layer.view(new_context_layer_shape)
|
|
|
|
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
|
|
|
|
return outputs
|
|
|
|
|
|
# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2
|
|
class Dinov2SelfOutput(nn.Module):
|
|
"""
|
|
The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the
|
|
layernorm applied before each block.
|
|
"""
|
|
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__()
|
|
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
|
|
def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.dropout(hidden_states)
|
|
|
|
return hidden_states
|
|
|
|
|
|
# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2
|
|
class Dinov2Attention(nn.Module):
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__()
|
|
self.attention = Dinov2SelfAttention(config)
|
|
self.output = Dinov2SelfOutput(config)
|
|
self.pruned_heads = set()
|
|
|
|
def prune_heads(self, heads: Set[int]) -> None:
|
|
if len(heads) == 0:
|
|
return
|
|
heads, index = find_pruneable_heads_and_indices(
|
|
heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
|
|
)
|
|
|
|
# Prune linear layers
|
|
self.attention.query = prune_linear_layer(self.attention.query, index)
|
|
self.attention.key = prune_linear_layer(self.attention.key, index)
|
|
self.attention.value = prune_linear_layer(self.attention.value, index)
|
|
self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
|
|
|
|
# Update hyper params and store pruned heads
|
|
self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
|
|
self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
|
|
self.pruned_heads = self.pruned_heads.union(heads)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
|
|
self_outputs = self.attention(hidden_states, head_mask, output_attentions)
|
|
|
|
attention_output = self.output(self_outputs[0], hidden_states)
|
|
|
|
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
|
|
return outputs
|
|
|
|
|
|
class Dinov2LayerScale(nn.Module):
|
|
def __init__(self, config) -> None:
|
|
super().__init__()
|
|
self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))
|
|
|
|
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
|
|
return hidden_state * self.lambda1
|
|
|
|
|
|
# Copied from transformers.models.beit.modeling_beit.drop_path
|
|
def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
|
|
"""
|
|
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
|
|
|
|
Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
|
|
however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
|
|
See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
|
|
layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
|
|
argument.
|
|
"""
|
|
if drop_prob == 0.0 or not training:
|
|
return input
|
|
keep_prob = 1 - drop_prob
|
|
shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
|
|
random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
|
|
random_tensor.floor_() # binarize
|
|
output = input.div(keep_prob) * random_tensor
|
|
return output
|
|
|
|
|
|
# Copied from transformers.models.beit.modeling_beit.BeitDropPath
|
|
class Dinov2DropPath(nn.Module):
|
|
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
|
|
|
|
def __init__(self, drop_prob: Optional[float] = None) -> None:
|
|
super().__init__()
|
|
self.drop_prob = drop_prob
|
|
|
|
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
|
return drop_path(hidden_states, self.drop_prob, self.training)
|
|
|
|
def extra_repr(self) -> str:
|
|
return "p={}".format(self.drop_prob)
|
|
|
|
|
|
class Dinov2MLP(nn.Module):
|
|
def __init__(self, config) -> None:
|
|
super().__init__()
|
|
in_features = out_features = config.hidden_size
|
|
hidden_features = int(config.hidden_size * config.mlp_ratio)
|
|
self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
|
|
if isinstance(config.hidden_act, str):
|
|
self.activation = ACT2FN[config.hidden_act]
|
|
else:
|
|
self.activation = config.hidden_act
|
|
self.fc2 = nn.Linear(hidden_features, out_features, bias=True)
|
|
|
|
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
|
|
hidden_state = self.fc1(hidden_state)
|
|
hidden_state = self.activation(hidden_state)
|
|
hidden_state = self.fc2(hidden_state)
|
|
return hidden_state
|
|
|
|
|
|
class Dinov2SwiGLUFFN(nn.Module):
|
|
def __init__(self, config) -> None:
|
|
super().__init__()
|
|
in_features = out_features = config.hidden_size
|
|
hidden_features = int(config.hidden_size * config.mlp_ratio)
|
|
hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
|
|
|
|
self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True)
|
|
self.weights_out = nn.Linear(hidden_features, out_features, bias=True)
|
|
|
|
def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
|
|
hidden_state = self.weights_in(hidden_state)
|
|
x1, x2 = hidden_state.chunk(2, dim=-1)
|
|
hidden = nn.functional.silu(x1) * x2
|
|
return self.weights_out(hidden)
|
|
|
|
|
|
class Dinov2Layer(nn.Module):
|
|
"""This corresponds to the Block class in the original implementation."""
|
|
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__()
|
|
|
|
self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
self.attention = Dinov2Attention(config)
|
|
self.layer_scale1 = Dinov2LayerScale(config)
|
|
self.drop_path = Dinov2DropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
|
|
|
|
self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
if config.use_swiglu_ffn:
|
|
self.mlp = Dinov2SwiGLUFFN(config)
|
|
else:
|
|
self.mlp = Dinov2MLP(config)
|
|
self.layer_scale2 = Dinov2LayerScale(config)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states: torch.Tensor,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
output_attentions: bool = False,
|
|
) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
|
|
self_attention_outputs = self.attention(
|
|
self.norm1(hidden_states), # 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.norm2(hidden_states)
|
|
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 Dinov2Encoder(nn.Module):
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.layer = nn.ModuleList([Dinov2Layer(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,
|
|
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,
|
|
output_attentions,
|
|
)
|
|
else:
|
|
layer_outputs = layer_module(hidden_states, layer_head_mask, 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 Dinov2PreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
config_class = Dinov2Config
|
|
base_model_prefix = "dinov2"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
|
|
def _init_weights(self, module: Union[nn.Linear, nn.Conv2d, nn.LayerNorm]) -> None:
|
|
"""Initialize the weights"""
|
|
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
|
# Upcast the input in `fp32` and cast it back to desired `dtype` to avoid
|
|
# `trunc_normal_cpu` not implemented in `half` issues
|
|
module.weight.data = nn.init.trunc_normal_(
|
|
module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range
|
|
).to(module.weight.dtype)
|
|
if module.bias is not None:
|
|
module.bias.data.zero_()
|
|
elif isinstance(module, nn.LayerNorm):
|
|
module.bias.data.zero_()
|
|
module.weight.data.fill_(1.0)
|
|
elif isinstance(module, Dinov2Embeddings):
|
|
module.position_embeddings.data = nn.init.trunc_normal_(
|
|
module.position_embeddings.data.to(torch.float32),
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
).to(module.position_embeddings.dtype)
|
|
|
|
module.cls_token.data = nn.init.trunc_normal_(
|
|
module.cls_token.data.to(torch.float32),
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
).to(module.cls_token.dtype)
|
|
|
|
|
|
DINOV2_START_DOCSTRING = r"""
|
|
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
|
|
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
|
|
behavior.
|
|
|
|
Parameters:
|
|
config ([`Dinov2Config`]): Model configuration class with all the parameters of the model.
|
|
Initializing with a config file does not load the weights associated with the model, only the
|
|
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
|
"""
|
|
|
|
DINOV2_BASE_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
|
|
[`BitImageProcessor.preprocess`] for details.
|
|
|
|
bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`):
|
|
Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Only relevant for
|
|
pre-training.
|
|
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
DINOV2_INPUTS_DOCSTRING = r"""
|
|
Args:
|
|
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
|
|
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
|
|
[`BitImageProcessor.preprocess`] for details.
|
|
|
|
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
|
|
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicates the head is **masked**.
|
|
|
|
output_attentions (`bool`, *optional*):
|
|
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
|
tensors for more detail.
|
|
output_hidden_states (`bool`, *optional*):
|
|
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
|
more detail.
|
|
return_dict (`bool`, *optional*):
|
|
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
|
"""
|
|
|
|
|
|
@add_start_docstrings(
|
|
"The bare DINOv2 Model transformer outputting raw hidden-states without any specific head on top.",
|
|
DINOV2_START_DOCSTRING,
|
|
)
|
|
class Dinov2Model(Dinov2PreTrainedModel):
|
|
def __init__(self, config: Dinov2Config):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.embeddings = Dinov2Embeddings(config)
|
|
self.encoder = Dinov2Encoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
|
|
return self.embeddings.patch_embeddings
|
|
|
|
def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
|
|
"""
|
|
Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
|
|
class PreTrainedModel
|
|
"""
|
|
for layer, heads in heads_to_prune.items():
|
|
self.encoder.layer[layer].attention.prune_heads(heads)
|
|
|
|
@add_start_docstrings_to_model_forward(DINOV2_BASE_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_CHECKPOINT_FOR_DOC,
|
|
output_type=BaseModelOutputWithPooling,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
modality="vision",
|
|
expected_output=_EXPECTED_OUTPUT_SHAPE,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
bool_masked_pos: Optional[torch.Tensor] = None,
|
|
head_mask: 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,
|
|
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,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Dinov2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state
|
|
of the [CLS] token) e.g. for ImageNet.
|
|
""",
|
|
DINOV2_START_DOCSTRING,
|
|
)
|
|
class Dinov2ForImageClassification(Dinov2PreTrainedModel):
|
|
def __init__(self, config: Dinov2Config) -> None:
|
|
super().__init__(config)
|
|
|
|
self.num_labels = config.num_labels
|
|
self.dinov2 = Dinov2Model(config)
|
|
|
|
# Classifier head
|
|
self.classifier = (
|
|
nn.Linear(config.hidden_size * 2, config.num_labels) if config.num_labels > 0 else nn.Identity()
|
|
)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
@add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING)
|
|
@add_code_sample_docstrings(
|
|
checkpoint=_IMAGE_CLASS_CHECKPOINT,
|
|
output_type=ImageClassifierOutput,
|
|
config_class=_CONFIG_FOR_DOC,
|
|
expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
|
|
)
|
|
def forward(
|
|
self,
|
|
pixel_values: Optional[torch.Tensor] = None,
|
|
head_mask: Optional[torch.Tensor] = None,
|
|
labels: Optional[torch.Tensor] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[tuple, ImageClassifierOutput]:
|
|
r"""
|
|
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
|
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
|
|
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
|
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.dinov2(
|
|
pixel_values,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0] # batch_size, sequence_length, hidden_size
|
|
|
|
cls_token = sequence_output[:, 0]
|
|
patch_tokens = sequence_output[:, 1:]
|
|
|
|
linear_input = torch.cat([cls_token, patch_tokens.mean(dim=1)], dim=1)
|
|
|
|
logits = self.classifier(linear_input)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(logits.device)
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = CrossEntropyLoss()
|
|
loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else output
|
|
|
|
return ImageClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
@add_start_docstrings(
|
|
"""
|
|
Dinov2 backbone, to be used with frameworks like DETR and MaskFormer.
|
|
""",
|
|
DINOV2_START_DOCSTRING,
|
|
)
|
|
class Dinov2Backbone(Dinov2PreTrainedModel, BackboneMixin):
|
|
def __init__(self, config):
|
|
super().__init__(config)
|
|
super()._init_backbone(config)
|
|
|
|
self.num_features = [config.hidden_size for _ in range(config.num_hidden_layers + 1)]
|
|
self.embeddings = Dinov2Embeddings(config)
|
|
self.encoder = Dinov2Encoder(config)
|
|
|
|
self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
|
|
|
|
# Initialize weights and apply final processing
|
|
self.post_init()
|
|
|
|
def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
|
|
return self.embeddings.patch_embeddings
|
|
|
|
@add_start_docstrings_to_model_forward(DINOV2_INPUTS_DOCSTRING)
|
|
@replace_return_docstrings(output_type=BackboneOutput, config_class=_CONFIG_FOR_DOC)
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> BackboneOutput:
|
|
"""
|
|
Returns:
|
|
|
|
Examples:
|
|
|
|
```python
|
|
>>> from transformers import AutoImageProcessor, AutoBackbone
|
|
>>> import torch
|
|
>>> from PIL import Image
|
|
>>> import requests
|
|
|
|
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
|
|
>>> image = Image.open(requests.get(url, stream=True).raw)
|
|
|
|
>>> processor = AutoImageProcessor.from_pretrained("facebook/dinov2-base")
|
|
>>> model = AutoBackbone.from_pretrained(
|
|
... "facebook/dinov2-base", out_features=["stage2", "stage5", "stage8", "stage11"]
|
|
... )
|
|
|
|
>>> inputs = processor(image, return_tensors="pt")
|
|
|
|
>>> outputs = model(**inputs)
|
|
>>> feature_maps = outputs.feature_maps
|
|
>>> list(feature_maps[-1].shape)
|
|
[1, 768, 16, 16]
|
|
```"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
output_hidden_states = (
|
|
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
|
)
|
|
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
|
|
|
embedding_output = self.embeddings(pixel_values)
|
|
|
|
outputs = self.encoder(
|
|
embedding_output, output_hidden_states=True, output_attentions=output_attentions, return_dict=return_dict
|
|
)
|
|
|
|
hidden_states = outputs.hidden_states if return_dict else outputs[1]
|
|
|
|
feature_maps = ()
|
|
for stage, hidden_state in zip(self.stage_names, hidden_states):
|
|
if stage in self.out_features:
|
|
if self.config.apply_layernorm:
|
|
hidden_state = self.layernorm(hidden_state)
|
|
if self.config.reshape_hidden_states:
|
|
hidden_state = hidden_state[:, 1:]
|
|
# this was actually a bug in the original implementation that we copied here,
|
|
# cause normally the order is height, width
|
|
batch_size, _, height, width = pixel_values.shape
|
|
patch_size = self.config.patch_size
|
|
hidden_state = hidden_state.reshape(batch_size, height // patch_size, width // patch_size, -1)
|
|
hidden_state = hidden_state.permute(0, 3, 1, 2).contiguous()
|
|
feature_maps += (hidden_state,)
|
|
|
|
if not return_dict:
|
|
if output_hidden_states:
|
|
output = (feature_maps,) + outputs[1:]
|
|
else:
|
|
output = (feature_maps,) + outputs[2:]
|
|
return output
|
|
|
|
return BackboneOutput(
|
|
feature_maps=feature_maps,
|
|
hidden_states=outputs.hidden_states if output_hidden_states else None,
|
|
attentions=outputs.attentions if output_attentions else None,
|
|
)
|