# 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 dataclasses import dataclass from typing import Dict, List, Optional, Set, Tuple, Union import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from transformers.activations import ACT2FN from transformers.modeling_outputs import ( BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput, ) from transformers.modeling_utils import PreTrainedModel from transformers.models.dinov2.configuration_dinov2 import Dinov2Config from transformers.pytorch_utils import ( find_pruneable_heads_and_indices, prune_linear_layer, ) from transformers.utils import ( add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings, ) from transformers.utils.backbone_utils import BackboneMixin 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-base" DINOV2_PRETRAINED_MODEL_ARCHIVE_LIST = [ "facebook/dinov2-base", # See all DINOv2 models at https://huggingface.co/models?filter=dinov2 ] 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)) # register as mask token as it's not used in optimization # to avoid the use of find_unused_parameters_true # self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size)) self.register_buffer("mask_token", 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) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=( height / math.sqrt(num_positions), width / math.sqrt(num_positions), ), mode="bicubic", align_corners=False, ) 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 patch_embeddings = self.patch_embeddings(pixel_values) embeddings = patch_embeddings 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.attention_probs_dropout_prob = config.attention_probs_dropout_prob 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) if hasattr(F, "scaled_dot_product_attention"): assert head_mask is None and not output_attentions new_size = hidden_states.size()[:-1] + ( self.num_attention_heads, self.attention_head_size, ) key_layer = self.key(hidden_states).reshape(new_size).transpose(1, 2) value_layer = self.value(hidden_states).reshape(new_size).transpose(1, 2) query_layer = mixed_query_layer.reshape(new_size).transpose(1, 2) context_layer = F.scaled_dot_product_attention( query_layer, key_layer, value_layer, dropout_p=self.attention_probs_dropout_prob, is_causal=False, ) context_layer = context_layer.transpose(1, 2).reshape( *hidden_states.size()[:-1], -1 ) else: 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.norm1_modulation = None self.attention = Dinov2Attention(config) self.layer_scale1 = Dinov2LayerScale(config) self.drop_path1 = ( 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) self.norm2_modulation = None if config.use_swiglu_ffn: self.mlp = Dinov2SwiGLUFFN(config) else: self.mlp = Dinov2MLP(config) self.layer_scale2 = Dinov2LayerScale(config) self.drop_path2 = ( Dinov2DropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity() ) def forward( self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None, modulation_cond: Optional[torch.Tensor] = None, output_attentions: bool = False, ) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]: hidden_states_norm = self.norm1(hidden_states) if self.norm1_modulation is not None: assert modulation_cond is not None hidden_states_norm = self.norm1_modulation( hidden_states_norm, modulation_cond ) self_attention_outputs = self.attention( hidden_states_norm, # 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 = attention_output + hidden_states # in Dinov2, layernorm is also applied after self-attention layer_output = self.norm2(hidden_states) if self.norm2_modulation is not None: assert modulation_cond is not None layer_output = self.norm2_modulation(layer_output, modulation_cond) layer_output = self.mlp(layer_output) layer_output = self.layer_scale2(layer_output) # second residual connection layer_output = layer_output + hidden_states outputs = (layer_output,) + outputs return outputs def register_ada_norm_modulation(self, norm1_mod: nn.Module, norm2_mod: nn.Module): self.norm1_modulation = norm1_mod self.norm2_modulation = norm2_mod # 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, modulation_cond: 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: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(layer_module), hidden_states, layer_head_mask, modulation_cond, use_reentrant=False, ) else: layer_outputs = layer_module( hidden_states, layer_head_mask, modulation_cond, 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) def _set_gradient_checkpointing( self, module: Dinov2Encoder, value: bool = False ) -> None: if isinstance(module, Dinov2Encoder): module.gradient_checkpointing = value 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. """ @dataclass class CustomBaseModelOutputWithPooling(BaseModelOutputWithPooling): patch_embeddings: Optional[torch.FloatTensor] = None @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 expand_input_channels(self, extra_input_channels: int) -> None: if extra_input_channels == 0: return conv_old = self.embeddings.patch_embeddings.projection conv_new = nn.Conv2d( self.config.num_channels + extra_input_channels, self.config.hidden_size, kernel_size=self.config.patch_size, stride=self.config.patch_size, ).to(self.device) with torch.no_grad(): conv_new.weight[:, :3] = conv_old.weight conv_new.bias = conv_old.bias self.embeddings.patch_embeddings.projection = conv_new del conv_old 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, modulation_cond: 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, modulation_cond=modulation_cond, 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 CustomBaseModelOutputWithPooling( last_hidden_state=sequence_output, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, patch_embeddings=embedding_output, ) def set_gradient_checkpointing(self, value: bool = False) -> None: self._set_gradient_checkpointing(self.encoder, value) @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, ) 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: batch_size, _, height, width = pixel_values.shape patch_size = self.config.patch_size hidden_state = hidden_state[:, 1:, :].reshape( batch_size, width // patch_size, height // 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, ) class CustomPatchEmbeddings(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, image_size: int, patch_size: int, num_channels: int, hidden_size: int ): super().__init__() 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 class CustomEmbeddings(nn.Module): """ Construct the CLS token, mask token, position and patch embeddings. """ def __init__( self, image_size: int, patch_size: int, num_channels: int, hidden_size: int ) -> None: super().__init__() self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.hidden_size = hidden_size self.cls_token = nn.Parameter(torch.randn(1, 1, self.hidden_size)) self.patch_embeddings = CustomPatchEmbeddings( image_size, patch_size, num_channels, hidden_size ) num_patches = self.patch_embeddings.num_patches self.position_embeddings = nn.Parameter( torch.randn(1, num_patches + 1, self.hidden_size) ) 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.patch_size width = width // self.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) patch_pos_embed = nn.functional.interpolate( patch_pos_embed, scale_factor=( height / math.sqrt(num_positions), width / math.sqrt(num_positions), ), mode="bicubic", align_corners=False, ) 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, ) -> torch.Tensor: batch_size, _, height, width = pixel_values.shape patch_embeddings = self.patch_embeddings(pixel_values) embeddings = patch_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 ) return embeddings