807 lines
33 KiB
Python
807 lines
33 KiB
Python
# coding=utf-8
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# Copyright 2023 Meta AI and The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch DINOv2 model."""
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import collections.abc
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from typing import Callable, Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
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from ...activations import ACT2FN
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from ...modeling_layers import GradientCheckpointingLayer
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from ...modeling_outputs import BackboneOutput, BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
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from ...modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel
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from ...pytorch_utils import find_pruneable_heads_and_indices, prune_linear_layer
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from ...utils import auto_docstring, logging, torch_int
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from ...utils.backbone_utils import BackboneMixin
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from .configuration_dinov2 import Dinov2Config
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logger = logging.get_logger(__name__)
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class Dinov2Embeddings(nn.Module):
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"""
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Construct the CLS token, mask token, position and patch embeddings.
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"""
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def __init__(self, config: Dinov2Config) -> None:
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super().__init__()
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self.cls_token = nn.Parameter(torch.randn(1, 1, config.hidden_size))
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if config.use_mask_token:
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self.mask_token = nn.Parameter(torch.zeros(1, config.hidden_size))
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self.patch_embeddings = Dinov2PatchEmbeddings(config)
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num_patches = self.patch_embeddings.num_patches
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self.position_embeddings = nn.Parameter(torch.randn(1, num_patches + 1, config.hidden_size))
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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self.patch_size = config.patch_size
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self.use_mask_token = config.use_mask_token
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self.config = config
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def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
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"""
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This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution
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images. This method is also adapted to support torch.jit tracing and interpolation at torch.float32 precision.
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Adapted from:
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- https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and
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- https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211
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"""
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num_patches = embeddings.shape[1] - 1
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num_positions = self.position_embeddings.shape[1] - 1
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# always interpolate when tracing to ensure the exported model works for dynamic input shapes
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if not torch.jit.is_tracing() and num_patches == num_positions and height == width:
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return self.position_embeddings
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class_pos_embed = self.position_embeddings[:, :1]
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patch_pos_embed = self.position_embeddings[:, 1:]
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dim = embeddings.shape[-1]
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new_height = height // self.patch_size
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new_width = width // self.patch_size
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sqrt_num_positions = torch_int(num_positions**0.5)
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patch_pos_embed = patch_pos_embed.reshape(1, sqrt_num_positions, sqrt_num_positions, dim)
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patch_pos_embed = patch_pos_embed.permute(0, 3, 1, 2)
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target_dtype = patch_pos_embed.dtype
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patch_pos_embed = nn.functional.interpolate(
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patch_pos_embed.to(torch.float32),
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size=(new_height, new_width),
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mode="bicubic",
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align_corners=False,
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).to(dtype=target_dtype)
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patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
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return torch.cat((class_pos_embed, patch_pos_embed), dim=1)
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def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
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batch_size, _, height, width = pixel_values.shape
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target_dtype = self.patch_embeddings.projection.weight.dtype
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embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
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if bool_masked_pos is not None and self.use_mask_token:
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embeddings = torch.where(
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bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings
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)
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# add the [CLS] token to the embedded patch tokens
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cls_tokens = self.cls_token.expand(batch_size, -1, -1)
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embeddings = torch.cat((cls_tokens, embeddings), dim=1)
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# add positional encoding to each token
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embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
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embeddings = self.dropout(embeddings)
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return embeddings
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class Dinov2PatchEmbeddings(nn.Module):
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"""
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This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
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`hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
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Transformer.
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"""
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def __init__(self, config):
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super().__init__()
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image_size, patch_size = config.image_size, config.patch_size
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num_channels, hidden_size = config.num_channels, config.hidden_size
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image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
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patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.num_patches = num_patches
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self.projection = nn.Conv2d(num_channels, hidden_size, kernel_size=patch_size, stride=patch_size)
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def forward(self, pixel_values: torch.Tensor) -> torch.Tensor:
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num_channels = pixel_values.shape[1]
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if num_channels != self.num_channels:
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raise ValueError(
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"Make sure that the channel dimension of the pixel values match with the one set in the configuration."
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f" Expected {self.num_channels} but got {num_channels}."
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)
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embeddings = self.projection(pixel_values).flatten(2).transpose(1, 2)
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return embeddings
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# Copied from transformers.models.vit.modeling_vit.eager_attention_forward
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def eager_attention_forward(
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module: nn.Module,
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query: torch.Tensor,
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key: torch.Tensor,
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value: torch.Tensor,
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attention_mask: Optional[torch.Tensor],
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scaling: float,
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dropout: float = 0.0,
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**kwargs,
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):
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling
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# Normalize the attention scores to probabilities.
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attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training)
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# Mask heads if we want to
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if attention_mask is not None:
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attn_weights = attn_weights * attention_mask
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attn_output = torch.matmul(attn_weights, value)
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attn_output = attn_output.transpose(1, 2).contiguous()
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return attn_output, attn_weights
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# Copied from transformers.models.vit.modeling_vit.ViTSelfAttention with ViT->Dinov2
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class Dinov2SelfAttention(nn.Module):
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def __init__(self, config: Dinov2Config) -> None:
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super().__init__()
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if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
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raise ValueError(
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f"The hidden size {config.hidden_size} is not a multiple of the number of attention "
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f"heads {config.num_attention_heads}."
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)
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self.config = config
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self.num_attention_heads = config.num_attention_heads
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self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.dropout_prob = config.attention_probs_dropout_prob
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self.scaling = self.attention_head_size**-0.5
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self.is_causal = False
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self.query = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.key = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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self.value = nn.Linear(config.hidden_size, self.all_head_size, bias=config.qkv_bias)
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def forward(
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self,
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hidden_states,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
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batch_size, seq_length, _ = hidden_states.shape
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key_layer = (
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self.key(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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value_layer = (
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self.value(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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query_layer = (
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self.query(hidden_states)
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.view(batch_size, -1, self.num_attention_heads, self.attention_head_size)
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.transpose(1, 2)
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)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and output_attentions:
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
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'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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context_layer, attention_probs = attention_interface(
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self,
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query_layer,
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key_layer,
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value_layer,
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head_mask,
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is_causal=self.is_causal,
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scaling=self.scaling,
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dropout=0.0 if not self.training else self.dropout_prob,
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)
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new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
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context_layer = context_layer.reshape(new_context_layer_shape)
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outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
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return outputs
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# Copied from transformers.models.vit.modeling_vit.ViTSelfOutput with ViT->Dinov2
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class Dinov2SelfOutput(nn.Module):
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"""
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The residual connection is defined in Dinov2Layer instead of here (as is the case with other models), due to the
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layernorm applied before each block.
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"""
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def __init__(self, config: Dinov2Config) -> None:
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super().__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
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hidden_states = self.dense(hidden_states)
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hidden_states = self.dropout(hidden_states)
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return hidden_states
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# Copied from transformers.models.vit.modeling_vit.ViTAttention with ViT->Dinov2
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class Dinov2Attention(nn.Module):
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def __init__(self, config: Dinov2Config) -> None:
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super().__init__()
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self.attention = Dinov2SelfAttention(config)
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self.output = Dinov2SelfOutput(config)
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self.pruned_heads = set()
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def prune_heads(self, heads: set[int]) -> None:
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if len(heads) == 0:
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return
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heads, index = find_pruneable_heads_and_indices(
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heads, self.attention.num_attention_heads, self.attention.attention_head_size, self.pruned_heads
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)
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# Prune linear layers
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self.attention.query = prune_linear_layer(self.attention.query, index)
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self.attention.key = prune_linear_layer(self.attention.key, index)
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self.attention.value = prune_linear_layer(self.attention.value, index)
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self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
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# Update hyper params and store pruned heads
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self.attention.num_attention_heads = self.attention.num_attention_heads - len(heads)
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self.attention.all_head_size = self.attention.attention_head_size * self.attention.num_attention_heads
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self.pruned_heads = self.pruned_heads.union(heads)
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
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self_outputs = self.attention(hidden_states, head_mask, output_attentions)
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attention_output = self.output(self_outputs[0], hidden_states)
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outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
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return outputs
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class Dinov2LayerScale(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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self.lambda1 = nn.Parameter(config.layerscale_value * torch.ones(config.hidden_size))
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def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
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return hidden_state * self.lambda1
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# Copied from transformers.models.beit.modeling_beit.drop_path
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def drop_path(input: torch.Tensor, drop_prob: float = 0.0, training: bool = False) -> torch.Tensor:
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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Comment by Ross Wightman: This is the same as the DropConnect impl I created for EfficientNet, etc networks,
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however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
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See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the
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layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the
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argument.
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"""
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if drop_prob == 0.0 or not training:
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return input
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keep_prob = 1 - drop_prob
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shape = (input.shape[0],) + (1,) * (input.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
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random_tensor = keep_prob + torch.rand(shape, dtype=input.dtype, device=input.device)
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random_tensor.floor_() # binarize
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output = input.div(keep_prob) * random_tensor
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return output
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# Copied from transformers.models.beit.modeling_beit.BeitDropPath
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class Dinov2DropPath(nn.Module):
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"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
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def __init__(self, drop_prob: Optional[float] = None) -> None:
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super().__init__()
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self.drop_prob = drop_prob
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return drop_path(hidden_states, self.drop_prob, self.training)
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def extra_repr(self) -> str:
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return f"p={self.drop_prob}"
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class Dinov2MLP(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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in_features = out_features = config.hidden_size
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hidden_features = int(config.hidden_size * config.mlp_ratio)
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self.fc1 = nn.Linear(in_features, hidden_features, bias=True)
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if isinstance(config.hidden_act, str):
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self.activation = ACT2FN[config.hidden_act]
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else:
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self.activation = config.hidden_act
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self.fc2 = nn.Linear(hidden_features, out_features, bias=True)
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def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
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hidden_state = self.fc1(hidden_state)
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hidden_state = self.activation(hidden_state)
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hidden_state = self.fc2(hidden_state)
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return hidden_state
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class Dinov2SwiGLUFFN(nn.Module):
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def __init__(self, config) -> None:
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super().__init__()
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in_features = out_features = config.hidden_size
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hidden_features = int(config.hidden_size * config.mlp_ratio)
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hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
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self.weights_in = nn.Linear(in_features, 2 * hidden_features, bias=True)
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self.weights_out = nn.Linear(hidden_features, out_features, bias=True)
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def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
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hidden_state = self.weights_in(hidden_state)
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x1, x2 = hidden_state.chunk(2, dim=-1)
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hidden = nn.functional.silu(x1) * x2
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return self.weights_out(hidden)
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class Dinov2Layer(GradientCheckpointingLayer):
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"""This corresponds to the Block class in the original implementation."""
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def __init__(self, config: Dinov2Config) -> None:
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super().__init__()
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self.norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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self.attention = Dinov2Attention(config)
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self.layer_scale1 = Dinov2LayerScale(config)
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self.drop_path = Dinov2DropPath(config.drop_path_rate) if config.drop_path_rate > 0.0 else nn.Identity()
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self.norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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if config.use_swiglu_ffn:
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self.mlp = Dinov2SwiGLUFFN(config)
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else:
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self.mlp = Dinov2MLP(config)
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self.layer_scale2 = Dinov2LayerScale(config)
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def forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: Optional[torch.Tensor] = None,
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output_attentions: bool = False,
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) -> Union[tuple[torch.Tensor, torch.Tensor], tuple[torch.Tensor]]:
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self_attention_outputs = self.attention(
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self.norm1(hidden_states), # in Dinov2, layernorm is applied before self-attention
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head_mask,
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output_attentions=output_attentions,
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)
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attention_output = self_attention_outputs[0]
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attention_output = self.layer_scale1(attention_output)
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outputs = self_attention_outputs[1:] # add self attentions if we output attention weights
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# first residual connection
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hidden_states = self.drop_path(attention_output) + hidden_states
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|
# 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
|
|
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring
|
|
class Dinov2PreTrainedModel(PreTrainedModel):
|
|
config: Dinov2Config
|
|
base_model_prefix = "dinov2"
|
|
main_input_name = "pixel_values"
|
|
supports_gradient_checkpointing = True
|
|
_no_split_modules = ["Dinov2Layer"]
|
|
_supports_sdpa = True
|
|
_supports_flash_attn = True
|
|
_supports_flex_attn = True
|
|
_supports_attention_backend = 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)
|
|
|
|
if self.config.use_mask_token:
|
|
module.mask_token.data.zero_()
|
|
elif isinstance(module, Dinov2LayerScale):
|
|
module.lambda1.data.fill_(self.config.layerscale_value)
|
|
|
|
|
|
@auto_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)
|
|
|
|
@auto_docstring
|
|
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]:
|
|
r"""
|
|
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.
|
|
"""
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
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.
|
|
"""
|
|
)
|
|
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()
|
|
|
|
@auto_docstring
|
|
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,
|
|
)
|
|
|
|
|
|
@auto_docstring(
|
|
custom_intro="""
|
|
Dinov2 backbone, to be used with frameworks like DETR and MaskFormer.
|
|
"""
|
|
)
|
|
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
|
|
|
|
@auto_docstring
|
|
def forward(
|
|
self,
|
|
pixel_values: torch.Tensor,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> BackboneOutput:
|
|
r"""
|
|
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,
|
|
)
|
|
|
|
|
|
__all__ = ["Dinov2ForImageClassification", "Dinov2Model", "Dinov2PreTrainedModel", "Dinov2Backbone"]
|