mirror of
https://git.datalinker.icu/comfyanonymous/ComfyUI
synced 2026-08-26 07:52:20 +08:00
397 lines
14 KiB
Python
397 lines
14 KiB
Python
from dataclasses import dataclass
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from typing import Optional
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import collections.abc
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import torch.nn as nn
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import torch
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@dataclass
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class DinoConfig():
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hidden_size: int = 1024
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use_mask_token: bool = True
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patch_size: int = 14
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image_size: int = 518
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num_channels: int = 3
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num_attention_heads: int = 16
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attention_probs_dropout_prob: float = 0.0
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hidden_dropout_prob: float = 0.0
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mlp_ratio: int = 4
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num_hidden_layers: int = 24
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layer_norm_eps: float = 1e-6
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qkv_bias: bool = True
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layerscale_value: float = 1.0
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drop_path_rate: float = 0.0
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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) -> 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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def interpolate_pos_encoding(self, embeddings: torch.Tensor, height: int, width: int) -> torch.Tensor:
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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 = 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: 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 pixel_values.shape[1] != 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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return self.projection(pixel_values).flatten(2).transpose(1, 2)
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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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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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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) -> None:
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super().__init__()
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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.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 transpose_for_scores(self, x: torch.Tensor) -> torch.Tensor:
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new_x_shape = x.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
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x = x.view(new_x_shape)
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return x.permute(0, 2, 1, 3)
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def forward(
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self, hidden_states, head_mask: torch.Tensor = None
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):
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key_layer = self.transpose_for_scores(self.key(hidden_states))
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value_layer = self.transpose_for_scores(self.value(hidden_states))
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query_layer = self.transpose_for_scores(self.query(hidden_states))
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context_layer, _ = eager_attention_forward(
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self,
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query = query_layer,
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key = key_layer,
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value = value_layer,
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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,)
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return outputs
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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: DinoConfig) -> 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) -> 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: DinoConfig) -> 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 forward(
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self,
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hidden_states: torch.Tensor,
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head_mask: torch.Tensor = None,
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):
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self_outputs = self.attention(hidden_states, head_mask)
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attention_output = self.output(self_outputs[0])
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outputs = (attention_output,)
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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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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: 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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class Dinov2MLP(nn.Module):
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def __init__(self, config):
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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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self.activation = nn.GELU()
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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 Dinov2Layer(nn.Module):
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"""This corresponds to the Block class in the original implementation."""
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def __init__(self, config: DinoConfig) -> 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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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: torch.Tensor = None,
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):
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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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)
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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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# 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
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layer_output = self.norm2(hidden_states)
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layer_output = self.mlp(layer_output)
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layer_output = self.layer_scale2(layer_output)
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# second residual connection
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layer_output = self.drop_path(layer_output) + hidden_states
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outputs = (layer_output,)
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return outputs
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class Dinov2Encoder(nn.Module):
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def __init__(self, config: DinoConfig) -> None:
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super().__init__()
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self.layer = nn.ModuleList([Dinov2Layer(config) for _ in range(config.num_hidden_layers)])
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def forward(self, hidden_states: torch.Tensor, head_mask: Optional[torch.Tensor] = None):
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for i, layer_module in enumerate(self.layer):
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layer_head_mask = head_mask[i] if head_mask is not None else None
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layer_outputs = layer_module(hidden_states, layer_head_mask)
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hidden_states = layer_outputs[0]
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return hidden_states
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class Dinov2Model(nn.Module):
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def __init__(self, config: DinoConfig):
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super().__init__()
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self.config = config
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self.embeddings = Dinov2Embeddings(config)
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self.encoder = Dinov2Encoder(config)
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self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
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def get_input_embeddings(self) -> Dinov2PatchEmbeddings:
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return self.embeddings.patch_embeddings
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def forward(
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self,
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pixel_values: torch.Tensor,
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bool_masked_pos: Optional[torch.Tensor] = None,
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head_mask: Optional[torch.Tensor] = None,
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):
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embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos)
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encoder_outputs = self.encoder(
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embedding_output,
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head_mask = head_mask,
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)
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sequence_output = encoder_outputs[0]
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sequence_output = self.layernorm(sequence_output)
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return sequence_output |