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#
# This software may be used and distributed in accordance with
# the terms of the DINOv3 License Agreement.
import logging
from functools import partial
from typing import Dict, List, Optional, Sequence, Union
import numpy as np
import torch
import torch.nn.functional as F
import torch.nn.init
from torch import Tensor, nn
logger = logging.getLogger("dinov3")
def drop_path(x: Tensor, drop_prob: float = 0.0, training: bool = False) -> Tensor:
if drop_prob == 0.0 or not training:
return x
keep_prob = 1 - drop_prob
shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets
random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
random_tensor.floor_() # binarize
output = x.div(keep_prob) * random_tensor
return output
class DropPath(nn.Module):
"""Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks)."""
def __init__(self, drop_prob=None) -> None:
super(DropPath, self).__init__()
self.drop_prob = drop_prob
def forward(self, x: Tensor) -> Tensor:
return drop_path(x, self.drop_prob, self.training)
class Block(nn.Module):
r"""ConvNeXt Block. There are two equivalent implementations:
(1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
(2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
We use (2) as we find it slightly faster in PyTorch
Args:
dim (int): Number of input channels.
drop_path (float): Stochastic depth rate. Default: 0.0
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
Source: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
"""
def __init__(self, dim, drop_path=0.0, layer_scale_init_value=1e-6):
super().__init__()
self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
self.norm = LayerNorm(dim, eps=1e-6)
self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
self.act = nn.GELU()
self.pwconv2 = nn.Linear(4 * dim, dim)
self.gamma = (
nn.Parameter(layer_scale_init_value * torch.ones((dim)), requires_grad=True)
if layer_scale_init_value > 0
else None
)
self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
def forward(self, x):
input = x
x = self.dwconv(x)
x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
x = self.norm(x)
x = self.pwconv1(x)
x = self.act(x)
x = self.pwconv2(x)
if self.gamma is not None:
x = self.gamma * x
x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
x = input + self.drop_path(x)
return x
class LayerNorm(nn.Module):
r"""LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
Source: https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.py
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape,)
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None] * x + self.bias[:, None, None]
return x
class ConvNeXt(nn.Module):
r"""
Code adapted from https://github.com/facebookresearch/ConvNeXt/blob/main/models/convnext.pyConvNeXt
A PyTorch impl of : `A ConvNet for the 2020s` -
https://arxiv.org/pdf/2201.03545.pdf
Args:
in_chans (int): Number of input image channels. Default: 3
num_classes (int): Number of classes for classification head. Default: 1000
depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
drop_path_rate (float): Stochastic depth rate. Default: 0.
layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
patch_size (int | None): Pseudo patch size. Used to resize feature maps to those of a ViT with a given patch size. If None, no resizing is performed
"""
def __init__(
self,
# original ConvNeXt arguments
in_chans: int = 3,
depths: List[int] = [3, 3, 9, 3],
dims: List[int] = [96, 192, 384, 768],
drop_path_rate: float = 0.0,
layer_scale_init_value: float = 1e-6,
# DINO arguments
patch_size: int | None = None,
**ignored_kwargs,
):
super().__init__()
if len(ignored_kwargs) > 0:
logger.warning(f"Ignored kwargs: {ignored_kwargs}")
del ignored_kwargs
# ==== ConvNeXt's original init =====
self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
stem = nn.Sequential(
nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
LayerNorm(dims[0], eps=1e-6, data_format="channels_first"),
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
nn.Conv2d(dims[i], dims[i + 1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
dp_rates = [x for x in np.linspace(0, drop_path_rate, sum(depths))]
cur = 0
for i in range(4):
stage = nn.Sequential(
*[
Block(dim=dims[i], drop_path=dp_rates[cur + j], layer_scale_init_value=layer_scale_init_value)
for j in range(depths[i])
]
)
self.stages.append(stage)
cur += depths[i]
self.norm = nn.LayerNorm(dims[-1], eps=1e-6) # final norm layer
# ==== End of ConvNeXt's original init =====
# ==== DINO adaptation ====
self.head = nn.Identity() # remove classification head
self.embed_dim = dims[-1]
self.embed_dims = dims # per layer dimensions
self.n_blocks = len(self.downsample_layers) # 4
self.chunked_blocks = False
self.n_storage_tokens = 0 # no registers
self.norms = nn.ModuleList([nn.Identity() for i in range(3)])
self.norms.append(self.norm)
self.patch_size = patch_size
self.input_pad_size = 4 # first convolution with kernel_size = 4, stride = 4
def init_weights(self):
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.LayerNorm):
module.reset_parameters()
if isinstance(module, LayerNorm):
module.weight = nn.Parameter(torch.ones(module.normalized_shape))
module.bias = nn.Parameter(torch.zeros(module.normalized_shape))
if isinstance(module, (nn.Conv2d, nn.Linear)):
torch.nn.init.trunc_normal_(module.weight, std=0.02)
nn.init.constant_(module.bias, 0)
def forward_features(self, x: Tensor | List[Tensor], masks: Optional[Tensor] = None) -> List[Dict[str, Tensor]]:
if isinstance(x, torch.Tensor):
return self.forward_features_list([x], [masks])[0]
else:
return self.forward_features_list(x, masks)
def forward_features_list(self, x_list: List[Tensor], masks_list: List[Tensor]) -> List[Dict[str, Tensor]]:
output = []
for x, masks in zip(x_list, masks_list):
h, w = x.shape[-2:]
for i in range(4):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
x_pool = x.mean([-2, -1]) # global average pooling, (N, C, H, W) -> (N, C)
x = torch.flatten(x, 2).transpose(1, 2)
# concat [CLS] and patch tokens as (N, HW + 1, C), then normalize
x_norm = self.norm(torch.cat([x_pool.unsqueeze(1), x], dim=1))
output.append(
{
"x_norm_clstoken": x_norm[:, 0],
"x_storage_tokens": x_norm[:, 1 : self.n_storage_tokens + 1],
"x_norm_patchtokens": x_norm[:, self.n_storage_tokens + 1 :],
"x_prenorm": x,
"masks": masks,
}
)
return output
def forward(self, *args, is_training=False, **kwargs):
ret = self.forward_features(*args, **kwargs)
if is_training:
return ret
else:
return self.head(ret["x_norm_clstoken"])
def _get_intermediate_layers(self, x, n=1):
h, w = x.shape[-2:]
output, total_block_len = [], len(self.downsample_layers)
blocks_to_take = range(total_block_len - n, total_block_len) if isinstance(n, int) else n
for i in range(total_block_len):
x = self.downsample_layers[i](x)
x = self.stages[i](x)
if i in blocks_to_take:
x_pool = x.mean([-2, -1])
x_patches = x
if self.patch_size is not None:
# Resize output feature maps to that of a ViT with given patch_size
x_patches = nn.functional.interpolate(
x,
size=(h // self.patch_size, w // self.patch_size),
mode="bilinear",
antialias=True,
)
output.append(
[
x_pool, # CLS (B x C)
x_patches, # B x C x H x W
]
)
assert len(output) == len(blocks_to_take), f"only {len(output)} / {len(blocks_to_take)} blocks found"
return output
def get_intermediate_layers(
self,
x,
n: Union[int, Sequence] = 1, # Layers or n last layers to take,
reshape: bool = False,
return_class_token: bool = False,
norm: bool = True,
):
outputs = self._get_intermediate_layers(x, n)
if norm:
nchw_shapes = [out[-1].shape for out in outputs]
if isinstance(n, int):
norms = self.norms[-n:]
else:
norms = [self.norms[i] for i in n]
outputs = [
(
norm(cls_token), # N x C
norm(patches.flatten(-2, -1).permute(0, 2, 1)), # N x HW x C
)
for (cls_token, patches), norm in zip(outputs, norms)
]
if reshape:
outputs = [
(cls_token, patches.permute(0, 2, 1).reshape(*nchw).contiguous())
for (cls_token, patches), nchw in zip(outputs, nchw_shapes)
]
elif not reshape:
# force B x N x C format for patch tokens
outputs = [(cls_token, patches.flatten(-2, -1).permute(0, 2, 1)) for (cls_token, patches) in outputs]
class_tokens = [out[0] for out in outputs]
outputs = [out[1] for out in outputs]
if return_class_token:
return tuple(zip(outputs, class_tokens))
return tuple(outputs)
convnext_sizes = {
"tiny": dict(
depths=[3, 3, 9, 3],
dims=[96, 192, 384, 768],
),
"small": dict(
depths=[3, 3, 27, 3],
dims=[96, 192, 384, 768],
),
"base": dict(
depths=[3, 3, 27, 3],
dims=[128, 256, 512, 1024],
),
"large": dict(
depths=[3, 3, 27, 3],
dims=[192, 384, 768, 1536],
),
}
def get_convnext_arch(arch_name):
size_dict = None
query_sizename = arch_name.split("_")[1]
try:
size_dict = convnext_sizes[query_sizename]
except KeyError:
raise NotImplementedError("didn't recognize vit size string")
return partial(
ConvNeXt,
**size_dict,
)
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