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import timm
import torch
import types
import numpy as np
import torch.nn.functional as F
from .utils import forward_adapted_unflatten, make_backbone_default
from timm.models.beit import gen_relative_position_index
from torch.utils.checkpoint import checkpoint
from typing import Optional
def forward_beit(pretrained, x):
return forward_adapted_unflatten(pretrained, x, "forward_features")
def patch_embed_forward(self, x):
"""
Modification of timm.models.layers.patch_embed.py: PatchEmbed.forward to support arbitrary window sizes.
"""
x = self.proj(x)
if self.flatten:
x = x.flatten(2).transpose(1, 2)
x = self.norm(x)
return x
def _get_rel_pos_bias(self, window_size):
"""
Modification of timm.models.beit.py: Attention._get_rel_pos_bias to support arbitrary window sizes.
"""
old_height = 2 * self.window_size[0] - 1
old_width = 2 * self.window_size[1] - 1
new_height = 2 * window_size[0] - 1
new_width = 2 * window_size[1] - 1
old_relative_position_bias_table = self.relative_position_bias_table
old_num_relative_distance = self.num_relative_distance
new_num_relative_distance = new_height * new_width + 3
old_sub_table = old_relative_position_bias_table[:old_num_relative_distance - 3]
old_sub_table = old_sub_table.reshape(1, old_width, old_height, -1).permute(0, 3, 1, 2)
new_sub_table = F.interpolate(old_sub_table, size=(new_height, new_width), mode="bilinear")
new_sub_table = new_sub_table.permute(0, 2, 3, 1).reshape(new_num_relative_distance - 3, -1)
new_relative_position_bias_table = torch.cat(
[new_sub_table, old_relative_position_bias_table[old_num_relative_distance - 3:]])
key = str(window_size[1]) + "," + str(window_size[0])
if key not in self.relative_position_indices.keys():
self.relative_position_indices[key] = gen_relative_position_index(window_size)
relative_position_bias = new_relative_position_bias_table[
self.relative_position_indices[key].view(-1)].view(
window_size[0] * window_size[1] + 1,
window_size[0] * window_size[1] + 1, -1) # Wh*Ww,Wh*Ww,nH
relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous() # nH, Wh*Ww, Wh*Ww
return relative_position_bias.unsqueeze(0)
def attention_forward(self, x, resolution, shared_rel_pos_bias: Optional[torch.Tensor] = None):
"""
Modification of timm.models.beit.py: Attention.forward to support arbitrary window sizes.
"""
B, N, C = x.shape
qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
q = q * self.scale
attn = (q @ k.transpose(-2, -1))
if self.relative_position_bias_table is not None:
window_size = tuple(np.array(resolution) // 16)
attn = attn + self._get_rel_pos_bias(window_size)
if shared_rel_pos_bias is not None:
attn = attn + shared_rel_pos_bias
attn = attn.softmax(dim=-1)
attn = self.attn_drop(attn)
x = (attn @ v).transpose(1, 2).reshape(B, N, -1)
x = self.proj(x)
x = self.proj_drop(x)
return x
def block_forward(self, x, resolution, shared_rel_pos_bias: Optional[torch.Tensor] = None):
"""
Modification of timm.models.beit.py: Block.forward to support arbitrary window sizes.
"""
if self.gamma_1 is None:
x = x + self.drop_path(self.attn(self.norm1(x), resolution, shared_rel_pos_bias=shared_rel_pos_bias))
x = x + self.drop_path(self.mlp(self.norm2(x)))
else:
x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x), resolution,
shared_rel_pos_bias=shared_rel_pos_bias))
x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x)))
return x
def beit_forward_features(self, x):
"""
Modification of timm.models.beit.py: Beit.forward_features to support arbitrary window sizes.
"""
resolution = x.shape[2:]
x = self.patch_embed(x)
x = torch.cat((self.cls_token.expand(x.shape[0], -1, -1), x), dim=1)
if self.pos_embed is not None:
x = x + self.pos_embed
x = self.pos_drop(x)
rel_pos_bias = self.rel_pos_bias() if self.rel_pos_bias is not None else None
for blk in self.blocks:
if self.grad_checkpointing and not torch.jit.is_scripting():
x = checkpoint(blk, x, shared_rel_pos_bias=rel_pos_bias)
else:
x = blk(x, resolution, shared_rel_pos_bias=rel_pos_bias)
x = self.norm(x)
return x
def _make_beit_backbone(
model,
features=[96, 192, 384, 768],
size=[384, 384],
hooks=[0, 4, 8, 11],
vit_features=768,
use_readout="ignore",
start_index=1,
start_index_readout=1,
):
backbone = make_backbone_default(model, features, size, hooks, vit_features, use_readout, start_index,
start_index_readout)
backbone.model.patch_embed.forward = types.MethodType(patch_embed_forward, backbone.model.patch_embed)
backbone.model.forward_features = types.MethodType(beit_forward_features, backbone.model)
for block in backbone.model.blocks:
attn = block.attn
attn._get_rel_pos_bias = types.MethodType(_get_rel_pos_bias, attn)
attn.forward = types.MethodType(attention_forward, attn)
attn.relative_position_indices = {}
block.forward = types.MethodType(block_forward, block)
return backbone
def _make_pretrained_beitl16_512(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("beit_large_patch16_512", pretrained=pretrained)
hooks = [5, 11, 17, 23] if hooks is None else hooks
features = [256, 512, 1024, 1024]
return _make_beit_backbone(
model,
features=features,
size=[512, 512],
hooks=hooks,
vit_features=1024,
use_readout=use_readout,
)
def _make_pretrained_beitl16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("beit_large_patch16_384", pretrained=pretrained)
hooks = [5, 11, 17, 23] if hooks is None else hooks
return _make_beit_backbone(
model,
features=[256, 512, 1024, 1024],
hooks=hooks,
vit_features=1024,
use_readout=use_readout,
)
def _make_pretrained_beitb16_384(pretrained, use_readout="ignore", hooks=None):
model = timm.create_model("beit_base_patch16_384", pretrained=pretrained)
hooks = [2, 5, 8, 11] if hooks is None else hooks
return _make_beit_backbone(
model,
features=[96, 192, 384, 768],
hooks=hooks,
use_readout=use_readout,
)
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